Spaces:
Running
on
Zero
Running
on
Zero
File size: 3,267 Bytes
45ee559 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 |
import os
from trainer import Trainer, TrainerArgs
from TTS.config.shared_configs import BaseAudioConfig
from TTS.tts.configs.shared_configs import BaseDatasetConfig, CapacitronVAEConfig
from TTS.tts.configs.tacotron2_config import Tacotron2Config
from TTS.tts.datasets import load_tts_samples
from TTS.tts.models.tacotron2 import Tacotron2
from TTS.tts.utils.text.tokenizer import TTSTokenizer
from TTS.utils.audio import AudioProcessor
output_path = os.path.dirname(os.path.abspath(__file__))
data_path = "/srv/data/blizzard2013/segmented"
# Using LJSpeech like dataset processing for the blizzard dataset
dataset_config = BaseDatasetConfig(
formatter="ljspeech",
meta_file_train="metadata.csv",
path=data_path,
)
audio_config = BaseAudioConfig(
sample_rate=24000,
do_trim_silence=True,
trim_db=60.0,
signal_norm=True,
mel_fmin=80.0,
mel_fmax=12000,
spec_gain=25.0,
log_func="np.log10",
ref_level_db=20,
preemphasis=0.0,
min_level_db=-100,
)
# Using the standard Capacitron config
capacitron_config = CapacitronVAEConfig(capacitron_VAE_loss_alpha=1.0)
config = Tacotron2Config(
run_name="Blizzard-Capacitron-T2",
audio=audio_config,
capacitron_vae=capacitron_config,
use_capacitron_vae=True,
batch_size=246, # Tune this to your gpu
max_audio_len=6 * 24000, # Tune this to your gpu
min_audio_len=1 * 24000,
eval_batch_size=16,
num_loader_workers=12,
num_eval_loader_workers=8,
precompute_num_workers=24,
run_eval=True,
test_delay_epochs=5,
r=2,
optimizer="CapacitronOptimizer",
optimizer_params={"RAdam": {"betas": [0.9, 0.998], "weight_decay": 1e-6}, "SGD": {"lr": 1e-5, "momentum": 0.9}},
attention_type="dynamic_convolution",
grad_clip=0.0, # Important! We overwrite the standard grad_clip with capacitron_grad_clip
double_decoder_consistency=False,
epochs=1000,
text_cleaner="phoneme_cleaners",
use_phonemes=True,
phoneme_language="en-us",
phonemizer="espeak",
phoneme_cache_path=os.path.join(data_path, "phoneme_cache"),
stopnet_pos_weight=15,
print_step=25,
print_eval=True,
mixed_precision=False,
output_path=output_path,
datasets=[dataset_config],
lr=1e-3,
lr_scheduler="StepwiseGradualLR",
lr_scheduler_params={
"gradual_learning_rates": [
[0, 1e-3],
[2e4, 5e-4],
[4e4, 3e-4],
[6e4, 1e-4],
[8e4, 5e-5],
]
},
scheduler_after_epoch=False, # scheduler doesn't work without this flag
seq_len_norm=True,
loss_masking=False,
decoder_loss_alpha=1.0,
postnet_loss_alpha=1.0,
postnet_diff_spec_alpha=1.0,
decoder_diff_spec_alpha=1.0,
decoder_ssim_alpha=1.0,
postnet_ssim_alpha=1.0,
)
ap = AudioProcessor(**config.audio.to_dict())
tokenizer, config = TTSTokenizer.init_from_config(config)
train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
model = Tacotron2(config, ap, tokenizer, speaker_manager=None)
trainer = Trainer(
TrainerArgs(),
config,
output_path,
model=model,
train_samples=train_samples,
eval_samples=eval_samples,
training_assets={"audio_processor": ap},
)
trainer.fit()
|