diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1.log deleted file mode 100644 index bca4d42ae6852b05a97c4081e6b8f77d8f0752c5..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.1.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.1.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:34 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.1.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.1.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:40,398 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:40,545 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:58:41,774 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:58:41,789 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:58:41,789 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:41,789 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:58:41,790 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:41,790 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:58:41,790 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/config.yaml -[wieling-3-a100] 2023-12-01 15:58:41,807 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.1.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.1.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=16 threads=1 -# Ended (code 0) at Fri Dec 1 15:58:50 UTC 2023, elapsed time 16 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/config.yaml deleted file mode 100644 index d01504e93cd38a03cd0b4873b4f158c3d896f103..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.1.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.1.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/train/feats_lengths_stats.npz deleted file mode 100644 index fcebf9b4b7a5d201356cc1d494900df0e30a01fb..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:ddfdb5fa7d569f4cfd147a3b2c063b468bc622eef7ac57458ca7e4c3afded949 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/train/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/train/feats_stats.npz deleted file mode 100644 index 604e05acae18378029c45b6e7bad18df0065616c..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/train/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:4410bc31eace8e651b586b137f0b39fafa8920aaf121ca372393331ef4abeadf -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/train/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/train/sids_shape deleted file mode 100644 index 86994642bd77bb19173c91d47a9c0cb67a391ec1..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/train/sids_shape +++ /dev/null @@ -1,249 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-Hoogelaandsters-0175 147882 -Hoogelaandsters-0179 105946 -Hoogelaandsters-0183 40465 -Hoogelaandsters-0187 54444 -Hoogelaandsters-0191 158183 -Hoogelaandsters-0195 78724 -Hoogelaandsters-0199 64008 -Hoogelaandsters-0204 143468 -Hoogelaandsters-0208 47822 -Hoogelaandsters-0212 45615 -Hoogelaandsters-0216 150826 -Hoogelaandsters-0220 125810 -Hoogelaandsters-0224 114774 -Hoogelaandsters-0228 107417 -Hoogelaandsters-0232 153768 -Hoogelaandsters-0236 139054 -Hoogelaandsters-0240 149354 -Hoogelaandsters-0244 83874 -Hoogelaandsters-0248 69158 -Hoogelaandsters-0252 112567 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/train/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/train/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/train/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/train/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/train/text_shape deleted file mode 100644 index ff9f69078d64c3ed806d913d9d0194e1e34eb520..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/train/text_shape +++ /dev/null @@ -1,249 +0,0 @@ -Hoogelaandsters-0002 64 -Hoogelaandsters-0006 54 -Hoogelaandsters-0010 29 -Hoogelaandsters-0014 97 -Hoogelaandsters-0018 94 -Hoogelaandsters-0022 62 -Hoogelaandsters-0026 54 -Hoogelaandsters-0030 51 -Hoogelaandsters-0034 52 -Hoogelaandsters-0038 50 -Hoogelaandsters-0042 114 -Hoogelaandsters-0046 241 -Hoogelaandsters-0050 32 -Hoogelaandsters-0054 42 -Hoogelaandsters-0058 48 -Hoogelaandsters-0062 118 -Hoogelaandsters-0066 72 -Hoogelaandsters-0071 95 -Hoogelaandsters-0075 108 -Hoogelaandsters-0079 110 -Hoogelaandsters-0083 110 -Hoogelaandsters-0087 67 -Hoogelaandsters-0091 60 -Hoogelaandsters-0095 70 -Hoogelaandsters-0099 61 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92 -Hoogelaandsters-0235 66 -Hoogelaandsters-0239 54 -Hoogelaandsters-0243 94 -Hoogelaandsters-0247 41 -Hoogelaandsters-0251 43 -Hoogelaandsters-0167 66 -Hoogelaandsters-0171 54 -Hoogelaandsters-0175 113 -Hoogelaandsters-0179 78 -Hoogelaandsters-0183 39 -Hoogelaandsters-0187 44 -Hoogelaandsters-0191 93 -Hoogelaandsters-0195 58 -Hoogelaandsters-0199 51 -Hoogelaandsters-0204 111 -Hoogelaandsters-0208 35 -Hoogelaandsters-0212 27 -Hoogelaandsters-0216 110 -Hoogelaandsters-0220 73 -Hoogelaandsters-0224 47 -Hoogelaandsters-0228 66 -Hoogelaandsters-0232 101 -Hoogelaandsters-0236 94 -Hoogelaandsters-0240 104 -Hoogelaandsters-0244 50 -Hoogelaandsters-0248 44 -Hoogelaandsters-0252 85 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/valid/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/valid/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/valid/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/valid/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/valid/feats_lengths_stats.npz deleted file mode 100644 index 6b137530565b7e5cbc3c4f1ba9761e58e60360ae..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:92b775aaa5d2948c527ad29e192999a8388c2e6fd81fad2475ee7da577f3594a -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/valid/feats_stats.npz deleted file mode 100644 index 6cd2a432ad4c1efd9661bf13abb78382b3ac5a35..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:4abdc1055a5330f17559bc4667c89f68c0ab191e3debc39c5735481cdf9d19ef -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/valid/sids_shape deleted file mode 100644 index 60f3c214e689a6427e4e5e95bc506476010b5cb1..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/valid/sids_shape +++ /dev/null @@ -1,5 +0,0 @@ -Hoogelaandsters-0001 1 -Hoogelaandsters-0269 1 -Hoogelaandsters-0068 1 -Hoogelaandsters-0135 1 -Hoogelaandsters-0202 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/valid/speech_shape deleted file mode 100644 index d5191dfe9045d0d04d77336caf83fcd8a7ed15dd..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/valid/speech_shape +++ /dev/null @@ -1,5 +0,0 @@ -Hoogelaandsters-0001 195705 -Hoogelaandsters-0269 119189 -Hoogelaandsters-0068 136111 -Hoogelaandsters-0135 87552 -Hoogelaandsters-0202 109624 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/valid/text_shape deleted file mode 100644 index 7880bf0adf7ee4acbd7154f9815ea3b30074f4b1..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.1/valid/text_shape +++ /dev/null @@ -1,5 +0,0 @@ -Hoogelaandsters-0001 111 -Hoogelaandsters-0269 86 -Hoogelaandsters-0068 91 -Hoogelaandsters-0135 62 -Hoogelaandsters-0202 75 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10.log deleted file mode 100644 index 699212649bf5f89740c75ad1f623cafc3ebbc630..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.10.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.10.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:34 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.10.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.10.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:40,202 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:40,315 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:58:41,727 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:58:41,759 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:58:41,759 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:41,759 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:58:41,759 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:41,759 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:58:41,759 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/config.yaml -[wieling-3-a100] 2023-12-01 15:58:41,790 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.10.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.10.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=18 threads=1 -# Ended (code 0) at Fri Dec 1 15:58:52 UTC 2023, elapsed time 18 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/config.yaml deleted file mode 100644 index 179b17f14ed4ae565122252d3dcc4ba2ea8ac851..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.10.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.10.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/train/feats_lengths_stats.npz deleted file mode 100644 index 5517437f87e86bd00f84849ce2aaa341d8c76019..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:af79191d13bfcc12aecb8430b36098d2678e2934adbe5d1a061dee1deef5773e -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/train/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/train/feats_stats.npz deleted file mode 100644 index db8a38d4e39ad4f40023635b6956d8b617f8bc9d..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/train/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:574f2692fe20cefd55fe03010ac1815121a4aa53069aa042c591fb317d7804b0 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/train/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/train/sids_shape deleted file mode 100644 index 732413d769e972888c8ab2671955668683693784..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/train/sids_shape +++ /dev/null @@ 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-Hoogelaandsters-2522-MoanMorn 67878 -Hoogelaandsters-2526-MoanMorn 139164 -Hoogelaandsters-2530-MoanMorn 150036 -Hoogelaandsters-2534-MoanMorn 163176 -Hoogelaandsters-2539-MoanMorn 115911 -Hoogelaandsters-2543-MoanMorn 387694 -Hoogelaandsters-2547-MoanMorn 160101 -Hoogelaandsters-2551-MoanMorn 178543 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/train/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/train/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/train/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/train/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/train/text_shape deleted file mode 100644 index 9d818823191998396a6a3c9655123d519abaf83c..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/train/text_shape +++ /dev/null @@ -1,249 +0,0 @@ -Hoogelaandsters-2288-MoanMorn 68 -Hoogelaandsters-2294-MoanMorn 95 -Hoogelaandsters-2299-MoanMorn 42 -Hoogelaandsters-2303-MoanMorn 54 -Hoogelaandsters-2307-MoanMorn 55 -Hoogelaandsters-2311-MoanMorn 42 -Hoogelaandsters-2316-MoanMorn 81 -Hoogelaandsters-2320-MoanMorn 149 -Hoogelaandsters-2324-MoanMorn 41 -Hoogelaandsters-2328-MoanMorn 104 -Hoogelaandsters-2332-MoanMorn 162 -Hoogelaandsters-2336-MoanMorn 53 -Hoogelaandsters-2340-MoanMorn 34 -Hoogelaandsters-2344-MoanMorn 23 -Hoogelaandsters-2348-MoanMorn 98 -Hoogelaandsters-2352-MoanMorn 90 -Hoogelaandsters-2356-MoanMorn 86 -Hoogelaandsters-2361-MoanMorn 51 -Hoogelaandsters-2365-MoanMorn 45 -Hoogelaandsters-2369-MoanMorn 119 -Hoogelaandsters-2373-MoanMorn 129 -Hoogelaandsters-2377-MoanMorn 50 -Hoogelaandsters-2381-MoanMorn 74 -Hoogelaandsters-2386-MoanMorn 57 -Hoogelaandsters-2390-MoanMorn 185 -Hoogelaandsters-2395-MoanMorn 30 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-Hoogelaandsters-2472-MoanMorn 76 -Hoogelaandsters-2476-MoanMorn 74 -Hoogelaandsters-2480-MoanMorn 76 -Hoogelaandsters-2485-MoanMorn 49 -Hoogelaandsters-2489-MoanMorn 90 -Hoogelaandsters-2493-MoanMorn 36 -Hoogelaandsters-2498-MoanMorn 67 -Hoogelaandsters-2502-MoanMorn 71 -Hoogelaandsters-2506-MoanMorn 44 -Hoogelaandsters-2510-MoanMorn 134 -Hoogelaandsters-2514-MoanMorn 139 -Hoogelaandsters-2518-MoanMorn 217 -Hoogelaandsters-2522-MoanMorn 44 -Hoogelaandsters-2526-MoanMorn 102 -Hoogelaandsters-2530-MoanMorn 96 -Hoogelaandsters-2534-MoanMorn 92 -Hoogelaandsters-2539-MoanMorn 86 -Hoogelaandsters-2543-MoanMorn 249 -Hoogelaandsters-2547-MoanMorn 102 -Hoogelaandsters-2551-MoanMorn 97 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/valid/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/valid/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/valid/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/valid/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/valid/feats_lengths_stats.npz deleted file mode 100644 index a256833e5e1a4348ef71de459c4cb705b2692c6d..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:15ffdfea3d7f1876d6a022d37c05b82e4b1fce8aefbb321bd0cddd482ac406c8 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/valid/feats_stats.npz deleted file mode 100644 index e0ed310539ff85ceb16b07e969bb4f3d25764514..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:eb9a150225e097511ae2eaa906b3c3fe455256237be5df75b824e8c2ae27d3e5 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/valid/sids_shape deleted file mode 100644 index 05ca965243cda927fe0bee7f7d7a82129d6f5540..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/valid/sids_shape +++ /dev/null @@ -1,5 +0,0 @@ -Hoogelaandsters-3047-MoanMorn 1 -Hoogelaandsters-3322-OpBuus 1 -Hoogelaandsters-3115-MoanMorn 1 -Hoogelaandsters-3184-MoanMorn 1 -Hoogelaandsters-3251-MoanMorn 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/valid/speech_shape deleted file mode 100644 index 835dcc52d4b239adef22c8cefbc9c3aaecedcb21..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/valid/speech_shape +++ /dev/null @@ -1,5 +0,0 @@ -Hoogelaandsters-3047-MoanMorn 152884 -Hoogelaandsters-3322-OpBuus 41382 -Hoogelaandsters-3115-MoanMorn 54997 -Hoogelaandsters-3184-MoanMorn 112079 -Hoogelaandsters-3251-MoanMorn 176275 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/valid/text_shape deleted file mode 100644 index 7220bc5f095426ba582a12a58aee7aa461818cdf..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.10/valid/text_shape +++ /dev/null @@ -1,5 +0,0 @@ -Hoogelaandsters-3047-MoanMorn 84 -Hoogelaandsters-3322-OpBuus 21 -Hoogelaandsters-3115-MoanMorn 32 -Hoogelaandsters-3184-MoanMorn 60 -Hoogelaandsters-3251-MoanMorn 113 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11.log deleted file mode 100644 index fd49ea4c692c798244868c59d19a2a87a27b1395..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.11.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.11.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:34 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.11.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.11.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:40,493 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:40,627 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:58:41,832 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:58:41,847 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:58:41,847 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:41,847 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:58:41,847 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:41,847 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:58:41,848 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/config.yaml -[wieling-3-a100] 2023-12-01 15:58:41,866 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.11.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.11.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=18 threads=1 -# Ended (code 0) at Fri Dec 1 15:58:52 UTC 2023, elapsed time 18 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/config.yaml deleted file mode 100644 index a5df96f9f53fce43e6306ea9545f150029aa4d73..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.11.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.11.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/train/feats_lengths_stats.npz deleted file mode 100644 index da9e0b1dd6f1b7a05413170aafb312385e84295e..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:8fe5b424d637b31235fd2b5fab1dd19d7aae7171c7ba6a8652d681775beb1a09 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/train/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/train/feats_stats.npz deleted file mode 100644 index 23c56c30d82381563122ca478ccceaf6e6fe205a..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/train/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:5b6b29e56a828f743a9e1137c738e65a6c821a5e18d1c4c8c801663798432712 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/train/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/train/sids_shape deleted file mode 100644 index d8dd5edd6512ea44f84fefdf4e1e13cb4f024994..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/train/sids_shape +++ /dev/null @@ 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144 -Hoogelaandsters-2790-MoanMorn 74 -Hoogelaandsters-2794-MoanMorn 46 -Hoogelaandsters-2799-MoanMorn 17 -Hoogelaandsters-2807-MoanMorn 24 -Hoogelaandsters-2811-MoanMorn 60 -Hoogelaandsters-2721-MoanMorn 43 -Hoogelaandsters-2725-MoanMorn 112 -Hoogelaandsters-2729-MoanMorn 143 -Hoogelaandsters-2733-MoanMorn 84 -Hoogelaandsters-2737-MoanMorn 36 -Hoogelaandsters-2742-MoanMorn 64 -Hoogelaandsters-2746-MoanMorn 65 -Hoogelaandsters-2750-MoanMorn 59 -Hoogelaandsters-2754-MoanMorn 74 -Hoogelaandsters-2758-MoanMorn 72 -Hoogelaandsters-2762-MoanMorn 160 -Hoogelaandsters-2767-MoanMorn 108 -Hoogelaandsters-2771-MoanMorn 102 -Hoogelaandsters-2775-MoanMorn 126 -Hoogelaandsters-2779-MoanMorn 35 -Hoogelaandsters-2783-MoanMorn 48 -Hoogelaandsters-2787-MoanMorn 83 -Hoogelaandsters-2791-MoanMorn 21 -Hoogelaandsters-2795-MoanMorn 178 -Hoogelaandsters-2800-MoanMorn 43 -Hoogelaandsters-2808-MoanMorn 49 -Hoogelaandsters-2812-MoanMorn 40 -Hoogelaandsters-2722-MoanMorn 123 -Hoogelaandsters-2726-MoanMorn 46 -Hoogelaandsters-2730-MoanMorn 65 -Hoogelaandsters-2734-MoanMorn 29 -Hoogelaandsters-2738-MoanMorn 102 -Hoogelaandsters-2743-MoanMorn 63 -Hoogelaandsters-2747-MoanMorn 140 -Hoogelaandsters-2751-MoanMorn 34 -Hoogelaandsters-2755-MoanMorn 170 -Hoogelaandsters-2759-MoanMorn 89 -Hoogelaandsters-2763-MoanMorn 170 -Hoogelaandsters-2768-MoanMorn 57 -Hoogelaandsters-2772-MoanMorn 82 -Hoogelaandsters-2776-MoanMorn 108 -Hoogelaandsters-2780-MoanMorn 203 -Hoogelaandsters-2784-MoanMorn 219 -Hoogelaandsters-2788-MoanMorn 86 -Hoogelaandsters-2792-MoanMorn 115 -Hoogelaandsters-2796-MoanMorn 72 -Hoogelaandsters-2801-MoanMorn 75 -Hoogelaandsters-2809-MoanMorn 82 -Hoogelaandsters-2813-MoanMorn 56 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/valid/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/valid/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/valid/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/valid/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/valid/feats_lengths_stats.npz deleted file mode 100644 index 704afc6ac0fe0a78be588e7c930bdb4ee07f881d..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:d91f08590d0800f915c76c97f826494001a888019e15e8cb5906b40df85e42f5 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/valid/feats_stats.npz deleted file mode 100644 index 57074946bf665c5f6a7714d6ca497c6a853f5a77..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:2bcc2334a15eb36d64c9c834c251b257c11f2f3609d48e3b645a451272efd35f -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/valid/sids_shape deleted file mode 100644 index fa1b29080a4eeb3623f66f189599110f3061ae14..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/valid/sids_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-0001-AigenOardegheden 1 -Oldambsters-0215-AigenOardegheden 1 -Oldambsters-0054-AigenOardegheden 1 -Oldambsters-0106-AigenOardegheden 1 -Oldambsters-0160-AigenOardegheden 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/valid/speech_shape deleted file mode 100644 index cf893de80f4b91883439697c67a993fd7b7746c0..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/valid/speech_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-0001-AigenOardegheden 115718 -Oldambsters-0215-AigenOardegheden 156715 -Oldambsters-0054-AigenOardegheden 129824 -Oldambsters-0106-AigenOardegheden 70560 -Oldambsters-0160-AigenOardegheden 50803 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/valid/text_shape deleted file mode 100644 index 9d4d6c8ce9132cec1cc009efdffa049807fcb45a..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.11/valid/text_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-0001-AigenOardegheden 89 -Oldambsters-0215-AigenOardegheden 110 -Oldambsters-0054-AigenOardegheden 7 -Oldambsters-0106-AigenOardegheden 44 -Oldambsters-0160-AigenOardegheden 34 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12.log deleted file mode 100644 index e0fc38d465f2fc22fa14e3a51b8f8691d85cdc5b..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.12.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.12.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:34 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.12.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.12.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:40,886 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:41,003 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:58:42,381 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:58:42,453 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:58:42,453 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:42,453 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:58:42,453 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:42,454 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:58:42,454 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/config.yaml -[wieling-3-a100] 2023-12-01 15:58:42,480 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.12.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.12.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=18 threads=1 -# Ended (code 0) at Fri Dec 1 15:58:52 UTC 2023, elapsed time 18 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/config.yaml deleted file mode 100644 index 091844e8f3cd39395fc5bb831adce101e313f6bd..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.12.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.12.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/train/feats_lengths_stats.npz deleted file mode 100644 index 95fd04185a6c610343ad005ff6933dba926d4f1b..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version 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a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/train/text_shape +++ /dev/null @@ -1,249 +0,0 @@ -Hoogelaandsters-2815-MoanMorn 56 -Hoogelaandsters-2819-MoanMorn 90 -Hoogelaandsters-2823-MoanMorn 71 -Hoogelaandsters-2827-MoanMorn 41 -Hoogelaandsters-2832-MoanMorn 156 -Hoogelaandsters-2837-MoanMorn 25 -Hoogelaandsters-2842-MoanMorn 153 -Hoogelaandsters-2846-MoanMorn 103 -Hoogelaandsters-2850-MoanMorn 66 -Hoogelaandsters-2854-MoanMorn 14 -Hoogelaandsters-2858-MoanMorn 49 -Hoogelaandsters-2862-MoanMorn 51 -Hoogelaandsters-2866-MoanMorn 38 -Hoogelaandsters-2871-MoanMorn 77 -Hoogelaandsters-2875-MoanMorn 175 -Hoogelaandsters-2879-MoanMorn 209 -Hoogelaandsters-2883-MoanMorn 38 -Hoogelaandsters-2887-MoanMorn 119 -Hoogelaandsters-2891-MoanMorn 30 -Hoogelaandsters-2895-MoanMorn 115 -Hoogelaandsters-2899-MoanMorn 82 -Hoogelaandsters-2903-MoanMorn 24 -Hoogelaandsters-2908-MoanMorn 42 -Hoogelaandsters-2912-MoanMorn 90 -Hoogelaandsters-2916-MoanMorn 156 -Hoogelaandsters-2921-MoanMorn 168 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-Hoogelaandsters-3060-MoanMorn 53 -Hoogelaandsters-3064-MoanMorn 28 -Hoogelaandsters-3068-MoanMorn 64 -Hoogelaandsters-3072-MoanMorn 28 -Hoogelaandsters-3076-MoanMorn 57 -Hoogelaandsters-2992-MoanMorn 148 -Hoogelaandsters-2996-MoanMorn 89 -Hoogelaandsters-3000-MoanMorn 204 -Hoogelaandsters-3004-MoanMorn 41 -Hoogelaandsters-3008-MoanMorn 84 -Hoogelaandsters-3012-MoanMorn 82 -Hoogelaandsters-3016-MoanMorn 60 -Hoogelaandsters-3020-MoanMorn 71 -Hoogelaandsters-3024-MoanMorn 70 -Hoogelaandsters-3028-MoanMorn 275 -Hoogelaandsters-3032-MoanMorn 48 -Hoogelaandsters-3036-MoanMorn 76 -Hoogelaandsters-3040-MoanMorn 32 -Hoogelaandsters-3044-MoanMorn 55 -Hoogelaandsters-3049-MoanMorn 181 -Hoogelaandsters-3053-MoanMorn 57 -Hoogelaandsters-3057-MoanMorn 169 -Hoogelaandsters-3061-MoanMorn 81 -Hoogelaandsters-3065-MoanMorn 42 -Hoogelaandsters-3069-MoanMorn 112 -Hoogelaandsters-3073-MoanMorn 49 -Hoogelaandsters-3077-MoanMorn 26 -Hoogelaandsters-2993-MoanMorn 70 -Hoogelaandsters-2997-MoanMorn 244 -Hoogelaandsters-3001-MoanMorn 120 -Hoogelaandsters-3005-MoanMorn 63 -Hoogelaandsters-3009-MoanMorn 100 -Hoogelaandsters-3013-MoanMorn 56 -Hoogelaandsters-3017-MoanMorn 127 -Hoogelaandsters-3021-MoanMorn 54 -Hoogelaandsters-3025-MoanMorn 71 -Hoogelaandsters-3029-MoanMorn 63 -Hoogelaandsters-3033-MoanMorn 70 -Hoogelaandsters-3037-MoanMorn 55 -Hoogelaandsters-3041-MoanMorn 230 -Hoogelaandsters-3045-MoanMorn 32 -Hoogelaandsters-3050-MoanMorn 93 -Hoogelaandsters-3054-MoanMorn 42 -Hoogelaandsters-3058-MoanMorn 240 -Hoogelaandsters-3062-MoanMorn 36 -Hoogelaandsters-3066-MoanMorn 71 -Hoogelaandsters-3070-MoanMorn 29 -Hoogelaandsters-3074-MoanMorn 59 -Hoogelaandsters-3078-MoanMorn 21 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/valid/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/valid/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/valid/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/valid/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/valid/feats_lengths_stats.npz deleted file mode 100644 index f9a0046d5e0643d5c91e9932400783ffe3421387..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:9341ead24fbaac1add4110b27dcb667b4cc72f021be14d858be73e54cb7d617d -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/valid/feats_stats.npz deleted file mode 100644 index fee798939d1950aa58628d4e0a6242db889e022a..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:1b4e140e4cba6133e6e6fc91bcc6a83dfe06814bc526a4fa92fdaa002e6e17ba -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/valid/sids_shape deleted file mode 100644 index a8b85adc022e839fc5993bb884d5e9187d26e37d..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/valid/sids_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-0270-AigenOardegheden 1 -Oldambsters-0483-AigenOardegheden 1 -Oldambsters-0322-AigenOardegheden 1 -Oldambsters-0375-AigenOardegheden 1 -Oldambsters-0430-AigenOardegheden 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/valid/speech_shape deleted file mode 100644 index fdabc171c998720b2559207b38c8a369eb797dbf..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/valid/speech_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-0270-AigenOardegheden 132609 -Oldambsters-0483-AigenOardegheden 106960 -Oldambsters-0322-AigenOardegheden 94986 -Oldambsters-0375-AigenOardegheden 128856 -Oldambsters-0430-AigenOardegheden 81850 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/valid/text_shape deleted file mode 100644 index 0739fcb2a3d7fa2a1e50b1ecb8acc3094b7837d9..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.12/valid/text_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-0270-AigenOardegheden 84 -Oldambsters-0483-AigenOardegheden 51 -Oldambsters-0322-AigenOardegheden 37 -Oldambsters-0375-AigenOardegheden 110 -Oldambsters-0430-AigenOardegheden 52 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13.log deleted file mode 100644 index ee501d30fe0d76f941183d356ab288f281a6c415..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.13.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.13.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:34 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.13.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.13.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:40,400 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:40,546 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:58:41,851 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:58:41,866 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:58:41,867 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:41,867 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:58:41,867 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:41,867 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:58:41,867 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/config.yaml -[wieling-3-a100] 2023-12-01 15:58:41,885 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.13.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.13.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=18 threads=1 -# Ended (code 0) at Fri Dec 1 15:58:52 UTC 2023, elapsed time 18 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/config.yaml deleted file mode 100644 index 67b020e93ad4a865a3d245dad19bb4f09f5ac071..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.13.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.13.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/train/feats_lengths_stats.npz deleted file mode 100644 index d0aaf18ebd412e98a584862305ae579161ec7a7a..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:a5911d250e907c912cc65a4a6ece4d694f1cfa1c3d4897a360cae306d0108219 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/train/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/train/feats_stats.npz deleted file mode 100644 index 67e38294c33b6d991ea8dcce82e80d31b639bea4..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/train/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:97e52f56f3a88212fdb83aca7027584614548ae7d20a596c46aa0b9cb847f80d -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/train/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/train/sids_shape deleted file mode 100644 index fcb5192e03af1e764cbd73e54b587f2ff2b41650..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/train/sids_shape +++ /dev/null @@ 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-Hoogelaandsters-3340-OpBuus 141088 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/train/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/train/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/train/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/train/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/train/text_shape deleted file mode 100644 index bfe340f4012d9b56e8c155ba02fc68a6f29212e6..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/train/text_shape +++ /dev/null @@ -1,249 +0,0 @@ -Hoogelaandsters-3080-MoanMorn 107 -Hoogelaandsters-3084-MoanMorn 56 -Hoogelaandsters-3088-MoanMorn 83 -Hoogelaandsters-3092-MoanMorn 157 -Hoogelaandsters-3097-MoanMorn 71 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-Hoogelaandsters-3268-MoanMorn 25 -Hoogelaandsters-3272-MoanMorn 184 -Hoogelaandsters-3276-OpBuus 109 -Hoogelaandsters-3280-OpBuus 187 -Hoogelaandsters-3284-OpBuus 233 -Hoogelaandsters-3291-OpBuus 131 -Hoogelaandsters-3295-OpBuus 9 -Hoogelaandsters-3299-OpBuus 80 -Hoogelaandsters-3305-OpBuus 178 -Hoogelaandsters-3309-OpBuus 98 -Hoogelaandsters-3313-OpBuus 16 -Hoogelaandsters-3317-OpBuus 122 -Hoogelaandsters-3321-OpBuus 29 -Hoogelaandsters-3326-OpBuus 64 -Hoogelaandsters-3330-OpBuus 51 -Hoogelaandsters-3334-OpBuus 182 -Hoogelaandsters-3339-OpBuus 87 -Hoogelaandsters-3248-MoanMorn 131 -Hoogelaandsters-3253-MoanMorn 204 -Hoogelaandsters-3257-MoanMorn 118 -Hoogelaandsters-3261-MoanMorn 157 -Hoogelaandsters-3265-MoanMorn 48 -Hoogelaandsters-3269-MoanMorn 138 -Hoogelaandsters-3273-MoanMorn 92 -Hoogelaandsters-3277-OpBuus 228 -Hoogelaandsters-3281-OpBuus 48 -Hoogelaandsters-3287-OpBuus 25 -Hoogelaandsters-3292-OpBuus 55 -Hoogelaandsters-3296-OpBuus 25 -Hoogelaandsters-3300-OpBuus 110 -Hoogelaandsters-3306-OpBuus 135 -Hoogelaandsters-3310-OpBuus 35 -Hoogelaandsters-3314-OpBuus 94 -Hoogelaandsters-3318-OpBuus 88 -Hoogelaandsters-3323-OpBuus 61 -Hoogelaandsters-3327-OpBuus 32 -Hoogelaandsters-3331-OpBuus 45 -Hoogelaandsters-3335-OpBuus 46 -Hoogelaandsters-3340-OpBuus 88 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/valid/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/valid/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/valid/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/valid/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/valid/feats_lengths_stats.npz deleted file mode 100644 index f1bdcb95b666240cc3dae99ccc00dea4704854ce..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:7f42ceb8a1eca3ee4e72fbadf22ad7dc12b040fb9bb3928bdfed2237e6cd0946 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/valid/feats_stats.npz deleted file mode 100644 index c7ce2599b7a6bb7477ccbe2b8ae8dc58dc0efb55..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:936b264ccdb8d6c45b4597556ab48f58c56473baf36a1f5b263ac3a0a48c6fd1 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/valid/sids_shape deleted file mode 100644 index 58822800c07e3309c9b0508ffc6e449aa1649de6..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/valid/sids_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-0536-AigenOardegheden 1 -Oldambsters-0751-DeVerhoalen 1 -Oldambsters-0591-AigenOardegheden 1 -Oldambsters-0646-DeVerhoalen 1 -Oldambsters-0698-DeVerhoalen 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/valid/speech_shape deleted file mode 100644 index 84f47ecfca1d53fd9a935245142b4e692b42b3dd..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/valid/speech_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-0536-AigenOardegheden 59270 -Oldambsters-0751-DeVerhoalen 246285 -Oldambsters-0591-AigenOardegheden 59270 -Oldambsters-0646-DeVerhoalen 208028 -Oldambsters-0698-DeVerhoalen 64551 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/valid/text_shape deleted file mode 100644 index aa0a0929fe56986d2aea0fe005ff0906e8cbc408..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.13/valid/text_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-0536-AigenOardegheden 46 -Oldambsters-0751-DeVerhoalen 177 -Oldambsters-0591-AigenOardegheden 48 -Oldambsters-0646-DeVerhoalen 114 -Oldambsters-0698-DeVerhoalen 28 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14.log deleted file mode 100644 index 8a27b49930da3e1a055df4c126c4e0125f321172..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.14.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.14.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:34 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.14.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.14.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:40,221 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:40,336 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:58:41,509 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:58:41,525 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:58:41,525 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:41,525 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:58:41,525 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:41,525 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:58:41,525 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/config.yaml -[wieling-3-a100] 2023-12-01 15:58:41,543 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.14.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.14.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=18 threads=1 -# Ended (code 0) at Fri Dec 1 15:58:52 UTC 2023, elapsed time 18 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/config.yaml deleted file mode 100644 index cec7c07e66a4ea897deac52654db0d45823eba01..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.14.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.14.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/train/feats_lengths_stats.npz deleted file mode 100644 index 58e3283ca6247138830efbac42d8b0f7bca7bf2d..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:d95e92d91b1abbfea2617340745bf3d6ccbfcf88866a42a961ec5b9a787b8bf1 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/train/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/train/feats_stats.npz deleted file mode 100644 index 1e88159164391504524f61c5dfb1ab1fe9cf99f7..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/train/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:ab346cb7e0e6daa6dbeeb6564badec21598f9fb263f984675c3e44e14d8ebc45 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/train/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/train/sids_shape deleted file mode 100644 index c4f15f7ad4ea2912fd7ea06f55267fa385d4e345..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/train/sids_shape +++ /dev/null @@ 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a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/train/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/train/speech_shape deleted file mode 100644 index f7dd3779cc095e9e889ecf26e372599f2493651d..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/train/speech_shape +++ /dev/null @@ -1,249 +0,0 @@ -Hoogelaandsters-3342-OpBuus 45032 -Hoogelaandsters-3348-OpBuus 123211 -Hoogelaandsters-3352-OpBuus 212842 -Hoogelaandsters-3356-VOnJ 94329 -Hoogelaandsters-3360-VOnJ 66945 -Hoogelaandsters-3364-VOnJ 100508 -Hoogelaandsters-3368-VOnJ 112722 -Hoogelaandsters-3373-VOnJ 82249 -Hoogelaandsters-3378-VOnJ 51193 -Hoogelaandsters-3382-VOnJ 134327 -Hoogelaandsters-3386-VOnJ 90317 -Hoogelaandsters-3390-VOnJ 61954 -Hoogelaandsters-3394-VOnJ 120636 -Hoogelaandsters-3398-VOnJ 87612 -Oldambsters-0002-AigenOardegheden 22579 -Oldambsters-0006-AigenOardegheden 34796 -Oldambsters-0010-AigenOardegheden 62093 -Oldambsters-0014-AigenOardegheden 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39ca61714ca827c34053af9220e56c015023983e..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/train/text_shape +++ /dev/null @@ -1,249 +0,0 @@ -Hoogelaandsters-3342-OpBuus 28 -Hoogelaandsters-3348-OpBuus 75 -Hoogelaandsters-3352-OpBuus 121 -Hoogelaandsters-3356-VOnJ 55 -Hoogelaandsters-3360-VOnJ 36 -Hoogelaandsters-3364-VOnJ 65 -Hoogelaandsters-3368-VOnJ 71 -Hoogelaandsters-3373-VOnJ 62 -Hoogelaandsters-3378-VOnJ 27 -Hoogelaandsters-3382-VOnJ 89 -Hoogelaandsters-3386-VOnJ 52 -Hoogelaandsters-3390-VOnJ 34 -Hoogelaandsters-3394-VOnJ 76 -Hoogelaandsters-3398-VOnJ 56 -Oldambsters-0002-AigenOardegheden 21 -Oldambsters-0006-AigenOardegheden 25 -Oldambsters-0010-AigenOardegheden 42 -Oldambsters-0014-AigenOardegheden 60 -Oldambsters-0018-AigenOardegheden 17 -Oldambsters-0022-AigenOardegheden 61 -Oldambsters-0026-AigenOardegheden 83 -Oldambsters-0030-AigenOardegheden 65 -Oldambsters-0034-AigenOardegheden 80 -Oldambsters-0039-AigenOardegheden 109 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114 -Oldambsters-0058-AigenOardegheden 38 -Oldambsters-0062-AigenOardegheden 61 -Oldambsters-0066-AigenOardegheden 144 -Oldambsters-0070-AigenOardegheden 67 -Oldambsters-0074-AigenOardegheden 91 -Oldambsters-0078-AigenOardegheden 78 -Oldambsters-0082-AigenOardegheden 59 -Oldambsters-0086-AigenOardegheden 21 -Oldambsters-0090-AigenOardegheden 28 -Oldambsters-0094-AigenOardegheden 48 -Oldambsters-0098-AigenOardegheden 59 -Oldambsters-0102-AigenOardegheden 53 -Oldambsters-0107-AigenOardegheden 30 -Hoogelaandsters-3346-OpBuus 79 -Hoogelaandsters-3351-OpBuus 101 -Hoogelaandsters-3355-OpBuus 96 -Hoogelaandsters-3359-VOnJ 186 -Hoogelaandsters-3363-VOnJ 39 -Hoogelaandsters-3367-VOnJ 53 -Hoogelaandsters-3372-VOnJ 48 -Hoogelaandsters-3377-VOnJ 59 -Hoogelaandsters-3381-VOnJ 53 -Hoogelaandsters-3385-VOnJ 75 -Hoogelaandsters-3389-VOnJ 102 -Hoogelaandsters-3393-VOnJ 117 -Hoogelaandsters-3397-VOnJ 26 -Hoogelaandsters-3401-VOnJ 43 -Oldambsters-0005-AigenOardegheden 14 -Oldambsters-0009-AigenOardegheden 62 -Oldambsters-0013-AigenOardegheden 60 -Oldambsters-0017-AigenOardegheden 48 -Oldambsters-0021-AigenOardegheden 111 -Oldambsters-0025-AigenOardegheden 35 -Oldambsters-0029-AigenOardegheden 55 -Oldambsters-0033-AigenOardegheden 98 -Oldambsters-0037-AigenOardegheden 122 -Oldambsters-0042-AigenOardegheden 42 -Oldambsters-0046-AigenOardegheden 23 -Oldambsters-0050-AigenOardegheden 52 -Oldambsters-0055-AigenOardegheden 160 -Oldambsters-0059-AigenOardegheden 40 -Oldambsters-0063-AigenOardegheden 30 -Oldambsters-0067-AigenOardegheden 69 -Oldambsters-0071-AigenOardegheden 34 -Oldambsters-0075-AigenOardegheden 92 -Oldambsters-0079-AigenOardegheden 181 -Oldambsters-0083-AigenOardegheden 95 -Oldambsters-0087-AigenOardegheden 159 -Oldambsters-0091-AigenOardegheden 37 -Oldambsters-0095-AigenOardegheden 35 -Oldambsters-0099-AigenOardegheden 40 -Oldambsters-0103-AigenOardegheden 65 -Oldambsters-0108-AigenOardegheden 36 -Oldambsters-0110-AigenOardegheden 28 -Oldambsters-0114-AigenOardegheden 83 -Oldambsters-0118-AigenOardegheden 11 -Oldambsters-0122-AigenOardegheden 65 -Oldambsters-0126-AigenOardegheden 44 -Oldambsters-0130-AigenOardegheden 324 -Oldambsters-0134-AigenOardegheden 43 -Oldambsters-0138-AigenOardegheden 119 -Oldambsters-0142-AigenOardegheden 58 -Oldambsters-0146-AigenOardegheden 69 -Oldambsters-0150-AigenOardegheden 90 -Oldambsters-0155-AigenOardegheden 72 -Oldambsters-0159-AigenOardegheden 41 -Oldambsters-0164-AigenOardegheden 91 -Oldambsters-0168-AigenOardegheden 72 -Oldambsters-0172-AigenOardegheden 25 -Oldambsters-0176-AigenOardegheden 61 -Oldambsters-0180-AigenOardegheden 60 -Oldambsters-0184-AigenOardegheden 105 -Oldambsters-0192-AigenOardegheden 107 -Oldambsters-0196-AigenOardegheden 100 -Oldambsters-0200-AigenOardegheden 58 -Oldambsters-0205-AigenOardegheden 39 -Oldambsters-0111-AigenOardegheden 110 -Oldambsters-0115-AigenOardegheden 156 -Oldambsters-0119-AigenOardegheden 137 -Oldambsters-0123-AigenOardegheden 38 -Oldambsters-0127-AigenOardegheden 20 -Oldambsters-0131-AigenOardegheden 105 -Oldambsters-0135-AigenOardegheden 40 -Oldambsters-0139-AigenOardegheden 32 -Oldambsters-0143-AigenOardegheden 105 -Oldambsters-0147-AigenOardegheden 24 -Oldambsters-0151-AigenOardegheden 60 -Oldambsters-0156-AigenOardegheden 212 -Oldambsters-0161-AigenOardegheden 60 -Oldambsters-0165-AigenOardegheden 107 -Oldambsters-0169-AigenOardegheden 77 -Oldambsters-0173-AigenOardegheden 29 -Oldambsters-0177-AigenOardegheden 120 -Oldambsters-0181-AigenOardegheden 73 -Oldambsters-0186-AigenOardegheden 141 -Oldambsters-0193-AigenOardegheden 14 -Oldambsters-0197-AigenOardegheden 49 -Oldambsters-0201-AigenOardegheden 56 -Oldambsters-0112-AigenOardegheden 67 -Oldambsters-0116-AigenOardegheden 26 -Oldambsters-0120-AigenOardegheden 110 -Oldambsters-0124-AigenOardegheden 48 -Oldambsters-0128-AigenOardegheden 46 -Oldambsters-0132-AigenOardegheden 84 -Oldambsters-0136-AigenOardegheden 150 -Oldambsters-0140-AigenOardegheden 57 -Oldambsters-0144-AigenOardegheden 43 -Oldambsters-0148-AigenOardegheden 27 -Oldambsters-0152-AigenOardegheden 14 -Oldambsters-0157-AigenOardegheden 34 -Oldambsters-0162-AigenOardegheden 88 -Oldambsters-0166-AigenOardegheden 89 -Oldambsters-0170-AigenOardegheden 22 -Oldambsters-0174-AigenOardegheden 43 -Oldambsters-0178-AigenOardegheden 188 -Oldambsters-0182-AigenOardegheden 180 -Oldambsters-0187-AigenOardegheden 23 -Oldambsters-0194-AigenOardegheden 102 -Oldambsters-0198-AigenOardegheden 38 -Oldambsters-0203-AigenOardegheden 45 -Oldambsters-0113-AigenOardegheden 55 -Oldambsters-0117-AigenOardegheden 23 -Oldambsters-0121-AigenOardegheden 103 -Oldambsters-0125-AigenOardegheden 30 -Oldambsters-0129-AigenOardegheden 34 -Oldambsters-0133-AigenOardegheden 46 -Oldambsters-0137-AigenOardegheden 22 -Oldambsters-0141-AigenOardegheden 40 -Oldambsters-0145-AigenOardegheden 31 -Oldambsters-0149-AigenOardegheden 187 -Oldambsters-0154-AigenOardegheden 109 -Oldambsters-0158-AigenOardegheden 13 -Oldambsters-0163-AigenOardegheden 53 -Oldambsters-0167-AigenOardegheden 68 -Oldambsters-0171-AigenOardegheden 35 -Oldambsters-0175-AigenOardegheden 161 -Oldambsters-0179-AigenOardegheden 92 -Oldambsters-0183-AigenOardegheden 74 -Oldambsters-0188-AigenOardegheden 57 -Oldambsters-0195-AigenOardegheden 14 -Oldambsters-0199-AigenOardegheden 20 -Oldambsters-0204-AigenOardegheden 20 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/valid/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/valid/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/valid/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/valid/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/valid/feats_lengths_stats.npz deleted file mode 100644 index f9a8e86474a456d92e0d65c273ee61a60910edcb..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:ada2b680b05b975ce0e173586be31f20a7743e83b4487d9fd26b215be7788142 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/valid/feats_stats.npz deleted file mode 100644 index 0865d3e2aab46ddf7ca2aaddf57ab947f3c8d2b1..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:d671171ae144964fe0355661b3940ae48503663bd8854f949d0d4954fa5e15f6 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/valid/sids_shape deleted file mode 100644 index 857942971278d885bb19fa35c86be21ab200a60c..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/valid/sids_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-0805-DeVerhoalen 1 -Oldambsters-1020-DeVerhoalen 1 -Oldambsters-0858-DeVerhoalen 1 -Oldambsters-0911-DeVerhoalen 1 -Oldambsters-0967-DeVerhoalen 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/valid/speech_shape deleted file mode 100644 index 27be7fc8b45373eeffbc8187735590456be36c65..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/valid/speech_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-0805-DeVerhoalen 31046 -Oldambsters-1020-DeVerhoalen 342250 -Oldambsters-0858-DeVerhoalen 120553 -Oldambsters-0911-DeVerhoalen 112896 -Oldambsters-0967-DeVerhoalen 119321 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/valid/text_shape deleted file mode 100644 index 71216b4d29f4d70ea22cc8b747415430d3cb162d..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.14/valid/text_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-0805-DeVerhoalen 20 -Oldambsters-1020-DeVerhoalen 212 -Oldambsters-0858-DeVerhoalen 79 -Oldambsters-0911-DeVerhoalen 83 -Oldambsters-0967-DeVerhoalen 63 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15.log deleted file mode 100644 index 0cdc2120ed1a2b04307f41cfe1b994f0edaf988b..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.15.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.15.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:34 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.15.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.15.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:40,708 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:40,827 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:58:42,141 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:58:42,174 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:58:42,174 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:42,174 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:58:42,174 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:42,174 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:58:42,175 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/config.yaml -[wieling-3-a100] 2023-12-01 15:58:42,209 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.15.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.15.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=18 threads=1 -# Ended (code 0) at Fri Dec 1 15:58:52 UTC 2023, elapsed time 18 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/config.yaml deleted file mode 100644 index 453b94c28aed08c8743aa5d348374487a2836d6a..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.15.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.15.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/train/feats_lengths_stats.npz deleted file mode 100644 index 7d781bd3a138f85f6cc93c656b7cf95a59af126f..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:09902706b23bccde74c0bc5f8f8742e2d022d3273e11f7b04c1d7cd4cd2b6662 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/train/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/train/feats_stats.npz deleted file mode 100644 index dc892294187be2da838b170e67f5cdfbf991e23b..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/train/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:2f796b646c7c8c8813c6d232d4f47501320f4cd4f81cb56a8a5934a77ddbe65e -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/train/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/train/sids_shape deleted file mode 100644 index f73094af01af5f4f5e99a8d778dabd26042ef10f..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/train/sids_shape +++ /dev/null @@ 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-Oldambsters-0460-AigenOardegheden 96926 -Oldambsters-0464-AigenOardegheden 86466 -Oldambsters-0468-AigenOardegheden 151508 -Oldambsters-0379-AigenOardegheden 129395 -Oldambsters-0383-AigenOardegheden 79840 -Oldambsters-0387-AigenOardegheden 85748 -Oldambsters-0391-AigenOardegheden 88346 -Oldambsters-0395-AigenOardegheden 79027 -Oldambsters-0400-AigenOardegheden 132653 -Oldambsters-0404-AigenOardegheden 57187 -Oldambsters-0408-AigenOardegheden 99514 -Oldambsters-0412-AigenOardegheden 137498 -Oldambsters-0418-AigenOardegheden 118541 -Oldambsters-0422-AigenOardegheden 169916 -Oldambsters-0426-AigenOardegheden 220787 -Oldambsters-0431-AigenOardegheden 56448 -Oldambsters-0435-AigenOardegheden 39514 -Oldambsters-0439-AigenOardegheden 118541 -Oldambsters-0443-AigenOardegheden 67738 -Oldambsters-0447-AigenOardegheden 67738 -Oldambsters-0453-AigenOardegheden 115718 -Oldambsters-0457-AigenOardegheden 39514 -Oldambsters-0461-AigenOardegheden 41473 -Oldambsters-0465-AigenOardegheden 198885 -Oldambsters-0469-AigenOardegheden 61342 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/train/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/train/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/train/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/train/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/train/text_shape deleted file mode 100644 index fdc40c64b0fbe10783ba71e1e4899027b26647d3..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/train/text_shape +++ /dev/null @@ -1,249 +0,0 @@ -Oldambsters-0206-AigenOardegheden 15 -Oldambsters-0211-AigenOardegheden 33 -Oldambsters-0216-AigenOardegheden 44 -Oldambsters-0220-AigenOardegheden 71 -Oldambsters-0225-AigenOardegheden 102 -Oldambsters-0229-AigenOardegheden 122 -Oldambsters-0233-AigenOardegheden 36 -Oldambsters-0237-AigenOardegheden 52 -Oldambsters-0243-AigenOardegheden 30 -Oldambsters-0247-AigenOardegheden 32 -Oldambsters-0251-AigenOardegheden 99 -Oldambsters-0255-AigenOardegheden 88 -Oldambsters-0259-AigenOardegheden 19 -Oldambsters-0263-AigenOardegheden 57 -Oldambsters-0267-AigenOardegheden 94 -Oldambsters-0272-AigenOardegheden 146 -Oldambsters-0276-AigenOardegheden 191 -Oldambsters-0280-AigenOardegheden 192 -Oldambsters-0284-AigenOardegheden 28 -Oldambsters-0288-AigenOardegheden 72 -Oldambsters-0292-AigenOardegheden 26 -Oldambsters-0296-AigenOardegheden 48 -Oldambsters-0300-AigenOardegheden 90 -Oldambsters-0304-AigenOardegheden 91 -Oldambsters-0308-AigenOardegheden 161 -Oldambsters-0312-AigenOardegheden 42 -Oldambsters-0316-AigenOardegheden 37 -Oldambsters-0321-AigenOardegheden 148 -Oldambsters-0326-AigenOardegheden 81 -Oldambsters-0331-AigenOardegheden 30 -Oldambsters-0335-AigenOardegheden 25 -Oldambsters-0339-AigenOardegheden 29 -Oldambsters-0343-AigenOardegheden 140 -Oldambsters-0347-AigenOardegheden 27 -Oldambsters-0351-AigenOardegheden 176 -Oldambsters-0355-AigenOardegheden 107 -Oldambsters-0359-AigenOardegheden 67 -Oldambsters-0363-AigenOardegheden 141 -Oldambsters-0367-AigenOardegheden 38 -Oldambsters-0371-AigenOardegheden 75 -Oldambsters-0208-AigenOardegheden 92 -Oldambsters-0212-AigenOardegheden 93 -Oldambsters-0217-AigenOardegheden 40 -Oldambsters-0221-AigenOardegheden 36 -Oldambsters-0226-AigenOardegheden 110 -Oldambsters-0230-AigenOardegheden 29 -Oldambsters-0234-AigenOardegheden 25 -Oldambsters-0238-AigenOardegheden 35 -Oldambsters-0244-AigenOardegheden 23 -Oldambsters-0248-AigenOardegheden 23 -Oldambsters-0252-AigenOardegheden 89 -Oldambsters-0256-AigenOardegheden 32 -Oldambsters-0260-AigenOardegheden 30 -Oldambsters-0264-AigenOardegheden 84 -Oldambsters-0268-AigenOardegheden 17 -Oldambsters-0273-AigenOardegheden 64 -Oldambsters-0277-AigenOardegheden 188 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-Oldambsters-0222-AigenOardegheden 35 -Oldambsters-0227-AigenOardegheden 31 -Oldambsters-0231-AigenOardegheden 87 -Oldambsters-0235-AigenOardegheden 92 -Oldambsters-0239-AigenOardegheden 43 -Oldambsters-0245-AigenOardegheden 74 -Oldambsters-0249-AigenOardegheden 76 -Oldambsters-0253-AigenOardegheden 93 -Oldambsters-0257-AigenOardegheden 83 -Oldambsters-0261-AigenOardegheden 236 -Oldambsters-0265-AigenOardegheden 55 -Oldambsters-0269-AigenOardegheden 217 -Oldambsters-0274-AigenOardegheden 97 -Oldambsters-0278-AigenOardegheden 66 -Oldambsters-0282-AigenOardegheden 157 -Oldambsters-0286-AigenOardegheden 75 -Oldambsters-0290-AigenOardegheden 62 -Oldambsters-0294-AigenOardegheden 51 -Oldambsters-0298-AigenOardegheden 38 -Oldambsters-0302-AigenOardegheden 14 -Oldambsters-0306-AigenOardegheden 92 -Oldambsters-0310-AigenOardegheden 22 -Oldambsters-0314-AigenOardegheden 70 -Oldambsters-0319-AigenOardegheden 62 -Oldambsters-0324-AigenOardegheden 40 -Oldambsters-0328-AigenOardegheden 132 -Oldambsters-0333-AigenOardegheden 40 -Oldambsters-0337-AigenOardegheden 52 -Oldambsters-0341-AigenOardegheden 35 -Oldambsters-0345-AigenOardegheden 53 -Oldambsters-0349-AigenOardegheden 22 -Oldambsters-0353-AigenOardegheden 116 -Oldambsters-0357-AigenOardegheden 37 -Oldambsters-0361-AigenOardegheden 16 -Oldambsters-0365-AigenOardegheden 69 -Oldambsters-0369-AigenOardegheden 51 -Oldambsters-0373-AigenOardegheden 43 -Oldambsters-0210-AigenOardegheden 67 -Oldambsters-0214-AigenOardegheden 22 -Oldambsters-0219-AigenOardegheden 72 -Oldambsters-0224-AigenOardegheden 163 -Oldambsters-0228-AigenOardegheden 143 -Oldambsters-0232-AigenOardegheden 36 -Oldambsters-0236-AigenOardegheden 50 -Oldambsters-0240-AigenOardegheden 17 -Oldambsters-0246-AigenOardegheden 95 -Oldambsters-0250-AigenOardegheden 120 -Oldambsters-0254-AigenOardegheden 139 -Oldambsters-0258-AigenOardegheden 21 -Oldambsters-0262-AigenOardegheden 98 -Oldambsters-0266-AigenOardegheden 43 -Oldambsters-0271-AigenOardegheden 14 -Oldambsters-0275-AigenOardegheden 47 -Oldambsters-0279-AigenOardegheden 66 -Oldambsters-0283-AigenOardegheden 59 -Oldambsters-0287-AigenOardegheden 27 -Oldambsters-0291-AigenOardegheden 26 -Oldambsters-0295-AigenOardegheden 16 -Oldambsters-0299-AigenOardegheden 43 -Oldambsters-0303-AigenOardegheden 48 -Oldambsters-0307-AigenOardegheden 54 -Oldambsters-0311-AigenOardegheden 16 -Oldambsters-0315-AigenOardegheden 63 -Oldambsters-0320-AigenOardegheden 36 -Oldambsters-0325-AigenOardegheden 89 -Oldambsters-0330-AigenOardegheden 225 -Oldambsters-0334-AigenOardegheden 12 -Oldambsters-0338-AigenOardegheden 231 -Oldambsters-0342-AigenOardegheden 56 -Oldambsters-0346-AigenOardegheden 75 -Oldambsters-0350-AigenOardegheden 62 -Oldambsters-0354-AigenOardegheden 71 -Oldambsters-0358-AigenOardegheden 74 -Oldambsters-0362-AigenOardegheden 26 -Oldambsters-0366-AigenOardegheden 40 -Oldambsters-0370-AigenOardegheden 64 -Oldambsters-0374-AigenOardegheden 67 -Oldambsters-0376-AigenOardegheden 97 -Oldambsters-0380-AigenOardegheden 186 -Oldambsters-0384-AigenOardegheden 71 -Oldambsters-0388-AigenOardegheden 62 -Oldambsters-0392-AigenOardegheden 65 -Oldambsters-0397-AigenOardegheden 28 -Oldambsters-0401-AigenOardegheden 119 -Oldambsters-0405-AigenOardegheden 57 -Oldambsters-0409-AigenOardegheden 109 -Oldambsters-0413-AigenOardegheden 71 -Oldambsters-0419-AigenOardegheden 201 -Oldambsters-0423-AigenOardegheden 55 -Oldambsters-0427-AigenOardegheden 47 -Oldambsters-0432-AigenOardegheden 35 -Oldambsters-0436-AigenOardegheden 73 -Oldambsters-0440-AigenOardegheden 82 -Oldambsters-0444-AigenOardegheden 44 -Oldambsters-0449-AigenOardegheden 24 -Oldambsters-0454-AigenOardegheden 47 -Oldambsters-0458-AigenOardegheden 71 -Oldambsters-0462-AigenOardegheden 205 -Oldambsters-0466-AigenOardegheden 57 -Oldambsters-0470-AigenOardegheden 47 -Oldambsters-0377-AigenOardegheden 35 -Oldambsters-0381-AigenOardegheden 29 -Oldambsters-0385-AigenOardegheden 57 -Oldambsters-0389-AigenOardegheden 54 -Oldambsters-0393-AigenOardegheden 66 -Oldambsters-0398-AigenOardegheden 88 -Oldambsters-0402-AigenOardegheden 114 -Oldambsters-0406-AigenOardegheden 107 -Oldambsters-0410-AigenOardegheden 46 -Oldambsters-0415-AigenOardegheden 28 -Oldambsters-0420-AigenOardegheden 77 -Oldambsters-0424-AigenOardegheden 60 -Oldambsters-0428-AigenOardegheden 28 -Oldambsters-0433-AigenOardegheden 184 -Oldambsters-0437-AigenOardegheden 36 -Oldambsters-0441-AigenOardegheden 69 -Oldambsters-0445-AigenOardegheden 69 -Oldambsters-0450-AigenOardegheden 51 -Oldambsters-0455-AigenOardegheden 47 -Oldambsters-0459-AigenOardegheden 41 -Oldambsters-0463-AigenOardegheden 118 -Oldambsters-0467-AigenOardegheden 54 -Oldambsters-0378-AigenOardegheden 92 -Oldambsters-0382-AigenOardegheden 8 -Oldambsters-0386-AigenOardegheden 31 -Oldambsters-0390-AigenOardegheden 58 -Oldambsters-0394-AigenOardegheden 26 -Oldambsters-0399-AigenOardegheden 49 -Oldambsters-0403-AigenOardegheden 74 -Oldambsters-0407-AigenOardegheden 82 -Oldambsters-0411-AigenOardegheden 148 -Oldambsters-0416-AigenOardegheden 57 -Oldambsters-0421-AigenOardegheden 138 -Oldambsters-0425-AigenOardegheden 64 -Oldambsters-0429-AigenOardegheden 109 -Oldambsters-0434-AigenOardegheden 57 -Oldambsters-0438-AigenOardegheden 39 -Oldambsters-0442-AigenOardegheden 49 -Oldambsters-0446-AigenOardegheden 54 -Oldambsters-0452-AigenOardegheden 18 -Oldambsters-0456-AigenOardegheden 99 -Oldambsters-0460-AigenOardegheden 59 -Oldambsters-0464-AigenOardegheden 56 -Oldambsters-0468-AigenOardegheden 114 -Oldambsters-0379-AigenOardegheden 91 -Oldambsters-0383-AigenOardegheden 42 -Oldambsters-0387-AigenOardegheden 49 -Oldambsters-0391-AigenOardegheden 55 -Oldambsters-0395-AigenOardegheden 44 -Oldambsters-0400-AigenOardegheden 93 -Oldambsters-0404-AigenOardegheden 88 -Oldambsters-0408-AigenOardegheden 65 -Oldambsters-0412-AigenOardegheden 92 -Oldambsters-0418-AigenOardegheden 86 -Oldambsters-0422-AigenOardegheden 119 -Oldambsters-0426-AigenOardegheden 147 -Oldambsters-0431-AigenOardegheden 43 -Oldambsters-0435-AigenOardegheden 35 -Oldambsters-0439-AigenOardegheden 80 -Oldambsters-0443-AigenOardegheden 46 -Oldambsters-0447-AigenOardegheden 35 -Oldambsters-0453-AigenOardegheden 76 -Oldambsters-0457-AigenOardegheden 25 -Oldambsters-0461-AigenOardegheden 20 -Oldambsters-0465-AigenOardegheden 125 -Oldambsters-0469-AigenOardegheden 28 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/valid/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/valid/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/valid/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/valid/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/valid/feats_lengths_stats.npz deleted file mode 100644 index 43a56c8a3df5ceb5b6b1baa1e6fa3362c110a881..