├── funasr ├── auto │ ├── __init__.py │ ├── auto_tokenizer.py │ └── __pycache__ │ │ ├── __init__.cpython-310.pyc │ │ ├── auto_model.cpython-310.pyc │ │ ├── auto_frontend.cpython-310.pyc │ │ └── auto_tokenizer.cpython-310.pyc ├── bin │ ├── __init__.py │ ├── __pycache__ │ │ ├── export.cpython-310.pyc │ │ ├── train.cpython-310.pyc │ │ ├── __init__.cpython-310.pyc │ │ ├── inference.cpython-310.pyc │ │ ├── tokenize_text.cpython-310.pyc │ │ └── compute_audio_cmvn.cpython-310.pyc │ ├── inference.py │ └── export.py ├── datasets │ ├── __init__.py │ ├── audio_datasets │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── scp2len.cpython-310.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── datasets.cpython-310.pyc │ │ │ ├── index_ds.cpython-310.pyc │ │ │ ├── jsonl2scp.cpython-310.pyc │ │ │ ├── samplers.cpython-310.pyc │ │ │ ├── scp2jsonl.cpython-310.pyc │ │ │ ├── preprocessor.cpython-310.pyc │ │ │ ├── update_jsonl.cpython-310.pyc │ │ │ └── espnet_samplers.cpython-310.pyc │ │ └── preprocessor.py │ ├── large_datasets │ │ ├── __init__.py │ │ ├── utils │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── filter.cpython-310.pyc │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ ├── clipping.cpython-310.pyc │ │ │ │ ├── padding.cpython-310.pyc │ │ │ │ ├── tokenize.cpython-310.pyc │ │ │ │ ├── hotword_utils.cpython-310.pyc │ │ │ │ └── low_frame_rate.cpython-310.pyc │ │ │ ├── low_frame_rate.py │ │ │ ├── filter.py │ │ │ ├── clipping.py │ │ │ └── hotword_utils.py │ │ ├── datapipes │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── map.cpython-310.pyc │ │ │ │ ├── batch.cpython-310.pyc │ │ │ │ ├── filter.cpython-310.pyc │ │ │ │ └── __init__.cpython-310.pyc │ │ │ ├── map.py │ │ │ └── filter.py │ │ ├── __pycache__ │ │ │ ├── dataset.cpython-310.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── collate_fn.cpython-310.pyc │ │ │ ├── abs_iter_factory.cpython-310.pyc │ │ │ └── build_dataloader.cpython-310.pyc │ │ └── abs_iter_factory.py │ ├── llm_datasets │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── datasets.cpython-310.pyc │ │ │ └── preprocessor.cpython-310.pyc │ │ └── preprocessor.py │ ├── llm_datasets_vicuna │ │ ├── __init__.py │ │ └── __pycache__ │ │ │ ├── __init__.cpython-310.pyc │ │ │ └── datasets.cpython-310.pyc │ ├── sense_voice_datasets │ │ ├── __init__.py │ │ └── __pycache__ │ │ │ ├── __init__.cpython-310.pyc │ │ │ └── datasets.cpython-310.pyc │ ├── llm_datasets_qwenaudio │ │ ├── __init__.py │ │ └── __pycache__ │ │ │ ├── __init__.cpython-310.pyc │ │ │ └── datasets.cpython-310.pyc │ └── __pycache__ │ │ ├── __init__.cpython-310.pyc │ │ └── dataloader_entry.cpython-310.pyc ├── download │ ├── __init__.py │ ├── __pycache__ │ │ ├── file.cpython-310.pyc │ │ ├── __init__.cpython-310.pyc │ │ ├── download_from_hub.cpython-310.pyc │ │ ├── name_maps_from_hub.cpython-310.pyc │ │ ├── download_dataset_from_hub.cpython-310.pyc │ │ └── runtime_sdk_download_tool.cpython-310.pyc │ ├── download_dataset_from_hub.py │ └── name_maps_from_hub.py ├── frontends │ ├── __init__.py │ ├── utils │ │ ├── __init__.py │ │ └── __pycache__ │ │ │ ├── stft.cpython-310.pyc │ │ │ ├── dnn_wpe.cpython-310.pyc │ │ │ ├── log_mel.cpython-310.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── beamformer.cpython-310.pyc │ │ │ ├── frontend.cpython-310.pyc │ │ │ ├── complex_utils.cpython-310.pyc │ │ │ ├── dnn_beamformer.cpython-310.pyc │ │ │ ├── mask_estimator.cpython-310.pyc │ │ │ └── feature_transform.cpython-310.pyc │ ├── __pycache__ │ │ ├── fused.cpython-310.pyc │ │ ├── s3prl.cpython-310.pyc │ │ ├── __init__.cpython-310.pyc │ │ ├── default.cpython-310.pyc │ │ ├── windowing.cpython-310.pyc │ │ ├── wav_frontend.cpython-310.pyc │ │ ├── eend_ola_feature.cpython-310.pyc │ │ └── whisper_frontend.cpython-310.pyc │ └── eend_ola_feature.py ├── losses │ ├── __init__.py │ └── __pycache__ │ │ ├── __init__.cpython-310.pyc │ │ └── label_smoothing_loss.cpython-310.pyc ├── metrics │ ├── __init__.py │ ├── __pycache__ │ │ ├── wer.cpython-310.pyc │ │ ├── __init__.cpython-310.pyc │ │ ├── common.cpython-310.pyc │ │ ├── compute_acc.cpython-310.pyc │ │ ├── compute_eer.cpython-310.pyc │ │ └── compute_min_dcf.cpython-310.pyc │ └── compute_acc.py ├── models │ ├── __init__.py │ ├── bat │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── model.cpython-310.pyc │ │ │ └── __init__.cpython-310.pyc │ │ └── model.py │ ├── ctc │ │ ├── __init__.py │ │ └── __pycache__ │ │ │ ├── ctc.cpython-310.pyc │ │ │ └── __init__.cpython-310.pyc │ ├── eend │ │ ├── __init__.py │ │ ├── utils │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── power.cpython-310.pyc │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ ├── feature.cpython-310.pyc │ │ │ │ ├── losses.cpython-310.pyc │ │ │ │ ├── report.cpython-310.pyc │ │ │ │ └── kaldi_data.cpython-310.pyc │ │ │ └── losses.py │ │ └── __pycache__ │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── encoder.cpython-310.pyc │ │ │ ├── e2e_diar_eend_ola.cpython-310.pyc │ │ │ ├── eend_ola_dataloader.cpython-310.pyc │ │ │ └── encoder_decoder_attractor.cpython-310.pyc │ ├── lcbnet │ │ ├── __init__.py │ │ └── __pycache__ │ │ │ ├── encoder.cpython-310.pyc │ │ │ ├── model.cpython-310.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ └── attention.cpython-310.pyc │ ├── llm_asr │ │ ├── __init__.py │ │ └── __pycache__ │ │ │ ├── model.cpython-310.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ └── adaptor.cpython-310.pyc │ ├── lora │ │ ├── __init__.py │ │ └── __pycache__ │ │ │ ├── layers.cpython-310.pyc │ │ │ ├── utils.cpython-310.pyc │ │ │ └── __init__.cpython-310.pyc │ ├── mfcca │ │ ├── __init__.py │ │ └── __pycache__ │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── e2e_asr_mfcca.cpython-310.pyc │ │ │ ├── mfcca_encoder.cpython-310.pyc │ │ │ └── encoder_layer_mfcca.cpython-310.pyc │ ├── sa_asr │ │ ├── __init__.py │ │ └── __pycache__ │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── attention.cpython-310.pyc │ │ │ ├── e2e_sa_asr.cpython-310.pyc │ │ │ ├── beam_search_sa_asr.cpython-310.pyc │ │ │ └── transformer_decoder.cpython-310.pyc │ ├── sanm │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── model.cpython-310.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── attention.cpython-310.pyc │ │ │ ├── decoder.cpython-310.pyc │ │ │ ├── encoder.cpython-310.pyc │ │ │ ├── multihead_att.cpython-310.pyc │ │ │ └── positionwise_feed_forward.cpython-310.pyc │ │ ├── model.py │ │ └── positionwise_feed_forward.py │ ├── scama │ │ ├── __init__.py │ │ └── __pycache__ │ │ │ ├── model.cpython-310.pyc │ │ │ ├── utils.cpython-310.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── decoder.cpython-310.pyc │ │ │ ├── encoder.cpython-310.pyc │ │ │ ├── beam_search.cpython-310.pyc │ │ │ └── chunk_utilis.cpython-310.pyc │ ├── sond │ │ ├── __init__.py │ │ ├── encoder │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ ├── ci_scorers.cpython-310.pyc │ │ │ │ ├── conv_encoder.cpython-310.pyc │ │ │ │ ├── fsmn_encoder.cpython-310.pyc │ │ │ │ ├── resnet34_encoder.cpython-310.pyc │ │ │ │ ├── ecapa_tdnn_encoder.cpython-310.pyc │ │ │ │ └── self_attention_encoder.cpython-310.pyc │ │ │ └── ci_scorers.py │ │ ├── pooling │ │ │ ├── __init__.py │ │ │ └── __pycache__ │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ ├── pooling_layers.cpython-310.pyc │ │ │ │ └── statistic_pooling.cpython-310.pyc │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── attention.cpython-310.pyc │ │ │ ├── sv_decoder.cpython-310.pyc │ │ │ ├── e2e_diar_sond.cpython-310.pyc │ │ │ └── label_aggregation.cpython-310.pyc │ │ └── sv_decoder.py │ ├── specaug │ │ ├── __init__.py │ │ └── __pycache__ │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── specaug.cpython-310.pyc │ │ │ ├── profileaug.cpython-310.pyc │ │ │ ├── time_warp.cpython-310.pyc │ │ │ └── mask_along_axis.cpython-310.pyc │ ├── uniasr │ │ ├── __init__.py │ │ └── __pycache__ │ │ │ ├── model.cpython-310.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ └── beam_search.cpython-310.pyc │ ├── whisper │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── model.cpython-310.pyc │ │ │ └── __init__.cpython-310.pyc │ │ └── template.yaml │ ├── xvector │ │ ├── __init__.py │ │ └── __pycache__ │ │ │ ├── e2e_sv.cpython-310.pyc │ │ │ └── __init__.cpython-310.pyc │ ├── branchformer │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── cgmlp.cpython-310.pyc │ │ │ ├── model.cpython-310.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── encoder.cpython-310.pyc │ │ │ └── fastformer.cpython-310.pyc │ │ └── model.py │ ├── campplus │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── model.cpython-310.pyc │ │ │ ├── utils.cpython-310.