readme: ---
  tags:
  - espnet
  - audio
  - self-supervised-learning
  language: en
  datasets:
  - librispeech
  license: cc-by-4.0
  ---
  
  ## ESPnet2 SSL model 
  
  ### `simpleoier/simpleoier_librispeech_hubert_iter1_train_ssl_torchaudiohubert_base_960h_pretrain_it1_raw`
  
  This model was trained by simpleoier using librispeech recipe in [espnet](https://github.com/espnet/espnet/).
  
  ### Demo: How to use in ESPnet2
  
  Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
  if you haven't done that already.
  
  ```bash
  cd espnet
  git checkout 753f40d61813436d4e76660904d02eaed7a6649e
  pip install -e .
  cd egs2/librispeech/ssl1
  ./run.sh --skip_data_prep false --skip_train true --download_model simpleoier/simpleoier_librispeech_hubert_iter1_train_ssl_torchaudiohubert_base_960h_pretrain_it1_raw
  ```
  
  
  
  ## SSL config
  
  <details><summary>expand</summary>
  
  ```
  config: conf/tuning/train_ssl_torchaudiohubert_base_960h_pretrain_it1.yaml
  print_config: false
  log_level: INFO
  dry_run: false
  iterator_type: sequence
  output_dir: exp/hubert_iter1_train_ssl_torchaudiohubert_base_960h_pretrain_it1_raw
  ngpu: 1
  seed: 0
  num_workers: 64
  num_att_plot: 3
  dist_backend: nccl
  dist_init_method: env://
  dist_world_size: 8
  dist_rank: 0
  local_rank: 0
  dist_master_addr: localhost
  dist_master_port: 49251
  dist_launcher: null
  multiprocessing_distributed: true
  unused_parameters: true
  sharded_ddp: false
  cudnn_enabled: true
  cudnn_benchmark: false
  cudnn_deterministic: true
  collect_stats: false
  write_collected_feats: false
  max_epoch: 250
  patience: null
  val_scheduler_criterion:
  - valid
  - loss
  early_stopping_criterion:
  - valid
  - loss
  - min
  best_model_criterion:
  -   - valid
      - loss
      - min
  keep_nbest_models: 10
  nbest_averaging_interval: 0
  grad_clip: 5.0
  grad_clip_type: 2.0
  grad_noise: false
  accum_grad: 2
  no_forward_run: false
  resume: true
  train_dtype: float32
  use_amp: true
  log_interval: null
  use_matplotlib: true
  use_tensorboard: true
  create_graph_in_tensorboard: false
  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: 20
  valid_batch_size: null
  batch_bins: 45000000
  valid_batch_bins: null
  train_shape_file:
  - exp/hubert_iter1_stats_raw/train/speech_shape
  - exp/hubert_iter1_stats_raw/train/text_shape.word
  valid_shape_file:
  - exp/hubert_iter1_stats_raw/valid/speech_shape
  - exp/hubert_iter1_stats_raw/valid/text_shape.word
  batch_type: numel
  valid_batch_type: null
  fold_length:
  - 80000
  - 400
  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/train_960/wav.scp
      - speech
      - sound
  -   - dump/raw/train_960/text.km.kmeans_iter1_hubert_train_960_portion0.1
      - text
      - text
  valid_data_path_and_name_and_type:
  -   - dump/raw/dev/wav.scp
      - speech
      - sound
  -   - dump/raw/dev/text.km.kmeans_iter1_hubert_train_960_portion0.1
      - text
      - text
  allow_variable_data_keys: false
  max_cache_size: 0.0
  max_cache_fd: 32
  valid_max_cache_size: null
  optim: adam
  optim_conf:
      lr: 0.0005
  scheduler: warmuplr
  scheduler_conf:
      warmup_steps: 32000
  token_list:
  - '386'
  - '160'
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