readme: ---
  tags:
  - espnet
  - audio
  - audio-to-audio
  language: en
  datasets:
  - wsj0_2mix
  license: cc-by-4.0
  ---
  
  ## ESPnet2 ENH model 
  
  ### `lichenda/Chenda_Li_wsj0_2mix_enh_dprnn_tasnet`
  
  This model was trained by LiChenda using wsj0_2mix recipe in [espnet](https://github.com/espnet/espnet/).
  
  Imported from [zenodo](https://zenodo.org/record/4688000).
  
  ### Demo: How to use in ESPnet2
  
  ```bash
  cd espnet
  git checkout 54919e2529d6f58f4550d4a72960f57b83f66dc9
  pip install -e .
  cd egs2/wsj0_2mix/enh1
  ./run.sh --skip_data_prep false --skip_train true --download_model lichenda/Chenda_Li_wsj0_2mix_enh_dprnn_tasnet
  ```
  
  <!-- Generated by ./scripts/utils/show_enh_score.sh -->
  # RESULTS
  ## Environments
  - date: `Thu Apr 15 00:03:19 CST 2021`
  - python version: `3.7.10 (default, Feb 26 2021, 18:47:35)  [GCC 7.3.0]`
  - espnet version: `espnet 0.9.8`
  - pytorch version: `pytorch 1.5.0`
  - Git hash: `2aa2f151b5929dc9ffa4df39a8d8c26ca4dbdb85`
    - Commit date: `Tue Mar 30 09:08:27 2021 +0900`
  
  
  ## enh_train_enh_dprnn_tasnet_raw
  
  config: conf/tuning/train_enh_dprnn_tasnet.yaml
  
  |dataset|STOI|SAR|SDR|SIR|
  |---|---|---|---|---|
  |enhanced_cv_min_8k|0.960037|19.0476|18.5438|29.1591|
  |enhanced_tt_min_8k|0.968376|18.8209|18.2925|28.929|
  
  ## ENH config
  
  <details><summary>expand</summary>
  
  ```
  config: conf/tuning/train_enh_dprnn_tasnet.yaml
  print_config: false
  log_level: INFO
  dry_run: false
  iterator_type: chunk
  output_dir: exp/enh_train_enh_dprnn_tasnet_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: 45126
  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: 150
  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
  use_tensorboard: true
  use_wandb: false
  wandb_project: null
  wandb_id: null
  detect_anomaly: false
  pretrain_path: null
  init_param: []
  freeze_param: []
  num_iters_per_epoch: null
  batch_size: 4
  valid_batch_size: null
  batch_bins: 1000000
  valid_batch_bins: null
  train_shape_file:
  - exp/enh_stats_8k/train/speech_mix_shape
  - exp/enh_stats_8k/train/speech_ref1_shape
  - exp/enh_stats_8k/train/speech_ref2_shape
  valid_shape_file:
  - exp/enh_stats_8k/valid/speech_mix_shape
  - exp/enh_stats_8k/valid/speech_ref1_shape
  - exp/enh_stats_8k/valid/speech_ref2_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: 32000
  chunk_shift_ratio: 0.5
  num_cache_chunks: 1024
  train_data_path_and_name_and_type:
  -   - dump/raw/tr_min_8k/wav.scp
      - speech_mix
      - sound
  -   - dump/raw/tr_min_8k/spk1.scp
      - speech_ref1
      - sound
  -   - dump/raw/tr_min_8k/spk2.scp
      - speech_ref2
      - sound
  valid_data_path_and_name_and_type:
  -   - dump/raw/cv_min_8k/wav.scp
      - speech_mix
      - sound
  -   - dump/raw/cv_min_8k/spk1.scp
      - speech_ref1
      - sound
  -   - dump/raw/cv_min_8k/spk2.scp
      - speech_ref2
      - 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: 0.7
      patience: 1
  init: xavier_uniform
  model_conf:
      loss_type: si_snr
  use_preprocessor: false
  encoder: conv
  encoder_conf:
      channel: 64
      kernel_size: 2
      stride: 1
  separator: dprnn
  separator_conf:
      num_spk: 2
      layer: 6
      rnn_type: lstm
      bidirectional: true
      nonlinear: relu
      unit: 128
      segment_size: 250
      dropout: 0.1
  decoder: conv
  decoder_conf:
      channel: 64
      kernel_size: 2
      stride: 1
  required:
  - output_dir
  version: 0.9.8
  distributed: true
  ```
  
  </details>
  
  
  
  ### 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{ESPnet-SE,
    author = {Chenda Li and Jing Shi and Wangyou Zhang and Aswin Shanmugam Subramanian and Xuankai Chang and 
    Naoyuki Kamo and Moto Hira and Tomoki Hayashi and Christoph B{"{o}}ddeker and Zhuo Chen and Shinji Watanabe},
    title = {ESPnet-SE: End-To-E