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:a11583ada125033bbc917e1c086e8c0b8d616049fd265fda3f0ad68c9eb5ff4e -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/valid/feats_stats.npz deleted file mode 100644 index e4ce14762f0ad3ed9094551b38f5ec550c0aa0e1..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:0e7b1e65a148b220d15d534a1e7f11cbfa8e84f917e6505158e9bce489c959ee -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/valid/sids_shape deleted file mode 100644 index 1a3b1cfbb2761206fba69ededd387a6808bbeffe..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/valid/sids_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-1073-DeVerhoalen 1 -Oldambsters-1284-WaitGoud 1 -Oldambsters-1126-DeVerhoalen 1 -Oldambsters-1179-DeVerhoalen 1 -Oldambsters-1232-DeVerhoalen 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/valid/speech_shape deleted file mode 100644 index cbe91e80428c79dd294318b867b7765ec539e428..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/valid/speech_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-1073-DeVerhoalen 129698 -Oldambsters-1284-WaitGoud 293119 -Oldambsters-1126-DeVerhoalen 179115 -Oldambsters-1179-DeVerhoalen 64915 -Oldambsters-1232-DeVerhoalen 135515 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/valid/text_shape deleted file mode 100644 index 601271640b65cfaf459770d568eeb69166a13327..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.15/valid/text_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-1073-DeVerhoalen 68 -Oldambsters-1284-WaitGoud 172 -Oldambsters-1126-DeVerhoalen 108 -Oldambsters-1179-DeVerhoalen 33 -Oldambsters-1232-DeVerhoalen 90 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16.log deleted file mode 100644 index 1a681c0d30c5634dc4ba7ce9c95dc7b4c673f2e5..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.16.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.16.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:34 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.16.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.16.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:40,816 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:40,932 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:58:42,360 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:58:42,438 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:58:42,438 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:42,438 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:58:42,438 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:42,438 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:58:42,439 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/config.yaml -[wieling-3-a100] 2023-12-01 15:58:42,497 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.16.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.16.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=18 threads=1 -# Ended (code 0) at Fri Dec 1 15:58:52 UTC 2023, elapsed time 18 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/config.yaml deleted file mode 100644 index af954b1d4ec5d9dc225ca99416a34711a970bc48..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.16.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.16.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/train/feats_lengths_stats.npz deleted file mode 100644 index fedd001b3bbbfdb5d0bd05c26fbec8afa6af8803..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:57f94639338735cebb4e5ff0ceeed10530bbb166f06f4d9b0b9a98c9229dee6c -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/train/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/train/feats_stats.npz deleted file mode 100644 index fc018f2ea20d2162ff7f59708d7a2c26510aa81a..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/train/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:ba717076ac08aa8fabd426f057b8343d1cb6dd082f5382ddf0b16919288405e4 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/train/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/train/sids_shape deleted file mode 100644 index f6c797a9f795847f81b34346deb302b2c7178d37..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/train/sids_shape +++ /dev/null @@ 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-Oldambsters-0719-DeVerhoalen 12 -Oldambsters-0723-DeVerhoalen 162 -Oldambsters-0727-DeVerhoalen 66 -Oldambsters-0732-DeVerhoalen 69 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/valid/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/valid/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/valid/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/valid/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/valid/feats_lengths_stats.npz deleted file mode 100644 index 62eea6016edd83ddf8464084fa0e5e94ba64ebcf..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:66af0b3c770f076e344e1f77dbcbe4bbd9c2110ed2e232586b66ddd10401471c -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/valid/feats_stats.npz deleted file mode 100644 index f7bb3dd7f80c981499f603c4ec8fff39e8ffeb02..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:4128d1fdf9625c434da3a63fe45ae94084167b5971aee903999f3cd726cb0a3d -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/valid/sids_shape deleted file mode 100644 index 4c77f871346d95360e8f0f05b6fccb86abe6afc0..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/valid/sids_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-1339-WaitGoud 1 -Oldambsters-1556-WaitGoud 1 -Oldambsters-1396-WaitGoud 1 -Oldambsters-1450-WaitGoud 1 -Oldambsters-1502-WaitGoud 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/valid/speech_shape deleted file mode 100644 index 45b9d29c825022c7b1b0b4fefa92f174c2eab43d..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/valid/speech_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-1339-WaitGoud 45158 -Oldambsters-1556-WaitGoud 91785 -Oldambsters-1396-WaitGoud 128375 -Oldambsters-1450-WaitGoud 69434 -Oldambsters-1502-WaitGoud 123885 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/valid/text_shape deleted file mode 100644 index 522567f3c80304f914c90235c16fbff7ac08cd3e..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.16/valid/text_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-1339-WaitGoud 30 -Oldambsters-1556-WaitGoud 61 -Oldambsters-1396-WaitGoud 96 -Oldambsters-1450-WaitGoud 38 -Oldambsters-1502-WaitGoud 82 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17.log deleted file mode 100644 index 2cbbab2dce3fd9f2ea3340c8c55783552ea59910..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.17.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.17.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:50 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.17.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.17.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:56,542 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:56,656 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:58:57,906 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:58:57,921 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:58:57,921 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:57,921 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:58:57,922 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:57,922 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:58:57,922 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/config.yaml -[wieling-3-a100] 2023-12-01 15:58:57,940 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.17.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.17.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=18 threads=1 -# Ended (code 0) at Fri Dec 1 15:59:08 UTC 2023, elapsed time 18 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/config.yaml deleted file mode 100644 index 9b9bb12939e18c65a3c3bd1027fb56f3762a020a..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.17.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.17.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/train/feats_lengths_stats.npz deleted file mode 100644 index 9c95dc892a2bc340b0e76f84c3aff011a848c618..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:ed69ab9c7d5d4004efaae1a4cf837dca60c650297a122056d3e5bd4848c90e9c -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/train/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/train/feats_stats.npz deleted file mode 100644 index e55ef8f5d0feb73b07653f7ad4ed47947aeb2471..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/train/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:88079f875120d9ed3f3b27845cdce735d36bb121234ac1245b55edebba2c1a58 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/train/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/train/sids_shape deleted file mode 100644 index c990ab27b05dd4b117caf11a9a5c880873ab871b..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/train/sids_shape +++ /dev/null @@ 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-Oldambsters-0907-DeVerhoalen 50 -Oldambsters-0912-DeVerhoalen 176 -Oldambsters-0916-DeVerhoalen 81 -Oldambsters-0920-DeVerhoalen 80 -Oldambsters-0925-DeVerhoalen 45 -Oldambsters-0929-DeVerhoalen 63 -Oldambsters-0933-DeVerhoalen 28 -Oldambsters-0937-DeVerhoalen 77 -Oldambsters-0942-DeVerhoalen 110 -Oldambsters-0946-DeVerhoalen 36 -Oldambsters-0950-DeVerhoalen 67 -Oldambsters-0954-DeVerhoalen 28 -Oldambsters-0959-DeVerhoalen 63 -Oldambsters-0963-DeVerhoalen 101 -Oldambsters-0969-DeVerhoalen 78 -Oldambsters-0973-DeVerhoalen 9 -Oldambsters-0977-DeVerhoalen 107 -Oldambsters-0981-DeVerhoalen 73 -Oldambsters-0985-DeVerhoalen 49 -Oldambsters-0990-DeVerhoalen 38 -Oldambsters-0994-DeVerhoalen 39 -Oldambsters-0998-DeVerhoalen 122 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/valid/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/valid/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/valid/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/valid/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/valid/feats_lengths_stats.npz deleted file mode 100644 index 668c8d51f755a0ef115a7495e47cb38d5200bb12..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:266bcef4d5d3d43e97ad564b0acb98616350a0d7eabc658e784deaf8ca32fed6 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/valid/feats_stats.npz deleted file mode 100644 index 86e98d0c3d42fd2d22d304d00f91dd81c9c8b0b6..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:9ce224b62576948947b23a915ea43dc542e498c3a60156add7561f15ac9e3bb5 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/valid/sids_shape deleted file mode 100644 index 71f571dadec00b856bfa581ac0a77ad47bc50e8f..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/valid/sids_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-1616-WaitGoud 1 -Oldambsters-1828-WaitGoud 1 -Oldambsters-1668-WaitGoud 1 -Oldambsters-1723-WaitGoud 1 -Oldambsters-1776-WaitGoud 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/valid/speech_shape deleted file mode 100644 index bf29d7dc25470f9cffcd6ee8a8a96af135f8c587..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/valid/speech_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-1616-WaitGoud 60462 -Oldambsters-1828-WaitGoud 99951 -Oldambsters-1668-WaitGoud 67738 -Oldambsters-1723-WaitGoud 157875 -Oldambsters-1776-WaitGoud 45158 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/valid/text_shape deleted file mode 100644 index ff72c6a526f936109a91d035d12b554c0e349190..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.17/valid/text_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-1616-WaitGoud 29 -Oldambsters-1828-WaitGoud 70 -Oldambsters-1668-WaitGoud 53 -Oldambsters-1723-WaitGoud 99 -Oldambsters-1776-WaitGoud 28 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18.log deleted file mode 100644 index b7f0f6b711830dfe04e4a2498192c1ddc183e5e2..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.18.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.18.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:50 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.18.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.18.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:56,645 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:56,756 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:58:57,938 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:58:57,953 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:58:57,953 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:57,953 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:58:57,953 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:57,953 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:58:57,954 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/config.yaml -[wieling-3-a100] 2023-12-01 15:58:57,972 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.18.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.18.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=18 threads=1 -# Ended (code 0) at Fri Dec 1 15:59:08 UTC 2023, elapsed time 18 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/config.yaml deleted file mode 100644 index 7eb09667538100b30a795b14b77f9c3572a604ba..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.18.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.18.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/train/feats_lengths_stats.npz deleted file mode 100644 index 595d63d4c6eb6eed2b27f77ff94f0a61cce7f534..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:9a112f4c283a3b595178f89f8a56a1c7e9a3c5e5fdb9b4255b4ffc64930c9014 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/train/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/train/feats_stats.npz deleted file mode 100644 index b227f86eaee26fd3560c2d9fccdd73e940f8bec7..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/train/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:e70fb2a519b09db97d95c5632d8c02403f8d7501843aea48ca1b8826f372547f -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/train/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/train/sids_shape deleted file mode 100644 index 507c07939d8687a8d1e73080ad18703f8bc41b6b..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/train/sids_shape +++ /dev/null @@ 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-Oldambsters-1196-DeVerhoalen 17 -Oldambsters-1200-DeVerhoalen 47 -Oldambsters-1205-DeVerhoalen 9 -Oldambsters-1210-DeVerhoalen 4 -Oldambsters-1214-DeVerhoalen 19 -Oldambsters-1218-DeVerhoalen 35 -Oldambsters-1222-DeVerhoalen 32 -Oldambsters-1226-DeVerhoalen 92 -Oldambsters-1230-DeVerhoalen 55 -Oldambsters-1235-DeVerhoalen 18 -Oldambsters-1239-DeVerhoalen 128 -Oldambsters-1243-DeVerhoalen 169 -Oldambsters-1247-DeVerhoalen 13 -Oldambsters-1251-DeVerhoalen 165 -Oldambsters-1255-WaitGoud 79 -Oldambsters-1259-WaitGoud 62 -Oldambsters-1167-DeVerhoalen 34 -Oldambsters-1172-DeVerhoalen 24 -Oldambsters-1176-DeVerhoalen 76 -Oldambsters-1181-DeVerhoalen 43 -Oldambsters-1185-DeVerhoalen 165 -Oldambsters-1189-DeVerhoalen 146 -Oldambsters-1193-DeVerhoalen 27 -Oldambsters-1197-DeVerhoalen 75 -Oldambsters-1201-DeVerhoalen 62 -Oldambsters-1207-DeVerhoalen 79 -Oldambsters-1211-DeVerhoalen 23 -Oldambsters-1215-DeVerhoalen 66 -Oldambsters-1219-DeVerhoalen 91 -Oldambsters-1223-DeVerhoalen 37 -Oldambsters-1227-DeVerhoalen 132 -Oldambsters-1231-DeVerhoalen 60 -Oldambsters-1236-DeVerhoalen 71 -Oldambsters-1240-DeVerhoalen 53 -Oldambsters-1244-DeVerhoalen 71 -Oldambsters-1248-DeVerhoalen 43 -Oldambsters-1252-DeVerhoalen 69 -Oldambsters-1256-WaitGoud 18 -Oldambsters-1168-DeVerhoalen 52 -Oldambsters-1173-DeVerhoalen 90 -Oldambsters-1177-DeVerhoalen 71 -Oldambsters-1182-DeVerhoalen 30 -Oldambsters-1186-DeVerhoalen 124 -Oldambsters-1190-DeVerhoalen 131 -Oldambsters-1194-DeVerhoalen 48 -Oldambsters-1198-DeVerhoalen 33 -Oldambsters-1202-DeVerhoalen 22 -Oldambsters-1208-DeVerhoalen 33 -Oldambsters-1212-DeVerhoalen 31 -Oldambsters-1216-DeVerhoalen 40 -Oldambsters-1220-DeVerhoalen 54 -Oldambsters-1224-DeVerhoalen 129 -Oldambsters-1228-DeVerhoalen 165 -Oldambsters-1233-DeVerhoalen 93 -Oldambsters-1237-DeVerhoalen 100 -Oldambsters-1241-DeVerhoalen 25 -Oldambsters-1245-DeVerhoalen 101 -Oldambsters-1249-DeVerhoalen 51 -Oldambsters-1253-DeVerhoalen 58 -Oldambsters-1257-WaitGoud 33 -Oldambsters-1169-DeVerhoalen 145 -Oldambsters-1174-DeVerhoalen 39 -Oldambsters-1178-DeVerhoalen 24 -Oldambsters-1183-DeVerhoalen 49 -Oldambsters-1187-DeVerhoalen 48 -Oldambsters-1191-DeVerhoalen 13 -Oldambsters-1195-DeVerhoalen 40 -Oldambsters-1199-DeVerhoalen 59 -Oldambsters-1203-DeVerhoalen 33 -Oldambsters-1209-DeVerhoalen 9 -Oldambsters-1213-DeVerhoalen 9 -Oldambsters-1217-DeVerhoalen 19 -Oldambsters-1221-DeVerhoalen 173 -Oldambsters-1225-DeVerhoalen 81 -Oldambsters-1229-DeVerhoalen 130 -Oldambsters-1234-DeVerhoalen 13 -Oldambsters-1238-DeVerhoalen 18 -Oldambsters-1242-DeVerhoalen 77 -Oldambsters-1246-DeVerhoalen 11 -Oldambsters-1250-DeVerhoalen 74 -Oldambsters-1254-DeVerhoalen 174 -Oldambsters-1258-WaitGoud 62 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/valid/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/valid/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/valid/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/valid/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/valid/feats_lengths_stats.npz deleted file mode 100644 index bf17160a202098bebc1939133183ceb5fd4ca5d0..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:e4b0a41e01f551c11dc25db4707fec06f0a6bc601b3dde2239c9b522859f161c -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/valid/feats_stats.npz deleted file mode 100644 index 77f07a23e1d49a35cc751a48fbd91c49e25c9598..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:ed7a4818066c266ef3339caffc4c79013303cc868198f0c95ae0c67ea0d09bb5 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/valid/sids_shape deleted file mode 100644 index 19766ec1392fc8f9f148255d454a6b651cffe7f4..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/valid/sids_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-1881-WaitGoud 1 -Oldambsters-2096-WaitGoud 1 -Oldambsters-1937-WaitGoud 1 -Oldambsters-1990-WaitGoud 1 -Oldambsters-2043-WaitGoud 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/valid/speech_shape deleted file mode 100644 index 7b904e30698c165b0b3c3fdf9f87601b2376d8f3..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/valid/speech_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-1881-WaitGoud 62093 -Oldambsters-2096-WaitGoud 117682 -Oldambsters-1937-WaitGoud 35891 -Oldambsters-1990-WaitGoud 70399 -Oldambsters-2043-WaitGoud 167594 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/valid/text_shape deleted file mode 100644 index 0f0bc851b0b97d2cc6fd99ce958d64a24e633458..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.18/valid/text_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-1881-WaitGoud 41 -Oldambsters-2096-WaitGoud 78 -Oldambsters-1937-WaitGoud 12 -Oldambsters-1990-WaitGoud 43 -Oldambsters-2043-WaitGoud 101 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19.log deleted file mode 100644 index 0384df3bf356f01857d5e36cb7dd2c9e7d24a80f..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.19.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.19.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:50 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.19.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.19.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:56,553 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:56,722 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:58:57,891 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:58:57,906 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:58:57,906 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:57,906 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:58:57,906 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:57,907 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:58:57,907 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/config.yaml -[wieling-3-a100] 2023-12-01 15:58:57,925 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.19.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.19.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=17 threads=1 -# Ended (code 0) at Fri Dec 1 15:59:08 UTC 2023, elapsed time 17 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/config.yaml deleted file mode 100644 index 0823840cab76c4a3a2fd225374f79a630f8b6376..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.19.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.19.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/train/feats_lengths_stats.npz deleted file mode 100644 index 7fd8e73d00611531d1b3a189d427339c89c55675..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:539657d40b05a4faf7ada36274614b498967f73721fb377211f8286baeafbf1b -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/train/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/train/feats_stats.npz deleted file mode 100644 index 0a61dde7059b778d2ea35ee78e1deae430cc4c7e..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/train/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:c45e526fcaeec0217f2cec1fe6a61f4a1d20ab91bc8472e7c05714b0cad1b1a1 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/train/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/train/sids_shape deleted file mode 100644 index 88b09facb6cbc06a3da118d85a15ebd51eac42e3..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/train/sids_shape +++ /dev/null @@ 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-Oldambsters-1472-WaitGoud 27 -Oldambsters-1476-WaitGoud 62 -Oldambsters-1480-WaitGoud 36 -Oldambsters-1485-WaitGoud 41 -Oldambsters-1489-WaitGoud 35 -Oldambsters-1494-WaitGoud 117 -Oldambsters-1498-WaitGoud 45 -Oldambsters-1503-WaitGoud 41 -Oldambsters-1507-WaitGoud 101 -Oldambsters-1512-WaitGoud 61 -Oldambsters-1516-WaitGoud 37 -Oldambsters-1520-WaitGoud 24 -Oldambsters-1524-WaitGoud 39 -Oldambsters-1528-WaitGoud 44 -Oldambsters-1436-WaitGoud 32 -Oldambsters-1440-WaitGoud 24 -Oldambsters-1444-WaitGoud 41 -Oldambsters-1448-WaitGoud 84 -Oldambsters-1453-WaitGoud 232 -Oldambsters-1457-WaitGoud 71 -Oldambsters-1461-WaitGoud 119 -Oldambsters-1465-WaitGoud 43 -Oldambsters-1469-WaitGoud 169 -Oldambsters-1473-WaitGoud 24 -Oldambsters-1477-WaitGoud 176 -Oldambsters-1481-WaitGoud 18 -Oldambsters-1486-WaitGoud 139 -Oldambsters-1490-WaitGoud 179 -Oldambsters-1495-WaitGoud 61 -Oldambsters-1499-WaitGoud 77 -Oldambsters-1504-WaitGoud 57 -Oldambsters-1509-WaitGoud 71 -Oldambsters-1513-WaitGoud 196 -Oldambsters-1517-WaitGoud 79 -Oldambsters-1521-WaitGoud 133 -Oldambsters-1525-WaitGoud 34 -Oldambsters-1437-WaitGoud 80 -Oldambsters-1441-WaitGoud 53 -Oldambsters-1445-WaitGoud 85 -Oldambsters-1449-WaitGoud 133 -Oldambsters-1454-WaitGoud 38 -Oldambsters-1458-WaitGoud 82 -Oldambsters-1462-WaitGoud 27 -Oldambsters-1466-WaitGoud 55 -Oldambsters-1470-WaitGoud 90 -Oldambsters-1474-WaitGoud 64 -Oldambsters-1478-WaitGoud 41 -Oldambsters-1482-WaitGoud 83 -Oldambsters-1487-WaitGoud 48 -Oldambsters-1491-WaitGoud 112 -Oldambsters-1496-WaitGoud 209 -Oldambsters-1500-WaitGoud 48 -Oldambsters-1505-WaitGoud 63 -Oldambsters-1510-WaitGoud 34 -Oldambsters-1514-WaitGoud 26 -Oldambsters-1518-WaitGoud 16 -Oldambsters-1522-WaitGoud 59 -Oldambsters-1526-WaitGoud 75 -Oldambsters-1438-WaitGoud 27 -Oldambsters-1442-WaitGoud 80 -Oldambsters-1446-WaitGoud 72 -Oldambsters-1451-WaitGoud 38 -Oldambsters-1455-WaitGoud 82 -Oldambsters-1459-WaitGoud 8 -Oldambsters-1463-WaitGoud 36 -Oldambsters-1467-WaitGoud 114 -Oldambsters-1471-WaitGoud 65 -Oldambsters-1475-WaitGoud 32 -Oldambsters-1479-WaitGoud 43 -Oldambsters-1484-WaitGoud 106 -Oldambsters-1488-WaitGoud 25 -Oldambsters-1492-WaitGoud 17 -Oldambsters-1497-WaitGoud 39 -Oldambsters-1501-WaitGoud 34 -Oldambsters-1506-WaitGoud 67 -Oldambsters-1511-WaitGoud 108 -Oldambsters-1515-WaitGoud 31 -Oldambsters-1519-WaitGoud 37 -Oldambsters-1523-WaitGoud 28 -Oldambsters-1527-WaitGoud 42 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/valid/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/valid/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/valid/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/valid/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/valid/feats_lengths_stats.npz deleted file mode 100644 index f86a9f61b68ba7c34f8210afb47ec5c75b373805..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:3d71563265e3207658cbcc1227e2a7056c23eaa8336908c16efa859ed075d8c8 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/valid/feats_stats.npz deleted file mode 100644 index fbee10a1e3b703b0d969684d1197997f8e31e224..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:7e317218c36e68253ff6b51457fa6c18b2977d49fb25410658d722b627a9c1a8 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/valid/sids_shape deleted file mode 100644 index 11b6605bd5e413a8887fe95e92d3c65f05976338..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/valid/sids_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-2152-WaitGoud 1 -Oldambsters-2364-WaitGoud 1 -Oldambsters-2204-WaitGoud 1 -Oldambsters-2258-WaitGoud 1 -Oldambsters-2312-WaitGoud 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/valid/speech_shape deleted file mode 100644 index 90d88e01cf50603c9ca8adddf71208df00694c1c..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/valid/speech_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-2152-WaitGoud 72804 -Oldambsters-2364-WaitGoud 45158 -Oldambsters-2204-WaitGoud 101138 -Oldambsters-2258-WaitGoud 67738 -Oldambsters-2312-WaitGoud 86367 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/valid/text_shape deleted file mode 100644 index 5f2ba88c74a7445dd0f7531d2bb649f166efa917..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.19/valid/text_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-2152-WaitGoud 56 -Oldambsters-2364-WaitGoud 22 -Oldambsters-2204-WaitGoud 65 -Oldambsters-2258-WaitGoud 48 -Oldambsters-2312-WaitGoud 60 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2.log deleted file mode 100644 index 709c3a656df628959f944e10554301769dec41ee..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.2.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.2.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:34 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.2.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.2.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:40,885 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:41,134 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:58:43,055 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:58:43,087 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:58:43,087 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:43,087 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:58:43,087 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:43,087 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:58:43,087 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2/config.yaml -[wieling-3-a100] 2023-12-01 15:58:43,141 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.2.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.2.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=17 threads=1 -# Ended (code 0) at Fri Dec 1 15:58:51 UTC 2023, elapsed time 17 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2/config.yaml deleted file mode 100644 index c8593b664e124bf9e2144f18128d39b00be8bb7c..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.2.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.2.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2/train/feats_lengths_stats.npz deleted file mode 100644 index 38436731d0eb0767581e9547486d08f78dc76824..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version 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/dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2/valid/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2/valid/feats_lengths_stats.npz deleted file mode 100644 index 1d82daa40ae145fbd5b023463a81f290863ae0f0..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:87118cb615861c009c702a6b97e4b3c8d67555e82807918e5ea23ac8f68d26cc -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2/valid/feats_stats.npz deleted file mode 100644 index 36ccf6fde6b8b035f84e9a8158e2a28f0050af44..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:2a5986ebb9718a9c35f59755fafc3c3a24087c572a85424cd3ddba034119be68 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2/valid/sids_shape deleted file mode 100644 index e2a512b2261ef560fa25281d7658843b2455b16a..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2/valid/sids_shape +++ /dev/null @@ -1,5 +0,0 @@ -Hoogelaandsters-0336 1 -Hoogelaandsters-0604 1 -Hoogelaandsters-0403 1 -Hoogelaandsters-0470 1 -Hoogelaandsters-0537 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2/valid/speech_shape deleted file mode 100644 index 167b39f6547fea73da9646bcf122684d08b91bdc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2/valid/speech_shape +++ /dev/null @@ -1,5 +0,0 @@ -Hoogelaandsters-0336 111096 -Hoogelaandsters-0604 38258 -Hoogelaandsters-0403 80194 -Hoogelaandsters-0470 41200 -Hoogelaandsters-0537 50030 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2/valid/text_shape deleted file mode 100644 index caa74cd6a07c4148f659207c687d9b1a14b21d01..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.2/valid/text_shape +++ /dev/null @@ -1,5 +0,0 @@ -Hoogelaandsters-0336 69 -Hoogelaandsters-0604 25 -Hoogelaandsters-0403 54 -Hoogelaandsters-0470 38 -Hoogelaandsters-0537 37 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20.log deleted file mode 100644 index 5817c42ab84a950095d302928bc0a07a1a26265c..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.20.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.20.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:51 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.20.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.20.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:56,739 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:56,861 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:58:58,037 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:58:58,052 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:58:58,052 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:58,052 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:58:58,052 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:58,052 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:58:58,053 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/config.yaml -[wieling-3-a100] 2023-12-01 15:58:58,071 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.20.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.20.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=17 threads=1 -# Ended (code 0) at Fri Dec 1 15:59:08 UTC 2023, elapsed time 17 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/config.yaml deleted file mode 100644 index 91ae32473c1de02b6884505a3f38e2eb9b0cef5f..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.20.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.20.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/train/feats_lengths_stats.npz deleted file mode 100644 index b84dbac97f4af6a7e24d9928ebf47f42b877fe2e..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:994d414920bf1e65c28fe403d783454dcc5645307a203ad1e5c2fb1f92300c36 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/train/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/train/feats_stats.npz deleted file mode 100644 index 70ad925179229784dab64693582cf190eed081c7..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/train/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:b18ff9e445288baaf5f73ab2ff4e101018685ed3311487df8c785f2208b4091d -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/train/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/train/sids_shape deleted file mode 100644 index 0e188fae1426340ade11b5bc687818d95b95d55d..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/train/sids_shape +++ /dev/null @@ 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122 -Oldambsters-1742-WaitGoud 216 -Oldambsters-1746-WaitGoud 252 -Oldambsters-1750-WaitGoud 46 -Oldambsters-1754-WaitGoud 66 -Oldambsters-1758-WaitGoud 42 -Oldambsters-1763-WaitGoud 66 -Oldambsters-1767-WaitGoud 72 -Oldambsters-1771-WaitGoud 16 -Oldambsters-1775-WaitGoud 20 -Oldambsters-1780-WaitGoud 31 -Oldambsters-1784-WaitGoud 31 -Oldambsters-1788-WaitGoud 76 -Oldambsters-1792-WaitGoud 90 -Oldambsters-1796-WaitGoud 126 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/valid/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/valid/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/valid/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/valid/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/valid/feats_lengths_stats.npz deleted file mode 100644 index 678a09b60f5e32dc266bad3ca15d9a9145537c08..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:86b09e16d0d1f1f3a0d9f5cf46b276b24df29df803aa75fd3430d423340e368d -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/valid/feats_stats.npz deleted file mode 100644 index c29091808e8e4f0e7202e5f35ca15225d234ab35..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:5ed7c055db040d5ec25203b8fdf286675c655014ad707a12020854793576c037 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/valid/sids_shape deleted file mode 100644 index 4dc67d7900cefa5d2ce5bd2b39fa763ee06cc798..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/valid/sids_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-2419-WaitGoud 1 -Oldambsters-2629-WaitGoud 1 -Oldambsters-2471-WaitGoud 1 -Oldambsters-2524-WaitGoud 1 -Oldambsters-2577-WaitGoud 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/valid/speech_shape deleted file mode 100644 index de4775df426e64eda09bdc3ceb2d4acf9076be4d..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/valid/speech_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-2419-WaitGoud 103266 -Oldambsters-2629-WaitGoud 141039 -Oldambsters-2471-WaitGoud 103131 -Oldambsters-2524-WaitGoud 36691 -Oldambsters-2577-WaitGoud 124142 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/valid/text_shape deleted file mode 100644 index 224c098375b8744a65afcd6f6b908a31bffd542c..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.20/valid/text_shape +++ /dev/null @@ -1,5 +0,0 @@ -Oldambsters-2419-WaitGoud 65 -Oldambsters-2629-WaitGoud 88 -Oldambsters-2471-WaitGoud 76 -Oldambsters-2524-WaitGoud 20 -Oldambsters-2577-WaitGoud 88 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21.log deleted file mode 100644 index 7718bb9982b622dddfa3a998b30e0ca1c8e1f464..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.21.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.21.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:51 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.21.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.21.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:56,900 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:57,015 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:58:58,273 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:58:58,289 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:58:58,289 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:58,289 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:58:58,289 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:58,289 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:58:58,289 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/config.yaml -[wieling-3-a100] 2023-12-01 15:58:58,307 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.21.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.21.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=18 threads=1 -# Ended (code 0) at Fri Dec 1 15:59:09 UTC 2023, elapsed time 18 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/config.yaml deleted file mode 100644 index 76035d1f9fd45cad297d2153758edf9aa1a52fc3..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.21.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.21.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/train/feats_lengths_stats.npz deleted file mode 100644 index b3428d92c95b37891c6dc10f2cb5734f9479a912..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:b169b7e2269b38fa7bc0f37fde635c4e104435e3fc55de898eca1b4bfa5390d7 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/train/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/train/feats_stats.npz deleted file mode 100644 index 04a918a997e372a37a8298ba45625b581a56e38f..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/train/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:d350f11cd7c91a7e94200c6ced139ee1d449fd31666e108ee32b9ad6ac010bdf -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/train/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/train/sids_shape deleted file mode 100644 index 8f269c7ef94b508fc7ad9c87640352ed3d902fde..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/train/sids_shape +++ /dev/null @@ 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-Oldambsters-1999-WaitGoud 103 -Oldambsters-2003-WaitGoud 75 -Oldambsters-2007-WaitGoud 55 -Oldambsters-2011-WaitGoud 101 -Oldambsters-2015-WaitGoud 68 -Oldambsters-2019-WaitGoud 188 -Oldambsters-2023-WaitGoud 27 -Oldambsters-2028-WaitGoud 41 -Oldambsters-2032-WaitGoud 132 -Oldambsters-2036-WaitGoud 30 -Oldambsters-2040-WaitGoud 125 -Oldambsters-2045-WaitGoud 255 -Oldambsters-2049-WaitGoud 268 -Oldambsters-2053-WaitGoud 45 -Oldambsters-2057-WaitGoud 186 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/valid/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/valid/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/valid/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/valid/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/valid/feats_lengths_stats.npz deleted file mode 100644 index 158e272b4f5a44ec3e03de742babd9a9292c9ed7..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:4c56db8323abca11784c24daf93e2a91ca3ce37c67c16bcc8c55671568da8d2e -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/valid/feats_stats.npz deleted file mode 100644 index 33b1c00df4fee3d3f92a47f4f250e61eb4b629ce..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:534492d8515ce6e0827b625909f7707be9979005a13498973b5664cd6fbdb31a -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/valid/sids_shape deleted file mode 100644 index 9ef1b1648d94efc1df6b44d7faee997a745ccc7e..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/valid/sids_shape +++ /dev/null @@ -1,5 +0,0 @@ -Westerkertaaiers-0001-KomVerhoal 1 -Westerkertaaiers-0178-KomVerhoal 1 -Westerkertaaiers-0044-KomVerhoal 1 -Westerkertaaiers-0087-KomVerhoal 1 -Westerkertaaiers-0132-KomVerhoal 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/valid/speech_shape deleted file mode 100644 index 662b5e4e771eace6d61ca37718d25618573c604d..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/valid/speech_shape +++ /dev/null @@ -1,5 +0,0 @@ -Westerkertaaiers-0001-KomVerhoal 283653 -Westerkertaaiers-0178-KomVerhoal 227129 -Westerkertaaiers-0044-KomVerhoal 120330 -Westerkertaaiers-0087-KomVerhoal 109026 -Westerkertaaiers-0132-KomVerhoal 258786 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/valid/text_shape deleted file mode 100644 index 7bea143253e912dfd4befae96ec986d6ecc9756a..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.21/valid/text_shape +++ /dev/null @@ -1,5 +0,0 @@ -Westerkertaaiers-0001-KomVerhoal 157 -Westerkertaaiers-0178-KomVerhoal 151 -Westerkertaaiers-0044-KomVerhoal 63 -Westerkertaaiers-0087-KomVerhoal 60 -Westerkertaaiers-0132-KomVerhoal 170 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22.log deleted file mode 100644 index 05db975bca1d82e5161632b777b14e54ad1b748c..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.22.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.22.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:51 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.22.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.22.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:57,091 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:57,207 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:58:58,513 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:58:58,545 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:58:58,545 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:58,545 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:58:58,545 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:58,545 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:58:58,545 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/config.yaml -[wieling-3-a100] 2023-12-01 15:58:58,564 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.22.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.22.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=18 threads=1 -# Ended (code 0) at Fri Dec 1 15:59:09 UTC 2023, elapsed time 18 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/config.yaml deleted file mode 100644 index 6ba907679a67086d1631931cfebfe512fb8d5fbf..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.22.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.22.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/train/feats_lengths_stats.npz deleted file mode 100644 index cd9f1ef0d6a5e325b68a16729fe4d67f14605b05..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:0ecd49dd74e25573c229d90fd7e7045c3edcb157c8ccbd7ecaa113dba10d7d14 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/train/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/train/feats_stats.npz deleted file mode 100644 index 76466772b621564cf38323c02eb75b0d145399ec..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/train/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:f7155fbd1f07a480cebfd5f924ff964ec200b7a0342d45e6cc6dabc2109dae05 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/train/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/train/sids_shape deleted file mode 100644 index c80718b4cdd7c115a957b35c1325de4c15b4b6f1..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/train/sids_shape +++ /dev/null @@ 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b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/valid/feats_lengths_stats.npz deleted file mode 100644 index add9c5c9e8c50380ea760efc039954e4d260e9b8..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:41838c9fb85336d96c9d8d3dc78f4ac567f13ae8bf96e5978cb53f8c549c4356 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/valid/feats_stats.npz deleted file mode 100644 index 3abffbdc5eee93ec04fcb7c10a40c40b8a937a3a..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:305073d77ec8007417d438b2c5489aa059b5bf0fe59c2f84f5570edcbd940328 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/valid/sids_shape deleted file mode 100644 index a2e0560729d0ab229da88b50dfeab05bdfa77a3d..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/valid/sids_shape +++ /dev/null @@ -1,5 +0,0 @@ -Westerkertaaiers-0222-KomVerhoal 1 -Westerkertaaiers-0397-KomVerhoal 1 -Westerkertaaiers-0265-KomVerhoal 1 -Westerkertaaiers-0309-KomVerhoal 1 -Westerkertaaiers-0354-KomVerhoal 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/valid/speech_shape deleted file mode 100644 index 0cad3072521eb04a851de040371e8ae3510a7822..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/valid/speech_shape +++ /dev/null @@ -1,5 +0,0 @@ -Westerkertaaiers-0222-KomVerhoal 91899 -Westerkertaaiers-0397-KomVerhoal 72759 -Westerkertaaiers-0265-KomVerhoal 107492 -Westerkertaaiers-0309-KomVerhoal 148206 -Westerkertaaiers-0354-KomVerhoal 56448 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/valid/text_shape deleted file mode 100644 index 846f48f0eaa869bc4a695cc71baa750deddb4294..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.22/valid/text_shape +++ /dev/null @@ -1,5 +0,0 @@ -Westerkertaaiers-0222-KomVerhoal 66 -Westerkertaaiers-0397-KomVerhoal 36 -Westerkertaaiers-0265-KomVerhoal 69 -Westerkertaaiers-0309-KomVerhoal 78 -Westerkertaaiers-0354-KomVerhoal 34 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23.log deleted file mode 100644 index 6781253f7046b18a15a13f4d7fca5e4115f4d15b..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.23.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.23.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:52 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.23.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.23.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:57,010 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:57,126 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:58:58,504 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:58:58,520 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:58:58,520 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:58,520 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:58:58,520 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:58,520 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:58:58,520 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/config.yaml -[wieling-3-a100] 2023-12-01 15:58:58,563 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.23.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.23.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=15 threads=1 -# Ended (code 0) at Fri Dec 1 15:59:07 UTC 2023, elapsed time 15 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/config.yaml deleted file mode 100644 index 5dccdcd2bdf7a8e779a83cd2b51759774a1f88a3..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.23.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.23.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/train/feats_lengths_stats.npz deleted file mode 100644 index db790ccfab689870d3841912249fc46ad38e2646..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:5b365865a1608343fe9900253fd28b16ffb95fe3d1b14c5f77b5b180ec872df2 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/train/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/train/feats_stats.npz deleted file mode 100644 index 97959b7be23ffabba02af48c09c79bda70f474b3..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/train/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:976329f11688a8b470d89b02770898b3755e7f6d4cd0b972f11c69651e881a88 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/train/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/train/sids_shape deleted file mode 100644 index 8a12a37397ad80e8d8bdef0fd867088ab821ccbf..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/train/sids_shape +++ /dev/null @@ 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38 -Oldambsters-2521-WaitGoud 158 -Oldambsters-2526-WaitGoud 88 -Oldambsters-2530-WaitGoud 84 -Oldambsters-2535-WaitGoud 55 -Oldambsters-2539-WaitGoud 62 -Oldambsters-2543-WaitGoud 96 -Oldambsters-2547-WaitGoud 205 -Oldambsters-2551-WaitGoud 47 -Oldambsters-2555-WaitGoud 101 -Oldambsters-2559-WaitGoud 17 -Oldambsters-2563-WaitGoud 82 -Oldambsters-2567-WaitGoud 63 -Oldambsters-2571-WaitGoud 52 -Oldambsters-2575-WaitGoud 49 -Oldambsters-2580-WaitGoud 60 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/valid/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/valid/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/valid/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/valid/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/valid/feats_lengths_stats.npz deleted file mode 100644 index 785db77692273ac52d696a7bb6470be647371b73..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:561cdb7a835d09611bbc5c829e796b995c4327f44c3ceba1a348404660172d83 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/valid/feats_stats.npz deleted file mode 100644 index 26ebcfd43ebcf2302358d9087a7f8690e4b42dd2..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:bb765263dfc44a72829029ab7faf75f3382ce69ddc84a12c474a623218518660 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/valid/sids_shape deleted file mode 100644 index bf5069f2fe0074ca23468c5c1b1da3bbff9bbfdd..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/valid/sids_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-0441-KomVerhoal 1 -Westerkertaaiers-0484-KomVerhoal 1 -Westerkertaaiers-0527-KomVerhoal 1 -Westerkertaaiers-0570-KomVerhoal 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/valid/speech_shape deleted file mode 100644 index be200e903f7ea36fca463db556f5eadc0b2e9367..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/valid/speech_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-0441-KomVerhoal 123440 -Westerkertaaiers-0484-KomVerhoal 148304 -Westerkertaaiers-0527-KomVerhoal 59441 -Westerkertaaiers-0570-KomVerhoal 133724 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/valid/text_shape deleted file mode 100644 index 5ce228f875d62b3d545445ffc840b85daaf88285..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.23/valid/text_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-0441-KomVerhoal 69 -Westerkertaaiers-0484-KomVerhoal 85 -Westerkertaaiers-0527-KomVerhoal 42 -Westerkertaaiers-0570-KomVerhoal 82 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24.log deleted file mode 100644 index 37ec189f9cad0b2d5ae7e04e564544e2f3299ac5..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.24.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.24.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:52 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.24.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.24.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:57,337 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:57,454 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:58:59,262 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:58:59,316 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:58:59,316 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:59,316 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:58:59,316 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:59,316 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:58:59,316 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/config.yaml -[wieling-3-a100] 2023-12-01 15:58:59,342 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.24.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.24.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=17 threads=1 -# Ended (code 0) at Fri Dec 1 15:59:09 UTC 2023, elapsed time 17 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/config.yaml deleted file mode 100644 index 7de74671d95c9384faa9e270bf4115896c4f8ad9..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.24.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.24.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/train/feats_lengths_stats.npz deleted file mode 100644 index 71a2d0f14c17d653fd8e401350206f82b5f2941b..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:56a66bf77f74db9fc0ea44b6c71e14bf621c2b484b9bada382271f5bf875de3c -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/train/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/train/feats_stats.npz deleted file mode 100644 index 0d3f4de1c71cb3483d98bc874bb8d4f933596012..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/train/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:130421c4b56808ebef40ead01af367d3c4058539da79e6badcf7169faab23ffd -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/train/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/train/sids_shape deleted file mode 100644 index 3b2ae1b70930c7eb3e9ef1546e38ef62f903d071..