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── components.cpython-310.pyc │ │ │ └── cluster_backend.cpython-310.pyc │ │ └── template.yaml │ ├── conformer │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── model.cpython-310.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ └── encoder.cpython-310.pyc │ │ └── model.py │ ├── data2vec │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── utils.cpython-310.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── data2vec.cpython-310.pyc │ │ │ ├── data_utils.cpython-310.pyc │ │ │ ├── ema_module.cpython-310.pyc │ │ │ ├── wav2vec2.cpython-310.pyc │ │ │ ├── quant_noise.cpython-310.pyc │ │ │ ├── grad_multiply.cpython-310.pyc │ │ │ ├── data2vec_encoder.cpython-310.pyc │ │ │ └── multihead_attention.cpython-310.pyc │ │ └── grad_multiply.py │ ├── emotion2vec │ │ ├── __init__.py │ │ └── __pycache__ │ │ │ ├── base.cpython-310.pyc │ │ │ ├── audio.cpython-310.pyc │ │ │ ├── model.cpython-310.pyc │ │ │ ├── modules.cpython-310.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── timm_modules.cpython-310.pyc │ │ │ └── fairseq_modules.cpython-310.pyc │ ├── llm_asr_nar │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── adaptor.cpython-310.pyc │ │ │ ├── model.cpython-310.pyc │ │ │ └── __init__.cpython-310.pyc │ │ └── adaptor.py │ ├── model_hf │ │ ├── __init__.py │ │ └── __pycache__ │ │ │ └── __init__.cpython-310.pyc │ ├── mossformer │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── e2e_ss.cpython-310.pyc │ │ │ ├── mossformer.cpython-310.pyc │ │ │ ├── mossformer_decoder.cpython-310.pyc │ │ │ └── mossformer_encoder.cpython-310.pyc │ │ └── mossformer_decoder.py │ ├── normalize │ │ ├── __init__.py │ │ └── __pycache__ │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── global_mvn.cpython-310.pyc │ │ │ └── utterance_mvn.cpython-310.pyc │ ├── paraformer │ │ ├── __init__.py │ │ └── __pycache__ │ │ │ ├── model.cpython-310.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── decoder.cpython-310.pyc │ │ │ ├── search.cpython-310.pyc │ │ │ ├── cif_predictor.cpython-310.pyc │ │ │ └── export_meta.cpython-310.pyc │ ├── qwen_audio │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── audio.cpython-310.pyc │ │ │ ├── model.cpython-310.pyc │ │ │ └── __init__.cpython-310.pyc │ │ └── template.yaml │ ├── rwkv_bat │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── rwkv.cpython-310.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── rwkv_encoder.cpython-310.pyc │ │ │ ├── rwkv_attention.cpython-310.pyc │ │ │ ├── rwkv_feed_forward.cpython-310.pyc │ │ │ └── rwkv_subsampling.cpython-310.pyc │ │ ├── template.yaml │ │ ├── cuda_decoder │ │ │ └── wkv_op.cpp │ │ └── cuda_encoder │ │ │ └── wkv_op.cpp │ ├── sense_voice │ │ ├── __init__.py │ │ ├── whisper_lib │ │ │ ├── version.py │ │ │ ├── __main__.py │ │ │ ├── normalizers │ │ │ │ ├── __init__.py │ │ │ │ └── __pycache__ │ │ │ │ │ ├── basic.cpython-310.pyc │ │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ │ └── english.cpython-310.pyc │ │ │ └── __pycache__ │ │ │ │ ├── audio.cpython-310.pyc │ │ │ │ ├── model.cpython-310.pyc │ │ │ │ ├── utils.cpython-310.pyc │ │ │ │ ├── timing.cpython-310.pyc │ │ │ │ ├── version.cpython-310.pyc │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ ├── __main__.cpython-310.pyc │ │ │ │ ├── decoding.cpython-310.pyc │ │ │ │ ├── tokenizer.cpython-310.pyc │ │ │ │ ├── transcribe.cpython-310.pyc │ │ │ │ └── triton_ops.cpython-310.pyc │ │ ├── __pycache__ │ │ │ ├── decoder.cpython-310.pyc │ │ │ ├── encoder.cpython-310.pyc │ │ │ ├── model.cpython-310.pyc │ │ │ ├── rwkv_v4.cpython-310.pyc │ │ │ ├── rwkv_v5.cpython-310.pyc │ │ │ ├── rwkv_v6.cpython-310.pyc │ │ │ ├── search.cpython-310.pyc │ │ │ └── __init__.cpython-310.pyc │ │ ├── cuda │ │ │ ├── wkv_op.cpp │ │ │ ├── wkv6_op.cpp │ │ │ └── wkv5_op.cpp │ │ └── encoder.py │ ├── transducer │ │ ├── __init__.py │ │ └── __pycache__ │ │ │ ├── model.cpython-310.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── joint_network.cpython-310.pyc │ │ │ ├── rnn_decoder.cpython-310.pyc │ │ │ ├── rnnt_decoder.cpython-310.pyc │ │ │ └── beam_search_transducer.cpython-310.pyc │ ├── transformer │ │ ├── __init__.py │ │ ├── utils │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── mask.cpython-310.pyc │ │ │ │ ├── repeat.cpython-310.pyc │ │ │ │ ├── vgg2l.cpython-310.pyc │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ ├── add_sos_eos.cpython-310.pyc │ │ │ │ ├── lightconv.cpython-310.pyc │ │ │ │ ├── lightconv2d.cpython-310.pyc │ │ │ │ ├── nets_utils.cpython-310.pyc │ │ │ │ ├── subsampling.cpython-310.pyc │ │ │ │ ├── dynamic_conv.cpython-310.pyc │ │ │ │ ├── dynamic_conv2d.cpython-310.pyc │ │ │ │ ├── multi_layer_conv.cpython-310.pyc │ │ │ │ └── subsampling_without_posenc.cpython-310.pyc │ │ │ └── add_sos_eos.py │ │ ├── scorers │ │ │ ├── __init__.py │ │ │ └── __pycache__ │ │ │ │ ├── ctc.cpython-310.pyc │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ ├── length_bonus.cpython-310.pyc │ │ │ │ ├── ctc_prefix_score.cpython-310.pyc │ │ │ │ └── scorer_interface.cpython-310.pyc │ │ ├── __pycache__ │ │ │ ├── decoder.cpython-310.pyc │ │ │ ├── encoder.cpython-310.pyc │ │ │ ├── model.cpython-310.pyc │ │ │ ├── search.cpython-310.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── attention.cpython-310.pyc │ │ │ ├── embedding.cpython-310.pyc │ │ │ ├── layer_norm.cpython-310.pyc │ │ │ └── positionwise_feed_forward.cpython-310.pyc │ │ └── positionwise_feed_forward.py │ ├── whisper_lid │ │ ├── __init__.py │ │ ├── eres2net │ │ │ ├── __init__.py │ │ │ ├── __pycache__ │ │ │ │ ├── ResNet.cpython-310.pyc │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ ├── fusion.cpython-310.pyc │ │ │ │ ├── simple_avg.cpython-310.pyc │ │ │ │ └── pooling_layers.cpython-310.pyc │ │ │ ├── simple_avg.py │ │ │ └── fusion.py │ │ ├── __pycache__ │ │ │ ├── decoder.cpython-310.pyc │ │ │ ├── encoder.cpython-310.pyc │ │ │ ├── model.cpython-310.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ └── lid_predictor.cpython-310.pyc │ │ └── lid_predictor.py │ ├── bicif_paraformer │ │ ├── __init__.py │ │ └── __pycache__ │ │ │ ├── model.cpython-310.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── export_meta.cpython-310.pyc │ │ │ └── cif_predictor.cpython-310.pyc │ ├── conformer_rwkv │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── model.cpython-310.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ └── decoder.cpython-310.pyc │ │ └── model.py │ ├── ct_transformer │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── model.cpython-310.pyc │ │ │ ├── utils.cpython-310.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ └── export_meta.cpython-310.pyc │ │ └── template.yaml │ ├── e_branchformer │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── model.cpython-310.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ └── encoder.cpython-310.pyc │ │ └── model.py │ ├── language_model │ │ ├── __init__.py │ │ ├── rnn │ │ │ ├── __init__.py │ │ │ └── __pycache__ │ │ │ │ ├── __init__.cpython-310.pyc │ │ │ │ ├── argument.cpython-310.pyc │ │ │ │ ├── attentions.cpython-310.pyc │ │ │ │ ├── decoders.cpython-310.pyc │ │ │ │ └── encoders.cpython-310.pyc │ │ └── __pycache__ │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── seq_rnn_lm.cpython-310.pyc │ │ │ ├── transformer_lm.cpython-310.pyc │ │ │ └── transformer_encoder.cpython-310.pyc │ ├── monotonic_aligner │ │ ├── __init__.py │ │ └── __pycache__ │ │ │ ├── model.cpython-310.pyc │ │ │ └── __init__.cpython-310.pyc │ ├── seaco_paraformer │ │ ├── __init__.py │ │ └── __pycache__ │ │ │ ├── model.cpython-310.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ └── export_meta.cpython-310.pyc │ ├── contextual_paraformer │ │ ├── __init__.py │ │ └── __pycache__ │ │ │ ├── decoder.cpython-310.pyc │ │ │ ├── model.cpython-310.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ └── export_meta.cpython-310.pyc │ ├── fsmn_vad_streaming │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── model.cpython-310.pyc │ │ │ ├── encoder.cpython-310.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ └── export_meta.cpython-310.pyc │ │ └── template.yaml │ ├── paraformer_streaming │ │ ├── __init__.py │ │ └── __pycache__ │ │ │ ├── model.cpython-310.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ └── export_meta.cpython-310.pyc │ ├── ct_transformer_streaming │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── model.cpython-310.