readme: ---
  tags:
  - espnet
  - audio
  - audio-to-audio
  language: en
  datasets:
  - wsj0_2mix
  license: cc-by-4.0
  ---
  
  ## ESPnet2 ENH model 
  
  ### `Yulinfeng/wsj0_2mix_enh_train_enh_mdc_raw_valid.si_snr.ave`
  
  This model was trained by earthmanylf using wsj0_2mix recipe in [espnet](https://github.com/espnet/espnet/).
  
  ### Demo: How to use in ESPnet2
  
  ```bash
  cd espnet
  git checkout ec1acec03d109f06d829b80862e0388f7234d0d1
  pip install -e .
  cd egs2/wsj0_2mix/enh1
  ./run.sh --skip_data_prep false --skip_train true --download_model Yulinfeng/wsj0_2mix_enh_train_enh_mdc_raw_valid.si_snr.ave
  ```
  
  <!-- Generated by ./scripts/utils/show_enh_score.sh -->
  # RESULTS
  ## Environments
  - date: `Thu Mar  3 17:10:03 CST 2022`
  - python version: `3.8.10 (default, May 19 2021, 18:05:58)  [GCC 7.3.0]`
  - espnet version: `espnet 0.10.7a1`
  - pytorch version: `pytorch 1.5.1+cu101`
  - Git hash: `ec1acec03d109f06d829b80862e0388f7234d0d1`
    - Commit date: `Fri Feb 25 14:12:45 2022 +0800`
  
  
  ## ..
  
  config: conf/tuning/train_enh_mdc.yaml
  
  |dataset|PESQ|STOI|SAR|SDR|SIR|SI_SNR|
  |---|---|---|---|---|---|---|
  |enhanced_cv_min_8k|2.20|0.84|9.62|8.57|17.27|8.03|
  |enhanced_tt_min_8k|2.18|0.85|9.56|8.50|17.28|7.97|
  
  ## ENH config
  
  <details><summary>expand</summary>
  
  ```
  config: conf/tuning/train_enh_mdc.yaml
  print_config: false
  log_level: INFO
  dry_run: false
  iterator_type: sequence
  output_dir: exp/enh_train_enh_mdc_raw
  ngpu: 1
  seed: 0
  num_workers: 4
  num_att_plot: 3
  dist_backend: nccl
  dist_init_method: env://
  dist_world_size: null
  dist_rank: null
  local_rank: 0
  dist_master_addr: null
  dist_master_port: null
  dist_launcher: null
  multiprocessing_distributed: false
  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: 10
  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
  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: 8
  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: 500
  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: 1.0e-07
  scheduler: reducelronplateau
  scheduler_conf:
      mode: min
      factor: 0.7
      patience: 1
  init: xavier_uniform
  model_conf:
      stft_consistency: false
      loss_type: mask_mse
      mask_type: PSM
      ref_channel: 0
  criterions:
  -   name: dpcl
      conf:
          loss_type: mdc
      wrapper: dpcl
      wrapper_conf:
          weight: 1.0
  use_preprocessor: false
  encoder: stft
  encoder_conf:
      n_fft: 256
      hop_length: 128
  separator: dpcl
  separator_conf:
      rnn_type: blstm
      num_spk: 2
      nonlinear: relu
      layer: 2
      unit: 500
      dropout: 0.1
      emb_D: 40
  decoder: stft
  decoder_conf:
      n_fft: 256
      hop_length: 128
  required:
  - output_dir
  version: 0.10.7a1
  distributed: false
  ```
  
  </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 