readme: ---
  tags:
  - espnet
  - audio
  - diarization
  language: noinfo
  datasets:
  - callhome
  license: cc-by-4.0
  ---
  
  ## ESPnet2 DIAR model 
  
  ### `YushiUeda/callhome_adapt_real`
  
  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_real
  ```
  
  <!-- Generated by scripts/utils/show_diar_result.sh -->
  # RESULTS
  ## Environments
  - date: `Mon Jun 20 10:30:23 EDT 2022`
  - python version: `3.7.11 (default, Jul 27 2021, 14:32:16)  [GCC 7.5.0]`
  - espnet version: `espnet 202205`
  - pytorch version: `pytorch 1.9.1+cu102`
  - Git hash: `fc62b1ce3e50c5ef8a2ac8cedb0d92ac41df54ca`
    - Commit date: `Thu Jun 9 16:29:52 2022 +0900`
  
  ## diar_train_diar_eda_adapt_real_lr0001
  ### DER
  diarized_callhome2_spkall
  |threshold_median_collar|DER|
  |---|---|
  |result_th0.3_med11_collar0.25|22.29|
  |result_th0.3_med1_collar0.25|23.27|
  |result_th0.4_med11_collar0.25|19.85|
  |result_th0.4_med1_collar0.25|20.80|
  |result_th0.5_med11_collar0.25|19.26|
  |result_th0.5_med1_collar0.25|20.18|
  |result_th0.6_med11_collar0.25|20.24|
  |result_th0.6_med1_collar0.25|21.08|
  |result_th0.7_med11_collar0.25|22.38|
  |result_th0.7_med1_collar0.25|23.17|
  
  ## 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_real_lr0001
  ngpu: 1
  seed: 0
  num_workers: 1
  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: 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: 16
  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_adapt_simu/latest.pth
  ignore_init_mismatch: false
  freeze_param: []
  num_iters_per_epoch: null
  batch_size: 1
  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: sorted
  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/callhome1_spkall/wav.scp
      - speech
      - sound
  -   - dump/raw/callhome1_spkall/espnet_rttm
      - spk_labels
      - rttm
  valid_data_path_and_name_and_type:
  -   - dump/raw/callhome2_spkall/wav.scp
      - speech
      - sound
  -   - dump/raw/callhome2_spkall/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: 0.001
  scheduler: null
  scheduler_conf: {}
  num_spk: 7
  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: false
  required:
  - output_dir
  version: '202204'
  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