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/train/sids_shape +++ /dev/null @@ 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-Westerkertaaiers-0084-KomVerhoal 51 -Westerkertaaiers-0089-KomVerhoal 111 -Westerkertaaiers-0095-KomVerhoal 109 -Westerkertaaiers-0099-KomVerhoal 50 -Westerkertaaiers-0103-KomVerhoal 30 -Westerkertaaiers-0107-KomVerhoal 69 -Westerkertaaiers-0112-KomVerhoal 82 -Westerkertaaiers-0116-KomVerhoal 145 -Westerkertaaiers-0120-KomVerhoal 98 -Westerkertaaiers-0124-KomVerhoal 196 -Westerkertaaiers-0128-KomVerhoal 76 -Westerkertaaiers-0133-KomVerhoal 121 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/valid/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/valid/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/valid/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/valid/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/valid/feats_lengths_stats.npz deleted file mode 100644 index a193af9e1d678791bed93450054bbeb8325e7207..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:af325684fb3ede5528169a5ff1935ecaaf068de609013246dd03d4f65a6bdb10 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/valid/feats_stats.npz deleted file mode 100644 index 4f091f3ececb491e19d86106f64ad1b372aa728a..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:7475ae0896d648cc761f305510d9e767f43f44e97c0abfd00aabffda602772ef -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/valid/sids_shape deleted file mode 100644 index 0537468389cd76cf3c85cfb88914c96a11e48a48..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/valid/sids_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-0613-KomVerhoal 1 -Westerkertaaiers-0662-KomVerhoal 1 -Westerkertaaiers-0706-KomVerhoal 1 -Westerkertaaiers-0752-SchiersteVerhoal 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/valid/speech_shape deleted file mode 100644 index e394fd909567de72670bc86b7ecbf1e12bf5cebf..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/valid/speech_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-0613-KomVerhoal 213091 -Westerkertaaiers-0662-KomVerhoal 234602 -Westerkertaaiers-0706-KomVerhoal 111690 -Westerkertaaiers-0752-SchiersteVerhoal 289381 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/valid/text_shape deleted file mode 100644 index 57b80cbf8cd17b1baabf8e726c8a3334f0c1eff8..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.24/valid/text_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-0613-KomVerhoal 126 -Westerkertaaiers-0662-KomVerhoal 151 -Westerkertaaiers-0706-KomVerhoal 73 -Westerkertaaiers-0752-SchiersteVerhoal 176 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25.log deleted file mode 100644 index 1d4aed5b733e9a6fd1702fe25a751c1924603e7a..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.25.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.25.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:52 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.25.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.25.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:58,021 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:58,138 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:59:00,950 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:59:00,977 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:59:00,978 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:59:00,978 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:59:00,978 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:59:00,978 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:59:00,978 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/config.yaml -[wieling-3-a100] 2023-12-01 15:59:01,020 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.25.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.25.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=19 threads=1 -# Ended (code 0) at Fri Dec 1 15:59:11 UTC 2023, elapsed time 19 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/config.yaml deleted file mode 100644 index 3aca31d5f28eb7088a5591271e674940f8e74189..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.25.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.25.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/train/feats_lengths_stats.npz deleted file mode 100644 index e6525b3afcb463c61c4644167676c0e46006f9ce..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version 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-Westerkertaaiers-0384-KomVerhoal 59 -Westerkertaaiers-0388-KomVerhoal 25 -Westerkertaaiers-0392-KomVerhoal 50 -Westerkertaaiers-0396-KomVerhoal 14 -Westerkertaaiers-0401-KomVerhoal 113 -Westerkertaaiers-0405-KomVerhoal 31 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/valid/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/valid/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/valid/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/valid/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/valid/feats_lengths_stats.npz deleted file mode 100644 index fafefe5bd6648b391e5b066c9c5c1ed9beab3857..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:62f6da6b2fd5046d90ff25b11aa3147eae3b5cf80e4fd0e14f63ecb69ce5ef9d -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/valid/feats_stats.npz deleted file mode 100644 index 3990b453976730af6ad5f875b03e89bbdb661406..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:124c7cd9479834e94a340645d851220c20c76fd03df17fa6b73e63423f7855d6 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/valid/sids_shape deleted file mode 100644 index 8a2007163575c6a64daf2823d2a401abdc3e1701..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/valid/sids_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-0796-SchiersteVerhoal 1 -Westerkertaaiers-0842-SchiersteVerhoal 1 -Westerkertaaiers-0887-SchiersteVerhoal 1 -Westerkertaaiers-0930-SchiersteVerhoal 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/valid/speech_shape deleted file mode 100644 index ff7aa3953361037aa6207bf6ed6bb7abc50000dd..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/valid/speech_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-0796-SchiersteVerhoal 247959 -Westerkertaaiers-0842-SchiersteVerhoal 126228 -Westerkertaaiers-0887-SchiersteVerhoal 62260 -Westerkertaaiers-0930-SchiersteVerhoal 166522 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/valid/text_shape deleted file mode 100644 index 4f612dd37e715f92367e447303080c6542ddf341..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.25/valid/text_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-0796-SchiersteVerhoal 107 -Westerkertaaiers-0842-SchiersteVerhoal 79 -Westerkertaaiers-0887-SchiersteVerhoal 38 -Westerkertaaiers-0930-SchiersteVerhoal 107 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26.log deleted file mode 100644 index 6bb7f05e7e2705efe548cb6006c2104f162bd3bf..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.26.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.26.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:52 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.26.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.26.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:57,765 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:57,880 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:59:00,440 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:59:00,456 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:59:00,456 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:59:00,456 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:59:00,456 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:59:00,456 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:59:00,456 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/config.yaml -[wieling-3-a100] 2023-12-01 15:59:00,475 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.26.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.26.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=18 threads=1 -# Ended (code 0) at Fri Dec 1 15:59:10 UTC 2023, elapsed time 18 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/config.yaml deleted file mode 100644 index ac7add3c6868827b7ae4be6278e6235433361b84..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.26.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.26.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/train/feats_lengths_stats.npz deleted file mode 100644 index 23a6e1771942f0d71dcd3c6a495c21aae5668348..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:2ea0947af9765f44b3c016e1de9635a75ad3bd96753f2e5dd82f59633ca75c23 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/train/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/train/feats_stats.npz deleted file mode 100644 index 4c5d3ec6659b8ae174eb95a49dd9e6faa0bfb7ea..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/train/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:45378aca307f5af427a974fac5933ea912749b51fa19c0f1f7684ed878265d6d -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/train/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/train/sids_shape deleted file mode 100644 index 0d0713fa6c14b4a8c68de5d7e8042cc36afd87bb..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/train/sids_shape +++ /dev/null @@ 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-Westerkertaaiers-0652-KomVerhoal 86 -Westerkertaaiers-0657-KomVerhoal 115 -Westerkertaaiers-0663-KomVerhoal 80 -Westerkertaaiers-0667-KomVerhoal 69 -Westerkertaaiers-0672-KomVerhoal 194 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/valid/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/valid/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/valid/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/valid/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/valid/feats_lengths_stats.npz deleted file mode 100644 index fb02081da48f4527f87d3af7940bcc1c6481d389..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:7874a2e52e6f99cf39c220ff135aa3eb4fd06d5b2e569c97d5f1bef1a677ecb3 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/valid/feats_stats.npz deleted file mode 100644 index d04665279046aa1f9f6a417565fc70ed8568196d..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:aaf070cb3e823c33f2d2e46383ee90298cca21cd57434fd5cb6c2fbcd770a243 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/valid/sids_shape deleted file mode 100644 index f29a3412f00f2d5427be308e579094ffa59522da..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/valid/sids_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-0974-SchiersteVerhoal 1 -Westerkertaaiers-1017-SchiersteVerhoal 1 -Westerkertaaiers-1062-SchiersteVerhoal 1 -Westerkertaaiers-1105-SchiersteVerhoal 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/valid/speech_shape deleted file mode 100644 index 7cf9d159774ff0696e2ad9a864d5a201b5f0c8e8..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/valid/speech_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-0974-SchiersteVerhoal 95922 -Westerkertaaiers-1017-SchiersteVerhoal 258233 -Westerkertaaiers-1062-SchiersteVerhoal 133988 -Westerkertaaiers-1105-SchiersteVerhoal 56448 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/valid/text_shape deleted file mode 100644 index c22678fac892152710f3aaf46d750ed2c03b488a..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.26/valid/text_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-0974-SchiersteVerhoal 43 -Westerkertaaiers-1017-SchiersteVerhoal 142 -Westerkertaaiers-1062-SchiersteVerhoal 81 -Westerkertaaiers-1105-SchiersteVerhoal 35 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27.log deleted file mode 100644 index 9bea28cf2fda10140c5e9648a60a6f80ddfa75ab..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.27.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.27.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:52 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.27.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.27.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:57,857 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:57,974 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:59:00,586 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:59:00,618 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:59:00,618 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:59:00,618 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:59:00,618 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:59:00,618 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:59:00,618 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/config.yaml -[wieling-3-a100] 2023-12-01 15:59:00,652 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.27.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.27.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=18 threads=1 -# Ended (code 0) at Fri Dec 1 15:59:10 UTC 2023, elapsed time 18 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/config.yaml deleted file mode 100644 index 89df3bbdd0284b52be66a3794cabd8a8e5427e74..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.27.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.27.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/train/feats_lengths_stats.npz deleted file mode 100644 index 571e23b7e7e02f2b824a2a626e5b0e54c6941699..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:876882454b082187542e22fbc8a28638f94a06107067e56fc4f52e738681c935 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/train/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/train/feats_stats.npz deleted file mode 100644 index bfde141af0fe27457e2c9463e70d8705eb04e179..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/train/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:194a2309c8fff2d94afafac10e071d2029c5db780f8782c7f1e5b07cd520b0fd -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/train/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/train/sids_shape deleted file mode 100644 index a08c3a180d0bd17626969ef3630d1f24f4a21073..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/train/sids_shape +++ /dev/null @@ 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-Westerkertaaiers-0915-SchiersteVerhoal 170 -Westerkertaaiers-0919-SchiersteVerhoal 67 -Westerkertaaiers-0923-SchiersteVerhoal 64 -Westerkertaaiers-0927-SchiersteVerhoal 79 -Westerkertaaiers-0932-SchiersteVerhoal 52 -Westerkertaaiers-0936-SchiersteVerhoal 35 -Westerkertaaiers-0849-SchiersteVerhoal 75 -Westerkertaaiers-0853-SchiersteVerhoal 223 -Westerkertaaiers-0857-SchiersteVerhoal 135 -Westerkertaaiers-0861-SchiersteVerhoal 187 -Westerkertaaiers-0865-SchiersteVerhoal 47 -Westerkertaaiers-0870-SchiersteVerhoal 61 -Westerkertaaiers-0874-SchiersteVerhoal 34 -Westerkertaaiers-0879-SchiersteVerhoal 13 -Westerkertaaiers-0883-SchiersteVerhoal 84 -Westerkertaaiers-0888-SchiersteVerhoal 29 -Westerkertaaiers-0892-SchiersteVerhoal 65 -Westerkertaaiers-0896-SchiersteVerhoal 113 -Westerkertaaiers-0900-SchiersteVerhoal 63 -Westerkertaaiers-0904-SchiersteVerhoal 76 -Westerkertaaiers-0908-SchiersteVerhoal 95 -Westerkertaaiers-0912-SchiersteVerhoal 24 -Westerkertaaiers-0916-SchiersteVerhoal 67 -Westerkertaaiers-0920-SchiersteVerhoal 175 -Westerkertaaiers-0924-SchiersteVerhoal 45 -Westerkertaaiers-0928-SchiersteVerhoal 140 -Westerkertaaiers-0933-SchiersteVerhoal 87 -Westerkertaaiers-0937-SchiersteVerhoal 85 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/valid/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/valid/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/valid/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/valid/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/valid/feats_lengths_stats.npz deleted file mode 100644 index 5097dcf27a7f0d773d0d169b714cc5be6ef4b975..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:d4f61f991e0864ded9e1246e51f83888e0ee1fca95ce24df305fe433c0d6f4b1 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/valid/feats_stats.npz deleted file mode 100644 index 8563e3372f411016b5326f8177e52dbca8bc0a66..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:3bc3d55881ceab198938d0f4d9c3d2d58514c2714dde1fe4eb273b559f876a33 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/valid/sids_shape deleted file mode 100644 index 8b587b38cf6451b7e146132d39f9637772ce9d61..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/valid/sids_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-1152-SchiersteVerhoal 1 -Westerkertaaiers-1195-SchiersteVerhoal 1 -Westerkertaaiers-1242-SchiersteVerhoal 1 -Westerkertaaiers-1287-SchiersteVerhoal 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/valid/speech_shape deleted file mode 100644 index ce66c0c040960e974d4f1647acb4290c9a93dd47..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/valid/speech_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-1152-SchiersteVerhoal 47206 -Westerkertaaiers-1195-SchiersteVerhoal 167614 -Westerkertaaiers-1242-SchiersteVerhoal 209708 -Westerkertaaiers-1287-SchiersteVerhoal 241614 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/valid/text_shape deleted file mode 100644 index 63289349f3b5490e59656695e0931b42048a7336..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.27/valid/text_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-1152-SchiersteVerhoal 27 -Westerkertaaiers-1195-SchiersteVerhoal 116 -Westerkertaaiers-1242-SchiersteVerhoal 130 -Westerkertaaiers-1287-SchiersteVerhoal 149 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28.log deleted file mode 100644 index d8e8458e931ab5b5c3d15a186fd51ca9d59385b7..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.28.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.28.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:52 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.28.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.28.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:57,990 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:58,178 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:59:00,916 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:59:00,987 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:59:00,987 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:59:00,987 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:59:00,987 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:59:00,987 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:59:00,987 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/config.yaml -[wieling-3-a100] 2023-12-01 15:59:01,029 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.28.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.28.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=18 threads=1 -# Ended (code 0) at Fri Dec 1 15:59:10 UTC 2023, elapsed time 18 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/config.yaml deleted file mode 100644 index 57229b0f6a822ef82b99a117ed7d447af8341b66..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.28.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.28.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/train/feats_lengths_stats.npz deleted file mode 100644 index 6bebee3ca02cc844a6473a019c92ab1969ad6ddb..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version 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a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/train/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/train/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/train/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/train/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/train/text_shape deleted file mode 100644 index d9a28e925bd28614eed1f7bae5e81825c99c8b47..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/train/text_shape +++ /dev/null @@ -1,248 +0,0 @@ -Westerkertaaiers-0938-SchiersteVerhoal 100 -Westerkertaaiers-0942-SchiersteVerhoal 58 -Westerkertaaiers-0946-SchiersteVerhoal 64 -Westerkertaaiers-0950-SchiersteVerhoal 90 -Westerkertaaiers-0954-SchiersteVerhoal 42 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-Westerkertaaiers-1150-SchiersteVerhoal 67 -Westerkertaaiers-1155-SchiersteVerhoal 18 -Westerkertaaiers-1159-SchiersteVerhoal 148 -Westerkertaaiers-1163-SchiersteVerhoal 30 -Westerkertaaiers-1167-SchiersteVerhoal 74 -Westerkertaaiers-1171-SchiersteVerhoal 43 -Westerkertaaiers-1175-SchiersteVerhoal 72 -Westerkertaaiers-1179-SchiersteVerhoal 125 -Westerkertaaiers-1183-SchiersteVerhoal 97 -Westerkertaaiers-1187-SchiersteVerhoal 75 -Westerkertaaiers-1191-SchiersteVerhoal 65 -Westerkertaaiers-1196-SchiersteVerhoal 54 -Westerkertaaiers-1200-SchiersteVerhoal 39 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/valid/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/valid/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/valid/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/valid/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/valid/feats_lengths_stats.npz deleted file mode 100644 index 1f727eb4b5227e69edea3764f0cfa82c0849e1e6..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:9a918153b2b35310bf70e385e03de1c7cb9ad62663363b1231cf450021b17379 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/valid/feats_stats.npz deleted file mode 100644 index c68915d8b1b6c87a00b99351740e38cc2a156b0f..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:f7b71ccc823b2ff0fce98e8f3ce2e6d3ab4c08ad6f57b680b81ff89cbec85a13 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/valid/sids_shape deleted file mode 100644 index fb41f5862b6939e3da871e6e817f5d7bc97149a1..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/valid/sids_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-1331-SchiersteVerhoal 1 -Westerkertaaiers-1374-SchiersteVerhoal 1 -Westerkertaaiers-1423-SlagTwaalf 1 -Westerkertaaiers-1466-SlagTwaalf 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/valid/speech_shape deleted file mode 100644 index bde76723e3feb2d8c4286000e59b3739da358ebc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/valid/speech_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-1331-SchiersteVerhoal 104429 -Westerkertaaiers-1374-SchiersteVerhoal 89519 -Westerkertaaiers-1423-SlagTwaalf 42960 -Westerkertaaiers-1466-SlagTwaalf 150298 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/valid/text_shape deleted file mode 100644 index 3c6eea2749f47a99f7f69d348f1a114ff41a6648..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.28/valid/text_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-1331-SchiersteVerhoal 65 -Westerkertaaiers-1374-SchiersteVerhoal 48 -Westerkertaaiers-1423-SlagTwaalf 17 -Westerkertaaiers-1466-SlagTwaalf 63 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29.log deleted file mode 100644 index 656bf3bde412bb712750ffea73fcd1459e7202ef..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.29.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.29.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:52 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.29.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.29.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:58,441 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:58,658 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:59:01,065 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:59:01,105 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:59:01,105 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:59:01,105 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:59:01,105 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:59:01,106 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:59:01,106 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/config.yaml -[wieling-3-a100] 2023-12-01 15:59:01,140 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.29.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.29.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=19 threads=1 -# Ended (code 0) at Fri Dec 1 15:59:11 UTC 2023, elapsed time 19 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/config.yaml deleted file mode 100644 index 6e612c1d3c1c65eeb78f4ad6722aee72711539f9..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.29.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.29.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/train/feats_lengths_stats.npz deleted file mode 100644 index bf0cc0387cc418bfcc75a6c1dd7adf8da6045629..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:6df191541b88dc3d104e631897aeb336bb18b15a8683162db2c88f112bb0c5b3 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/train/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/train/feats_stats.npz deleted file mode 100644 index c68574582fa5479be66be2ce1c446a71c75435ad..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/train/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:8b10a93b4958ff5991bfca68a41efd2743d038502db87e3f7a93d09a2937b3c9 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/train/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/train/sids_shape deleted file mode 100644 index 8576b63364f91e439b68e7b34b145732443b0e41..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/train/sids_shape +++ /dev/null @@ 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-Westerkertaaiers-1463-SlagTwaalf 63 -Westerkertaaiers-1468-SlagTwaalf 81 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/valid/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/valid/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/valid/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/valid/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/valid/feats_lengths_stats.npz deleted file mode 100644 index 7a2db1ea7db133db52263d1584fa5434223159c6..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:f2a00e97b36cb830a20d09915721795f534fc6394a4e85b705c7adc81f9cd83c -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/valid/feats_stats.npz deleted file mode 100644 index 7fccab6ac744d2629406167347d37473d16b47aa..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:0821af6b401ff9483f618fc04b0362d4057e2766239c7ee4aca50b8ea19541f8 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/valid/sids_shape deleted file mode 100644 index 0b7d1fe4742544564285c580949fc0336efd4e2f..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/valid/sids_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-1509-SlagTwaalf 1 -Westerkertaaiers-1552-SlagTwaalf 1 -Westerkertaaiers-1595-SlagTwaalf 1 -Westerkertaaiers-1639-SlagTwaalf 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/valid/speech_shape deleted file mode 100644 index 57eec7c09f1fecd3c6117bc24924097323ef77e9..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/valid/speech_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-1509-SlagTwaalf 181598 -Westerkertaaiers-1552-SlagTwaalf 214737 -Westerkertaaiers-1595-SlagTwaalf 52483 -Westerkertaaiers-1639-SlagTwaalf 139969 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/valid/text_shape deleted file mode 100644 index 5d45686f384b22a5b18e35f2296b72f7c4904999..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.29/valid/text_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-1509-SlagTwaalf 111 -Westerkertaaiers-1552-SlagTwaalf 128 -Westerkertaaiers-1595-SlagTwaalf 18 -Westerkertaaiers-1639-SlagTwaalf 75 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3.log deleted file mode 100644 index 8f14e800774c50df24da1cd83b060765564f472c..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.3.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.3.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:34 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.3.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.3.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:40,761 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:40,881 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:58:42,815 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:58:42,870 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:58:42,870 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:42,870 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:58:42,870 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:42,870 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:58:42,870 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/config.yaml -[wieling-3-a100] 2023-12-01 15:58:42,912 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.3.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.3.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=17 threads=1 -# Ended (code 0) at Fri Dec 1 15:58:51 UTC 2023, elapsed time 17 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/config.yaml deleted file mode 100644 index 2081245999f9e032f01c3079e79c4fdb976c469e..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.3.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.3.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/train/feats_lengths_stats.npz deleted file mode 100644 index d261987d03f9afed3edea6a69d888eb2998599a6..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version 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-Hoogelaandsters-0748 13 -Hoogelaandsters-0752 24 -Hoogelaandsters-0756 84 -Hoogelaandsters-0760 19 -Hoogelaandsters-0675 36 -Hoogelaandsters-0679 9 -Hoogelaandsters-0683 64 -Hoogelaandsters-0687 31 -Hoogelaandsters-0691 18 -Hoogelaandsters-0696 25 -Hoogelaandsters-0700 30 -Hoogelaandsters-0704 45 -Hoogelaandsters-0708 17 -Hoogelaandsters-0712 20 -Hoogelaandsters-0716 74 -Hoogelaandsters-0720 49 -Hoogelaandsters-0724 39 -Hoogelaandsters-0728 44 -Hoogelaandsters-0732 103 -Hoogelaandsters-0736 25 -Hoogelaandsters-0741 78 -Hoogelaandsters-0745 18 -Hoogelaandsters-0749 18 -Hoogelaandsters-0753 107 -Hoogelaandsters-0757 20 -Hoogelaandsters-0761 34 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/valid/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/valid/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/valid/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/valid/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/valid/feats_lengths_stats.npz deleted file mode 100644 index 1f68d308ea9ca4441d15b0ea467607b3fe7d751f..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:ae38b324f8c96d89fb3e01c093eca25fcf9117baca7965f6fb63c94ca0e13c71 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/valid/feats_stats.npz deleted file mode 100644 index 5450c7cec35115f57539fc56af56f34c617cdfd9..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:3cea8b53aa9f94d2a845cded45bf2cb256549a18d8d8c1a3d0e48e1d837a2ac7 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/valid/sids_shape deleted file mode 100644 index 0b78c2aad370123ee5645bd9b3f3e794e06b422a..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/valid/sids_shape +++ /dev/null @@ -1,5 +0,0 @@ -Hoogelaandsters-0671 1 -Hoogelaandsters-0942 1 -Hoogelaandsters-0740 1 -Hoogelaandsters-0808 1 -Hoogelaandsters-0875 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/valid/speech_shape deleted file mode 100644 index 67915c430778f926afe8ba3f27bf4814354f90b9..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/valid/speech_shape +++ /dev/null @@ -1,5 +0,0 @@ -Hoogelaandsters-0671 69158 -Hoogelaandsters-0942 55103 -Hoogelaandsters-0740 50030 -Hoogelaandsters-0808 170690 -Hoogelaandsters-0875 42672 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/valid/text_shape deleted file mode 100644 index a4d8ac97acf1f1893fa9c6b75b10cf38fa6d624e..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.3/valid/text_shape +++ /dev/null @@ -1,5 +0,0 @@ -Hoogelaandsters-0671 45 -Hoogelaandsters-0942 44 -Hoogelaandsters-0740 37 -Hoogelaandsters-0808 88 -Hoogelaandsters-0875 19 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30.log deleted file mode 100644 index 49a8f67009026ab24878c01b64fde6bb6df0b397..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.30.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.30.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:52 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.30.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.30.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:58,037 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:58,154 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:59:00,698 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:59:00,730 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:59:00,730 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:59:00,730 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:59:00,730 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:59:00,730 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:59:00,730 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/config.yaml -[wieling-3-a100] 2023-12-01 15:59:00,764 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.30.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.30.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=17 threads=1 -# Ended (code 0) at Fri Dec 1 15:59:09 UTC 2023, elapsed time 17 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/config.yaml deleted file mode 100644 index 97f40a9e884ee3679ab2ff86c4346fa9d1185409..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.30.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.30.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/train/feats_lengths_stats.npz deleted file mode 100644 index bd6dec47d2c7457b499cf57b6c96846ab13ba4de..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version 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deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/train/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/train/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/train/text_shape deleted file mode 100644 index 999eaece69cf3802e9f32df50ee0a0ca34536287..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/train/text_shape +++ /dev/null @@ -1,248 +0,0 @@ -Westerkertaaiers-1469-SlagTwaalf 46 -Westerkertaaiers-1473-SlagTwaalf 37 -Westerkertaaiers-1477-SlagTwaalf 90 -Westerkertaaiers-1481-SlagTwaalf 141 -Westerkertaaiers-1485-SlagTwaalf 58 -Westerkertaaiers-1489-SlagTwaalf 31 -Westerkertaaiers-1493-SlagTwaalf 67 -Westerkertaaiers-1497-SlagTwaalf 40 -Westerkertaaiers-1501-SlagTwaalf 94 -Westerkertaaiers-1505-SlagTwaalf 69 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-Westerkertaaiers-1711-SlagTwaalf 102 -Westerkertaaiers-1715-SlagTwaalf 11 -Westerkertaaiers-1719-SlagTwaalf 57 -Westerkertaaiers-1723-SlagTwaalf 91 -Westerkertaaiers-1636-SlagTwaalf 69 -Westerkertaaiers-1641-SlagTwaalf 55 -Westerkertaaiers-1645-SlagTwaalf 53 -Westerkertaaiers-1649-SlagTwaalf 9 -Westerkertaaiers-1653-SlagTwaalf 68 -Westerkertaaiers-1657-SlagTwaalf 104 -Westerkertaaiers-1661-SlagTwaalf 50 -Westerkertaaiers-1665-SlagTwaalf 61 -Westerkertaaiers-1670-SlagTwaalf 30 -Westerkertaaiers-1674-SlagTwaalf 27 -Westerkertaaiers-1679-SlagTwaalf 48 -Westerkertaaiers-1683-SlagTwaalf 48 -Westerkertaaiers-1688-SlagTwaalf 69 -Westerkertaaiers-1692-SlagTwaalf 73 -Westerkertaaiers-1696-SlagTwaalf 80 -Westerkertaaiers-1700-SlagTwaalf 18 -Westerkertaaiers-1704-SlagTwaalf 48 -Westerkertaaiers-1708-SlagTwaalf 27 -Westerkertaaiers-1712-SlagTwaalf 37 -Westerkertaaiers-1716-SlagTwaalf 43 -Westerkertaaiers-1720-SlagTwaalf 56 -Westerkertaaiers-1724-SlagTwaalf 39 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/valid/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/valid/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/valid/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/valid/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/valid/feats_lengths_stats.npz deleted file mode 100644 index 73bc1b4abc016d4c331780a31d063e9c6f62d146..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:13a7fdbbec419f02d0087f7163af779f6a61c7d602dc84dfcf32d997f107c22c -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/valid/feats_stats.npz deleted file mode 100644 index a975763c92973dc5cf0c5305b507f919c48e1956..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:3806218c07d17537e3eede3ef29288121649d137e6480389b983d30e77207def -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/valid/sids_shape deleted file mode 100644 index 4c67d579e718f7fd2461a5af3aa193f083edc323..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/valid/sids_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-1684-SlagTwaalf 1 -Westerkertaaiers-1727-SlagTwaalf 1 -Westerkertaaiers-1771-SlagTwaalf 1 -Westerkertaaiers-1815-SlagTwaalf 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/valid/speech_shape deleted file mode 100644 index 7ddfbd51d7aee7cb4b3d2d2993b010ac9537dbaa..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/valid/speech_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-1684-SlagTwaalf 107159 -Westerkertaaiers-1727-SlagTwaalf 113217 -Westerkertaaiers-1771-SlagTwaalf 134468 -Westerkertaaiers-1815-SlagTwaalf 39514 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/valid/text_shape deleted file mode 100644 index 448924020a97055f3b8fb317056352cf90a7334f..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.30/valid/text_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-1684-SlagTwaalf 58 -Westerkertaaiers-1727-SlagTwaalf 55 -Westerkertaaiers-1771-SlagTwaalf 67 -Westerkertaaiers-1815-SlagTwaalf 19 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31.log deleted file mode 100644 index 0cc6b6795d6434c92b50e7e3ce45a343f296c1d2..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.31.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.31.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:52 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.31.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.31.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:58,492 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:58,696 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:59:01,383 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:59:01,399 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:59:01,409 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:59:01,409 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:59:01,409 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:59:01,409 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:59:01,409 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/config.yaml -[wieling-3-a100] 2023-12-01 15:59:01,428 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.31.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.31.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=17 threads=1 -# Ended (code 0) at Fri Dec 1 15:59:09 UTC 2023, elapsed time 17 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/config.yaml deleted file mode 100644 index fdb902131238f39fbe48a17b5fe9dda58063451d..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.31.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.31.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/train/feats_lengths_stats.npz deleted file mode 100644 index e44fe15b4b21919ef3c39edd83aa63d4833bfaad..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version 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8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/train/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/train/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/train/text_shape deleted file mode 100644 index c309c3f0cd7375bf03dcafc560c24d159646a9bd..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/train/text_shape +++ /dev/null @@ -1,248 +0,0 @@ -Westerkertaaiers-1725-SlagTwaalf 96 -Westerkertaaiers-1730-SlagTwaalf 34 -Westerkertaaiers-1734-SlagTwaalf 160 -Westerkertaaiers-1738-SlagTwaalf 16 -Westerkertaaiers-1742-SlagTwaalf 28 -Westerkertaaiers-1746-SlagTwaalf 12 -Westerkertaaiers-1750-SlagTwaalf 91 -Westerkertaaiers-1755-SlagTwaalf 100 -Westerkertaaiers-1759-SlagTwaalf 26 -Westerkertaaiers-1763-SlagTwaalf 26 -Westerkertaaiers-1767-SlagTwaalf 147 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88 -Westerkertaaiers-1981-SlagTwaalf 32 -Westerkertaaiers-1895-SlagTwaalf 34 -Westerkertaaiers-1899-SlagTwaalf 28 -Westerkertaaiers-1904-SlagTwaalf 72 -Westerkertaaiers-1908-SlagTwaalf 21 -Westerkertaaiers-1913-SlagTwaalf 80 -Westerkertaaiers-1917-SlagTwaalf 68 -Westerkertaaiers-1921-SlagTwaalf 49 -Westerkertaaiers-1925-SlagTwaalf 266 -Westerkertaaiers-1929-SlagTwaalf 39 -Westerkertaaiers-1933-SlagTwaalf 21 -Westerkertaaiers-1937-SlagTwaalf 127 -Westerkertaaiers-1941-SlagTwaalf 61 -Westerkertaaiers-1945-SlagTwaalf 44 -Westerkertaaiers-1950-SlagTwaalf 26 -Westerkertaaiers-1954-SlagTwaalf 86 -Westerkertaaiers-1958-SlagTwaalf 45 -Westerkertaaiers-1962-SlagTwaalf 51 -Westerkertaaiers-1966-SlagTwaalf 86 -Westerkertaaiers-1970-SlagTwaalf 73 -Westerkertaaiers-1974-SlagTwaalf 57 -Westerkertaaiers-1978-SlagTwaalf 63 -Westerkertaaiers-1982-SlagTwaalf 50 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/valid/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/valid/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/valid/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/valid/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/valid/feats_lengths_stats.npz deleted file mode 100644 index e6c5da0c4741af8ed1cdb637635fb6f76efd9646..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:e4ef300a3c44a6c34340bab132587af73a4de3a263c47fb6386d72b26531eeee -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/valid/feats_stats.npz deleted file mode 100644 index 1f0b6080854c7536e226c611754ebd7f9715dbc5..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:4a6230164f0e8eb54193922f3bfd1763c4279849ff954f091d40f0b4acf5c2c1 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/valid/sids_shape deleted file mode 100644 index 1e1957f1cd83898cd426b0ce35351515c5db74dd..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/valid/sids_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-1858-SlagTwaalf 1 -Westerkertaaiers-1902-SlagTwaalf 1 -Westerkertaaiers-1946-SlagTwaalf 1 -Westerkertaaiers-1989-SlagTwaalf 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/valid/speech_shape deleted file mode 100644 index 4810b6ec916611b6b035bd5e6b231caeedb10237..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/valid/speech_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-1858-SlagTwaalf 84672 -Westerkertaaiers-1902-SlagTwaalf 297712 -Westerkertaaiers-1946-SlagTwaalf 58858 -Westerkertaaiers-1989-SlagTwaalf 106959 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/valid/text_shape deleted file mode 100644 index c56a6e9b38127da1a924dd769e9ac86dc0f37056..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.31/valid/text_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-1858-SlagTwaalf 46 -Westerkertaaiers-1902-SlagTwaalf 184 -Westerkertaaiers-1946-SlagTwaalf 36 -Westerkertaaiers-1989-SlagTwaalf 57 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32.log deleted file mode 100644 index 0fdea905e86f4b031e2fb236d07d42e5f02dd019..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.32.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.32.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:53 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.32.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.32.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:58,765 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:58,936 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:59:01,351 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:59:01,411 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:59:01,412 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:59:01,412 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:59:01,412 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:59:01,412 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:59:01,412 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/config.yaml -[wieling-3-a100] 2023-12-01 15:59:01,458 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.32.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.32.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=16 threads=1 -# Ended (code 0) at Fri Dec 1 15:59:09 UTC 2023, elapsed time 16 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/config.yaml deleted file mode 100644 index cb94c5a6d81e6adaefc7f8e4bf9f89d22b9946f0..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.32.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.32.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/train/feats_lengths_stats.npz deleted file mode 100644 index a914e704bb9a6c228188f30eafdac049b050a476..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:f240385d5a773857a85e1cea6ea6eadbfdbe5051e2b7a98d884d10e7fc8a1908 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/train/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/train/feats_stats.npz deleted file mode 100644 index 3842adaa4e506c7a3a33aaf9ad59e7846a928fb6..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/train/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:a27ba4e0f42e88cd0e3d9d267281e794c8e01b65a695ed3268fbbf0dab2d02b1 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/train/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/train/sids_shape deleted file mode 100644 index f5e354c3abb89873097fd47519aa13c93899cd3b..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/train/sids_shape +++ /dev/null @@ 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b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/valid/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/valid/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/valid/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/valid/feats_lengths_stats.npz deleted file mode 100644 index d3ceb9736443b599fb1bd20e384dfc39e03f7441..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:e14968b579f25708dcc76877204efd5a0c4dda9252389fafceffe196893b1678 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/valid/feats_stats.npz deleted file mode 100644 index 3549df8d471d150ad73a6b6141d2cf22488bd66a..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:20ee71ed23c4b66c50fb0ee6cecf324eeb9ce31ed47ffc3aaafde6ada115a3cf -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/valid/sids_shape deleted file mode 100644 index babd61bf85da5fc0a716da6a62ab9e27346f4643..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/valid/sids_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-2033-SlagTwaalf 1 -Westerkertaaiers-2076-SlagTwaalf 1 -Westerkertaaiers-2120-SlagTwaalf 1 -Westerkertaaiers-2163-SlagTwaalf 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/valid/speech_shape deleted file mode 100644 index e9cbe466e4a85e4e2aca2682da993d9a56bb9021..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/valid/speech_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-2033-SlagTwaalf 169071 -Westerkertaaiers-2076-SlagTwaalf 133441 -Westerkertaaiers-2120-SlagTwaalf 48824 -Westerkertaaiers-2163-SlagTwaalf 59270 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/valid/text_shape deleted file mode 100644 index dbce0e28c00ed9c635e22cbc5f9f10ed2f876ccf..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.32/valid/text_shape +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-2033-SlagTwaalf 101 -Westerkertaaiers-2076-SlagTwaalf 96 -Westerkertaaiers-2120-SlagTwaalf 24 -Westerkertaaiers-2163-SlagTwaalf 32 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4.log deleted file mode 100644 index 6b765e63ad7ae22a6e96b0c2e5e9db598b331ae6..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.4.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.4.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:34 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.4.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.4.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:40,330 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:40,444 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:58:41,719 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:58:41,735 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:58:41,735 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:41,735 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:58:41,735 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:41,735 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:58:41,736 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/config.yaml -[wieling-3-a100] 2023-12-01 15:58:41,754 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.4.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.4.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=17 threads=1 -# Ended (code 0) at Fri Dec 1 15:58:51 UTC 2023, elapsed time 17 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/config.yaml deleted file mode 100644 index 0d662e1267d70a54e180c5e36b00bcd6fe7a68e8..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.4.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.4.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/train/feats_lengths_stats.npz deleted file mode 100644 index 868f20000d0fa452abb9907b4eed5e46eb1c9a56..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:2dc81f22c6af66a0aa2505527e23ec392191a9b505029ffaf50b15b875c30057 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/train/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/train/feats_stats.npz deleted file mode 100644 index 8542c3014c851a6046bfbb0e748bbc27a1b5c2b9..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/train/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:c2ea91412bd9c93bb726114b5fc2c42c5ee0fbae7cd3088f671cbdca4f9ce02d -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/train/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/train/sids_shape deleted file mode 100644 index bab40f38f1648dc52e816aae581c34337242349d..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/train/sids_shape +++ /dev/null @@ -1,249 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-Hoogelaandsters-0937 77824 -Hoogelaandsters-0941 127233 -Hoogelaandsters-0946 30209 -Hoogelaandsters-0950 190555 -Hoogelaandsters-0954 100864 -Hoogelaandsters-0958 48128 -Hoogelaandsters-0962 71935 -Hoogelaandsters-0966 136448 -Hoogelaandsters-0970 73472 -Hoogelaandsters-0974 53761 -Hoogelaandsters-0978 62464 -Hoogelaandsters-0982 157952 -Hoogelaandsters-0986 109568 -Hoogelaandsters-0990 75159 -Hoogelaandsters-0994 128754 -Hoogelaandsters-0998 103168 -Hoogelaandsters-1002 93696 -Hoogelaandsters-1006 33844 -Hoogelaandsters-1011 97617 -Hoogelaandsters-1015 82176 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/train/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/train/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/train/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/train/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/train/text_shape deleted file mode 100644 index c296e4299e6f6da419cc09091bfa80b31a17244a..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/train/text_shape +++ /dev/null @@ -1,249 +0,0 @@ -Hoogelaandsters-0763 10 -Hoogelaandsters-0768 30 -Hoogelaandsters-0772 42 -Hoogelaandsters-0776 56 -Hoogelaandsters-0780 36 -Hoogelaandsters-0784 48 -Hoogelaandsters-0788 42 -Hoogelaandsters-0792 42 -Hoogelaandsters-0796 21 -Hoogelaandsters-0800 37 -Hoogelaandsters-0804 60 -Hoogelaandsters-0809 24 -Hoogelaandsters-0813 30 -Hoogelaandsters-0817 16 -Hoogelaandsters-0821 36 -Hoogelaandsters-0825 35 -Hoogelaandsters-0829 53 -Hoogelaandsters-0833 35 -Hoogelaandsters-0837 27 -Hoogelaandsters-0841 46 -Hoogelaandsters-0845 26 -Hoogelaandsters-0849 27 -Hoogelaandsters-0853 22 -Hoogelaandsters-0857 72 -Hoogelaandsters-0861 63 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-Hoogelaandsters-1001 67 -Hoogelaandsters-1005 79 -Hoogelaandsters-1010 70 -Hoogelaandsters-1014 81 -Hoogelaandsters-0929 38 -Hoogelaandsters-0933 56 -Hoogelaandsters-0937 55 -Hoogelaandsters-0941 90 -Hoogelaandsters-0946 20 -Hoogelaandsters-0950 111 -Hoogelaandsters-0954 83 -Hoogelaandsters-0958 35 -Hoogelaandsters-0962 51 -Hoogelaandsters-0966 95 -Hoogelaandsters-0970 55 -Hoogelaandsters-0974 42 -Hoogelaandsters-0978 55 -Hoogelaandsters-0982 105 -Hoogelaandsters-0986 53 -Hoogelaandsters-0990 46 -Hoogelaandsters-0994 63 -Hoogelaandsters-0998 65 -Hoogelaandsters-1002 60 -Hoogelaandsters-1006 22 -Hoogelaandsters-1011 71 -Hoogelaandsters-1015 57 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/valid/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/valid/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/valid/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/valid/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/valid/feats_lengths_stats.npz deleted file mode 100644 index 2578aae87ef495f290d08f005daf14b8f497ebc3..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:d4b9804cd22832ed2c77df08090106346cb1cbd59d290cfacf17b4e892e0603c -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/valid/feats_stats.npz deleted file mode 100644 index 631f80ac704cfbd62ef00a6aa39dbefdc2e3dc29..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:315160146e132e4265a4ab45e74f47efbb52f4607a63446d090e58674f66d96c -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/valid/sids_shape deleted file mode 100644 index 6f1d749035be76d4d3f68a249621387be96abe86..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/valid/sids_shape +++ /dev/null @@ -1,5 +0,0 @@ -Hoogelaandsters-1009 1 -Hoogelaandsters-1277 1 -Hoogelaandsters-1076 1 -Hoogelaandsters-1143 1 -Hoogelaandsters-1210 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/valid/speech_shape deleted file mode 100644 index 3d32997954d8b08d5b0260d4b4699300f94818b5..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/valid/speech_shape +++ /dev/null @@ -1,5 +0,0 @@ -Hoogelaandsters-1009 66561 -Hoogelaandsters-1277 45615 -Hoogelaandsters-1076 242944 -Hoogelaandsters-1143 45056 -Hoogelaandsters-1210 52972 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/valid/text_shape deleted file mode 100644 index 1c9332e72826558b08f9579452aaf0aa0520388b..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.4/valid/text_shape +++ /dev/null @@ -1,5 +0,0 @@ -Hoogelaandsters-1009 45 -Hoogelaandsters-1277 43 -Hoogelaandsters-1076 177 -Hoogelaandsters-1143 21 -Hoogelaandsters-1210 37 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5.log deleted file mode 100644 index 82aed69e941d490fc3cc57333da5cafc51d0b271..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.5.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.5.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:34 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.5.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.5.