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voxforge_it_e_branchformer

pyf98 / voxforge_it_e_branchformer

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Total runs: 3
24-hour runs: 0
7-day runs: 1
30-day runs: -2
Model's Last Updated: December 30 2022
automatic-speech-recognition

Introduction of voxforge_it_e_branchformer

Model Details of voxforge_it_e_branchformer

ESPnet2 ASR model
pyf98/voxforge_it_e_branchformer

This model was trained by Yifan Peng using voxforge recipe in espnet .

References:

Demo: How to use in ESPnet2

Follow the ESPnet installation instructions if you haven't done that already. 

cd espnet
git checkout bf8c8f00194bdfed8ca388d8b20d14791b7d270e
pip install -e .
cd egs2/voxforge/asr1
./run.sh --skip_data_prep false --skip_train true --download_model pyf98/voxforge_it_e_branchformer

RESULTS

Environments
  • date: Thu Dec 29 01:48:29 EST 2022
  • 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: bf8c8f00194bdfed8ca388d8b20d14791b7d270e
    • Commit date: Wed Dec 28 22:43:13 2022 -0500
asr_train_asr_e_branchformer_e12_mlp1024_linear1024_raw_it_char_normalize_confnorm_varsFalse
WER
dataset Snt Wrd Corr Sub Del Ins Err S.Err
decode_asr_asr_model_valid.acc.ave/dt_it 1035 12587 71.3 24.0 4.7 3.8 32.5 95.5
decode_asr_asr_model_valid.acc.ave/et_it 1103 13699 72.8 22.7 4.5 3.1 30.2 91.7
CER
dataset Snt Wrd Corr Sub Del Ins Err S.Err
decode_asr_asr_model_valid.acc.ave/dt_it 1035 75494 93.2 3.7 3.1 2.0 8.8 95.5
decode_asr_asr_model_valid.acc.ave/et_it 1103 81228 93.8 3.5 2.7 1.8 8.0 91.7
TER
dataset Snt Wrd Corr Sub Del Ins Err S.Err
ASR config
expand 
config: conf/tuning/train_asr_e_branchformer_e12_mlp1024_linear1024.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_e_branchformer_e12_mlp1024_linear1024_raw_it_char_normalize_confnorm_varsFalse
ngpu: 1
seed: 0
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: 100
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: 1
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: 128
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_it_char/train/speech_shape
- exp/asr_stats_raw_it_char/train/text_shape.char
valid_shape_file:
- exp/asr_stats_raw_it_char/valid/speech_shape
- exp/asr_stats_raw_it_char/valid/text_shape.char
batch_type: folded
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/tr_it/wav.scp
    - speech
    - sound
-   - dump/raw/tr_it/text
    - text
    - text
valid_data_path_and_name_and_type:
-   - dump/raw/dt_it/wav.scp
    - speech
    - sound
-   - dump/raw/dt_it/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: 0.002
scheduler: warmuplr
scheduler_conf:
    warmup_steps: 10000
token_list:
- <blank>
- <unk>
- <space>
- A
- E
- I
- O
- R
- N
- L
- S
- T
- C
- D
- U
- M
- P
- V
- G
- F
- H
- B
- Q
- Z
- ''''
- Ò
- À
- È
- Ú
- X
- W
- Í
- É
- Y
- K
- J
- '1'
- <sos/eos>
init: null
input_size: null
ctc_conf:
    dropout_rate: 0.0
    ctc_type: builtin
    reduce: true
    ignore_nan_grad: null
    zero_infinity: true
joint_net_conf: null
use_preprocessor: true
token_type: char
bpemodel: null
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
short_noise_thres: 0.5
frontend: default
frontend_conf:
    fs: 16k
specaug: null
specaug_conf: {}
normalize: global_mvn
normalize_conf:
    stats_file: exp/asr_stats_raw_it_char/train/feats_stats.npz
    norm_vars: false
model: espnet
model_conf:
    ctc_weight: 0.3
    lsm_weight: 0.1
    length_normalized_loss: false
preencoder: null
preencoder_conf: {}
encoder: e_branchformer
encoder_conf:
    output_size: 256
    attention_heads: 4
    attention_layer_type: rel_selfattn
    pos_enc_layer_type: rel_pos
    rel_pos_type: latest
    cgmlp_linear_units: 1024
    cgmlp_conv_kernel: 31
    use_linear_after_conv: false
    gate_activation: identity
    num_blocks: 12
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    attention_dropout_rate: 0.1
    input_layer: conv2d
    layer_drop_rate: 0.0
    linear_units: 1024
    positionwise_layer_type: linear
    use_ffn: true
    macaron_ffn: true
    merge_conv_kernel: 31
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
    attention_heads: 4
    linear_units: 2048
    num_blocks: 6
    dropout_rate: 0.1
    positional_dropout_rate: 0.1
    self_attention_dropout_rate: 0.0
    src_attention_dropout_rate: 0.0
preprocessor: default
preprocessor_conf: {}
required:
- output_dir
- token_list
version: '202211'
distributed: false
Citing ESPnet 
@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 arXiv: 

@misc{watanabe2018espnet,
  title={ESPnet: End-to-End Speech Processing Toolkit}, 
  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},
  year={2018},
  eprint={1804.00015},
  archivePrefix={arXiv},
  primaryClass={cs.CL}
}

Runs of pyf98 voxforge_it_e_branchformer on huggingface.co

3
Total runs
0
24-hour runs
1
3-day runs
1
7-day runs
-2
30-day runs

More Information About voxforge_it_e_branchformer huggingface.co Model

More voxforge_it_e_branchformer license Visit here:

https://choosealicense.com/licenses/cc-by-4.0

voxforge_it_e_branchformer huggingface.co

voxforge_it_e_branchformer huggingface.co is an AI model on huggingface.co that provides voxforge_it_e_branchformer's model effect (), which can be used instantly with this pyf98 voxforge_it_e_branchformer model. huggingface.co supports a free trial of the voxforge_it_e_branchformer model, and also provides paid use of the voxforge_it_e_branchformer. Support call voxforge_it_e_branchformer model through api, including Node.js, Python, http.

voxforge_it_e_branchformer huggingface.co Url

https://huggingface.co/pyf98/voxforge_it_e_branchformer

pyf98 voxforge_it_e_branchformer online free

voxforge_it_e_branchformer huggingface.co is an online trial and call api platform, which integrates voxforge_it_e_branchformer's modeling effects, including api services, and provides a free online trial of voxforge_it_e_branchformer, you can try voxforge_it_e_branchformer online for free by clicking the link below.

pyf98 voxforge_it_e_branchformer online free url in huggingface.co:

https://huggingface.co/pyf98/voxforge_it_e_branchformer

voxforge_it_e_branchformer install

voxforge_it_e_branchformer is an open source model from GitHub that offers a free installation service, and any user can find voxforge_it_e_branchformer on GitHub to install. At the same time, huggingface.co provides the effect of voxforge_it_e_branchformer install, users can directly use voxforge_it_e_branchformer installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

voxforge_it_e_branchformer install url in huggingface.co:

https://huggingface.co/pyf98/voxforge_it_e_branchformer

Url of voxforge_it_e_branchformer

voxforge_it_e_branchformer huggingface.co Url

Provider of voxforge_it_e_branchformer huggingface.co

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