--- tags: - espnet - audio - audio-to-audio language: datasets: - chime4 license: cc-by-4.0 --- ## ESPnet2 ENH model ### `espnet/Wangyou_Zhang_chime4_enh_train_enh_beamformer_mvdr_raw` This model was trained by Wangyou Zhang using chime4 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet pip install -e . cd egs2/chime4/enh1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/Wangyou_Zhang_chime4_enh_train_enh_beamformer_mvdr_raw ``` ## ENH config
expand ``` config: conf/tuning/train_enh_beamformer_mvdr.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/enh_train_enh_beamformer_mvdr_raw ngpu: 1 seed: 0 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: 35841 dist_launcher: null multiprocessing_distributed: true cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 70 patience: 4 val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - si_snr - max - - valid - loss - min keep_nbest_models: 1 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 unused_parameters: false use_tensorboard: true use_wandb: false wandb_project: null wandb_id: null pretrain_path: null init_param: [] freeze_param: [] num_iters_per_epoch: null batch_size: 8 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null train_shape_file: - exp/enh_stats_16k/train/speech_mix_shape - exp/enh_stats_16k/train/speech_ref1_shape - exp/enh_stats_16k/train/noise_ref1_shape valid_shape_file: - exp/enh_stats_16k/valid/speech_mix_shape - exp/enh_stats_16k/valid/speech_ref1_shape - exp/enh_stats_16k/valid/noise_ref1_shape batch_type: folded valid_batch_type: null fold_length: - 80000 - 80000 - 80000 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/tr05_simu_isolated_6ch_track/wav.scp - speech_mix - sound - - dump/raw/tr05_simu_isolated_6ch_track/spk1.scp - speech_ref1 - sound - - dump/raw/tr05_simu_isolated_6ch_track/noise1.scp - noise_ref1 - sound valid_data_path_and_name_and_type: - - dump/raw/dt05_simu_isolated_6ch_track/wav.scp - speech_mix - sound - - dump/raw/dt05_simu_isolated_6ch_track/spk1.scp - speech_ref1 - sound - - dump/raw/dt05_simu_isolated_6ch_track/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-08 weight_decay: 0 scheduler: reducelronplateau scheduler_conf: mode: min factor: 0.5 patience: 1 init: xavier_uniform model_conf: loss_type: mask_mse mask_type: PSM^2 use_preprocessor: false encoder: stft encoder_conf: n_fft: 512 hop_length: 128 separator: wpe_beamformer separator_conf: num_spk: 1 loss_type: mask_mse use_wpe: false wnet_type: blstmp wlayers: 3 wunits: 300 wprojs: 320 wdropout_rate: 0.0 taps: 5 delay: 3 use_dnn_mask_for_wpe: true use_beamformer: true bnet_type: blstmp blayers: 3 bunits: 512 bprojs: 512 badim: 320 ref_channel: 3 use_noise_mask: true beamformer_type: mvdr_souden bdropout_rate: 0.0 decoder: stft decoder_conf: n_fft: 512 hop_length: 128 required: - output_dir version: 0.9.7 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} } @inproceedings{li2021espnetse, title={{ESPnet-SE}: End-to-End Speech Enhancement and Separation Toolkit Designed for {ASR} Integration}, author={Li, Chenda and Shi, Jing and Zhang, Wangyou and Subramanian, Aswin Shanmugam and Chang, Xuankai and Kamo, Naoyuki and Hira, Moto and Hayashi, Tomoki and Boeddeker, Christoph and Chen, Zhuo and Watanabe, Shinji}, booktitle={Proc. IEEE Spoken Language Technology Workshop (SLT)}, pages={785--792}, year={2021}, } ``` 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} } @inproceedings{li2021espnetse, title={{ESPnet-SE}: End-to-End Speech Enhancement and Separation Toolkit Designed for {ASR} Integration}, author={Li, Chenda and Shi, Jing and Zhang, Wangyou and Subramanian, Aswin Shanmugam and Chang, Xuankai and Kamo, Naoyuki and Hira, Moto and Hayashi, Tomoki and Boeddeker, Christoph and Chen, Zhuo and Watanabe, Shinji}, year={2020}, eprint={2011.03706}, archivePrefix={arXiv}, primaryClass={eess.AS} } ```