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:41,311 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:41,430 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:58:43,279 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:58:43,312 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:58:43,313 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:43,313 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:58:43,313 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:43,353 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:58:43,353 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/config.yaml -[wieling-3-a100] 2023-12-01 15:58:43,412 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.5.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.5.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=18 threads=1 -# Ended (code 0) at Fri Dec 1 15:58:52 UTC 2023, elapsed time 18 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/config.yaml deleted file mode 100644 index f76f43d109ff419faff962471af1af71ca37ba15..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.5.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.5.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/train/feats_lengths_stats.npz deleted file mode 100644 index 74bbb85f3bfda2771da32b23ca24a02650d89dd9..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:1bc0cc355682ec3c1e92dd434bda5ee2b2468d85da4316875ee363dc86a9e640 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/train/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/train/feats_stats.npz deleted file mode 100644 index 1ec6c4271329dd8cf5dc4e88cd5a3364c5eb41d8..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/train/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:94ad985bee74f11d8e4e7026acf4be6c33f3b90a608f45bd239195061364dc0f -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/train/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/train/sids_shape deleted file mode 100644 index 84974a32650a5970b19ff5dbb5da5d7ae369000e..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/train/sids_shape +++ /dev/null @@ -1,249 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-Hoogelaandsters-1190 217042 -Hoogelaandsters-1194 207477 -Hoogelaandsters-1198 29428 -Hoogelaandsters-1202 159654 -Hoogelaandsters-1206 286201 -Hoogelaandsters-1211 86080 -Hoogelaandsters-1215 31636 -Hoogelaandsters-1219 172898 -Hoogelaandsters-1223 77252 -Hoogelaandsters-1227 64008 -Hoogelaandsters-1231 62537 -Hoogelaandsters-1235 72102 -Hoogelaandsters-1239 51501 -Hoogelaandsters-1243 24278 -Hoogelaandsters-1247 53708 -Hoogelaandsters-1251 26486 -Hoogelaandsters-1255 58122 -Hoogelaandsters-1259 50030 -Hoogelaandsters-1263 80930 -Hoogelaandsters-1267 105210 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/train/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/train/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/train/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/train/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/train/text_shape deleted file mode 100644 index 7a71bae94089b2437389acad269294e6d79a5bf0..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/train/text_shape +++ /dev/null @@ -1,249 +0,0 @@ -Hoogelaandsters-1017 29 -Hoogelaandsters-1021 119 -Hoogelaandsters-1025 77 -Hoogelaandsters-1029 21 -Hoogelaandsters-1033 29 -Hoogelaandsters-1037 69 -Hoogelaandsters-1041 23 -Hoogelaandsters-1045 79 -Hoogelaandsters-1049 43 -Hoogelaandsters-1053 34 -Hoogelaandsters-1057 113 -Hoogelaandsters-1061 96 -Hoogelaandsters-1065 61 -Hoogelaandsters-1069 56 -Hoogelaandsters-1073 78 -Hoogelaandsters-1078 87 -Hoogelaandsters-1082 56 -Hoogelaandsters-1086 87 -Hoogelaandsters-1090 93 -Hoogelaandsters-1094 37 -Hoogelaandsters-1098 33 -Hoogelaandsters-1102 44 -Hoogelaandsters-1106 121 -Hoogelaandsters-1110 35 -Hoogelaandsters-1114 29 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100 -Hoogelaandsters-1180 41 -Hoogelaandsters-1184 84 -Hoogelaandsters-1188 86 -Hoogelaandsters-1192 24 -Hoogelaandsters-1196 41 -Hoogelaandsters-1200 97 -Hoogelaandsters-1204 70 -Hoogelaandsters-1208 125 -Hoogelaandsters-1213 24 -Hoogelaandsters-1217 22 -Hoogelaandsters-1221 135 -Hoogelaandsters-1225 155 -Hoogelaandsters-1229 16 -Hoogelaandsters-1233 75 -Hoogelaandsters-1237 60 -Hoogelaandsters-1241 33 -Hoogelaandsters-1245 29 -Hoogelaandsters-1249 82 -Hoogelaandsters-1253 44 -Hoogelaandsters-1257 34 -Hoogelaandsters-1261 31 -Hoogelaandsters-1265 25 -Hoogelaandsters-1181 65 -Hoogelaandsters-1185 66 -Hoogelaandsters-1189 134 -Hoogelaandsters-1193 58 -Hoogelaandsters-1197 22 -Hoogelaandsters-1201 69 -Hoogelaandsters-1205 23 -Hoogelaandsters-1209 144 -Hoogelaandsters-1214 38 -Hoogelaandsters-1218 72 -Hoogelaandsters-1222 42 -Hoogelaandsters-1226 83 -Hoogelaandsters-1230 52 -Hoogelaandsters-1234 78 -Hoogelaandsters-1238 165 -Hoogelaandsters-1242 28 -Hoogelaandsters-1246 19 -Hoogelaandsters-1250 30 -Hoogelaandsters-1254 18 -Hoogelaandsters-1258 20 -Hoogelaandsters-1262 34 -Hoogelaandsters-1266 30 -Hoogelaandsters-1182 25 -Hoogelaandsters-1186 31 -Hoogelaandsters-1190 139 -Hoogelaandsters-1194 98 -Hoogelaandsters-1198 28 -Hoogelaandsters-1202 126 -Hoogelaandsters-1206 173 -Hoogelaandsters-1211 61 -Hoogelaandsters-1215 26 -Hoogelaandsters-1219 102 -Hoogelaandsters-1223 48 -Hoogelaandsters-1227 31 -Hoogelaandsters-1231 39 -Hoogelaandsters-1235 43 -Hoogelaandsters-1239 35 -Hoogelaandsters-1243 18 -Hoogelaandsters-1247 49 -Hoogelaandsters-1251 27 -Hoogelaandsters-1255 31 -Hoogelaandsters-1259 45 -Hoogelaandsters-1263 63 -Hoogelaandsters-1267 51 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/valid/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/valid/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/valid/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/valid/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/valid/feats_lengths_stats.npz deleted file mode 100644 index 06382c07d719d2f656953e6134dccdd3889c0c46..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:fccf94a66512ee6a3997cedfecc17c15e9c5799d9fec1edbf1786b66bdc22a41 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/valid/feats_stats.npz deleted file mode 100644 index 093d1c26ed0d037e6f9db9afd2883af239773455..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:d334ed2b36439d6f5ac7376214d30da1f27104babf71993121e03774e4eba5ff -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/valid/sids_shape deleted file mode 100644 index a1125a93bdb016177858cafa82ee7ee7f27cbaf7..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/valid/sids_shape +++ /dev/null @@ -1,5 +0,0 @@ -Hoogelaandsters-1344 1 -Hoogelaandsters-1613 1 -Hoogelaandsters-1412 1 -Hoogelaandsters-1479 1 -Hoogelaandsters-1546 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/valid/speech_shape deleted file mode 100644 index a62a2061651a6178314c7a4f961fce8294fabc2b..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/valid/speech_shape +++ /dev/null @@ -1,5 +0,0 @@ -Hoogelaandsters-1344 85345 -Hoogelaandsters-1613 75044 -Hoogelaandsters-1412 64008 -Hoogelaandsters-1479 43408 -Hoogelaandsters-1546 36788 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/valid/text_shape deleted file mode 100644 index 548a9de86f6e2ece66c542433fb31bf215a4da0c..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.5/valid/text_shape +++ /dev/null @@ -1,5 +0,0 @@ -Hoogelaandsters-1344 62 -Hoogelaandsters-1613 44 -Hoogelaandsters-1412 50 -Hoogelaandsters-1479 32 -Hoogelaandsters-1546 30 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6.log deleted file mode 100644 index 3cfae2680e07ef6ffc53dcba9afeed56d9af5a31..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.6.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.6.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:34 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.6.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.6.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:40,455 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:40,590 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:58:41,805 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:58:41,820 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:58:41,821 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:41,821 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:58:41,821 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:41,821 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:58:41,821 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/config.yaml -[wieling-3-a100] 2023-12-01 15:58:41,839 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.6.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.6.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=16 threads=1 -# Ended (code 0) at Fri Dec 1 15:58:50 UTC 2023, elapsed time 16 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/config.yaml deleted file mode 100644 index 642c097ed274e2c43b6ca51f01be80debefe3071..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.6.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.6.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/train/feats_lengths_stats.npz deleted file mode 100644 index cb3b47709ce0813851a7b55a602398e72e4d9d11..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version 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a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/train/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/train/text_shape deleted file mode 100644 index 247008415bb65cdcde64847244beff6605fcd030..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/train/text_shape +++ /dev/null @@ -1,249 +0,0 @@ -Hoogelaandsters-1269 15 -Hoogelaandsters-1273 46 -Hoogelaandsters-1278 33 -Hoogelaandsters-1282 74 -Hoogelaandsters-1286 46 -Hoogelaandsters-1290 169 -Hoogelaandsters-1294 23 -Hoogelaandsters-1298 34 -Hoogelaandsters-1302 34 -Hoogelaandsters-1306 12 -Hoogelaandsters-1310 21 -Hoogelaandsters-1314 64 -Hoogelaandsters-1318 61 -Hoogelaandsters-1322 31 -Hoogelaandsters-1326 59 -Hoogelaandsters-1330 72 -Hoogelaandsters-1334 202 -Hoogelaandsters-1338 27 -Hoogelaandsters-1342 52 -Hoogelaandsters-1347 46 -Hoogelaandsters-1351 31 -Hoogelaandsters-1355 35 -Hoogelaandsters-1359 48 -Hoogelaandsters-1363 34 -Hoogelaandsters-1367 72 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-Hoogelaandsters-1438 50 -Hoogelaandsters-1442 95 -Hoogelaandsters-1446 60 -Hoogelaandsters-1450 34 -Hoogelaandsters-1454 63 -Hoogelaandsters-1458 72 -Hoogelaandsters-1462 44 -Hoogelaandsters-1466 29 -Hoogelaandsters-1470 91 -Hoogelaandsters-1474 29 -Hoogelaandsters-1478 50 -Hoogelaandsters-1483 78 -Hoogelaandsters-1487 28 -Hoogelaandsters-1491 36 -Hoogelaandsters-1495 28 -Hoogelaandsters-1499 31 -Hoogelaandsters-1503 47 -Hoogelaandsters-1507 68 -Hoogelaandsters-1511 20 -Hoogelaandsters-1515 82 -Hoogelaandsters-1519 25 -Hoogelaandsters-1435 65 -Hoogelaandsters-1439 56 -Hoogelaandsters-1443 109 -Hoogelaandsters-1447 27 -Hoogelaandsters-1451 37 -Hoogelaandsters-1455 19 -Hoogelaandsters-1459 62 -Hoogelaandsters-1463 65 -Hoogelaandsters-1467 46 -Hoogelaandsters-1471 24 -Hoogelaandsters-1475 45 -Hoogelaandsters-1480 29 -Hoogelaandsters-1484 109 -Hoogelaandsters-1488 50 -Hoogelaandsters-1492 78 -Hoogelaandsters-1496 78 -Hoogelaandsters-1500 25 -Hoogelaandsters-1504 25 -Hoogelaandsters-1508 84 -Hoogelaandsters-1512 28 -Hoogelaandsters-1516 20 -Hoogelaandsters-1520 44 -Hoogelaandsters-1436 24 -Hoogelaandsters-1440 102 -Hoogelaandsters-1444 35 -Hoogelaandsters-1448 45 -Hoogelaandsters-1452 67 -Hoogelaandsters-1456 41 -Hoogelaandsters-1460 96 -Hoogelaandsters-1464 20 -Hoogelaandsters-1468 34 -Hoogelaandsters-1472 64 -Hoogelaandsters-1476 76 -Hoogelaandsters-1481 44 -Hoogelaandsters-1485 58 -Hoogelaandsters-1489 94 -Hoogelaandsters-1493 74 -Hoogelaandsters-1497 136 -Hoogelaandsters-1501 15 -Hoogelaandsters-1505 41 -Hoogelaandsters-1509 62 -Hoogelaandsters-1513 44 -Hoogelaandsters-1517 30 -Hoogelaandsters-1521 67 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/valid/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/valid/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/valid/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/valid/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/valid/feats_lengths_stats.npz deleted file mode 100644 index a4f3d7db5112d16f4180dcf91f9a755ac41856e0..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:22a1c19c3b932a34a8a64e7575ef06293a5372f4f375dbf69e9ff5276bfdc278 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/valid/feats_stats.npz deleted file mode 100644 index 90002bfa9c1dd170e59475315f223d4cba779c14..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:65419a75280be829851ff2eab03604e6511df5729c8953ecda8ec4f4a8fee1e8 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/valid/sids_shape deleted file mode 100644 index 62187e7b0112b99918dcdef8009014fd4d0848ac..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/valid/sids_shape +++ /dev/null @@ -1,5 +0,0 @@ -Hoogelaandsters-1680 1 -Hoogelaandsters-1949 1 -Hoogelaandsters-1748 1 -Hoogelaandsters-1815 1 -Hoogelaandsters-1882 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/valid/speech_shape deleted file mode 100644 index ceacafb2af7068b6a04e8156d6b0c28ec973289c..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/valid/speech_shape +++ /dev/null @@ -1,5 +0,0 @@ -Hoogelaandsters-1680 48558 -Hoogelaandsters-1949 104474 -Hoogelaandsters-1748 80930 -Hoogelaandsters-1815 64744 -Hoogelaandsters-1882 52972 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/valid/text_shape deleted file mode 100644 index fdc0cb7b90f48895eece1fdcc7d4c476b965a72a..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.6/valid/text_shape +++ /dev/null @@ -1,5 +0,0 @@ -Hoogelaandsters-1680 35 -Hoogelaandsters-1949 64 -Hoogelaandsters-1748 28 -Hoogelaandsters-1815 28 -Hoogelaandsters-1882 32 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7.log deleted file mode 100644 index 2b45ca27edfabe6e2209e1fc45c1e429920de244..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.7.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.7.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:34 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.7.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.7.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:41,087 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:41,203 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:58:42,975 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:58:43,003 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:58:43,003 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:43,003 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:58:43,003 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:43,003 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:58:43,003 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/config.yaml -[wieling-3-a100] 2023-12-01 15:58:43,037 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.7.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.7.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=18 threads=1 -# Ended (code 0) at Fri Dec 1 15:58:52 UTC 2023, elapsed time 18 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/config.yaml deleted file mode 100644 index f4c257f69f616179db1ef1cc1d33212f7d8d11e1..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.7.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.7.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/train/feats_lengths_stats.npz deleted file mode 100644 index f5359c0493c0883814c1813d1453f86a1957efb4..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:1b9296d23c1ba3efda3df87bcf59d46ca9f9690aac5dc7dacc4feff7a5a319c8 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/train/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/train/feats_stats.npz deleted file mode 100644 index 78013db17ec14f89c56e04d7d4e436f809b0f46f..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/train/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:f40f6ae13098472cccdfcdb7540cfb31df3387052d2233a34a9a11affa3e60d5 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/train/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/train/sids_shape deleted file mode 100644 index 992cdec8c28e6dbc3fe22093d4c09ec497a62367..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/train/sids_shape +++ /dev/null @@ -1,249 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-Hoogelaandsters-1762 18 -Hoogelaandsters-1766 32 -Hoogelaandsters-1770 13 -Hoogelaandsters-1774 22 -Hoogelaandsters-1689 66 -Hoogelaandsters-1693 46 -Hoogelaandsters-1697 22 -Hoogelaandsters-1701 28 -Hoogelaandsters-1705 93 -Hoogelaandsters-1709 48 -Hoogelaandsters-1713 52 -Hoogelaandsters-1717 27 -Hoogelaandsters-1721 31 -Hoogelaandsters-1725 49 -Hoogelaandsters-1730 43 -Hoogelaandsters-1734 83 -Hoogelaandsters-1738 87 -Hoogelaandsters-1742 86 -Hoogelaandsters-1746 102 -Hoogelaandsters-1751 9 -Hoogelaandsters-1755 117 -Hoogelaandsters-1759 63 -Hoogelaandsters-1763 15 -Hoogelaandsters-1767 74 -Hoogelaandsters-1771 67 -Hoogelaandsters-1775 52 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/valid/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/valid/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/valid/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/valid/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/valid/feats_lengths_stats.npz deleted file mode 100644 index e14b13193aeaa2b67f65f89e6c8a61e49fe13c7c..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/valid/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:b7a475b8d0f42e966833689c904e532989a5e5d51b6319854d4f41ef2a56415b -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/valid/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/valid/feats_stats.npz deleted file mode 100644 index e3ebe5f8ea253203899f30e29d4d55b3134d4ace..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:c624a7437ded496c427c8820aa70d265a7016abea7f16f1bff3f0b2307904f83 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/valid/sids_shape deleted file mode 100644 index 18428e5befc8cb40d5df4edace03d8d1b534d704..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/valid/sids_shape +++ /dev/null @@ -1,5 +0,0 @@ -Hoogelaandsters-2016 1 -Hoogelaandsters-2290-MoanMorn 1 -Hoogelaandsters-2083-MoanMorn 1 -Hoogelaandsters-2150-MoanMorn 1 -Hoogelaandsters-2222-MoanMorn 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/valid/speech_shape deleted file mode 100644 index 8df75b83fd4ca7baeb04df01eea1e7895435f61d..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/valid/speech_shape +++ /dev/null @@ -1,5 +0,0 @@ -Hoogelaandsters-2016 26486 -Hoogelaandsters-2290-MoanMorn 353296 -Hoogelaandsters-2083-MoanMorn 63815 -Hoogelaandsters-2150-MoanMorn 153518 -Hoogelaandsters-2222-MoanMorn 184820 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/valid/text_shape deleted file mode 100644 index 3cafbd5fdf50777576c6e3fa0240e3bba4a8b054..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.7/valid/text_shape +++ /dev/null @@ -1,5 +0,0 @@ -Hoogelaandsters-2016 25 -Hoogelaandsters-2290-MoanMorn 218 -Hoogelaandsters-2083-MoanMorn 43 -Hoogelaandsters-2150-MoanMorn 86 -Hoogelaandsters-2222-MoanMorn 103 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8.log deleted file mode 100644 index f2273f5df665f2d94c7d56d00cc7a9b6e7363250..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.8.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.8.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:34 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.8.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.8.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:40,615 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:40,852 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:58:42,290 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:58:42,306 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:58:42,306 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:42,306 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:58:42,306 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:42,306 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:58:42,306 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8/config.yaml -[wieling-3-a100] 2023-12-01 15:58:42,356 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.8.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.8.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=16 threads=1 -# Ended (code 0) at Fri Dec 1 15:58:50 UTC 2023, elapsed time 16 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8/config.yaml deleted file mode 100644 index 6cce20a687af8860373cdac2424f3f68c840c6c0..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.8.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.8.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8/train/feats_lengths_stats.npz deleted file mode 100644 index 60b1990f5c6f1f63417148eb783fb92d5d9b5493..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:955e0565fbb33dcaf40cd48dfd6fa20c292f5c4ad7404676d312f9b0a6d2242b -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8/train/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8/train/feats_stats.npz deleted file mode 100644 index 824db953b0e77edeeaf4f78c146ee396983081c8..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8/train/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:bbdfff53e6737ec99a86816344b21d21ed0033392e106a5af9ad94168a73e0f6 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8/train/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8/train/sids_shape deleted file mode 100644 index b3b602b27991ae33f73271484d5e6cec6766e936..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8/train/sids_shape +++ /dev/null @@ -1,249 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a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8/valid/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:4a8a4cb4eb3ee68c3123fb3a49dd6a06d0987e82cc20baf9d31f468660a4f25a -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8/valid/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8/valid/sids_shape deleted file mode 100644 index 3b7bcc8f0c3da3854c47acaf9704278fdc00fbb5..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8/valid/sids_shape +++ /dev/null @@ -1,5 +0,0 @@ -Hoogelaandsters-2357-MoanMorn 1 -Hoogelaandsters-2630-MoanMorn 1 -Hoogelaandsters-2428-MoanMorn 1 -Hoogelaandsters-2496-MoanMorn 1 -Hoogelaandsters-2563-MoanMorn 1 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8/valid/speech_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8/valid/speech_shape deleted file mode 100644 index d8a8b21904d8af05d594ea72087abc6d7df89fef..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8/valid/speech_shape +++ /dev/null @@ -1,5 +0,0 @@ -Hoogelaandsters-2357-MoanMorn 109040 -Hoogelaandsters-2630-MoanMorn 304495 -Hoogelaandsters-2428-MoanMorn 57967 -Hoogelaandsters-2496-MoanMorn 208844 -Hoogelaandsters-2563-MoanMorn 359222 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8/valid/stats_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8/valid/stats_keys deleted file mode 100644 index 8b395786dbc0bf6d417ffb91823949cfaf8116bc..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8/valid/stats_keys +++ /dev/null @@ -1,2 +0,0 @@ -feats -feats_lengths diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8/valid/text_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8/valid/text_shape deleted file mode 100644 index fc7e14abfd4559e87e859b7decaf249ce2fa2237..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.8/valid/text_shape +++ /dev/null @@ -1,5 +0,0 @@ -Hoogelaandsters-2357-MoanMorn 66 -Hoogelaandsters-2630-MoanMorn 197 -Hoogelaandsters-2428-MoanMorn 23 -Hoogelaandsters-2496-MoanMorn 132 -Hoogelaandsters-2563-MoanMorn 196 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.9.log b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.9.log deleted file mode 100644 index 00a778c5221a4a76a6cd13ac4ec7f78457bab181..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.9.log +++ /dev/null @@ -1,1152 +0,0 @@ -# python3 -m espnet2.bin.gan_tts_train --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.9.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.9.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.9 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -# Started at Fri Dec 1 15:58:34 UTC 2023 -# -/data2/p280965/tts/espnet/tools/venv/bin/python3 /data2/p280965/tts/espnet/espnet2/bin/gan_tts_train.py --collect_stats true --write_collected_feats false --use_preprocessor true --token_type char --token_list dump/token_list/char/tokens.txt --non_linguistic_symbols none --cleaner none --g2p none --normalize none --pitch_normalize none --energy_normalize none --train_data_path_and_name_and_type dump/raw/train_nodev/text,text,text --train_data_path_and_name_and_type dump/raw/train_nodev/wav.scp,speech,sound --valid_data_path_and_name_and_type dump/raw/train_dev/text,text,text --valid_data_path_and_name_and_type dump/raw/train_dev/wav.scp,speech,sound --train_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.9.scp --valid_shape_file exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.9.scp --output_dir exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.9 --config conf/train_vits.yaml --feats_extract fbank --feats_extract_conf n_fft=1024 --feats_extract_conf hop_length=256 --feats_extract_conf win_length=null --feats_extract_conf fs=22050 --feats_extract_conf fmin=80 --feats_extract_conf fmax=7600 --feats_extract_conf n_mels=80 --pitch_extract_conf fs=22050 --pitch_extract_conf n_fft=1024 --pitch_extract_conf hop_length=256 --pitch_extract_conf f0max=400 --pitch_extract_conf f0min=80 --energy_extract_conf fs=22050 --energy_extract_conf n_fft=1024 --energy_extract_conf hop_length=256 --energy_extract_conf win_length=null --train_data_path_and_name_and_type dump/raw/train_nodev/utt2sid,sids,text_int --valid_data_path_and_name_and_type dump/raw/train_dev/utt2sid,sids,text_int --use_wandb true --wandb_project GROTTS --wandb_name VITS_lr_3.0e-4 --init_param downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv --batch_size 40 --batch_bins 10000000 -[wieling-3-a100] 2023-12-01 15:58:40,834 (gan_tts:293) INFO: Vocabulary size: 46 -[wieling-3-a100] 2023-12-01 15:58:40,965 (encoder:174) INFO: encoder self-attention layer type = relative self-attention -/data2/p280965/tts/espnet/tools/venv/lib/python3.9/site-packages/torch/nn/utils/weight_norm.py:30: UserWarning: torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm. - warnings.warn("torch.nn.utils.weight_norm is deprecated in favor of torch.nn.utils.parametrizations.weight_norm.") -/data2/p280965/tts/espnet/espnet2/gan_tts/vits/monotonic_align/__init__.py:19: UserWarning: Cython version is not available. Fallback to 'EXPERIMETAL' numba version. If you want to use the cython version, please build it as follows: `cd espnet2/gan_tts/vits/monotonic_align; python setup.py build_ext --inplace` - warnings.warn( -[wieling-3-a100] 2023-12-01 15:58:42,297 (abs_task:1268) INFO: pytorch.version=2.1.0+cu121, cuda.available=True, cudnn.version=8902, cudnn.benchmark=False, cudnn.deterministic=False -[wieling-3-a100] 2023-12-01 15:58:42,313 (abs_task:1269) INFO: Model structure: -ESPnetGANTTSModel( - (feats_extract): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=80, fmax=7600, htk=False) - ) - (tts): VITS( - (generator): VITSGenerator( - (text_encoder): TextEncoder( - (emb): Embedding(46, 192) - (encoder): Encoder( - (embed): Sequential( - (0): RelPositionalEncoding( - (dropout): Dropout(p=0.0, inplace=False) - ) - ) - (encoders): MultiSequential( - (0): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (1): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (2): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (3): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (4): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - (5): EncoderLayer( - (self_attn): RelPositionMultiHeadedAttention( - (linear_q): Linear(in_features=192, out_features=192, bias=True) - (linear_k): Linear(in_features=192, out_features=192, bias=True) - (linear_v): Linear(in_features=192, out_features=192, bias=True) - (linear_out): Linear(in_features=192, out_features=192, bias=True) - (dropout): Dropout(p=0.1, inplace=False) - (linear_pos): Linear(in_features=192, out_features=192, bias=False) - ) - (feed_forward): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (feed_forward_macaron): MultiLayeredConv1d( - (w_1): Conv1d(192, 768, kernel_size=(3,), stride=(1,), padding=(1,)) - (w_2): Conv1d(768, 192, kernel_size=(3,), stride=(1,), padding=(1,)) - (dropout): Dropout(p=0.1, inplace=False) - ) - (norm_ff): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_mha): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (norm_ff_macaron): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - (dropout): Dropout(p=0.1, inplace=False) - ) - ) - (after_norm): LayerNorm((192,), eps=1e-12, elementwise_affine=True) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (decoder): HiFiGANGenerator( - (input_conv): Conv1d(192, 512, kernel_size=(7,), stride=(1,), padding=(3,)) - (upsamples): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(512, 256, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(256, 128, kernel_size=(16,), stride=(8,), padding=(4,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(128, 64, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - (3): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): ConvTranspose1d(64, 32, kernel_size=(4,), stride=(2,), padding=(1,)) - ) - ) - (blocks): ModuleList( - (0): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (1): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (2): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(256, 256, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (3): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (4): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (5): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(128, 128, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (6): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (7): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (8): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(64, 64, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - (9): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(5,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - (10): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(9,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(15,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(7,), stride=(1,), padding=(3,)) - ) - ) - ) - (11): ResidualBlock( - (convs1): ModuleList( - (0): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - (1): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(15,), dilation=(3,)) - ) - (2): Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(25,), dilation=(5,)) - ) - ) - (convs2): ModuleList( - (0-2): 3 x Sequential( - (0): LeakyReLU(negative_slope=0.1) - (1): Conv1d(32, 32, kernel_size=(11,), stride=(1,), padding=(5,)) - ) - ) - ) - ) - (output_conv): Sequential( - (0): LeakyReLU(negative_slope=0.01) - (1): Conv1d(32, 1, kernel_size=(7,), stride=(1,), padding=(3,)) - (2): Tanh() - ) - (global_conv): Conv1d(256, 512, kernel_size=(1,), stride=(1,)) - ) - (posterior_encoder): PosteriorEncoder( - (input_conv): Conv1d(80, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-15): 16 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 384, kernel_size=(1,), stride=(1,)) - ) - (flow): ResidualAffineCouplingBlock( - (flows): ModuleList( - (0): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (1): FlipFlow() - (2): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (3): FlipFlow() - (4): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (5): FlipFlow() - (6): ResidualAffineCouplingLayer( - (input_conv): Conv1d(96, 192, kernel_size=(1,), stride=(1,)) - (encoder): WaveNet( - (conv_layers): ModuleList( - (0-3): 4 x ResidualBlock( - (conv): Conv1d(192, 384, kernel_size=(5,), stride=(1,), padding=(2,)) - (conv1x1_glo): Conv1d1x1(256, 384, kernel_size=(1,), stride=(1,), bias=False) - (conv1x1_out): Conv1d1x1(192, 384, kernel_size=(1,), stride=(1,)) - ) - ) - ) - (proj): Conv1d(192, 96, kernel_size=(1,), stride=(1,)) - ) - (7): FlipFlow() - ) - ) - (duration_predictor): StochasticDurationPredictor( - (pre): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (log_flow): LogFlow() - (flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (post_pre): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (post_dds): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.5, inplace=False) - ) - ) - ) - (post_proj): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (post_flows): ModuleList( - (0): ElementwiseAffineFlow() - (1): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (2): FlipFlow() - (3): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (4): FlipFlow() - (5): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (6): FlipFlow() - (7): ConvFlow( - (input_conv): Conv1d(1, 192, kernel_size=(1,), stride=(1,)) - (dds_conv): DilatedDepthSeparableConv( - (convs): ModuleList( - (0): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(1,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (1): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(3,), dilation=(3,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - (2): Sequential( - (0): Conv1d(192, 192, kernel_size=(3,), stride=(1,), padding=(9,), dilation=(9,), groups=192) - (1): Transpose() - (2): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (3): Transpose() - (4): GELU(approximate='none') - (5): Conv1d(192, 192, kernel_size=(1,), stride=(1,)) - (6): Transpose() - (7): LayerNorm((192,), eps=1e-05, elementwise_affine=True) - (8): Transpose() - (9): GELU(approximate='none') - (10): Dropout(p=0.0, inplace=False) - ) - ) - ) - (proj): Conv1d(192, 29, kernel_size=(1,), stride=(1,)) - ) - (8): FlipFlow() - ) - (global_conv): Conv1d(256, 192, kernel_size=(1,), stride=(1,)) - ) - (global_emb): Embedding(4, 256) - ) - (discriminator): HiFiGANMultiScaleMultiPeriodDiscriminator( - (msd): HiFiGANMultiScaleDiscriminator( - (discriminators): ModuleList( - (0): HiFiGANScaleDiscriminator( - (layers): ModuleList( - (0): Sequential( - (0): Conv1d(1, 128, kernel_size=(15,), stride=(1,), padding=(7,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv1d(128, 128, kernel_size=(41,), stride=(2,), padding=(20,), groups=4) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv1d(128, 256, kernel_size=(41,), stride=(2,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv1d(256, 512, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv1d(512, 1024, kernel_size=(41,), stride=(4,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (5): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(41,), stride=(1,), padding=(20,), groups=16) - (1): LeakyReLU(negative_slope=0.1) - ) - (6): Sequential( - (0): Conv1d(1024, 1024, kernel_size=(5,), stride=(1,), padding=(2,)) - (1): LeakyReLU(negative_slope=0.1) - ) - (7): Conv1d(1024, 1, kernel_size=(3,), stride=(1,), padding=(1,)) - ) - ) - ) - ) - (mpd): HiFiGANMultiPeriodDiscriminator( - (discriminators): ModuleList( - (0-4): 5 x HiFiGANPeriodDiscriminator( - (convs): ModuleList( - (0): Sequential( - (0): Conv2d(1, 32, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (1): Sequential( - (0): Conv2d(32, 128, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (2): Sequential( - (0): Conv2d(128, 512, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (3): Sequential( - (0): Conv2d(512, 1024, kernel_size=(5, 1), stride=(3, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - (4): Sequential( - (0): Conv2d(1024, 1024, kernel_size=(5, 1), stride=(1, 1), padding=(2, 0)) - (1): LeakyReLU(negative_slope=0.1) - ) - ) - (output_conv): Conv2d(1024, 1, kernel_size=(2, 1), stride=(1, 1), padding=(1, 0)) - ) - ) - ) - ) - (generator_adv_loss): GeneratorAdversarialLoss() - (discriminator_adv_loss): DiscriminatorAdversarialLoss() - (feat_match_loss): FeatureMatchLoss() - (mel_loss): MelSpectrogramLoss( - (wav_to_mel): LogMelFbank( - (stft): Stft(n_fft=1024, win_length=1024, hop_length=256, center=True, normalized=False, onesided=True) - (logmel): LogMel(sr=22050, n_fft=1024, n_mels=80, fmin=0, fmax=11025.0, htk=False) - ) - ) - (kl_loss): KLDivergenceLoss() - ) -) - -Model summary: - Class Name: ESPnetGANTTSModel - Total Number of model parameters: 96.24 M - Number of trainable parameters: 96.24 M (100.0%) - Size: 384.96 MB - Type: torch.float32 -[wieling-3-a100] 2023-12-01 15:58:42,313 (abs_task:1272) INFO: Optimizer: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:42,313 (abs_task:1273) INFO: Scheduler: -[wieling-3-a100] 2023-12-01 15:58:42,313 (abs_task:1272) INFO: Optimizer2: -AdamW ( -Parameter Group 0 - amsgrad: False - betas: [0.8, 0.99] - capturable: False - differentiable: False - eps: 1e-09 - foreach: None - fused: None - initial_lr: 0.0003 - lr: 0.0003 - maximize: False - weight_decay: 0.0 -) -[wieling-3-a100] 2023-12-01 15:58:42,313 (abs_task:1273) INFO: Scheduler2: -[wieling-3-a100] 2023-12-01 15:58:42,313 (abs_task:1282) INFO: Saving the configuration in exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.9/config.yaml -[wieling-3-a100] 2023-12-01 15:58:42,363 (abs_task:1293) INFO: Namespace(config='conf/train_vits.yaml', print_config=False, log_level='INFO', drop_last_iter=False, dry_run=False, iterator_type='sequence', valid_iterator_type=None, output_dir='exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.9', ngpu=0, seed=67823, num_workers=4, num_att_plot=3, dist_backend='nccl', dist_init_method='env://', dist_world_size=None, dist_rank=None, local_rank=None, dist_master_addr=None, dist_master_port=None, dist_launcher=None, multiprocessing_distributed=False, unused_parameters=True, sharded_ddp=False, cudnn_enabled=True, cudnn_benchmark=False, cudnn_deterministic=False, collect_stats=True, write_collected_feats=False, max_epoch=1000, patience=None, val_scheduler_criterion=('valid', 'loss'), early_stopping_criterion=('valid', 'loss', 'min'), best_model_criterion=[['train', 'total_count', 'max']], keep_nbest_models=10, nbest_averaging_interval=0, grad_clip=-1, grad_clip_type=2.0, grad_noise=False, accum_grad=1, no_forward_run=False, resume=False, train_dtype='float32', use_amp=False, log_interval=50, use_matplotlib=True, use_tensorboard=True, create_graph_in_tensorboard=False, use_wandb=True, wandb_project='GROTTS', wandb_id=None, wandb_entity=None, wandb_name='VITS_lr_3.0e-4', wandb_model_log_interval=-1, detect_anomaly=False, use_lora=False, save_lora_only=True, lora_conf={}, pretrain_path=None, init_param=['downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv'], ignore_init_mismatch=False, freeze_param=[], num_iters_per_epoch=1000, batch_size=40, valid_batch_size=None, batch_bins=10000000, valid_batch_bins=None, train_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.9.scp'], valid_shape_file=['exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.9.scp'], batch_type='numel', valid_batch_type=None, fold_length=[], sort_in_batch='descending', shuffle_within_batch=False, sort_batch='descending', multiple_iterator=False, chunk_length=500, chunk_shift_ratio=0.5, num_cache_chunks=1024, chunk_excluded_key_prefixes=[], chunk_default_fs=None, train_data_path_and_name_and_type=[('dump/raw/train_nodev/text', 'text', 'text'), ('dump/raw/train_nodev/wav.scp', 'speech', 'sound'), ('dump/raw/train_nodev/utt2sid', 'sids', 'text_int')], valid_data_path_and_name_and_type=[('dump/raw/train_dev/text', 'text', 'text'), ('dump/raw/train_dev/wav.scp', 'speech', 'sound'), ('dump/raw/train_dev/utt2sid', 'sids', 'text_int')], allow_variable_data_keys=False, max_cache_size=0.0, max_cache_fd=32, allow_multi_rates=False, valid_max_cache_size=None, exclude_weight_decay=False, exclude_weight_decay_conf={}, optim='adamw', optim_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler='exponentiallr', scheduler_conf={'gamma': 0.999875}, optim2='adamw', optim2_conf={'lr': 0.0003, 'betas': [0.8, 0.99], 'eps': 1e-09, 'weight_decay': 0.0}, scheduler2='exponentiallr', scheduler2_conf={'gamma': 0.999875}, generator_first=False, token_list=['', '', '', 'e', 'n', 'a', 'o', 't', 'i', 'r', 'd', 's', 'k', 'l', 'm', 'u', 'g', 'h', 'w', 'v', '.', 'z', 'b', 'p', ',', 'j', 'c', 'f', '‘', '’', ':', '?', 'ö', "'", '!', '-', ';', 'ò', 'è', 'ì', 'é', 'y', 'ë', 'x', 'q', ''], odim=None, model_conf={}, use_preprocessor=True, token_type='char', bpemodel=None, non_linguistic_symbols=None, cleaner=None, g2p=None, feats_extract='fbank', feats_extract_conf={'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'fs': 22050, 'fmin': 80, 'fmax': 7600, 'n_mels': 80}, normalize=None, normalize_conf={}, tts='vits', tts_conf={'generator_type': 'vits_generator', 'generator_params': {'hidden_channels': 192, 'spks': 4, 'global_channels': 256, 'segment_size': 32, 'text_encoder_attention_heads': 2, 'text_encoder_ffn_expand': 4, 'text_encoder_blocks': 6, 'text_encoder_positionwise_layer_type': 'conv1d', 'text_encoder_positionwise_conv_kernel_size': 3, 'text_encoder_positional_encoding_layer_type': 'rel_pos', 'text_encoder_self_attention_layer_type': 'rel_selfattn', 'text_encoder_activation_type': 'swish', 'text_encoder_normalize_before': True, 'text_encoder_dropout_rate': 0.1, 'text_encoder_positional_dropout_rate': 0.0, 'text_encoder_attention_dropout_rate': 0.1, 'use_macaron_style_in_text_encoder': True, 'use_conformer_conv_in_text_encoder': False, 'text_encoder_conformer_kernel_size': -1, 'decoder_kernel_size': 7, 'decoder_channels': 512, 'decoder_upsample_scales': [8, 8, 2, 2], 'decoder_upsample_kernel_sizes': [16, 16, 4, 4], 'decoder_resblock_kernel_sizes': [3, 7, 11], 'decoder_resblock_dilations': [[1, 3, 5], [1, 3, 5], [1, 3, 5]], 'use_weight_norm_in_decoder': True, 'posterior_encoder_kernel_size': 5, 'posterior_encoder_layers': 16, 'posterior_encoder_stacks': 1, 'posterior_encoder_base_dilation': 1, 'posterior_encoder_dropout_rate': 0.0, 'use_weight_norm_in_posterior_encoder': True, 'flow_flows': 4, 'flow_kernel_size': 5, 'flow_base_dilation': 1, 'flow_layers': 4, 'flow_dropout_rate': 0.0, 'use_weight_norm_in_flow': True, 'use_only_mean_in_flow': True, 'stochastic_duration_predictor_kernel_size': 3, 'stochastic_duration_predictor_dropout_rate': 0.5, 'stochastic_duration_predictor_flows': 4, 'stochastic_duration_predictor_dds_conv_layers': 3, 'vocabs': 46, 'aux_channels': 80}, 'discriminator_type': 'hifigan_multi_scale_multi_period_discriminator', 'discriminator_params': {'scales': 1, 'scale_downsample_pooling': 'AvgPool1d', 'scale_downsample_pooling_params': {'kernel_size': 4, 'stride': 2, 'padding': 2}, 'scale_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [15, 41, 5, 3], 'channels': 128, 'max_downsample_channels': 1024, 'max_groups': 16, 'bias': True, 'downsample_scales': [2, 2, 4, 4, 1], 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': False, 'use_spectral_norm': False}, 'follow_official_norm': False, 'periods': [2, 3, 5, 7, 11], 'period_discriminator_params': {'in_channels': 1, 'out_channels': 1, 'kernel_sizes': [5, 3], 'channels': 32, 'downsample_scales': [3, 3, 3, 3, 1], 'max_downsample_channels': 1024, 'bias': True, 'nonlinear_activation': 'LeakyReLU', 'nonlinear_activation_params': {'negative_slope': 0.1}, 'use_weight_norm': True, 'use_spectral_norm': False}}, 'generator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'discriminator_adv_loss_params': {'average_by_discriminators': False, 'loss_type': 'mse'}, 'feat_match_loss_params': {'average_by_discriminators': False, 'average_by_layers': False, 'include_final_outputs': True}, 'mel_loss_params': {'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None, 'window': 'hann', 'n_mels': 80, 'fmin': 0, 'fmax': None, 'log_base': None}, 'lambda_adv': 1.0, 'lambda_mel': 45.0, 'lambda_feat_match': 2.0, 'lambda_dur': 1.0, 'lambda_kl': 1.0, 'sampling_rate': 22050, 'cache_generator_outputs': True}, pitch_extract=None, pitch_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'f0max': 400, 'f0min': 80}, pitch_normalize=None, pitch_normalize_conf={}, energy_extract=None, energy_extract_conf={'fs': 22050, 'n_fft': 1024, 'hop_length': 256, 'win_length': None}, energy_normalize=None, energy_normalize_conf={}, required=['output_dir', 'token_list'], version='202310', distributed=False) -# Accounting: time=19 threads=1 -# Ended (code 0) at Fri Dec 1 15:58:53 UTC 2023, elapsed time 19 seconds diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.9/config.yaml b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.9/config.yaml deleted file mode 100644 index 8db07e2429bca0afd740b867c95d320cd90a6bd6..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.9/config.yaml +++ /dev/null @@ -1,383 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.9 -ngpu: 0 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: null -dist_rank: null -local_rank: null -dist_master_addr: null -dist_master_port: null -dist_launcher: null -multiprocessing_distributed: false -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: true -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: false -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/train.9.scp -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.9.scp -batch_type: numel -valid_batch_type: null -fold_length: [] -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: null -normalize_conf: {} -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - f0max: 400 - f0min: 80 -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: false diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.9/train/batch_keys b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.9/train/batch_keys deleted file mode 100644 index d033b933d9e3acd4836bb88080c217d77e1d7b52..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.9/train/batch_keys +++ /dev/null @@ -1,3 +0,0 @@ -text -speech -sids diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.9/train/feats_lengths_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.9/train/feats_lengths_stats.npz deleted file mode 100644 index 6ae4dfa363a1909ac4be49f50394e0df4356bafb..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.9/train/feats_lengths_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:819b2913dfa22de73aa009f06e30ecd948ee26aced851a9c60caa90f66dab301 -size 778 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.9/train/feats_stats.npz b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.9/train/feats_stats.npz deleted file mode 100644 index 9336a6127c56c9da26cbab4f793dcc95d6a38b5c..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.9/train/feats_stats.npz +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:057024ac9a577053dcb73fe0e9c3b6ec34a92665b0facf6692f131e17ee1ef75 -size 1402 diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.9/train/sids_shape b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.9/train/sids_shape deleted file mode 100644 index c4a2fe12594f18f49b961ee47b9878cd70620b13..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/stats.9/train/sids_shape +++ /dev/null @@ -1,249 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downloads/datasets/wietsedv/GroTTS/flacs/Hoogelaandsters/Hoogelaandsters-0942.flac diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.30.scp b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.30.scp deleted file mode 100644 index 6f01a1be2ece77bef08bea9d35f54bafd3606aec..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.30.scp +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-1684-SlagTwaalf downloads/datasets/wietsedv/GroTTS/flacs/Westerkertaaiers/Westerkertaaiers-1684-SlagTwaalf.flac -Westerkertaaiers-1727-SlagTwaalf downloads/datasets/wietsedv/GroTTS/flacs/Westerkertaaiers/Westerkertaaiers-1727-SlagTwaalf.flac -Westerkertaaiers-1771-SlagTwaalf downloads/datasets/wietsedv/GroTTS/flacs/Westerkertaaiers/Westerkertaaiers-1771-SlagTwaalf.flac -Westerkertaaiers-1815-SlagTwaalf downloads/datasets/wietsedv/GroTTS/flacs/Westerkertaaiers/Westerkertaaiers-1815-SlagTwaalf.flac diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.31.scp b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.31.scp deleted file mode 100644 index a71ae1a4cc0c6a6aa0a10a3bc03f3bff37d8b149..