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── attention.cpython-310.pyc │ │ │ ├── encoder.cpython-310.pyc │ │ │ └── export_meta.cpython-310.pyc │ │ ├── attention.py │ │ └── template.yaml │ ├── eres2net │ │ ├── __init__.py │ │ ├── __pycache__ │ │ │ ├── fusion.cpython-310.pyc │ │ │ ├── __init__.cpython-310.pyc │ │ │ ├── eres2net.cpython-310.pyc │ │ │ └── eres2net_aug.cpython-310.pyc │ │ └── fusion.py │ └── __pycache__ │ │ └── __init__.cpython-310.pyc ├── tokenizer │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-310.pyc │ │ ├── cleaner.cpython-310.pyc │ │ ├── abs_tokenizer.cpython-310.pyc │ │ ├── hf_tokenizer.cpython-310.pyc │ │ ├── build_tokenizer.cpython-310.pyc │ │ ├── char_tokenizer.cpython-310.pyc │ │ ├── korean_cleaner.cpython-310.pyc │ │ ├── word_tokenizer.cpython-310.pyc │ │ ├── phoneme_tokenizer.cpython-310.pyc │ │ ├── token_id_converter.cpython-310.pyc │ │ ├── whisper_tokenizer.cpython-310.pyc │ │ └── sentencepiece_tokenizer.cpython-310.pyc │ ├── hf_tokenizer.py │ ├── whisper_tokenizer.py │ ├── cleaner.py │ └── sentencepiece_tokenizer.py ├── utils │ ├── __init__.py │ ├── __pycache__ │ │ ├── misc.cpython-310.pyc │ │ ├── types.cpython-310.pyc │ │ ├── __init__.cpython-310.pyc │ │ ├── load_utils.cpython-310.pyc │ │ ├── vad_utils.cpython-310.pyc │ │ ├── export_utils.cpython-310.pyc │ │ ├── datadir_writer.cpython-310.pyc │ │ ├── speaker_utils.cpython-310.pyc │ │ ├── timestamp_tools.cpython-310.pyc │ │ ├── torch_function.cpython-310.pyc │ │ ├── postprocess_utils.cpython-310.pyc │ │ └── install_model_requirements.cpython-310.pyc │ └── install_model_requirements.py ├── version.txt ├── train_utils │ ├── __init__.py │ ├── __pycache__ │ │ ├── __init__.cpython-310.pyc │ │ ├── trainer.cpython-310.pyc │ │ ├── initialize.cpython-310.pyc │ │ ├── device_funcs.cpython-310.pyc │ │ ├── model_summary.cpython-310.pyc │ │ ├── recursive_op.cpython-310.pyc │ │ ├── forward_adaptor.cpython-310.pyc │ │ ├── add_gradient_noise.cpython-310.pyc │ │ ├── set_all_random_seed.cpython-310.pyc │ │ ├── average_nbest_models.cpython-310.pyc │ │ └── load_pretrained_model.cpython-310.pyc │ ├── set_all_random_seed.py │ ├── forward_adaptor.py │ ├── add_gradient_noise.py │ └── recursive_op.py ├── optimizers │ ├── __pycache__ │ │ ├── sgd.cpython-310.pyc │ │ ├── __init__.cpython-310.pyc │ │ └── fairseq_adam.cpython-310.pyc │ ├── __init__.py │ └── sgd.py ├── schedulers │ ├── __pycache__ │ │ ├── noam_lr.cpython-310.pyc │ │ ├── __init__.cpython-310.pyc │ │ ├── warmup_lr.cpython-310.pyc │ │ ├── lambdalr_cus.cpython-310.pyc │ │ ├── abs_scheduler.cpython-310.pyc │ │ └── tri_stage_scheduler.cpython-310.pyc │ ├── __init__.py │ ├── lambdalr_cus.py │ └── warmup_lr.py └── __init__.py └── funasr_onnx ├── utils └── __init__.py └── __init__.py /funasr/auto/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/bin/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/datasets/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/download/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/frontends/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/losses/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/metrics/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/tokenizer/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/utils/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/version.txt: -------------------------------------------------------------------------------- 1 | 1.0.26 -------------------------------------------------------------------------------- /funasr/models/bat/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/ctc/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/eend/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/lcbnet/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/llm_asr/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/lora/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/mfcca/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/sa_asr/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/sanm/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/scama/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/sond/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/specaug/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/uniasr/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/whisper/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/xvector/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/train_utils/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr_onnx/utils/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/branchformer/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/campplus/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/conformer/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/data2vec/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/eend/utils/__init__.py: 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/funasr/models/normalize/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/paraformer/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/qwen_audio/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/rwkv_bat/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/sense_voice/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/sond/encoder/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/sond/pooling/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/transducer/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/transformer/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/whisper_lid/__init__.py: 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-------------------------------------------------------------------------------- /funasr/models/conformer_rwkv/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/ct_transformer/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/e_branchformer/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/language_model/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/monotonic_aligner/__init__.py: 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-------------------------------------------------------------------------------- /funasr/datasets/sense_voice_datasets/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/contextual_paraformer/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/fsmn_vad_streaming/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/paraformer_streaming/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/whisper_lid/eres2net/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/datasets/large_datasets/datapipes/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/datasets/llm_datasets_qwenaudio/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/models/ct_transformer_streaming/__init__.py: -------------------------------------------------------------------------------- 1 | -------------------------------------------------------------------------------- /funasr/frontends/utils/__init__.py: -------------------------------------------------------------------------------- 1 | """Initialize sub package.""" 2 | -------------------------------------------------------------------------------- /funasr/models/language_model/rnn/__init__.py: -------------------------------------------------------------------------------- 1 | """Initialize sub package.""" 2 | -------------------------------------------------------------------------------- /funasr/models/sense_voice/whisper_lib/version.py: -------------------------------------------------------------------------------- 1 | __version__ = "20231117" 2 | -------------------------------------------------------------------------------- /funasr/models/transformer/scorers/__init__.py: -------------------------------------------------------------------------------- 1 | """Initialize sub package.""" 2 | -------------------------------------------------------------------------------- /funasr/models/sense_voice/whisper_lib/__main__.py: -------------------------------------------------------------------------------- 1 | # from .transcribe import cli 2 | # 3 | # cli() 4 | -------------------------------------------------------------------------------- /funasr/models/eres2net/__init__.py: -------------------------------------------------------------------------------- 1 | from .eres2net import ERes2Net 2 | from .eres2net_aug import ERes2NetAug 3 | -------------------------------------------------------------------------------- /funasr/auto/auto_tokenizer.py: -------------------------------------------------------------------------------- 1 | class AutoTokenizer: 2 | """ 3 | Undo 4 | """ 5 | 6 | def __init__(self): 7 | pass 8 | -------------------------------------------------------------------------------- /funasr/bin/__pycache__/export.cpython-310.pyc: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/pika-online/funasr_seaco_paraformer_onnx_with_timestamp/HEAD/funasr/bin/__pycache__/export.cpython-310.pyc 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torch.random.manual_seed(seed) 11 | -------------------------------------------------------------------------------- /funasr_onnx/__init__.py: -------------------------------------------------------------------------------- 1 | # -*- encoding: utf-8 -*- 2 | from .paraformer_bin import Paraformer, ContextualParaformer, SeacoParaformer 3 | from .vad_bin import Fsmn_vad 4 | from .vad_bin import Fsmn_vad_online 5 | from .punc_bin import CT_Transformer 6 | from .punc_bin import CT_Transformer_VadRealtime 7 | -------------------------------------------------------------------------------- /funasr/datasets/large_datasets/abs_iter_factory.py: -------------------------------------------------------------------------------- 1 | from abc import ABC 2 | from abc import abstractmethod 3 | from typing import Iterator 4 | 5 | 6 | class AbsIterFactory(ABC): 7 | @abstractmethod 8 | def build_iter(self, epoch: int, shuffle: bool = None) -> Iterator: 9 | raise NotImplementedError 10 | -------------------------------------------------------------------------------- /funasr/models/branchformer/model.py: -------------------------------------------------------------------------------- 1 | import logging 2 | 3 | from funasr.models.transformer.model import Transformer 4 | from funasr.register import tables 5 | 6 | 7 | @tables.register("model_classes", "Branchformer") 8 | class Branchformer(Transformer): 9 | """CTC-attention hybrid Encoder-Decoder model""" 10 | 11 | def __init__( 12 | self, 13 | *args, 14 | **kwargs, 15 | ): 16 | 17 | super().__init__(*args, **kwargs) 18 | -------------------------------------------------------------------------------- /funasr/models/e_branchformer/model.py: -------------------------------------------------------------------------------- 1 | import logging 2 | 3 | from funasr.models.transformer.model import Transformer 4 | from funasr.register import tables 5 | 6 | 7 | @tables.register("model_classes", "EBranchformer") 8 | class EBranchformer(Transformer): 9 | """CTC-attention hybrid Encoder-Decoder model""" 10 | 11 | def __init__( 12 | self, 13 | *args, 14 | **kwargs, 15 | ): 16 | 17 | super().__init__(*args, **kwargs) 18 | -------------------------------------------------------------------------------- /funasr/models/conformer/model.py: -------------------------------------------------------------------------------- 1 | import logging 2 | 3 | import torch 4 | 5 | from funasr.models.transformer.model import Transformer 6 | from funasr.register import tables 7 | 8 | 9 | @tables.register("model_classes", "Conformer") 10 | class Conformer(Transformer): 11 | """CTC-attention hybrid Encoder-Decoder model""" 12 | 13 | def __init__( 14 | self, 15 | *args, 16 | **kwargs, 17 | ): 18 | 19 | super().__init__(*args, **kwargs) 20 | -------------------------------------------------------------------------------- /funasr/models/conformer_rwkv/model.py: -------------------------------------------------------------------------------- 1 | import logging 2 | 3 | import torch 4 | 5 | from funasr.models.transformer.model import Transformer 6 | from funasr.register import tables 7 | 8 | 9 | @tables.register("model_classes", "Conformer") 10 | class Conformer(Transformer): 11 | """CTC-attention hybrid Encoder-Decoder model""" 12 | 13 | def __init__( 14 | self, 15 | *args, 16 | **kwargs, 17 | ): 18 | 19 | super().__init__(*args, **kwargs) 20 | -------------------------------------------------------------------------------- /funasr/tokenizer/hf_tokenizer.py: -------------------------------------------------------------------------------- 1 | from funasr.register import tables 2 | 3 | 4 | @tables.register("tokenizer_classes", "HuggingfaceTokenizer") 5 | def HuggingfaceTokenizer(init_param_path, **kwargs): 6 | try: 7 | from transformers import AutoTokenizer 8 | except: 9 | # print("If you want to use hugging, please `pip install -U transformers`") 10 | pass 11 | tokenizer = AutoTokenizer.from_pretrained(init_param_path) 12 | 13 | return tokenizer 14 | -------------------------------------------------------------------------------- /funasr/optimizers/__init__.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from funasr.optimizers.fairseq_adam import FairseqAdam 3 | from funasr.optimizers.sgd import SGD 4 | 5 | optim_classes = dict( 6 | adam=torch.optim.Adam, 7 | fairseq_adam=FairseqAdam, 8 | adamw=torch.optim.AdamW, 9 | sgd=SGD, 10 | adadelta=torch.optim.Adadelta, 11 | adagrad=torch.optim.Adagrad, 12 | adamax=torch.optim.Adamax, 13 | asgd=torch.optim.ASGD, 14 | lbfgs=torch.optim.LBFGS, 15 | rmsprop=torch.optim.RMSprop, 16 | rprop=torch.optim.Rprop, 17 | ) 18 | -------------------------------------------------------------------------------- /funasr/models/data2vec/grad_multiply.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) Facebook, Inc. and its affiliates. 2 | # 3 | # This source code is licensed under the MIT license found in the 4 | # LICENSE file in the root directory of this source tree. 5 | 6 | import torch 7 | 8 | 9 | class GradMultiply(torch.autograd.Function): 10 | @staticmethod 11 | def forward(ctx, x, scale): 12 | ctx.scale = scale 13 | res = x.new(x) 14 | return res 15 | 16 | @staticmethod 17 | def backward(ctx, grad): 18 | return grad * ctx.scale, None 19 | -------------------------------------------------------------------------------- /funasr/datasets/large_datasets/datapipes/map.py: -------------------------------------------------------------------------------- 1 | from torch.utils.data import IterableDataset 2 | 3 | 4 | def default_fn(data): 5 | return data 6 | 7 | 8 | class MapperIterDataPipe(IterableDataset): 9 | 10 | def __init__(self, datapipe, fn=default_fn): 11 | self.datapipe = datapipe 12 | self.fn = fn 13 | 14 | def set_epoch(self, epoch): 15 | self.datapipe.set_epoch(epoch) 16 | 17 | def __iter__(self): 18 | assert callable(self.fn) 19 | for data in self.datapipe: 20 | yield self.fn(data) 21 | -------------------------------------------------------------------------------- /funasr/download/download_dataset_from_hub.py: -------------------------------------------------------------------------------- 1 | def download_dataset(): 2 | pass 3 | 4 | 5 | def download_dataset_from_ms(**kwargs): 6 | from modelscope.msdatasets import MsDataset 7 | 8 | dataset_name = kwargs.get("dataset_name", "speech_asr/speech_asr_aishell1_trainsets") 9 | subset_name = kwargs.get("subset_name", "default") 10 | split = kwargs.get("split", "train") 11 | data_dump_dir = kwargs.get("data_dump_dir", None) 12 | ds = MsDataset.load( 13 | dataset_name=dataset_name, subset_name=subset_name, split=split, cache_dir=data_dump_dir 14 | ) 15 | -------------------------------------------------------------------------------- /funasr/models/whisper_lid/eres2net/simple_avg.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | from funasr.models.encoder.abs_encoder import AbsEncoder 4 | from funasr.modules.nets_utils import make_pad_mask 5 | 6 | 7 | class SimpleAvg(AbsEncoder): 8 | def __init__(self, feat_dim): 9 | super(SimpleAvg, self).__init__() 10 | self.feat_dim = feat_dim 11 | 12 | def forward(self, x, ilens): 13 | mask = ~make_pad_mask(ilens, maxlen=x.shape[1]).to(x.device) 14 | avg_x = (x * mask[:, :, None]).sum(1) / mask.sum(-1)[:, None] 15 | return avg_x 16 | 17 | def output_size(self) -> int: 18 | return self.feat_dim 19 | -------------------------------------------------------------------------------- /funasr/datasets/large_datasets/datapipes/filter.py: -------------------------------------------------------------------------------- 1 | from torch.utils.data import IterableDataset 2 | 3 | 4 | def default_fn(data): 5 | return data 6 | 7 | 8 | class FilterIterDataPipe(IterableDataset): 9 | 10 | def __init__(self, datapipe, fn=default_fn): 11 | self.datapipe = datapipe 12 | self.fn = fn 13 | 14 | def set_epoch(self, epoch): 15 | self.datapipe.set_epoch(epoch) 16 | 17 | def __iter__(self): 18 | assert callable(self.fn) 19 | for data in self.datapipe: 20 | if self.fn(data): 21 | yield data 22 | else: 23 | continue 24 | -------------------------------------------------------------------------------- /funasr/models/campplus/template.yaml: -------------------------------------------------------------------------------- 1 | # This is an example that demonstrates how to configure a model file. 2 | # You can modify the configuration according to your own requirements. 3 | 4 | # to print the register_table: 5 | # from funasr.register import tables 6 | # tables.print() 7 | 8 | # network architecture 9 | model: CAMPPlus 10 | model_conf: 11 | feat_dim: 80 12 | embedding_size: 192 13 | growth_rate: 32 14 | bn_size: 4 15 | init_channels: 128 16 | config_str: 'batchnorm-relu' 17 | memory_efficient: True 18 | output_level: 'segment' 19 | 20 | # frontend related 21 | frontend: WavFrontend 22 | frontend_conf: 23 | fs: 16000 24 | -------------------------------------------------------------------------------- /funasr/models/sanm/model.