readme: ---
  tags:
  - espnet
  - audio
  - automatic-speech-recognition
  language: en
  datasets:
  - gigaspeech
  license: cc-by-4.0
  ---
  
  ## ESPnet2 ASR model 
  
  ### `pyf98/gigaspeech_e_branchformer`
  
  This model was trained by Yifan Peng using gigaspeech recipe in [espnet](https://github.com/espnet/espnet/).
  
  References:
  - [E-Branchformer: Branchformer with Enhanced merging for speech recognition (SLT 2022)](https://arxiv.org/abs/2210.00077)
  - [Branchformer: Parallel MLP-Attention Architectures to Capture Local and Global Context for Speech Recognition and Understanding (ICML 2022)](https://proceedings.mlr.press/v162/peng22a.html)
  
  ### 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 197dc412eab82e9bab008f00fbcb922c824d8cf2
  pip install -e .
  cd egs2/gigaspeech/asr1
  ./run.sh --skip_data_prep false --skip_train true --download_model pyf98/gigaspeech_e_branchformer
  ```
  
  <!-- Generated by scripts/utils/show_asr_result.sh -->
  # RESULTS
  ## Environments
  - date: `Sat Jan 21 17:54:14 EST 2023`
  - python version: `3.9.15 (main, Nov 24 2022, 14:31:59)  [GCC 11.2.0]`
  - espnet version: `espnet 202211`
  - pytorch version: `pytorch 1.12.1`
  - Git hash: `197dc412eab82e9bab008f00fbcb922c824d8cf2`
    - Commit date: `Sat Jan 21 13:59:20 2023 -0500`
  
  ## asr_train_asr_e_branchformer_e17_size512_mlp3072_linear1024_layerdrop_raw_en_bpe5000
  ### WER
  
  |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
  |---|---|---|---|---|---|---|---|---|
  |decode_asr_asr_model_valid.acc.ave/dev|5715|129240|91.1|6.3|2.6|3.1|11.9|74.0|
  |decode_asr_asr_model_valid.acc.ave/test|19930|392325|90.6|6.9|2.5|2.3|11.8|67.1|
  
  ### CER
  
  |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
  |---|---|---|---|---|---|---|---|---|
  |decode_asr_asr_model_valid.acc.ave/dev|5715|673778|96.8|1.4|1.8|2.6|5.8|74.0|
  |decode_asr_asr_model_valid.acc.ave/test|19930|2056231|96.5|1.6|1.9|2.0|5.5|67.1|
  
  ### TER
  
  |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
  |---|---|---|---|---|---|---|---|---|
  |decode_asr_asr_model_valid.acc.ave/dev|5715|160740|91.4|5.4|3.3|3.2|11.8|74.0|
  |decode_asr_asr_model_valid.acc.ave/test|19930|493006|90.8|6.0|3.1|2.7|11.9|67.1|
  
  ## ASR config
  
  <details><summary>expand</summary>
  
  ```
  config: conf/tuning/train_asr_e_branchformer_e17_size512_mlp3072_linear1024_layerdrop.yaml
  print_config: false
  log_level: INFO
  dry_run: false
  iterator_type: sequence
  output_dir: exp/asr_train_asr_e_branchformer_e17_size512_mlp3072_linear1024_layerdrop_raw_en_bpe5000
  ngpu: 1
  seed: 0
  num_workers: 4
  num_att_plot: 3
  dist_backend: nccl
  dist_init_method: env://
  dist_world_size: 5
  dist_rank: 0
  local_rank: 0
  dist_master_addr: localhost
  dist_master_port: 46971
  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: 30
  patience: null
  val_scheduler_criterion:
  - valid
  - loss
  early_stopping_criterion:
  - valid
  - loss
  - min
  best_model_criterion:
  -   - valid
      - acc
      - max
  keep_nbest_models: 10
  nbest_averaging_interval: 0
  grad_clip: 5.0
  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
  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: 35000000
  valid_batch_bins: null
  train_shape_file:
  - exp/asr_stats_raw_en_bpe5000/train/speech_shape
  - exp/asr_stats_raw_en_bpe5000/train/text_shape.bpe
  valid_shape_file:
  - exp/asr_stats_raw_en_bpe5000/valid/speech_shape
  - exp/asr_stats_raw_en_bpe5000/valid/text_shape.bpe
  batch_type: numel
  valid_batch_type: null
  fold_length:
  - 80000
  - 150
  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/wav.scp
      - speech
      - kaldi_ark
  -   - dump/raw/train/text
      - text
      - text
  valid_data_path_and_name_and_type:
  -   - dump/raw/dev/wav.scp
      - speech
      - kaldi_ark
  -   - dump/raw/dev/text
      - 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: 0.0015
  scheduler: warmuplr
  scheduler_conf:
      warmup_steps: 25000
  token_list:
  - <blank>
  - <unk>
  - S
  - ▁THE
  - ▁TO
  - ▁OF
  - ▁A
  - ▁AND
  - ''''
  - ▁THAT
  - ▁IN
  - ▁YOU
  - ▁I
  - ▁IT
  - T
  - ▁IS
  - ▁WAS
  - ED
  - ▁WE
  - ▁FOR
  - ING
  - ▁THIS
  - D
  - ▁ON
  - ▁BE
  - ▁WITH
  - ▁HAVE
  - ▁SO
  - ▁HE
  - RE
  - ▁THEY
  - ▁ARE
  - ▁NOT
  - ▁AS
  - ▁LIKE
  - ▁AT
  - ▁KNOW
  - ▁WHAT
  - LY
  - ▁CAN
  - ▁DO
  - ▁ABOUT
  - ▁ALL
  - ▁HIS
  - M
  - ▁HAD
  - '-'
  - ▁ONE
  - ▁OR
  - ▁FROM
  - ▁THERE
  - ▁M