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.31.scp +++ /dev/null @@ -1,4 +0,0 @@ -Westerkertaaiers-1858-SlagTwaalf downloads/datasets/wietsedv/GroTTS/flacs/Westerkertaaiers/Westerkertaaiers-1858-SlagTwaalf.flac -Westerkertaaiers-1902-SlagTwaalf downloads/datasets/wietsedv/GroTTS/flacs/Westerkertaaiers/Westerkertaaiers-1902-SlagTwaalf.flac -Westerkertaaiers-1946-SlagTwaalf downloads/datasets/wietsedv/GroTTS/flacs/Westerkertaaiers/Westerkertaaiers-1946-SlagTwaalf.flac -Westerkertaaiers-1989-SlagTwaalf downloads/datasets/wietsedv/GroTTS/flacs/Westerkertaaiers/Westerkertaaiers-1989-SlagTwaalf.flac diff --git a/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.32.scp b/exp-vits-lr-3e-4/tts_stats_raw_char/logdir/valid.32.scp deleted file mode 100644 index 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-Hoogelaandsters-3367-VOnJ 90215 -Hoogelaandsters-3368-VOnJ 112722 -Hoogelaandsters-3369-VOnJ 121956 -Hoogelaandsters-3371-VOnJ 133821 -Hoogelaandsters-3372-VOnJ 90317 -Hoogelaandsters-3373-VOnJ 82249 -Hoogelaandsters-3375-VOnJ 173283 -Hoogelaandsters-3376-VOnJ 51953 -Hoogelaandsters-3377-VOnJ 89114 -Hoogelaandsters-3378-VOnJ 51193 -Hoogelaandsters-3379-VOnJ 95962 -Hoogelaandsters-3380-VOnJ 124696 -Hoogelaandsters-3381-VOnJ 70036 -Hoogelaandsters-3382-VOnJ 134327 -Hoogelaandsters-3383-VOnJ 97683 -Hoogelaandsters-3384-VOnJ 165028 -Hoogelaandsters-3385-VOnJ 133182 -Hoogelaandsters-3386-VOnJ 90317 -Hoogelaandsters-3387-VOnJ 103970 -Hoogelaandsters-3388-VOnJ 77248 -Hoogelaandsters-3389-VOnJ 154273 -Hoogelaandsters-3390-VOnJ 61954 -Hoogelaandsters-3391-VOnJ 308295 -Hoogelaandsters-3392-VOnJ 188147 -Hoogelaandsters-3393-VOnJ 183176 -Hoogelaandsters-3394-VOnJ 120636 -Hoogelaandsters-3395-VOnJ 360989 -Hoogelaandsters-3396-VOnJ 81818 -Hoogelaandsters-3397-VOnJ 50803 -Hoogelaandsters-3398-VOnJ 87612 -Hoogelaandsters-3399-VOnJ 224648 -Hoogelaandsters-3400-VOnJ 115718 -Hoogelaandsters-3401-VOnJ 69725 -Oldambsters-0002-AigenOardegheden 22579 -Oldambsters-0003-AigenOardegheden 146219 -Oldambsters-0004-AigenOardegheden 166522 -Oldambsters-0005-AigenOardegheden 25402 -Oldambsters-0006-AigenOardegheden 34796 -Oldambsters-0007-AigenOardegheden 59270 -Oldambsters-0008-AigenOardegheden 57579 -Oldambsters-0009-AigenOardegheden 58200 -Oldambsters-0010-AigenOardegheden 62093 -Oldambsters-0011-AigenOardegheden 92281 -Oldambsters-0012-AigenOardegheden 172737 -Oldambsters-0013-AigenOardegheden 106500 -Oldambsters-0014-AigenOardegheden 84672 -Oldambsters-0015-AigenOardegheden 47981 -Oldambsters-0016-AigenOardegheden 42336 -Oldambsters-0017-AigenOardegheden 68763 -Oldambsters-0018-AigenOardegheden 52385 -Oldambsters-0019-AigenOardegheden 47981 -Oldambsters-0020-AigenOardegheden 101999 -Oldambsters-0021-AigenOardegheden 127938 -Oldambsters-0022-AigenOardegheden 89981 -Oldambsters-0023-AigenOardegheden 297410 -Oldambsters-0024-AigenOardegheden 148030 -Oldambsters-0025-AigenOardegheden 49739 -Oldambsters-0026-AigenOardegheden 148734 -Oldambsters-0027-AigenOardegheden 123138 -Oldambsters-0028-AigenOardegheden 64681 -Oldambsters-0029-AigenOardegheden 85668 -Oldambsters-0030-AigenOardegheden 93139 -Oldambsters-0031-AigenOardegheden 112392 -Oldambsters-0032-AigenOardegheden 91623 -Oldambsters-0033-AigenOardegheden 143922 -Oldambsters-0034-AigenOardegheden 108479 -Oldambsters-0035-AigenOardegheden 107251 -Oldambsters-0036-AigenOardegheden 91772 -Oldambsters-0037-AigenOardegheden 161723 -Oldambsters-0039-AigenOardegheden 197201 -Oldambsters-0040-AigenOardegheden 67738 -Oldambsters-0041-AigenOardegheden 67738 -Oldambsters-0042-AigenOardegheden 56448 -Oldambsters-0043-AigenOardegheden 82118 -Oldambsters-0044-AigenOardegheden 39514 -Oldambsters-0045-AigenOardegheden 105821 -Oldambsters-0046-AigenOardegheden 39514 -Oldambsters-0047-AigenOardegheden 25402 -Oldambsters-0048-AigenOardegheden 56565 -Oldambsters-0049-AigenOardegheden 113912 -Oldambsters-0050-AigenOardegheden 76205 -Oldambsters-0051-AigenOardegheden 79027 -Oldambsters-0052-AigenOardegheden 35800 -Oldambsters-0053-AigenOardegheden 149587 -Oldambsters-0055-AigenOardegheden 98784 -Oldambsters-0056-AigenOardegheden 33869 -Oldambsters-0057-AigenOardegheden 22579 -Oldambsters-0058-AigenOardegheden 61102 -Oldambsters-0059-AigenOardegheden 63784 -Oldambsters-0060-AigenOardegheden 53626 -Oldambsters-0061-AigenOardegheden 174989 -Oldambsters-0062-AigenOardegheden 70560 -Oldambsters-0063-AigenOardegheden 45062 -Oldambsters-0064-AigenOardegheden 63057 -Oldambsters-0065-AigenOardegheden 155232 -Oldambsters-0066-AigenOardegheden 93480 -Oldambsters-0067-AigenOardegheden 84672 -Oldambsters-0068-AigenOardegheden 39514 -Oldambsters-0069-AigenOardegheden 50117 -Oldambsters-0070-AigenOardegheden 95135 -Oldambsters-0071-AigenOardegheden 50803 -Oldambsters-0072-AigenOardegheden 31046 -Oldambsters-0073-AigenOardegheden 131723 -Oldambsters-0074-AigenOardegheden 120393 -Oldambsters-0075-AigenOardegheden 109746 -Oldambsters-0076-AigenOardegheden 70560 -Oldambsters-0077-AigenOardegheden 202215 -Oldambsters-0078-AigenOardegheden 120349 -Oldambsters-0079-AigenOardegheden 298152 -Oldambsters-0080-AigenOardegheden 113633 -Oldambsters-0081-AigenOardegheden 125091 -Oldambsters-0082-AigenOardegheden 76205 -Oldambsters-0083-AigenOardegheden 132653 -Oldambsters-0084-AigenOardegheden 62093 -Oldambsters-0085-AigenOardegheden 36691 -Oldambsters-0086-AigenOardegheden 36691 -Oldambsters-0087-AigenOardegheden 247303 -Oldambsters-0088-AigenOardegheden 29744 -Oldambsters-0089-AigenOardegheden 90317 -Oldambsters-0090-AigenOardegheden 42336 -Oldambsters-0091-AigenOardegheden 57170 -Oldambsters-0092-AigenOardegheden 67738 -Oldambsters-0093-AigenOardegheden 172315 -Oldambsters-0094-AigenOardegheden 73382 -Oldambsters-0095-AigenOardegheden 64915 -Oldambsters-0096-AigenOardegheden 62694 -Oldambsters-0097-AigenOardegheden 129439 -Oldambsters-0098-AigenOardegheden 81275 -Oldambsters-0099-AigenOardegheden 50803 -Oldambsters-0100-AigenOardegheden 81850 -Oldambsters-0101-AigenOardegheden 127144 -Oldambsters-0102-AigenOardegheden 85973 -Oldambsters-0103-AigenOardegheden 95962 -Oldambsters-0104-AigenOardegheden 77952 -Oldambsters-0105-AigenOardegheden 57199 -Oldambsters-0107-AigenOardegheden 42336 -Oldambsters-0108-AigenOardegheden 39514 -Oldambsters-0110-AigenOardegheden 43856 -Oldambsters-0111-AigenOardegheden 130255 -Oldambsters-0112-AigenOardegheden 79027 -Oldambsters-0113-AigenOardegheden 61497 -Oldambsters-0114-AigenOardegheden 101606 -Oldambsters-0115-AigenOardegheden 203670 -Oldambsters-0116-AigenOardegheden 28224 -Oldambsters-0117-AigenOardegheden 28224 -Oldambsters-0118-AigenOardegheden 25402 -Oldambsters-0119-AigenOardegheden 201829 -Oldambsters-0120-AigenOardegheden 129830 -Oldambsters-0121-AigenOardegheden 158756 -Oldambsters-0122-AigenOardegheden 84672 -Oldambsters-0123-AigenOardegheden 59270 -Oldambsters-0124-AigenOardegheden 83802 -Oldambsters-0125-AigenOardegheden 45158 -Oldambsters-0126-AigenOardegheden 64755 -Oldambsters-0127-AigenOardegheden 31046 -Oldambsters-0128-AigenOardegheden 56448 -Oldambsters-0129-AigenOardegheden 49971 -Oldambsters-0130-AigenOardegheden 424154 -Oldambsters-0131-AigenOardegheden 154609 -Oldambsters-0132-AigenOardegheden 115718 -Oldambsters-0133-AigenOardegheden 77564 -Oldambsters-0134-AigenOardegheden 47981 -Oldambsters-0135-AigenOardegheden 65329 -Oldambsters-0136-AigenOardegheden 187308 -Oldambsters-0137-AigenOardegheden 39514 -Oldambsters-0138-AigenOardegheden 145810 -Oldambsters-0139-AigenOardegheden 39514 -Oldambsters-0140-AigenOardegheden 64915 -Oldambsters-0141-AigenOardegheden 62918 -Oldambsters-0142-AigenOardegheden 77892 -Oldambsters-0143-AigenOardegheden 149929 -Oldambsters-0144-AigenOardegheden 67738 -Oldambsters-0145-AigenOardegheden 39514 -Oldambsters-0146-AigenOardegheden 147639 -Oldambsters-0147-AigenOardegheden 50803 -Oldambsters-0148-AigenOardegheden 44017 -Oldambsters-0149-AigenOardegheden 256509 -Oldambsters-0150-AigenOardegheden 126455 -Oldambsters-0151-AigenOardegheden 76205 -Oldambsters-0152-AigenOardegheden 29269 -Oldambsters-0154-AigenOardegheden 143942 -Oldambsters-0155-AigenOardegheden 101606 -Oldambsters-0156-AigenOardegheden 39514 -Oldambsters-0157-AigenOardegheden 53626 -Oldambsters-0158-AigenOardegheden 33869 -Oldambsters-0159-AigenOardegheden 76205 -Oldambsters-0161-AigenOardegheden 70560 -Oldambsters-0162-AigenOardegheden 101606 -Oldambsters-0163-AigenOardegheden 81789 -Oldambsters-0164-AigenOardegheden 129330 -Oldambsters-0165-AigenOardegheden 153416 -Oldambsters-0166-AigenOardegheden 129658 -Oldambsters-0167-AigenOardegheden 93722 -Oldambsters-0168-AigenOardegheden 87494 -Oldambsters-0169-AigenOardegheden 104280 -Oldambsters-0170-AigenOardegheden 33869 -Oldambsters-0171-AigenOardegheden 48273 -Oldambsters-0172-AigenOardegheden 33869 -Oldambsters-0173-AigenOardegheden 52607 -Oldambsters-0174-AigenOardegheden 62093 -Oldambsters-0175-AigenOardegheden 231437 -Oldambsters-0176-AigenOardegheden 93139 -Oldambsters-0177-AigenOardegheden 172629 -Oldambsters-0178-AigenOardegheden 239703 -Oldambsters-0179-AigenOardegheden 147415 -Oldambsters-0180-AigenOardegheden 85912 -Oldambsters-0181-AigenOardegheden 118115 -Oldambsters-0182-AigenOardegheden 61798 -Oldambsters-0183-AigenOardegheden 117011 -Oldambsters-0184-AigenOardegheden 139411 -Oldambsters-0186-AigenOardegheden 97861 -Oldambsters-0187-AigenOardegheden 36691 -Oldambsters-0188-AigenOardegheden 81185 -Oldambsters-0192-AigenOardegheden 83175 -Oldambsters-0193-AigenOardegheden 34432 -Oldambsters-0194-AigenOardegheden 137115 -Oldambsters-0195-AigenOardegheden 25402 -Oldambsters-0196-AigenOardegheden 135882 -Oldambsters-0197-AigenOardegheden 73382 -Oldambsters-0198-AigenOardegheden 47981 -Oldambsters-0199-AigenOardegheden 31046 -Oldambsters-0200-AigenOardegheden 94486 -Oldambsters-0201-AigenOardegheden 82266 -Oldambsters-0203-AigenOardegheden 67738 -Oldambsters-0204-AigenOardegheden 31046 -Oldambsters-0205-AigenOardegheden 47981 -Oldambsters-0206-AigenOardegheden 22579 -Oldambsters-0208-AigenOardegheden 158775 -Oldambsters-0209-AigenOardegheden 90317 -Oldambsters-0210-AigenOardegheden 90317 -Oldambsters-0211-AigenOardegheden 53679 -Oldambsters-0212-AigenOardegheden 124186 -Oldambsters-0213-AigenOardegheden 50803 -Oldambsters-0214-AigenOardegheden 28224 -Oldambsters-0216-AigenOardegheden 62093 -Oldambsters-0217-AigenOardegheden 55198 -Oldambsters-0218-AigenOardegheden 142823 -Oldambsters-0219-AigenOardegheden 122190 -Oldambsters-0220-AigenOardegheden 93139 -Oldambsters-0221-AigenOardegheden 86249 -Oldambsters-0222-AigenOardegheden 62871 -Oldambsters-0224-AigenOardegheden 219619 -Oldambsters-0225-AigenOardegheden 145111 -Oldambsters-0226-AigenOardegheden 165389 -Oldambsters-0227-AigenOardegheden 47981 -Oldambsters-0228-AigenOardegheden 198557 -Oldambsters-0229-AigenOardegheden 153396 -Oldambsters-0230-AigenOardegheden 50803 -Oldambsters-0231-AigenOardegheden 115718 -Oldambsters-0232-AigenOardegheden 49760 -Oldambsters-0233-AigenOardegheden 54712 -Oldambsters-0234-AigenOardegheden 36691 -Oldambsters-0235-AigenOardegheden 114679 -Oldambsters-0236-AigenOardegheden 87454 -Oldambsters-0237-AigenOardegheden 67162 -Oldambsters-0238-AigenOardegheden 47981 -Oldambsters-0239-AigenOardegheden 73817 -Oldambsters-0240-AigenOardegheden 22579 -Oldambsters-0243-AigenOardegheden 42336 -Oldambsters-0244-AigenOardegheden 42336 -Oldambsters-0245-AigenOardegheden 148572 -Oldambsters-0246-AigenOardegheden 64915 -Oldambsters-0247-AigenOardegheden 42336 -Oldambsters-0248-AigenOardegheden 36691 -Oldambsters-0249-AigenOardegheden 102447 -Oldambsters-0250-AigenOardegheden 174367 -Oldambsters-0251-AigenOardegheden 142814 -Oldambsters-0252-AigenOardegheden 124464 -Oldambsters-0253-AigenOardegheden 136082 -Oldambsters-0254-AigenOardegheden 206256 -Oldambsters-0255-AigenOardegheden 132653 -Oldambsters-0256-AigenOardegheden 53368 -Oldambsters-0257-AigenOardegheden 131492 -Oldambsters-0258-AigenOardegheden 46360 -Oldambsters-0259-AigenOardegheden 25402 -Oldambsters-0260-AigenOardegheden 47778 -Oldambsters-0261-AigenOardegheden 359998 -Oldambsters-0262-AigenOardegheden 135133 -Oldambsters-0263-AigenOardegheden 106347 -Oldambsters-0264-AigenOardegheden 146926 -Oldambsters-0265-AigenOardegheden 112896 -Oldambsters-0266-AigenOardegheden 64915 -Oldambsters-0267-AigenOardegheden 128244 -Oldambsters-0268-AigenOardegheden 33869 -Oldambsters-0269-AigenOardegheden 258126 -Oldambsters-0271-AigenOardegheden 33531 -Oldambsters-0272-AigenOardegheden 237656 -Oldambsters-0273-AigenOardegheden 105074 -Oldambsters-0274-AigenOardegheden 141092 -Oldambsters-0275-AigenOardegheden 56192 -Oldambsters-0276-AigenOardegheden 276823 -Oldambsters-0277-AigenOardegheden 241574 -Oldambsters-0278-AigenOardegheden 95617 -Oldambsters-0279-AigenOardegheden 93139 -Oldambsters-0280-AigenOardegheden 310464 -Oldambsters-0281-AigenOardegheden 183282 -Oldambsters-0282-AigenOardegheden 44680 -Oldambsters-0283-AigenOardegheden 81850 -Oldambsters-0284-AigenOardegheden 45158 -Oldambsters-0285-AigenOardegheden 67738 -Oldambsters-0286-AigenOardegheden 129792 -Oldambsters-0287-AigenOardegheden 47981 -Oldambsters-0288-AigenOardegheden 90317 -Oldambsters-0289-AigenOardegheden 56448 -Oldambsters-0290-AigenOardegheden 79027 -Oldambsters-0291-AigenOardegheden 36691 -Oldambsters-0292-AigenOardegheden 33869 -Oldambsters-0293-AigenOardegheden 32386 -Oldambsters-0294-AigenOardegheden 76205 -Oldambsters-0295-AigenOardegheden 28224 -Oldambsters-0296-AigenOardegheden 92151 -Oldambsters-0297-AigenOardegheden 75230 -Oldambsters-0298-AigenOardegheden 89815 -Oldambsters-0299-AigenOardegheden 81850 -Oldambsters-0300-AigenOardegheden 127807 -Oldambsters-0301-AigenOardegheden 75196 -Oldambsters-0302-AigenOardegheden 31046 -Oldambsters-0303-AigenOardegheden 64915 -Oldambsters-0304-AigenOardegheden 144527 -Oldambsters-0305-AigenOardegheden 146418 -Oldambsters-0306-AigenOardegheden 110074 -Oldambsters-0307-AigenOardegheden 100088 -Oldambsters-0308-AigenOardegheden 238378 -Oldambsters-0309-AigenOardegheden 174796 -Oldambsters-0310-AigenOardegheden 44942 -Oldambsters-0311-AigenOardegheden 25402 -Oldambsters-0312-AigenOardegheden 81045 -Oldambsters-0313-AigenOardegheden 72353 -Oldambsters-0314-AigenOardegheden 112656 -Oldambsters-0315-AigenOardegheden 73382 -Oldambsters-0316-AigenOardegheden 39514 -Oldambsters-0318-AigenOardegheden 76013 -Oldambsters-0319-AigenOardegheden 95962 -Oldambsters-0320-AigenOardegheden 64915 -Oldambsters-0321-AigenOardegheden 223927 -Oldambsters-0323-AigenOardegheden 118541 -Oldambsters-0324-AigenOardegheden 62093 -Oldambsters-0325-AigenOardegheden 117522 -Oldambsters-0326-AigenOardegheden 160608 -Oldambsters-0327-AigenOardegheden 91290 -Oldambsters-0328-AigenOardegheden 99018 -Oldambsters-0330-AigenOardegheden 107226 -Oldambsters-0331-AigenOardegheden 43058 -Oldambsters-0332-AigenOardegheden 156675 -Oldambsters-0333-AigenOardegheden 64764 -Oldambsters-0334-AigenOardegheden 25402 -Oldambsters-0335-AigenOardegheden 36250 -Oldambsters-0336-AigenOardegheden 81065 -Oldambsters-0337-AigenOardegheden 90317 -Oldambsters-0338-AigenOardegheden 305120 -Oldambsters-0339-AigenOardegheden 47981 -Oldambsters-0340-AigenOardegheden 103629 -Oldambsters-0341-AigenOardegheden 53626 -Oldambsters-0342-AigenOardegheden 76646 -Oldambsters-0343-AigenOardegheden 177128 -Oldambsters-0344-AigenOardegheden 121008 -Oldambsters-0345-AigenOardegheden 84876 -Oldambsters-0346-AigenOardegheden 121363 -Oldambsters-0347-AigenOardegheden 44668 -Oldambsters-0348-AigenOardegheden 191365 -Oldambsters-0349-AigenOardegheden 42839 -Oldambsters-0350-AigenOardegheden 81843 -Oldambsters-0351-AigenOardegheden 251202 -Oldambsters-0352-AigenOardegheden 104429 -Oldambsters-0353-AigenOardegheden 189195 -Oldambsters-0354-AigenOardegheden 123568 -Oldambsters-0355-AigenOardegheden 135475 -Oldambsters-0356-AigenOardegheden 215435 -Oldambsters-0357-AigenOardegheden 60691 -Oldambsters-0358-AigenOardegheden 107251 -Oldambsters-0359-AigenOardegheden 92785 -Oldambsters-0360-AigenOardegheden 42336 -Oldambsters-0361-AigenOardegheden 22579 -Oldambsters-0362-AigenOardegheden 36691 -Oldambsters-0363-AigenOardegheden 213598 -Oldambsters-0364-AigenOardegheden 47981 -Oldambsters-0365-AigenOardegheden 127980 -Oldambsters-0366-AigenOardegheden 66076 -Oldambsters-0367-AigenOardegheden 71580 -Oldambsters-0368-AigenOardegheden 70499 -Oldambsters-0369-AigenOardegheden 71605 -Oldambsters-0370-AigenOardegheden 94546 -Oldambsters-0371-AigenOardegheden 92563 -Oldambsters-0372-AigenOardegheden 168995 -Oldambsters-0373-AigenOardegheden 62727 -Oldambsters-0374-AigenOardegheden 61576 -Oldambsters-0376-AigenOardegheden 205083 -Oldambsters-0377-AigenOardegheden 60775 -Oldambsters-0378-AigenOardegheden 110074 -Oldambsters-0379-AigenOardegheden 129395 -Oldambsters-0380-AigenOardegheden 39514 -Oldambsters-0381-AigenOardegheden 36691 -Oldambsters-0382-AigenOardegheden 26778 -Oldambsters-0383-AigenOardegheden 79840 -Oldambsters-0384-AigenOardegheden 104429 -Oldambsters-0385-AigenOardegheden 67738 -Oldambsters-0386-AigenOardegheden 50803 -Oldambsters-0387-AigenOardegheden 85748 -Oldambsters-0388-AigenOardegheden 76205 -Oldambsters-0389-AigenOardegheden 70560 -Oldambsters-0390-AigenOardegheden 64915 -Oldambsters-0391-AigenOardegheden 88346 -Oldambsters-0392-AigenOardegheden 98784 -Oldambsters-0393-AigenOardegheden 97836 -Oldambsters-0394-AigenOardegheden 64382 -Oldambsters-0395-AigenOardegheden 79027 -Oldambsters-0397-AigenOardegheden 53241 -Oldambsters-0398-AigenOardegheden 118541 -Oldambsters-0399-AigenOardegheden 81850 -Oldambsters-0400-AigenOardegheden 132653 -Oldambsters-0401-AigenOardegheden 182316 -Oldambsters-0402-AigenOardegheden 161777 -Oldambsters-0403-AigenOardegheden 103938 -Oldambsters-0404-AigenOardegheden 57187 -Oldambsters-0405-AigenOardegheden 84672 -Oldambsters-0406-AigenOardegheden 161438 -Oldambsters-0407-AigenOardegheden 101810 -Oldambsters-0408-AigenOardegheden 99514 -Oldambsters-0409-AigenOardegheden 171410 -Oldambsters-0410-AigenOardegheden 81331 -Oldambsters-0411-AigenOardegheden 210866 -Oldambsters-0412-AigenOardegheden 137498 -Oldambsters-0413-AigenOardegheden 98277 -Oldambsters-0415-AigenOardegheden 31046 -Oldambsters-0416-AigenOardegheden 74180 -Oldambsters-0418-AigenOardegheden 118541 -Oldambsters-0419-AigenOardegheden 89925 -Oldambsters-0420-AigenOardegheden 98784 -Oldambsters-0421-AigenOardegheden 195556 -Oldambsters-0422-AigenOardegheden 169916 -Oldambsters-0423-AigenOardegheden 84938 -Oldambsters-0424-AigenOardegheden 93139 -Oldambsters-0425-AigenOardegheden 79814 -Oldambsters-0426-AigenOardegheden 220787 -Oldambsters-0427-AigenOardegheden 77573 -Oldambsters-0428-AigenOardegheden 64915 -Oldambsters-0429-AigenOardegheden 173720 -Oldambsters-0431-AigenOardegheden 56448 -Oldambsters-0432-AigenOardegheden 50803 -Oldambsters-0433-AigenOardegheden 256413 -Oldambsters-0434-AigenOardegheden 114394 -Oldambsters-0435-AigenOardegheden 39514 -Oldambsters-0436-AigenOardegheden 90317 -Oldambsters-0437-AigenOardegheden 77190 -Oldambsters-0438-AigenOardegheden 56448 -Oldambsters-0439-AigenOardegheden 118541 -Oldambsters-0440-AigenOardegheden 149204 -Oldambsters-0441-AigenOardegheden 87494 -Oldambsters-0442-AigenOardegheden 62093 -Oldambsters-0443-AigenOardegheden 67738 -Oldambsters-0444-AigenOardegheden 79875 -Oldambsters-0445-AigenOardegheden 115718 -Oldambsters-0446-AigenOardegheden 93068 -Oldambsters-0447-AigenOardegheden 67738 -Oldambsters-0449-AigenOardegheden 45158 -Oldambsters-0450-AigenOardegheden 75115 -Oldambsters-0452-AigenOardegheden 28224 -Oldambsters-0453-AigenOardegheden 115718 -Oldambsters-0454-AigenOardegheden 75130 -Oldambsters-0455-AigenOardegheden 97220 -Oldambsters-0456-AigenOardegheden 141808 -Oldambsters-0457-AigenOardegheden 39514 -Oldambsters-0458-AigenOardegheden 104429 -Oldambsters-0459-AigenOardegheden 64915 -Oldambsters-0460-AigenOardegheden 96926 -Oldambsters-0461-AigenOardegheden 41473 -Oldambsters-0462-AigenOardegheden 304829 -Oldambsters-0463-AigenOardegheden 170354 -Oldambsters-0464-AigenOardegheden 86466 -Oldambsters-0465-AigenOardegheden 198885 -Oldambsters-0466-AigenOardegheden 93139 -Oldambsters-0467-AigenOardegheden 73675 -Oldambsters-0468-AigenOardegheden 151508 -Oldambsters-0469-AigenOardegheden 61342 -Oldambsters-0470-AigenOardegheden 73382 -Oldambsters-0471-AigenOardegheden 64915 -Oldambsters-0472-AigenOardegheden 76205 -Oldambsters-0473-AigenOardegheden 258908 -Oldambsters-0474-AigenOardegheden 39514 -Oldambsters-0475-AigenOardegheden 118541 -Oldambsters-0476-AigenOardegheden 64915 -Oldambsters-0477-AigenOardegheden 332449 -Oldambsters-0478-AigenOardegheden 98784 -Oldambsters-0479-AigenOardegheden 90814 -Oldambsters-0480-AigenOardegheden 42336 -Oldambsters-0481-AigenOardegheden 50803 -Oldambsters-0482-AigenOardegheden 93139 -Oldambsters-0484-AigenOardegheden 110053 -Oldambsters-0485-AigenOardegheden 76786 -Oldambsters-0486-AigenOardegheden 104021 -Oldambsters-0487-AigenOardegheden 193957 -Oldambsters-0488-AigenOardegheden 136330 -Oldambsters-0489-AigenOardegheden 76205 -Oldambsters-0490-AigenOardegheden 197568 -Oldambsters-0491-AigenOardegheden 165601 -Oldambsters-0492-AigenOardegheden 289324 -Oldambsters-0493-AigenOardegheden 133244 -Oldambsters-0494-AigenOardegheden 84124 -Oldambsters-0495-AigenOardegheden 144356 -Oldambsters-0496-AigenOardegheden 88426 -Oldambsters-0497-AigenOardegheden 95962 -Oldambsters-0498-AigenOardegheden 134236 -Oldambsters-0499-AigenOardegheden 50803 -Oldambsters-0500-AigenOardegheden 141120 -Oldambsters-0501-AigenOardegheden 62093 -Oldambsters-0502-AigenOardegheden 130406 -Oldambsters-0503-AigenOardegheden 179924 -Oldambsters-0504-AigenOardegheden 104429 -Oldambsters-0505-AigenOardegheden 125112 -Oldambsters-0506-AigenOardegheden 104649 -Oldambsters-0507-AigenOardegheden 178544 -Oldambsters-0508-AigenOardegheden 139964 -Oldambsters-0509-AigenOardegheden 122201 -Oldambsters-0510-AigenOardegheden 64237 -Oldambsters-0511-AigenOardegheden 64915 -Oldambsters-0512-AigenOardegheden 58654 -Oldambsters-0514-AigenOardegheden 42703 -Oldambsters-0515-AigenOardegheden 107251 -Oldambsters-0516-AigenOardegheden 41957 -Oldambsters-0517-AigenOardegheden 205368 -Oldambsters-0518-AigenOardegheden 44720 -Oldambsters-0519-AigenOardegheden 132829 -Oldambsters-0520-AigenOardegheden 79027 -Oldambsters-0521-AigenOardegheden 231098 -Oldambsters-0522-AigenOardegheden 110074 -Oldambsters-0523-AigenOardegheden 140501 -Oldambsters-0524-AigenOardegheden 43921 -Oldambsters-0525-AigenOardegheden 296742 -Oldambsters-0526-AigenOardegheden 79027 -Oldambsters-0527-AigenOardegheden 207241 -Oldambsters-0528-AigenOardegheden 289064 -Oldambsters-0529-AigenOardegheden 331143 -Oldambsters-0530-AigenOardegheden 341593 -Oldambsters-0531-AigenOardegheden 73133 -Oldambsters-0532-AigenOardegheden 87494 -Oldambsters-0533-AigenOardegheden 143184 -Oldambsters-0534-AigenOardegheden 243866 -Oldambsters-0535-AigenOardegheden 147896 -Oldambsters-0537-AigenOardegheden 111890 -Oldambsters-0538-AigenOardegheden 209453 -Oldambsters-0539-AigenOardegheden 59525 -Oldambsters-0540-AigenOardegheden 145916 -Oldambsters-0541-AigenOardegheden 79069 -Oldambsters-0543-AigenOardegheden 102685 -Oldambsters-0544-AigenOardegheden 264261 -Oldambsters-0545-AigenOardegheden 45158 -Oldambsters-0546-AigenOardegheden 163694 -Oldambsters-0547-AigenOardegheden 101606 -Oldambsters-0548-AigenOardegheden 118541 -Oldambsters-0549-AigenOardegheden 56448 -Oldambsters-0550-AigenOardegheden 83581 -Oldambsters-0551-AigenOardegheden 56150 -Oldambsters-0552-AigenOardegheden 46933 -Oldambsters-0553-AigenOardegheden 81850 -Oldambsters-0554-AigenOardegheden 198147 -Oldambsters-0555-AigenOardegheden 102501 -Oldambsters-0556-AigenOardegheden 107251 -Oldambsters-0557-AigenOardegheden 95962 -Oldambsters-0558-AigenOardegheden 45158 -Oldambsters-0559-AigenOardegheden 128615 -Oldambsters-0561-AigenOardegheden 137111 -Oldambsters-0562-AigenOardegheden 99665 -Oldambsters-0563-AigenOardegheden 56114 -Oldambsters-0564-AigenOardegheden 76673 -Oldambsters-0565-AigenOardegheden 67738 -Oldambsters-0566-AigenOardegheden 315212 -Oldambsters-0567-AigenOardegheden 47981 -Oldambsters-0568-AigenOardegheden 75987 -Oldambsters-0569-AigenOardegheden 110074 -Oldambsters-0570-AigenOardegheden 67738 -Oldambsters-0571-AigenOardegheden 76096 -Oldambsters-0572-AigenOardegheden 94025 -Oldambsters-0574-AigenOardegheden 51670 -Oldambsters-0575-AigenOardegheden 64915 -Oldambsters-0576-AigenOardegheden 174428 -Oldambsters-0577-AigenOardegheden 42169 -Oldambsters-0578-AigenOardegheden 332704 -Oldambsters-0579-AigenOardegheden 42336 -Oldambsters-0580-AigenOardegheden 78096 -Oldambsters-0581-AigenOardegheden 33279 -Oldambsters-0582-AigenOardegheden 169344 -Oldambsters-0584-AigenOardegheden 300481 -Oldambsters-0585-AigenOardegheden 70560 -Oldambsters-0586-AigenOardegheden 61026 -Oldambsters-0587-AigenOardegheden 50749 -Oldambsters-0588-AigenOardegheden 95962 -Oldambsters-0589-AigenOardegheden 59270 -Oldambsters-0590-AigenOardegheden 78327 -Oldambsters-0592-AigenOardegheden 79179 -Oldambsters-0593-AigenOardegheden 132636 -Oldambsters-0594-AigenOardegheden 33869 -Oldambsters-0595-AigenOardegheden 58030 -Oldambsters-0596-AigenOardegheden 45158 -Oldambsters-0597-AigenOardegheden 207654 -Oldambsters-0598-AigenOardegheden 155616 -Oldambsters-0599-AigenOardegheden 122225 -Oldambsters-0600-AigenOardegheden 77504 -Oldambsters-0601-AigenOardegheden 70560 -Oldambsters-0602-AigenOardegheden 47981 -Oldambsters-0603-AigenOardegheden 76205 -Oldambsters-0604-AigenOardegheden 106237 -Oldambsters-0605-AigenOardegheden 169344 -Oldambsters-0606-AigenOardegheden 39514 -Oldambsters-0607-AigenOardegheden 78057 -Oldambsters-0608-AigenOardegheden 43392 -Oldambsters-0609-AigenOardegheden 151605 -Oldambsters-0610-AigenOardegheden 25402 -Oldambsters-0611-AigenOardegheden 60687 -Oldambsters-0612-AigenOardegheden 79027 -Oldambsters-0613-AigenOardegheden 28224 -Oldambsters-0614-AigenOardegheden 53626 -Oldambsters-0615-AigenOardegheden 106309 -Oldambsters-0616-AigenOardegheden 59270 -Oldambsters-0617-AigenOardegheden 86374 -Oldambsters-0618-AigenOardegheden 48659 -Oldambsters-0619-AigenOardegheden 56678 -Oldambsters-0620-AigenOardegheden 45158 -Oldambsters-0621-AigenOardegheden 42336 -Oldambsters-0622-AigenOardegheden 42336 -Oldambsters-0623-AigenOardegheden 42336 -Oldambsters-0625-AigenOardegheden 56448 -Oldambsters-0626-AigenOardegheden 130755 -Oldambsters-0629-AigenOardegheden 50803 -Oldambsters-0630-AigenOardegheden 28224 -Oldambsters-0631-AigenOardegheden 79763 -Oldambsters-0632-AigenOardegheden 159828 -Oldambsters-0633-AigenOardegheden 224384 -Oldambsters-0634-AigenOardegheden 59270 -Oldambsters-0635-AigenOardegheden 127907 -Oldambsters-0636-AigenOardegheden 84672 -Oldambsters-0637-AigenOardegheden 61746 -Oldambsters-0638-AigenOardegheden 59270 -Oldambsters-0639-AigenOardegheden 33869 -Oldambsters-0640-AigenOardegheden 205848 -Oldambsters-0641-AigenOardegheden 112470 -Oldambsters-0642-AigenOardegheden 59270 -Oldambsters-0643-AigenOardegheden 28224 -Oldambsters-0644-DeVerhoalen 53784 -Oldambsters-0645-DeVerhoalen 90490 -Oldambsters-0647-DeVerhoalen 125930 -Oldambsters-0648-DeVerhoalen 58249 -Oldambsters-0649-DeVerhoalen 41617 -Oldambsters-0650-DeVerhoalen 83589 -Oldambsters-0651-DeVerhoalen 74486 -Oldambsters-0652-DeVerhoalen 270573 -Oldambsters-0653-DeVerhoalen 150870 -Oldambsters-0654-DeVerhoalen 157310 -Oldambsters-0655-DeVerhoalen 74521 -Oldambsters-0656-DeVerhoalen 154165 -Oldambsters-0657-DeVerhoalen 53626 -Oldambsters-0658-DeVerhoalen 200578 -Oldambsters-0659-DeVerhoalen 80349 -Oldambsters-0660-DeVerhoalen 22579 -Oldambsters-0661-DeVerhoalen 50803 -Oldambsters-0662-DeVerhoalen 87494 -Oldambsters-0663-DeVerhoalen 110508 -Oldambsters-0664-DeVerhoalen 390431 -Oldambsters-0665-DeVerhoalen 34534 -Oldambsters-0666-DeVerhoalen 152758 -Oldambsters-0667-DeVerhoalen 235942 -Oldambsters-0668-DeVerhoalen 42336 -Oldambsters-0669-DeVerhoalen 139352 -Oldambsters-0670-DeVerhoalen 79027 -Oldambsters-0671-DeVerhoalen 338233 -Oldambsters-0672-DeVerhoalen 112896 -Oldambsters-0673-DeVerhoalen 63202 -Oldambsters-0674-DeVerhoalen 163699 -Oldambsters-0675-DeVerhoalen 87494 -Oldambsters-0676-DeVerhoalen 50803 -Oldambsters-0677-DeVerhoalen 141120 -Oldambsters-0678-DeVerhoalen 345576 -Oldambsters-0679-DeVerhoalen 374427 -Oldambsters-0680-DeVerhoalen 53626 -Oldambsters-0681-DeVerhoalen 39514 -Oldambsters-0682-DeVerhoalen 108146 -Oldambsters-0683-DeVerhoalen 239904 -Oldambsters-0684-DeVerhoalen 76428 -Oldambsters-0685-DeVerhoalen 58558 -Oldambsters-0686-DeVerhoalen 115334 -Oldambsters-0687-DeVerhoalen 62093 -Oldambsters-0688-DeVerhoalen 133788 -Oldambsters-0689-DeVerhoalen 25402 -Oldambsters-0690-DeVerhoalen 240818 -Oldambsters-0691-DeVerhoalen 178872 -Oldambsters-0692-DeVerhoalen 215303 -Oldambsters-0693-DeVerhoalen 73382 -Oldambsters-0694-DeVerhoalen 118541 -Oldambsters-0695-DeVerhoalen 261418 -Oldambsters-0696-DeVerhoalen 230997 -Oldambsters-0697-DeVerhoalen 113790 -Oldambsters-0699-DeVerhoalen 76205 -Oldambsters-0700-DeVerhoalen 64915 -Oldambsters-0701-DeVerhoalen 150986 -Oldambsters-0702-DeVerhoalen 160681 -Oldambsters-0703-DeVerhoalen 155232 -Oldambsters-0704-DeVerhoalen 89471 -Oldambsters-0705-DeVerhoalen 91450 -Oldambsters-0706-DeVerhoalen 27991 -Oldambsters-0707-DeVerhoalen 27845 -Oldambsters-0708-DeVerhoalen 25402 -Oldambsters-0709-DeVerhoalen 98784 -Oldambsters-0710-DeVerhoalen 156764 -Oldambsters-0711-DeVerhoalen 269558 -Oldambsters-0712-DeVerhoalen 39514 -Oldambsters-0713-DeVerhoalen 196530 -Oldambsters-0714-DeVerhoalen 233481 -Oldambsters-0715-DeVerhoalen 76205 -Oldambsters-0716-DeVerhoalen 114943 -Oldambsters-0717-DeVerhoalen 85546 -Oldambsters-0718-DeVerhoalen 179770 -Oldambsters-0719-DeVerhoalen 204069 -Oldambsters-0720-DeVerhoalen 137032 -Oldambsters-0721-DeVerhoalen 64728 -Oldambsters-0722-DeVerhoalen 339892 -Oldambsters-0723-DeVerhoalen 219943 -Oldambsters-0724-DeVerhoalen 93139 -Oldambsters-0725-DeVerhoalen 74025 -Oldambsters-0726-DeVerhoalen 47981 -Oldambsters-0727-DeVerhoalen 107554 -Oldambsters-0728-DeVerhoalen 64915 -Oldambsters-0729-DeVerhoalen 112896 -Oldambsters-0731-DeVerhoalen 184250 -Oldambsters-0732-DeVerhoalen 113999 -Oldambsters-0733-DeVerhoalen 359309 -Oldambsters-0734-DeVerhoalen 270985 -Oldambsters-0735-DeVerhoalen 245858 -Oldambsters-0736-DeVerhoalen 53626 -Oldambsters-0737-DeVerhoalen 47981 -Oldambsters-0738-DeVerhoalen 103314 -Oldambsters-0739-DeVerhoalen 79027 -Oldambsters-0740-DeVerhoalen 110074 -Oldambsters-0741-DeVerhoalen 164807 -Oldambsters-0742-DeVerhoalen 112896 -Oldambsters-0743-DeVerhoalen 301075 -Oldambsters-0744-DeVerhoalen 247310 -Oldambsters-0745-DeVerhoalen 124186 -Oldambsters-0746-DeVerhoalen 191612 -Oldambsters-0747-DeVerhoalen 183456 -Oldambsters-0748-DeVerhoalen 93322 -Oldambsters-0749-DeVerhoalen 210125 -Oldambsters-0750-DeVerhoalen 69992 -Oldambsters-0752-DeVerhoalen 95962 -Oldambsters-0753-DeVerhoalen 78803 -Oldambsters-0754-DeVerhoalen 103511 -Oldambsters-0755-DeVerhoalen 112896 -Oldambsters-0756-DeVerhoalen 112896 -Oldambsters-0757-DeVerhoalen 129830 -Oldambsters-0758-DeVerhoalen 172652 -Oldambsters-0759-DeVerhoalen 115796 -Oldambsters-0760-DeVerhoalen 89143 -Oldambsters-0761-DeVerhoalen 182943 -Oldambsters-0762-DeVerhoalen 143942 -Oldambsters-0763-DeVerhoalen 135475 -Oldambsters-0764-DeVerhoalen 31046 -Oldambsters-0765-DeVerhoalen 80873 -Oldambsters-0766-DeVerhoalen 114776 -Oldambsters-0767-DeVerhoalen 118541 -Oldambsters-0768-DeVerhoalen 47981 -Oldambsters-0769-DeVerhoalen 75363 -Oldambsters-0770-DeVerhoalen 122379 -Oldambsters-0771-DeVerhoalen 76205 -Oldambsters-0772-DeVerhoalen 45158 -Oldambsters-0773-DeVerhoalen 31046 -Oldambsters-0774-DeVerhoalen 39514 -Oldambsters-0775-DeVerhoalen 131222 -Oldambsters-0776-DeVerhoalen 27773 -Oldambsters-0777-DeVerhoalen 198996 -Oldambsters-0778-DeVerhoalen 73382 -Oldambsters-0779-DeVerhoalen 112159 -Oldambsters-0780-DeVerhoalen 32247 -Oldambsters-0781-DeVerhoalen 147527 -Oldambsters-0782-DeVerhoalen 135475 -Oldambsters-0783-DeVerhoalen 59270 -Oldambsters-0784-DeVerhoalen 233658 -Oldambsters-0785-DeVerhoalen 33869 -Oldambsters-0786-DeVerhoalen 36691 -Oldambsters-0787-DeVerhoalen 124186 -Oldambsters-0788-DeVerhoalen 171786 -Oldambsters-0789-DeVerhoalen 182447 -Oldambsters-0790-DeVerhoalen 61860 -Oldambsters-0791-DeVerhoalen 73323 -Oldambsters-0792-DeVerhoalen 28224 -Oldambsters-0793-DeVerhoalen 33869 -Oldambsters-0796-DeVerhoalen 136545 -Oldambsters-0797-DeVerhoalen 107688 -Oldambsters-0798-DeVerhoalen 136077 -Oldambsters-0799-DeVerhoalen 275549 -Oldambsters-0800-DeVerhoalen 205131 -Oldambsters-0801-DeVerhoalen 158417 -Oldambsters-0802-DeVerhoalen 121400 -Oldambsters-0803-DeVerhoalen 128950 -Oldambsters-0804-DeVerhoalen 90317 -Oldambsters-0806-DeVerhoalen 73382 -Oldambsters-0807-DeVerhoalen 121363 -Oldambsters-0808-DeVerhoalen 171007 -Oldambsters-0809-DeVerhoalen 205171 -Oldambsters-0810-DeVerhoalen 59270 -Oldambsters-0811-DeVerhoalen 76205 -Oldambsters-0812-DeVerhoalen 39514 -Oldambsters-0814-DeVerhoalen 329515 -Oldambsters-0815-DeVerhoalen 282240 -Oldambsters-0816-DeVerhoalen 68471 -Oldambsters-0817-DeVerhoalen 83454 -Oldambsters-0818-DeVerhoalen 31046 -Oldambsters-0819-DeVerhoalen 59270 -Oldambsters-0820-DeVerhoalen 31046 -Oldambsters-0821-DeVerhoalen 70323 -Oldambsters-0822-DeVerhoalen 109652 -Oldambsters-0823-DeVerhoalen 45158 -Oldambsters-0825-DeVerhoalen 264837 -Oldambsters-0826-DeVerhoalen 253167 -Oldambsters-0827-DeVerhoalen 60848 -Oldambsters-0828-DeVerhoalen 349698 -Oldambsters-0829-DeVerhoalen 162408 -Oldambsters-0830-DeVerhoalen 155232 -Oldambsters-0831-DeVerhoalen 46856 -Oldambsters-0832-DeVerhoalen 305426 -Oldambsters-0833-DeVerhoalen 124489 -Oldambsters-0834-DeVerhoalen 149587 -Oldambsters-0835-DeVerhoalen 268761 -Oldambsters-0836-DeVerhoalen 85525 -Oldambsters-0837-DeVerhoalen 50803 -Oldambsters-0838-DeVerhoalen 107251 -Oldambsters-0839-DeVerhoalen 169344 -Oldambsters-0840-DeVerhoalen 101248 -Oldambsters-0841-DeVerhoalen 151907 -Oldambsters-0842-DeVerhoalen 425269 -Oldambsters-0844-DeVerhoalen 45116 -Oldambsters-0845-DeVerhoalen 103850 -Oldambsters-0846-DeVerhoalen 104210 -Oldambsters-0847-DeVerhoalen 120156 -Oldambsters-0848-DeVerhoalen 119958 -Oldambsters-0849-DeVerhoalen 248785 -Oldambsters-0850-DeVerhoalen 141142 -Oldambsters-0851-DeVerhoalen 110074 -Oldambsters-0852-DeVerhoalen 84376 -Oldambsters-0853-DeVerhoalen 129830 -Oldambsters-0854-DeVerhoalen 128529 -Oldambsters-0855-DeVerhoalen 106271 -Oldambsters-0856-DeVerhoalen 244282 -Oldambsters-0857-DeVerhoalen 218929 -Oldambsters-0859-DeVerhoalen 192126 -Oldambsters-0860-DeVerhoalen 56448 -Oldambsters-0861-DeVerhoalen 25402 -Oldambsters-0862-DeVerhoalen 129830 -Oldambsters-0863-DeVerhoalen 67738 -Oldambsters-0864-DeVerhoalen 139982 -Oldambsters-0865-DeVerhoalen 124186 -Oldambsters-0866-DeVerhoalen 129655 -Oldambsters-0867-DeVerhoalen 53626 -Oldambsters-0868-DeVerhoalen 49961 -Oldambsters-0869-DeVerhoalen 160877 -Oldambsters-0870-DeVerhoalen 104429 -Oldambsters-0871-DeVerhoalen 149587 -Oldambsters-0872-DeVerhoalen 42336 -Oldambsters-0873-DeVerhoalen 253557 -Oldambsters-0874-DeVerhoalen 39514 -Oldambsters-0875-DeVerhoalen 31046 -Oldambsters-0876-DeVerhoalen 85868 -Oldambsters-0877-DeVerhoalen 183812 -Oldambsters-0878-DeVerhoalen 84672 -Oldambsters-0879-DeVerhoalen 123938 -Oldambsters-0880-DeVerhoalen 243355 -Oldambsters-0881-DeVerhoalen 76205 -Oldambsters-0882-DeVerhoalen 164053 -Oldambsters-0883-DeVerhoalen 64561 -Oldambsters-0884-DeVerhoalen 56448 -Oldambsters-0885-DeVerhoalen 152410 -Oldambsters-0886-DeVerhoalen 152410 -Oldambsters-0887-DeVerhoalen 79027 -Oldambsters-0888-DeVerhoalen 98784 -Oldambsters-0889-DeVerhoalen 124186 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-Westerkertaaiers-0860-SchiersteVerhoal 73382 -Westerkertaaiers-0861-SchiersteVerhoal 286788 -Westerkertaaiers-0862-SchiersteVerhoal 92305 -Westerkertaaiers-0863-SchiersteVerhoal 102812 -Westerkertaaiers-0864-SchiersteVerhoal 135106 -Westerkertaaiers-0865-SchiersteVerhoal 81768 -Westerkertaaiers-0866-SchiersteVerhoal 90317 -Westerkertaaiers-0867-SchiersteVerhoal 190436 -Westerkertaaiers-0868-SchiersteVerhoal 90030 -Westerkertaaiers-0870-SchiersteVerhoal 101606 -Westerkertaaiers-0871-SchiersteVerhoal 126622 -Westerkertaaiers-0872-SchiersteVerhoal 270463 -Westerkertaaiers-0873-SchiersteVerhoal 90456 -Westerkertaaiers-0874-SchiersteVerhoal 54069 -Westerkertaaiers-0875-SchiersteVerhoal 100428 -Westerkertaaiers-0877-SchiersteVerhoal 106295 -Westerkertaaiers-0878-SchiersteVerhoal 106275 -Westerkertaaiers-0879-SchiersteVerhoal 36691 -Westerkertaaiers-0880-SchiersteVerhoal 105898 -Westerkertaaiers-0881-SchiersteVerhoal 122733 -Westerkertaaiers-0882-SchiersteVerhoal 208853 -Westerkertaaiers-0883-SchiersteVerhoal 138029 -Westerkertaaiers-0884-SchiersteVerhoal 144141 -Westerkertaaiers-0885-SchiersteVerhoal 147786 -Westerkertaaiers-0886-SchiersteVerhoal 124908 -Westerkertaaiers-0888-SchiersteVerhoal 47981 -Westerkertaaiers-0889-SchiersteVerhoal 93139 -Westerkertaaiers-0890-SchiersteVerhoal 208491 -Westerkertaaiers-0891-SchiersteVerhoal 72225 -Westerkertaaiers-0892-SchiersteVerhoal 92096 -Westerkertaaiers-0893-SchiersteVerhoal 233952 -Westerkertaaiers-0894-SchiersteVerhoal 162349 -Westerkertaaiers-0895-SchiersteVerhoal 56448 -Westerkertaaiers-0896-SchiersteVerhoal 167309 -Westerkertaaiers-0897-SchiersteVerhoal 154696 -Westerkertaaiers-0898-SchiersteVerhoal 173197 -Westerkertaaiers-0899-SchiersteVerhoal 98117 -Westerkertaaiers-0900-SchiersteVerhoal 116949 -Westerkertaaiers-0901-SchiersteVerhoal 169591 -Westerkertaaiers-0902-SchiersteVerhoal 164278 -Westerkertaaiers-0903-SchiersteVerhoal 140927 -Westerkertaaiers-0904-SchiersteVerhoal 125134 -Westerkertaaiers-0905-SchiersteVerhoal 85543 -Westerkertaaiers-0906-SchiersteVerhoal 179217 -Westerkertaaiers-0907-SchiersteVerhoal 81498 -Westerkertaaiers-0908-SchiersteVerhoal 163108 -Westerkertaaiers-0909-SchiersteVerhoal 100170 -Westerkertaaiers-0910-SchiersteVerhoal 74960 -Westerkertaaiers-0911-SchiersteVerhoal 82595 -Westerkertaaiers-0912-SchiersteVerhoal 53941 -Westerkertaaiers-0913-SchiersteVerhoal 106978 -Westerkertaaiers-0914-SchiersteVerhoal 107585 -Westerkertaaiers-0915-SchiersteVerhoal 290356 -Westerkertaaiers-0916-SchiersteVerhoal 108476 -Westerkertaaiers-0917-SchiersteVerhoal 172023 -Westerkertaaiers-0918-SchiersteVerhoal 109361 -Westerkertaaiers-0919-SchiersteVerhoal 97384 -Westerkertaaiers-0920-SchiersteVerhoal 293683 -Westerkertaaiers-0921-SchiersteVerhoal 163699 -Westerkertaaiers-0922-SchiersteVerhoal 121150 -Westerkertaaiers-0923-SchiersteVerhoal 121247 -Westerkertaaiers-0924-SchiersteVerhoal 94724 -Westerkertaaiers-0925-SchiersteVerhoal 112182 -Westerkertaaiers-0926-SchiersteVerhoal 98692 -Westerkertaaiers-0927-SchiersteVerhoal 152352 -Westerkertaaiers-0928-SchiersteVerhoal 205484 -Westerkertaaiers-0929-SchiersteVerhoal 155426 -Westerkertaaiers-0931-SchiersteVerhoal 45158 -Westerkertaaiers-0932-SchiersteVerhoal 87494 -Westerkertaaiers-0933-SchiersteVerhoal 148133 -Westerkertaaiers-0934-SchiersteVerhoal 221491 -Westerkertaaiers-0935-SchiersteVerhoal 84265 -Westerkertaaiers-0936-SchiersteVerhoal 75914 -Westerkertaaiers-0937-SchiersteVerhoal 148563 -Westerkertaaiers-0938-SchiersteVerhoal 210750 -Westerkertaaiers-0939-SchiersteVerhoal 63725 -Westerkertaaiers-0940-SchiersteVerhoal 102450 -Westerkertaaiers-0941-SchiersteVerhoal 140255 -Westerkertaaiers-0942-SchiersteVerhoal 92722 -Westerkertaaiers-0943-SchiersteVerhoal 129319 -Westerkertaaiers-0944-SchiersteVerhoal 154413 -Westerkertaaiers-0945-SchiersteVerhoal 64336 -Westerkertaaiers-0946-SchiersteVerhoal 84562 -Westerkertaaiers-0947-SchiersteVerhoal 176797 -Westerkertaaiers-0948-SchiersteVerhoal 145223 -Westerkertaaiers-0949-SchiersteVerhoal 144152 -Westerkertaaiers-0950-SchiersteVerhoal 158213 -Westerkertaaiers-0951-SchiersteVerhoal 70116 -Westerkertaaiers-0952-SchiersteVerhoal 180839 -Westerkertaaiers-0953-SchiersteVerhoal 92374 -Westerkertaaiers-0954-SchiersteVerhoal 71988 -Westerkertaaiers-0955-SchiersteVerhoal 232731 -Westerkertaaiers-0956-SchiersteVerhoal 95581 -Westerkertaaiers-0957-SchiersteVerhoal 113117 -Westerkertaaiers-0958-SchiersteVerhoal 42257 -Westerkertaaiers-0959-SchiersteVerhoal 204658 -Westerkertaaiers-0960-SchiersteVerhoal 235458 -Westerkertaaiers-0961-SchiersteVerhoal 215376 -Westerkertaaiers-0962-SchiersteVerhoal 86776 -Westerkertaaiers-0963-SchiersteVerhoal 158975 -Westerkertaaiers-0964-SchiersteVerhoal 219861 -Westerkertaaiers-0965-SchiersteVerhoal 143555 -Westerkertaaiers-0966-SchiersteVerhoal 159912 -Westerkertaaiers-0967-SchiersteVerhoal 138409 -Westerkertaaiers-0968-SchiersteVerhoal 152324 -Westerkertaaiers-0969-SchiersteVerhoal 209012 -Westerkertaaiers-0971-SchiersteVerhoal 172211 -Westerkertaaiers-0972-SchiersteVerhoal 128423 -Westerkertaaiers-0973-SchiersteVerhoal 417175 -Westerkertaaiers-0975-SchiersteVerhoal 180023 -Westerkertaaiers-0976-SchiersteVerhoal 129213 -Westerkertaaiers-0977-SchiersteVerhoal 65451 -Westerkertaaiers-0978-SchiersteVerhoal 181554 -Westerkertaaiers-0979-SchiersteVerhoal 270104 -Westerkertaaiers-0980-SchiersteVerhoal 182374 -Westerkertaaiers-0981-SchiersteVerhoal 140696 -Westerkertaaiers-0982-SchiersteVerhoal 102942 -Westerkertaaiers-0983-SchiersteVerhoal 81850 -Westerkertaaiers-0984-SchiersteVerhoal 110841 -Westerkertaaiers-0985-SchiersteVerhoal 125783 -Westerkertaaiers-0986-SchiersteVerhoal 263189 -Westerkertaaiers-0987-SchiersteVerhoal 326471 -Westerkertaaiers-0988-SchiersteVerhoal 342827 -Westerkertaaiers-0989-SchiersteVerhoal 76205 -Westerkertaaiers-0990-SchiersteVerhoal 176072 -Westerkertaaiers-0991-SchiersteVerhoal 182589 -Westerkertaaiers-0992-SchiersteVerhoal 129512 -Westerkertaaiers-0993-SchiersteVerhoal 261174 -Westerkertaaiers-0994-SchiersteVerhoal 291225 -Westerkertaaiers-0995-SchiersteVerhoal 178831 -Westerkertaaiers-0996-SchiersteVerhoal 150692 -Westerkertaaiers-0997-SchiersteVerhoal 170542 -Westerkertaaiers-0998-SchiersteVerhoal 134280 -Westerkertaaiers-0999-SchiersteVerhoal 272523 -Westerkertaaiers-1000-SchiersteVerhoal 181677 -Westerkertaaiers-1001-SchiersteVerhoal 132144 -Westerkertaaiers-1002-SchiersteVerhoal 74555 -Westerkertaaiers-1003-SchiersteVerhoal 139418 -Westerkertaaiers-1004-SchiersteVerhoal 138568 -Westerkertaaiers-1005-SchiersteVerhoal 96709 -Westerkertaaiers-1006-SchiersteVerhoal 216298 -Westerkertaaiers-1007-SchiersteVerhoal 332555 -Westerkertaaiers-1008-SchiersteVerhoal 301327 -Westerkertaaiers-1009-SchiersteVerhoal 371632 -Westerkertaaiers-1010-SchiersteVerhoal 253459 -Westerkertaaiers-1011-SchiersteVerhoal 180569 -Westerkertaaiers-1012-SchiersteVerhoal 231677 -Westerkertaaiers-1013-SchiersteVerhoal 275422 -Westerkertaaiers-1014-SchiersteVerhoal 171151 -Westerkertaaiers-1015-SchiersteVerhoal 331321 -Westerkertaaiers-1018-SchiersteVerhoal 148274 -Westerkertaaiers-1019-SchiersteVerhoal 81850 -Westerkertaaiers-1020-SchiersteVerhoal 56448 -Westerkertaaiers-1021-SchiersteVerhoal 176028 -Westerkertaaiers-1022-SchiersteVerhoal 162135 -Westerkertaaiers-1023-SchiersteVerhoal 63920 -Westerkertaaiers-1024-SchiersteVerhoal 69831 -Westerkertaaiers-1025-SchiersteVerhoal 279314 -Westerkertaaiers-1026-SchiersteVerhoal 76205 -Westerkertaaiers-1028-SchiersteVerhoal 127220 -Westerkertaaiers-1029-SchiersteVerhoal 77759 -Westerkertaaiers-1030-SchiersteVerhoal 59270 -Westerkertaaiers-1031-SchiersteVerhoal 115451 -Westerkertaaiers-1032-SchiersteVerhoal 73382 -Westerkertaaiers-1033-SchiersteVerhoal 68614 -Westerkertaaiers-1034-SchiersteVerhoal 100789 -Westerkertaaiers-1035-SchiersteVerhoal 141120 -Westerkertaaiers-1036-SchiersteVerhoal 84611 -Westerkertaaiers-1037-SchiersteVerhoal 269087 -Westerkertaaiers-1038-SchiersteVerhoal 73382 -Westerkertaaiers-1039-SchiersteVerhoal 54779 -Westerkertaaiers-1040-SchiersteVerhoal 39514 -Westerkertaaiers-1041-SchiersteVerhoal 67738 -Westerkertaaiers-1042-SchiersteVerhoal 50803 -Westerkertaaiers-1043-SchiersteVerhoal 143324 -Westerkertaaiers-1044-SchiersteVerhoal 107251 -Westerkertaaiers-1045-SchiersteVerhoal 64915 -Westerkertaaiers-1046-SchiersteVerhoal 60174 -Westerkertaaiers-1047-SchiersteVerhoal 47004 -Westerkertaaiers-1048-SchiersteVerhoal 45158 -Westerkertaaiers-1049-SchiersteVerhoal 89290 -Westerkertaaiers-1050-SchiersteVerhoal 142059 -Westerkertaaiers-1051-SchiersteVerhoal 81850 -Westerkertaaiers-1052-SchiersteVerhoal 162088 -Westerkertaaiers-1053-SchiersteVerhoal 45158 -Westerkertaaiers-1054-SchiersteVerhoal 85331 -Westerkertaaiers-1055-SchiersteVerhoal 242199 -Westerkertaaiers-1056-SchiersteVerhoal 87084 -Westerkertaaiers-1058-SchiersteVerhoal 54997 -Westerkertaaiers-1059-SchiersteVerhoal 104881 -Westerkertaaiers-1060-SchiersteVerhoal 215834 -Westerkertaaiers-1061-SchiersteVerhoal 107997 -Westerkertaaiers-1063-SchiersteVerhoal 53091 -Westerkertaaiers-1064-SchiersteVerhoal 288393 -Westerkertaaiers-1065-SchiersteVerhoal 53065 -Westerkertaaiers-1066-SchiersteVerhoal 101116 -Westerkertaaiers-1067-SchiersteVerhoal 53626 -Westerkertaaiers-1068-SchiersteVerhoal 45158 -Westerkertaaiers-1069-SchiersteVerhoal 46618 -Westerkertaaiers-1070-SchiersteVerhoal 186212 -Westerkertaaiers-1071-SchiersteVerhoal 91509 -Westerkertaaiers-1072-SchiersteVerhoal 116682 -Westerkertaaiers-1073-SchiersteVerhoal 107025 -Westerkertaaiers-1074-SchiersteVerhoal 190931 -Westerkertaaiers-1075-SchiersteVerhoal 182581 -Westerkertaaiers-1076-SchiersteVerhoal 85562 -Westerkertaaiers-1077-SchiersteVerhoal 64892 -Westerkertaaiers-1078-SchiersteVerhoal 39514 -Westerkertaaiers-1079-SchiersteVerhoal 67738 -Westerkertaaiers-1080-SchiersteVerhoal 72279 -Westerkertaaiers-1081-SchiersteVerhoal 72108 -Westerkertaaiers-1082-SchiersteVerhoal 87494 -Westerkertaaiers-1083-SchiersteVerhoal 239737 -Westerkertaaiers-1084-SchiersteVerhoal 216182 -Westerkertaaiers-1085-SchiersteVerhoal 87494 -Westerkertaaiers-1086-SchiersteVerhoal 180453 -Westerkertaaiers-1087-SchiersteVerhoal 63186 -Westerkertaaiers-1088-SchiersteVerhoal 91998 -Westerkertaaiers-1089-SchiersteVerhoal 81414 -Westerkertaaiers-1090-SchiersteVerhoal 47981 -Westerkertaaiers-1091-SchiersteVerhoal 47955 -Westerkertaaiers-1092-SchiersteVerhoal 73382 -Westerkertaaiers-1093-SchiersteVerhoal 203595 -Westerkertaaiers-1094-SchiersteVerhoal 231092 -Westerkertaaiers-1095-SchiersteVerhoal 91960 -Westerkertaaiers-1096-SchiersteVerhoal 53923 -Westerkertaaiers-1097-SchiersteVerhoal 140102 -Westerkertaaiers-1098-SchiersteVerhoal 59669 -Westerkertaaiers-1099-SchiersteVerhoal 53626 -Westerkertaaiers-1100-SchiersteVerhoal 137992 -Westerkertaaiers-1101-SchiersteVerhoal 156004 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152252 -Westerkertaaiers-1777-SlagTwaalf 127008 -Westerkertaaiers-1778-SlagTwaalf 189572 -Westerkertaaiers-1779-SlagTwaalf 142761 -Westerkertaaiers-1780-SlagTwaalf 76209 -Westerkertaaiers-1781-SlagTwaalf 39514 -Westerkertaaiers-1783-SlagTwaalf 70415 -Westerkertaaiers-1784-SlagTwaalf 69511 -Westerkertaaiers-1785-SlagTwaalf 137617 -Westerkertaaiers-1786-SlagTwaalf 40726 -Westerkertaaiers-1787-SlagTwaalf 68216 -Westerkertaaiers-1788-SlagTwaalf 46353 -Westerkertaaiers-1789-SlagTwaalf 98711 -Westerkertaaiers-1790-SlagTwaalf 62539 -Westerkertaaiers-1791-SlagTwaalf 45174 -Westerkertaaiers-1792-SlagTwaalf 97279 -Westerkertaaiers-1793-SlagTwaalf 42336 -Westerkertaaiers-1794-SlagTwaalf 166883 -Westerkertaaiers-1795-SlagTwaalf 233960 -Westerkertaaiers-1796-SlagTwaalf 153479 -Westerkertaaiers-1797-SlagTwaalf 80615 -Westerkertaaiers-1798-SlagTwaalf 153859 -Westerkertaaiers-1799-SlagTwaalf 79027 -Westerkertaaiers-1800-SlagTwaalf 74132 -Westerkertaaiers-1801-SlagTwaalf 135777 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-Westerkertaaiers-1851-SlagTwaalf 98784 -Westerkertaaiers-1852-SlagTwaalf 69094 -Westerkertaaiers-1853-SlagTwaalf 137391 -Westerkertaaiers-1854-SlagTwaalf 60099 -Westerkertaaiers-1855-SlagTwaalf 63767 -Westerkertaaiers-1856-SlagTwaalf 89003 -Westerkertaaiers-1857-SlagTwaalf 45158 -Westerkertaaiers-1859-SlagTwaalf 50708 -Westerkertaaiers-1860-SlagTwaalf 56183 -Westerkertaaiers-1861-SlagTwaalf 105897 -Westerkertaaiers-1862-SlagTwaalf 86530 -Westerkertaaiers-1863-SlagTwaalf 75305 -Westerkertaaiers-1864-SlagTwaalf 114925 -Westerkertaaiers-1865-SlagTwaalf 76205 -Westerkertaaiers-1866-SlagTwaalf 192007 -Westerkertaaiers-1867-SlagTwaalf 47981 -Westerkertaaiers-1868-SlagTwaalf 58004 -Westerkertaaiers-1869-SlagTwaalf 138558 -Westerkertaaiers-1870-SlagTwaalf 57402 -Westerkertaaiers-1872-SlagTwaalf 139170 -Westerkertaaiers-1873-SlagTwaalf 64131 -Westerkertaaiers-1874-SlagTwaalf 77864 -Westerkertaaiers-1875-SlagTwaalf 75634 -Westerkertaaiers-1876-SlagTwaalf 62093 -Westerkertaaiers-1877-SlagTwaalf 246203 -Westerkertaaiers-1878-SlagTwaalf 96977 -Westerkertaaiers-1879-SlagTwaalf 187688 -Westerkertaaiers-1880-SlagTwaalf 208222 -Westerkertaaiers-1881-SlagTwaalf 49796 -Westerkertaaiers-1882-SlagTwaalf 49039 -Westerkertaaiers-1883-SlagTwaalf 53626 -Westerkertaaiers-1884-SlagTwaalf 128013 -Westerkertaaiers-1885-SlagTwaalf 76205 -Westerkertaaiers-1886-SlagTwaalf 104014 -Westerkertaaiers-1887-SlagTwaalf 84672 -Westerkertaaiers-1888-SlagTwaalf 104609 -Westerkertaaiers-1889-SlagTwaalf 142635 -Westerkertaaiers-1890-SlagTwaalf 37506 -Westerkertaaiers-1891-SlagTwaalf 56338 -Westerkertaaiers-1892-SlagTwaalf 97043 -Westerkertaaiers-1893-SlagTwaalf 96061 -Westerkertaaiers-1894-SlagTwaalf 59353 -Westerkertaaiers-1895-SlagTwaalf 50803 -Westerkertaaiers-1896-SlagTwaalf 326906 -Westerkertaaiers-1897-SlagTwaalf 167062 -Westerkertaaiers-1898-SlagTwaalf 132704 -Westerkertaaiers-1899-SlagTwaalf 57203 -Westerkertaaiers-1900-SlagTwaalf 126903 -Westerkertaaiers-1901-SlagTwaalf 128893 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-Hoogelaandsters-3254-MoanMorn 37 -Hoogelaandsters-3255-MoanMorn 48 -Hoogelaandsters-3256-MoanMorn 23 -Hoogelaandsters-3257-MoanMorn 118 -Hoogelaandsters-3258-MoanMorn 137 -Hoogelaandsters-3259-MoanMorn 210 -Hoogelaandsters-3260-MoanMorn 70 -Hoogelaandsters-3261-MoanMorn 157 -Hoogelaandsters-3262-MoanMorn 105 -Hoogelaandsters-3263-MoanMorn 142 -Hoogelaandsters-3264-MoanMorn 128 -Hoogelaandsters-3265-MoanMorn 48 -Hoogelaandsters-3266-MoanMorn 37 -Hoogelaandsters-3267-MoanMorn 58 -Hoogelaandsters-3268-MoanMorn 25 -Hoogelaandsters-3269-MoanMorn 138 -Hoogelaandsters-3270-MoanMorn 97 -Hoogelaandsters-3271-MoanMorn 60 -Hoogelaandsters-3272-MoanMorn 184 -Hoogelaandsters-3273-MoanMorn 92 -Hoogelaandsters-3274-OpBuus 84 -Hoogelaandsters-3275-OpBuus 111 -Hoogelaandsters-3276-OpBuus 109 -Hoogelaandsters-3277-OpBuus 228 -Hoogelaandsters-3278-OpBuus 68 -Hoogelaandsters-3279-OpBuus 128 -Hoogelaandsters-3280-OpBuus 187 -Hoogelaandsters-3281-OpBuus 48 -Hoogelaandsters-3282-OpBuus 218 -Hoogelaandsters-3283-OpBuus 259 -Hoogelaandsters-3284-OpBuus 233 -Hoogelaandsters-3287-OpBuus 25 -Hoogelaandsters-3289-OpBuus 19 -Hoogelaandsters-3290-OpBuus 116 -Hoogelaandsters-3291-OpBuus 131 -Hoogelaandsters-3292-OpBuus 55 -Hoogelaandsters-3293-OpBuus 87 -Hoogelaandsters-3294-OpBuus 84 -Hoogelaandsters-3295-OpBuus 9 -Hoogelaandsters-3296-OpBuus 25 -Hoogelaandsters-3297-OpBuus 63 -Hoogelaandsters-3298-OpBuus 178 -Hoogelaandsters-3299-OpBuus 80 -Hoogelaandsters-3300-OpBuus 110 -Hoogelaandsters-3302-OpBuus 39 -Hoogelaandsters-3304-OpBuus 247 -Hoogelaandsters-3305-OpBuus 178 -Hoogelaandsters-3306-OpBuus 135 -Hoogelaandsters-3307-OpBuus 38 -Hoogelaandsters-3308-OpBuus 28 -Hoogelaandsters-3309-OpBuus 98 -Hoogelaandsters-3310-OpBuus 35 -Hoogelaandsters-3311-OpBuus 87 -Hoogelaandsters-3312-OpBuus 95 -Hoogelaandsters-3313-OpBuus 16 -Hoogelaandsters-3314-OpBuus 94 -Hoogelaandsters-3315-OpBuus 19 -Hoogelaandsters-3316-OpBuus 62 -Hoogelaandsters-3317-OpBuus 122 -Hoogelaandsters-3318-OpBuus 88 -Hoogelaandsters-3319-OpBuus 55 -Hoogelaandsters-3320-OpBuus 55 -Hoogelaandsters-3321-OpBuus 29 -Hoogelaandsters-3323-OpBuus 61 -Hoogelaandsters-3324-OpBuus 38 -Hoogelaandsters-3325-OpBuus 83 -Hoogelaandsters-3326-OpBuus 64 -Hoogelaandsters-3327-OpBuus 32 -Hoogelaandsters-3328-OpBuus 132 -Hoogelaandsters-3329-OpBuus 44 -Hoogelaandsters-3330-OpBuus 51 -Hoogelaandsters-3331-OpBuus 45 -Hoogelaandsters-3332-OpBuus 99 -Hoogelaandsters-3333-OpBuus 46 -Hoogelaandsters-3334-OpBuus 182 -Hoogelaandsters-3335-OpBuus 46 -Hoogelaandsters-3336-OpBuus 62 -Hoogelaandsters-3337-OpBuus 98 -Hoogelaandsters-3339-OpBuus 87 -Hoogelaandsters-3340-OpBuus 88 -Hoogelaandsters-3341-OpBuus 43 -Hoogelaandsters-3342-OpBuus 28 -Hoogelaandsters-3343-OpBuus 66 -Hoogelaandsters-3345-OpBuus 94 -Hoogelaandsters-3346-OpBuus 79 -Hoogelaandsters-3348-OpBuus 75 -Hoogelaandsters-3349-OpBuus 258 -Hoogelaandsters-3350-OpBuus 210 -Hoogelaandsters-3351-OpBuus 101 -Hoogelaandsters-3352-OpBuus 121 -Hoogelaandsters-3353-OpBuus 257 -Hoogelaandsters-3354-OpBuus 120 -Hoogelaandsters-3355-OpBuus 96 -Hoogelaandsters-3356-VOnJ 55 -Hoogelaandsters-3357-VOnJ 174 -Hoogelaandsters-3358-VOnJ 64 -Hoogelaandsters-3359-VOnJ 186 -Hoogelaandsters-3360-VOnJ 36 -Hoogelaandsters-3361-VOnJ 133 -Hoogelaandsters-3362-VOnJ 77 -Hoogelaandsters-3363-VOnJ 39 -Hoogelaandsters-3364-VOnJ 65 -Hoogelaandsters-3365-VOnJ 41 -Hoogelaandsters-3366-VOnJ 55 -Hoogelaandsters-3367-VOnJ 53 -Hoogelaandsters-3368-VOnJ 71 -Hoogelaandsters-3369-VOnJ 73 -Hoogelaandsters-3371-VOnJ 72 -Hoogelaandsters-3372-VOnJ 48 -Hoogelaandsters-3373-VOnJ 62 -Hoogelaandsters-3375-VOnJ 114 -Hoogelaandsters-3376-VOnJ 30 -Hoogelaandsters-3377-VOnJ 59 -Hoogelaandsters-3378-VOnJ 27 -Hoogelaandsters-3379-VOnJ 75 -Hoogelaandsters-3380-VOnJ 74 -Hoogelaandsters-3381-VOnJ 53 -Hoogelaandsters-3382-VOnJ 89 -Hoogelaandsters-3383-VOnJ 66 -Hoogelaandsters-3384-VOnJ 105 -Hoogelaandsters-3385-VOnJ 75 -Hoogelaandsters-3386-VOnJ 52 -Hoogelaandsters-3387-VOnJ 53 -Hoogelaandsters-3388-VOnJ 47 -Hoogelaandsters-3389-VOnJ 102 -Hoogelaandsters-3390-VOnJ 34 -Hoogelaandsters-3391-VOnJ 202 -Hoogelaandsters-3392-VOnJ 120 -Hoogelaandsters-3393-VOnJ 117 -Hoogelaandsters-3394-VOnJ 76 -Hoogelaandsters-3395-VOnJ 229 -Hoogelaandsters-3396-VOnJ 43 -Hoogelaandsters-3397-VOnJ 26 -Hoogelaandsters-3398-VOnJ 56 -Hoogelaandsters-3399-VOnJ 147 -Hoogelaandsters-3400-VOnJ 72 -Hoogelaandsters-3401-VOnJ 43 -Oldambsters-0002-AigenOardegheden 21 -Oldambsters-0003-AigenOardegheden 119 -Oldambsters-0004-AigenOardegheden 126 -Oldambsters-0005-AigenOardegheden 14 -Oldambsters-0006-AigenOardegheden 25 -Oldambsters-0007-AigenOardegheden 39 -Oldambsters-0008-AigenOardegheden 45 -Oldambsters-0009-AigenOardegheden 62 -Oldambsters-0010-AigenOardegheden 42 -Oldambsters-0011-AigenOardegheden 62 -Oldambsters-0012-AigenOardegheden 120 -Oldambsters-0013-AigenOardegheden 60 -Oldambsters-0014-AigenOardegheden 60 -Oldambsters-0015-AigenOardegheden 33 -Oldambsters-0016-AigenOardegheden 26 -Oldambsters-0017-AigenOardegheden 48 -Oldambsters-0018-AigenOardegheden 17 -Oldambsters-0019-AigenOardegheden 25 -Oldambsters-0020-AigenOardegheden 62 -Oldambsters-0021-AigenOardegheden 111 -Oldambsters-0022-AigenOardegheden 61 -Oldambsters-0023-AigenOardegheden 206 -Oldambsters-0024-AigenOardegheden 78 -Oldambsters-0025-AigenOardegheden 35 -Oldambsters-0026-AigenOardegheden 83 -Oldambsters-0027-AigenOardegheden 76 -Oldambsters-0028-AigenOardegheden 41 -Oldambsters-0029-AigenOardegheden 55 -Oldambsters-0030-AigenOardegheden 65 -Oldambsters-0031-AigenOardegheden 70 -Oldambsters-0032-AigenOardegheden 67 -Oldambsters-0033-AigenOardegheden 98 -Oldambsters-0034-AigenOardegheden 80 -Oldambsters-0035-AigenOardegheden 76 -Oldambsters-0036-AigenOardegheden 44 -Oldambsters-0037-AigenOardegheden 122 -Oldambsters-0039-AigenOardegheden 109 -Oldambsters-0040-AigenOardegheden 42 -Oldambsters-0041-AigenOardegheden 57 -Oldambsters-0042-AigenOardegheden 42 -Oldambsters-0043-AigenOardegheden 52 -Oldambsters-0044-AigenOardegheden 25 -Oldambsters-0045-AigenOardegheden 77 -Oldambsters-0046-AigenOardegheden 23 -Oldambsters-0047-AigenOardegheden 11 -Oldambsters-0048-AigenOardegheden 39 -Oldambsters-0049-AigenOardegheden 59 -Oldambsters-0050-AigenOardegheden 52 -Oldambsters-0051-AigenOardegheden 50 -Oldambsters-0052-AigenOardegheden 13 -Oldambsters-0053-AigenOardegheden 114 -Oldambsters-0055-AigenOardegheden 160 -Oldambsters-0056-AigenOardegheden 31 -Oldambsters-0057-AigenOardegheden 12 -Oldambsters-0058-AigenOardegheden 38 -Oldambsters-0059-AigenOardegheden 40 -Oldambsters-0060-AigenOardegheden 26 -Oldambsters-0061-AigenOardegheden 122 -Oldambsters-0062-AigenOardegheden 61 -Oldambsters-0063-AigenOardegheden 30 -Oldambsters-0064-AigenOardegheden 34 -Oldambsters-0065-AigenOardegheden 124 -Oldambsters-0066-AigenOardegheden 144 -Oldambsters-0067-AigenOardegheden 69 -Oldambsters-0068-AigenOardegheden 29 -Oldambsters-0069-AigenOardegheden 26 -Oldambsters-0070-AigenOardegheden 67 -Oldambsters-0071-AigenOardegheden 34 -Oldambsters-0072-AigenOardegheden 23 -Oldambsters-0073-AigenOardegheden 104 -Oldambsters-0074-AigenOardegheden 91 -Oldambsters-0075-AigenOardegheden 92 -Oldambsters-0076-AigenOardegheden 51 -Oldambsters-0077-AigenOardegheden 118 -Oldambsters-0078-AigenOardegheden 78 -Oldambsters-0079-AigenOardegheden 181 -Oldambsters-0080-AigenOardegheden 60 -Oldambsters-0081-AigenOardegheden 71 -Oldambsters-0082-AigenOardegheden 59 -Oldambsters-0083-AigenOardegheden 95 -Oldambsters-0084-AigenOardegheden 38 -Oldambsters-0085-AigenOardegheden 26 -Oldambsters-0086-AigenOardegheden 21 -Oldambsters-0087-AigenOardegheden 159 -Oldambsters-0088-AigenOardegheden 15 -Oldambsters-0089-AigenOardegheden 61 -Oldambsters-0090-AigenOardegheden 28 -Oldambsters-0091-AigenOardegheden 37 -Oldambsters-0092-AigenOardegheden 43 -Oldambsters-0093-AigenOardegheden 112 -Oldambsters-0094-AigenOardegheden 48 -Oldambsters-0095-AigenOardegheden 35 -Oldambsters-0096-AigenOardegheden 34 -Oldambsters-0097-AigenOardegheden 84 -Oldambsters-0098-AigenOardegheden 59 -Oldambsters-0099-AigenOardegheden 40 -Oldambsters-0100-AigenOardegheden 279 -Oldambsters-0101-AigenOardegheden 76 -Oldambsters-0102-AigenOardegheden 53 -Oldambsters-0103-AigenOardegheden 65 -Oldambsters-0104-AigenOardegheden 61 -Oldambsters-0105-AigenOardegheden 29 -Oldambsters-0107-AigenOardegheden 30 -Oldambsters-0108-AigenOardegheden 36 -Oldambsters-0110-AigenOardegheden 28 -Oldambsters-0111-AigenOardegheden 110 -Oldambsters-0112-AigenOardegheden 67 -Oldambsters-0113-AigenOardegheden 55 -Oldambsters-0114-AigenOardegheden 83 -Oldambsters-0115-AigenOardegheden 156 -Oldambsters-0116-AigenOardegheden 26 -Oldambsters-0117-AigenOardegheden 23 -Oldambsters-0118-AigenOardegheden 11 -Oldambsters-0119-AigenOardegheden 137 -Oldambsters-0120-AigenOardegheden 110 -Oldambsters-0121-AigenOardegheden 103 -Oldambsters-0122-AigenOardegheden 65 -Oldambsters-0123-AigenOardegheden 38 -Oldambsters-0124-AigenOardegheden 48 -Oldambsters-0125-AigenOardegheden 30 -Oldambsters-0126-AigenOardegheden 44 -Oldambsters-0127-AigenOardegheden 20 -Oldambsters-0128-AigenOardegheden 46 -Oldambsters-0129-AigenOardegheden 34 -Oldambsters-0130-AigenOardegheden 324 -Oldambsters-0131-AigenOardegheden 105 -Oldambsters-0132-AigenOardegheden 84 -Oldambsters-0133-AigenOardegheden 46 -Oldambsters-0134-AigenOardegheden 43 -Oldambsters-0135-AigenOardegheden 40 -Oldambsters-0136-AigenOardegheden 150 -Oldambsters-0137-AigenOardegheden 22 -Oldambsters-0138-AigenOardegheden 119 -Oldambsters-0139-AigenOardegheden 32 -Oldambsters-0140-AigenOardegheden 57 -Oldambsters-0141-AigenOardegheden 40 -Oldambsters-0142-AigenOardegheden 58 -Oldambsters-0143-AigenOardegheden 105 -Oldambsters-0144-AigenOardegheden 43 -Oldambsters-0145-AigenOardegheden 31 -Oldambsters-0146-AigenOardegheden 69 -Oldambsters-0147-AigenOardegheden 24 -Oldambsters-0148-AigenOardegheden 27 -Oldambsters-0149-AigenOardegheden 187 -Oldambsters-0150-AigenOardegheden 90 -Oldambsters-0151-AigenOardegheden 60 -Oldambsters-0152-AigenOardegheden 14 -Oldambsters-0154-AigenOardegheden 109 -Oldambsters-0155-AigenOardegheden 72 -Oldambsters-0156-AigenOardegheden 212 -Oldambsters-0157-AigenOardegheden 34 -Oldambsters-0158-AigenOardegheden 13 -Oldambsters-0159-AigenOardegheden 41 -Oldambsters-0161-AigenOardegheden 60 -Oldambsters-0162-AigenOardegheden 88 -Oldambsters-0163-AigenOardegheden 53 -Oldambsters-0164-AigenOardegheden 91 -Oldambsters-0165-AigenOardegheden 107 -Oldambsters-0166-AigenOardegheden 89 -Oldambsters-0167-AigenOardegheden 68 -Oldambsters-0168-AigenOardegheden 72 -Oldambsters-0169-AigenOardegheden 77 -Oldambsters-0170-AigenOardegheden 22 -Oldambsters-0171-AigenOardegheden 35 -Oldambsters-0172-AigenOardegheden 25 -Oldambsters-0173-AigenOardegheden 29 -Oldambsters-0174-AigenOardegheden 43 -Oldambsters-0175-AigenOardegheden 161 -Oldambsters-0176-AigenOardegheden 61 -Oldambsters-0177-AigenOardegheden 120 -Oldambsters-0178-AigenOardegheden 188 -Oldambsters-0179-AigenOardegheden 92 -Oldambsters-0180-AigenOardegheden 60 -Oldambsters-0181-AigenOardegheden 73 -Oldambsters-0182-AigenOardegheden 180 -Oldambsters-0183-AigenOardegheden 74 -Oldambsters-0184-AigenOardegheden 105 -Oldambsters-0186-AigenOardegheden 141 -Oldambsters-0187-AigenOardegheden 23 -Oldambsters-0188-AigenOardegheden 57 -Oldambsters-0192-AigenOardegheden 107 -Oldambsters-0193-AigenOardegheden 14 -Oldambsters-0194-AigenOardegheden 102 -Oldambsters-0195-AigenOardegheden 14 -Oldambsters-0196-AigenOardegheden 100 -Oldambsters-0197-AigenOardegheden 49 -Oldambsters-0198-AigenOardegheden 38 -Oldambsters-0199-AigenOardegheden 20 -Oldambsters-0200-AigenOardegheden 58 -Oldambsters-0201-AigenOardegheden 56 -Oldambsters-0203-AigenOardegheden 45 -Oldambsters-0204-AigenOardegheden 20 -Oldambsters-0205-AigenOardegheden 39 -Oldambsters-0206-AigenOardegheden 15 -Oldambsters-0208-AigenOardegheden 92 -Oldambsters-0209-AigenOardegheden 50 -Oldambsters-0210-AigenOardegheden 67 -Oldambsters-0211-AigenOardegheden 33 -Oldambsters-0212-AigenOardegheden 93 -Oldambsters-0213-AigenOardegheden 37 -Oldambsters-0214-AigenOardegheden 22 -Oldambsters-0216-AigenOardegheden 44 -Oldambsters-0217-AigenOardegheden 40 -Oldambsters-0218-AigenOardegheden 112 -Oldambsters-0219-AigenOardegheden 72 -Oldambsters-0220-AigenOardegheden 71 -Oldambsters-0221-AigenOardegheden 36 -Oldambsters-0222-AigenOardegheden 35 -Oldambsters-0224-AigenOardegheden 163 -Oldambsters-0225-AigenOardegheden 102 -Oldambsters-0226-AigenOardegheden 110 -Oldambsters-0227-AigenOardegheden 31 -Oldambsters-0228-AigenOardegheden 143 -Oldambsters-0229-AigenOardegheden 122 -Oldambsters-0230-AigenOardegheden 29 -Oldambsters-0231-AigenOardegheden 87 -Oldambsters-0232-AigenOardegheden 36 -Oldambsters-0233-AigenOardegheden 36 -Oldambsters-0234-AigenOardegheden 25 -Oldambsters-0235-AigenOardegheden 92 -Oldambsters-0236-AigenOardegheden 50 -Oldambsters-0237-AigenOardegheden 52 -Oldambsters-0238-AigenOardegheden 35 -Oldambsters-0239-AigenOardegheden 43 -Oldambsters-0240-AigenOardegheden 