readme: ---
  tags:
  - espnet
  - audio
  - diarization
  language: noinfo
  datasets:
  - mini_librispeech
  license: cc-by-4.0
  ---
  
  ## ESPnet2 DIAR model 
  
  ### `jkang/espnet2_mini_librispeech_diar`
  
  This model was trained by jaekookang using mini_librispeech recipe in [espnet](https://github.com/espnet/espnet/).
  
  ### Demo: How to use in ESPnet2
  
  ```bash
  cd espnet
  git checkout e08a89e0a43db7fc12bec835c62a000ad10bd417
  pip install -e .
  cd egs2/mini_librispeech/diar1
  ./run.sh --skip_data_prep false --skip_train true --download_model jkang/espnet2_mini_librispeech_diar
  ```
  
  <!-- Generated by scripts/utils/show_diar_result.sh -->
  # RESULTS
  ## Environments
  - date: `Tue Feb  8 16:41:16 KST 2022`
  - python version: `3.9.7 (default, Sep 16 2021, 13:09:58)  [GCC 7.5.0]`
  - espnet version: `espnet 0.10.6a1`
  - pytorch version: `pytorch 1.10.1`
  - Git hash: `e08a89e0a43db7fc12bec835c62a000ad10bd417`
    - Commit date: `Sun Feb 6 18:54:20 2022 -0500`
  
  ## diar_train_diar_raw
  ### DER
  dev_clean_2_ns2_beta2_500
  |threshold_median_collar|DER|
  |---|---|
  |result_th0.3_med11_collar0.0|31.39|
  |result_th0.3_med1_collar0.0|31.78|
  |result_th0.4_med11_collar0.0|29.99|
  |result_th0.4_med1_collar0.0|30.61|
  |result_th0.5_med11_collar0.0|29.28|
  |result_th0.5_med1_collar0.0|30.19|
  |result_th0.6_med11_collar0.0|29.50|
  |result_th0.6_med1_collar0.0|30.66|
  |result_th0.7_med11_collar0.0|30.90|
  |result_th0.7_med1_collar0.0|32.38|
  
  ## DIAR config
  
  <details><summary>expand</summary>
  
  ```
  config: conf/train_diar.yaml
  print_config: false
  log_level: INFO
  dry_run: false
  iterator_type: chunk
  output_dir: exp/diar_train_diar_raw
  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: 100
  patience: 3
  val_scheduler_criterion:
  - valid
  - loss
  early_stopping_criterion:
  - valid
  - loss
  - min
  best_model_criterion:
  -   - valid
      - acc
      - max
  keep_nbest_models: 3
  nbest_averaging_interval: 0
  grad_clip: 5
  grad_clip_type: 2.0
  grad_noise: false
  accum_grad: 2
  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: 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: 200000
  chunk_shift_ratio: 0.5
  num_cache_chunks: 64
  train_data_path_and_name_and_type:
  -   - dump/raw/simu/data/train_clean_5_ns2_beta2_500/wav.scp
      - speech
      - sound
  -   - dump/raw/simu/data/train_clean_5_ns2_beta2_500/espnet_rttm
      - spk_labels
      - rttm
  valid_data_path_and_name_and_type:
  -   - dump/raw/simu/data/dev_clean_2_ns2_beta2_500/wav.scp
      - speech
      - sound
  -   - dump/raw/simu/data/dev_clean_2_ns2_beta2_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: 0.01
  scheduler: noamlr
  scheduler_conf:
      warmup_steps: 1000
  num_spk: 2
  init: xavier_uniform
  input_size: null
  model_conf:
      attractor_weight: 1.0
  use_preprocessor: true
  frontend: default
  frontend_conf:
      fs: 8k
      hop_length: 128
  specaug: null
  specaug_conf: {}
  normalize: global_mvn
  normalize_conf:
      stats_file: exp/diar_stats_8k/train/feats_stats.npz
  encoder: transformer
  encoder_conf:
      input_layer: linear
      num_blocks: 2
      linear_units: 512
      dropout_rate: 0.1
      output_size: 256
      attention_heads: 4
      attention_dropout_rate: 0.0
  decoder: linear
  decoder_conf: {}
  label_aggregator: label_aggregator
  label_aggregator_conf: {}
  attractor: null
  attractor_conf: {}
  required:
  - output_dir
  version: 0.10.6a1
  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}
  }
  
  
  
  
  ```
  
  or ar