readme: ---
  tags:
  - espnet
  - audio
  - audio-to-audio
  language: en
  datasets:
  - dns_icassp21
  license: cc-by-4.0
  ---
  
  ## ESPnet2 ENH model 
  
  ### `espnet/dns_icassp21_enh_train_enh_tcn_tf_raw`
  
  This model was trained by Yoshiki using dns_icassp21 recipe in [espnet](https://github.com/espnet/espnet/).
  
  ### Demo: How to use in ESPnet2
  
  ```bash
  cd espnet
  
  pip install -e .
  cd egs2/dns_icassp21/enh1
  ./run.sh --skip_data_prep false --skip_train true --download_model espnet/dns_icassp21_enh_train_enh_tcn_tf_raw
  ```
  
  <!-- Generated by ./scripts/utils/show_enh_score.sh -->
  # RESULTS
  ## Environments
  - date: `Thu Apr 21 21:49:46 UTC 2022`
  - python version: `3.7.4 (default, Aug 13 2019, 20:35:49)  [GCC 7.3.0]`
  - espnet version: `espnet 202204`
  - pytorch version: `pytorch 1.10.1+cu111`
  - Git hash: ``
    - Commit date: ``
  
  
  ## ..
  
  config: ./conf/train.yaml
  
  |dataset|STOI|SAR|SDR|SIR|SI_SNR|
  |---|---|---|---|---|---|
  |enhanced_cv_synthetic|0.93|18.96|18.96|0.00|18.79|
  |enhanced_tt_synthetic_track_1|0.77|14.19|14.19|0.00|12.15|
  
  ## ENH config
  
  <details><summary>expand</summary>
  
  ```
  config: ./conf/train.yaml
  print_config: false
  log_level: INFO
  dry_run: false
  iterator_type: sequence
  output_dir: exp/enh_train_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: 32787
  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: 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: 128
  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
  valid_shape_file:
  - exp/enh_stats_16k/valid/speech_mix_shape
  - exp/enh_stats_16k/valid/speech_ref1_shape
  batch_type: folded
  valid_batch_type: null
  fold_length:
  - 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_synthetic/wav.scp
      - speech_mix
      - sound
  -   - dump/raw/tr_synthetic/spk1.scp
      - speech_ref1
      - sound
  valid_data_path_and_name_and_type:
  -   - dump/raw/cv_synthetic/wav.scp
      - speech_mix
      - sound
  -   - dump/raw/cv_synthetic/spk1.scp
      - speech_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: 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: null
  criterions:
  -   name: si_snr
      conf:
          eps: 1.0e-07
      wrapper: pit
      wrapper_conf:
          weight: 1.0
          independent_perm: true
  use_preprocessor: false
  encoder: stft
  encoder_conf:
      n_fft: 320
      hop_length: 160
  separator: tcn
  separator_conf:
      num_spk: 1
      layer: 4
      stack: 3
      bottleneck_dim: 128
      hidden_dim: 512
      kernel: 3
      causal: true
      norm_type: gLN
      nonlinear: relu
  decoder: stft
  decoder_conf:
      n_fft: 320
      hop_length: 160
  required:
  - output_dir
  version: '202204'
  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-End Speech Enhancement and Separation Toolkit Designed for {ASR} Integration},
    booktitle = {{IEEE} Spoken Language T