17 -Oldambsters-0243-AigenOardegheden 30 -Oldambsters-0244-AigenOardegheden 23 -Oldambsters-0245-AigenOardegheden 74 -Oldambsters-0246-AigenOardegheden 95 -Oldambsters-0247-AigenOardegheden 32 -Oldambsters-0248-AigenOardegheden 23 -Oldambsters-0249-AigenOardegheden 76 -Oldambsters-0250-AigenOardegheden 120 -Oldambsters-0251-AigenOardegheden 99 -Oldambsters-0252-AigenOardegheden 89 -Oldambsters-0253-AigenOardegheden 93 -Oldambsters-0254-AigenOardegheden 139 -Oldambsters-0255-AigenOardegheden 88 -Oldambsters-0256-AigenOardegheden 32 -Oldambsters-0257-AigenOardegheden 83 -Oldambsters-0258-AigenOardegheden 21 -Oldambsters-0259-AigenOardegheden 19 -Oldambsters-0260-AigenOardegheden 30 -Oldambsters-0261-AigenOardegheden 236 -Oldambsters-0262-AigenOardegheden 98 -Oldambsters-0263-AigenOardegheden 57 -Oldambsters-0264-AigenOardegheden 84 -Oldambsters-0265-AigenOardegheden 55 -Oldambsters-0266-AigenOardegheden 43 -Oldambsters-0267-AigenOardegheden 94 -Oldambsters-0268-AigenOardegheden 17 -Oldambsters-0269-AigenOardegheden 217 -Oldambsters-0271-AigenOardegheden 14 -Oldambsters-0272-AigenOardegheden 146 -Oldambsters-0273-AigenOardegheden 64 -Oldambsters-0274-AigenOardegheden 97 -Oldambsters-0275-AigenOardegheden 47 -Oldambsters-0276-AigenOardegheden 191 -Oldambsters-0277-AigenOardegheden 188 -Oldambsters-0278-AigenOardegheden 66 -Oldambsters-0279-AigenOardegheden 66 -Oldambsters-0280-AigenOardegheden 192 -Oldambsters-0281-AigenOardegheden 136 -Oldambsters-0282-AigenOardegheden 157 -Oldambsters-0283-AigenOardegheden 59 -Oldambsters-0284-AigenOardegheden 28 -Oldambsters-0285-AigenOardegheden 136 -Oldambsters-0286-AigenOardegheden 75 -Oldambsters-0287-AigenOardegheden 27 -Oldambsters-0288-AigenOardegheden 72 -Oldambsters-0289-AigenOardegheden 109 -Oldambsters-0290-AigenOardegheden 62 -Oldambsters-0291-AigenOardegheden 26 -Oldambsters-0292-AigenOardegheden 26 -Oldambsters-0293-AigenOardegheden 15 -Oldambsters-0294-AigenOardegheden 51 -Oldambsters-0295-AigenOardegheden 16 -Oldambsters-0296-AigenOardegheden 48 -Oldambsters-0297-AigenOardegheden 58 -Oldambsters-0298-AigenOardegheden 38 -Oldambsters-0299-AigenOardegheden 43 -Oldambsters-0300-AigenOardegheden 90 -Oldambsters-0301-AigenOardegheden 46 -Oldambsters-0302-AigenOardegheden 14 -Oldambsters-0303-AigenOardegheden 48 -Oldambsters-0304-AigenOardegheden 91 -Oldambsters-0305-AigenOardegheden 100 -Oldambsters-0306-AigenOardegheden 92 -Oldambsters-0307-AigenOardegheden 54 -Oldambsters-0308-AigenOardegheden 161 -Oldambsters-0309-AigenOardegheden 104 -Oldambsters-0310-AigenOardegheden 22 -Oldambsters-0311-AigenOardegheden 16 -Oldambsters-0312-AigenOardegheden 42 -Oldambsters-0313-AigenOardegheden 33 -Oldambsters-0314-AigenOardegheden 70 -Oldambsters-0315-AigenOardegheden 63 -Oldambsters-0316-AigenOardegheden 37 -Oldambsters-0318-AigenOardegheden 58 -Oldambsters-0319-AigenOardegheden 62 -Oldambsters-0320-AigenOardegheden 36 -Oldambsters-0321-AigenOardegheden 148 -Oldambsters-0323-AigenOardegheden 91 -Oldambsters-0324-AigenOardegheden 40 -Oldambsters-0325-AigenOardegheden 89 -Oldambsters-0326-AigenOardegheden 81 -Oldambsters-0327-AigenOardegheden 40 -Oldambsters-0328-AigenOardegheden 132 -Oldambsters-0330-AigenOardegheden 225 -Oldambsters-0331-AigenOardegheden 30 -Oldambsters-0332-AigenOardegheden 110 -Oldambsters-0333-AigenOardegheden 40 -Oldambsters-0334-AigenOardegheden 12 -Oldambsters-0335-AigenOardegheden 25 -Oldambsters-0336-AigenOardegheden 57 -Oldambsters-0337-AigenOardegheden 52 -Oldambsters-0338-AigenOardegheden 231 -Oldambsters-0339-AigenOardegheden 29 -Oldambsters-0340-AigenOardegheden 67 -Oldambsters-0341-AigenOardegheden 35 -Oldambsters-0342-AigenOardegheden 56 -Oldambsters-0343-AigenOardegheden 140 -Oldambsters-0344-AigenOardegheden 72 -Oldambsters-0345-AigenOardegheden 53 -Oldambsters-0346-AigenOardegheden 75 -Oldambsters-0347-AigenOardegheden 27 -Oldambsters-0348-AigenOardegheden 115 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147 -Westerkertaaiers-1768-SlagTwaalf 49 -Westerkertaaiers-1769-SlagTwaalf 18 -Westerkertaaiers-1770-SlagTwaalf 46 -Westerkertaaiers-1772-SlagTwaalf 47 -Westerkertaaiers-1773-SlagTwaalf 49 -Westerkertaaiers-1774-SlagTwaalf 52 -Westerkertaaiers-1775-SlagTwaalf 40 -Westerkertaaiers-1776-SlagTwaalf 87 -Westerkertaaiers-1777-SlagTwaalf 80 -Westerkertaaiers-1778-SlagTwaalf 99 -Westerkertaaiers-1779-SlagTwaalf 73 -Westerkertaaiers-1780-SlagTwaalf 42 -Westerkertaaiers-1781-SlagTwaalf 15 -Westerkertaaiers-1783-SlagTwaalf 37 -Westerkertaaiers-1784-SlagTwaalf 31 -Westerkertaaiers-1785-SlagTwaalf 90 -Westerkertaaiers-1786-SlagTwaalf 14 -Westerkertaaiers-1787-SlagTwaalf 50 -Westerkertaaiers-1788-SlagTwaalf 20 -Westerkertaaiers-1789-SlagTwaalf 54 -Westerkertaaiers-1790-SlagTwaalf 25 -Westerkertaaiers-1791-SlagTwaalf 23 -Westerkertaaiers-1792-SlagTwaalf 65 -Westerkertaaiers-1793-SlagTwaalf 26 -Westerkertaaiers-1794-SlagTwaalf 94 -Westerkertaaiers-1795-SlagTwaalf 156 -Westerkertaaiers-1796-SlagTwaalf 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40 -Westerkertaaiers-1851-SlagTwaalf 53 -Westerkertaaiers-1852-SlagTwaalf 33 -Westerkertaaiers-1853-SlagTwaalf 84 -Westerkertaaiers-1854-SlagTwaalf 31 -Westerkertaaiers-1855-SlagTwaalf 23 -Westerkertaaiers-1856-SlagTwaalf 48 -Westerkertaaiers-1857-SlagTwaalf 18 -Westerkertaaiers-1859-SlagTwaalf 13 -Westerkertaaiers-1860-SlagTwaalf 26 -Westerkertaaiers-1861-SlagTwaalf 43 -Westerkertaaiers-1862-SlagTwaalf 41 -Westerkertaaiers-1863-SlagTwaalf 41 -Westerkertaaiers-1864-SlagTwaalf 50 -Westerkertaaiers-1865-SlagTwaalf 39 -Westerkertaaiers-1866-SlagTwaalf 109 -Westerkertaaiers-1867-SlagTwaalf 23 -Westerkertaaiers-1868-SlagTwaalf 28 -Westerkertaaiers-1869-SlagTwaalf 83 -Westerkertaaiers-1870-SlagTwaalf 32 -Westerkertaaiers-1872-SlagTwaalf 80 -Westerkertaaiers-1873-SlagTwaalf 25 -Westerkertaaiers-1874-SlagTwaalf 46 -Westerkertaaiers-1875-SlagTwaalf 46 -Westerkertaaiers-1876-SlagTwaalf 38 -Westerkertaaiers-1877-SlagTwaalf 161 -Westerkertaaiers-1878-SlagTwaalf 60 -Westerkertaaiers-1879-SlagTwaalf 107 -Westerkertaaiers-1880-SlagTwaalf 124 -Westerkertaaiers-1881-SlagTwaalf 22 -Westerkertaaiers-1882-SlagTwaalf 24 -Westerkertaaiers-1883-SlagTwaalf 32 -Westerkertaaiers-1884-SlagTwaalf 74 -Westerkertaaiers-1885-SlagTwaalf 47 -Westerkertaaiers-1886-SlagTwaalf 54 -Westerkertaaiers-1887-SlagTwaalf 56 -Westerkertaaiers-1888-SlagTwaalf 63 -Westerkertaaiers-1889-SlagTwaalf 81 -Westerkertaaiers-1890-SlagTwaalf 12 -Westerkertaaiers-1891-SlagTwaalf 33 -Westerkertaaiers-1892-SlagTwaalf 49 -Westerkertaaiers-1893-SlagTwaalf 47 -Westerkertaaiers-1894-SlagTwaalf 30 -Westerkertaaiers-1895-SlagTwaalf 34 -Westerkertaaiers-1896-SlagTwaalf 183 -Westerkertaaiers-1897-SlagTwaalf 92 -Westerkertaaiers-1898-SlagTwaalf 73 -Westerkertaaiers-1899-SlagTwaalf 28 -Westerkertaaiers-1900-SlagTwaalf 73 -Westerkertaaiers-1901-SlagTwaalf 61 -Westerkertaaiers-1903-SlagTwaalf 51 -Westerkertaaiers-1904-SlagTwaalf 72 -Westerkertaaiers-1905-SlagTwaalf 42 -Westerkertaaiers-1906-SlagTwaalf 40 -Westerkertaaiers-1907-SlagTwaalf 69 -Westerkertaaiers-1908-SlagTwaalf 21 -Westerkertaaiers-1909-SlagTwaalf 21 -Westerkertaaiers-1911-SlagTwaalf 67 -Westerkertaaiers-1912-SlagTwaalf 21 -Westerkertaaiers-1913-SlagTwaalf 80 -Westerkertaaiers-1914-SlagTwaalf 93 -Westerkertaaiers-1915-SlagTwaalf 19 -Westerkertaaiers-1916-SlagTwaalf 58 -Westerkertaaiers-1917-SlagTwaalf 68 -Westerkertaaiers-1918-SlagTwaalf 77 -Westerkertaaiers-1919-SlagTwaalf 132 -Westerkertaaiers-1920-SlagTwaalf 100 -Westerkertaaiers-1921-SlagTwaalf 49 -Westerkertaaiers-1922-SlagTwaalf 18 -Westerkertaaiers-1923-SlagTwaalf 92 -Westerkertaaiers-1924-SlagTwaalf 135 -Westerkertaaiers-1925-SlagTwaalf 266 -Westerkertaaiers-1926-SlagTwaalf 30 -Westerkertaaiers-1927-SlagTwaalf 92 -Westerkertaaiers-1928-SlagTwaalf 43 -Westerkertaaiers-1929-SlagTwaalf 39 -Westerkertaaiers-1930-SlagTwaalf 41 -Westerkertaaiers-1931-SlagTwaalf 20 -Westerkertaaiers-1932-SlagTwaalf 70 -Westerkertaaiers-1933-SlagTwaalf 21 -Westerkertaaiers-1934-SlagTwaalf 69 -Westerkertaaiers-1935-SlagTwaalf 35 -Westerkertaaiers-1936-SlagTwaalf 35 -Westerkertaaiers-1937-SlagTwaalf 127 -Westerkertaaiers-1938-SlagTwaalf 27 -Westerkertaaiers-1939-SlagTwaalf 27 -Westerkertaaiers-1940-SlagTwaalf 44 -Westerkertaaiers-1941-SlagTwaalf 61 -Westerkertaaiers-1942-SlagTwaalf 44 -Westerkertaaiers-1943-SlagTwaalf 24 -Westerkertaaiers-1944-SlagTwaalf 30 -Westerkertaaiers-1945-SlagTwaalf 44 -Westerkertaaiers-1947-SlagTwaalf 61 -Westerkertaaiers-1948-SlagTwaalf 57 -Westerkertaaiers-1949-SlagTwaalf 36 -Westerkertaaiers-1950-SlagTwaalf 26 -Westerkertaaiers-1951-SlagTwaalf 27 -Westerkertaaiers-1952-SlagTwaalf 53 -Westerkertaaiers-1953-SlagTwaalf 10 -Westerkertaaiers-1954-SlagTwaalf 86 -Westerkertaaiers-1955-SlagTwaalf 49 -Westerkertaaiers-1956-SlagTwaalf 116 -Westerkertaaiers-1957-SlagTwaalf 21 -Westerkertaaiers-1958-SlagTwaalf 45 -Westerkertaaiers-1959-SlagTwaalf 45 -Westerkertaaiers-1960-SlagTwaalf 79 -Westerkertaaiers-1961-SlagTwaalf 55 -Westerkertaaiers-1962-SlagTwaalf 51 -Westerkertaaiers-1963-SlagTwaalf 71 -Westerkertaaiers-1964-SlagTwaalf 48 -Westerkertaaiers-1965-SlagTwaalf 19 -Westerkertaaiers-1966-SlagTwaalf 86 -Westerkertaaiers-1967-SlagTwaalf 100 -Westerkertaaiers-1968-SlagTwaalf 76 -Westerkertaaiers-1969-SlagTwaalf 137 -Westerkertaaiers-1970-SlagTwaalf 73 -Westerkertaaiers-1971-SlagTwaalf 45 -Westerkertaaiers-1972-SlagTwaalf 19 -Westerkertaaiers-1973-SlagTwaalf 20 -Westerkertaaiers-1974-SlagTwaalf 57 -Westerkertaaiers-1975-SlagTwaalf 65 -Westerkertaaiers-1976-SlagTwaalf 30 -Westerkertaaiers-1977-SlagTwaalf 88 -Westerkertaaiers-1978-SlagTwaalf 63 -Westerkertaaiers-1979-SlagTwaalf 46 -Westerkertaaiers-1980-SlagTwaalf 155 -Westerkertaaiers-1981-SlagTwaalf 32 -Westerkertaaiers-1982-SlagTwaalf 50 -Westerkertaaiers-1983-SlagTwaalf 69 -Westerkertaaiers-1984-SlagTwaalf 139 -Westerkertaaiers-1985-SlagTwaalf 57 -Westerkertaaiers-1986-SlagTwaalf 38 -Westerkertaaiers-1987-SlagTwaalf 50 -Westerkertaaiers-1988-SlagTwaalf 33 -Westerkertaaiers-1990-SlagTwaalf 90 -Westerkertaaiers-1991-SlagTwaalf 74 -Westerkertaaiers-1992-SlagTwaalf 41 -Westerkertaaiers-1993-SlagTwaalf 102 -Westerkertaaiers-1994-SlagTwaalf 52 -Westerkertaaiers-1995-SlagTwaalf 86 -Westerkertaaiers-1996-SlagTwaalf 37 -Westerkertaaiers-1997-SlagTwaalf 118 -Westerkertaaiers-1998-SlagTwaalf 91 -Westerkertaaiers-1999-SlagTwaalf 49 -Westerkertaaiers-2000-SlagTwaalf 32 -Westerkertaaiers-2001-SlagTwaalf 48 -Westerkertaaiers-2002-SlagTwaalf 47 -Westerkertaaiers-2003-SlagTwaalf 50 -Westerkertaaiers-2004-SlagTwaalf 144 -Westerkertaaiers-2005-SlagTwaalf 58 -Westerkertaaiers-2006-SlagTwaalf 101 -Westerkertaaiers-2007-SlagTwaalf 77 -Westerkertaaiers-2009-SlagTwaalf 30 -Westerkertaaiers-2010-SlagTwaalf 71 -Westerkertaaiers-2011-SlagTwaalf 37 -Westerkertaaiers-2012-SlagTwaalf 56 -Westerkertaaiers-2013-SlagTwaalf 22 -Westerkertaaiers-2014-SlagTwaalf 66 -Westerkertaaiers-2015-SlagTwaalf 25 -Westerkertaaiers-2016-SlagTwaalf 101 -Westerkertaaiers-2017-SlagTwaalf 120 -Westerkertaaiers-2018-SlagTwaalf 24 -Westerkertaaiers-2019-SlagTwaalf 138 -Westerkertaaiers-2020-SlagTwaalf 91 -Westerkertaaiers-2021-SlagTwaalf 43 -Westerkertaaiers-2022-SlagTwaalf 81 -Westerkertaaiers-2023-SlagTwaalf 26 -Westerkertaaiers-2024-SlagTwaalf 118 -Westerkertaaiers-2025-SlagTwaalf 149 -Westerkertaaiers-2026-SlagTwaalf 51 -Westerkertaaiers-2027-SlagTwaalf 117 -Westerkertaaiers-2028-SlagTwaalf 102 -Westerkertaaiers-2029-SlagTwaalf 35 -Westerkertaaiers-2030-SlagTwaalf 52 -Westerkertaaiers-2031-SlagTwaalf 17 -Westerkertaaiers-2032-SlagTwaalf 62 -Westerkertaaiers-2034-SlagTwaalf 53 -Westerkertaaiers-2035-SlagTwaalf 47 -Westerkertaaiers-2036-SlagTwaalf 164 -Westerkertaaiers-2037-SlagTwaalf 91 -Westerkertaaiers-2038-SlagTwaalf 43 -Westerkertaaiers-2039-SlagTwaalf 53 -Westerkertaaiers-2040-SlagTwaalf 25 -Westerkertaaiers-2041-SlagTwaalf 20 -Westerkertaaiers-2042-SlagTwaalf 68 -Westerkertaaiers-2043-SlagTwaalf 29 -Westerkertaaiers-2044-SlagTwaalf 69 -Westerkertaaiers-2045-SlagTwaalf 102 -Westerkertaaiers-2046-SlagTwaalf 74 -Westerkertaaiers-2047-SlagTwaalf 44 -Westerkertaaiers-2048-SlagTwaalf 43 -Westerkertaaiers-2049-SlagTwaalf 107 -Westerkertaaiers-2050-SlagTwaalf 163 -Westerkertaaiers-2051-SlagTwaalf 65 -Westerkertaaiers-2052-SlagTwaalf 30 -Westerkertaaiers-2053-SlagTwaalf 99 -Westerkertaaiers-2054-SlagTwaalf 51 -Westerkertaaiers-2055-SlagTwaalf 99 -Westerkertaaiers-2056-SlagTwaalf 48 -Westerkertaaiers-2057-SlagTwaalf 96 -Westerkertaaiers-2058-SlagTwaalf 97 -Westerkertaaiers-2059-SlagTwaalf 70 -Westerkertaaiers-2060-SlagTwaalf 146 -Westerkertaaiers-2061-SlagTwaalf 101 -Westerkertaaiers-2062-SlagTwaalf 18 -Westerkertaaiers-2063-SlagTwaalf 35 -Westerkertaaiers-2064-SlagTwaalf 23 -Westerkertaaiers-2065-SlagTwaalf 71 -Westerkertaaiers-2066-SlagTwaalf 22 -Westerkertaaiers-2067-SlagTwaalf 54 -Westerkertaaiers-2068-SlagTwaalf 64 -Westerkertaaiers-2069-SlagTwaalf 71 -Westerkertaaiers-2070-SlagTwaalf 41 -Westerkertaaiers-2071-SlagTwaalf 25 -Westerkertaaiers-2072-SlagTwaalf 51 -Westerkertaaiers-2073-SlagTwaalf 29 -Westerkertaaiers-2074-SlagTwaalf 106 -Westerkertaaiers-2075-SlagTwaalf 26 -Westerkertaaiers-2077-SlagTwaalf 54 -Westerkertaaiers-2078-SlagTwaalf 15 -Westerkertaaiers-2079-SlagTwaalf 99 -Westerkertaaiers-2080-SlagTwaalf 63 -Westerkertaaiers-2081-SlagTwaalf 28 -Westerkertaaiers-2082-SlagTwaalf 27 -Westerkertaaiers-2083-SlagTwaalf 67 -Westerkertaaiers-2084-SlagTwaalf 114 -Westerkertaaiers-2085-SlagTwaalf 96 -Westerkertaaiers-2086-SlagTwaalf 50 -Westerkertaaiers-2087-SlagTwaalf 87 -Westerkertaaiers-2088-SlagTwaalf 24 -Westerkertaaiers-2090-SlagTwaalf 64 -Westerkertaaiers-2091-SlagTwaalf 43 -Westerkertaaiers-2092-SlagTwaalf 16 -Westerkertaaiers-2093-SlagTwaalf 39 -Westerkertaaiers-2094-SlagTwaalf 55 -Westerkertaaiers-2095-SlagTwaalf 88 -Westerkertaaiers-2096-SlagTwaalf 56 -Westerkertaaiers-2097-SlagTwaalf 83 -Westerkertaaiers-2098-SlagTwaalf 85 -Westerkertaaiers-2099-SlagTwaalf 100 -Westerkertaaiers-2100-SlagTwaalf 67 -Westerkertaaiers-2101-SlagTwaalf 27 -Westerkertaaiers-2102-SlagTwaalf 25 -Westerkertaaiers-2103-SlagTwaalf 49 -Westerkertaaiers-2104-SlagTwaalf 181 -Westerkertaaiers-2105-SlagTwaalf 31 -Westerkertaaiers-2106-SlagTwaalf 35 -Westerkertaaiers-2107-SlagTwaalf 107 -Westerkertaaiers-2108-SlagTwaalf 163 -Westerkertaaiers-2109-SlagTwaalf 131 -Westerkertaaiers-2110-SlagTwaalf 261 -Westerkertaaiers-2111-SlagTwaalf 72 -Westerkertaaiers-2112-SlagTwaalf 97 -Westerkertaaiers-2113-SlagTwaalf 28 -Westerkertaaiers-2114-SlagTwaalf 101 -Westerkertaaiers-2115-SlagTwaalf 65 -Westerkertaaiers-2116-SlagTwaalf 53 -Westerkertaaiers-2117-SlagTwaalf 90 -Westerkertaaiers-2118-SlagTwaalf 40 -Westerkertaaiers-2119-SlagTwaalf 64 -Westerkertaaiers-2121-SlagTwaalf 28 -Westerkertaaiers-2122-SlagTwaalf 21 -Westerkertaaiers-2123-SlagTwaalf 31 -Westerkertaaiers-2124-SlagTwaalf 70 -Westerkertaaiers-2125-SlagTwaalf 34 -Westerkertaaiers-2126-SlagTwaalf 88 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-Oldambsters-0614-AigenOardegheden 42,46 -Oldambsters-0615-AigenOardegheden 33,46 -Oldambsters-0616-AigenOardegheden 36,46 -Oldambsters-0617-AigenOardegheden 65,46 -Oldambsters-0618-AigenOardegheden 31,46 -Oldambsters-0619-AigenOardegheden 26,46 -Oldambsters-0620-AigenOardegheden 37,46 -Oldambsters-0621-AigenOardegheden 25,46 -Oldambsters-0622-AigenOardegheden 26,46 -Oldambsters-0623-AigenOardegheden 31,46 -Oldambsters-0625-AigenOardegheden 32,46 -Oldambsters-0626-AigenOardegheden 103,46 -Oldambsters-0629-AigenOardegheden 40,46 -Oldambsters-0630-AigenOardegheden 18,46 -Oldambsters-0631-AigenOardegheden 62,46 -Oldambsters-0632-AigenOardegheden 122,46 -Oldambsters-0633-AigenOardegheden 149,46 -Oldambsters-0634-AigenOardegheden 39,46 -Oldambsters-0635-AigenOardegheden 80,46 -Oldambsters-0636-AigenOardegheden 57,46 -Oldambsters-0637-AigenOardegheden 45,46 -Oldambsters-0638-AigenOardegheden 38,46 -Oldambsters-0639-AigenOardegheden 23,46 -Oldambsters-0640-AigenOardegheden 164,46 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-Oldambsters-0669-DeVerhoalen 94,46 -Oldambsters-0670-DeVerhoalen 54,46 -Oldambsters-0671-DeVerhoalen 215,46 -Oldambsters-0672-DeVerhoalen 75,46 -Oldambsters-0673-DeVerhoalen 26,46 -Oldambsters-0674-DeVerhoalen 103,46 -Oldambsters-0675-DeVerhoalen 55,46 -Oldambsters-0676-DeVerhoalen 31,46 -Oldambsters-0677-DeVerhoalen 102,46 -Oldambsters-0678-DeVerhoalen 208,46 -Oldambsters-0679-DeVerhoalen 233,46 -Oldambsters-0680-DeVerhoalen 38,46 -Oldambsters-0681-DeVerhoalen 22,46 -Oldambsters-0682-DeVerhoalen 55,46 -Oldambsters-0683-DeVerhoalen 151,46 -Oldambsters-0684-DeVerhoalen 30,46 -Oldambsters-0685-DeVerhoalen 30,46 -Oldambsters-0686-DeVerhoalen 57,46 -Oldambsters-0687-DeVerhoalen 47,46 -Oldambsters-0688-DeVerhoalen 90,46 -Oldambsters-0689-DeVerhoalen 10,46 -Oldambsters-0690-DeVerhoalen 175,46 -Oldambsters-0691-DeVerhoalen 99,46 -Oldambsters-0692-DeVerhoalen 149,46 -Oldambsters-0693-DeVerhoalen 49,46 -Oldambsters-0694-DeVerhoalen 82,46 -Oldambsters-0695-DeVerhoalen 161,46 -Oldambsters-0696-DeVerhoalen 97,46 -Oldambsters-0697-DeVerhoalen 68,46 -Oldambsters-0699-DeVerhoalen 43,46 -Oldambsters-0700-DeVerhoalen 40,46 -Oldambsters-0701-DeVerhoalen 82,46 -Oldambsters-0702-DeVerhoalen 94,46 -Oldambsters-0703-DeVerhoalen 91,46 -Oldambsters-0704-DeVerhoalen 233,46 -Oldambsters-0705-DeVerhoalen 55,46 -Oldambsters-0706-DeVerhoalen 7,46 -Oldambsters-0707-DeVerhoalen 7,46 -Oldambsters-0708-DeVerhoalen 7,46 -Oldambsters-0709-DeVerhoalen 53,46 -Oldambsters-0710-DeVerhoalen 89,46 -Oldambsters-0711-DeVerhoalen 175,46 -Oldambsters-0712-DeVerhoalen 24,46 -Oldambsters-0713-DeVerhoalen 117,46 -Oldambsters-0714-DeVerhoalen 139,46 -Oldambsters-0715-DeVerhoalen 44,46 -Oldambsters-0716-DeVerhoalen 75,46 -Oldambsters-0717-DeVerhoalen 58,46 -Oldambsters-0718-DeVerhoalen 113,46 -Oldambsters-0719-DeVerhoalen 12,46 -Oldambsters-0720-DeVerhoalen 95,46 -Oldambsters-0721-DeVerhoalen 42,46 -Oldambsters-0722-DeVerhoalen 202,46 -Oldambsters-0723-DeVerhoalen 162,46 -Oldambsters-0724-DeVerhoalen 71,46 -Oldambsters-0725-DeVerhoalen 39,46 -Oldambsters-0726-DeVerhoalen 42,46 -Oldambsters-0727-DeVerhoalen 66,46 -Oldambsters-0728-DeVerhoalen 51,46 -Oldambsters-0729-DeVerhoalen 81,46 -Oldambsters-0731-DeVerhoalen 109,46 -Oldambsters-0732-DeVerhoalen 69,46 -Oldambsters-0733-DeVerhoalen 235,46 -Oldambsters-0734-DeVerhoalen 180,46 -Oldambsters-0735-DeVerhoalen 26,46 -Oldambsters-0736-DeVerhoalen 32,46 -Oldambsters-0737-DeVerhoalen 32,46 -Oldambsters-0738-DeVerhoalen 57,46 -Oldambsters-0739-DeVerhoalen 48,46 -Oldambsters-0740-DeVerhoalen 68,46 -Oldambsters-0741-DeVerhoalen 96,46 -Oldambsters-0742-DeVerhoalen 68,46 -Oldambsters-0743-DeVerhoalen 181,46 -Oldambsters-0744-DeVerhoalen 167,46 -Oldambsters-0745-DeVerhoalen 79,46 -Oldambsters-0746-DeVerhoalen 108,46 -Oldambsters-0747-DeVerhoalen 107,46 -Oldambsters-0748-DeVerhoalen 44,46 -Oldambsters-0749-DeVerhoalen 124,46 -Oldambsters-0750-DeVerhoalen 41,46 -Oldambsters-0752-DeVerhoalen 68,46 -Oldambsters-0753-DeVerhoalen 46,46 -Oldambsters-0754-DeVerhoalen 67,46 -Oldambsters-0755-DeVerhoalen 76,46 -Oldambsters-0756-DeVerhoalen 84,46 -Oldambsters-0757-DeVerhoalen 92,46 -Oldambsters-0758-DeVerhoalen 80,46 -Oldambsters-0759-DeVerhoalen 71,46 -Oldambsters-0760-DeVerhoalen 57,46 -Oldambsters-0761-DeVerhoalen 114,46 -Oldambsters-0762-DeVerhoalen 80,46 -Oldambsters-0763-DeVerhoalen 73,46 -Oldambsters-0764-DeVerhoalen 10,46 -Oldambsters-0765-DeVerhoalen 36,46 -Oldambsters-0766-DeVerhoalen 49,46 -Oldambsters-0767-DeVerhoalen 73,46 -Oldambsters-0768-DeVerhoalen 33,46 -Oldambsters-0769-DeVerhoalen 44,46 -Oldambsters-0770-DeVerhoalen 82,46 -Oldambsters-0771-DeVerhoalen 76,46 -Oldambsters-0772-DeVerhoalen 29,46 -Oldambsters-0773-DeVerhoalen 15,46 -Oldambsters-0774-DeVerhoalen 28,46 -Oldambsters-0775-DeVerhoalen 74,46 -Oldambsters-0776-DeVerhoalen 12,46 -Oldambsters-0777-DeVerhoalen 101,46 -Oldambsters-0778-DeVerhoalen 40,46 -Oldambsters-0779-DeVerhoalen 68,46 -Oldambsters-0780-DeVerhoalen 10,46 -Oldambsters-0781-DeVerhoalen 91,46 -Oldambsters-0782-DeVerhoalen 93,46 -Oldambsters-0783-DeVerhoalen 41,46 -Oldambsters-0784-DeVerhoalen 161,46 -Oldambsters-0785-DeVerhoalen 186,46 -Oldambsters-0786-DeVerhoalen 29,46 -Oldambsters-0787-DeVerhoalen 81,46 -Oldambsters-0788-DeVerhoalen 89,46 -Oldambsters-0789-DeVerhoalen 122,46 -Oldambsters-0790-DeVerhoalen 39,46 -Oldambsters-0791-DeVerhoalen 46,46 -Oldambsters-0792-DeVerhoalen 18,46 -Oldambsters-0793-DeVerhoalen 18,46 -Oldambsters-0796-DeVerhoalen 85,46 -Oldambsters-0797-DeVerhoalen 66,46 -Oldambsters-0798-DeVerhoalen 69,46 -Oldambsters-0799-DeVerhoalen 148,46 -Oldambsters-0800-DeVerhoalen 107,46 -Oldambsters-0801-DeVerhoalen 84,46 -Oldambsters-0802-DeVerhoalen 61,46 -Oldambsters-0803-DeVerhoalen 71,46 -Oldambsters-0804-DeVerhoalen 54,46 -Oldambsters-0806-DeVerhoalen 43,46 -Oldambsters-0807-DeVerhoalen 81,46 -Oldambsters-0808-DeVerhoalen 90,46 -Oldambsters-0809-DeVerhoalen 117,46 -Oldambsters-0810-DeVerhoalen 43,46 -Oldambsters-0811-DeVerhoalen 33,46 -Oldambsters-0812-DeVerhoalen 22,46 -Oldambsters-0814-DeVerhoalen 203,46 -Oldambsters-0815-DeVerhoalen 178,46 -Oldambsters-0816-DeVerhoalen 45,46 -Oldambsters-0817-DeVerhoalen 55,46 -Oldambsters-0818-DeVerhoalen 13,46 -Oldambsters-0819-DeVerhoalen 40,46 -Oldambsters-0820-DeVerhoalen 19,46 -Oldambsters-0821-DeVerhoalen 86,46 -Oldambsters-0822-DeVerhoalen 75,46 -Oldambsters-0823-DeVerhoalen 25,46 -Oldambsters-0825-DeVerhoalen 175,46 -Oldambsters-0826-DeVerhoalen 151,46 -Oldambsters-0827-DeVerhoalen 31,46 -Oldambsters-0828-DeVerhoalen 212,46 -Oldambsters-0829-DeVerhoalen 88,46 -Oldambsters-0830-DeVerhoalen 104,46 -Oldambsters-0831-DeVerhoalen 19,46 -Oldambsters-0832-DeVerhoalen 94,46 -Oldambsters-0833-DeVerhoalen 61,46 -Oldambsters-0834-DeVerhoalen 102,46 -Oldambsters-0835-DeVerhoalen 189,46 -Oldambsters-0836-DeVerhoalen 42,46 -Oldambsters-0837-DeVerhoalen 34,46 -Oldambsters-0838-DeVerhoalen 115,46 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-Oldambsters-0868-DeVerhoalen 28,46 -Oldambsters-0869-DeVerhoalen 112,46 -Oldambsters-0870-DeVerhoalen 78,46 -Oldambsters-0871-DeVerhoalen 42,46 -Oldambsters-0872-DeVerhoalen 18,46 -Oldambsters-0873-DeVerhoalen 175,46 -Oldambsters-0874-DeVerhoalen 28,46 -Oldambsters-0875-DeVerhoalen 23,46 -Oldambsters-0876-DeVerhoalen 61,46 -Oldambsters-0877-DeVerhoalen 123,46 -Oldambsters-0878-DeVerhoalen 59,46 -Oldambsters-0879-DeVerhoalen 81,46 -Oldambsters-0880-DeVerhoalen 159,46 -Oldambsters-0881-DeVerhoalen 49,46 -Oldambsters-0882-DeVerhoalen 104,46 -Oldambsters-0883-DeVerhoalen 43,46 -Oldambsters-0884-DeVerhoalen 45,46 -Oldambsters-0885-DeVerhoalen 122,46 -Oldambsters-0886-DeVerhoalen 107,46 -Oldambsters-0887-DeVerhoalen 50,46 -Oldambsters-0888-DeVerhoalen 56,46 -Oldambsters-0889-DeVerhoalen 97,46 -Oldambsters-0890-DeVerhoalen 62,46 -Oldambsters-0891-DeVerhoalen 74,46 -Oldambsters-0892-DeVerhoalen 56,46 -Oldambsters-0893-DeVerhoalen 42,46 -Oldambsters-0894-DeVerhoalen 38,46 -Oldambsters-0895-DeVerhoalen 20,46 -Oldambsters-0896-DeVerhoalen 63,46 -Oldambsters-0899-DeVerhoalen 56,46 -Oldambsters-0900-DeVerhoalen 30,46 -Oldambsters-0901-DeVerhoalen 28,46 -Oldambsters-0902-DeVerhoalen 54,46 -Oldambsters-0903-DeVerhoalen 66,46 -Oldambsters-0904-DeVerhoalen 28,46 -Oldambsters-0905-DeVerhoalen 16,46 -Oldambsters-0906-DeVerhoalen 12,46 -Oldambsters-0907-DeVerhoalen 50,46 -Oldambsters-0908-DeVerhoalen 8,46 -Oldambsters-0909-DeVerhoalen 13,46 -Oldambsters-0910-DeVerhoalen 49,46 -Oldambsters-0912-DeVerhoalen 176,46 -Oldambsters-0913-DeVerhoalen 97,46 -Oldambsters-0914-DeVerhoalen 7,46 -Oldambsters-0915-DeVerhoalen 19,46 -Oldambsters-0916-DeVerhoalen 81,46 -Oldambsters-0917-DeVerhoalen 81,46 -Oldambsters-0918-DeVerhoalen 143,46 -Oldambsters-0919-DeVerhoalen 37,46 -Oldambsters-0920-DeVerhoalen 80,46 -Oldambsters-0921-DeVerhoalen 88,46 -Oldambsters-0922-DeVerhoalen 54,46 -Oldambsters-0923-DeVerhoalen 50,46 -Oldambsters-0925-DeVerhoalen 45,46 -Oldambsters-0926-DeVerhoalen 124,46 -Oldambsters-0927-DeVerhoalen 69,46 -Oldambsters-0928-DeVerhoalen 183,46 -Oldambsters-0929-DeVerhoalen 63,46 -Oldambsters-0930-DeVerhoalen 27,46 -Oldambsters-0931-DeVerhoalen 98,46 -Oldambsters-0932-DeVerhoalen 108,46 -Oldambsters-0933-DeVerhoalen 28,46 -Oldambsters-0934-DeVerhoalen 67,46 -Oldambsters-0935-DeVerhoalen 24,46 -Oldambsters-0936-DeVerhoalen 57,46 -Oldambsters-0937-DeVerhoalen 77,46 -Oldambsters-0938-DeVerhoalen 27,46 -Oldambsters-0939-DeVerhoalen 12,46 -Oldambsters-0941-DeVerhoalen 80,46 -Oldambsters-0942-DeVerhoalen 110,46 -Oldambsters-0943-DeVerhoalen 31,46 -Oldambsters-0944-DeVerhoalen 11,46 -Oldambsters-0945-DeVerhoalen 26,46 -Oldambsters-0946-DeVerhoalen 36,46 -Oldambsters-0947-DeVerhoalen 57,46 -Oldambsters-0948-DeVerhoalen 53,46 -Oldambsters-0949-DeVerhoalen 62,46 -Oldambsters-0950-DeVerhoalen 67,46 -Oldambsters-0951-DeVerhoalen 55,46 -Oldambsters-0952-DeVerhoalen 27,46 -Oldambsters-0953-DeVerhoalen 64,46 -Oldambsters-0954-DeVerhoalen 28,46 -Oldambsters-0955-DeVerhoalen 41,46 -Oldambsters-0956-DeVerhoalen 125,46 -Oldambsters-0958-DeVerhoalen 94,46 -Oldambsters-0959-DeVerhoalen 63,46 -Oldambsters-0960-DeVerhoalen 49,46 -Oldambsters-0961-DeVerhoalen 50,46 -Oldambsters-0962-DeVerhoalen 36,46 -Oldambsters-0963-DeVerhoalen 101,46 -Oldambsters-0964-DeVerhoalen 69,46 -Oldambsters-0965-DeVerhoalen 22,46 -Oldambsters-0968-DeVerhoalen 52,46 -Oldambsters-0969-DeVerhoalen 78,46 -Oldambsters-0970-DeVerhoalen 55,46 -Oldambsters-0971-DeVerhoalen 51,46 -Oldambsters-0972-DeVerhoalen 97,46 -Oldambsters-0973-DeVerhoalen 9,46 -Oldambsters-0974-DeVerhoalen 34,46 -Oldambsters-0975-DeVerhoalen 62,46 -Oldambsters-0976-DeVerhoalen 35,46 -Oldambsters-0977-DeVerhoalen 107,46 -Oldambsters-0978-DeVerhoalen 50,46 -Oldambsters-0979-DeVerhoalen 226,46 -Oldambsters-0980-DeVerhoalen 113,46 -Oldambsters-0981-DeVerhoalen 73,46 -Oldambsters-0982-DeVerhoalen 98,46 -Oldambsters-0983-DeVerhoalen 41,46 -Oldambsters-0984-DeVerhoalen 64,46 -Oldambsters-0985-DeVerhoalen 49,46 -Oldambsters-0986-DeVerhoalen 30,46 -Oldambsters-0987-DeVerhoalen 13,46 -Oldambsters-0988-DeVerhoalen 23,46 -Oldambsters-0990-DeVerhoalen 38,46 -Oldambsters-0991-DeVerhoalen 91,46 -Oldambsters-0992-DeVerhoalen 12,46 -Oldambsters-0993-DeVerhoalen 53,46 -Oldambsters-0994-DeVerhoalen 39,46 -Oldambsters-0995-DeVerhoalen 124,46 -Oldambsters-0996-DeVerhoalen 109,46 -Oldambsters-0997-DeVerhoalen 48,46 -Oldambsters-0998-DeVerhoalen 122,46 -Oldambsters-0999-DeVerhoalen 76,46 -Oldambsters-1000-DeVerhoalen 84,46 -Oldambsters-1001-DeVerhoalen 167,46 -Oldambsters-1002-DeVerhoalen 83,46 -Oldambsters-1003-DeVerhoalen 128,46 -Oldambsters-1004-DeVerhoalen 62,46 -Oldambsters-1005-DeVerhoalen 50,46 -Oldambsters-1006-DeVerhoalen 26,46 -Oldambsters-1007-DeVerhoalen 62,46 -Oldambsters-1008-DeVerhoalen 45,46 -Oldambsters-1009-DeVerhoalen 151,46 -Oldambsters-1010-DeVerhoalen 34,46 -Oldambsters-1011-DeVerhoalen 61,46 -Oldambsters-1012-DeVerhoalen 21,46 -Oldambsters-1013-DeVerhoalen 116,46 -Oldambsters-1014-DeVerhoalen 37,46 -Oldambsters-1016-DeVerhoalen 56,46 -Oldambsters-1017-DeVerhoalen 40,46 -Oldambsters-1018-DeVerhoalen 57,46 -Oldambsters-1019-DeVerhoalen 91,46 -Oldambsters-1021-DeVerhoalen 64,46 -Oldambsters-1022-DeVerhoalen 25,46 -Oldambsters-1023-DeVerhoalen 80,46 -Oldambsters-1024-DeVerhoalen 83,46 -Oldambsters-1025-DeVerhoalen 37,46 -Oldambsters-1026-DeVerhoalen 232,46 -Oldambsters-1027-DeVerhoalen 42,46 -Oldambsters-1028-DeVerhoalen 200,46 -Oldambsters-1029-DeVerhoalen 37,46 -Oldambsters-1030-DeVerhoalen 90,46 -Oldambsters-1031-DeVerhoalen 38,46 -Oldambsters-1032-DeVerhoalen 56,46 -Oldambsters-1033-DeVerhoalen 134,46 -Oldambsters-1034-DeVerhoalen 124,46 -Oldambsters-1035-DeVerhoalen 97,46 -Oldambsters-1036-DeVerhoalen 231,46 -Oldambsters-1037-DeVerhoalen 95,46 -Oldambsters-1038-DeVerhoalen 23,46 -Oldambsters-1039-DeVerhoalen 106,46 -Oldambsters-1040-DeVerhoalen 48,46 -Oldambsters-1041-DeVerhoalen 185,46 -Oldambsters-1042-DeVerhoalen 68,46 -Oldambsters-1043-DeVerhoalen 75,46 -Oldambsters-1044-DeVerhoalen 30,46 -Oldambsters-1046-DeVerhoalen 52,46 -Oldambsters-1047-DeVerhoalen 115,46 -Oldambsters-1048-DeVerhoalen 93,46 -Oldambsters-1049-DeVerhoalen 89,46 -Oldambsters-1050-DeVerhoalen 71,46 -Oldambsters-1051-DeVerhoalen 42,46 -Oldambsters-1052-DeVerhoalen 105,46 -Oldambsters-1053-DeVerhoalen 171,46 -Oldambsters-1054-DeVerhoalen 89,46 -Oldambsters-1055-DeVerhoalen 102,46 -Oldambsters-1056-DeVerhoalen 55,46 -Oldambsters-1057-DeVerhoalen 31,46 -Oldambsters-1058-DeVerhoalen 70,46 -Oldambsters-1059-DeVerhoalen 38,46 -Oldambsters-1060-DeVerhoalen 100,46 -Oldambsters-1061-DeVerhoalen 100,46 -Oldambsters-1062-DeVerhoalen 84,46 -Oldambsters-1063-DeVerhoalen 18,46 -Oldambsters-1064-DeVerhoalen 38,46 -Oldambsters-1065-DeVerhoalen 61,46 -Oldambsters-1066-DeVerhoalen 46,46 -Oldambsters-1067-DeVerhoalen 65,46 -Oldambsters-1068-DeVerhoalen 121,46 -Oldambsters-1069-DeVerhoalen 58,46 -Oldambsters-1070-DeVerhoalen 57,46 -Oldambsters-1071-DeVerhoalen 80,46 -Oldambsters-1072-DeVerhoalen 191,46 -Oldambsters-1074-DeVerhoalen 36,46 -Oldambsters-1075-DeVerhoalen 101,46 -Oldambsters-1076-DeVerhoalen 94,46 -Oldambsters-1077-DeVerhoalen 49,46 -Oldambsters-1078-DeVerhoalen 75,46 -Oldambsters-1079-DeVerhoalen 34,46 -Oldambsters-1080-DeVerhoalen 104,46 -Oldambsters-1081-DeVerhoalen 35,46 -Oldambsters-1082-DeVerhoalen 120,46 -Oldambsters-1083-DeVerhoalen 112,46 -Oldambsters-1084-DeVerhoalen 43,46 -Oldambsters-1085-DeVerhoalen 44,46 -Oldambsters-1086-DeVerhoalen 66,46 -Oldambsters-1087-DeVerhoalen 29,46 -Oldambsters-1088-DeVerhoalen 27,46 -Oldambsters-1089-DeVerhoalen 19,46 -Oldambsters-1090-DeVerhoalen 62,46 -Oldambsters-1091-DeVerhoalen 160,46 -Oldambsters-1092-DeVerhoalen 33,46 -Oldambsters-1093-DeVerhoalen 56,46 -Oldambsters-1094-DeVerhoalen 154,46 -Oldambsters-1095-DeVerhoalen 91,46 -Oldambsters-1096-DeVerhoalen 90,46 -Oldambsters-1097-DeVerhoalen 203,46 -Oldambsters-1098-DeVerhoalen 63,46 -Oldambsters-1099-DeVerhoalen 88,46 -Oldambsters-1100-DeVerhoalen 86,46 -Oldambsters-1101-DeVerhoalen 253,46 -Oldambsters-1102-DeVerhoalen 106,46 -Oldambsters-1103-DeVerhoalen 45,46 -Oldambsters-1104-DeVerhoalen 127,46 -Oldambsters-1105-DeVerhoalen 39,46 -Oldambsters-1106-DeVerhoalen 44,46 -Oldambsters-1107-DeVerhoalen 55,46 -Oldambsters-1108-DeVerhoalen 191,46 -Oldambsters-1109-DeVerhoalen 24,46 -Oldambsters-1110-DeVerhoalen 238,46 -Oldambsters-1111-DeVerhoalen 51,46 -Oldambsters-1112-DeVerhoalen 62,46 -Oldambsters-1113-DeVerhoalen 62,46 -Oldambsters-1114-DeVerhoalen 116,46 -Oldambsters-1115-DeVerhoalen 65,46 -Oldambsters-1116-DeVerhoalen 39,46 -Oldambsters-1117-DeVerhoalen 97,46 -Oldambsters-1118-DeVerhoalen 23,46 -Oldambsters-1119-DeVerhoalen 42,46 -Oldambsters-1120-DeVerhoalen 74,46 -Oldambsters-1121-DeVerhoalen 21,46 -Oldambsters-1122-DeVerhoalen 23,46 -Oldambsters-1123-DeVerhoalen 42,46 -Oldambsters-1125-DeVerhoalen 45,46 -Oldambsters-1127-DeVerhoalen 125,46 -Oldambsters-1128-DeVerhoalen 46,46 -Oldambsters-1129-DeVerhoalen 125,46 -Oldambsters-1130-DeVerhoalen 91,46 -Oldambsters-1131-DeVerhoalen 68,46 -Oldambsters-1132-DeVerhoalen 88,46 -Oldambsters-1133-DeVerhoalen 125,46 -Oldambsters-1134-DeVerhoalen 66,46 -Oldambsters-1135-DeVerhoalen 130,46 -Oldambsters-1136-DeVerhoalen 92,46 -Oldambsters-1137-DeVerhoalen 17,46 -Oldambsters-1138-DeVerhoalen 27,46 -Oldambsters-1139-DeVerhoalen 70,46 -Oldambsters-1140-DeVerhoalen 85,46 -Oldambsters-1141-DeVerhoalen 33,46 -Oldambsters-1142-DeVerhoalen 126,46 -Oldambsters-1143-DeVerhoalen 124,46 -Oldambsters-1144-DeVerhoalen 156,46 -Oldambsters-1145-DeVerhoalen 40,46 -Oldambsters-1146-DeVerhoalen 71,46 -Oldambsters-1147-DeVerhoalen 18,46 -Oldambsters-1148-DeVerhoalen 37,46 -Oldambsters-1149-DeVerhoalen 33,46 -Oldambsters-1150-DeVerhoalen 74,46 -Oldambsters-1151-DeVerhoalen 25,46 -Oldambsters-1152-DeVerhoalen 45,46 -Oldambsters-1153-DeVerhoalen 72,46 -Oldambsters-1154-DeVerhoalen 28,46 -Oldambsters-1155-DeVerhoalen 46,46 -Oldambsters-1156-DeVerhoalen 28,46 -Oldambsters-1157-DeVerhoalen 37,46 -Oldambsters-1158-DeVerhoalen 148,46 -Oldambsters-1159-DeVerhoalen 55,46 -Oldambsters-1160-DeVerhoalen 69,46 -Oldambsters-1161-DeVerhoalen 71,46 -Oldambsters-1162-DeVerhoalen 40,46 -Oldambsters-1163-DeVerhoalen 85,46 -Oldambsters-1164-DeVerhoalen 45,46 -Oldambsters-1165-DeVerhoalen 100,46 -Oldambsters-1166-DeVerhoalen 181,46 -Oldambsters-1167-DeVerhoalen 34,46 -Oldambsters-1168-DeVerhoalen 52,46 -Oldambsters-1169-DeVerhoalen 145,46 -Oldambsters-1171-DeVerhoalen 24,46 -Oldambsters-1172-DeVerhoalen 24,46 -Oldambsters-1173-DeVerhoalen 90,46 -Oldambsters-1174-DeVerhoalen 39,46 -Oldambsters-1175-DeVerhoalen 118,46 -Oldambsters-1176-DeVerhoalen 76,46 -Oldambsters-1177-DeVerhoalen 71,46 -Oldambsters-1178-DeVerhoalen 24,46 -Oldambsters-1180-DeVerhoalen 29,46 -Oldambsters-1181-DeVerhoalen 43,46 -Oldambsters-1182-DeVerhoalen 30,46 -Oldambsters-1183-DeVerhoalen 49,46 -Oldambsters-1184-DeVerhoalen 118,46 -Oldambsters-1185-DeVerhoalen 165,46 -Oldambsters-1186-DeVerhoalen 124,46 -Oldambsters-1187-DeVerhoalen 48,46 -Oldambsters-1188-DeVerhoalen 45,46 -Oldambsters-1189-DeVerhoalen 146,46 -Oldambsters-1190-DeVerhoalen 131,46 -Oldambsters-1191-DeVerhoalen 13,46 -Oldambsters-1192-DeVerhoalen 23,46 -Oldambsters-1193-DeVerhoalen 27,46 -Oldambsters-1194-DeVerhoalen 48,46 -Oldambsters-1195-DeVerhoalen 40,46 -Oldambsters-1196-DeVerhoalen 17,46 -Oldambsters-1197-DeVerhoalen 75,46 -Oldambsters-1198-DeVerhoalen 33,46 -Oldambsters-1199-DeVerhoalen 59,46 -Oldambsters-1200-DeVerhoalen 47,46 -Oldambsters-1201-DeVerhoalen 62,46 -Oldambsters-1202-DeVerhoalen 22,46 -Oldambsters-1203-DeVerhoalen 33,46 -Oldambsters-1205-DeVerhoalen 9,46 -Oldambsters-1207-DeVerhoalen 79,46 -Oldambsters-1208-DeVerhoalen 33,46 -Oldambsters-1209-DeVerhoalen 9,46 -Oldambsters-1210-DeVerhoalen 4,46 -Oldambsters-1211-DeVerhoalen 23,46 -Oldambsters-1212-DeVerhoalen 31,46 -Oldambsters-1213-DeVerhoalen 9,46 -Oldambsters-1214-DeVerhoalen 19,46 -Oldambsters-1215-DeVerhoalen 66,46 -Oldambsters-1216-DeVerhoalen 40,46 -Oldambsters-1217-DeVerhoalen 19,46 -Oldambsters-1218-DeVerhoalen 35,46 -Oldambsters-1219-DeVerhoalen 91,46 -Oldambsters-1220-DeVerhoalen 54,46 -Oldambsters-1221-DeVerhoalen 173,46 -Oldambsters-1222-DeVerhoalen 32,46 -Oldambsters-1223-DeVerhoalen 37,46 -Oldambsters-1224-DeVerhoalen 129,46 -Oldambsters-1225-DeVerhoalen 81,46 -Oldambsters-1226-DeVerhoalen 92,46 -Oldambsters-1227-DeVerhoalen 132,46 -Oldambsters-1228-DeVerhoalen 165,46 -Oldambsters-1229-DeVerhoalen 130,46 -Oldambsters-1230-DeVerhoalen 55,46 -Oldambsters-1231-DeVerhoalen 60,46 -Oldambsters-1233-DeVerhoalen 93,46 -Oldambsters-1234-DeVerhoalen 13,46 -Oldambsters-1235-DeVerhoalen 18,46 -Oldambsters-1236-DeVerhoalen 71,46 -Oldambsters-1237-DeVerhoalen 100,46 -Oldambsters-1238-DeVerhoalen 18,46 -Oldambsters-1239-DeVerhoalen 128,46 -Oldambsters-1240-DeVerhoalen 53,46 -Oldambsters-1241-DeVerhoalen 25,46 -Oldambsters-1242-DeVerhoalen 77,46 -Oldambsters-1243-DeVerhoalen 169,46 -Oldambsters-1244-DeVerhoalen 71,46 -Oldambsters-1245-DeVerhoalen 101,46 -Oldambsters-1246-DeVerhoalen 11,46 -Oldambsters-1247-DeVerhoalen 13,46 -Oldambsters-1248-DeVerhoalen 43,46 -Oldambsters-1249-DeVerhoalen 51,46 -Oldambsters-1250-DeVerhoalen 74,46 -Oldambsters-1251-DeVerhoalen 165,46 -Oldambsters-1252-DeVerhoalen 69,46 -Oldambsters-1253-DeVerhoalen 58,46 -Oldambsters-1254-DeVerhoalen 174,46 -Oldambsters-1255-WaitGoud 79,46 -Oldambsters-1256-WaitGoud 18,46 -Oldambsters-1257-WaitGoud 33,46 -Oldambsters-1258-WaitGoud 62,46 -Oldambsters-1259-WaitGoud 62,46 -Oldambsters-1260-WaitGoud 60,46 -Oldambsters-1261-WaitGoud 77,46 -Oldambsters-1262-WaitGoud 57,46 -Oldambsters-1263-WaitGoud 82,46 -Oldambsters-1264-WaitGoud 58,46 -Oldambsters-1265-WaitGoud 180,46 -Oldambsters-1266-WaitGoud 42,46 -Oldambsters-1267-WaitGoud 33,46 -Oldambsters-1268-WaitGoud 88,46 -Oldambsters-1269-WaitGoud 118,46 -Oldambsters-1270-WaitGoud 64,46 -Oldambsters-1271-WaitGoud 48,46 -Oldambsters-1272-WaitGoud 9,46 -Oldambsters-1273-WaitGoud 33,46 -Oldambsters-1274-WaitGoud 68,46 -Oldambsters-1275-WaitGoud 44,46 -Oldambsters-1276-WaitGoud 55,46 -Oldambsters-1277-WaitGoud 42,46 -Oldambsters-1278-WaitGoud 21,46 -Oldambsters-1279-WaitGoud 87,46 -Oldambsters-1280-WaitGoud 91,46 -Oldambsters-1281-WaitGoud 138,46 -Oldambsters-1282-WaitGoud 40,46 -Oldambsters-1283-WaitGoud 87,46 -Oldambsters-1285-WaitGoud 107,46 -Oldambsters-1286-WaitGoud 93,46 -Oldambsters-1287-WaitGoud 51,46 -Oldambsters-1288-WaitGoud 69,46 -Oldambsters-1290-WaitGoud 102,46 -Oldambsters-1291-WaitGoud 72,46 -Oldambsters-1292-WaitGoud 33,46 -Oldambsters-1293-WaitGoud 197,46 -Oldambsters-1294-WaitGoud 66,46 -Oldambsters-1295-WaitGoud 89,46 -Oldambsters-1296-WaitGoud 36,46 -Oldambsters-1297-WaitGoud 101,46 -Oldambsters-1298-WaitGoud 13,46 -Oldambsters-1299-WaitGoud 51,46 -Oldambsters-1300-WaitGoud 70,46 -Oldambsters-1301-WaitGoud 29,46 -Oldambsters-1302-WaitGoud 148,46 -Oldambsters-1303-WaitGoud 45,46 -Oldambsters-1304-WaitGoud 28,46 -Oldambsters-1305-WaitGoud 167,46 -Oldambsters-1306-WaitGoud 97,46 -Oldambsters-1307-WaitGoud 57,46 -Oldambsters-1308-WaitGoud 47,46 -Oldambsters-1309-WaitGoud 74,46 -Oldambsters-1310-WaitGoud 66,46 -Oldambsters-1311-WaitGoud 100,46 -Oldambsters-1312-WaitGoud 62,46 -Oldambsters-1313-WaitGoud 48,46 -Oldambsters-1314-WaitGoud 59,46 -Oldambsters-1315-WaitGoud 65,46 -Oldambsters-1316-WaitGoud 27,46 -Oldambsters-1317-WaitGoud 120,46 -Oldambsters-1318-WaitGoud 20,46 -Oldambsters-1319-WaitGoud 31,46 -Oldambsters-1320-WaitGoud 34,46 -Oldambsters-1321-WaitGoud 96,46 -Oldambsters-1322-WaitGoud 47,46 -Oldambsters-1323-WaitGoud 102,46 -Oldambsters-1324-WaitGoud 52,46 -Oldambsters-1325-WaitGoud 116,46 -Oldambsters-1326-WaitGoud 89,46 -Oldambsters-1327-WaitGoud 33,46 -Oldambsters-1328-WaitGoud 109,46 -Oldambsters-1329-WaitGoud 93,46 -Oldambsters-1331-WaitGoud 45,46 -Oldambsters-1332-WaitGoud 58,46 -Oldambsters-1333-WaitGoud 112,46 -Oldambsters-1334-WaitGoud 71,46 -Oldambsters-1335-WaitGoud 209,46 -Oldambsters-1336-WaitGoud 135,46 -Oldambsters-1337-WaitGoud 73,46 -Oldambsters-1340-WaitGoud 93,46 -Oldambsters-1343-WaitGoud 33,46 -Oldambsters-1344-WaitGoud 74,46 -Oldambsters-1345-WaitGoud 30,46 -Oldambsters-1346-WaitGoud 44,46 -Oldambsters-1347-WaitGoud 69,46 -Oldambsters-1348-WaitGoud 28,46 -Oldambsters-1349-WaitGoud 110,46 -Oldambsters-1350-WaitGoud 53,46 -Oldambsters-1351-WaitGoud 69,46 -Oldambsters-1352-WaitGoud 55,46 -Oldambsters-1353-WaitGoud 50,46 -Oldambsters-1354-WaitGoud 30,46 -Oldambsters-1355-WaitGoud 79,46 -Oldambsters-1357-WaitGoud 41,46 -Oldambsters-1359-WaitGoud 82,46 -Oldambsters-1360-WaitGoud 104,46 -Oldambsters-1361-WaitGoud 54,46 -Oldambsters-1362-WaitGoud 76,46 -Oldambsters-1363-WaitGoud 26,46 -Oldambsters-1364-WaitGoud 61,46 -Oldambsters-1365-WaitGoud 50,46 -Oldambsters-1366-WaitGoud 37,46 -Oldambsters-1367-WaitGoud 47,46 -Oldambsters-1368-WaitGoud 53,46 -Oldambsters-1369-WaitGoud 93,46 -Oldambsters-1370-WaitGoud 25,46 -Oldambsters-1371-WaitGoud 153,46 -Oldambsters-1372-WaitGoud 131,46 -Oldambsters-1373-WaitGoud 51,46 -Oldambsters-1374-WaitGoud 97,46 -Oldambsters-1375-WaitGoud 34,46 -Oldambsters-1376-WaitGoud 45,46 -Oldambsters-1377-WaitGoud 53,46 -Oldambsters-1378-WaitGoud 45,46 -Oldambsters-1379-WaitGoud 141,46 -Oldambsters-1380-WaitGoud 70,46 -Oldambsters-1381-WaitGoud 67,46 -Oldambsters-1382-WaitGoud 45,46 -Oldambsters-1383-WaitGoud 20,46 -Oldambsters-1384-WaitGoud 31,46 -Oldambsters-1385-WaitGoud 72,46 -Oldambsters-1386-WaitGoud 47,46 -Oldambsters-1387-WaitGoud 141,46 -Oldambsters-1388-WaitGoud 112,46 -Oldambsters-1389-WaitGoud 33,46 -Oldambsters-1390-WaitGoud 24,46 -Oldambsters-1391-WaitGoud 25,46 -Oldambsters-1392-WaitGoud 42,46 -Oldambsters-1394-WaitGoud 56,46 -Oldambsters-1397-WaitGoud 74,46 -Oldambsters-1398-WaitGoud 42,46 -Oldambsters-1399-WaitGoud 69,46 -Oldambsters-1400-WaitGoud 42,46 -Oldambsters-1402-WaitGoud 50,46 -Oldambsters-1403-WaitGoud 82,46 -Oldambsters-1404-WaitGoud 63,46 -Oldambsters-1405-WaitGoud 34,46 -Oldambsters-1406-WaitGoud 88,46 -Oldambsters-1407-WaitGoud 29,46 -Oldambsters-1408-WaitGoud 22,46 -Oldambsters-1409-WaitGoud 105,46 -Oldambsters-1410-WaitGoud 92,46 -Oldambsters-1411-WaitGoud 107,46 -Oldambsters-1412-WaitGoud 148,46 -Oldambsters-1413-WaitGoud 76,46 -Oldambsters-1414-WaitGoud 115,46 -Oldambsters-1415-WaitGoud 132,46 -Oldambsters-1416-WaitGoud 17,46 -Oldambsters-1417-WaitGoud 180,46 -Oldambsters-1418-WaitGoud 15,46 -Oldambsters-1420-WaitGoud 39,46 -Oldambsters-1421-WaitGoud 132,46 -Oldambsters-1422-WaitGoud 82,46 -Oldambsters-1423-WaitGoud 46,46 -Oldambsters-1424-WaitGoud 32,46 -Oldambsters-1425-WaitGoud 74,46 -Oldambsters-1426-WaitGoud 52,46 -Oldambsters-1427-WaitGoud 103,46 -Oldambsters-1428-WaitGoud 48,46 -Oldambsters-1430-WaitGoud 52,46 -Oldambsters-1431-WaitGoud 84,46 -Oldambsters-1432-WaitGoud 47,46 -Oldambsters-1433-WaitGoud 71,46 -Oldambsters-1434-WaitGoud 42,46 -Oldambsters-1435-WaitGoud 71,46 -Oldambsters-1436-WaitGoud 32,46 -Oldambsters-1437-WaitGoud 80,46 -Oldambsters-1438-WaitGoud 27,46 -Oldambsters-1439-WaitGoud 27,46 -Oldambsters-1440-WaitGoud 24,46 -Oldambsters-1441-WaitGoud 53,46 -Oldambsters-1442-WaitGoud 80,46 -Oldambsters-1443-WaitGoud 39,46 -Oldambsters-1444-WaitGoud 41,46 -Oldambsters-1445-WaitGoud 85,46 -Oldambsters-1446-WaitGoud 72,46 -Oldambsters-1447-WaitGoud 38,46 -Oldambsters-1448-WaitGoud 84,46 -Oldambsters-1449-WaitGoud 133,46 -Oldambsters-1451-WaitGoud 38,46 -Oldambsters-1452-WaitGoud 69,46 -Oldambsters-1453-WaitGoud 232,46 -Oldambsters-1454-WaitGoud 38,46 -Oldambsters-1455-WaitGoud 82,46 -Oldambsters-1456-WaitGoud 23,46 -Oldambsters-1457-WaitGoud 71,46 -Oldambsters-1458-WaitGoud 82,46 -Oldambsters-1459-WaitGoud 8,46 -Oldambsters-1460-WaitGoud 152,46 -Oldambsters-1461-WaitGoud 119,46 -Oldambsters-1462-WaitGoud 27,46 -Oldambsters-1463-WaitGoud 36,46 -Oldambsters-1464-WaitGoud 51,46 -Oldambsters-1465-WaitGoud 43,46 -Oldambsters-1466-WaitGoud 55,46 -Oldambsters-1467-WaitGoud 114,46 -Oldambsters-1468-WaitGoud 67,46 -Oldambsters-1469-WaitGoud 169,46 -Oldambsters-1470-WaitGoud 90,46 -Oldambsters-1471-WaitGoud 65,46 -Oldambsters-1472-WaitGoud 27,46 -Oldambsters-1473-WaitGoud 24,46 -Oldambsters-1474-WaitGoud 64,46 -Oldambsters-1475-WaitGoud 32,46 -Oldambsters-1476-WaitGoud 62,46 -Oldambsters-1477-WaitGoud 176,46 -Oldambsters-1478-WaitGoud 41,46 -Oldambsters-1479-WaitGoud 43,46 -Oldambsters-1480-WaitGoud 36,46 -Oldambsters-1481-WaitGoud 18,46 -Oldambsters-1482-WaitGoud 83,46 -Oldambsters-1484-WaitGoud 106,46 -Oldambsters-1485-WaitGoud 41,46 -Oldambsters-1486-WaitGoud 139,46 -Oldambsters-1487-WaitGoud 48,46 -Oldambsters-1488-WaitGoud 25,46 -Oldambsters-1489-WaitGoud 35,46 -Oldambsters-1490-WaitGoud 179,46 -Oldambsters-1491-WaitGoud 112,46 -Oldambsters-1492-WaitGoud 17,46 -Oldambsters-1494-WaitGoud 117,46 -Oldambsters-1495-WaitGoud 61,46 -Oldambsters-1496-WaitGoud 209,46 -Oldambsters-1497-WaitGoud 39,46 -Oldambsters-1498-WaitGoud 45,46 -Oldambsters-1499-WaitGoud 77,46 -Oldambsters-1500-WaitGoud 48,46 -Oldambsters-1501-WaitGoud 34,46 -Oldambsters-1503-WaitGoud 41,46 -Oldambsters-1504-WaitGoud 57,46 -Oldambsters-1505-WaitGoud 63,46 -Oldambsters-1506-WaitGoud 67,46 -Oldambsters-1507-WaitGoud 101,46 -Oldambsters-1509-WaitGoud 71,46 -Oldambsters-1510-WaitGoud 34,46 -Oldambsters-1511-WaitGoud 108,46 -Oldambsters-1512-WaitGoud 61,46 -Oldambsters-1513-WaitGoud 196,46 -Oldambsters-1514-WaitGoud 26,46 -Oldambsters-1515-WaitGoud 31,46 -Oldambsters-1516-WaitGoud 37,46 -Oldambsters-1517-WaitGoud 79,46 -Oldambsters-1518-WaitGoud 16,46 -Oldambsters-1519-WaitGoud 37,46 -Oldambsters-1520-WaitGoud 24,46 -Oldambsters-1521-WaitGoud 133,46 -Oldambsters-1522-WaitGoud 59,46 -Oldambsters-1523-WaitGoud 28,46 -Oldambsters-1524-WaitGoud 39,46 -Oldambsters-1525-WaitGoud 34,46 -Oldambsters-1526-WaitGoud 75,46 -Oldambsters-1527-WaitGoud 42,46 -Oldambsters-1528-WaitGoud 44,46 -Oldambsters-1529-WaitGoud 29,46 -Oldambsters-1530-WaitGoud 61,46 -Oldambsters-1531-WaitGoud 32,46 -Oldambsters-1532-WaitGoud 112,46 -Oldambsters-1533-WaitGoud 26,46 -Oldambsters-1534-WaitGoud 74,46 -Oldambsters-1535-WaitGoud 61,46 -Oldambsters-1536-WaitGoud 72,46 -Oldambsters-1537-WaitGoud 28,46 -Oldambsters-1538-WaitGoud 303,46 -Oldambsters-1539-WaitGoud 61,46 -Oldambsters-1540-WaitGoud 38,46 -Oldambsters-1541-WaitGoud 194,46 -Oldambsters-1542-WaitGoud 40,46 -Oldambsters-1543-WaitGoud 48,46 -Oldambsters-1544-WaitGoud 16,46 -Oldambsters-1545-WaitGoud 85,46 -Oldambsters-1547-WaitGoud 70,46 -Oldambsters-1548-WaitGoud 84,46 -Oldambsters-1549-WaitGoud 55,46 -Oldambsters-1550-WaitGoud 43,46 -Oldambsters-1551-WaitGoud 61,46 -Oldambsters-1552-WaitGoud 161,46 -Oldambsters-1553-WaitGoud 19,46 -Oldambsters-1554-WaitGoud 136,46 -Oldambsters-1555-WaitGoud 30,46 -Oldambsters-1557-WaitGoud 49,46 -Oldambsters-1558-WaitGoud 44,46 -Oldambsters-1561-WaitGoud 329,46 -Oldambsters-1562-WaitGoud 53,46 -Oldambsters-1563-WaitGoud 70,46 -Oldambsters-1564-WaitGoud 53,46 -Oldambsters-1565-WaitGoud 65,46 -Oldambsters-1566-WaitGoud 69,46 -Oldambsters-1568-WaitGoud 79,46 -Oldambsters-1569-WaitGoud 84,46 -Oldambsters-1570-WaitGoud 96,46 -Oldambsters-1571-WaitGoud 153,46 -Oldambsters-1573-WaitGoud 24,46 -Oldambsters-1574-WaitGoud 27,46 -Oldambsters-1575-WaitGoud 40,46 -Oldambsters-1576-WaitGoud 110,46 -Oldambsters-1577-WaitGoud 48,46 -Oldambsters-1578-WaitGoud 54,46 -Oldambsters-1582-WaitGoud 83,46 -Oldambsters-1583-WaitGoud 56,46 -Oldambsters-1584-WaitGoud 144,46 -Oldambsters-1585-WaitGoud 47,46 -Oldambsters-1586-WaitGoud 37,46 -Oldambsters-1587-WaitGoud 28,46 -Oldambsters-1588-WaitGoud 23,46 -Oldambsters-1590-WaitGoud 20,46 -Oldambsters-1591-WaitGoud 42,46 -Oldambsters-1592-WaitGoud 39,46 -Oldambsters-1593-WaitGoud 51,46 -Oldambsters-1594-WaitGoud 62,46 -Oldambsters-1595-WaitGoud 122,46 -Oldambsters-1596-WaitGoud 154,46 -Oldambsters-1597-WaitGoud 119,46 -Oldambsters-1598-WaitGoud 45,46 -Oldambsters-1599-WaitGoud 34,46 -Oldambsters-1600-WaitGoud 85,46 -Oldambsters-1601-WaitGoud 28,46 -Oldambsters-1602-WaitGoud 31,46 -Oldambsters-1603-WaitGoud 31,46 -Oldambsters-1604-WaitGoud 83,46 -Oldambsters-1605-WaitGoud 31,46 -Oldambsters-1606-WaitGoud 107,46 -Oldambsters-1607-WaitGoud 106,46 -Oldambsters-1608-WaitGoud 60,46 -Oldambsters-1609-WaitGoud 43,46 -Oldambsters-1610-WaitGoud 43,46 -Oldambsters-1611-WaitGoud 28,46 -Oldambsters-1612-WaitGoud 22,46 -Oldambsters-1613-WaitGoud 22,46 -Oldambsters-1614-WaitGoud 51,46 -Oldambsters-1615-WaitGoud 93,46 -Oldambsters-1617-WaitGoud 78,46 -Oldambsters-1618-WaitGoud 14,46 -Oldambsters-1619-WaitGoud 83,46 -Oldambsters-1620-WaitGoud 67,46 -Oldambsters-1621-WaitGoud 29,46 -Oldambsters-1622-WaitGoud 43,46 -Oldambsters-1623-WaitGoud 59,46 -Oldambsters-1624-WaitGoud 21,46 -Oldambsters-1625-WaitGoud 116,46 -Oldambsters-1626-WaitGoud 231,46 -Oldambsters-1627-WaitGoud 95,46 -Oldambsters-1628-WaitGoud 65,46 -Oldambsters-1629-WaitGoud 177,46 -Oldambsters-1630-WaitGoud 50,46 -Oldambsters-1631-WaitGoud 127,46 -Oldambsters-1632-WaitGoud 125,46 -Oldambsters-1633-WaitGoud 119,46 -Oldambsters-1634-WaitGoud 34,46 -Oldambsters-1635-WaitGoud 49,46 -Oldambsters-1636-WaitGoud 177,46 -Oldambsters-1637-WaitGoud 49,46 -Oldambsters-1638-WaitGoud 50,46 -Oldambsters-1639-WaitGoud 74,46 -Oldambsters-1640-WaitGoud 91,46 -Oldambsters-1641-WaitGoud 121,46 -Oldambsters-1642-WaitGoud 286,46 -Oldambsters-1643-WaitGoud 23,46 -Oldambsters-1644-WaitGoud 189,46 -Oldambsters-1645-WaitGoud 31,46 -Oldambsters-1646-WaitGoud 18,46 -Oldambsters-1647-WaitGoud 217,46 -Oldambsters-1648-WaitGoud 287,46 -Oldambsters-1649-WaitGoud 118,46 -Oldambsters-1650-WaitGoud 194,46 -Oldambsters-1651-WaitGoud 114,46 -Oldambsters-1652-WaitGoud 15,46 -Oldambsters-1653-WaitGoud 26,46 -Oldambsters-1654-WaitGoud 26,46 -Oldambsters-1655-WaitGoud 49,46 -Oldambsters-1656-WaitGoud 61,46 -Oldambsters-1657-WaitGoud 51,46 -Oldambsters-1658-WaitGoud 140,46 -Oldambsters-1659-WaitGoud 21,46 -Oldambsters-1660-WaitGoud 217,46 -Oldambsters-1661-WaitGoud 47,46 -Oldambsters-1662-WaitGoud 27,46 -Oldambsters-1663-WaitGoud 133,46 -Oldambsters-1664-WaitGoud 163,46 -Oldambsters-1665-WaitGoud 71,46 -Oldambsters-1666-WaitGoud 35,46 -Oldambsters-1667-WaitGoud 95,46 -Oldambsters-1670-WaitGoud 18,46 -Oldambsters-1671-WaitGoud 32,46 -Oldambsters-1672-WaitGoud 256,46 -Oldambsters-1673-WaitGoud 31,46 -Oldambsters-1674-WaitGoud 164,46 -Oldambsters-1675-WaitGoud 100,46 -Oldambsters-1676-WaitGoud 118,46 -Oldambsters-1677-WaitGoud 40,46 -Oldambsters-1678-WaitGoud 28,46 -Oldambsters-1679-WaitGoud 23,46 -Oldambsters-1680-WaitGoud 33,46 -Oldambsters-1681-WaitGoud 42,46 -Oldambsters-1682-WaitGoud 135,46 -Oldambsters-1685-WaitGoud 133,46 -Oldambsters-1686-WaitGoud 43,46 -Oldambsters-1687-WaitGoud 23,46 -Oldambsters-1688-WaitGoud 202,46 -Oldambsters-1689-WaitGoud 294,46 -Oldambsters-1690-WaitGoud 41,46 -Oldambsters-1691-WaitGoud 43,46 -Oldambsters-1692-WaitGoud 44,46 -Oldambsters-1693-WaitGoud 72,46 -Oldambsters-1694-WaitGoud 53,46 -Oldambsters-1695-WaitGoud 134,46 -Oldambsters-1696-WaitGoud 97,46 -Oldambsters-1697-WaitGoud 137,46 -Oldambsters-1698-WaitGoud 13,46 -Oldambsters-1699-WaitGoud 20,46 -Oldambsters-1701-WaitGoud 44,46 -Oldambsters-1702-WaitGoud 19,46 -Oldambsters-1703-WaitGoud 115,46 -Oldambsters-1704-WaitGoud 96,46 -Oldambsters-1705-WaitGoud 67,46 -Oldambsters-1706-WaitGoud 71,46 -Oldambsters-1707-WaitGoud 60,46 -Oldambsters-1708-WaitGoud 32,46 -Oldambsters-1709-WaitGoud 29,46 -Oldambsters-1710-WaitGoud 21,46 -Oldambsters-1711-WaitGoud 30,46 -Oldambsters-1712-WaitGoud 41,46 -Oldambsters-1713-WaitGoud 75,46 -Oldambsters-1714-WaitGoud 36,46 -Oldambsters-1715-WaitGoud 96,46 -Oldambsters-1717-WaitGoud 33,46 -Oldambsters-1718-WaitGoud 44,46 -Oldambsters-1719-WaitGoud 53,46 -Oldambsters-1720-WaitGoud 36,46 -Oldambsters-1721-WaitGoud 38,46 -Oldambsters-1722-WaitGoud 57,46 -Oldambsters-1724-WaitGoud 77,46 -Oldambsters-1725-WaitGoud 234,46 -Oldambsters-1726-WaitGoud 49,46 -Oldambsters-1727-WaitGoud 51,46 -Oldambsters-1728-WaitGoud 100,46 -Oldambsters-1729-WaitGoud 37,46 -Oldambsters-1730-WaitGoud 248,46 -Oldambsters-1731-WaitGoud 139,46 -Oldambsters-1732-WaitGoud 77,46 -Oldambsters-1733-WaitGoud 112,46 -Oldambsters-1734-WaitGoud 178,46 -Oldambsters-1735-WaitGoud 54,46 -Oldambsters-1736-WaitGoud 151,46 -Oldambsters-1737-WaitGoud 38,46 -Oldambsters-1738-WaitGoud 122,46 -Oldambsters-1739-WaitGoud 142,46 -Oldambsters-1740-WaitGoud 39,46 -Oldambsters-1741-WaitGoud 131,46 -Oldambsters-1742-WaitGoud 216,46 -Oldambsters-1743-WaitGoud 92,46 -Oldambsters-1744-WaitGoud 21,46 -Oldambsters-1745-WaitGoud 124,46 -Oldambsters-1746-WaitGoud 252,46 -Oldambsters-1747-WaitGoud 130,46 -Oldambsters-1748-WaitGoud 53,46 -Oldambsters-1749-WaitGoud 32,46 -Oldambsters-1750-WaitGoud 46,46 -Oldambsters-1751-WaitGoud 151,46 -Oldambsters-1752-WaitGoud 49,46 -Oldambsters-1753-WaitGoud 103,46 -Oldambsters-1754-WaitGoud 66,46 -Oldambsters-1755-WaitGoud 80,46 -Oldambsters-1756-WaitGoud 127,46 -Oldambsters-1757-WaitGoud 106,46 -Oldambsters-1758-WaitGoud 42,46 -Oldambsters-1759-WaitGoud 81,46 -Oldambsters-1761-WaitGoud 29,46 -Oldambsters-1762-WaitGoud 119,46 -Oldambsters-1763-WaitGoud 66,46 -Oldambsters-1764-WaitGoud 73,46 -Oldambsters-1765-WaitGoud 217,46 -Oldambsters-1766-WaitGoud 76,46 -Oldambsters-1767-WaitGoud 72,46 -Oldambsters-1768-WaitGoud 67,46 -Oldambsters-1769-WaitGoud 108,46 -Oldambsters-1770-WaitGoud 134,46 -Oldambsters-1771-WaitGoud 16,46 -Oldambsters-1772-WaitGoud 23,46 -Oldambsters-1773-WaitGoud 13,46 -Oldambsters-1774-WaitGoud 20,46 -Oldambsters-1775-WaitGoud 20,46 -Oldambsters-1777-WaitGoud 28,46 -Oldambsters-1778-WaitGoud 15,46 -Oldambsters-1779-WaitGoud 21,46 -Oldambsters-1780-WaitGoud 31,46 -Oldambsters-1781-WaitGoud 37,46 -Oldambsters-1782-WaitGoud 128,46 -Oldambsters-1783-WaitGoud 30,46 -Oldambsters-1784-WaitGoud 31,46 -Oldambsters-1785-WaitGoud 98,46 -Oldambsters-1786-WaitGoud 85,46 -Oldambsters-1787-WaitGoud 127,46 -Oldambsters-1788-WaitGoud 76,46 -Oldambsters-1789-WaitGoud 88,46 -Oldambsters-1790-WaitGoud 55,46 -Oldambsters-1791-WaitGoud 118,46 -Oldambsters-1792-WaitGoud 90,46 -Oldambsters-1793-WaitGoud 50,46 -Oldambsters-1794-WaitGoud 57,46 -Oldambsters-1795-WaitGoud 83,46 -Oldambsters-1796-WaitGoud 126,46 -Oldambsters-1797-WaitGoud 133,46 -Oldambsters-1798-WaitGoud 15,46 -Oldambsters-1799-WaitGoud 76,46 -Oldambsters-1800-WaitGoud 93,46 -Oldambsters-1801-WaitGoud 93,46 -Oldambsters-1802-WaitGoud 152,46 -Oldambsters-1803-WaitGoud 230,46 -Oldambsters-1804-WaitGoud 165,46 -Oldambsters-1805-WaitGoud 36,46 -Oldambsters-1806-WaitGoud 28,46 -Oldambsters-1807-WaitGoud 73,46 -Oldambsters-1808-WaitGoud 54,46 -Oldambsters-1809-WaitGoud 52,46 -Oldambsters-1810-WaitGoud 101,46 -Oldambsters-1811-WaitGoud 174,46 -Oldambsters-1812-WaitGoud 39,46 -Oldambsters-1813-WaitGoud 52,46 -Oldambsters-1814-WaitGoud 12,46 -Oldambsters-1815-WaitGoud 60,46 -Oldambsters-1816-WaitGoud 113,46 -Oldambsters-1817-WaitGoud 32,46 -Oldambsters-1818-WaitGoud 90,46 -Oldambsters-1819-WaitGoud 17,46 -Oldambsters-1820-WaitGoud 16,46 -Oldambsters-1821-WaitGoud 33,46 -Oldambsters-1822-WaitGoud 57,46 -Oldambsters-1823-WaitGoud 39,46 -Oldambsters-1824-WaitGoud 48,46 -Oldambsters-1825-WaitGoud 191,46 -Oldambsters-1826-WaitGoud 28,46 -Oldambsters-1827-WaitGoud 89,46 -Oldambsters-1829-WaitGoud 13,46 -Oldambsters-1830-WaitGoud 33,46 -Oldambsters-1831-WaitGoud 51,46 -Oldambsters-1832-WaitGoud 206,46 -Oldambsters-1833-WaitGoud 85,46 -Oldambsters-1834-WaitGoud 132,46 -Oldambsters-1835-WaitGoud 27,46 -Oldambsters-1836-WaitGoud 46,46 -Oldambsters-1837-WaitGoud 53,46 -Oldambsters-1838-WaitGoud 16,46 -Oldambsters-1839-WaitGoud 132,46 -Oldambsters-1840-WaitGoud 37,46 -Oldambsters-1841-WaitGoud 18,46 -Oldambsters-1842-WaitGoud 54,46 -Oldambsters-1843-WaitGoud 45,46 -Oldambsters-1844-WaitGoud 94,46 -Oldambsters-1845-WaitGoud 143,46 -Oldambsters-1846-WaitGoud 74,46 -Oldambsters-1847-WaitGoud 23,46 -Oldambsters-1848-WaitGoud 85,46 -Oldambsters-1849-WaitGoud 140,46 -Oldambsters-1850-WaitGoud 118,46 -Oldambsters-1851-WaitGoud 191,46 -Oldambsters-1852-WaitGoud 36,46 -Oldambsters-1853-WaitGoud 50,46 -Oldambsters-1854-WaitGoud 178,46 -Oldambsters-1855-WaitGoud 145,46 -Oldambsters-1856-WaitGoud 147,46 -Oldambsters-1857-WaitGoud 82,46 -Oldambsters-1858-WaitGoud 85,46 -Oldambsters-1859-WaitGoud 32,46 -Oldambsters-1860-WaitGoud 41,46 -Oldambsters-1861-WaitGoud 169,46 -Oldambsters-1862-WaitGoud 89,46 -Oldambsters-1863-WaitGoud 30,46 -Oldambsters-1864-WaitGoud 94,46 -Oldambsters-1865-WaitGoud 79,46 -Oldambsters-1866-WaitGoud 229,46 -Oldambsters-1867-WaitGoud 88,46 -Oldambsters-1868-WaitGoud 35,46 -Oldambsters-1869-WaitGoud 26,46 -Oldambsters-1870-WaitGoud 206,46 -Oldambsters-1871-WaitGoud 44,46 -Oldambsters-1872-WaitGoud 29,46 -Oldambsters-1873-WaitGoud 176,46 -Oldambsters-1874-WaitGoud 35,46 -Oldambsters-1875-WaitGoud 75,46 -Oldambsters-1876-WaitGoud 52,46 -Oldambsters-1877-WaitGoud 68,46 -Oldambsters-1878-WaitGoud 51,46 -Oldambsters-1879-WaitGoud 141,46 -Oldambsters-1882-WaitGoud 32,46 -Oldambsters-1884-WaitGoud 64,46 -Oldambsters-1885-WaitGoud 106,46 -Oldambsters-1886-WaitGoud 54,46 -Oldambsters-1887-WaitGoud 66,46 -Oldambsters-1888-WaitGoud 23,46 -Oldambsters-1889-WaitGoud 182,46 -Oldambsters-1890-WaitGoud 147,46 -Oldambsters-1891-WaitGoud 18,46 -Oldambsters-1892-WaitGoud 168,46 -Oldambsters-1893-WaitGoud 16,46 -Oldambsters-1894-WaitGoud 49,46 -Oldambsters-1895-WaitGoud 35,46 -Oldambsters-1896-WaitGoud 76,46 -Oldambsters-1897-WaitGoud 55,46 -Oldambsters-1898-WaitGoud 37,46 -Oldambsters-1899-WaitGoud 30,46 -Oldambsters-1900-WaitGoud 69,46 -Oldambsters-1901-WaitGoud 64,46 -Oldambsters-1902-WaitGoud 