--- datasets: - LIUM/tedlium language: - en metrics: - wer library_name: espnet pipeline_tag: automatic-speech-recognition --- ## ESPnet2 ASR model ### `espnet/tedlium3` This model was trained by Dongwei Jiang using tedlium3 recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 ```bash cd espnet git checkout ff841366229d539eb74d23ac999cae7c0cc62cad pip install -e . cd egs2/tedlium3/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/dongwei_tedlium3_asr_conformer_external_lm ``` # RESULTS ## Environments - date: `Mon Mar 27 04:02:03 EDT 2023` - python version: `3.8.16 (default, Mar 2 2023, 03:21:46) [GCC 11.2.0]` - espnet version: `espnet 202301` - pytorch version: `pytorch 1.8.1` - Git hash: `ff841366229d539eb74d23ac999cae7c0cc62cad` - Commit date: `Mon Feb 20 12:23:15 2023 -0500` ## exp/asr_train_raw_en_bpe500_sp ### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_lm_lm_train_lm_en_bpe500_valid.loss.ave_asr_model_valid.acc.ave/dev|507|17783|93.2|3.2|3.5|1.0|7.8|68.6| |decode_lm_lm_train_lm_en_bpe500_valid.loss.ave_asr_model_valid.acc.ave/test|1155|27500|93.9|2.7|3.4|0.7|6.8|61.1| ### CER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_lm_lm_train_lm_en_bpe500_valid.loss.ave_asr_model_valid.acc.ave/dev|507|95429|96.2|0.7|3.1|0.9|4.7|68.6| |decode_lm_lm_train_lm_en_bpe500_valid.loss.ave_asr_model_valid.acc.ave/test|1155|145066|96.6|0.6|2.8|0.6|4.1|61.1| ### TER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |decode_lm_lm_train_lm_en_bpe500_valid.loss.ave_asr_model_valid.acc.ave/dev|507|36002|95.0|2.2|2.8|0.9|5.8|68.6| |decode_lm_lm_train_lm_en_bpe500_valid.loss.ave_asr_model_valid.acc.ave/test|1155|54206|95.5|1.7|2.7|0.6|5.1|61.1| ## ASR config
expand ``` config: conf/train.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_raw_en_bpe500_sp ngpu: 1 seed: 2022 num_workers: 6 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: 4 dist_rank: 0 local_rank: 0 dist_master_addr: localhost dist_master_port: 46711 dist_launcher: null multiprocessing_distributed: true unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 50 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: 20 valid_batch_size: null batch_bins: 50000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_en_bpe500_sp/train/speech_shape - exp/asr_stats_raw_en_bpe500_sp/train/text_shape.bpe valid_shape_file: - exp/asr_stats_raw_en_bpe500_sp/valid/speech_shape - exp/asr_stats_raw_en_bpe500_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_sp/wav.scp - speech - kaldi_ark - - dump/raw/train_sp/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 exclude_weight_decay: false exclude_weight_decay_conf: {} optim: adam optim_conf: lr: 0.002 weight_decay: 1.0e-06 scheduler: warmuplr scheduler_conf: warmup_steps: 15000 token_list: - - - '[unk]' - ▁ - s - ▁the - t - ▁and - e - ▁a - ▁to - d - ▁of - '''' - n - ing - ▁in - ▁that - re - ▁i - c - o - u - ▁we - y - a - ed - ▁it - ▁you - i - m - ▁is - er - p - g - w - al - ▁this - ▁so - f - le - b - ar - ▁f - k - ▁c - r - in - or - ▁for - ▁be - ve - ▁was - te - th - ▁do - es - ly - ▁they - ro - ▁are - ▁with - ▁have - an - v - ch - ▁on - se - lo - ▁but - en - ri - li - ▁what - it - ic - ▁can - l - ur - ce - ent - ▁me - ▁b - ▁ma - ▁he - ra - ▁de - ll - at - ▁about - ▁one - ▁not - ne - ▁all - ▁my - ter - el - il - ▁there - 'on' - ad - ▁mo - ol - ation - nd - ▁like - ▁people - po - ▁at - ▁us - us - ▁g - ci - ▁our - h - pe - ▁as - ▁from - vi - ▁if - as - ▁ex - ▁con - ▁an - ver - ▁out - ▁just - un - ▁see - la - ▁di - ▁when - ▁now - ▁p - ha - ▁who - ck - ▁these - ▁because - ▁or - ▁know - ion - ir - ▁co - ▁up - ▁pa - ment - ▁think - ge - ▁how - ide - ▁by - ul - ity - ▁go - ▁get - ▁ho - ive - ▁very - ate - ng - ▁no - ▁had - ac - ▁bo - ry - ▁more - ▁them - ▁some - mi - ▁time - ▁your - me - ▁going - op - am - per - et - ▁would - ru - ure - ti - ist - ▁their - x - ▁were - ▁look - ▁pro - ▁which - ▁work - tion - est - ty - im - z - ta - ▁want - ▁two - age - ▁really - om - ma - ers - ting - ▁world - co - ▁way - ▁don - wa - hi - tra - ▁la - ▁here - able - lu - ▁other - mo - ies - ▁has - ▁could - j - ▁make - ally - ▁sta - ten - ▁will - ▁un - ig - ▁where - ▁into - ke - ▁than - ▁comp - ▁actually - tic - sh - ▁did - tor - fa - ical - ▁she - ▁years - ▁say - one - ted - ▁things - ph - ▁new - ▁pre - ▁any - ▁thousand - ▁been - ▁inter - ▁his - ▁com - ▁need - nce - ▁right - ▁take - ▁even - ▁over - ▁start - ▁hundred - min - ▁sp - ▁those - ▁car - ▁then - mp - ap - ▁first - les - ize - ▁every - ba - ▁something - ▁well - ard - ▁str - ▁back - und - ia - pl - ki - ho - ▁call - ▁most - ▁also - bi - ▁thing - ▁life - um - ▁said - ▁kind - ▁lot - ▁much - va - ▁ra - ▁little - ▁dr - ▁got - ▁come - ful - ▁talk - ▁part - ▁day - ant - ction - ▁happen - ▁only - ▁many - ▁wo - pri - ▁her - ▁br - qui - ▁mean - ▁three - iv - ▁different - ugh - ain - ▁human - ance - ▁change - ▁let - ▁real - ▁show - ▁good - ▁around - ▁through - ▁jo - bu - ▁down - ight - ga - ▁why - ▁live - ff - ▁tell - ▁put - ▁idea - port - ▁same - ▁give - ated - ish - ible - ▁though - ious - ▁problem - ▁five - par - ▁fact - ▁cha - ition - ▁year - ▁big - ▁plan - ▁great - ▁find - ▁four - ▁app - ▁after - ▁system - ▁place - ▁em - ▁build - ▁percent - ▁again - ▁point - ▁learn - ▁own - ▁long - ▁made - ▁today - ▁nine - ities - ▁gene - ▁six - ▁question - light - ▁should - ▁came - ▁feel - ▁turn - ▁person - ▁end - ▁hu - ▁design - ▁help - ▁brain - ▁last - ▁create - ▁important - ▁before - ▁high - ▁never - ▁trans - ▁another - ▁him - ▁eight - ▁might - ▁understand - ▁power - ▁better - q - ▁found - ▁play - ▁twenty - ▁still - ▁school - ▁each - ▁seven - ▁together - ▁few - ▁hand - ▁example - que - ▁next - ▁million - ▁story - ▁women - ▁under - ▁number - ▁course - ▁water - ▁ago - ▁grow - ▁between - ▁develop - ▁america - ▁sort - ▁technology - ▁believe - ▁second - ▁small - ▁maybe - ▁become - press - ▁health - ▁space - ▁word - ▁hard - ▁children - ▁organ - ▁always - ▁country - ▁reason - ▁experience - ▁large - ▁everything - ▁friend - ▁project - ▁computer - ▁fifty - ▁money - ▁information - graph - ▁walk - ization - ▁africa - ▁picture - ▁process - ▁teach - ▁enough - ▁elect - ▁thirty - '0' - '1' - '2' - '9' - '3' - '5' - '8' - '4' - '7' - '6' - '&' - + - '#' - '@' - '*' - \ - ^ - R - _ - '-' - '%' - '=' - $ - M - ā - ']' - E - U - A - G - '[' - 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: bpe bpemodel: data/en_token_list/bpe_unigram500/bpe.model 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 aux_ctc_tasks: [] frontend: default frontend_conf: n_fft: 512 win_length: 400 hop_length: 160 fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 27 num_freq_mask: 2 apply_time_mask: true time_mask_width_ratio_range: - 0.0 - 0.05 num_time_mask: 5 normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_en_bpe500_sp/train/feats_stats.npz model: espnet model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false preencoder: null preencoder_conf: {} encoder: conformer encoder_conf: output_size: 256 attention_heads: 4 linear_units: 1024 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d normalize_before: true macaron_style: true rel_pos_type: latest pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish use_cnn_module: true cnn_module_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: 0.1 src_attention_dropout_rate: 0.1 preprocessor: default preprocessor_conf: {} required: - output_dir - token_list version: '202301' distributed: true ```
### Citing ESPnet ```BibTex @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: ```bibtex @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} } ```