ESPnet2 pretrained model, siddhana/fsc_unseen_asr_train_asr_hubert_transformer_adam_specaug_finetune_raw_en_word_valid.acc.ave_5best, fs=16k, lang=en
Creators: name: Siddhana
Publisher: name: Zenodo
Year: 2021
Subjects: ESPnet
deep-learning
python
pytorch
speech-recognition
speech-synthesis
speech-translation
machine-translation

description: This model was trained by siddhana using fsc_unseen recipe in espnet. <strong>Python API</strong> <pre><code class="language-python">See https://github.com/espnet/espnet_model_zoo</code></pre> <strong>Evaluate in the recipe</strong> <pre><code class="language-bash">git clone https://github.com/espnet/espnet cd espnet pip install -e . cd egs2/fsc_unseen/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model siddhana/fsc_unseen_asr_train_asr_hubert_transformer_adam_specaug_finetune_raw_en_word_valid.acc.ave_5best</code> </pre> <strong>Results</strong> <pre><code> # RESULTS ## Environments - date: `Mon Oct 11 13:11:36 2021 -0400` - python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]` - espnet version: `espnet 0.10.3a2` - pytorch version: `pytorch 1.8.1+cu102` - Git hash: `4e7d2ba3510463ae744d1a6d98f18388ad929a9d` - Commit date: `Mon Nov 8 16:28:44 2021 -0500` ## Using Transformer based encoder-decoder with Hubert pre encoder and decoding sentence with spectral augmentation and predicting transcript along with intent - ASR config: [conf/tuning/train_asr_hubert_transformer_adam_specaug.yaml](conf/tuning/train_asr_hubert_transformer_adam_specaug.yaml) - token_type: word - keep_nbest_models: 5 |dataset|Snt|Intent Classification (%)| |---|---|---| |inference_asr_model_valid.acc.ave_5best/spk_test|3366|98.5| |inference_asr_model_valid.acc.ave_5best/utt_test|3970|86.4| |inference_asr_model_valid.acc.ave_5best/valid|2624|98.8| ###ASR results #### WER |dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err| |---|---|---|---|---|---|---|---|---| |inference_asr_model_valid.acc.ave_5best/spk_test|3366|14806|99.1|0.5|0.4|0.4|1.3|3.1| |inference_asr_model_valid.acc.ave_5best/utt_test|3970|17199|91.0|6.5|2.5|5.3|14.4|49.3| |inference_asr_model_valid.acc.ave_5best/valid|2624|11295|99.3|0.4|0.2|0.2|0.9|2.1|</code></pre> <strong>ASR config</strong> <pre><code>config: conf/tuning/train_asr_hubert_transformer_adam_specaug_finetune.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_hubert_transformer_adam_specaug_finetune_raw_en_word ngpu: 1 seed: 0 num_workers: 1 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: 80 patience: null val_scheduler_criterion: - valid - loss early_stopping_criterion: - valid - loss - min best_model_criterion: - - train - loss - min - - valid - loss - min - - train - acc - max - - valid - acc - max keep_nbest_models: 5 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: false log_interval: null use_tensorboard: true 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: 1000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_en_word/train/speech_shape - exp/asr_stats_raw_en_word/train/text_shape.word valid_shape_file: - exp/asr_stats_raw_en_word/valid/speech_shape - exp/asr_stats_raw_en_word/valid/text_shape.word 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/train/wav.scp - speech - sound - - dump/raw/train/text - text - text valid_data_path_and_name_and_type: - - dump/raw/valid/wav.scp - speech - sound - - dump/raw/valid/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: 0.0002 scheduler: warmuplr scheduler_conf: warmup_steps: 25000 token_list: - - - the - turn - lights - in - up - 'on' - down - heat - temperature - switch - kitchen - 'off' - bedroom - washroom - increase_volume_none - volume - decrease_volume_none - language - bathroom - my - to - decrease - increase - heating - increase_heat_washroom - music - decrease_heat_washroom - bring - increase_heat_none - activate_lights_washroom - decrease_heat_none - change_language_none_none - too - me - activate_lights_kitchen - i - lamp - activate_music_none - set - deactivate_music_none - increase_heat_bedroom - decrease_heat_bedroom - increase_heat_kitchen - sound - decrease_heat_kitchen - loud - deactivate_lights_bedroom - deactivate_lights_kitchen - activate_lights_bedroom - need - bring_newspaper_none - newspaper - bring_socks_none - socks - bring_shoes_none - shoes - activate_lights_none - louder - deactivate_lights_none - deactivate_lights_washroom - it - bring_juice_none - juice - get - could - you - change_language_Chinese_none - chinese - activate_lamp_none - deactivate_lamp_none - hear - make - stop - some - play - change - please - ok - now - main - fetch - go - change_language_Korean_none - korean - practice - change_language_German_none - german - change_language_English_none - english - phones - lower - thats - pause - its - this - quiet - audio - quieter - far - a - different - start - put - resume - max - couldnt - anything - phone - reduce - softer - cant - that - levels - more - less - mute - video - is - low - allow - open - settings - languages - device - use - system - init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: true model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false extract_feats_in_collect_stats: false use_preprocessor: true token_type: word 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' frontend: s3prl frontend_conf: frontend_conf: upstream: hubert_large_ll60k download_dir: ./hub multilayer_feature: true 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 - 30 num_freq_mask: 2 apply_time_mask: true time_mask_width_range: - 0 - 40 num_time_mask: 2 normalize: utterance_mvn normalize_conf: {} preencoder: linear preencoder_conf: input_size: 1024 output_size: 80 encoder: transformer encoder_conf: output_size: 256 attention_heads: 4 linear_units: 2048 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.0 input_layer: conv2d normalize_before: true 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 required: - output_dir - token_list version: 0.10.3a2 distributed: false</code></pre> <strong>LM config</strong> <pre><code>NONE</code></pre>