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- encoding: utf-8 -*- 3 | # Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. 4 | # MIT License (https://opensource.org/licenses/MIT) 5 | 6 | import logging 7 | 8 | import torch 9 | 10 | from funasr.models.transformer.model import Transformer 11 | from funasr.register import tables 12 | 13 | 14 | @tables.register("model_classes", "SANM") 15 | class SANM(Transformer): 16 | """ 17 | Author: Zhifu Gao, Shiliang Zhang, Ming Lei, Ian McLoughlin 18 | San-m: Memory equipped self-attention for end-to-end speech recognition 19 | https://arxiv.org/abs/2006.01713 20 | """ 21 | 22 | def __init__( 23 | self, 24 | *args, 25 | **kwargs, 26 | ): 27 | 28 | super().__init__(*args, **kwargs) 29 | -------------------------------------------------------------------------------- /funasr/optimizers/sgd.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | 4 | class SGD(torch.optim.SGD): 5 | """Thin inheritance of torch.optim.SGD to bind the required arguments, 'lr' 6 | 7 | Note that 8 | the arguments of the optimizer invoked by AbsTask.main() 9 | must have default value except for 'param'. 10 | 11 | I can't understand why only SGD.lr doesn't have the default value. 12 | """ 13 | 14 | def __init__( 15 | self, 16 | params, 17 | lr: float = 0.1, 18 | momentum: float = 0.0, 19 | dampening: float = 0.0, 20 | weight_decay: float = 0.0, 21 | nesterov: bool = False, 22 | ): 23 | super().__init__( 24 | params, 25 | lr=lr, 26 | momentum=momentum, 27 | dampening=dampening, 28 | weight_decay=weight_decay, 29 | nesterov=nesterov, 30 | ) 31 | -------------------------------------------------------------------------------- /funasr/models/sond/encoder/ci_scorers.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch.nn import functional as F 3 | 4 | 5 | class DotScorer(torch.nn.Module): 6 | def __init__(self): 7 | super().__init__() 8 | 9 | def forward( 10 | self, 11 | xs_pad: torch.Tensor, 12 | spk_emb: torch.Tensor, 13 | ): 14 | # xs_pad: B, T, D 15 | # spk_emb: B, N, D 16 | scores = torch.matmul(xs_pad, spk_emb.transpose(1, 2)) 17 | return scores 18 | 19 | 20 | class CosScorer(torch.nn.Module): 21 | def __init__(self): 22 | super().__init__() 23 | 24 | def forward( 25 | self, 26 | xs_pad: torch.Tensor, 27 | spk_emb: torch.Tensor, 28 | ): 29 | # xs_pad: B, T, D 30 | # spk_emb: B, N, D 31 | scores = F.cosine_similarity(xs_pad.unsqueeze(2), spk_emb.unsqueeze(1), dim=-1) 32 | return scores 33 | -------------------------------------------------------------------------------- /funasr/bin/inference.py: -------------------------------------------------------------------------------- 1 | import hydra 2 | import logging 3 | from omegaconf import DictConfig, OmegaConf, ListConfig 4 | 5 | from funasr.auto.auto_model import AutoModel 6 | 7 | 8 | @hydra.main(config_name=None, version_base=None) 9 | def main_hydra(cfg: DictConfig): 10 | def to_plain_list(cfg_item): 11 | if isinstance(cfg_item, ListConfig): 12 | return OmegaConf.to_container(cfg_item, resolve=True) 13 | elif isinstance(cfg_item, DictConfig): 14 | return {k: to_plain_list(v) for k, v in cfg_item.items()} 15 | else: 16 | return cfg_item 17 | 18 | kwargs = to_plain_list(cfg) 19 | 20 | if kwargs.get("debug", False): 21 | import pdb 22 | 23 | pdb.set_trace() 24 | model = AutoModel(**kwargs) 25 | res = model.generate(input=kwargs["input"]) 26 | print(res) 27 | 28 | 29 | if __name__ == "__main__": 30 | main_hydra() 31 | -------------------------------------------------------------------------------- /funasr/models/ct_transformer_streaming/attention.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- encoding: utf-8 -*- 3 | # Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. 4 | # MIT License (https://opensource.org/licenses/MIT) 5 | 6 | import torch 7 | from funasr.models.sanm.attention import MultiHeadedAttentionSANM 8 | 9 | 10 | class MultiHeadedAttentionSANMwithMask(MultiHeadedAttentionSANM): 11 | def __init__(self, *args, **kwargs): 12 | super().__init__(*args, **kwargs) 13 | 14 | def forward(self, x, mask, mask_shfit_chunk=None, mask_att_chunk_encoder=None): 15 | q_h, k_h, v_h, v = self.forward_qkv(x) 16 | fsmn_memory = self.forward_fsmn(v, mask[0], mask_shfit_chunk) 17 | q_h = q_h * self.d_k ** (-0.5) 18 | scores = torch.matmul(q_h, k_h.transpose(-2, -1)) 19 | att_outs = self.forward_attention(v_h, scores, mask[1], mask_att_chunk_encoder) 20 | return att_outs + fsmn_memory 21 | -------------------------------------------------------------------------------- /funasr/models/whisper_lid/lid_predictor.py: -------------------------------------------------------------------------------- 1 | from funasr.register import tables 2 | from funasr.models.whisper_lid.eres2net.ResNet import ( 3 | ERes2Net, 4 | BasicBlockERes2Net, 5 | BasicBlockERes2Net_diff_AFF, 6 | ) 7 | 8 | 9 | @tables.register("lid_predictor_classes", "LidPredictor") 10 | class LidPredictor(ERes2Net): 11 | def __init__( 12 | self, 13 | block=BasicBlockERes2Net, 14 | block_fuse=BasicBlockERes2Net_diff_AFF, 15 | num_blocks=[3, 4, 6, 3], 16 | m_channels=32, 17 | feat_dim=80, 18 | embedding_size=192, 19 | pooling_func="TSTP", 20 | two_emb_layer=False, 21 | ): 22 | super(LidPredictor, self).__init__( 23 | block=block, 24 | block_fuse=block_fuse, 25 | num_blocks=num_blocks, 26 | m_channels=m_channels, 27 | feat_dim=feat_dim, 28 | embedding_size=embedding_size, 29 | pooling_func=pooling_func, 30 | two_emb_layer=two_emb_layer, 31 | ) 32 | -------------------------------------------------------------------------------- /funasr/schedulers/__init__.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.multiprocessing 3 | import torch.nn 4 | import torch.optim 5 | 6 | from funasr.schedulers.noam_lr import NoamLR 7 | from funasr.schedulers.tri_stage_scheduler import TriStageLR 8 | from funasr.schedulers.warmup_lr import WarmupLR 9 | from funasr.schedulers.lambdalr_cus import CustomLambdaLR 10 | 11 | scheduler_classes = dict( 12 | ReduceLROnPlateau=torch.optim.lr_scheduler.ReduceLROnPlateau, 13 | lambdalr=torch.optim.lr_scheduler.LambdaLR, 14 | steplr=torch.optim.lr_scheduler.StepLR, 15 | multisteplr=torch.optim.lr_scheduler.MultiStepLR, 16 | exponentiallr=torch.optim.lr_scheduler.ExponentialLR, 17 | CosineAnnealingLR=torch.optim.lr_scheduler.CosineAnnealingLR, 18 | noamlr=NoamLR, 19 | warmuplr=WarmupLR, 20 | tri_stage=TriStageLR, 21 | cycliclr=torch.optim.lr_scheduler.CyclicLR, 22 | onecyclelr=torch.optim.lr_scheduler.OneCycleLR, 23 | CosineAnnealingWarmRestarts=torch.optim.lr_scheduler.CosineAnnealingWarmRestarts, 24 | custom_lambdalr=CustomLambdaLR, 25 | ) 26 | -------------------------------------------------------------------------------- /funasr/models/eres2net/fusion.py: -------------------------------------------------------------------------------- 1 | # Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved. 2 | # Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0) 3 | 4 | import torch 5 | import torch.nn as nn 6 | 7 | 8 | class AFF(nn.Module): 9 | 10 | def __init__(self, channels=64, r=4): 11 | super(AFF, self).__init__() 12 | inter_channels = int(channels // r) 13 | 14 | self.local_att = nn.Sequential( 15 | nn.Conv2d(channels * 2, inter_channels, kernel_size=1, stride=1, padding=0), 16 | nn.BatchNorm2d(inter_channels), 17 | nn.SiLU(inplace=True), 18 | nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0), 19 | nn.BatchNorm2d(channels), 20 | ) 21 | 22 | def forward(self, x, ds_y): 23 | xa = torch.cat((x, ds_y), dim=1) 24 | x_att = self.local_att(xa) 25 | x_att = 1.0 + torch.tanh(x_att) 26 | xo = torch.mul(x, x_att) + torch.mul(ds_y, 2.0 - x_att) 27 | 28 | return xo 29 | -------------------------------------------------------------------------------- /funasr/models/llm_asr_nar/adaptor.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | 4 | from funasr.register import tables 5 | 6 | 7 | @tables.register("adaptor_classes", "Linear") 8 | class Linear(nn.Module): 9 | def __init__(self, downsample_rate, encoder_dim, llm_dim, ffn_dim: int = 2048, **kwargs): 10 | super().__init__() 11 | self.k = downsample_rate 12 | self.encoder_dim = encoder_dim 13 | self.llm_dim = llm_dim 14 | self.linear1 = nn.Linear(self.encoder_dim * self.k, ffn_dim) 15 | self.relu = nn.ReLU() 16 | self.linear2 = nn.Linear(ffn_dim, self.llm_dim) 17 | 18 | def forward(self, x): 19 | batch_size, seq_len, dim = x.size() 20 | num_frames_to_discard = seq_len % self.k 21 | if num_frames_to_discard > 0: 22 | x = x[:, :-num_frames_to_discard, :] 23 | seq_len = x.size(1) 24 | 25 | x = x.contiguous() 26 | x = x.view(batch_size, seq_len // self.k, dim * self.k) 27 | x = self.linear1(x) 28 | x = self.relu(x) 29 | x = self.linear2(x) 30 | return x 31 | -------------------------------------------------------------------------------- /funasr/models/whisper_lid/eres2net/fusion.py: -------------------------------------------------------------------------------- 1 | # Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved. 2 | # Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0) 3 | 4 | import torch 5 | import torch.nn as nn 6 | 7 | 8 | class AFF(nn.Module): 9 | 10 | def __init__(self, channels=64, r=4): 11 | super(AFF, self).