--- tags: - espnet - audio - diarization language: en datasets: - librimix license: cc-by-4.0 --- ## ESPnet2 DIAR model ### `soumi-maiti/libri2mix_eend_ss` This model was trained by soumimaiti using librimix recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout d837c97c88f13ffe655a30bcff93d814f212b225 pip install -e . cd egs2/librimix/enh_diar1 ./run.sh --skip_data_prep false --skip_train true --download_model soumi-maiti/libri2mix_eend_ss ``` ## DIAR config
expand ``` config: conf/tuning/train_diar_enh_convtasnet_2.yaml print_config: false log_level: INFO dry_run: false iterator_type: chunk output_dir: exp/diar_enh_train_diar_enh_convtasnet_2_raw ngpu: 1 seed: 0 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 4 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 55259 dist_launcher: null multiprocessing_distributed: true unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 100 patience: 4 val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - loss_enh - min keep_nbest_models: 1 nbest_averaging_interval: 0 grad_clip: 5.0 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: null use_matplotlib: true use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 16 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/diar_enh_stats_8k/train/speech_shape - exp/diar_enh_stats_8k/train/text_shape - exp/diar_enh_stats_8k/train/speech_ref1_shape - exp/diar_enh_stats_8k/train/speech_ref2_shape - exp/diar_enh_stats_8k/train/noise_ref1_shape valid_shape_file: - exp/diar_enh_stats_8k/valid/speech_shape - exp/diar_enh_stats_8k/valid/text_shape - exp/diar_enh_stats_8k/valid/speech_ref1_shape - exp/diar_enh_stats_8k/valid/speech_ref2_shape - exp/diar_enh_stats_8k/valid/noise_ref1_shape batch_type: folded valid_batch_type: null fold_length: - 800 - 80000 - 80000 - 80000 - 80000 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 24000 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train/wav.scp - speech - sound - - dump/raw/train/espnet_rttm - text - rttm - - dump/raw/train/spk1.scp - speech_ref1 - sound - - dump/raw/train/spk2.scp - speech_ref2 - sound - - dump/raw/train/noise1.scp - noise_ref1 - sound valid_data_path_and_name_and_type: - - dump/raw/dev/wav.scp - speech - sound - - dump/raw/dev/espnet_rttm - text - rttm - - dump/raw/dev/spk1.scp - speech_ref1 - sound - - dump/raw/dev/spk2.scp - speech_ref2 - sound - - dump/raw/dev/noise1.scp - noise_ref1 - sound allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.001 eps: 1.0e-07 weight_decay: 0 scheduler: reducelronplateau scheduler_conf: mode: min factor: 0.5 patience: 1 token_list: null src_token_list: null init: xavier_uniform input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: null zero_infinity: true enh_criterions: - name: si_snr conf: eps: 1.0e-07 wrapper: pit wrapper_conf: weight: 1.0 independent_perm: true diar_num_spk: 2 diar_input_size: 128 enh_model_conf: loss_type: si_snr asr_model_conf: ctc_weight: 0.5 interctc_weight: 0.0 ignore_id: -1 lsm_weight: 0.0 length_normalized_loss: false report_cer: true report_wer: true sym_space: sym_blank: extract_feats_in_collect_stats: true st_model_conf: stft_consistency: false loss_type: mask_mse mask_type: null diar_model_conf: diar_weight: 0.2 attractor_weight: 0.2 subtask_series: - enh - diar model_conf: calc_enh_loss: true bypass_enh_prob: 0 use_preprocessor: true token_type: bpe bpemodel: null src_token_type: bpe src_bpemodel: null non_linguistic_symbols: null cleaner: null g2p: null enh_encoder: conv enh_encoder_conf: channel: 512 kernel_size: 16 stride: 8 enh_separator: tcn_nomask enh_separator_conf: layer: 8 stack: 3 bottleneck_dim: 128 hidden_dim: 512 kernel: 3 causal: false norm_type: gLN enh_decoder: conv enh_decoder_conf: channel: 512 kernel_size: 16 stride: 8 enh_mask_module: multi_mask enh_mask_module_conf: max_num_spk: 3 mask_nonlinear: relu bottleneck_dim: 128 frontend: null frontend_conf: {} specaug: null specaug_conf: {} normalize: utterance_mvn normalize_conf: {} asr_preencoder: null asr_preencoder_conf: {} asr_encoder: rnn asr_encoder_conf: {} asr_postencoder: null asr_postencoder_conf: {} asr_decoder: rnn asr_decoder_conf: {} st_preencoder: null st_preencoder_conf: {} st_encoder: rnn st_encoder_conf: {} st_postencoder: null st_postencoder_conf: {} st_decoder: rnn st_decoder_conf: {} st_extra_asr_decoder: rnn st_extra_asr_decoder_conf: {} st_extra_mt_decoder: rnn st_extra_mt_decoder_conf: {} diar_frontend: null diar_frontend_conf: {} diar_specaug: null diar_specaug_conf: {} diar_normalize: utterance_mvn diar_normalize_conf: {} diar_encoder: transformer diar_encoder_conf: input_layer: conv2d8 num_blocks: 4 linear_units: 512 dropout_rate: 0.1 output_size: 256 attention_heads: 4 attention_dropout_rate: 0.1 diar_decoder: linear diar_decoder_conf: {} label_aggregator: label_aggregator label_aggregator_conf: win_length: 256 hop_length: 64 diar_attractor: rnn diar_attractor_conf: unit: 256 layer: 1 dropout: 0.0 attractor_grad: true required: - output_dir version: '202205' distributed: true ```
### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```