readme: ---
  tags:
  - espnet
  - audio
  - diarization
  language: noinfo
  datasets:
  - callhome
  license: cc-by-4.0
  ---
  
  ## ESPnet2 DIAR model 
  
  ### `YushiUeda/callhome_adapt_simu`
  
  This model was trained by YushiUeda using callhome recipe in [espnet](https://github.com/espnet/espnet/).
  
  ### Demo: How to use in ESPnet2
  
  ```bash
  cd espnet
  git checkout 0cabe65afd362122e77b04e2e967986a91de0fd8
  pip install -e .
  cd egs2/callhome/diar1
  ./run.sh --skip_data_prep false --skip_train true --download_model YushiUeda/callhome_adapt_simu
  ```
  
  
  
  ## DIAR config
  
  <details><summary>expand</summary>
  
  ```
  config: conf/tuning/train_diar_eda_adapt.yaml
  print_config: false
  log_level: INFO
  dry_run: false
  iterator_type: sequence
  output_dir: exp/diar_train_diar_eda_adapt_simu
  ngpu: 1
  seed: 0
  num_workers: 1
  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: 43777
  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: 50
  patience: null
  val_scheduler_criterion:
  - valid
  - loss
  early_stopping_criterion:
  - valid
  - loss
  - min
  best_model_criterion:
  -   - valid
      - acc
      - max
  -   - train
      - acc
      - max
  keep_nbest_models: 10
  nbest_averaging_interval: 0
  grad_clip: 5
  grad_clip_type: 2.0
  grad_noise: false
  accum_grad: 4
  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:
  - exp/diar_train_diar_eda_5_raw/latest.pth
  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_stats_8k/train/speech_shape
  - exp/diar_stats_8k/train/spk_labels_shape
  valid_shape_file:
  - exp/diar_stats_8k/valid/speech_shape
  - exp/diar_stats_8k/valid/spk_labels_shape
  batch_type: folded
  valid_batch_type: null
  fold_length:
  - 80000
  - 800
  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/simu/data/swb_sre_tr_ns1n2n3n4_beta2n2n5n9_100000/wav.scp
      - speech
      - sound
  -   - dump/raw/simu/data/swb_sre_tr_ns1n2n3n4_beta2n2n5n9_100000/espnet_rttm
      - spk_labels
      - rttm
  valid_data_path_and_name_and_type:
  -   - dump/raw/simu/data/swb_sre_cv_ns1n2n3n4_beta2n2n5n9_500/wav.scp
      - speech
      - sound
  -   - dump/raw/simu/data/swb_sre_cv_ns1n2n3n4_beta2n2n5n9_500/espnet_rttm
      - spk_labels
      - rttm
  allow_variable_data_keys: false
  max_cache_size: 0.0
  max_cache_fd: 32
  valid_max_cache_size: null
  optim: adam
  optim_conf:
      lr: 0.0001
  scheduler: null
  scheduler_conf: {}
  num_spk: 4
  init: null
  input_size: null
  model_conf:
      attractor_weight: 1.0
  use_preprocessor: true
  frontend: default
  frontend_conf:
      fs: 8k
      hop_length: 128
  specaug: specaug
  specaug_conf:
      apply_time_warp: false
      apply_freq_mask: true
      freq_mask_width_range:
      - 0
      - 30
      num_freq_mask: 2
      apply_time_mask: true
      time_mask_width_range:
      - 0
      - 40
      num_time_mask: 2
  normalize: global_mvn
  normalize_conf:
      stats_file: exp/diar_stats_8k/train/feats_stats.npz
  encoder: transformer
  encoder_conf:
      input_layer: conv2d
      num_blocks: 4
      linear_units: 512
      dropout_rate: 0.1
      output_size: 256
      attention_heads: 4
      attention_dropout_rate: 0.1
  decoder: linear
  decoder_conf: {}
  label_aggregator: label_aggregator
  label_aggregator_conf:
      win_length: 1024
      hop_length: 512
  attractor: rnn
  attractor_conf:
      unit: 256
      layer: 1
      dropout: 0.0
      attractor_grad: true
  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}
  }
  
  
  
  
  ```
  
  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}
  }
  ```
  