__init__() 12 | inter_channels = int(channels // r) 13 | 14 | self.local_att = nn.Sequential( 15 | nn.Conv2d(channels * 2, inter_channels, kernel_size=1, stride=1, padding=0), 16 | nn.BatchNorm2d(inter_channels), 17 | nn.SiLU(inplace=True), 18 | nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0), 19 | nn.BatchNorm2d(channels), 20 | ) 21 | 22 | def forward(self, x, ds_y): 23 | xa = torch.cat((x, ds_y), dim=1) 24 | x_att = self.local_att(xa) 25 | x_att = 1.0 + torch.tanh(x_att) 26 | xo = torch.mul(x, x_att) + torch.mul(ds_y, 2.0 - x_att) 27 | 28 | return xo 29 | -------------------------------------------------------------------------------- /funasr/datasets/llm_datasets/preprocessor.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | import torch 4 | import logging 5 | import concurrent.futures 6 | import librosa 7 | import torch.distributed as dist 8 | from typing import Collection 9 | import torch 10 | import torchaudio 11 | from torch import nn 12 | import random 13 | import re 14 | import string 15 | from funasr.tokenizer.cleaner import TextCleaner 16 | from funasr.register import tables 17 | 18 | 19 | @tables.register("preprocessor_classes", "TextPreprocessRemovePunctuation") 20 | class TextPreprocessRemovePunctuation(nn.Module): 21 | def __init__(self, **kwargs): 22 | super().__init__() 23 | 24 | def forward(self, text, **kwargs): 25 | # 定义英文标点符号 26 | en_punct = string.punctuation 27 | # 定义中文标点符号（部分常用的） 28 | cn_punct = "。？！，、；：“”‘’（）《》【】…—～·" 29 | # 合并英文和中文标点符号 30 | all_punct = en_punct + cn_punct 31 | # 创建正则表达式模式，匹配任何在all_punct中的字符 32 | punct_pattern = re.compile("[{}]".format(re.escape(all_punct))) 33 | # 使用正则表达式的sub方法替换掉这些字符 34 | return punct_pattern.sub("", text) 35 | -------------------------------------------------------------------------------- /funasr/models/transformer/utils/add_sos_eos.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | 4 | # Copyright 2019 Shigeki Karita 5 | # Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) 6 | 7 | """Unility functions for Transformer.""" 8 | 9 | import torch 10 | from funasr.models.transformer.utils.nets_utils import pad_list 11 | 12 | 13 | def add_sos_eos(ys_pad, sos, eos, ignore_id): 14 | """Add and labels. 15 | 16 | :param torch.Tensor ys_pad: batch of padded target sequences (B, Lmax) 17 | :param int sos: index of 18 | :param int eos: index of 19 | :param int ignore_id: index of padding 20 | :return: padded tensor (B, Lmax) 21 | :rtype: torch.Tensor 22 | :return: padded tensor (B, Lmax) 23 | :rtype: torch.Tensor 24 | """ 25 | 26 | _sos = ys_pad.new([sos]) 27 | _eos = ys_pad.new([eos]) 28 | ys = [y[y != ignore_id] for y in ys_pad] # parse padded ys 29 | ys_in = [torch.cat([_sos, y], dim=0) for y in ys] 30 | ys_out = [torch.cat([y, _eos], dim=0) for y in ys] 31 | return pad_list(ys_in, eos), pad_list(ys_out, ignore_id) 32 | -------------------------------------------------------------------------------- /funasr/datasets/large_datasets/utils/low_frame_rate.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | def build_LFR_features(data, m, n): 5 | """ 6 | Actually, this implements stacking frames and skipping frames. 7 | if m = 1 and n = 1, just return the origin features. 8 | if m = 1 and n > 1, it works like skipping. 9 | if m > 1 and n = 1, it works like stacking but only support right frames. 10 | if m > 1 and n > 1, it works like LFR. 11 | 12 | Args: 13 | inputs_batch: inputs is T x D np.ndarray 14 | m: number of frames to stack 15 | n: number of frames to skip 16 | """ 17 | 18 | LFR_inputs = [] 19 | T = data.shape[0] 20 | T_lfr = int(np.ceil(T / n)) 21 | for i in range(T_lfr): 22 | if m <= T - i * n: 23 | LFR_inputs.append(np.hstack(data[i * n : i * n + m])) 24 | else: 25 | num_padding = m - (T - i * n) 26 | frame = np.hstack(data[i * n :]) 27 | for _ in range(num_padding): 28 | frame = np.hstack((frame, data[-1])) 29 | LFR_inputs.append(frame) 30 | return np.vstack(LFR_inputs) 31 | -------------------------------------------------------------------------------- /funasr/train_utils/forward_adaptor.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | 4 | class ForwardAdaptor(torch.nn.Module): 5 | """Wrapped module to parallelize specified method 6 | 7 | torch.nn.DataParallel parallelizes only "forward()" 8 | and, maybe, the method having the other name can't be applied 9 | except for wrapping the module just like this class. 10 | 11 | Examples: 12 | >>> class A(torch.nn.Module): 13 | ... def foo(self, x): 14 | ... ... 15 | >>> model = A() 16 | >>> model = ForwardAdaptor(model, "foo") 17 | >>> model = torch.nn.DataParallel(model, device_ids=[0, 1]) 18 | >>> x = torch.randn(2, 10) 19 | >>> model(x) 20 | """ 21 | 22 | def __init__(self, module: torch.nn.Module, name: str): 23 | super().__init__() 24 | self.module = module 25 | self.name = name 26 | if not hasattr(module, name): 27 | raise ValueError(f"{module} doesn't have {name}") 28 | 29 | def forward(self, *args, **kwargs): 30 | func = getattr(self.module, self.name) 31 | return func(*args, **kwargs) 32 | -------------------------------------------------------------------------------- /funasr/train_utils/add_gradient_noise.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | 4 | def add_gradient_noise( 5 | model: torch.nn.Module, 6 | iteration: int, 7 | duration: float = 100, 8 | eta: float = 1.0, 9 | scale_factor: float = 0.55, 10 | ): 11 | """Adds noise from a standard normal distribution to the gradients. 12 | 13 | The standard deviation (`sigma`) is controlled 14 | by the three hyper-parameters below. 15 | `sigma` goes to zero (no noise) with more iterations. 16 | 17 | Args: 18 | model: Model. 19 | iteration: Number of iterations. 20 | duration: {100, 1000}: Number of durations to control 21 | the interval of the `sigma` change. 22 | eta: {0.01, 0.3, 1.0}: The magnitude of `sigma`. 23 | scale_factor: {0.55}: The scale of `sigma`. 24 | """ 25 | interval = (iteration // duration) + 1 26 | sigma = eta / interval**scale_factor 27 | for param in model.parameters(): 28 | if param.grad is not None: 29 | _shape = param.grad.size() 30 | noise = sigma * torch.randn(_shape).to(param.device) 31 | param.grad += noise 32 | -------------------------------------------------------------------------------- /funasr/models/whisper/template.yaml: -------------------------------------------------------------------------------- 1 | # This is an example that demonstrates how to configure a model file. 2 | # You can modify the configuration according to your own requirements. 3 | 4 | # to print the register_table: 5 | # from funasr.register import tables 6 | # tables.print() 7 | 8 | # network architecture 9 | model: WhisperWarp 10 | model_conf: 11 | lsm_weight: 0.1 12 | length_normalized_loss: true 13 | hub: funasr # openai 14 | 15 | 16 | 17 | # only use for hub == funasr, 18 | # if hub == openai, dims is automaticall download 19 | dims: 20 | n_mels: 128 21 | n_vocab: 51866 22 | n_audio_ctx: 1500 23 | n_audio_state: 1280 24 | n_audio_head: 20 25 | n_audio_layer: 32 26 | n_text_ctx: 448 27 | n_text_state: 1280 28 | n_text_head: 20 29 | n_text_layer: 32 30 | 31 | # frontend related 32 | frontend: WhisperFrontend 33 | frontend_conf: 34 | fs: 16000 35 | n_mels: ${dims.n_mels} 36 | do_pad_trim: true 37 | 38 | tokenizer: WhisperTokenizer 39 | tokenizer_conf: 40 | language: null 41 | task: transcribe 42 | is_multilingual: true 43 | num_languages: 100 44 | 45 | scope_map: [none, "model."] -------------------------------------------------------------------------------- /funasr/datasets/large_datasets/utils/filter.