readme: ---
  tags:
  - espnet
  - audio
  - automatic-speech-recognition
  language: en
  datasets:
  - librispeech_100
  license: cc-by-4.0
  ---
  
  ## ESPnet2 ASR model 
  
  ### `pyf98/librispeech_100_ctc_e_branchformer`
  
  This model was trained by Yifan Peng using librispeech_100 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 5fbaedd0555de4e205172d9e5b34a98cbf9d265e
  pip install -e .
  cd egs2/librispeech_100/asr1
  ./run.sh --skip_data_prep false --skip_train true --download_model pyf98/librispeech_100_ctc_e_branchformer
  ```
  
  <!-- Generated by scripts/utils/show_asr_result.sh -->
  # RESULTS
  ## Environments
  - date: `Sun Jan  1 15:05:07 CST 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: `b12e08c955276daa015cc40cf4f5977d87233db2`
    - Commit date: `Thu Dec 29 07:10:24 2022 -0500`
  
  ## asr_train_asr_ctc_e_branchformer_e12_raw_en_bpe5000_sp
  ### WER
  
  |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
  |---|---|---|---|---|---|---|---|---|
  |decode_ctc_bs1_asr_model_valid.cer_ctc.ave/dev_clean|2703|54402|91.8|7.5|0.7|1.0|9.2|70.1|
  |decode_ctc_bs1_asr_model_valid.cer_ctc.ave/dev_other|2864|50948|80.4|17.4|2.2|2.8|22.4|87.8|
  |decode_ctc_bs1_asr_model_valid.cer_ctc.ave/test_clean|2620|52576|91.5|7.7|0.8|1.1|9.6|70.3|
  |decode_ctc_bs1_asr_model_valid.cer_ctc.ave/test_other|2939|52343|79.5|18.1|2.4|2.6|23.1|88.6|
  
  ### CER
  
  |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
  |---|---|---|---|---|---|---|---|---|
  |decode_ctc_bs1_asr_model_valid.cer_ctc.ave/dev_clean|2703|288456|97.1|1.2|1.7|1.1|4.0|70.1|
  |decode_ctc_bs1_asr_model_valid.cer_ctc.ave/dev_other|2864|265951|91.2|4.5|4.3|3.0|11.8|87.8|
  |decode_ctc_bs1_asr_model_valid.cer_ctc.ave/test_clean|2620|281530|97.0|1.3|1.7|1.2|4.2|70.3|
  |decode_ctc_bs1_asr_model_valid.cer_ctc.ave/test_other|2939|272758|90.8|4.5|4.7|3.0|12.2|88.6|
  
  ### TER
  
  |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
  |---|---|---|---|---|---|---|---|---|
  |decode_ctc_bs1_asr_model_valid.cer_ctc.ave/dev_clean|2703|69558|89.6|5.9|4.5|0.9|11.3|70.1|
  |decode_ctc_bs1_asr_model_valid.cer_ctc.ave/dev_other|2864|64524|77.9|14.8|7.2|3.0|25.1|87.8|
  |decode_ctc_bs1_asr_model_valid.cer_ctc.ave/test_clean|2620|66983|89.6|6.0|4.4|1.0|11.4|70.3|
  |decode_ctc_bs1_asr_model_valid.cer_ctc.ave/test_other|2939|66650|77.2|15.2|7.6|2.8|25.7|88.6|
  
  ## ASR config
  
  <details><summary>expand</summary>
  
  ```
  config: /scratch/bbjs/peng6/espnet-arch/egs2/librispeech_100/asr1/conf/tuning/train_asr_ctc_e_branchformer_e12.yaml
  print_config: false
  log_level: INFO
  dry_run: false
  iterator_type: sequence
  output_dir: exp/asr_train_asr_ctc_e_branchformer_e12_raw_en_bpe5000_sp
  ngpu: 1
  seed: 2022
  num_workers: 4
  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: 70
  patience: null
  val_scheduler_criterion:
  - valid
  - loss
  early_stopping_criterion:
  - valid
  - loss
  - min
  best_model_criterion:
  -   - valid
      - cer_ctc
      - min
  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: 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: 16000000
  valid_batch_bins: null
  train_shape_file:
  - exp/asr_stats_raw_en_bpe5000_sp/train/speech_shape
  - exp/asr_stats_raw_en_bpe5000_sp/train/text_shape.bpe
  valid_shape_file:
  - exp/asr_stats_raw_en_bpe5000_sp/valid/speech_shape
  - exp/asr_stats_raw_en_bpe5000_sp/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_clean_100_sp/wav.scp
      - speech
      - kaldi_ark
  -   - dump/raw/train_clean_100_sp/text
      - text
      - text