--- tags: - espnet - audio - universa language: multilingual datasets: - universa_unite license: cc-by-4.0 --- ## ESPnet2 universa model ### `espnet/arecho_scale_v0.1-large-decoder` This model was trained by ftshijt using universa_unite recipe in [espnet](https://github.com/espnet/espnet/). ### 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 0b68ffd26362f4b50e7c73942c5bbdbc0a220bd4 pip install -e . cd egs2/universa_unite/uni_versa1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/arecho_scale_v0.1-large-decoder ``` ## universa config
expand ``` config: conf/train_aruniversa_wavlm_large.yaml print_config: false log_level: INFO drop_last_iter: false dry_run: false iterator_type: sequence valid_iterator_type: null output_dir: exp/universa_universa_ar_overall_scale_token_wavlm_large ngpu: 1 seed: 777 num_workers: 1 num_att_plot: 0 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 use_deepspeed: false deepspeed_config: null gradient_as_bucket_view: true ddp_comm_hook: null cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: false use_tf32: false 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: - - train - loss - min - - valid - loss - min - - train - acc - max - - valid - acc - max keep_nbest_models: 1 nbest_averaging_interval: 0 grad_clip: -1 grad_clip_type: 2.0 grad_noise: false accum_grad: 2 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: 50 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 use_adapter: false adapter: lora save_strategy: all adapter_conf: {} pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: - frontend.upstream num_iters_per_epoch: null batch_size: 16 valid_batch_size: null batch_bins: 1000000 valid_batch_bins: null category_sample_size: 10 train_shape_file: - exp/universa_stats_overall_scale/train/audio_shape - exp/universa_stats_overall_scale/train/ref_audio_shape valid_shape_file: - exp/universa_stats_overall_scale/valid/audio_shape - exp/universa_stats_overall_scale/valid/ref_audio_shape batch_type: sorted valid_batch_type: null fold_length: - 256000 sort_in_batch: descending shuffle_within_batch: false sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 chunk_excluded_key_prefixes: [] chunk_default_fs: null chunk_max_abs_length: null chunk_discard_short_samples: true train_data_path_and_name_and_type: - - dump/raw/overall_scale/wav.scp - audio - kaldi_ark - - dump/raw/overall_scale/metric.scp - metrics - metric - - dump/raw/overall_scale/ref_wav.scp - ref_audio - kaldi_ark valid_data_path_and_name_and_type: - - dump/raw/overall_dev/wav.scp - audio - kaldi_ark - - dump/raw/overall_dev/metric.scp - metrics - metric - - dump/raw/overall_dev/ref_wav.scp - ref_audio - kaldi_ark multi_task_dataset: false allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 allow_multi_rates: false valid_max_cache_size: null exclude_weight_decay: false exclude_weight_decay_conf: {} optim: adamw optim_conf: lr: 0.001 scheduler: warmuplr scheduler_conf: warmup_steps: 25000 metric2id: dump/raw/overall_scale/metric2id metric2type: dump/raw/overall_scale/metric2type metric_pad_value: -100 token_list: null metric_token_info: data/token_list/metric_500_percentile_overall_scale_w-numerical/tokens.json metric_token_pad_value: 0 tokenize_numerical_metric: true init: null model_conf: {} use_ref_audio: true use_ref_text: false use_preprocessor: true token_type: bpe bpemodel: null non_linguistic_symbols: null cleaner: null g2p: null sequential_metric: true randomize_sequential_metric: true frontend: s3prl frontend_conf: frontend_conf: upstream: wavlm_large download_dir: ./hub multilayer_feature: true universa: ar_universa universa_conf: embedding_dim: 512 audio_encoder_type: transformer audio_encoder_params: num_blocks: 4 attention_heads: 4 linear_units: 1024 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.1 input_layer: conv2d normalize_before: true concat_after: false positionwise_layer_type: linear positionwise_conv_kernel_size: 1 layer_drop_rate: 0.1 qk_norm: false use_flash_attn: false cross_attention_type: multihead cross_attention_params: n_head: 2 dropout_rate: 0.1 metric_decoder_params: num_blocks: 12 attention_heads: 8 linear_units: 2048 dropout_rate: 0.1 positional_dropout_rate: 0.1 src_attention_dropout_rate: 0.1 self_attention_dropout_rate: 0.1 input_layer: embed normalize_before: true concat_after: false layer_drop_rate: 0.1 qk_norm: false use_flash_attn: false use_rope: true lsm_weight: 0.1 sym_sos: sym_eos: required: - output_dir - metric2id version: '202503' distributed: false ```
### 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} } ```