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python 2 | 3 | 4 | def filter( 5 | data, speech_length_min=100, speech_length_max=15000, token_length_min=0, token_length_max=200 6 | ): 7 | assert "speech" in data or "text" in data 8 | 9 | if "speech" in data and "text" in data: 10 | if "sampling_rate" in data: 11 | speech_length = (data["speech"].shape[0] / data["sampling_rate"]) * 1000.0 12 | else: 13 | speech_length = data["speech"].shape[0] 14 | num_tokens = len(data["text"]) 15 | return ( 16 | speech_length_min < speech_length < speech_length_max 17 | and token_length_min < num_tokens < token_length_max 18 | ) 19 | elif "speech" in data: 20 | if "sampling_rate" in data: 21 | speech_length = (data["speech"].shape[0] / data["sampling_rate"]) * 1000.0 22 | else: 23 | speech_length = data["speech"].shape[0] 24 | return speech_length_min < speech_length < speech_length_max 25 | else: 26 | num_tokens = len(data["text"]) 27 | return token_length_min < num_tokens < token_length_max 28 | -------------------------------------------------------------------------------- /funasr/utils/install_model_requirements.py: -------------------------------------------------------------------------------- 1 | import subprocess 2 | 3 | 4 | def install_requirements(requirements_path): 5 | try: 6 | result = subprocess.run( 7 | ["pip", "install", "-r", requirements_path], 8 | stdout=subprocess.PIPE, 9 | stderr=subprocess.PIPE, 10 | text=True, 11 | ) 12 | 13 | # check status 14 | if result.returncode == 0: 15 | print("install model requirements successfully") 16 | return True 17 | else: 18 | print("fail to install model requirements! ") 19 | print("error", result.stderr) 20 | return False 21 | except Exception as e: 22 | result = subprocess.run( 23 | ["pip", "install", "-r", requirements_path], 24 | stdout=subprocess.PIPE, 25 | stderr=subprocess.PIPE, 26 | text=True, 27 | ) 28 | 29 | # check status 30 | if result.returncode == 0: 31 | print("install model requirements successfully") 32 | return True 33 | else: 34 | print("fail to install model requirements! ") 35 | print("error", result.stderr) 36 | return False 37 | -------------------------------------------------------------------------------- /funasr/models/sanm/positionwise_feed_forward.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | 4 | 5 | """Positionwise feed forward layer definition.""" 6 | 7 | import torch 8 | 9 | from funasr.models.transformer.layer_norm import LayerNorm 10 | 11 | 12 | class PositionwiseFeedForwardDecoderSANM(torch.nn.Module): 13 | """Positionwise feed forward layer. 14 | 15 | Args: 16 | idim (int): Input dimenstion. 17 | hidden_units (int): The number of hidden units. 18 | dropout_rate (float): Dropout rate. 19 | 20 | """ 21 | 22 | def __init__(self, idim, hidden_units, dropout_rate, adim=None, activation=torch.nn.ReLU()): 23 | """Construct an PositionwiseFeedForward object.""" 24 | super(PositionwiseFeedForwardDecoderSANM, self).__init__() 25 | self.w_1 = torch.nn.Linear(idim, hidden_units) 26 | self.w_2 = torch.nn.Linear(hidden_units, idim if adim is None else adim, bias=False) 27 | self.dropout = torch.nn.Dropout(dropout_rate) 28 | self.activation = activation 29 | self.norm = LayerNorm(hidden_units) 30 | 31 | def forward(self, x): 32 | """Forward function.""" 33 | return self.w_2(self.norm(self.dropout(self.activation(self.w_1(x))))) 34 | -------------------------------------------------------------------------------- /funasr/models/qwen_audio/template.yaml: -------------------------------------------------------------------------------- 1 | # This is an example that demonstrates how to configure a model file. 2 | # You can modify the configuration according to your own requirements. 3 | 4 | # to print the register_table: 5 | # from funasr.register import tables 6 | # tables.print() 7 | 8 | # network architecture 9 | model: WhisperWarp 10 | model_conf: 11 | lsm_weight: 0.1 12 | length_normalized_loss: true 13 | hub: funasr # openai 14 | init_param_path: null # large-v2 or large-v3 if hub == "openai" 15 | 16 | 17 | 18 | # only use for hub == funasr, 19 | # if hub == openai, whisper_dims is automaticall download 20 | whisper_dims: 21 | 'n_mels': 80 22 | 'n_vocab': 51865 23 | 'n_audio_ctx': 1500 24 | 'n_audio_state': 1280 25 | 'n_audio_head': 20 26 | 'n_audio_layer': 32 27 | 'n_text_ctx': 448 28 | 'n_text_state': 1280 29 | 'n_text_head': 20 30 | 'n_text_layer': 32 31 | 32 | # frontend related 33 | frontend: WhisperFrontend 34 | frontend_conf: 35 | fs: 16000 36 | n_mels: 80 37 | do_pad_trim: true 38 | 39 | tokenizer: WhisperTokenizer 40 | tokenizer_conf: 41 | language: null 42 | task: transcribe 43 | is_multilingual: true 44 | num_languages: 99 45 | 46 | scope_map: ['none', "model."] 47 | -------------------------------------------------------------------------------- /funasr/models/ct_transformer_streaming/template.yaml: -------------------------------------------------------------------------------- 1 | # This is an example that demonstrates how to configure a model file. 2 | # You can modify the configuration according to your own requirements. 3 | 4 | # to print the register_table: 5 | # from funasr.register import tables 6 | # tables.print() 7 | 8 | model: CTTransformerStreaming 9 | model_conf: 10 | ignore_id: 0 11 | embed_unit: 256 12 | att_unit: 256 13 | dropout_rate: 0.1 14 | punc_list: 15 | - 16 | - _ 17 | - ， 18 | - 。 19 | - ？ 20 | - 、 21 | punc_weight: 22 | - 1.0 23 | - 1.0 24 | - 1.0 25 | - 1.0 26 | - 1.0 27 | - 1.0 28 | sentence_end_id: 3 29 | 30 | encoder: SANMVadEncoder 31 | encoder_conf: 32 | input_size: 256 33 | output_size: 256 34 | attention_heads: 8 35 | linear_units: 1024 36 | num_blocks: 3 37 | dropout_rate: 0.1 38 | positional_dropout_rate: 0.1 39 | attention_dropout_rate: 0.0 40 | input_layer: pe 41 | pos_enc_class: SinusoidalPositionEncoder 42 | normalize_before: true 43 | kernel_size: 11 44 | sanm_shfit: 5 45 | selfattention_layer_type: sanm 46 | padding_idx: 0 47 | 48 | tokenizer: CharTokenizer 49 | tokenizer_conf: 50 | unk_symbol: -------------------------------------------------------------------------------- /funasr/models/ct_transformer/template.yaml: -------------------------------------------------------------------------------- 1 | # This is an example that demonstrates how to configure a model file. 2 | # You can modify the configuration according to your own requirements. 3 | 4 | # to print the register_table: 5 | # from funasr.register import tables 6 | # tables.print() 7 | 8 | model: CTTransformer 9 | model_conf: 10 | ignore_id: 0 11 | embed_unit: 256 12 | att_unit: 256 13 | dropout_rate: 0.1 14 | punc_list: 15 | - 16 | - _ 17 | - ',' 18 | - 。 19 | - '?' 20 | - 、 21 | punc_weight: 22 | - 1.0 23 | - 1.0 24 | - 1.0 25 | - 1.0 26 | - 1.0 27 | - 1.0 28 | sentence_end_id: 3 29 | 30 | encoder: SANMEncoder 31 | encoder_conf: 32 | input_size: 256 33 | output_size: 256 34 | attention_heads: 8 35 | linear_units: 1024 36 | num_blocks: 4 37 | dropout_rate: 0.1 38 | positional_dropout_rate: 0.1 39 | attention_dropout_rate: 0.0 40 | input_layer: pe 41 | pos_enc_class: SinusoidalPositionEncoder 42 | normalize_before: true 43 | kernel_size: 11 44 | sanm_shfit: 0 45 | selfattention_layer_type: sanm 46 | padding_idx: 0 47 | 48 | tokenizer: CharTokenizer 49 | tokenizer_conf: 50 | unk_symbol: 51 | 52 | 53 | 54 | -------------------------------------------------------------------------------- /funasr/bin/export.py: -------------------------------------------------------------------------------- 1 | import os 2 | import hydra 3 | import logging 4 | from omegaconf import DictConfig, OmegaConf, ListConfig 5 | 6 | from funasr.auto.auto_model import AutoModel 7 | 8 | 9 | @hydra.main(config_name=None, version_base=None) 10 | def main_hydra(cfg: DictConfig): 11 | def to_plain_list(cfg_item): 12 | if isinstance(cfg_item, ListConfig): 13 | return OmegaConf.to_container(cfg_item, resolve=True) 14 | elif isinstance(cfg_item, DictConfig): 15 | return {k: to_plain_list(v) for k, v in cfg_item.items()} 16 | else: 17 | return cfg_item 18 | 19 | kwargs = to_plain_list(cfg) 20 | 21 | if kwargs.get("debug", False): 22 | import pdb 23 | 24 | pdb.set_trace() 25 | 26 | if "device" not in kwargs: 27 | kwargs["device"] = "cpu" 28 | model = AutoModel(**kwargs) 29 | 30 | res = model.export( 31 | input=kwargs.get("input", None), 32 | type=kwargs.get("type", "onnx"), 33 | quantize=kwargs.get("quantize", False), 34 | fallback_num=kwargs.get("fallback-num", 5), 35 | calib_num=kwargs.get("calib_num", 100), 36 | opset_version=kwargs.get("opset_version", 14), 37 | ) 38 | print(res) 39 | 40 | 41 | if __name__ == "__main__": 42 | main_hydra() 43 | -------------------------------------------------------------------------------- /funasr/models/sense_voice/cuda/wkv_op.