104,46 -Oldambsters-1904-WaitGoud 27,46 -Oldambsters-1906-WaitGoud 20,46 -Oldambsters-1907-WaitGoud 35,46 -Oldambsters-1908-WaitGoud 47,46 -Oldambsters-1909-WaitGoud 96,46 -Oldambsters-1911-WaitGoud 16,46 -Oldambsters-1912-WaitGoud 81,46 -Oldambsters-1913-WaitGoud 123,46 -Oldambsters-1914-WaitGoud 147,46 -Oldambsters-1915-WaitGoud 70,46 -Oldambsters-1916-WaitGoud 36,46 -Oldambsters-1917-WaitGoud 89,46 -Oldambsters-1918-WaitGoud 10,46 -Oldambsters-1919-WaitGoud 27,46 -Oldambsters-1920-WaitGoud 296,46 -Oldambsters-1921-WaitGoud 59,46 -Oldambsters-1922-WaitGoud 52,46 -Oldambsters-1923-WaitGoud 96,46 -Oldambsters-1924-WaitGoud 29,46 -Oldambsters-1925-WaitGoud 20,46 -Oldambsters-1926-WaitGoud 23,46 -Oldambsters-1927-WaitGoud 49,46 -Oldambsters-1928-WaitGoud 55,46 -Oldambsters-1929-WaitGoud 18,46 -Oldambsters-1930-WaitGoud 170,46 -Oldambsters-1931-WaitGoud 221,46 -Oldambsters-1932-WaitGoud 49,46 -Oldambsters-1933-WaitGoud 51,46 -Oldambsters-1934-WaitGoud 68,46 -Oldambsters-1935-WaitGoud 69,46 -Oldambsters-1936-WaitGoud 264,46 -Oldambsters-1938-WaitGoud 38,46 -Oldambsters-1939-WaitGoud 41,46 -Oldambsters-1940-WaitGoud 146,46 -Oldambsters-1941-WaitGoud 52,46 -Oldambsters-1942-WaitGoud 84,46 -Oldambsters-1943-WaitGoud 31,46 -Oldambsters-1944-WaitGoud 15,46 -Oldambsters-1945-WaitGoud 131,46 -Oldambsters-1946-WaitGoud 21,46 -Oldambsters-1947-WaitGoud 72,46 -Oldambsters-1948-WaitGoud 178,46 -Oldambsters-1949-WaitGoud 145,46 -Oldambsters-1950-WaitGoud 16,46 -Oldambsters-1951-WaitGoud 24,46 -Oldambsters-1952-WaitGoud 84,46 -Oldambsters-1953-WaitGoud 24,46 -Oldambsters-1954-WaitGoud 40,46 -Oldambsters-1955-WaitGoud 92,46 -Oldambsters-1956-WaitGoud 55,46 -Oldambsters-1957-WaitGoud 13,46 -Oldambsters-1958-WaitGoud 41,46 -Oldambsters-1959-WaitGoud 100,46 -Oldambsters-1960-WaitGoud 170,46 -Oldambsters-1961-WaitGoud 16,46 -Oldambsters-1962-WaitGoud 71,46 -Oldambsters-1963-WaitGoud 45,46 -Oldambsters-1964-WaitGoud 66,46 -Oldambsters-1965-WaitGoud 89,46 -Oldambsters-1966-WaitGoud 54,46 -Oldambsters-1967-WaitGoud 34,46 -Oldambsters-1968-WaitGoud 124,46 -Oldambsters-1969-WaitGoud 77,46 -Oldambsters-1970-WaitGoud 57,46 -Oldambsters-1971-WaitGoud 44,46 -Oldambsters-1972-WaitGoud 23,46 -Oldambsters-1973-WaitGoud 42,46 -Oldambsters-1975-WaitGoud 129,46 -Oldambsters-1976-WaitGoud 163,46 -Oldambsters-1977-WaitGoud 196,46 -Oldambsters-1978-WaitGoud 84,46 -Oldambsters-1979-WaitGoud 11,46 -Oldambsters-1980-WaitGoud 282,46 -Oldambsters-1981-WaitGoud 227,46 -Oldambsters-1982-WaitGoud 52,46 -Oldambsters-1983-WaitGoud 182,46 -Oldambsters-1984-WaitGoud 24,46 -Oldambsters-1985-WaitGoud 70,46 -Oldambsters-1986-WaitGoud 58,46 -Oldambsters-1987-WaitGoud 36,46 -Oldambsters-1988-WaitGoud 79,46 -Oldambsters-1989-WaitGoud 151,46 -Oldambsters-1991-WaitGoud 40,46 -Oldambsters-1992-WaitGoud 48,46 -Oldambsters-1993-WaitGoud 238,46 -Oldambsters-1994-WaitGoud 41,46 -Oldambsters-1995-WaitGoud 18,46 -Oldambsters-1996-WaitGoud 33,46 -Oldambsters-1997-WaitGoud 6,46 -Oldambsters-1998-WaitGoud 57,46 -Oldambsters-1999-WaitGoud 103,46 -Oldambsters-2000-WaitGoud 75,46 -Oldambsters-2001-WaitGoud 180,46 -Oldambsters-2002-WaitGoud 87,46 -Oldambsters-2003-WaitGoud 75,46 -Oldambsters-2004-WaitGoud 151,46 -Oldambsters-2005-WaitGoud 81,46 -Oldambsters-2006-WaitGoud 96,46 -Oldambsters-2007-WaitGoud 55,46 -Oldambsters-2008-WaitGoud 127,46 -Oldambsters-2009-WaitGoud 71,46 -Oldambsters-2010-WaitGoud 117,46 -Oldambsters-2011-WaitGoud 101,46 -Oldambsters-2012-WaitGoud 101,46 -Oldambsters-2013-WaitGoud 134,46 -Oldambsters-2014-WaitGoud 104,46 -Oldambsters-2015-WaitGoud 68,46 -Oldambsters-2016-WaitGoud 126,46 -Oldambsters-2017-WaitGoud 132,46 -Oldambsters-2018-WaitGoud 82,46 -Oldambsters-2019-WaitGoud 188,46 -Oldambsters-2020-WaitGoud 57,46 -Oldambsters-2021-WaitGoud 65,46 -Oldambsters-2022-WaitGoud 45,46 -Oldambsters-2023-WaitGoud 27,46 -Oldambsters-2024-WaitGoud 39,46 -Oldambsters-2025-WaitGoud 36,46 -Oldambsters-2026-WaitGoud 34,46 -Oldambsters-2028-WaitGoud 41,46 -Oldambsters-2029-WaitGoud 51,46 -Oldambsters-2030-WaitGoud 87,46 -Oldambsters-2031-WaitGoud 20,46 -Oldambsters-2032-WaitGoud 132,46 -Oldambsters-2033-WaitGoud 86,46 -Oldambsters-2034-WaitGoud 17,46 -Oldambsters-2035-WaitGoud 153,46 -Oldambsters-2036-WaitGoud 30,46 -Oldambsters-2037-WaitGoud 92,46 -Oldambsters-2038-WaitGoud 156,46 -Oldambsters-2039-WaitGoud 47,46 -Oldambsters-2040-WaitGoud 125,46 -Oldambsters-2041-WaitGoud 37,46 -Oldambsters-2042-WaitGoud 95,46 -Oldambsters-2044-WaitGoud 70,46 -Oldambsters-2045-WaitGoud 255,46 -Oldambsters-2046-WaitGoud 236,46 -Oldambsters-2047-WaitGoud 217,46 -Oldambsters-2048-WaitGoud 85,46 -Oldambsters-2049-WaitGoud 268,46 -Oldambsters-2050-WaitGoud 89,46 -Oldambsters-2051-WaitGoud 26,46 -Oldambsters-2052-WaitGoud 26,46 -Oldambsters-2053-WaitGoud 45,46 -Oldambsters-2054-WaitGoud 57,46 -Oldambsters-2055-WaitGoud 28,46 -Oldambsters-2056-WaitGoud 9,46 -Oldambsters-2057-WaitGoud 186,46 -Oldambsters-2058-WaitGoud 20,46 -Oldambsters-2059-WaitGoud 134,46 -Oldambsters-2060-WaitGoud 209,46 -Oldambsters-2061-WaitGoud 188,46 -Oldambsters-2062-WaitGoud 229,46 -Oldambsters-2063-WaitGoud 83,46 -Oldambsters-2064-WaitGoud 145,46 -Oldambsters-2065-WaitGoud 41,46 -Oldambsters-2066-WaitGoud 249,46 -Oldambsters-2067-WaitGoud 57,46 -Oldambsters-2068-WaitGoud 27,46 -Oldambsters-2069-WaitGoud 287,46 -Oldambsters-2070-WaitGoud 39,46 -Oldambsters-2071-WaitGoud 202,46 -Oldambsters-2072-WaitGoud 46,46 -Oldambsters-2073-WaitGoud 50,46 -Oldambsters-2074-WaitGoud 121,46 -Oldambsters-2075-WaitGoud 68,46 -Oldambsters-2076-WaitGoud 59,46 -Oldambsters-2077-WaitGoud 110,46 -Oldambsters-2078-WaitGoud 40,46 -Oldambsters-2079-WaitGoud 93,46 -Oldambsters-2080-WaitGoud 17,46 -Oldambsters-2081-WaitGoud 23,46 -Oldambsters-2082-WaitGoud 53,46 -Oldambsters-2083-WaitGoud 107,46 -Oldambsters-2085-WaitGoud 22,46 -Oldambsters-2086-WaitGoud 42,46 -Oldambsters-2087-WaitGoud 42,46 -Oldambsters-2088-WaitGoud 38,46 -Oldambsters-2089-WaitGoud 22,46 -Oldambsters-2090-WaitGoud 23,46 -Oldambsters-2091-WaitGoud 49,46 -Oldambsters-2092-WaitGoud 37,46 -Oldambsters-2093-WaitGoud 73,46 -Oldambsters-2094-WaitGoud 152,46 -Oldambsters-2095-WaitGoud 158,46 -Oldambsters-2097-WaitGoud 21,46 -Oldambsters-2098-WaitGoud 28,46 -Oldambsters-2099-WaitGoud 61,46 -Oldambsters-2100-WaitGoud 152,46 -Oldambsters-2101-WaitGoud 21,46 -Oldambsters-2103-WaitGoud 17,46 -Oldambsters-2104-WaitGoud 16,46 -Oldambsters-2105-WaitGoud 46,46 -Oldambsters-2106-WaitGoud 118,46 -Oldambsters-2107-WaitGoud 64,46 -Oldambsters-2108-WaitGoud 112,46 -Oldambsters-2109-WaitGoud 117,46 -Oldambsters-2111-WaitGoud 93,46 -Oldambsters-2112-WaitGoud 90,46 -Oldambsters-2113-WaitGoud 77,46 -Oldambsters-2114-WaitGoud 30,46 -Oldambsters-2115-WaitGoud 29,46 -Oldambsters-2116-WaitGoud 55,46 -Oldambsters-2117-WaitGoud 229,46 -Oldambsters-2118-WaitGoud 26,46 -Oldambsters-2119-WaitGoud 99,46 -Oldambsters-2120-WaitGoud 43,46 -Oldambsters-2121-WaitGoud 137,46 -Oldambsters-2122-WaitGoud 144,46 -Oldambsters-2124-WaitGoud 20,46 -Oldambsters-2125-WaitGoud 62,46 -Oldambsters-2126-WaitGoud 61,46 -Oldambsters-2127-WaitGoud 34,46 -Oldambsters-2128-WaitGoud 46,46 -Oldambsters-2129-WaitGoud 29,46 -Oldambsters-2130-WaitGoud 19,46 -Oldambsters-2132-WaitGoud 102,46 -Oldambsters-2133-WaitGoud 127,46 -Oldambsters-2134-WaitGoud 21,46 -Oldambsters-2135-WaitGoud 64,46 -Oldambsters-2136-WaitGoud 55,46 -Oldambsters-2137-WaitGoud 28,46 -Oldambsters-2138-WaitGoud 32,46 -Oldambsters-2139-WaitGoud 17,46 -Oldambsters-2140-WaitGoud 74,46 -Oldambsters-2141-WaitGoud 62,46 -Oldambsters-2142-WaitGoud 174,46 -Oldambsters-2143-WaitGoud 165,46 -Oldambsters-2144-WaitGoud 43,46 -Oldambsters-2145-WaitGoud 29,46 -Oldambsters-2146-WaitGoud 84,46 -Oldambsters-2147-WaitGoud 51,46 -Oldambsters-2148-WaitGoud 32,46 -Oldambsters-2149-WaitGoud 32,46 -Oldambsters-2150-WaitGoud 45,46 -Oldambsters-2151-WaitGoud 89,46 -Oldambsters-2153-WaitGoud 68,46 -Oldambsters-2154-WaitGoud 81,46 -Oldambsters-2155-WaitGoud 29,46 -Oldambsters-2156-WaitGoud 35,46 -Oldambsters-2157-WaitGoud 83,46 -Oldambsters-2158-WaitGoud 122,46 -Oldambsters-2159-WaitGoud 82,46 -Oldambsters-2160-WaitGoud 99,46 -Oldambsters-2161-WaitGoud 143,46 -Oldambsters-2162-WaitGoud 82,46 -Oldambsters-2163-WaitGoud 81,46 -Oldambsters-2164-WaitGoud 75,46 -Oldambsters-2165-WaitGoud 80,46 -Oldambsters-2166-WaitGoud 82,46 -Oldambsters-2167-WaitGoud 80,46 -Oldambsters-2168-WaitGoud 125,46 -Oldambsters-2169-WaitGoud 66,46 -Oldambsters-2170-WaitGoud 56,46 -Oldambsters-2171-WaitGoud 141,46 -Oldambsters-2172-WaitGoud 93,46 -Oldambsters-2173-WaitGoud 75,46 -Oldambsters-2174-WaitGoud 244,46 -Oldambsters-2175-WaitGoud 89,46 -Oldambsters-2176-WaitGoud 87,46 -Oldambsters-2177-WaitGoud 133,46 -Oldambsters-2178-WaitGoud 137,46 -Oldambsters-2179-WaitGoud 32,46 -Oldambsters-2180-WaitGoud 89,46 -Oldambsters-2181-WaitGoud 56,46 -Oldambsters-2182-WaitGoud 260,46 -Oldambsters-2183-WaitGoud 130,46 -Oldambsters-2184-WaitGoud 140,46 -Oldambsters-2185-WaitGoud 49,46 -Oldambsters-2186-WaitGoud 135,46 -Oldambsters-2187-WaitGoud 141,46 -Oldambsters-2188-WaitGoud 65,46 -Oldambsters-2189-WaitGoud 103,46 -Oldambsters-2190-WaitGoud 159,46 -Oldambsters-2191-WaitGoud 83,46 -Oldambsters-2192-WaitGoud 97,46 -Oldambsters-2193-WaitGoud 45,46 -Oldambsters-2194-WaitGoud 81,46 -Oldambsters-2195-WaitGoud 144,46 -Oldambsters-2196-WaitGoud 222,46 -Oldambsters-2197-WaitGoud 166,46 -Oldambsters-2198-WaitGoud 148,46 -Oldambsters-2199-WaitGoud 28,46 -Oldambsters-2200-WaitGoud 55,46 -Oldambsters-2201-WaitGoud 60,46 -Oldambsters-2202-WaitGoud 74,46 -Oldambsters-2203-WaitGoud 87,46 -Oldambsters-2205-WaitGoud 100,46 -Oldambsters-2206-WaitGoud 18,46 -Oldambsters-2207-WaitGoud 67,46 -Oldambsters-2208-WaitGoud 40,46 -Oldambsters-2209-WaitGoud 24,46 -Oldambsters-2210-WaitGoud 210,46 -Oldambsters-2211-WaitGoud 80,46 -Oldambsters-2212-WaitGoud 65,46 -Oldambsters-2213-WaitGoud 169,46 -Oldambsters-2214-WaitGoud 49,46 -Oldambsters-2215-WaitGoud 269,46 -Oldambsters-2216-WaitGoud 88,46 -Oldambsters-2217-WaitGoud 118,46 -Oldambsters-2218-WaitGoud 128,46 -Oldambsters-2219-WaitGoud 49,46 -Oldambsters-2220-WaitGoud 41,46 -Oldambsters-2221-WaitGoud 96,46 -Oldambsters-2222-WaitGoud 136,46 -Oldambsters-2223-WaitGoud 81,46 -Oldambsters-2224-WaitGoud 110,46 -Oldambsters-2225-WaitGoud 33,46 -Oldambsters-2226-WaitGoud 58,46 -Oldambsters-2227-WaitGoud 65,46 -Oldambsters-2228-WaitGoud 123,46 -Oldambsters-2229-WaitGoud 118,46 -Oldambsters-2230-WaitGoud 129,46 -Oldambsters-2231-WaitGoud 34,46 -Oldambsters-2232-WaitGoud 25,46 -Oldambsters-2233-WaitGoud 28,46 -Oldambsters-2234-WaitGoud 61,46 -Oldambsters-2235-WaitGoud 19,46 -Oldambsters-2236-WaitGoud 49,46 -Oldambsters-2237-WaitGoud 23,46 -Oldambsters-2238-WaitGoud 56,46 -Oldambsters-2240-WaitGoud 94,46 -Oldambsters-2241-WaitGoud 184,46 -Oldambsters-2242-WaitGoud 56,46 -Oldambsters-2243-WaitGoud 65,46 -Oldambsters-2244-WaitGoud 53,46 -Oldambsters-2245-WaitGoud 157,46 -Oldambsters-2246-WaitGoud 70,46 -Oldambsters-2248-WaitGoud 69,46 -Oldambsters-2249-WaitGoud 129,46 -Oldambsters-2250-WaitGoud 79,46 -Oldambsters-2251-WaitGoud 62,46 -Oldambsters-2252-WaitGoud 132,46 -Oldambsters-2253-WaitGoud 117,46 -Oldambsters-2254-WaitGoud 101,46 -Oldambsters-2255-WaitGoud 208,46 -Oldambsters-2257-WaitGoud 55,46 -Oldambsters-2259-WaitGoud 31,46 -Oldambsters-2260-WaitGoud 59,46 -Oldambsters-2261-WaitGoud 38,46 -Oldambsters-2262-WaitGoud 35,46 -Oldambsters-2264-WaitGoud 124,46 -Oldambsters-2265-WaitGoud 37,46 -Oldambsters-2266-WaitGoud 75,46 -Oldambsters-2267-WaitGoud 97,46 -Oldambsters-2268-WaitGoud 87,46 -Oldambsters-2269-WaitGoud 94,46 -Oldambsters-2270-WaitGoud 51,46 -Oldambsters-2271-WaitGoud 34,46 -Oldambsters-2272-WaitGoud 59,46 -Oldambsters-2273-WaitGoud 103,46 -Oldambsters-2274-WaitGoud 81,46 -Oldambsters-2275-WaitGoud 43,46 -Oldambsters-2276-WaitGoud 105,46 -Oldambsters-2277-WaitGoud 67,46 -Oldambsters-2278-WaitGoud 43,46 -Oldambsters-2279-WaitGoud 46,46 -Oldambsters-2280-WaitGoud 60,46 -Oldambsters-2281-WaitGoud 50,46 -Oldambsters-2282-WaitGoud 33,46 -Oldambsters-2283-WaitGoud 30,46 -Oldambsters-2284-WaitGoud 54,46 -Oldambsters-2285-WaitGoud 49,46 -Oldambsters-2287-WaitGoud 76,46 -Oldambsters-2288-WaitGoud 67,46 -Oldambsters-2289-WaitGoud 32,46 -Oldambsters-2290-WaitGoud 77,46 -Oldambsters-2291-WaitGoud 100,46 -Oldambsters-2292-WaitGoud 92,46 -Oldambsters-2293-WaitGoud 134,46 -Oldambsters-2294-WaitGoud 82,46 -Oldambsters-2295-WaitGoud 29,46 -Oldambsters-2296-WaitGoud 27,46 -Oldambsters-2297-WaitGoud 118,46 -Oldambsters-2298-WaitGoud 83,46 -Oldambsters-2299-WaitGoud 209,46 -Oldambsters-2300-WaitGoud 150,46 -Oldambsters-2301-WaitGoud 120,46 -Oldambsters-2302-WaitGoud 85,46 -Oldambsters-2303-WaitGoud 65,46 -Oldambsters-2304-WaitGoud 53,46 -Oldambsters-2305-WaitGoud 116,46 -Oldambsters-2306-WaitGoud 67,46 -Oldambsters-2307-WaitGoud 89,46 -Oldambsters-2308-WaitGoud 49,46 -Oldambsters-2309-WaitGoud 42,46 -Oldambsters-2310-WaitGoud 49,46 -Oldambsters-2311-WaitGoud 199,46 -Oldambsters-2313-WaitGoud 24,46 -Oldambsters-2314-WaitGoud 163,46 -Oldambsters-2315-WaitGoud 42,46 -Oldambsters-2316-WaitGoud 30,46 -Oldambsters-2317-WaitGoud 46,46 -Oldambsters-2318-WaitGoud 79,46 -Oldambsters-2319-WaitGoud 90,46 -Oldambsters-2320-WaitGoud 60,46 -Oldambsters-2321-WaitGoud 125,46 -Oldambsters-2322-WaitGoud 42,46 -Oldambsters-2323-WaitGoud 34,46 -Oldambsters-2324-WaitGoud 31,46 -Oldambsters-2325-WaitGoud 42,46 -Oldambsters-2326-WaitGoud 107,46 -Oldambsters-2327-WaitGoud 55,46 -Oldambsters-2328-WaitGoud 60,46 -Oldambsters-2329-WaitGoud 46,46 -Oldambsters-2330-WaitGoud 55,46 -Oldambsters-2331-WaitGoud 51,46 -Oldambsters-2332-WaitGoud 101,46 -Oldambsters-2333-WaitGoud 111,46 -Oldambsters-2334-WaitGoud 79,46 -Oldambsters-2335-WaitGoud 25,46 -Oldambsters-2336-WaitGoud 147,46 -Oldambsters-2337-WaitGoud 220,46 -Oldambsters-2338-WaitGoud 96,46 -Oldambsters-2339-WaitGoud 77,46 -Oldambsters-2340-WaitGoud 139,46 -Oldambsters-2341-WaitGoud 78,46 -Oldambsters-2342-WaitGoud 23,46 -Oldambsters-2343-WaitGoud 56,46 -Oldambsters-2344-WaitGoud 30,46 -Oldambsters-2345-WaitGoud 57,46 -Oldambsters-2346-WaitGoud 54,46 -Oldambsters-2347-WaitGoud 65,46 -Oldambsters-2348-WaitGoud 60,46 -Oldambsters-2349-WaitGoud 39,46 -Oldambsters-2350-WaitGoud 25,46 -Oldambsters-2351-WaitGoud 90,46 -Oldambsters-2352-WaitGoud 35,46 -Oldambsters-2353-WaitGoud 224,46 -Oldambsters-2354-WaitGoud 60,46 -Oldambsters-2355-WaitGoud 220,46 -Oldambsters-2356-WaitGoud 56,46 -Oldambsters-2357-WaitGoud 59,46 -Oldambsters-2358-WaitGoud 97,46 -Oldambsters-2359-WaitGoud 159,46 -Oldambsters-2360-WaitGoud 48,46 -Oldambsters-2361-WaitGoud 97,46 -Oldambsters-2362-WaitGoud 82,46 -Oldambsters-2363-WaitGoud 130,46 -Oldambsters-2365-WaitGoud 37,46 -Oldambsters-2366-WaitGoud 223,46 -Oldambsters-2367-WaitGoud 18,46 -Oldambsters-2368-WaitGoud 57,46 -Oldambsters-2369-WaitGoud 87,46 -Oldambsters-2370-WaitGoud 55,46 -Oldambsters-2371-WaitGoud 62,46 -Oldambsters-2372-WaitGoud 74,46 -Oldambsters-2374-WaitGoud 52,46 -Oldambsters-2375-WaitGoud 89,46 -Oldambsters-2376-WaitGoud 48,46 -Oldambsters-2377-WaitGoud 86,46 -Oldambsters-2378-WaitGoud 58,46 -Oldambsters-2379-WaitGoud 105,46 -Oldambsters-2380-WaitGoud 107,46 -Oldambsters-2382-WaitGoud 75,46 -Oldambsters-2383-WaitGoud 135,46 -Oldambsters-2384-WaitGoud 93,46 -Oldambsters-2385-WaitGoud 45,46 -Oldambsters-2386-WaitGoud 63,46 -Oldambsters-2387-WaitGoud 44,46 -Oldambsters-2388-WaitGoud 37,46 -Oldambsters-2389-WaitGoud 143,46 -Oldambsters-2390-WaitGoud 37,46 -Oldambsters-2391-WaitGoud 157,46 -Oldambsters-2392-WaitGoud 91,46 -Oldambsters-2393-WaitGoud 27,46 -Oldambsters-2394-WaitGoud 15,46 -Oldambsters-2395-WaitGoud 14,46 -Oldambsters-2396-WaitGoud 66,46 -Oldambsters-2397-WaitGoud 28,46 -Oldambsters-2398-WaitGoud 14,46 -Oldambsters-2400-WaitGoud 45,46 -Oldambsters-2401-WaitGoud 42,46 -Oldambsters-2402-WaitGoud 68,46 -Oldambsters-2403-WaitGoud 50,46 -Oldambsters-2404-WaitGoud 100,46 -Oldambsters-2405-WaitGoud 175,46 -Oldambsters-2406-WaitGoud 40,46 -Oldambsters-2407-WaitGoud 81,46 -Oldambsters-2408-WaitGoud 82,46 -Oldambsters-2409-WaitGoud 48,46 -Oldambsters-2410-WaitGoud 22,46 -Oldambsters-2411-WaitGoud 165,46 -Oldambsters-2412-WaitGoud 105,46 -Oldambsters-2413-WaitGoud 68,46 -Oldambsters-2414-WaitGoud 33,46 -Oldambsters-2415-WaitGoud 101,46 -Oldambsters-2416-WaitGoud 82,46 -Oldambsters-2417-WaitGoud 50,46 -Oldambsters-2418-WaitGoud 10,46 -Oldambsters-2420-WaitGoud 106,46 -Oldambsters-2421-WaitGoud 58,46 -Oldambsters-2422-WaitGoud 74,46 -Oldambsters-2423-WaitGoud 33,46 -Oldambsters-2424-WaitGoud 139,46 -Oldambsters-2425-WaitGoud 33,46 -Oldambsters-2426-WaitGoud 25,46 -Oldambsters-2427-WaitGoud 118,46 -Oldambsters-2428-WaitGoud 38,46 -Oldambsters-2429-WaitGoud 249,46 -Oldambsters-2430-WaitGoud 83,46 -Oldambsters-2431-WaitGoud 113,46 -Oldambsters-2432-WaitGoud 125,46 -Oldambsters-2433-WaitGoud 299,46 -Oldambsters-2434-WaitGoud 28,46 -Oldambsters-2435-WaitGoud 21,46 -Oldambsters-2436-WaitGoud 48,46 -Oldambsters-2437-WaitGoud 49,46 -Oldambsters-2438-WaitGoud 119,46 -Oldambsters-2439-WaitGoud 62,46 -Oldambsters-2440-WaitGoud 22,46 -Oldambsters-2441-WaitGoud 56,46 -Oldambsters-2442-WaitGoud 137,46 -Oldambsters-2443-WaitGoud 101,46 -Oldambsters-2444-WaitGoud 58,46 -Oldambsters-2445-WaitGoud 31,46 -Oldambsters-2446-WaitGoud 34,46 -Oldambsters-2447-WaitGoud 63,46 -Oldambsters-2448-WaitGoud 106,46 -Oldambsters-2449-WaitGoud 78,46 -Oldambsters-2450-WaitGoud 31,46 -Oldambsters-2451-WaitGoud 123,46 -Oldambsters-2452-WaitGoud 69,46 -Oldambsters-2453-WaitGoud 77,46 -Oldambsters-2454-WaitGoud 85,46 -Oldambsters-2455-WaitGoud 47,46 -Oldambsters-2456-WaitGoud 193,46 -Oldambsters-2457-WaitGoud 58,46 -Oldambsters-2458-WaitGoud 61,46 -Oldambsters-2459-WaitGoud 55,46 -Oldambsters-2460-WaitGoud 64,46 -Oldambsters-2461-WaitGoud 72,46 -Oldambsters-2462-WaitGoud 24,46 -Oldambsters-2463-WaitGoud 50,46 -Oldambsters-2464-WaitGoud 137,46 -Oldambsters-2465-WaitGoud 93,46 -Oldambsters-2466-WaitGoud 12,46 -Oldambsters-2467-WaitGoud 12,46 -Oldambsters-2468-WaitGoud 73,46 -Oldambsters-2469-WaitGoud 156,46 -Oldambsters-2470-WaitGoud 118,46 -Oldambsters-2472-WaitGoud 124,46 -Oldambsters-2473-WaitGoud 55,46 -Oldambsters-2474-WaitGoud 47,46 -Oldambsters-2475-WaitGoud 25,46 -Oldambsters-2476-WaitGoud 189,46 -Oldambsters-2477-WaitGoud 66,46 -Oldambsters-2478-WaitGoud 116,46 -Oldambsters-2479-WaitGoud 110,46 -Oldambsters-2480-WaitGoud 83,46 -Oldambsters-2481-WaitGoud 31,46 -Oldambsters-2482-WaitGoud 142,46 -Oldambsters-2483-WaitGoud 205,46 -Oldambsters-2484-WaitGoud 157,46 -Oldambsters-2485-WaitGoud 31,46 -Oldambsters-2486-WaitGoud 157,46 -Oldambsters-2487-WaitGoud 139,46 -Oldambsters-2488-WaitGoud 52,46 -Oldambsters-2489-WaitGoud 66,46 -Oldambsters-2491-WaitGoud 112,46 -Oldambsters-2492-WaitGoud 147,46 -Oldambsters-2493-WaitGoud 99,46 -Oldambsters-2494-WaitGoud 47,46 -Oldambsters-2495-WaitGoud 146,46 -Oldambsters-2496-WaitGoud 122,46 -Oldambsters-2497-WaitGoud 103,46 -Oldambsters-2498-WaitGoud 75,46 -Oldambsters-2499-WaitGoud 78,46 -Oldambsters-2500-WaitGoud 53,46 -Oldambsters-2501-WaitGoud 69,46 -Oldambsters-2502-WaitGoud 181,46 -Oldambsters-2503-WaitGoud 30,46 -Oldambsters-2504-WaitGoud 138,46 -Oldambsters-2505-WaitGoud 158,46 -Oldambsters-2506-WaitGoud 103,46 -Oldambsters-2507-WaitGoud 30,46 -Oldambsters-2508-WaitGoud 44,46 -Oldambsters-2509-WaitGoud 137,46 -Oldambsters-2510-WaitGoud 84,46 -Oldambsters-2511-WaitGoud 132,46 -Oldambsters-2512-WaitGoud 60,46 -Oldambsters-2513-WaitGoud 57,46 -Oldambsters-2514-WaitGoud 48,46 -Oldambsters-2515-WaitGoud 185,46 -Oldambsters-2516-WaitGoud 94,46 -Oldambsters-2517-WaitGoud 38,46 -Oldambsters-2518-WaitGoud 77,46 -Oldambsters-2519-WaitGoud 100,46 -Oldambsters-2520-WaitGoud 117,46 -Oldambsters-2521-WaitGoud 158,46 -Oldambsters-2522-WaitGoud 20,46 -Oldambsters-2523-WaitGoud 77,46 -Oldambsters-2525-WaitGoud 102,46 -Oldambsters-2526-WaitGoud 88,46 -Oldambsters-2527-WaitGoud 99,46 -Oldambsters-2528-WaitGoud 13,46 -Oldambsters-2529-WaitGoud 40,46 -Oldambsters-2530-WaitGoud 84,46 -Oldambsters-2531-WaitGoud 68,46 -Oldambsters-2533-WaitGoud 19,46 -Oldambsters-2534-WaitGoud 14,46 -Oldambsters-2535-WaitGoud 55,46 -Oldambsters-2536-WaitGoud 110,46 -Oldambsters-2537-WaitGoud 55,46 -Oldambsters-2538-WaitGoud 71,46 -Oldambsters-2539-WaitGoud 62,46 -Oldambsters-2540-WaitGoud 68,46 -Oldambsters-2541-WaitGoud 205,46 -Oldambsters-2542-WaitGoud 21,46 -Oldambsters-2543-WaitGoud 96,46 -Oldambsters-2544-WaitGoud 85,46 -Oldambsters-2545-WaitGoud 151,46 -Oldambsters-2546-WaitGoud 51,46 -Oldambsters-2547-WaitGoud 205,46 -Oldambsters-2548-WaitGoud 32,46 -Oldambsters-2549-WaitGoud 20,46 -Oldambsters-2550-WaitGoud 25,46 -Oldambsters-2551-WaitGoud 47,46 -Oldambsters-2552-WaitGoud 27,46 -Oldambsters-2553-WaitGoud 54,46 -Oldambsters-2554-WaitGoud 96,46 -Oldambsters-2555-WaitGoud 101,46 -Oldambsters-2556-WaitGoud 74,46 -Oldambsters-2557-WaitGoud 124,46 -Oldambsters-2558-WaitGoud 77,46 -Oldambsters-2559-WaitGoud 17,46 -Oldambsters-2560-WaitGoud 65,46 -Oldambsters-2561-WaitGoud 20,46 -Oldambsters-2562-WaitGoud 85,46 -Oldambsters-2563-WaitGoud 82,46 -Oldambsters-2564-WaitGoud 68,46 -Oldambsters-2565-WaitGoud 137,46 -Oldambsters-2566-WaitGoud 158,46 -Oldambsters-2567-WaitGoud 63,46 -Oldambsters-2568-WaitGoud 68,46 -Oldambsters-2569-WaitGoud 178,46 -Oldambsters-2570-WaitGoud 73,46 -Oldambsters-2571-WaitGoud 52,46 -Oldambsters-2572-WaitGoud 56,46 -Oldambsters-2573-WaitGoud 295,46 -Oldambsters-2574-WaitGoud 76,46 -Oldambsters-2575-WaitGoud 49,46 -Oldambsters-2576-WaitGoud 72,46 -Oldambsters-2578-WaitGoud 71,46 -Oldambsters-2579-WaitGoud 44,46 -Oldambsters-2580-WaitGoud 60,46 -Oldambsters-2581-WaitGoud 202,46 -Oldambsters-2582-WaitGoud 48,46 -Oldambsters-2583-WaitGoud 34,46 -Oldambsters-2584-WaitGoud 129,46 -Oldambsters-2585-WaitGoud 150,46 -Oldambsters-2586-WaitGoud 148,46 -Oldambsters-2588-WaitGoud 99,46 -Oldambsters-2589-WaitGoud 60,46 -Oldambsters-2590-WaitGoud 128,46 -Oldambsters-2591-WaitGoud 69,46 -Oldambsters-2592-WaitGoud 72,46 -Oldambsters-2593-WaitGoud 25,46 -Oldambsters-2594-WaitGoud 96,46 -Oldambsters-2595-WaitGoud 48,46 -Oldambsters-2596-WaitGoud 52,46 -Oldambsters-2597-WaitGoud 124,46 -Oldambsters-2598-WaitGoud 180,46 -Oldambsters-2599-WaitGoud 54,46 -Oldambsters-2600-WaitGoud 66,46 -Oldambsters-2601-WaitGoud 42,46 -Oldambsters-2602-WaitGoud 169,46 -Oldambsters-2603-WaitGoud 133,46 -Oldambsters-2604-WaitGoud 195,46 -Oldambsters-2605-WaitGoud 109,46 -Oldambsters-2606-WaitGoud 152,46 -Oldambsters-2607-WaitGoud 20,46 -Oldambsters-2608-WaitGoud 85,46 -Oldambsters-2609-WaitGoud 132,46 -Oldambsters-2610-WaitGoud 109,46 -Oldambsters-2611-WaitGoud 97,46 -Oldambsters-2612-WaitGoud 120,46 -Oldambsters-2613-WaitGoud 99,46 -Oldambsters-2614-WaitGoud 94,46 -Oldambsters-2616-WaitGoud 12,46 -Oldambsters-2617-WaitGoud 22,46 -Oldambsters-2618-WaitGoud 118,46 -Oldambsters-2619-WaitGoud 91,46 -Oldambsters-2620-WaitGoud 55,46 -Oldambsters-2621-WaitGoud 66,46 -Oldambsters-2622-WaitGoud 26,46 -Oldambsters-2623-WaitGoud 20,46 -Oldambsters-2624-WaitGoud 88,46 -Oldambsters-2625-WaitGoud 235,46 -Oldambsters-2626-WaitGoud 203,46 -Oldambsters-2627-WaitGoud 15,46 -Oldambsters-2628-WaitGoud 72,46 -Oldambsters-2630-WaitGoud 46,46 -Oldambsters-2631-WaitGoud 89,46 -Oldambsters-2632-WaitGoud 77,46 -Oldambsters-2633-WaitGoud 40,46 -Oldambsters-2634-WaitGoud 36,46 -Oldambsters-2635-WaitGoud 45,46 -Oldambsters-2636-WaitGoud 65,46 -Oldambsters-2637-WaitGoud 45,46 -Oldambsters-2638-WaitGoud 103,46 -Oldambsters-2639-WaitGoud 92,46 -Oldambsters-2640-WaitGoud 174,46 -Oldambsters-2641-WaitGoud 32,46 -Oldambsters-2642-WaitGoud 73,46 -Oldambsters-2643-WaitGoud 86,46 -Oldambsters-2644-WaitGoud 163,46 -Oldambsters-2645-WaitGoud 95,46 -Oldambsters-2647-WaitGoud 164,46 -Oldambsters-2648-WaitGoud 133,46 -Oldambsters-2649-WaitGoud 176,46 -Oldambsters-2650-WaitGoud 243,46 -Oldambsters-2651-WaitGoud 114,46 -Oldambsters-2652-WaitGoud 121,46 -Oldambsters-2653-WaitGoud 21,46 -Oldambsters-2654-WaitGoud 155,46 -Oldambsters-2655-WaitGoud 99,46 -Oldambsters-2657-WaitGoud 106,46 -Oldambsters-2658-WaitGoud 59,46 -Oldambsters-2659-WaitGoud 137,46 -Oldambsters-2661-WaitGoud 87,46 -Oldambsters-2663-WaitGoud 28,46 -Oldambsters-2665-WaitGoud 28,46 -Oldambsters-2666-WaitGoud 62,46 -Oldambsters-2667-WaitGoud 30,46 -Oldambsters-2668-WaitGoud 121,46 -Oldambsters-2669-WaitGoud 49,46 -Oldambsters-2670-WaitGoud 36,46 -Oldambsters-2672-WaitGoud 87,46 -Oldambsters-2673-WaitGoud 14,46 -Oldambsters-2674-WaitGoud 35,46 -Oldambsters-2675-WaitGoud 97,46 -Oldambsters-2676-WaitGoud 67,46 -Oldambsters-2677-WaitGoud 74,46 -Oldambsters-2678-WaitGoud 51,46 -Oldambsters-2679-WaitGoud 29,46 -Oldambsters-2680-WaitGoud 76,46 -Oldambsters-2681-WaitGoud 30,46 -Oldambsters-2682-WaitGoud 77,46 -Oldambsters-2683-WaitGoud 99,46 -Oldambsters-2684-WaitGoud 79,46 -Oldambsters-2685-WaitGoud 82,46 -Oldambsters-2686-WaitGoud 173,46 -Oldambsters-2687-WaitGoud 101,46 -Oldambsters-2688-WaitGoud 101,46 -Oldambsters-2689-WaitGoud 75,46 -Oldambsters-2690-WaitGoud 90,46 -Oldambsters-2691-WaitGoud 101,46 -Oldambsters-2692-WaitGoud 69,46 -Oldambsters-2693-WaitGoud 46,46 -Oldambsters-2694-WaitGoud 101,46 -Oldambsters-2695-WaitGoud 42,46 -Oldambsters-2696-WaitGoud 53,46 -Oldambsters-2697-WaitGoud 56,46 -Oldambsters-2698-WaitGoud 57,46 -Oldambsters-2699-WaitGoud 117,46 -Oldambsters-2700-WaitGoud 87,46 -Oldambsters-2701-WaitGoud 102,46 -Oldambsters-2702-WaitGoud 139,46 -Oldambsters-2704-WaitGoud 35,46 -Oldambsters-2705-WaitGoud 34,46 -Oldambsters-2706-WaitGoud 90,46 -Oldambsters-2707-WaitGoud 20,46 -Oldambsters-2708-WaitGoud 168,46 -Oldambsters-2709-WaitGoud 50,46 -Oldambsters-2710-WaitGoud 103,46 -Oldambsters-2711-WaitGoud 65,46 -Oldambsters-2712-WaitGoud 130,46 -Oldambsters-2713-WaitGoud 184,46 -Westerkertaaiers-0002-KomVerhoal 25,46 -Westerkertaaiers-0003-KomVerhoal 43,46 -Westerkertaaiers-0004-KomVerhoal 57,46 -Westerkertaaiers-0005-KomVerhoal 76,46 -Westerkertaaiers-0006-KomVerhoal 95,46 -Westerkertaaiers-0007-KomVerhoal 96,46 -Westerkertaaiers-0008-KomVerhoal 54,46 -Westerkertaaiers-0009-KomVerhoal 41,46 -Westerkertaaiers-0010-KomVerhoal 72,46 -Westerkertaaiers-0011-KomVerhoal 48,46 -Westerkertaaiers-0012-KomVerhoal 31,46 -Westerkertaaiers-0013-KomVerhoal 38,46 -Westerkertaaiers-0014-KomVerhoal 48,46 -Westerkertaaiers-0015-KomVerhoal 77,46 -Westerkertaaiers-0016-KomVerhoal 219,46 -Westerkertaaiers-0017-KomVerhoal 100,46 -Westerkertaaiers-0018-KomVerhoal 20,46 -Westerkertaaiers-0019-KomVerhoal 66,46 -Westerkertaaiers-0020-KomVerhoal 38,46 -Westerkertaaiers-0021-KomVerhoal 37,46 -Westerkertaaiers-0022-KomVerhoal 31,46 -Westerkertaaiers-0023-KomVerhoal 57,46 -Westerkertaaiers-0024-KomVerhoal 103,46 -Westerkertaaiers-0025-KomVerhoal 31,46 -Westerkertaaiers-0026-KomVerhoal 53,46 -Westerkertaaiers-0027-KomVerhoal 57,46 -Westerkertaaiers-0028-KomVerhoal 18,46 -Westerkertaaiers-0029-KomVerhoal 26,46 -Westerkertaaiers-0030-KomVerhoal 128,46 -Westerkertaaiers-0031-KomVerhoal 117,46 -Westerkertaaiers-0032-KomVerhoal 44,46 -Westerkertaaiers-0033-KomVerhoal 67,46 -Westerkertaaiers-0034-KomVerhoal 60,46 -Westerkertaaiers-0035-KomVerhoal 97,46 -Westerkertaaiers-0036-KomVerhoal 88,46 -Westerkertaaiers-0037-KomVerhoal 118,46 -Westerkertaaiers-0038-KomVerhoal 48,46 -Westerkertaaiers-0039-KomVerhoal 175,46 -Westerkertaaiers-0040-KomVerhoal 225,46 -Westerkertaaiers-0041-KomVerhoal 52,46 -Westerkertaaiers-0042-KomVerhoal 69,46 -Westerkertaaiers-0043-KomVerhoal 121,46 -Westerkertaaiers-0045-KomVerhoal 61,46 -Westerkertaaiers-0046-KomVerhoal 163,46 -Westerkertaaiers-0047-KomVerhoal 101,46 -Westerkertaaiers-0048-KomVerhoal 95,46 -Westerkertaaiers-0049-KomVerhoal 66,46 -Westerkertaaiers-0050-KomVerhoal 54,46 -Westerkertaaiers-0051-KomVerhoal 84,46 -Westerkertaaiers-0052-KomVerhoal 142,46 -Westerkertaaiers-0053-KomVerhoal 71,46 -Westerkertaaiers-0054-KomVerhoal 48,46 -Westerkertaaiers-0055-KomVerhoal 56,46 -Westerkertaaiers-0056-KomVerhoal 101,46 -Westerkertaaiers-0057-KomVerhoal 67,46 -Westerkertaaiers-0058-KomVerhoal 43,46 -Westerkertaaiers-0059-KomVerhoal 114,46 -Westerkertaaiers-0060-KomVerhoal 49,46 -Westerkertaaiers-0061-KomVerhoal 137,46 -Westerkertaaiers-0062-KomVerhoal 43,46 -Westerkertaaiers-0063-KomVerhoal 71,46 -Westerkertaaiers-0064-KomVerhoal 134,46 -Westerkertaaiers-0065-KomVerhoal 114,46 -Westerkertaaiers-0066-KomVerhoal 29,46 -Westerkertaaiers-0067-KomVerhoal 64,46 -Westerkertaaiers-0068-KomVerhoal 13,46 -Westerkertaaiers-0069-KomVerhoal 32,46 -Westerkertaaiers-0070-KomVerhoal 33,46 -Westerkertaaiers-0071-KomVerhoal 69,46 -Westerkertaaiers-0072-KomVerhoal 49,46 -Westerkertaaiers-0073-KomVerhoal 193,46 -Westerkertaaiers-0074-KomVerhoal 76,46 -Westerkertaaiers-0075-KomVerhoal 80,46 -Westerkertaaiers-0076-KomVerhoal 180,46 -Westerkertaaiers-0077-KomVerhoal 121,46 -Westerkertaaiers-0078-KomVerhoal 177,46 -Westerkertaaiers-0079-KomVerhoal 54,46 -Westerkertaaiers-0080-KomVerhoal 20,46 -Westerkertaaiers-0081-KomVerhoal 23,46 -Westerkertaaiers-0082-KomVerhoal 84,46 -Westerkertaaiers-0083-KomVerhoal 61,46 -Westerkertaaiers-0084-KomVerhoal 51,46 -Westerkertaaiers-0085-KomVerhoal 90,46 -Westerkertaaiers-0086-KomVerhoal 40,46 -Westerkertaaiers-0088-KomVerhoal 35,46 -Westerkertaaiers-0089-KomVerhoal 111,46 -Westerkertaaiers-0090-KomVerhoal 41,46 -Westerkertaaiers-0091-KomVerhoal 152,46 -Westerkertaaiers-0093-KomVerhoal 147,46 -Westerkertaaiers-0095-KomVerhoal 109,46 -Westerkertaaiers-0096-KomVerhoal 132,46 -Westerkertaaiers-0097-KomVerhoal 124,46 -Westerkertaaiers-0098-KomVerhoal 121,46 -Westerkertaaiers-0099-KomVerhoal 50,46 -Westerkertaaiers-0100-KomVerhoal 97,46 -Westerkertaaiers-0101-KomVerhoal 32,46 -Westerkertaaiers-0102-KomVerhoal 141,46 -Westerkertaaiers-0103-KomVerhoal 30,46 -Westerkertaaiers-0104-KomVerhoal 58,46 -Westerkertaaiers-0105-KomVerhoal 39,46 -Westerkertaaiers-0106-KomVerhoal 147,46 -Westerkertaaiers-0107-KomVerhoal 69,46 -Westerkertaaiers-0108-KomVerhoal 115,46 -Westerkertaaiers-0109-KomVerhoal 109,46 -Westerkertaaiers-0111-KomVerhoal 96,46 -Westerkertaaiers-0112-KomVerhoal 82,46 -Westerkertaaiers-0113-KomVerhoal 93,46 -Westerkertaaiers-0114-KomVerhoal 25,46 -Westerkertaaiers-0115-KomVerhoal 259,46 -Westerkertaaiers-0116-KomVerhoal 145,46 -Westerkertaaiers-0117-KomVerhoal 113,46 -Westerkertaaiers-0118-KomVerhoal 74,46 -Westerkertaaiers-0119-KomVerhoal 131,46 -Westerkertaaiers-0120-KomVerhoal 98,46 -Westerkertaaiers-0121-KomVerhoal 71,46 -Westerkertaaiers-0122-KomVerhoal 177,46 -Westerkertaaiers-0123-KomVerhoal 219,46 -Westerkertaaiers-0124-KomVerhoal 196,46 -Westerkertaaiers-0125-KomVerhoal 26,46 -Westerkertaaiers-0126-KomVerhoal 84,46 -Westerkertaaiers-0127-KomVerhoal 185,46 -Westerkertaaiers-0128-KomVerhoal 76,46 -Westerkertaaiers-0129-KomVerhoal 20,46 -Westerkertaaiers-0130-KomVerhoal 191,46 -Westerkertaaiers-0131-KomVerhoal 89,46 -Westerkertaaiers-0133-KomVerhoal 121,46 -Westerkertaaiers-0134-KomVerhoal 106,46 -Westerkertaaiers-0135-KomVerhoal 118,46 -Westerkertaaiers-0136-KomVerhoal 75,46 -Westerkertaaiers-0137-KomVerhoal 98,46 -Westerkertaaiers-0138-KomVerhoal 80,46 -Westerkertaaiers-0140-KomVerhoal 29,46 -Westerkertaaiers-0142-KomVerhoal 125,46 -Westerkertaaiers-0143-KomVerhoal 238,46 -Westerkertaaiers-0144-KomVerhoal 33,46 -Westerkertaaiers-0146-KomVerhoal 99,46 -Westerkertaaiers-0147-KomVerhoal 31,46 -Westerkertaaiers-0148-KomVerhoal 130,46 -Westerkertaaiers-0149-KomVerhoal 134,46 -Westerkertaaiers-0150-KomVerhoal 225,46 -Westerkertaaiers-0151-KomVerhoal 96,46 -Westerkertaaiers-0152-KomVerhoal 40,46 -Westerkertaaiers-0153-KomVerhoal 137,46 -Westerkertaaiers-0154-KomVerhoal 240,46 -Westerkertaaiers-0155-KomVerhoal 127,46 -Westerkertaaiers-0156-KomVerhoal 72,46 -Westerkertaaiers-0157-KomVerhoal 117,46 -Westerkertaaiers-0158-KomVerhoal 123,46 -Westerkertaaiers-0159-KomVerhoal 140,46 -Westerkertaaiers-0160-KomVerhoal 233,46 -Westerkertaaiers-0161-KomVerhoal 135,46 -Westerkertaaiers-0162-KomVerhoal 79,46 -Westerkertaaiers-0163-KomVerhoal 179,46 -Westerkertaaiers-0164-KomVerhoal 44,46 -Westerkertaaiers-0166-KomVerhoal 107,46 -Westerkertaaiers-0167-KomVerhoal 209,46 -Westerkertaaiers-0170-KomVerhoal 197,46 -Westerkertaaiers-0171-KomVerhoal 106,46 -Westerkertaaiers-0172-KomVerhoal 40,46 -Westerkertaaiers-0173-KomVerhoal 264,46 -Westerkertaaiers-0174-KomVerhoal 151,46 -Westerkertaaiers-0175-KomVerhoal 9,46 -Westerkertaaiers-0177-KomVerhoal 186,46 -Westerkertaaiers-0179-KomVerhoal 73,46 -Westerkertaaiers-0180-KomVerhoal 186,46 -Westerkertaaiers-0181-KomVerhoal 47,46 -Westerkertaaiers-0182-KomVerhoal 106,46 -Westerkertaaiers-0183-KomVerhoal 55,46 -Westerkertaaiers-0184-KomVerhoal 45,46 -Westerkertaaiers-0185-KomVerhoal 80,46 -Westerkertaaiers-0186-KomVerhoal 150,46 -Westerkertaaiers-0187-KomVerhoal 95,46 -Westerkertaaiers-0188-KomVerhoal 26,46 -Westerkertaaiers-0189-KomVerhoal 71,46 -Westerkertaaiers-0190-KomVerhoal 28,46 -Westerkertaaiers-0191-KomVerhoal 68,46 -Westerkertaaiers-0192-KomVerhoal 48,46 -Westerkertaaiers-0193-KomVerhoal 146,46 -Westerkertaaiers-0194-KomVerhoal 87,46 -Westerkertaaiers-0195-KomVerhoal 74,46 -Westerkertaaiers-0196-KomVerhoal 84,46 -Westerkertaaiers-0197-KomVerhoal 93,46 -Westerkertaaiers-0198-KomVerhoal 185,46 -Westerkertaaiers-0199-KomVerhoal 57,46 -Westerkertaaiers-0200-KomVerhoal 97,46 -Westerkertaaiers-0201-KomVerhoal 57,46 -Westerkertaaiers-0202-KomVerhoal 141,46 -Westerkertaaiers-0203-KomVerhoal 130,46 -Westerkertaaiers-0204-KomVerhoal 19,46 -Westerkertaaiers-0205-KomVerhoal 37,46 -Westerkertaaiers-0207-KomVerhoal 87,46 -Westerkertaaiers-0208-KomVerhoal 129,46 -Westerkertaaiers-0209-KomVerhoal 82,46 -Westerkertaaiers-0210-KomVerhoal 108,46 -Westerkertaaiers-0211-KomVerhoal 81,46 -Westerkertaaiers-0212-KomVerhoal 158,46 -Westerkertaaiers-0213-KomVerhoal 75,46 -Westerkertaaiers-0214-KomVerhoal 83,46 -Westerkertaaiers-0215-KomVerhoal 128,46 -Westerkertaaiers-0216-KomVerhoal 139,46 -Westerkertaaiers-0217-KomVerhoal 264,46 -Westerkertaaiers-0218-KomVerhoal 218,46 -Westerkertaaiers-0219-KomVerhoal 65,46 -Westerkertaaiers-0221-KomVerhoal 179,46 -Westerkertaaiers-0223-KomVerhoal 122,46 -Westerkertaaiers-0224-KomVerhoal 138,46 -Westerkertaaiers-0225-KomVerhoal 213,46 -Westerkertaaiers-0226-KomVerhoal 113,46 -Westerkertaaiers-0227-KomVerhoal 284,46 -Westerkertaaiers-0228-KomVerhoal 103,46 -Westerkertaaiers-0229-KomVerhoal 180,46 -Westerkertaaiers-0230-KomVerhoal 180,46 -Westerkertaaiers-0231-KomVerhoal 135,46 -Westerkertaaiers-0232-KomVerhoal 99,46 -Westerkertaaiers-0233-KomVerhoal 55,46 -Westerkertaaiers-0234-KomVerhoal 161,46 -Westerkertaaiers-0235-KomVerhoal 114,46 -Westerkertaaiers-0236-KomVerhoal 87,46 -Westerkertaaiers-0237-KomVerhoal 55,46 -Westerkertaaiers-0238-KomVerhoal 18,46 -Westerkertaaiers-0239-KomVerhoal 99,46 -Westerkertaaiers-0240-KomVerhoal 103,46 -Westerkertaaiers-0241-KomVerhoal 55,46 -Westerkertaaiers-0242-KomVerhoal 93,46 -Westerkertaaiers-0243-KomVerhoal 148,46 -Westerkertaaiers-0244-KomVerhoal 267,46 -Westerkertaaiers-0245-KomVerhoal 145,46 -Westerkertaaiers-0246-KomVerhoal 171,46 -Westerkertaaiers-0247-KomVerhoal 117,46 -Westerkertaaiers-0248-KomVerhoal 62,46 -Westerkertaaiers-0249-KomVerhoal 37,46 -Westerkertaaiers-0250-KomVerhoal 134,46 -Westerkertaaiers-0251-KomVerhoal 59,46 -Westerkertaaiers-0252-KomVerhoal 223,46 -Westerkertaaiers-0253-KomVerhoal 48,46 -Westerkertaaiers-0254-KomVerhoal 194,46 -Westerkertaaiers-0255-KomVerhoal 28,46 -Westerkertaaiers-0256-KomVerhoal 95,46 -Westerkertaaiers-0257-KomVerhoal 79,46 -Westerkertaaiers-0258-KomVerhoal 111,46 -Westerkertaaiers-0259-KomVerhoal 186,46 -Westerkertaaiers-0260-KomVerhoal 70,46 -Westerkertaaiers-0261-KomVerhoal 145,46 -Westerkertaaiers-0262-KomVerhoal 77,46 -Westerkertaaiers-0263-KomVerhoal 79,46 -Westerkertaaiers-0264-KomVerhoal 106,46 -Westerkertaaiers-0266-KomVerhoal 47,46 -Westerkertaaiers-0267-KomVerhoal 67,46 -Westerkertaaiers-0268-KomVerhoal 46,46 -Westerkertaaiers-0269-KomVerhoal 18,46 -Westerkertaaiers-0270-KomVerhoal 96,46 -Westerkertaaiers-0272-KomVerhoal 227,46 -Westerkertaaiers-0273-KomVerhoal 141,46 -Westerkertaaiers-0274-KomVerhoal 121,46 -Westerkertaaiers-0275-KomVerhoal 242,46 -Westerkertaaiers-0276-KomVerhoal 112,46 -Westerkertaaiers-0277-KomVerhoal 109,46 -Westerkertaaiers-0278-KomVerhoal 95,46 -Westerkertaaiers-0279-KomVerhoal 162,46 -Westerkertaaiers-0280-KomVerhoal 104,46 -Westerkertaaiers-0281-KomVerhoal 34,46 -Westerkertaaiers-0282-KomVerhoal 125,46 -Westerkertaaiers-0283-KomVerhoal 270,46 -Westerkertaaiers-0284-KomVerhoal 275,46 -Westerkertaaiers-0285-KomVerhoal 138,46 -Westerkertaaiers-0286-KomVerhoal 88,46 -Westerkertaaiers-0287-KomVerhoal 61,46 -Westerkertaaiers-0288-KomVerhoal 110,46 -Westerkertaaiers-0289-KomVerhoal 116,46 -Westerkertaaiers-0290-KomVerhoal 207,46 -Westerkertaaiers-0291-KomVerhoal 83,46 -Westerkertaaiers-0292-KomVerhoal 190,46 -Westerkertaaiers-0295-KomVerhoal 138,46 -Westerkertaaiers-0296-KomVerhoal 16,46 -Westerkertaaiers-0297-KomVerhoal 22,46 -Westerkertaaiers-0298-KomVerhoal 87,46 -Westerkertaaiers-0299-KomVerhoal 43,46 -Westerkertaaiers-0301-KomVerhoal 194,46 -Westerkertaaiers-0302-KomVerhoal 134,46 -Westerkertaaiers-0303-KomVerhoal 222,46 -Westerkertaaiers-0304-KomVerhoal 68,46 -Westerkertaaiers-0305-KomVerhoal 174,46 -Westerkertaaiers-0306-KomVerhoal 132,46 -Westerkertaaiers-0307-KomVerhoal 117,46 -Westerkertaaiers-0308-KomVerhoal 124,46 -Westerkertaaiers-0310-KomVerhoal 142,46 -Westerkertaaiers-0311-KomVerhoal 103,46 -Westerkertaaiers-0312-KomVerhoal 185,46 -Westerkertaaiers-0313-KomVerhoal 100,46 -Westerkertaaiers-0314-KomVerhoal 84,46 -Westerkertaaiers-0315-KomVerhoal 118,46 -Westerkertaaiers-0316-KomVerhoal 72,46 -Westerkertaaiers-0317-KomVerhoal 88,46 -Westerkertaaiers-0319-KomVerhoal 16,46 -Westerkertaaiers-0320-KomVerhoal 94,46 -Westerkertaaiers-0321-KomVerhoal 249,46 -Westerkertaaiers-0322-KomVerhoal 48,46 -Westerkertaaiers-0323-KomVerhoal 236,46 -Westerkertaaiers-0324-KomVerhoal 67,46 -Westerkertaaiers-0325-KomVerhoal 52,46 -Westerkertaaiers-0326-KomVerhoal 117,46 -Westerkertaaiers-0327-KomVerhoal 166,46 -Westerkertaaiers-0329-KomVerhoal 35,46 -Westerkertaaiers-0331-KomVerhoal 219,46 -Westerkertaaiers-0332-KomVerhoal 205,46 -Westerkertaaiers-0333-KomVerhoal 112,46 -Westerkertaaiers-0334-KomVerhoal 123,46 -Westerkertaaiers-0335-KomVerhoal 188,46 -Westerkertaaiers-0336-KomVerhoal 227,46 -Westerkertaaiers-0337-KomVerhoal 110,46 -Westerkertaaiers-0338-KomVerhoal 67,46 -Westerkertaaiers-0339-KomVerhoal 83,46 -Westerkertaaiers-0340-KomVerhoal 232,46 -Westerkertaaiers-0341-KomVerhoal 138,46 -Westerkertaaiers-0342-KomVerhoal 137,46 -Westerkertaaiers-0344-KomVerhoal 132,46 -Westerkertaaiers-0345-KomVerhoal 49,46 -Westerkertaaiers-0346-KomVerhoal 78,46 -Westerkertaaiers-0347-KomVerhoal 82,46 -Westerkertaaiers-0348-KomVerhoal 73,46 -Westerkertaaiers-0349-KomVerhoal 44,46 -Westerkertaaiers-0350-KomVerhoal 66,46 -Westerkertaaiers-0351-KomVerhoal 50,46 -Westerkertaaiers-0352-KomVerhoal 62,46 -Westerkertaaiers-0353-KomVerhoal 41,46 -Westerkertaaiers-0355-KomVerhoal 57,46 -Westerkertaaiers-0356-KomVerhoal 115,46 -Westerkertaaiers-0357-KomVerhoal 69,46 -Westerkertaaiers-0358-KomVerhoal 99,46 -Westerkertaaiers-0359-KomVerhoal 69,46 -Westerkertaaiers-0360-KomVerhoal 92,46 -Westerkertaaiers-0361-KomVerhoal 45,46 -Westerkertaaiers-0362-KomVerhoal 25,46 -Westerkertaaiers-0363-KomVerhoal 45,46 -Westerkertaaiers-0364-KomVerhoal 84,46 -Westerkertaaiers-0365-KomVerhoal 41,46 -Westerkertaaiers-0366-KomVerhoal 31,46 -Westerkertaaiers-0367-KomVerhoal 68,46 -Westerkertaaiers-0368-KomVerhoal 36,46 -Westerkertaaiers-0369-KomVerhoal 20,46 -Westerkertaaiers-0370-KomVerhoal 233,46 -Westerkertaaiers-0371-KomVerhoal 45,46 -Westerkertaaiers-0372-KomVerhoal 91,46 -Westerkertaaiers-0373-KomVerhoal 22,46 -Westerkertaaiers-0374-KomVerhoal 63,46 -Westerkertaaiers-0375-KomVerhoal 61,46 -Westerkertaaiers-0376-KomVerhoal 30,46 -Westerkertaaiers-0377-KomVerhoal 39,46 -Westerkertaaiers-0378-KomVerhoal 85,46 -Westerkertaaiers-0379-KomVerhoal 79,46 -Westerkertaaiers-0380-KomVerhoal 38,46 -Westerkertaaiers-0381-KomVerhoal 84,46 -Westerkertaaiers-0382-KomVerhoal 89,46 -Westerkertaaiers-0383-KomVerhoal 59,46 -Westerkertaaiers-0384-KomVerhoal 59,46 -Westerkertaaiers-0385-KomVerhoal 56,46 -Westerkertaaiers-0386-KomVerhoal 51,46 -Westerkertaaiers-0387-KomVerhoal 186,46 -Westerkertaaiers-0388-KomVerhoal 25,46 -Westerkertaaiers-0389-KomVerhoal 62,46 -Westerkertaaiers-0390-KomVerhoal 82,46 -Westerkertaaiers-0391-KomVerhoal 68,46 -Westerkertaaiers-0392-KomVerhoal 50,46 -Westerkertaaiers-0393-KomVerhoal 79,46 -Westerkertaaiers-0394-KomVerhoal 34,46 -Westerkertaaiers-0395-KomVerhoal 71,46 -Westerkertaaiers-0396-KomVerhoal 14,46 -Westerkertaaiers-0398-KomVerhoal 46,46 -Westerkertaaiers-0399-KomVerhoal 75,46 -Westerkertaaiers-0400-KomVerhoal 126,46 -Westerkertaaiers-0401-KomVerhoal 113,46 -Westerkertaaiers-0402-KomVerhoal 138,46 -Westerkertaaiers-0403-KomVerhoal 86,46 -Westerkertaaiers-0404-KomVerhoal 95,46 -Westerkertaaiers-0405-KomVerhoal 31,46 -Westerkertaaiers-0407-KomVerhoal 93,46 -Westerkertaaiers-0408-KomVerhoal 43,46 -Westerkertaaiers-0409-KomVerhoal 49,46 -Westerkertaaiers-0410-KomVerhoal 61,46 -Westerkertaaiers-0411-KomVerhoal 73,46 -Westerkertaaiers-0412-KomVerhoal 115,46 -Westerkertaaiers-0413-KomVerhoal 98,46 -Westerkertaaiers-0414-KomVerhoal 73,46 -Westerkertaaiers-0415-KomVerhoal 81,46 -Westerkertaaiers-0416-KomVerhoal 139,46 -Westerkertaaiers-0417-KomVerhoal 63,46 -Westerkertaaiers-0418-KomVerhoal 24,46 -Westerkertaaiers-0419-KomVerhoal 195,46 -Westerkertaaiers-0420-KomVerhoal 71,46 -Westerkertaaiers-0421-KomVerhoal 47,46 -Westerkertaaiers-0422-KomVerhoal 61,46 -Westerkertaaiers-0423-KomVerhoal 78,46 -Westerkertaaiers-0424-KomVerhoal 53,46 -Westerkertaaiers-0425-KomVerhoal 87,46 -Westerkertaaiers-0426-KomVerhoal 163,46 -Westerkertaaiers-0427-KomVerhoal 60,46 -Westerkertaaiers-0428-KomVerhoal 109,46 -Westerkertaaiers-0429-KomVerhoal 35,46 -Westerkertaaiers-0430-KomVerhoal 63,46 -Westerkertaaiers-0431-KomVerhoal 127,46 -Westerkertaaiers-0432-KomVerhoal 18,46 -Westerkertaaiers-0433-KomVerhoal 55,46 -Westerkertaaiers-0434-KomVerhoal 48,46 -Westerkertaaiers-0435-KomVerhoal 61,46 -Westerkertaaiers-0436-KomVerhoal 61,46 -Westerkertaaiers-0437-KomVerhoal 51,46 -Westerkertaaiers-0438-KomVerhoal 34,46 -Westerkertaaiers-0439-KomVerhoal 97,46 -Westerkertaaiers-0440-KomVerhoal 59,46 -Westerkertaaiers-0442-KomVerhoal 41,46 -Westerkertaaiers-0443-KomVerhoal 29,46 -Westerkertaaiers-0444-KomVerhoal 134,46 -Westerkertaaiers-0446-KomVerhoal 56,46 -Westerkertaaiers-0447-KomVerhoal 90,46 -Westerkertaaiers-0448-KomVerhoal 50,46 -Westerkertaaiers-0449-KomVerhoal 201,46 -Westerkertaaiers-0450-KomVerhoal 26,46 -Westerkertaaiers-0451-KomVerhoal 94,46 -Westerkertaaiers-0452-KomVerhoal 111,46 -Westerkertaaiers-0453-KomVerhoal 18,46 -Westerkertaaiers-0454-KomVerhoal 68,46 -Westerkertaaiers-0455-KomVerhoal 133,46 -Westerkertaaiers-0456-KomVerhoal 133,46 -Westerkertaaiers-0457-KomVerhoal 92,46 -Westerkertaaiers-0458-KomVerhoal 156,46 -Westerkertaaiers-0459-KomVerhoal 53,46 -Westerkertaaiers-0460-KomVerhoal 48,46 -Westerkertaaiers-0461-KomVerhoal 79,46 -Westerkertaaiers-0462-KomVerhoal 62,46 -Westerkertaaiers-0463-KomVerhoal 121,46 -Westerkertaaiers-0464-KomVerhoal 39,46 -Westerkertaaiers-0465-KomVerhoal 36,46 -Westerkertaaiers-0467-KomVerhoal 140,46 -Westerkertaaiers-0468-KomVerhoal 262,46 -Westerkertaaiers-0469-KomVerhoal 86,46 -Westerkertaaiers-0470-KomVerhoal 70,46 -Westerkertaaiers-0471-KomVerhoal 114,46 -Westerkertaaiers-0472-KomVerhoal 191,46 -Westerkertaaiers-0473-KomVerhoal 114,46 -Westerkertaaiers-0474-KomVerhoal 144,46 -Westerkertaaiers-0475-KomVerhoal 81,46 -Westerkertaaiers-0476-KomVerhoal 59,46 -Westerkertaaiers-0477-KomVerhoal 99,46 -Westerkertaaiers-0478-KomVerhoal 121,46 -Westerkertaaiers-0479-KomVerhoal 69,46 -Westerkertaaiers-0480-KomVerhoal 49,46 -Westerkertaaiers-0481-KomVerhoal 38,46 -Westerkertaaiers-0483-KomVerhoal 61,46 -Westerkertaaiers-0485-KomVerhoal 58,46 -Westerkertaaiers-0486-KomVerhoal 58,46 -Westerkertaaiers-0487-KomVerhoal 132,46 -Westerkertaaiers-0488-KomVerhoal 40,46 -Westerkertaaiers-0489-KomVerhoal 48,46 -Westerkertaaiers-0490-KomVerhoal 50,46 -Westerkertaaiers-0491-KomVerhoal 107,46 -Westerkertaaiers-0492-KomVerhoal 112,46 -Westerkertaaiers-0493-KomVerhoal 107,46 -Westerkertaaiers-0494-KomVerhoal 80,46 -Westerkertaaiers-0495-KomVerhoal 52,46 -Westerkertaaiers-0496-KomVerhoal 86,46 -Westerkertaaiers-0497-KomVerhoal 62,46 -Westerkertaaiers-0498-KomVerhoal 154,46 -Westerkertaaiers-0499-KomVerhoal 72,46 -Westerkertaaiers-0500-KomVerhoal 138,46 -Westerkertaaiers-0501-KomVerhoal 35,46 -Westerkertaaiers-0502-KomVerhoal 79,46 -Westerkertaaiers-0503-KomVerhoal 77,46 -Westerkertaaiers-0504-KomVerhoal 82,46 -Westerkertaaiers-0505-KomVerhoal 260,46 -Westerkertaaiers-0506-KomVerhoal 142,46 -Westerkertaaiers-0507-KomVerhoal 108,46 -Westerkertaaiers-0508-KomVerhoal 43,46 -Westerkertaaiers-0509-KomVerhoal 137,46 -Westerkertaaiers-0510-KomVerhoal 64,46 -Westerkertaaiers-0511-KomVerhoal 123,46 -Westerkertaaiers-0512-KomVerhoal 205,46 -Westerkertaaiers-0513-KomVerhoal 172,46 -Westerkertaaiers-0514-KomVerhoal 147,46 -Westerkertaaiers-0515-KomVerhoal 50,46 -Westerkertaaiers-0516-KomVerhoal 142,46 -Westerkertaaiers-0517-KomVerhoal 32,46 -Westerkertaaiers-0518-KomVerhoal 24,46 -Westerkertaaiers-0519-KomVerhoal 74,46 -Westerkertaaiers-0520-KomVerhoal 48,46 -Westerkertaaiers-0521-KomVerhoal 56,46 -Westerkertaaiers-0522-KomVerhoal 47,46 -Westerkertaaiers-0523-KomVerhoal 12,46 -Westerkertaaiers-0524-KomVerhoal 249,46 -Westerkertaaiers-0525-KomVerhoal 105,46 -Westerkertaaiers-0526-KomVerhoal 246,46 -Westerkertaaiers-0528-KomVerhoal 115,46 -Westerkertaaiers-0529-KomVerhoal 194,46 -Westerkertaaiers-0530-KomVerhoal 218,46 -Westerkertaaiers-0531-KomVerhoal 68,46 -Westerkertaaiers-0532-KomVerhoal 37,46 -Westerkertaaiers-0533-KomVerhoal 145,46 -Westerkertaaiers-0534-KomVerhoal 21,46 -Westerkertaaiers-0535-KomVerhoal 46,46 -Westerkertaaiers-0536-KomVerhoal 49,46 -Westerkertaaiers-0537-KomVerhoal 103,46 -Westerkertaaiers-0538-KomVerhoal 54,46 -Westerkertaaiers-0539-KomVerhoal 74,46 -Westerkertaaiers-0540-KomVerhoal 23,46 -Westerkertaaiers-0541-KomVerhoal 63,46 -Westerkertaaiers-0542-KomVerhoal 101,46 -Westerkertaaiers-0543-KomVerhoal 67,46 -Westerkertaaiers-0544-KomVerhoal 113,46 -Westerkertaaiers-0545-KomVerhoal 43,46 -Westerkertaaiers-0546-KomVerhoal 23,46 -Westerkertaaiers-0547-KomVerhoal 52,46 -Westerkertaaiers-0548-KomVerhoal 44,46 -Westerkertaaiers-0549-KomVerhoal 155,46 -Westerkertaaiers-0550-KomVerhoal 66,46 -Westerkertaaiers-0552-KomVerhoal 40,46 -Westerkertaaiers-0553-KomVerhoal 61,46 -Westerkertaaiers-0554-KomVerhoal 46,46 -Westerkertaaiers-0555-KomVerhoal 21,46 -Westerkertaaiers-0556-KomVerhoal 67,46 -Westerkertaaiers-0557-KomVerhoal 43,46 -Westerkertaaiers-0558-KomVerhoal 105,46 -Westerkertaaiers-0559-KomVerhoal 107,46 -Westerkertaaiers-0560-KomVerhoal 38,46 -Westerkertaaiers-0561-KomVerhoal 83,46 -Westerkertaaiers-0562-KomVerhoal 55,46 -Westerkertaaiers-0563-KomVerhoal 33,46 -Westerkertaaiers-0564-KomVerhoal 63,46 -Westerkertaaiers-0565-KomVerhoal 139,46 -Westerkertaaiers-0566-KomVerhoal 111,46 -Westerkertaaiers-0567-KomVerhoal 71,46 -Westerkertaaiers-0568-KomVerhoal 47,46 -Westerkertaaiers-0569-KomVerhoal 61,46 -Westerkertaaiers-0571-KomVerhoal 189,46 -Westerkertaaiers-0572-KomVerhoal 46,46 -Westerkertaaiers-0573-KomVerhoal 29,46 -Westerkertaaiers-0574-KomVerhoal 90,46 -Westerkertaaiers-0575-KomVerhoal 126,46 -Westerkertaaiers-0576-KomVerhoal 59,46 -Westerkertaaiers-0577-KomVerhoal 98,46 -Westerkertaaiers-0578-KomVerhoal 103,46 -Westerkertaaiers-0579-KomVerhoal 151,46 -Westerkertaaiers-0580-KomVerhoal 96,46 -Westerkertaaiers-0581-KomVerhoal 108,46 -Westerkertaaiers-0582-KomVerhoal 105,46 -Westerkertaaiers-0583-KomVerhoal 67,46 -Westerkertaaiers-0584-KomVerhoal 88,46 -Westerkertaaiers-0585-KomVerhoal 109,46 -Westerkertaaiers-0586-KomVerhoal 204,46 -Westerkertaaiers-0587-KomVerhoal 287,46 -Westerkertaaiers-0588-KomVerhoal 88,46 -Westerkertaaiers-0589-KomVerhoal 109,46 -Westerkertaaiers-0590-KomVerhoal 72,46 -Westerkertaaiers-0591-KomVerhoal 112,46 -Westerkertaaiers-0593-KomVerhoal 56,46 -Westerkertaaiers-0594-KomVerhoal 138,46 -Westerkertaaiers-0595-KomVerhoal 122,46 -Westerkertaaiers-0596-KomVerhoal 152,46 -Westerkertaaiers-0597-KomVerhoal 114,46 -Westerkertaaiers-0598-KomVerhoal 37,46 -Westerkertaaiers-0599-KomVerhoal 243,46 -Westerkertaaiers-0600-KomVerhoal 250,46 -Westerkertaaiers-0601-KomVerhoal 19,46 -Westerkertaaiers-0602-KomVerhoal 72,46 -Westerkertaaiers-0603-KomVerhoal 221,46 -Westerkertaaiers-0604-KomVerhoal 47,46 -Westerkertaaiers-0605-KomVerhoal 178,46 -Westerkertaaiers-0606-KomVerhoal 116,46 -Westerkertaaiers-0607-KomVerhoal 84,46 -Westerkertaaiers-0608-KomVerhoal 256,46 -Westerkertaaiers-0609-KomVerhoal 42,46 -Westerkertaaiers-0610-KomVerhoal 75,46 -Westerkertaaiers-0611-KomVerhoal 209,46 -Westerkertaaiers-0612-KomVerhoal 175,46 -Westerkertaaiers-0614-KomVerhoal 39,46 -Westerkertaaiers-0615-KomVerhoal 261,46 -Westerkertaaiers-0616-KomVerhoal 119,46 -Westerkertaaiers-0617-KomVerhoal 139,46 -Westerkertaaiers-0618-KomVerhoal 105,46 -Westerkertaaiers-0619-KomVerhoal 62,46 -Westerkertaaiers-0620-KomVerhoal 99,46 -Westerkertaaiers-0621-KomVerhoal 74,46 -Westerkertaaiers-0622-KomVerhoal 58,46 -Westerkertaaiers-0624-KomVerhoal 26,46 -Westerkertaaiers-0625-KomVerhoal 236,46 -Westerkertaaiers-0626-KomVerhoal 54,46 -Westerkertaaiers-0627-KomVerhoal 191,46 -Westerkertaaiers-0628-KomVerhoal 111,46 -Westerkertaaiers-0629-KomVerhoal 33,46 -Westerkertaaiers-0631-KomVerhoal 101,46 -Westerkertaaiers-0632-KomVerhoal 12,46 -Westerkertaaiers-0633-KomVerhoal 38,46 -Westerkertaaiers-0634-KomVerhoal 140,46 -Westerkertaaiers-0635-KomVerhoal 52,46 -Westerkertaaiers-0636-KomVerhoal 61,46 -Westerkertaaiers-0637-KomVerhoal 199,46 -Westerkertaaiers-0638-KomVerhoal 145,46 -Westerkertaaiers-0639-KomVerhoal 127,46 -Westerkertaaiers-0640-KomVerhoal 103,46 -Westerkertaaiers-0641-KomVerhoal 26,46 -Westerkertaaiers-0642-KomVerhoal 243,46 -Westerkertaaiers-0644-KomVerhoal 139,46 -Westerkertaaiers-0646-KomVerhoal 133,46 -Westerkertaaiers-0647-KomVerhoal 86,46 -Westerkertaaiers-0648-KomVerhoal 211,46 -Westerkertaaiers-0649-KomVerhoal 155,46 -Westerkertaaiers-0650-KomVerhoal 114,46 -Westerkertaaiers-0651-KomVerhoal 97,46 -Westerkertaaiers-0652-KomVerhoal 86,46 -Westerkertaaiers-0654-KomVerhoal 79,46 -Westerkertaaiers-0655-KomVerhoal 102,46 -Westerkertaaiers-0656-KomVerhoal 99,46 -Westerkertaaiers-0657-KomVerhoal 115,46 -Westerkertaaiers-0658-KomVerhoal 127,46 -Westerkertaaiers-0660-KomVerhoal 74,46 -Westerkertaaiers-0661-KomVerhoal 61,46 -Westerkertaaiers-0663-KomVerhoal 80,46 -Westerkertaaiers-0664-KomVerhoal 60,46 -Westerkertaaiers-0665-KomVerhoal 102,46 -Westerkertaaiers-0666-KomVerhoal 67,46 -Westerkertaaiers-0667-KomVerhoal 69,46 -Westerkertaaiers-0669-KomVerhoal 91,46 -Westerkertaaiers-0670-KomVerhoal 149,46 -Westerkertaaiers-0671-KomVerhoal 47,46 -Westerkertaaiers-0672-KomVerhoal 194,46 -Westerkertaaiers-0673-KomVerhoal 130,46 -Westerkertaaiers-0674-KomVerhoal 122,46 -Westerkertaaiers-0675-KomVerhoal 59,46 -Westerkertaaiers-0676-KomVerhoal 73,46 -Westerkertaaiers-0677-KomVerhoal 130,46 -Westerkertaaiers-0678-KomVerhoal 84,46 -Westerkertaaiers-0679-KomVerhoal 146,46 -Westerkertaaiers-0680-KomVerhoal 93,46 -Westerkertaaiers-0682-KomVerhoal 75,46 -Westerkertaaiers-0683-KomVerhoal 192,46 -Westerkertaaiers-0684-KomVerhoal 84,46 -Westerkertaaiers-0685-KomVerhoal 58,46 -Westerkertaaiers-0686-KomVerhoal 72,46 -Westerkertaaiers-0687-KomVerhoal 116,46 -Westerkertaaiers-0688-KomVerhoal 152,46 -Westerkertaaiers-0689-KomVerhoal 121,46 -Westerkertaaiers-0690-KomVerhoal 190,46 -Westerkertaaiers-0691-KomVerhoal 59,46 -Westerkertaaiers-0692-KomVerhoal 104,46 -Westerkertaaiers-0693-KomVerhoal 49,46 -Westerkertaaiers-0694-KomVerhoal 106,46 -Westerkertaaiers-0696-KomVerhoal 112,46 -Westerkertaaiers-0697-KomVerhoal 115,46 -Westerkertaaiers-0698-KomVerhoal 265,46 -Westerkertaaiers-0699-KomVerhoal 45,46 -Westerkertaaiers-0700-KomVerhoal 224,46 -Westerkertaaiers-0701-KomVerhoal 49,46 -Westerkertaaiers-0702-KomVerhoal 87,46 -Westerkertaaiers-0703-KomVerhoal 74,46 -Westerkertaaiers-0704-KomVerhoal 57,46 -Westerkertaaiers-0705-KomVerhoal 230,46 -Westerkertaaiers-0707-KomVerhoal 292,46 -Westerkertaaiers-0708-KomVerhoal 65,46 -Westerkertaaiers-0709-KomVerhoal 181,46 -Westerkertaaiers-0710-KomVerhoal 49,46 -Westerkertaaiers-0711-KomVerhoal 159,46 -Westerkertaaiers-0712-KomVerhoal 115,46 -Westerkertaaiers-0713-KomVerhoal 63,46 -Westerkertaaiers-0715-KomVerhoal 130,46 -Westerkertaaiers-0716-KomVerhoal 62,46 -Westerkertaaiers-0717-KomVerhoal 52,46 -Westerkertaaiers-0718-KomVerhoal 79,46 -Westerkertaaiers-0719-KomVerhoal 23,46 -Westerkertaaiers-0720-KomVerhoal 141,46 -Westerkertaaiers-0721-KomVerhoal 15,46 -Westerkertaaiers-0722-KomVerhoal 33,46 -Westerkertaaiers-0723-KomVerhoal 140,46 -Westerkertaaiers-0724-KomVerhoal 25,46 -Westerkertaaiers-0725-KomVerhoal 87,46 -Westerkertaaiers-0726-KomVerhoal 119,46 -Westerkertaaiers-0727-KomVerhoal 94,46 -Westerkertaaiers-0728-KomVerhoal 70,46 -Westerkertaaiers-0729-KomVerhoal 52,46 -Westerkertaaiers-0730-KomVerhoal 32,46 -Westerkertaaiers-0731-KomVerhoal 61,46 -Westerkertaaiers-0732-KomVerhoal 42,46 -Westerkertaaiers-0733-KomVerhoal 40,46 -Westerkertaaiers-0734-KomVerhoal 24,46 -Westerkertaaiers-0735-KomVerhoal 64,46 -Westerkertaaiers-0736-KomVerhoal 54,46 -Westerkertaaiers-0737-KomVerhoal 41,46 -Westerkertaaiers-0738-KomVerhoal 125,46 -Westerkertaaiers-0739-KomVerhoal 89,46 -Westerkertaaiers-0740-KomVerhoal 137,46 -Westerkertaaiers-0741-KomVerhoal 93,46 -Westerkertaaiers-0743-KomVerhoal 38,46 -Westerkertaaiers-0744-KomVerhoal 72,46 -Westerkertaaiers-0745-KomVerhoal 29,46 -Westerkertaaiers-0746-KomVerhoal 123,46 -Westerkertaaiers-0747-KomVerhoal 72,46 -Westerkertaaiers-0749-SchiersteVerhoal 65,46 -Westerkertaaiers-0750-SchiersteVerhoal 107,46 -Westerkertaaiers-0751-SchiersteVerhoal 144,46 -Westerkertaaiers-0753-SchiersteVerhoal 93,46 -Westerkertaaiers-0754-SchiersteVerhoal 90,46 -Westerkertaaiers-0755-SchiersteVerhoal 113,46 -Westerkertaaiers-0756-SchiersteVerhoal 54,46 -Westerkertaaiers-0757-SchiersteVerhoal 33,46 -Westerkertaaiers-0758-SchiersteVerhoal 147,46 -Westerkertaaiers-0759-SchiersteVerhoal 116,46 -Westerkertaaiers-0760-SchiersteVerhoal 28,46 -Westerkertaaiers-0761-SchiersteVerhoal 32,46 -Westerkertaaiers-0762-SchiersteVerhoal 100,46 -Westerkertaaiers-0763-SchiersteVerhoal 172,46 -Westerkertaaiers-0764-SchiersteVerhoal 126,46 -Westerkertaaiers-0765-SchiersteVerhoal 58,46 -Westerkertaaiers-0766-SchiersteVerhoal 30,46 -Westerkertaaiers-0767-SchiersteVerhoal 86,46 -Westerkertaaiers-0768-SchiersteVerhoal 89,46 -Westerkertaaiers-0769-SchiersteVerhoal 53,46 -Westerkertaaiers-0770-SchiersteVerhoal 132,46 -Westerkertaaiers-0771-SchiersteVerhoal 123,46 -Westerkertaaiers-0772-SchiersteVerhoal 75,46 -Westerkertaaiers-0773-SchiersteVerhoal 20,46 -Westerkertaaiers-0774-SchiersteVerhoal 49,46 -Westerkertaaiers-0775-SchiersteVerhoal 62,46 -Westerkertaaiers-0776-SchiersteVerhoal 33,46 -Westerkertaaiers-0777-SchiersteVerhoal 24,46 -Westerkertaaiers-0778-SchiersteVerhoal 66,46 -Westerkertaaiers-0779-SchiersteVerhoal 113,46 -Westerkertaaiers-0781-SchiersteVerhoal 148,46 -Westerkertaaiers-0782-SchiersteVerhoal 93,46 -Westerkertaaiers-0783-SchiersteVerhoal 38,46 -Westerkertaaiers-0784-SchiersteVerhoal 180,46 -Westerkertaaiers-0785-SchiersteVerhoal 77,46 -Westerkertaaiers-0786-SchiersteVerhoal 