readme: ---
  tags:
  - espnet
  - audio
  - diarization
  language: 
  datasets:
  - mini_librispeech
  license: cc-by-4.0
  ---
  
  ## ESPnet2 DIAR model 
  
  ### `YushiUeda/test`
  
  This model was trained by Yushi Ueda using mini_librispeech recipe in [espnet](https://github.com/espnet/espnet/).
  
  ### Demo: How to use in ESPnet2
  
  ```bash
  cd espnet
  git checkout 4dfa2be4331d3d68f124aa5fd81f63217a7278a4
  pip install -e .
  cd egs2/mini_librispeech/diar1
  ./run.sh --skip_data_prep false --skip_train true --download_model YushiUeda/test
  ```
  
  <!-- Generated by scripts/utils/show_diar_result.sh -->
  # RESULTS
  ## Environments
  - date: `Wed Aug 25 23:29:07 EDT 2021`
  - python version: `3.7.11 (default, Jul 27 2021, 14:32:16)  [GCC 7.5.0]`
  - espnet version: `espnet 0.10.2a1`
  - pytorch version: `pytorch 1.9.0+cu102`
  - Git hash: `19bcd34f9395e01e54a97c4db5ecbcedb429dd92`
    - Commit date: `Tue Aug 24 19:50:44 2021 -0400`
  
  ## `diar_train_diar_raw_max_epoch20`
  ### DER
  `dev_clean_2_ns2_beta2_500`
  
  |threshold_median_collar|DER|
  |---|---|
  |result_th0.3_med1_collar0.0|32.42|
  |result_th0.3_med11_collar0.0|32.03|
  |result_th0.4_med1_collar0.0|30.96|
  |result_th0.4_med11_collar0.0|30.26|
  |result_th0.5_med1_collar0.0|30.35|
  |result_th0.5_med11_collar0.0|29.37|
  |result_th0.6_med1_collar0.0|30.77|
  |result_th0.6_med11_collar0.0|29.52|
  |result_th0.7_med1_collar0.0|32.60|
  |result_th0.7_med11_collar0.0|31.03|
  
  ## 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_max_epoch20
  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: 20
  patience: 3
  val_scheduler_criterion:
  - valid
  - loss
  early_stopping_criterion:
  - valid
  - loss
  - min
  best_model_criterion:
  -   - valid
      - acc
      - max
  keep_nbest_models: 3
  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_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:
      loss_type: pit
  use_preprocessor: true
  frontend: default
  frontend_conf:
      fs: 8k
      hop_length: 128
  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: {}
  required:
  - output_dir
  version: 0.10.2a1
  distributed: false
  ```
  
  </details>
  
  
  