157,46 -Westerkertaaiers-0787-SchiersteVerhoal 228,46 -Westerkertaaiers-0788-SchiersteVerhoal 63,46 -Westerkertaaiers-0789-SchiersteVerhoal 123,46 -Westerkertaaiers-0790-SchiersteVerhoal 60,46 -Westerkertaaiers-0791-SchiersteVerhoal 76,46 -Westerkertaaiers-0792-SchiersteVerhoal 165,46 -Westerkertaaiers-0793-SchiersteVerhoal 146,46 -Westerkertaaiers-0794-SchiersteVerhoal 94,46 -Westerkertaaiers-0795-SchiersteVerhoal 174,46 -Westerkertaaiers-0797-SchiersteVerhoal 14,46 -Westerkertaaiers-0798-SchiersteVerhoal 92,46 -Westerkertaaiers-0799-SchiersteVerhoal 56,46 -Westerkertaaiers-0800-SchiersteVerhoal 175,46 -Westerkertaaiers-0801-SchiersteVerhoal 11,46 -Westerkertaaiers-0802-SchiersteVerhoal 46,46 -Westerkertaaiers-0803-SchiersteVerhoal 16,46 -Westerkertaaiers-0804-SchiersteVerhoal 57,46 -Westerkertaaiers-0805-SchiersteVerhoal 26,46 -Westerkertaaiers-0806-SchiersteVerhoal 59,46 -Westerkertaaiers-0807-SchiersteVerhoal 29,46 -Westerkertaaiers-0808-SchiersteVerhoal 25,46 -Westerkertaaiers-0809-SchiersteVerhoal 101,46 -Westerkertaaiers-0810-SchiersteVerhoal 74,46 -Westerkertaaiers-0811-SchiersteVerhoal 126,46 -Westerkertaaiers-0812-SchiersteVerhoal 176,46 -Westerkertaaiers-0813-SchiersteVerhoal 93,46 -Westerkertaaiers-0814-SchiersteVerhoal 193,46 -Westerkertaaiers-0815-SchiersteVerhoal 33,46 -Westerkertaaiers-0816-SchiersteVerhoal 33,46 -Westerkertaaiers-0817-SchiersteVerhoal 79,46 -Westerkertaaiers-0818-SchiersteVerhoal 40,46 -Westerkertaaiers-0820-SchiersteVerhoal 61,46 -Westerkertaaiers-0822-SchiersteVerhoal 69,46 -Westerkertaaiers-0823-SchiersteVerhoal 83,46 -Westerkertaaiers-0825-SchiersteVerhoal 62,46 -Westerkertaaiers-0826-SchiersteVerhoal 100,46 -Westerkertaaiers-0827-SchiersteVerhoal 65,46 -Westerkertaaiers-0828-SchiersteVerhoal 108,46 -Westerkertaaiers-0829-SchiersteVerhoal 32,46 -Westerkertaaiers-0830-SchiersteVerhoal 125,46 -Westerkertaaiers-0831-SchiersteVerhoal 108,46 -Westerkertaaiers-0832-SchiersteVerhoal 76,46 -Westerkertaaiers-0833-SchiersteVerhoal 108,46 -Westerkertaaiers-0834-SchiersteVerhoal 96,46 -Westerkertaaiers-0835-SchiersteVerhoal 63,46 -Westerkertaaiers-0836-SchiersteVerhoal 52,46 -Westerkertaaiers-0837-SchiersteVerhoal 80,46 -Westerkertaaiers-0838-SchiersteVerhoal 48,46 -Westerkertaaiers-0839-SchiersteVerhoal 46,46 -Westerkertaaiers-0840-SchiersteVerhoal 111,46 -Westerkertaaiers-0841-SchiersteVerhoal 43,46 -Westerkertaaiers-0843-SchiersteVerhoal 62,46 -Westerkertaaiers-0844-SchiersteVerhoal 69,46 -Westerkertaaiers-0845-SchiersteVerhoal 155,46 -Westerkertaaiers-0846-SchiersteVerhoal 78,46 -Westerkertaaiers-0847-SchiersteVerhoal 133,46 -Westerkertaaiers-0848-SchiersteVerhoal 80,46 -Westerkertaaiers-0849-SchiersteVerhoal 75,46 -Westerkertaaiers-0850-SchiersteVerhoal 84,46 -Westerkertaaiers-0851-SchiersteVerhoal 102,46 -Westerkertaaiers-0852-SchiersteVerhoal 114,46 -Westerkertaaiers-0853-SchiersteVerhoal 223,46 -Westerkertaaiers-0854-SchiersteVerhoal 95,46 -Westerkertaaiers-0855-SchiersteVerhoal 48,46 -Westerkertaaiers-0856-SchiersteVerhoal 66,46 -Westerkertaaiers-0857-SchiersteVerhoal 135,46 -Westerkertaaiers-0858-SchiersteVerhoal 80,46 -Westerkertaaiers-0859-SchiersteVerhoal 119,46 -Westerkertaaiers-0860-SchiersteVerhoal 41,46 -Westerkertaaiers-0861-SchiersteVerhoal 187,46 -Westerkertaaiers-0862-SchiersteVerhoal 61,46 -Westerkertaaiers-0863-SchiersteVerhoal 65,46 -Westerkertaaiers-0864-SchiersteVerhoal 71,46 -Westerkertaaiers-0865-SchiersteVerhoal 47,46 -Westerkertaaiers-0866-SchiersteVerhoal 45,46 -Westerkertaaiers-0867-SchiersteVerhoal 124,46 -Westerkertaaiers-0868-SchiersteVerhoal 43,46 -Westerkertaaiers-0870-SchiersteVerhoal 61,46 -Westerkertaaiers-0871-SchiersteVerhoal 89,46 -Westerkertaaiers-0872-SchiersteVerhoal 194,46 -Westerkertaaiers-0873-SchiersteVerhoal 46,46 -Westerkertaaiers-0874-SchiersteVerhoal 34,46 -Westerkertaaiers-0875-SchiersteVerhoal 59,46 -Westerkertaaiers-0877-SchiersteVerhoal 68,46 -Westerkertaaiers-0878-SchiersteVerhoal 60,46 -Westerkertaaiers-0879-SchiersteVerhoal 13,46 -Westerkertaaiers-0880-SchiersteVerhoal 63,46 -Westerkertaaiers-0881-SchiersteVerhoal 78,46 -Westerkertaaiers-0882-SchiersteVerhoal 110,46 -Westerkertaaiers-0883-SchiersteVerhoal 84,46 -Westerkertaaiers-0884-SchiersteVerhoal 74,46 -Westerkertaaiers-0885-SchiersteVerhoal 69,46 -Westerkertaaiers-0886-SchiersteVerhoal 65,46 -Westerkertaaiers-0888-SchiersteVerhoal 29,46 -Westerkertaaiers-0889-SchiersteVerhoal 46,46 -Westerkertaaiers-0890-SchiersteVerhoal 107,46 -Westerkertaaiers-0891-SchiersteVerhoal 37,46 -Westerkertaaiers-0892-SchiersteVerhoal 65,46 -Westerkertaaiers-0893-SchiersteVerhoal 147,46 -Westerkertaaiers-0894-SchiersteVerhoal 94,46 -Westerkertaaiers-0895-SchiersteVerhoal 24,46 -Westerkertaaiers-0896-SchiersteVerhoal 113,46 -Westerkertaaiers-0897-SchiersteVerhoal 97,46 -Westerkertaaiers-0898-SchiersteVerhoal 101,46 -Westerkertaaiers-0899-SchiersteVerhoal 55,46 -Westerkertaaiers-0900-SchiersteVerhoal 63,46 -Westerkertaaiers-0901-SchiersteVerhoal 98,46 -Westerkertaaiers-0902-SchiersteVerhoal 103,46 -Westerkertaaiers-0903-SchiersteVerhoal 76,46 -Westerkertaaiers-0904-SchiersteVerhoal 76,46 -Westerkertaaiers-0905-SchiersteVerhoal 53,46 -Westerkertaaiers-0906-SchiersteVerhoal 106,46 -Westerkertaaiers-0907-SchiersteVerhoal 47,46 -Westerkertaaiers-0908-SchiersteVerhoal 95,46 -Westerkertaaiers-0909-SchiersteVerhoal 56,46 -Westerkertaaiers-0910-SchiersteVerhoal 36,46 -Westerkertaaiers-0911-SchiersteVerhoal 41,46 -Westerkertaaiers-0912-SchiersteVerhoal 24,46 -Westerkertaaiers-0913-SchiersteVerhoal 65,46 -Westerkertaaiers-0914-SchiersteVerhoal 61,46 -Westerkertaaiers-0915-SchiersteVerhoal 170,46 -Westerkertaaiers-0916-SchiersteVerhoal 67,46 -Westerkertaaiers-0917-SchiersteVerhoal 99,46 -Westerkertaaiers-0918-SchiersteVerhoal 53,46 -Westerkertaaiers-0919-SchiersteVerhoal 67,46 -Westerkertaaiers-0920-SchiersteVerhoal 175,46 -Westerkertaaiers-0921-SchiersteVerhoal 103,46 -Westerkertaaiers-0922-SchiersteVerhoal 63,46 -Westerkertaaiers-0923-SchiersteVerhoal 64,46 -Westerkertaaiers-0924-SchiersteVerhoal 45,46 -Westerkertaaiers-0925-SchiersteVerhoal 69,46 -Westerkertaaiers-0926-SchiersteVerhoal 53,46 -Westerkertaaiers-0927-SchiersteVerhoal 79,46 -Westerkertaaiers-0928-SchiersteVerhoal 140,46 -Westerkertaaiers-0929-SchiersteVerhoal 105,46 -Westerkertaaiers-0931-SchiersteVerhoal 21,46 -Westerkertaaiers-0932-SchiersteVerhoal 52,46 -Westerkertaaiers-0933-SchiersteVerhoal 87,46 -Westerkertaaiers-0934-SchiersteVerhoal 115,46 -Westerkertaaiers-0935-SchiersteVerhoal 49,46 -Westerkertaaiers-0936-SchiersteVerhoal 35,46 -Westerkertaaiers-0937-SchiersteVerhoal 85,46 -Westerkertaaiers-0938-SchiersteVerhoal 100,46 -Westerkertaaiers-0939-SchiersteVerhoal 26,46 -Westerkertaaiers-0940-SchiersteVerhoal 53,46 -Westerkertaaiers-0941-SchiersteVerhoal 85,46 -Westerkertaaiers-0942-SchiersteVerhoal 58,46 -Westerkertaaiers-0943-SchiersteVerhoal 63,46 -Westerkertaaiers-0944-SchiersteVerhoal 85,46 -Westerkertaaiers-0945-SchiersteVerhoal 44,46 -Westerkertaaiers-0946-SchiersteVerhoal 64,46 -Westerkertaaiers-0947-SchiersteVerhoal 114,46 -Westerkertaaiers-0948-SchiersteVerhoal 65,46 -Westerkertaaiers-0949-SchiersteVerhoal 83,46 -Westerkertaaiers-0950-SchiersteVerhoal 90,46 -Westerkertaaiers-0951-SchiersteVerhoal 33,46 -Westerkertaaiers-0952-SchiersteVerhoal 99,46 -Westerkertaaiers-0953-SchiersteVerhoal 43,46 -Westerkertaaiers-0954-SchiersteVerhoal 42,46 -Westerkertaaiers-0955-SchiersteVerhoal 134,46 -Westerkertaaiers-0956-SchiersteVerhoal 50,46 -Westerkertaaiers-0957-SchiersteVerhoal 58,46 -Westerkertaaiers-0958-SchiersteVerhoal 16,46 -Westerkertaaiers-0959-SchiersteVerhoal 118,46 -Westerkertaaiers-0960-SchiersteVerhoal 136,46 -Westerkertaaiers-0961-SchiersteVerhoal 122,46 -Westerkertaaiers-0962-SchiersteVerhoal 48,46 -Westerkertaaiers-0963-SchiersteVerhoal 82,46 -Westerkertaaiers-0964-SchiersteVerhoal 126,46 -Westerkertaaiers-0965-SchiersteVerhoal 73,46 -Westerkertaaiers-0966-SchiersteVerhoal 81,46 -Westerkertaaiers-0967-SchiersteVerhoal 75,46 -Westerkertaaiers-0968-SchiersteVerhoal 103,46 -Westerkertaaiers-0969-SchiersteVerhoal 120,46 -Westerkertaaiers-0971-SchiersteVerhoal 112,46 -Westerkertaaiers-0972-SchiersteVerhoal 74,46 -Westerkertaaiers-0973-SchiersteVerhoal 246,46 -Westerkertaaiers-0975-SchiersteVerhoal 126,46 -Westerkertaaiers-0976-SchiersteVerhoal 89,46 -Westerkertaaiers-0977-SchiersteVerhoal 34,46 -Westerkertaaiers-0978-SchiersteVerhoal 91,46 -Westerkertaaiers-0979-SchiersteVerhoal 145,46 -Westerkertaaiers-0980-SchiersteVerhoal 111,46 -Westerkertaaiers-0981-SchiersteVerhoal 83,46 -Westerkertaaiers-0982-SchiersteVerhoal 62,46 -Westerkertaaiers-0983-SchiersteVerhoal 36,46 -Westerkertaaiers-0984-SchiersteVerhoal 50,46 -Westerkertaaiers-0985-SchiersteVerhoal 76,46 -Westerkertaaiers-0986-SchiersteVerhoal 166,46 -Westerkertaaiers-0987-SchiersteVerhoal 191,46 -Westerkertaaiers-0988-SchiersteVerhoal 95,46 -Westerkertaaiers-0989-SchiersteVerhoal 48,46 -Westerkertaaiers-0990-SchiersteVerhoal 95,46 -Westerkertaaiers-0991-SchiersteVerhoal 118,46 -Westerkertaaiers-0992-SchiersteVerhoal 65,46 -Westerkertaaiers-0993-SchiersteVerhoal 148,46 -Westerkertaaiers-0994-SchiersteVerhoal 166,46 -Westerkertaaiers-0995-SchiersteVerhoal 115,46 -Westerkertaaiers-0996-SchiersteVerhoal 78,46 -Westerkertaaiers-0997-SchiersteVerhoal 92,46 -Westerkertaaiers-0998-SchiersteVerhoal 76,46 -Westerkertaaiers-0999-SchiersteVerhoal 168,46 -Westerkertaaiers-1000-SchiersteVerhoal 102,46 -Westerkertaaiers-1001-SchiersteVerhoal 87,46 -Westerkertaaiers-1002-SchiersteVerhoal 42,46 -Westerkertaaiers-1003-SchiersteVerhoal 93,46 -Westerkertaaiers-1004-SchiersteVerhoal 71,46 -Westerkertaaiers-1005-SchiersteVerhoal 49,46 -Westerkertaaiers-1006-SchiersteVerhoal 144,46 -Westerkertaaiers-1007-SchiersteVerhoal 199,46 -Westerkertaaiers-1008-SchiersteVerhoal 174,46 -Westerkertaaiers-1009-SchiersteVerhoal 243,46 -Westerkertaaiers-1010-SchiersteVerhoal 162,46 -Westerkertaaiers-1011-SchiersteVerhoal 107,46 -Westerkertaaiers-1012-SchiersteVerhoal 134,46 -Westerkertaaiers-1013-SchiersteVerhoal 161,46 -Westerkertaaiers-1014-SchiersteVerhoal 91,46 -Westerkertaaiers-1015-SchiersteVerhoal 225,46 -Westerkertaaiers-1018-SchiersteVerhoal 88,46 -Westerkertaaiers-1019-SchiersteVerhoal 52,46 -Westerkertaaiers-1020-SchiersteVerhoal 32,46 -Westerkertaaiers-1021-SchiersteVerhoal 121,46 -Westerkertaaiers-1022-SchiersteVerhoal 108,46 -Westerkertaaiers-1023-SchiersteVerhoal 32,46 -Westerkertaaiers-1024-SchiersteVerhoal 41,46 -Westerkertaaiers-1025-SchiersteVerhoal 174,46 -Westerkertaaiers-1026-SchiersteVerhoal 44,46 -Westerkertaaiers-1028-SchiersteVerhoal 81,46 -Westerkertaaiers-1029-SchiersteVerhoal 42,46 -Westerkertaaiers-1030-SchiersteVerhoal 31,46 -Westerkertaaiers-1031-SchiersteVerhoal 65,46 -Westerkertaaiers-1032-SchiersteVerhoal 36,46 -Westerkertaaiers-1033-SchiersteVerhoal 39,46 -Westerkertaaiers-1034-SchiersteVerhoal 72,46 -Westerkertaaiers-1035-SchiersteVerhoal 99,46 -Westerkertaaiers-1036-SchiersteVerhoal 45,46 -Westerkertaaiers-1037-SchiersteVerhoal 176,46 -Westerkertaaiers-1038-SchiersteVerhoal 38,46 -Westerkertaaiers-1039-SchiersteVerhoal 39,46 -Westerkertaaiers-1040-SchiersteVerhoal 24,46 -Westerkertaaiers-1041-SchiersteVerhoal 37,46 -Westerkertaaiers-1042-SchiersteVerhoal 21,46 -Westerkertaaiers-1043-SchiersteVerhoal 83,46 -Westerkertaaiers-1044-SchiersteVerhoal 69,46 -Westerkertaaiers-1045-SchiersteVerhoal 38,46 -Westerkertaaiers-1046-SchiersteVerhoal 30,46 -Westerkertaaiers-1047-SchiersteVerhoal 25,46 -Westerkertaaiers-1048-SchiersteVerhoal 22,46 -Westerkertaaiers-1049-SchiersteVerhoal 53,46 -Westerkertaaiers-1050-SchiersteVerhoal 93,46 -Westerkertaaiers-1051-SchiersteVerhoal 40,46 -Westerkertaaiers-1052-SchiersteVerhoal 101,46 -Westerkertaaiers-1053-SchiersteVerhoal 24,46 -Westerkertaaiers-1054-SchiersteVerhoal 54,46 -Westerkertaaiers-1055-SchiersteVerhoal 149,46 -Westerkertaaiers-1056-SchiersteVerhoal 49,46 -Westerkertaaiers-1058-SchiersteVerhoal 24,46 -Westerkertaaiers-1059-SchiersteVerhoal 50,46 -Westerkertaaiers-1060-SchiersteVerhoal 137,46 -Westerkertaaiers-1061-SchiersteVerhoal 70,46 -Westerkertaaiers-1063-SchiersteVerhoal 30,46 -Westerkertaaiers-1064-SchiersteVerhoal 163,46 -Westerkertaaiers-1065-SchiersteVerhoal 23,46 -Westerkertaaiers-1066-SchiersteVerhoal 55,46 -Westerkertaaiers-1067-SchiersteVerhoal 26,46 -Westerkertaaiers-1068-SchiersteVerhoal 25,46 -Westerkertaaiers-1069-SchiersteVerhoal 23,46 -Westerkertaaiers-1070-SchiersteVerhoal 123,46 -Westerkertaaiers-1071-SchiersteVerhoal 52,46 -Westerkertaaiers-1072-SchiersteVerhoal 67,46 -Westerkertaaiers-1073-SchiersteVerhoal 52,46 -Westerkertaaiers-1074-SchiersteVerhoal 118,46 -Westerkertaaiers-1075-SchiersteVerhoal 133,46 -Westerkertaaiers-1076-SchiersteVerhoal 49,46 -Westerkertaaiers-1077-SchiersteVerhoal 37,46 -Westerkertaaiers-1078-SchiersteVerhoal 18,46 -Westerkertaaiers-1079-SchiersteVerhoal 45,46 -Westerkertaaiers-1080-SchiersteVerhoal 40,46 -Westerkertaaiers-1081-SchiersteVerhoal 125,46 -Westerkertaaiers-1082-SchiersteVerhoal 44,46 -Westerkertaaiers-1083-SchiersteVerhoal 169,46 -Westerkertaaiers-1084-SchiersteVerhoal 138,46 -Westerkertaaiers-1085-SchiersteVerhoal 47,46 -Westerkertaaiers-1086-SchiersteVerhoal 111,46 -Westerkertaaiers-1087-SchiersteVerhoal 32,46 -Westerkertaaiers-1088-SchiersteVerhoal 52,46 -Westerkertaaiers-1089-SchiersteVerhoal 43,46 -Westerkertaaiers-1090-SchiersteVerhoal 26,46 -Westerkertaaiers-1091-SchiersteVerhoal 25,46 -Westerkertaaiers-1092-SchiersteVerhoal 43,46 -Westerkertaaiers-1093-SchiersteVerhoal 126,46 -Westerkertaaiers-1094-SchiersteVerhoal 159,46 -Westerkertaaiers-1095-SchiersteVerhoal 42,46 -Westerkertaaiers-1096-SchiersteVerhoal 35,46 -Westerkertaaiers-1097-SchiersteVerhoal 83,46 -Westerkertaaiers-1098-SchiersteVerhoal 34,46 -Westerkertaaiers-1099-SchiersteVerhoal 32,46 -Westerkertaaiers-1100-SchiersteVerhoal 78,46 -Westerkertaaiers-1101-SchiersteVerhoal 106,46 -Westerkertaaiers-1102-SchiersteVerhoal 87,46 -Westerkertaaiers-1103-SchiersteVerhoal 97,46 -Westerkertaaiers-1104-SchiersteVerhoal 62,46 -Westerkertaaiers-1106-SchiersteVerhoal 26,46 -Westerkertaaiers-1107-SchiersteVerhoal 54,46 -Westerkertaaiers-1108-SchiersteVerhoal 52,46 -Westerkertaaiers-1109-SchiersteVerhoal 69,46 -Westerkertaaiers-1111-SchiersteVerhoal 41,46 -Westerkertaaiers-1113-SchiersteVerhoal 24,46 -Westerkertaaiers-1114-SchiersteVerhoal 143,46 -Westerkertaaiers-1115-SchiersteVerhoal 71,46 -Westerkertaaiers-1116-SchiersteVerhoal 47,46 -Westerkertaaiers-1117-SchiersteVerhoal 48,46 -Westerkertaaiers-1118-SchiersteVerhoal 64,46 -Westerkertaaiers-1119-SchiersteVerhoal 20,46 -Westerkertaaiers-1120-SchiersteVerhoal 55,46 -Westerkertaaiers-1121-SchiersteVerhoal 165,46 -Westerkertaaiers-1122-SchiersteVerhoal 35,46 -Westerkertaaiers-1123-SchiersteVerhoal 52,46 -Westerkertaaiers-1124-SchiersteVerhoal 163,46 -Westerkertaaiers-1125-SchiersteVerhoal 90,46 -Westerkertaaiers-1126-SchiersteVerhoal 105,46 -Westerkertaaiers-1127-SchiersteVerhoal 101,46 -Westerkertaaiers-1128-SchiersteVerhoal 77,46 -Westerkertaaiers-1129-SchiersteVerhoal 39,46 -Westerkertaaiers-1130-SchiersteVerhoal 58,46 -Westerkertaaiers-1131-SchiersteVerhoal 21,46 -Westerkertaaiers-1132-SchiersteVerhoal 46,46 -Westerkertaaiers-1133-SchiersteVerhoal 102,46 -Westerkertaaiers-1134-SchiersteVerhoal 103,46 -Westerkertaaiers-1135-SchiersteVerhoal 67,46 -Westerkertaaiers-1136-SchiersteVerhoal 198,46 -Westerkertaaiers-1138-SchiersteVerhoal 118,46 -Westerkertaaiers-1140-SchiersteVerhoal 52,46 -Westerkertaaiers-1142-SchiersteVerhoal 143,46 -Westerkertaaiers-1143-SchiersteVerhoal 41,46 -Westerkertaaiers-1144-SchiersteVerhoal 35,46 -Westerkertaaiers-1145-SchiersteVerhoal 80,46 -Westerkertaaiers-1146-SchiersteVerhoal 247,46 -Westerkertaaiers-1147-SchiersteVerhoal 90,46 -Westerkertaaiers-1148-SchiersteVerhoal 97,46 -Westerkertaaiers-1149-SchiersteVerhoal 123,46 -Westerkertaaiers-1150-SchiersteVerhoal 67,46 -Westerkertaaiers-1151-SchiersteVerhoal 112,46 -Westerkertaaiers-1153-SchiersteVerhoal 77,46 -Westerkertaaiers-1154-SchiersteVerhoal 162,46 -Westerkertaaiers-1155-SchiersteVerhoal 18,46 -Westerkertaaiers-1156-SchiersteVerhoal 196,46 -Westerkertaaiers-1157-SchiersteVerhoal 172,46 -Westerkertaaiers-1158-SchiersteVerhoal 150,46 -Westerkertaaiers-1159-SchiersteVerhoal 148,46 -Westerkertaaiers-1160-SchiersteVerhoal 103,46 -Westerkertaaiers-1161-SchiersteVerhoal 131,46 -Westerkertaaiers-1162-SchiersteVerhoal 55,46 -Westerkertaaiers-1163-SchiersteVerhoal 30,46 -Westerkertaaiers-1164-SchiersteVerhoal 138,46 -Westerkertaaiers-1165-SchiersteVerhoal 104,46 -Westerkertaaiers-1166-SchiersteVerhoal 29,46 -Westerkertaaiers-1167-SchiersteVerhoal 74,46 -Westerkertaaiers-1168-SchiersteVerhoal 65,46 -Westerkertaaiers-1169-SchiersteVerhoal 93,46 -Westerkertaaiers-1170-SchiersteVerhoal 133,46 -Westerkertaaiers-1171-SchiersteVerhoal 43,46 -Westerkertaaiers-1172-SchiersteVerhoal 127,46 -Westerkertaaiers-1173-SchiersteVerhoal 131,46 -Westerkertaaiers-1174-SchiersteVerhoal 78,46 -Westerkertaaiers-1175-SchiersteVerhoal 72,46 -Westerkertaaiers-1176-SchiersteVerhoal 114,46 -Westerkertaaiers-1177-SchiersteVerhoal 37,46 -Westerkertaaiers-1178-SchiersteVerhoal 63,46 -Westerkertaaiers-1179-SchiersteVerhoal 125,46 -Westerkertaaiers-1180-SchiersteVerhoal 80,46 -Westerkertaaiers-1181-SchiersteVerhoal 120,46 -Westerkertaaiers-1182-SchiersteVerhoal 77,46 -Westerkertaaiers-1183-SchiersteVerhoal 97,46 -Westerkertaaiers-1184-SchiersteVerhoal 88,46 -Westerkertaaiers-1185-SchiersteVerhoal 119,46 -Westerkertaaiers-1186-SchiersteVerhoal 52,46 -Westerkertaaiers-1187-SchiersteVerhoal 75,46 -Westerkertaaiers-1188-SchiersteVerhoal 86,46 -Westerkertaaiers-1189-SchiersteVerhoal 41,46 -Westerkertaaiers-1190-SchiersteVerhoal 98,46 -Westerkertaaiers-1191-SchiersteVerhoal 65,46 -Westerkertaaiers-1192-SchiersteVerhoal 84,46 -Westerkertaaiers-1193-SchiersteVerhoal 169,46 -Westerkertaaiers-1194-SchiersteVerhoal 100,46 -Westerkertaaiers-1196-SchiersteVerhoal 54,46 -Westerkertaaiers-1197-SchiersteVerhoal 145,46 -Westerkertaaiers-1198-SchiersteVerhoal 149,46 -Westerkertaaiers-1199-SchiersteVerhoal 106,46 -Westerkertaaiers-1200-SchiersteVerhoal 39,46 -Westerkertaaiers-1201-SchiersteVerhoal 171,46 -Westerkertaaiers-1202-SchiersteVerhoal 92,46 -Westerkertaaiers-1203-SchiersteVerhoal 108,46 -Westerkertaaiers-1204-SchiersteVerhoal 87,46 -Westerkertaaiers-1205-SchiersteVerhoal 54,46 -Westerkertaaiers-1206-SchiersteVerhoal 134,46 -Westerkertaaiers-1207-SchiersteVerhoal 74,46 -Westerkertaaiers-1208-SchiersteVerhoal 64,46 -Westerkertaaiers-1209-SchiersteVerhoal 97,46 -Westerkertaaiers-1210-SchiersteVerhoal 75,46 -Westerkertaaiers-1211-SchiersteVerhoal 92,46 -Westerkertaaiers-1212-SchiersteVerhoal 68,46 -Westerkertaaiers-1213-SchiersteVerhoal 53,46 -Westerkertaaiers-1215-SchiersteVerhoal 139,46 -Westerkertaaiers-1216-SchiersteVerhoal 68,46 -Westerkertaaiers-1217-SchiersteVerhoal 228,46 -Westerkertaaiers-1218-SchiersteVerhoal 76,46 -Westerkertaaiers-1219-SchiersteVerhoal 101,46 -Westerkertaaiers-1220-SchiersteVerhoal 58,46 -Westerkertaaiers-1221-SchiersteVerhoal 80,46 -Westerkertaaiers-1223-SchiersteVerhoal 62,46 -Westerkertaaiers-1224-SchiersteVerhoal 102,46 -Westerkertaaiers-1225-SchiersteVerhoal 196,46 -Westerkertaaiers-1226-SchiersteVerhoal 41,46 -Westerkertaaiers-1227-SchiersteVerhoal 102,46 -Westerkertaaiers-1228-SchiersteVerhoal 84,46 -Westerkertaaiers-1231-SchiersteVerhoal 224,46 -Westerkertaaiers-1232-SchiersteVerhoal 100,46 -Westerkertaaiers-1233-SchiersteVerhoal 190,46 -Westerkertaaiers-1234-SchiersteVerhoal 299,46 -Westerkertaaiers-1235-SchiersteVerhoal 142,46 -Westerkertaaiers-1236-SchiersteVerhoal 91,46 -Westerkertaaiers-1237-SchiersteVerhoal 40,46 -Westerkertaaiers-1238-SchiersteVerhoal 115,46 -Westerkertaaiers-1239-SchiersteVerhoal 124,46 -Westerkertaaiers-1240-SchiersteVerhoal 69,46 -Westerkertaaiers-1241-SchiersteVerhoal 150,46 -Westerkertaaiers-1243-SchiersteVerhoal 159,46 -Westerkertaaiers-1245-SchiersteVerhoal 74,46 -Westerkertaaiers-1246-SchiersteVerhoal 57,46 -Westerkertaaiers-1247-SchiersteVerhoal 84,46 -Westerkertaaiers-1248-SchiersteVerhoal 69,46 -Westerkertaaiers-1249-SchiersteVerhoal 51,46 -Westerkertaaiers-1250-SchiersteVerhoal 161,46 -Westerkertaaiers-1251-SchiersteVerhoal 79,46 -Westerkertaaiers-1252-SchiersteVerhoal 31,46 -Westerkertaaiers-1253-SchiersteVerhoal 59,46 -Westerkertaaiers-1254-SchiersteVerhoal 57,46 -Westerkertaaiers-1255-SchiersteVerhoal 24,46 -Westerkertaaiers-1256-SchiersteVerhoal 20,46 -Westerkertaaiers-1257-SchiersteVerhoal 68,46 -Westerkertaaiers-1258-SchiersteVerhoal 50,46 -Westerkertaaiers-1259-SchiersteVerhoal 274,46 -Westerkertaaiers-1260-SchiersteVerhoal 60,46 -Westerkertaaiers-1261-SchiersteVerhoal 95,46 -Westerkertaaiers-1262-SchiersteVerhoal 56,46 -Westerkertaaiers-1263-SchiersteVerhoal 62,46 -Westerkertaaiers-1264-SchiersteVerhoal 9,46 -Westerkertaaiers-1265-SchiersteVerhoal 77,46 -Westerkertaaiers-1266-SchiersteVerhoal 66,46 -Westerkertaaiers-1267-SchiersteVerhoal 89,46 -Westerkertaaiers-1268-SchiersteVerhoal 44,46 -Westerkertaaiers-1269-SchiersteVerhoal 32,46 -Westerkertaaiers-1270-SchiersteVerhoal 52,46 -Westerkertaaiers-1271-SchiersteVerhoal 186,46 -Westerkertaaiers-1272-SchiersteVerhoal 94,46 -Westerkertaaiers-1274-SchiersteVerhoal 49,46 -Westerkertaaiers-1275-SchiersteVerhoal 104,46 -Westerkertaaiers-1276-SchiersteVerhoal 81,46 -Westerkertaaiers-1277-SchiersteVerhoal 122,46 -Westerkertaaiers-1278-SchiersteVerhoal 28,46 -Westerkertaaiers-1279-SchiersteVerhoal 160,46 -Westerkertaaiers-1280-SchiersteVerhoal 78,46 -Westerkertaaiers-1281-SchiersteVerhoal 75,46 -Westerkertaaiers-1282-SchiersteVerhoal 216,46 -Westerkertaaiers-1283-SchiersteVerhoal 64,46 -Westerkertaaiers-1284-SchiersteVerhoal 186,46 -Westerkertaaiers-1285-SchiersteVerhoal 93,46 -Westerkertaaiers-1286-SchiersteVerhoal 135,46 -Westerkertaaiers-1288-SchiersteVerhoal 154,46 -Westerkertaaiers-1289-SchiersteVerhoal 158,46 -Westerkertaaiers-1290-SchiersteVerhoal 66,46 -Westerkertaaiers-1291-SchiersteVerhoal 20,46 -Westerkertaaiers-1292-SchiersteVerhoal 51,46 -Westerkertaaiers-1293-SchiersteVerhoal 73,46 -Westerkertaaiers-1294-SchiersteVerhoal 61,46 -Westerkertaaiers-1295-SchiersteVerhoal 69,46 -Westerkertaaiers-1296-SchiersteVerhoal 74,46 -Westerkertaaiers-1297-SchiersteVerhoal 31,46 -Westerkertaaiers-1298-SchiersteVerhoal 41,46 -Westerkertaaiers-1299-SchiersteVerhoal 63,46 -Westerkertaaiers-1300-SchiersteVerhoal 93,46 -Westerkertaaiers-1301-SchiersteVerhoal 95,46 -Westerkertaaiers-1302-SchiersteVerhoal 60,46 -Westerkertaaiers-1303-SchiersteVerhoal 124,46 -Westerkertaaiers-1304-SchiersteVerhoal 65,46 -Westerkertaaiers-1305-SchiersteVerhoal 90,46 -Westerkertaaiers-1306-SchiersteVerhoal 94,46 -Westerkertaaiers-1307-SchiersteVerhoal 98,46 -Westerkertaaiers-1308-SchiersteVerhoal 38,46 -Westerkertaaiers-1309-SchiersteVerhoal 61,46 -Westerkertaaiers-1310-SchiersteVerhoal 98,46 -Westerkertaaiers-1311-SchiersteVerhoal 58,46 -Westerkertaaiers-1312-SchiersteVerhoal 100,46 -Westerkertaaiers-1313-SchiersteVerhoal 65,46 -Westerkertaaiers-1314-SchiersteVerhoal 53,46 -Westerkertaaiers-1315-SchiersteVerhoal 64,46 -Westerkertaaiers-1316-SchiersteVerhoal 119,46 -Westerkertaaiers-1317-SchiersteVerhoal 55,46 -Westerkertaaiers-1318-SchiersteVerhoal 44,46 -Westerkertaaiers-1319-SchiersteVerhoal 26,46 -Westerkertaaiers-1320-SchiersteVerhoal 66,46 -Westerkertaaiers-1321-SchiersteVerhoal 95,46 -Westerkertaaiers-1322-SchiersteVerhoal 242,46 -Westerkertaaiers-1324-SchiersteVerhoal 15,46 -Westerkertaaiers-1325-SchiersteVerhoal 83,46 -Westerkertaaiers-1326-SchiersteVerhoal 32,46 -Westerkertaaiers-1327-SchiersteVerhoal 86,46 -Westerkertaaiers-1328-SchiersteVerhoal 54,46 -Westerkertaaiers-1329-SchiersteVerhoal 66,46 -Westerkertaaiers-1330-SchiersteVerhoal 145,46 -Westerkertaaiers-1332-SchiersteVerhoal 96,46 -Westerkertaaiers-1333-SchiersteVerhoal 89,46 -Westerkertaaiers-1334-SchiersteVerhoal 87,46 -Westerkertaaiers-1335-SchiersteVerhoal 93,46 -Westerkertaaiers-1336-SchiersteVerhoal 68,46 -Westerkertaaiers-1337-SchiersteVerhoal 87,46 -Westerkertaaiers-1338-SchiersteVerhoal 65,46 -Westerkertaaiers-1339-SchiersteVerhoal 44,46 -Westerkertaaiers-1340-SchiersteVerhoal 89,46 -Westerkertaaiers-1341-SchiersteVerhoal 53,46 -Westerkertaaiers-1342-SchiersteVerhoal 43,46 -Westerkertaaiers-1343-SchiersteVerhoal 90,46 -Westerkertaaiers-1344-SchiersteVerhoal 52,46 -Westerkertaaiers-1345-SchiersteVerhoal 49,46 -Westerkertaaiers-1346-SchiersteVerhoal 77,46 -Westerkertaaiers-1347-SchiersteVerhoal 81,46 -Westerkertaaiers-1348-SchiersteVerhoal 87,46 -Westerkertaaiers-1349-SchiersteVerhoal 54,46 -Westerkertaaiers-1350-SchiersteVerhoal 96,46 -Westerkertaaiers-1351-SchiersteVerhoal 69,46 -Westerkertaaiers-1352-SchiersteVerhoal 59,46 -Westerkertaaiers-1353-SchiersteVerhoal 66,46 -Westerkertaaiers-1354-SchiersteVerhoal 49,46 -Westerkertaaiers-1355-SchiersteVerhoal 11,46 -Westerkertaaiers-1356-SchiersteVerhoal 127,46 -Westerkertaaiers-1357-SchiersteVerhoal 42,46 -Westerkertaaiers-1358-SchiersteVerhoal 50,46 -Westerkertaaiers-1359-SchiersteVerhoal 35,46 -Westerkertaaiers-1360-SchiersteVerhoal 42,46 -Westerkertaaiers-1361-SchiersteVerhoal 108,46 -Westerkertaaiers-1362-SchiersteVerhoal 71,46 -Westerkertaaiers-1363-SchiersteVerhoal 68,46 -Westerkertaaiers-1364-SchiersteVerhoal 134,46 -Westerkertaaiers-1365-SchiersteVerhoal 87,46 -Westerkertaaiers-1366-SchiersteVerhoal 91,46 -Westerkertaaiers-1367-SchiersteVerhoal 59,46 -Westerkertaaiers-1368-SchiersteVerhoal 29,46 -Westerkertaaiers-1369-SchiersteVerhoal 160,46 -Westerkertaaiers-1370-SchiersteVerhoal 93,46 -Westerkertaaiers-1371-SchiersteVerhoal 132,46 -Westerkertaaiers-1372-SchiersteVerhoal 79,46 -Westerkertaaiers-1373-SchiersteVerhoal 147,46 -Westerkertaaiers-1375-SchiersteVerhoal 111,46 -Westerkertaaiers-1376-SchiersteVerhoal 140,46 -Westerkertaaiers-1377-SchiersteVerhoal 22,46 -Westerkertaaiers-1378-SchiersteVerhoal 93,46 -Westerkertaaiers-1380-SchiersteVerhoal 250,46 -Westerkertaaiers-1381-SchiersteVerhoal 103,46 -Westerkertaaiers-1382-SchiersteVerhoal 156,46 -Westerkertaaiers-1386-SchiersteVerhoal 94,46 -Westerkertaaiers-1388-SchiersteVerhoal 74,46 -Westerkertaaiers-1390-SchiersteVerhoal 232,46 -Westerkertaaiers-1391-SchiersteVerhoal 225,46 -Westerkertaaiers-1392-SchiersteVerhoal 45,46 -Westerkertaaiers-1393-SlagTwaalf 67,46 -Westerkertaaiers-1394-SlagTwaalf 81,46 -Westerkertaaiers-1395-SlagTwaalf 31,46 -Westerkertaaiers-1396-SlagTwaalf 30,46 -Westerkertaaiers-1397-SlagTwaalf 36,46 -Westerkertaaiers-1398-SlagTwaalf 87,46 -Westerkertaaiers-1399-SlagTwaalf 72,46 -Westerkertaaiers-1400-SlagTwaalf 24,46 -Westerkertaaiers-1402-SlagTwaalf 18,46 -Westerkertaaiers-1403-SlagTwaalf 154,46 -Westerkertaaiers-1404-SlagTwaalf 100,46 -Westerkertaaiers-1405-SlagTwaalf 49,46 -Westerkertaaiers-1406-SlagTwaalf 73,46 -Westerkertaaiers-1407-SlagTwaalf 9,46 -Westerkertaaiers-1408-SlagTwaalf 51,46 -Westerkertaaiers-1409-SlagTwaalf 97,46 -Westerkertaaiers-1410-SlagTwaalf 18,46 -Westerkertaaiers-1411-SlagTwaalf 87,46 -Westerkertaaiers-1412-SlagTwaalf 189,46 -Westerkertaaiers-1413-SlagTwaalf 60,46 -Westerkertaaiers-1414-SlagTwaalf 92,46 -Westerkertaaiers-1415-SlagTwaalf 192,46 -Westerkertaaiers-1416-SlagTwaalf 112,46 -Westerkertaaiers-1417-SlagTwaalf 17,46 -Westerkertaaiers-1418-SlagTwaalf 56,46 -Westerkertaaiers-1419-SlagTwaalf 96,46 -Westerkertaaiers-1420-SlagTwaalf 34,46 -Westerkertaaiers-1421-SlagTwaalf 9,46 -Westerkertaaiers-1422-SlagTwaalf 106,46 -Westerkertaaiers-1424-SlagTwaalf 23,46 -Westerkertaaiers-1425-SlagTwaalf 25,46 -Westerkertaaiers-1426-SlagTwaalf 120,46 -Westerkertaaiers-1427-SlagTwaalf 17,46 -Westerkertaaiers-1428-SlagTwaalf 105,46 -Westerkertaaiers-1429-SlagTwaalf 75,46 -Westerkertaaiers-1430-SlagTwaalf 103,46 -Westerkertaaiers-1431-SlagTwaalf 52,46 -Westerkertaaiers-1432-SlagTwaalf 41,46 -Westerkertaaiers-1433-SlagTwaalf 12,46 -Westerkertaaiers-1434-SlagTwaalf 22,46 -Westerkertaaiers-1435-SlagTwaalf 91,46 -Westerkertaaiers-1436-SlagTwaalf 14,46 -Westerkertaaiers-1437-SlagTwaalf 10,46 -Westerkertaaiers-1438-SlagTwaalf 11,46 -Westerkertaaiers-1439-SlagTwaalf 123,46 -Westerkertaaiers-1440-SlagTwaalf 24,46 -Westerkertaaiers-1441-SlagTwaalf 16,46 -Westerkertaaiers-1442-SlagTwaalf 53,46 -Westerkertaaiers-1443-SlagTwaalf 30,46 -Westerkertaaiers-1444-SlagTwaalf 99,46 -Westerkertaaiers-1445-SlagTwaalf 127,46 -Westerkertaaiers-1446-SlagTwaalf 62,46 -Westerkertaaiers-1447-SlagTwaalf 40,46 -Westerkertaaiers-1448-SlagTwaalf 40,46 -Westerkertaaiers-1449-SlagTwaalf 49,46 -Westerkertaaiers-1450-SlagTwaalf 39,46 -Westerkertaaiers-1451-SlagTwaalf 61,46 -Westerkertaaiers-1452-SlagTwaalf 70,46 -Westerkertaaiers-1453-SlagTwaalf 35,46 -Westerkertaaiers-1454-SlagTwaalf 20,46 -Westerkertaaiers-1455-SlagTwaalf 70,46 -Westerkertaaiers-1456-SlagTwaalf 72,46 -Westerkertaaiers-1457-SlagTwaalf 138,46 -Westerkertaaiers-1458-SlagTwaalf 44,46 -Westerkertaaiers-1459-SlagTwaalf 34,46 -Westerkertaaiers-1460-SlagTwaalf 27,46 -Westerkertaaiers-1461-SlagTwaalf 104,46 -Westerkertaaiers-1462-SlagTwaalf 37,46 -Westerkertaaiers-1463-SlagTwaalf 63,46 -Westerkertaaiers-1464-SlagTwaalf 22,46 -Westerkertaaiers-1465-SlagTwaalf 85,46 -Westerkertaaiers-1467-SlagTwaalf 56,46 -Westerkertaaiers-1468-SlagTwaalf 81,46 -Westerkertaaiers-1469-SlagTwaalf 46,46 -Westerkertaaiers-1470-SlagTwaalf 122,46 -Westerkertaaiers-1471-SlagTwaalf 86,46 -Westerkertaaiers-1472-SlagTwaalf 106,46 -Westerkertaaiers-1473-SlagTwaalf 37,46 -Westerkertaaiers-1474-SlagTwaalf 15,46 -Westerkertaaiers-1475-SlagTwaalf 23,46 -Westerkertaaiers-1476-SlagTwaalf 30,46 -Westerkertaaiers-1477-SlagTwaalf 90,46 -Westerkertaaiers-1478-SlagTwaalf 30,46 -Westerkertaaiers-1479-SlagTwaalf 163,46 -Westerkertaaiers-1480-SlagTwaalf 75,46 -Westerkertaaiers-1481-SlagTwaalf 141,46 -Westerkertaaiers-1482-SlagTwaalf 83,46 -Westerkertaaiers-1483-SlagTwaalf 35,46 -Westerkertaaiers-1484-SlagTwaalf 68,46 -Westerkertaaiers-1485-SlagTwaalf 58,46 -Westerkertaaiers-1486-SlagTwaalf 24,46 -Westerkertaaiers-1487-SlagTwaalf 60,46 -Westerkertaaiers-1488-SlagTwaalf 48,46 -Westerkertaaiers-1489-SlagTwaalf 31,46 -Westerkertaaiers-1490-SlagTwaalf 95,46 -Westerkertaaiers-1491-SlagTwaalf 36,46 -Westerkertaaiers-1492-SlagTwaalf 36,46 -Westerkertaaiers-1493-SlagTwaalf 67,46 -Westerkertaaiers-1494-SlagTwaalf 8,46 -Westerkertaaiers-1495-SlagTwaalf 54,46 -Westerkertaaiers-1496-SlagTwaalf 86,46 -Westerkertaaiers-1497-SlagTwaalf 40,46 -Westerkertaaiers-1498-SlagTwaalf 92,46 -Westerkertaaiers-1499-SlagTwaalf 51,46 -Westerkertaaiers-1500-SlagTwaalf 53,46 -Westerkertaaiers-1501-SlagTwaalf 94,46 -Westerkertaaiers-1502-SlagTwaalf 34,46 -Westerkertaaiers-1503-SlagTwaalf 76,46 -Westerkertaaiers-1504-SlagTwaalf 44,46 -Westerkertaaiers-1505-SlagTwaalf 69,46 -Westerkertaaiers-1506-SlagTwaalf 64,46 -Westerkertaaiers-1507-SlagTwaalf 25,46 -Westerkertaaiers-1508-SlagTwaalf 28,46 -Westerkertaaiers-1510-SlagTwaalf 67,46 -Westerkertaaiers-1511-SlagTwaalf 163,46 -Westerkertaaiers-1512-SlagTwaalf 56,46 -Westerkertaaiers-1513-SlagTwaalf 47,46 -Westerkertaaiers-1514-SlagTwaalf 93,46 -Westerkertaaiers-1515-SlagTwaalf 67,46 -Westerkertaaiers-1516-SlagTwaalf 79,46 -Westerkertaaiers-1517-SlagTwaalf 102,46 -Westerkertaaiers-1518-SlagTwaalf 116,46 -Westerkertaaiers-1519-SlagTwaalf 24,46 -Westerkertaaiers-1520-SlagTwaalf 27,46 -Westerkertaaiers-1521-SlagTwaalf 78,46 -Westerkertaaiers-1522-SlagTwaalf 51,46 -Westerkertaaiers-1523-SlagTwaalf 73,46 -Westerkertaaiers-1524-SlagTwaalf 67,46 -Westerkertaaiers-1525-SlagTwaalf 28,46 -Westerkertaaiers-1526-SlagTwaalf 28,46 -Westerkertaaiers-1527-SlagTwaalf 35,46 -Westerkertaaiers-1528-SlagTwaalf 73,46 -Westerkertaaiers-1529-SlagTwaalf 66,46 -Westerkertaaiers-1530-SlagTwaalf 49,46 -Westerkertaaiers-1531-SlagTwaalf 19,46 -Westerkertaaiers-1532-SlagTwaalf 81,46 -Westerkertaaiers-1533-SlagTwaalf 39,46 -Westerkertaaiers-1534-SlagTwaalf 10,46 -Westerkertaaiers-1535-SlagTwaalf 34,46 -Westerkertaaiers-1536-SlagTwaalf 52,46 -Westerkertaaiers-1537-SlagTwaalf 22,46 -Westerkertaaiers-1538-SlagTwaalf 27,46 -Westerkertaaiers-1539-SlagTwaalf 107,46 -Westerkertaaiers-1540-SlagTwaalf 109,46 -Westerkertaaiers-1541-SlagTwaalf 41,46 -Westerkertaaiers-1542-SlagTwaalf 31,46 -Westerkertaaiers-1543-SlagTwaalf 63,46 -Westerkertaaiers-1544-SlagTwaalf 78,46 -Westerkertaaiers-1545-SlagTwaalf 39,46 -Westerkertaaiers-1546-SlagTwaalf 38,46 -Westerkertaaiers-1547-SlagTwaalf 81,46 -Westerkertaaiers-1548-SlagTwaalf 79,46 -Westerkertaaiers-1549-SlagTwaalf 36,46 -Westerkertaaiers-1550-SlagTwaalf 121,46 -Westerkertaaiers-1551-SlagTwaalf 32,46 -Westerkertaaiers-1553-SlagTwaalf 70,46 -Westerkertaaiers-1554-SlagTwaalf 10,46 -Westerkertaaiers-1555-SlagTwaalf 53,46 -Westerkertaaiers-1556-SlagTwaalf 99,46 -Westerkertaaiers-1557-SlagTwaalf 97,46 -Westerkertaaiers-1558-SlagTwaalf 83,46 -Westerkertaaiers-1559-SlagTwaalf 138,46 -Westerkertaaiers-1560-SlagTwaalf 55,46 -Westerkertaaiers-1561-SlagTwaalf 37,46 -Westerkertaaiers-1562-SlagTwaalf 69,46 -Westerkertaaiers-1563-SlagTwaalf 85,46 -Westerkertaaiers-1564-SlagTwaalf 43,46 -Westerkertaaiers-1565-SlagTwaalf 26,46 -Westerkertaaiers-1566-SlagTwaalf 55,46 -Westerkertaaiers-1567-SlagTwaalf 158,46 -Westerkertaaiers-1568-SlagTwaalf 18,46 -Westerkertaaiers-1569-SlagTwaalf 127,46 -Westerkertaaiers-1570-SlagTwaalf 80,46 -Westerkertaaiers-1571-SlagTwaalf 54,46 -Westerkertaaiers-1572-SlagTwaalf 91,46 -Westerkertaaiers-1573-SlagTwaalf 48,46 -Westerkertaaiers-1574-SlagTwaalf 38,46 -Westerkertaaiers-1575-SlagTwaalf 56,46 -Westerkertaaiers-1576-SlagTwaalf 36,46 -Westerkertaaiers-1577-SlagTwaalf 78,46 -Westerkertaaiers-1578-SlagTwaalf 34,46 -Westerkertaaiers-1579-SlagTwaalf 35,46 -Westerkertaaiers-1580-SlagTwaalf 51,46 -Westerkertaaiers-1581-SlagTwaalf 57,46 -Westerkertaaiers-1582-SlagTwaalf 51,46 -Westerkertaaiers-1583-SlagTwaalf 41,46 -Westerkertaaiers-1584-SlagTwaalf 126,46 -Westerkertaaiers-1585-SlagTwaalf 175,46 -Westerkertaaiers-1586-SlagTwaalf 102,46 -Westerkertaaiers-1587-SlagTwaalf 21,46 -Westerkertaaiers-1588-SlagTwaalf 27,46 -Westerkertaaiers-1589-SlagTwaalf 24,46 -Westerkertaaiers-1590-SlagTwaalf 20,46 -Westerkertaaiers-1591-SlagTwaalf 101,46 -Westerkertaaiers-1592-SlagTwaalf 163,46 -Westerkertaaiers-1593-SlagTwaalf 21,46 -Westerkertaaiers-1594-SlagTwaalf 23,46 -Westerkertaaiers-1596-SlagTwaalf 12,46 -Westerkertaaiers-1597-SlagTwaalf 159,46 -Westerkertaaiers-1598-SlagTwaalf 94,46 -Westerkertaaiers-1599-SlagTwaalf 91,46 -Westerkertaaiers-1600-SlagTwaalf 23,46 -Westerkertaaiers-1601-SlagTwaalf 109,46 -Westerkertaaiers-1602-SlagTwaalf 61,46 -Westerkertaaiers-1603-SlagTwaalf 63,46 -Westerkertaaiers-1604-SlagTwaalf 143,46 -Westerkertaaiers-1605-SlagTwaalf 84,46 -Westerkertaaiers-1606-SlagTwaalf 54,46 -Westerkertaaiers-1607-SlagTwaalf 195,46 -Westerkertaaiers-1608-SlagTwaalf 28,46 -Westerkertaaiers-1609-SlagTwaalf 74,46 -Westerkertaaiers-1610-SlagTwaalf 40,46 -Westerkertaaiers-1611-SlagTwaalf 25,46 -Westerkertaaiers-1612-SlagTwaalf 31,46 -Westerkertaaiers-1613-SlagTwaalf 21,46 -Westerkertaaiers-1615-SlagTwaalf 11,46 -Westerkertaaiers-1616-SlagTwaalf 41,46 -Westerkertaaiers-1617-SlagTwaalf 30,46 -Westerkertaaiers-1618-SlagTwaalf 44,46 -Westerkertaaiers-1619-SlagTwaalf 75,46 -Westerkertaaiers-1620-SlagTwaalf 22,46 -Westerkertaaiers-1621-SlagTwaalf 78,46 -Westerkertaaiers-1622-SlagTwaalf 47,46 -Westerkertaaiers-1623-SlagTwaalf 80,46 -Westerkertaaiers-1624-SlagTwaalf 32,46 -Westerkertaaiers-1625-SlagTwaalf 34,46 -Westerkertaaiers-1626-SlagTwaalf 11,46 -Westerkertaaiers-1627-SlagTwaalf 88,46 -Westerkertaaiers-1628-SlagTwaalf 177,46 -Westerkertaaiers-1629-SlagTwaalf 72,46 -Westerkertaaiers-1630-SlagTwaalf 89,46 -Westerkertaaiers-1631-SlagTwaalf 102,46 -Westerkertaaiers-1632-SlagTwaalf 34,46 -Westerkertaaiers-1633-SlagTwaalf 53,46 -Westerkertaaiers-1634-SlagTwaalf 63,46 -Westerkertaaiers-1635-SlagTwaalf 30,46 -Westerkertaaiers-1636-SlagTwaalf 69,46 -Westerkertaaiers-1637-SlagTwaalf 107,46 -Westerkertaaiers-1638-SlagTwaalf 40,46 -Westerkertaaiers-1640-SlagTwaalf 11,46 -Westerkertaaiers-1641-SlagTwaalf 55,46 -Westerkertaaiers-1642-SlagTwaalf 31,46 -Westerkertaaiers-1643-SlagTwaalf 114,46 -Westerkertaaiers-1644-SlagTwaalf 71,46 -Westerkertaaiers-1645-SlagTwaalf 53,46 -Westerkertaaiers-1646-SlagTwaalf 43,46 -Westerkertaaiers-1647-SlagTwaalf 14,46 -Westerkertaaiers-1648-SlagTwaalf 90,46 -Westerkertaaiers-1649-SlagTwaalf 9,46 -Westerkertaaiers-1650-SlagTwaalf 34,46 -Westerkertaaiers-1651-SlagTwaalf 35,46 -Westerkertaaiers-1652-SlagTwaalf 19,46 -Westerkertaaiers-1653-SlagTwaalf 68,46 -Westerkertaaiers-1654-SlagTwaalf 82,46 -Westerkertaaiers-1655-SlagTwaalf 155,46 -Westerkertaaiers-1656-SlagTwaalf 44,46 -Westerkertaaiers-1657-SlagTwaalf 104,46 -Westerkertaaiers-1658-SlagTwaalf 42,46 -Westerkertaaiers-1659-SlagTwaalf 35,46 -Westerkertaaiers-1660-SlagTwaalf 87,46 -Westerkertaaiers-1661-SlagTwaalf 50,46 -Westerkertaaiers-1662-SlagTwaalf 29,46 -Westerkertaaiers-1663-SlagTwaalf 24,46 -Westerkertaaiers-1664-SlagTwaalf 40,46 -Westerkertaaiers-1665-SlagTwaalf 61,46 -Westerkertaaiers-1666-SlagTwaalf 34,46 -Westerkertaaiers-1667-SlagTwaalf 34,46 -Westerkertaaiers-1668-SlagTwaalf 41,46 -Westerkertaaiers-1670-SlagTwaalf 30,46 -Westerkertaaiers-1671-SlagTwaalf 59,46 -Westerkertaaiers-1672-SlagTwaalf 14,46 -Westerkertaaiers-1673-SlagTwaalf 58,46 -Westerkertaaiers-1674-SlagTwaalf 27,46 -Westerkertaaiers-1676-SlagTwaalf 31,46 -Westerkertaaiers-1677-SlagTwaalf 80,46 -Westerkertaaiers-1678-SlagTwaalf 49,46 -Westerkertaaiers-1679-SlagTwaalf 48,46 -Westerkertaaiers-1680-SlagTwaalf 18,46 -Westerkertaaiers-1681-SlagTwaalf 31,46 -Westerkertaaiers-1682-SlagTwaalf 89,46 -Westerkertaaiers-1683-SlagTwaalf 48,46 -Westerkertaaiers-1685-SlagTwaalf 105,46 -Westerkertaaiers-1686-SlagTwaalf 108,46 -Westerkertaaiers-1687-SlagTwaalf 55,46 -Westerkertaaiers-1688-SlagTwaalf 69,46 -Westerkertaaiers-1689-SlagTwaalf 118,46 -Westerkertaaiers-1690-SlagTwaalf 72,46 -Westerkertaaiers-1691-SlagTwaalf 18,46 -Westerkertaaiers-1692-SlagTwaalf 73,46 -Westerkertaaiers-1693-SlagTwaalf 79,46 -Westerkertaaiers-1694-SlagTwaalf 101,46 -Westerkertaaiers-1695-SlagTwaalf 25,46 -Westerkertaaiers-1696-SlagTwaalf 80,46 -Westerkertaaiers-1697-SlagTwaalf 20,46 -Westerkertaaiers-1698-SlagTwaalf 19,46 -Westerkertaaiers-1699-SlagTwaalf 22,46 -Westerkertaaiers-1700-SlagTwaalf 18,46 -Westerkertaaiers-1701-SlagTwaalf 70,46 -Westerkertaaiers-1702-SlagTwaalf 41,46 -Westerkertaaiers-1703-SlagTwaalf 25,46 -Westerkertaaiers-1704-SlagTwaalf 48,46 -Westerkertaaiers-1705-SlagTwaalf 55,46 -Westerkertaaiers-1706-SlagTwaalf 208,46 -Westerkertaaiers-1707-SlagTwaalf 45,46 -Westerkertaaiers-1708-SlagTwaalf 27,46 -Westerkertaaiers-1709-SlagTwaalf 28,46 -Westerkertaaiers-1710-SlagTwaalf 66,46 -Westerkertaaiers-1711-SlagTwaalf 102,46 -Westerkertaaiers-1712-SlagTwaalf 37,46 -Westerkertaaiers-1713-SlagTwaalf 53,46 -Westerkertaaiers-1714-SlagTwaalf 65,46 -Westerkertaaiers-1715-SlagTwaalf 11,46 -Westerkertaaiers-1716-SlagTwaalf 43,46 -Westerkertaaiers-1717-SlagTwaalf 126,46 -Westerkertaaiers-1718-SlagTwaalf 32,46 -Westerkertaaiers-1719-SlagTwaalf 57,46 -Westerkertaaiers-1720-SlagTwaalf 56,46 -Westerkertaaiers-1721-SlagTwaalf 41,46 -Westerkertaaiers-1722-SlagTwaalf 32,46 -Westerkertaaiers-1723-SlagTwaalf 91,46 -Westerkertaaiers-1724-SlagTwaalf 39,46 -Westerkertaaiers-1725-SlagTwaalf 96,46 -Westerkertaaiers-1726-SlagTwaalf 33,46 -Westerkertaaiers-1728-SlagTwaalf 74,46 -Westerkertaaiers-1729-SlagTwaalf 38,46 -Westerkertaaiers-1730-SlagTwaalf 34,46 -Westerkertaaiers-1731-SlagTwaalf 73,46 -Westerkertaaiers-1732-SlagTwaalf 100,46 -Westerkertaaiers-1733-SlagTwaalf 40,46 -Westerkertaaiers-1734-SlagTwaalf 160,46 -Westerkertaaiers-1735-SlagTwaalf 32,46 -Westerkertaaiers-1736-SlagTwaalf 44,46 -Westerkertaaiers-1737-SlagTwaalf 33,46 -Westerkertaaiers-1738-SlagTwaalf 16,46 -Westerkertaaiers-1739-SlagTwaalf 92,46 -Westerkertaaiers-1740-SlagTwaalf 101,46 -Westerkertaaiers-1741-SlagTwaalf 36,46 -Westerkertaaiers-1742-SlagTwaalf 28,46 -Westerkertaaiers-1743-SlagTwaalf 48,46 -Westerkertaaiers-1744-SlagTwaalf 67,46 -Westerkertaaiers-1745-SlagTwaalf 21,46 -Westerkertaaiers-1746-SlagTwaalf 12,46 -Westerkertaaiers-1747-SlagTwaalf 77,46 -Westerkertaaiers-1748-SlagTwaalf 28,46 -Westerkertaaiers-1749-SlagTwaalf 57,46 -Westerkertaaiers-1750-SlagTwaalf 91,46 -Westerkertaaiers-1751-SlagTwaalf 25,46 -Westerkertaaiers-1752-SlagTwaalf 21,46 -Westerkertaaiers-1754-SlagTwaalf 57,46 -Westerkertaaiers-1755-SlagTwaalf 100,46 -Westerkertaaiers-1756-SlagTwaalf 8,46 -Westerkertaaiers-1757-SlagTwaalf 51,46 -Westerkertaaiers-1758-SlagTwaalf 22,46 -Westerkertaaiers-1759-SlagTwaalf 26,46 -Westerkertaaiers-1760-SlagTwaalf 35,46 -Westerkertaaiers-1761-SlagTwaalf 81,46 -Westerkertaaiers-1762-SlagTwaalf 141,46 -Westerkertaaiers-1763-SlagTwaalf 26,46 -Westerkertaaiers-1764-SlagTwaalf 148,46 -Westerkertaaiers-1765-SlagTwaalf 44,46 -Westerkertaaiers-1766-SlagTwaalf 246,46 -Westerkertaaiers-1767-SlagTwaalf 147,46 -Westerkertaaiers-1768-SlagTwaalf 49,46 -Westerkertaaiers-1769-SlagTwaalf 18,46 -Westerkertaaiers-1770-SlagTwaalf 46,46 -Westerkertaaiers-1772-SlagTwaalf 47,46 -Westerkertaaiers-1773-SlagTwaalf 49,46 -Westerkertaaiers-1774-SlagTwaalf 52,46 -Westerkertaaiers-1775-SlagTwaalf 40,46 -Westerkertaaiers-1776-SlagTwaalf 87,46 -Westerkertaaiers-1777-SlagTwaalf 80,46 -Westerkertaaiers-1778-SlagTwaalf 99,46 -Westerkertaaiers-1779-SlagTwaalf 73,46 -Westerkertaaiers-1780-SlagTwaalf 42,46 -Westerkertaaiers-1781-SlagTwaalf 15,46 -Westerkertaaiers-1783-SlagTwaalf 37,46 -Westerkertaaiers-1784-SlagTwaalf 31,46 -Westerkertaaiers-1785-SlagTwaalf 90,46 -Westerkertaaiers-1786-SlagTwaalf 14,46 -Westerkertaaiers-1787-SlagTwaalf 50,46 -Westerkertaaiers-1788-SlagTwaalf 20,46 -Westerkertaaiers-1789-SlagTwaalf 54,46 -Westerkertaaiers-1790-SlagTwaalf 25,46 -Westerkertaaiers-1791-SlagTwaalf 23,46 -Westerkertaaiers-1792-SlagTwaalf 65,46 -Westerkertaaiers-1793-SlagTwaalf 26,46 -Westerkertaaiers-1794-SlagTwaalf 94,46 -Westerkertaaiers-1795-SlagTwaalf 156,46 -Westerkertaaiers-1796-SlagTwaalf 86,46 -Westerkertaaiers-1797-SlagTwaalf 50,46 -Westerkertaaiers-1798-SlagTwaalf 83,46 -Westerkertaaiers-1799-SlagTwaalf 42,46 -Westerkertaaiers-1800-SlagTwaalf 42,46 -Westerkertaaiers-1801-SlagTwaalf 83,46 -Westerkertaaiers-1802-SlagTwaalf 117,46 -Westerkertaaiers-1803-SlagTwaalf 13,46 -Westerkertaaiers-1804-SlagTwaalf 14,46 -Westerkertaaiers-1805-SlagTwaalf 28,46 -Westerkertaaiers-1806-SlagTwaalf 71,46 -Westerkertaaiers-1807-SlagTwaalf 35,46 -Westerkertaaiers-1808-SlagTwaalf 98,46 -Westerkertaaiers-1809-SlagTwaalf 37,46 -Westerkertaaiers-1810-SlagTwaalf 45,46 -Westerkertaaiers-1811-SlagTwaalf 18,46 -Westerkertaaiers-1812-SlagTwaalf 104,46 -Westerkertaaiers-1813-SlagTwaalf 57,46 -Westerkertaaiers-1814-SlagTwaalf 31,46 -Westerkertaaiers-1816-SlagTwaalf 82,46 -Westerkertaaiers-1817-SlagTwaalf 25,46 -Westerkertaaiers-1818-SlagTwaalf 58,46 -Westerkertaaiers-1819-SlagTwaalf 44,46 -Westerkertaaiers-1820-SlagTwaalf 46,46 -Westerkertaaiers-1821-SlagTwaalf 103,46 -Westerkertaaiers-1822-SlagTwaalf 60,46 -Westerkertaaiers-1823-SlagTwaalf 43,46 -Westerkertaaiers-1824-SlagTwaalf 126,46 -Westerkertaaiers-1825-SlagTwaalf 19,46 -Westerkertaaiers-1826-SlagTwaalf 21,46 -Westerkertaaiers-1827-SlagTwaalf 57,46 -Westerkertaaiers-1828-SlagTwaalf 23,46 -Westerkertaaiers-1829-SlagTwaalf 38,46 -Westerkertaaiers-1830-SlagTwaalf 72,46 -Westerkertaaiers-1831-SlagTwaalf 21,46 -Westerkertaaiers-1832-SlagTwaalf 18,46 -Westerkertaaiers-1833-SlagTwaalf 49,46 -Westerkertaaiers-1834-SlagTwaalf 128,46 -Westerkertaaiers-1835-SlagTwaalf 55,46 -Westerkertaaiers-1836-SlagTwaalf 19,46 -Westerkertaaiers-1837-SlagTwaalf 20,46 -Westerkertaaiers-1838-SlagTwaalf 26,46 -Westerkertaaiers-1839-SlagTwaalf 120,46 -Westerkertaaiers-1840-SlagTwaalf 38,46 -Westerkertaaiers-1841-SlagTwaalf 17,46 -Westerkertaaiers-1842-SlagTwaalf 65,46 -Westerkertaaiers-1843-SlagTwaalf 65,46 -Westerkertaaiers-1844-SlagTwaalf 24,46 -Westerkertaaiers-1845-SlagTwaalf 35,46 -Westerkertaaiers-1846-SlagTwaalf 42,46 -Westerkertaaiers-1847-SlagTwaalf 67,46 -Westerkertaaiers-1848-SlagTwaalf 70,46 -Westerkertaaiers-1849-SlagTwaalf 25,46 -Westerkertaaiers-1850-SlagTwaalf 40,46 -Westerkertaaiers-1851-SlagTwaalf 53,46 -Westerkertaaiers-1852-SlagTwaalf 33,46 -Westerkertaaiers-1853-SlagTwaalf 84,46 -Westerkertaaiers-1854-SlagTwaalf 31,46 -Westerkertaaiers-1855-SlagTwaalf 23,46 -Westerkertaaiers-1856-SlagTwaalf 48,46 -Westerkertaaiers-1857-SlagTwaalf 18,46 -Westerkertaaiers-1859-SlagTwaalf 13,46 -Westerkertaaiers-1860-SlagTwaalf 26,46 -Westerkertaaiers-1861-SlagTwaalf 43,46 -Westerkertaaiers-1862-SlagTwaalf 41,46 -Westerkertaaiers-1863-SlagTwaalf 41,46 -Westerkertaaiers-1864-SlagTwaalf 50,46 -Westerkertaaiers-1865-SlagTwaalf 39,46 -Westerkertaaiers-1866-SlagTwaalf 109,46 -Westerkertaaiers-1867-SlagTwaalf 23,46 -Westerkertaaiers-1868-SlagTwaalf 28,46 -Westerkertaaiers-1869-SlagTwaalf 83,46 -Westerkertaaiers-1870-SlagTwaalf 32,46 -Westerkertaaiers-1872-SlagTwaalf 80,46 -Westerkertaaiers-1873-SlagTwaalf 25,46 -Westerkertaaiers-1874-SlagTwaalf 46,46 -Westerkertaaiers-1875-SlagTwaalf 46,46 -Westerkertaaiers-1876-SlagTwaalf 38,46 -Westerkertaaiers-1877-SlagTwaalf 161,46 -Westerkertaaiers-1878-SlagTwaalf 60,46 -Westerkertaaiers-1879-SlagTwaalf 107,46 -Westerkertaaiers-1880-SlagTwaalf 124,46 -Westerkertaaiers-1881-SlagTwaalf 22,46 -Westerkertaaiers-1882-SlagTwaalf 24,46 -Westerkertaaiers-1883-SlagTwaalf 32,46 -Westerkertaaiers-1884-SlagTwaalf 74,46 -Westerkertaaiers-1885-SlagTwaalf 47,46 -Westerkertaaiers-1886-SlagTwaalf 54,46 -Westerkertaaiers-1887-SlagTwaalf 56,46 -Westerkertaaiers-1888-SlagTwaalf 63,46 -Westerkertaaiers-1889-SlagTwaalf 81,46 -Westerkertaaiers-1890-SlagTwaalf 12,46 -Westerkertaaiers-1891-SlagTwaalf 33,46 -Westerkertaaiers-1892-SlagTwaalf 49,46 -Westerkertaaiers-1893-SlagTwaalf 47,46 -Westerkertaaiers-1894-SlagTwaalf 30,46 -Westerkertaaiers-1895-SlagTwaalf 34,46 -Westerkertaaiers-1896-SlagTwaalf 183,46 -Westerkertaaiers-1897-SlagTwaalf 92,46 -Westerkertaaiers-1898-SlagTwaalf 73,46 -Westerkertaaiers-1899-SlagTwaalf 28,46 -Westerkertaaiers-1900-SlagTwaalf 73,46 -Westerkertaaiers-1901-SlagTwaalf 61,46 -Westerkertaaiers-1903-SlagTwaalf 51,46 -Westerkertaaiers-1904-SlagTwaalf 72,46 -Westerkertaaiers-1905-SlagTwaalf 42,46 -Westerkertaaiers-1906-SlagTwaalf 40,46 -Westerkertaaiers-1907-SlagTwaalf 69,46 -Westerkertaaiers-1908-SlagTwaalf 21,46 -Westerkertaaiers-1909-SlagTwaalf 21,46 -Westerkertaaiers-1911-SlagTwaalf 67,46 -Westerkertaaiers-1912-SlagTwaalf 21,46 -Westerkertaaiers-1913-SlagTwaalf 80,46 -Westerkertaaiers-1914-SlagTwaalf 93,46 -Westerkertaaiers-1915-SlagTwaalf 19,46 -Westerkertaaiers-1916-SlagTwaalf 58,46 -Westerkertaaiers-1917-SlagTwaalf 68,46 -Westerkertaaiers-1918-SlagTwaalf 77,46 -Westerkertaaiers-1919-SlagTwaalf 132,46 -Westerkertaaiers-1920-SlagTwaalf 100,46 -Westerkertaaiers-1921-SlagTwaalf 49,46 -Westerkertaaiers-1922-SlagTwaalf 18,46 -Westerkertaaiers-1923-SlagTwaalf 92,46 -Westerkertaaiers-1924-SlagTwaalf 135,46 -Westerkertaaiers-1925-SlagTwaalf 266,46 -Westerkertaaiers-1926-SlagTwaalf 30,46 -Westerkertaaiers-1927-SlagTwaalf 92,46 -Westerkertaaiers-1928-SlagTwaalf 43,46 -Westerkertaaiers-1929-SlagTwaalf 39,46 -Westerkertaaiers-1930-SlagTwaalf 41,46 -Westerkertaaiers-1931-SlagTwaalf 20,46 -Westerkertaaiers-1932-SlagTwaalf 70,46 -Westerkertaaiers-1933-SlagTwaalf 21,46 -Westerkertaaiers-1934-SlagTwaalf 69,46 -Westerkertaaiers-1935-SlagTwaalf 35,46 -Westerkertaaiers-1936-SlagTwaalf 35,46 -Westerkertaaiers-1937-SlagTwaalf 127,46 -Westerkertaaiers-1938-SlagTwaalf 27,46 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bbb701ecf5f3c5daf125b7869cdbd28b6d2ffb7c..0000000000000000000000000000000000000000 --- a/exp-vits-lr-3e-4/tts_vits/config.yaml +++ /dev/null @@ -1,379 +0,0 @@ -config: conf/train_vits.yaml -print_config: false -log_level: INFO -drop_last_iter: false -dry_run: false -iterator_type: sequence -valid_iterator_type: null -output_dir: exp-vits-lr-3e-4/tts_vits -ngpu: 1 -seed: 67823 -num_workers: 4 -num_att_plot: 3 -dist_backend: nccl -dist_init_method: env:// -dist_world_size: 2 -dist_rank: 0 -local_rank: 0 -dist_master_addr: localhost -dist_master_port: 53471 -dist_launcher: null -multiprocessing_distributed: true -unused_parameters: true -sharded_ddp: false -cudnn_enabled: true -cudnn_benchmark: false -cudnn_deterministic: false -collect_stats: false -write_collected_feats: false -max_epoch: 1000 -patience: null -val_scheduler_criterion: -- valid -- loss -early_stopping_criterion: -- valid -- loss -- min -best_model_criterion: -- - train - - total_count - - max -keep_nbest_models: 10 -nbest_averaging_interval: 0 -grad_clip: -1 -grad_clip_type: 2.0 -grad_noise: false -accum_grad: 1 -no_forward_run: false -resume: true -train_dtype: float32 -use_amp: false -log_interval: 50 -use_matplotlib: true -use_tensorboard: true -create_graph_in_tensorboard: false -use_wandb: true -wandb_project: GROTTS -wandb_id: null -wandb_entity: null -wandb_name: VITS_lr_3.0e-4 -wandb_model_log_interval: -1 -detect_anomaly: false -use_lora: false -save_lora_only: true -lora_conf: {} -pretrain_path: null -init_param: -- downloads/espnet/kan-bayashi_ljspeech_vits/exp/tts_train_vits_raw_phn_tacotron_g2p_en_no_space/train.total_count.ave_10best.pth:tts:tts:tts.generator.text_encoder,tts.generator.posterior_encoder.input_conv -ignore_init_mismatch: false -freeze_param: [] -num_iters_per_epoch: 1000 -batch_size: 40 -valid_batch_size: null -batch_bins: 10000000 -valid_batch_bins: null -train_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/train/text_shape.char -- exp-vits-lr-3e-4/tts_stats_raw_char/train/speech_shape -valid_shape_file: -- exp-vits-lr-3e-4/tts_stats_raw_char/valid/text_shape.char -- exp-vits-lr-3e-4/tts_stats_raw_char/valid/speech_shape -batch_type: numel -valid_batch_type: null -fold_length: -- 150 -- 204800 -sort_in_batch: descending -shuffle_within_batch: false -sort_batch: descending -multiple_iterator: false -chunk_length: 500 -chunk_shift_ratio: 0.5 -num_cache_chunks: 1024 -chunk_excluded_key_prefixes: [] -chunk_default_fs: null -train_data_path_and_name_and_type: -- - dump/raw/train_nodev/text - - text - - text -- - dump/raw/train_nodev/wav.scp - - speech - - sound -- - dump/raw/train_nodev/utt2sid - - sids - - text_int -valid_data_path_and_name_and_type: -- - dump/raw/train_dev/text - - text - - text -- - dump/raw/train_dev/wav.scp - - speech - - sound -- - dump/raw/train_dev/utt2sid - - sids - - text_int -allow_variable_data_keys: false -max_cache_size: 0.0 -max_cache_fd: 32 -allow_multi_rates: false -valid_max_cache_size: null -exclude_weight_decay: false -exclude_weight_decay_conf: {} -optim: adamw -optim_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler: exponentiallr -scheduler_conf: - gamma: 0.999875 -optim2: adamw -optim2_conf: - lr: 0.0003 - betas: - - 0.8 - - 0.99 - eps: 1.0e-09 - weight_decay: 0.0 -scheduler2: exponentiallr -scheduler2_conf: - gamma: 0.999875 -generator_first: false -token_list: -- -- -- -- e -- n -- a -- o -- t -- i -- r -- d -- s -- k -- l -- m -- u -- g -- h -- w -- v -- . -- z -- b -- p -- ',' -- j -- c -- f -- ‘ -- ’ -- ':' -- '?' -- ö -- '''' -- '!' -- '-' -- ; -- ò -- è -- ì -- é -- y -- ë -- x -- q -- -odim: null -model_conf: {} -use_preprocessor: true -token_type: char -bpemodel: null -non_linguistic_symbols: null -cleaner: null -g2p: null -feats_extract: fbank -feats_extract_conf: - n_fft: 1024 - hop_length: 256 - win_length: null - fs: 22050 - fmin: 80 - fmax: 7600 - n_mels: 80 -normalize: global_mvn -normalize_conf: - stats_file: exp-vits-lr-3e-4/tts_stats_raw_char/train/feats_stats.npz -tts: vits -tts_conf: - generator_type: vits_generator - generator_params: - hidden_channels: 192 - spks: 4 - global_channels: 256 - segment_size: 32 - text_encoder_attention_heads: 2 - text_encoder_ffn_expand: 4 - text_encoder_blocks: 6 - text_encoder_positionwise_layer_type: conv1d - text_encoder_positionwise_conv_kernel_size: 3 - text_encoder_positional_encoding_layer_type: rel_pos - text_encoder_self_attention_layer_type: rel_selfattn - text_encoder_activation_type: swish - text_encoder_normalize_before: true - text_encoder_dropout_rate: 0.1 - text_encoder_positional_dropout_rate: 0.0 - text_encoder_attention_dropout_rate: 0.1 - use_macaron_style_in_text_encoder: true - use_conformer_conv_in_text_encoder: false - text_encoder_conformer_kernel_size: -1 - decoder_kernel_size: 7 - decoder_channels: 512 - decoder_upsample_scales: - - 8 - - 8 - - 2 - - 2 - decoder_upsample_kernel_sizes: - - 16 - - 16 - - 4 - - 4 - decoder_resblock_kernel_sizes: - - 3 - - 7 - - 11 - decoder_resblock_dilations: - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - - - 1 - - 3 - - 5 - use_weight_norm_in_decoder: true - posterior_encoder_kernel_size: 5 - posterior_encoder_layers: 16 - posterior_encoder_stacks: 1 - posterior_encoder_base_dilation: 1 - posterior_encoder_dropout_rate: 0.0 - use_weight_norm_in_posterior_encoder: true - flow_flows: 4 - flow_kernel_size: 5 - flow_base_dilation: 1 - flow_layers: 4 - flow_dropout_rate: 0.0 - use_weight_norm_in_flow: true - use_only_mean_in_flow: true - stochastic_duration_predictor_kernel_size: 3 - stochastic_duration_predictor_dropout_rate: 0.5 - stochastic_duration_predictor_flows: 4 - stochastic_duration_predictor_dds_conv_layers: 3 - vocabs: 46 - aux_channels: 80 - discriminator_type: hifigan_multi_scale_multi_period_discriminator - discriminator_params: - scales: 1 - scale_downsample_pooling: AvgPool1d - scale_downsample_pooling_params: - kernel_size: 4 - stride: 2 - padding: 2 - scale_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 15 - - 41 - - 5 - - 3 - channels: 128 - max_downsample_channels: 1024 - max_groups: 16 - bias: true - downsample_scales: - - 2 - - 2 - - 4 - - 4 - - 1 - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: false - use_spectral_norm: false - follow_official_norm: false - periods: - - 2 - - 3 - - 5 - - 7 - - 11 - period_discriminator_params: - in_channels: 1 - out_channels: 1 - kernel_sizes: - - 5 - - 3 - channels: 32 - downsample_scales: - - 3 - - 3 - - 3 - - 3 - - 1 - max_downsample_channels: 1024 - bias: true - nonlinear_activation: LeakyReLU - nonlinear_activation_params: - negative_slope: 0.1 - use_weight_norm: true - use_spectral_norm: false - generator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - discriminator_adv_loss_params: - average_by_discriminators: false - loss_type: mse - feat_match_loss_params: - average_by_discriminators: false - average_by_layers: false - include_final_outputs: true - mel_loss_params: - fs: 22050 - n_fft: 1024 - hop_length: 256 - win_length: null - window: hann - n_mels: 80 - fmin: 0 - fmax: null - log_base: null - lambda_adv: 1.0 - lambda_mel: 45.0 - lambda_feat_match: 2.0 - lambda_dur: 1.0 - lambda_kl: 1.0 - sampling_rate: 22050 - cache_generator_outputs: true -pitch_extract: null -pitch_extract_conf: {} -pitch_normalize: null -pitch_normalize_conf: {} -energy_extract: null -energy_extract_conf: {} -energy_normalize: null -energy_normalize_conf: {} -required: -- output_dir -- token_list -version: '202310' -distributed: true diff --git a/exp-vits-lr-3e-4/tts_vits/images/discriminator_backward_time.png b/exp-vits-lr-3e-4/tts_vits/images/discriminator_backward_time.png deleted file mode 100644 index 472d505bfecf598c4f4294e61e66938da6fae84a..0000000000000000000000000000000000000000 Binary files a/exp-vits-lr-3e-4/tts_vits/images/discriminator_backward_time.png 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