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | 3 | void cuda_forward(int B, int T, int C, float *w, float *u, float *k, float *v, float *y); 4 | void cuda_backward(int B, int T, int C, float *w, float *u, float *k, float *v, float *gy, float *gw, float *gu, float *gk, float *gv); 5 | 6 | void forward(int64_t B, int64_t T, int64_t C, torch::Tensor &w, torch::Tensor &u, torch::Tensor &k, torch::Tensor &v, torch::Tensor &y) { 7 | cuda_forward(B, T, C, w.data_ptr(), u.data_ptr(), k.data_ptr(), v.data_ptr(), y.data_ptr()); 8 | } 9 | void backward(int64_t B, int64_t T, int64_t C, torch::Tensor &w, torch::Tensor &u, torch::Tensor &k, torch::Tensor &v, torch::Tensor &gy, torch::Tensor &gw, torch::Tensor &gu, torch::Tensor &gk, torch::Tensor &gv) { 10 | cuda_backward(B, T, C, w.data_ptr(), u.data_ptr(), k.data_ptr(), v.data_ptr(), gy.data_ptr(), gw.data_ptr(), gu.data_ptr(), gk.data_ptr(), gv.data_ptr()); 11 | } 12 | 13 | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { 14 | m.def("forward", &forward, "wkv forward"); 15 | m.def("backward", &backward, "wkv backward"); 16 | } 17 | 18 | TORCH_LIBRARY(wkv, m) { 19 | m.def("forward", forward); 20 | m.def("backward", backward); 21 | } 22 | -------------------------------------------------------------------------------- /funasr/__init__.py: -------------------------------------------------------------------------------- 1 | """Initialize funasr package.""" 2 | 3 | import os 4 | import pkgutil 5 | import importlib 6 | 7 | dirname = os.path.dirname(__file__) 8 | version_file = os.path.join(dirname, "version.txt") 9 | with open(version_file, "r") as f: 10 | __version__ = f.read().strip() 11 | 12 | 13 | import importlib 14 | import pkgutil 15 | 16 | 17 | def import_submodules(package, recursive=True): 18 | if isinstance(package, str): 19 | try: 20 | package = importlib.import_module(package) 21 | except Exception as e: 22 | # 如果想要看到导入错误的具体信息，可以取消注释下面的行 23 | # print(f"Failed to import {package}: {e}") 24 | pass 25 | results = {} 26 | if not isinstance(package, str): 27 | for loader, name, is_pkg in pkgutil.walk_packages(package.__path__, package.__name__ + "."): 28 | try: 29 | results[name] = importlib.import_module(name) 30 | except Exception as e: 31 | # 如果想要看到导入错误的具体信息，可以取消注释下面的行 32 | # print(f"Failed to import {name}: {e}") 33 | pass 34 | if recursive and is_pkg: 35 | results.update(import_submodules(name)) 36 | return results 37 | 38 | 39 | import_submodules(__name__) 40 | 41 | from funasr.auto.auto_model import AutoModel 42 | from funasr.auto.auto_frontend import AutoFrontend 43 | 44 | os.environ["HYDRA_FULL_ERROR"] = "1" 45 | -------------------------------------------------------------------------------- /funasr/schedulers/lambdalr_cus.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch.optim.lr_scheduler import _LRScheduler 3 | 4 | 5 | # class CustomLambdaLR(_LRScheduler): 6 | # def __init__(self, optimizer, warmup_steps, last_epoch=-1): 7 | # self.warmup_steps = warmup_steps 8 | # super().__init__(optimizer, last_epoch) 9 | # 10 | # def get_lr(self): 11 | # if self.last_epoch < self.warmup_steps: 12 | # return [ 13 | # base_lr * min(self.last_epoch / self.warmup_steps, 1) for base_lr in self.base_lrs 14 | # ] 15 | # else: 16 | # return [base_lr for base_lr in self.base_lrs] 17 | 18 | 19 | class CustomLambdaLR(_LRScheduler): 20 | def __init__( 21 | self, 22 | optimizer, 23 | warmup_steps: int = 25000, 24 | total_steps: int = 500000, 25 | last_epoch=-1, 26 | verbose=False, 27 | ): 28 | self.warmup_steps = warmup_steps 29 | self.total_steps = total_steps 30 | super().__init__(optimizer, last_epoch, verbose) 31 | 32 | def get_lr(self): 33 | 34 | step = self.last_epoch + 1 35 | if step < self.warmup_steps: 36 | lr_scale = step / self.warmup_steps 37 | else: 38 | lr_scale = max( 39 | 0.0, 1 - (step - self.warmup_steps) / (self.total_steps - self.warmup_steps) 40 | ) 41 | return [base_lr * lr_scale for base_lr in self.base_lrs] 42 | -------------------------------------------------------------------------------- /funasr/metrics/compute_acc.py: -------------------------------------------------------------------------------- 1 | import torch 2 | 3 | 4 | def th_accuracy(pad_outputs, pad_targets, ignore_label): 5 | """Calculate accuracy. 6 | 7 | Args: 8 | pad_outputs (Tensor): Prediction tensors (B * Lmax, D). 9 | pad_targets (LongTensor): Target label tensors (B, Lmax, D). 10 | ignore_label (int): Ignore label id. 11 | 12 | Returns: 13 | float: Accuracy value (0.0 - 1.0). 14 | 15 | """ 16 | pad_pred = pad_outputs.view( 17 | pad_targets.size(0), pad_targets.size(1), pad_outputs.size(1) 18 | ).argmax(2) 19 | mask = pad_targets != ignore_label 20 | numerator = torch.sum(pad_pred.masked_select(mask) == pad_targets.masked_select(mask)) 21 | denominator = torch.sum(mask) 22 | return float(numerator) / float(denominator) 23 | 24 | 25 | def compute_accuracy(pad_outputs, pad_targets, ignore_label): 26 | """Calculate accuracy. 27 | 28 | Args: 29 | pad_outputs (LongTensor): Prediction tensors (B, Lmax). 30 | pad_targets (LongTensor): Target label tensors (B, Lmax). 31 | ignore_label (int): Ignore label id. 32 | 33 | Returns: 34 | float: Accuracy value (0.0 - 1.0). 35 | 36 | """ 37 | mask = pad_targets != ignore_label 38 | numerator = torch.sum(pad_outputs.masked_select(mask) == pad_targets.masked_select(mask)) 39 | denominator = torch.sum(mask) 40 | return numerator.float() / denominator.float() # (FIX:MZY):return torch.Tensor type 41 | -------------------------------------------------------------------------------- /funasr/models/rwkv_bat/template.yaml: -------------------------------------------------------------------------------- 1 | # network architecture 2 | model: Transducer 3 | model_conf: 4 | auxiliary_ctc_weight: 0.0 5 | 6 | # encoder 7 | encoder: RWKVEncoder 8 | encoder_conf: 9 | kernel: 3 10 | subsampling_factor: 4 11 | output_size: 512 12 | num_blocks: 18 13 | time_reduction_factor: 2 14 | att_dropout_rate: 0.1 15 | ffn_dropout_rate: 0.1 16 | dropout_rate: 0.1 17 | 18 | # decoder (prediction network) 19 | decoder: rnnt_decoder 20 | decoder_conf: 21 | embed_size: 512 22 | hidden_size: 512 23 | embed_dropout_rate: 0.1 24 | dropout_rate: 0.1 25 | use_embed_mask: false 26 | 27 | # joint network 28 | joint_network: joint_network 29 | joint_network_conf: 30 | joint_space_size: 512 31 | 32 | frontend: WavFrontend 33 | frontend_conf: 34 | fs: 16000 35 | window: hamming 36 | n_mels: 80 37 | frame_length: 25 38 | frame_shift: 10 39 | lfr_m: 1 40 | lfr_n: 1 41 | upsacle_samples: true 42 | 43 | specaug: SpecAugLFR 44 | specaug_conf: 45 | apply_time_warp: false 46 | time_warp_window: 5 47 | time_warp_mode: bicubic 48 | apply_freq_mask: true 49 | freq_mask_width_range: 50 | - 0 51 | - 30 52 | lfr_rate: 6 53 | num_freq_mask: 1 54 | apply_time_mask: true 55 | time_mask_width_range: 56 | - 0 57 | - 12 58 | num_time_mask: 1 59 | 60 | tokenizer: CharTokenizer 61 | tokenizer_conf: 62 | unk_symbol: 63 | split_with_space: true 64 | -------------------------------------------------------------------------------- /funasr/models/bat/model.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- encoding: utf-8 -*- 3 | # Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. 4 | # MIT License (https://opensource.org/licenses/MIT) 5 | 6 | import time 7 | import torch 8 | import logging 9 | from contextlib import contextmanager 10 | from typing import Dict, Optional, Tuple 11 | from distutils.version import LooseVersion 12 | 13 | from funasr.register import tables 14 | from funasr.utils import postprocess_utils 15 | from funasr.utils.datadir_writer import DatadirWriter 16 | from funasr.models.transducer.model import Transducer 17 | from funasr.train_utils.device_funcs import force_gatherable 18 | from funasr.models.transformer.scorers.ctc import CTCPrefixScorer 19 | from funasr.losses.label_smoothing_loss import LabelSmoothingLoss 20 | from funasr.models.transformer.scorers.length_bonus import LengthBonus 21 | from funasr.models.transformer.utils.nets_utils import get_transducer_task_io 22 | from funasr.utils.load_utils import load_audio_text_image_video, extract_fbank 23 | from funasr.models.transducer.beam_search_transducer import BeamSearchTransducer 24 | 25 | 26 | if LooseVersion(torch.__version__) >= LooseVersion("1.6.0"): 27 | from torch.cuda.amp import autocast 28 | else: 29 | # Nothing to do if torch<1.6.0 30 | @contextmanager 31 | def autocast(enabled=True): 32 | yield 33 | 34 | 35 | @tables.register("model_classes", "BAT") # TODO: BAT training 36 | class BAT(Transducer): 37 | pass 38 | -------------------------------------------------------------------------------- /funasr/models/sense_voice/cuda/wkv6_op.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include "ATen/ATen.h" 3 | typedef at::BFloat16 bf16; 4 | //typedef float bf16; 5 | 6 | void cuda_forward(int B, int T, int C, int H, bf16 *r, bf16 *k, bf16 *v, float *w, bf16 *u, bf16 *y); 7 | void cuda_backward(int B, int T, int C, int H, bf16 *r, bf16 *k, bf16 *v, float *w, bf16 *u, bf16 *gy, bf16 *gr, bf16 *gk, bf16 *gv, bf16 *gw, bf16 *gu); 8 | 9 | void forward(int64_t B, int64_t T, int64_t C, int64_t H, torch::Tensor &r, torch::Tensor &k, torch::Tensor &v, torch::Tensor &w, torch::Tensor &u, torch::Tensor &y) { 10 | cuda_forward(B, T, C, H, r.data_ptr(), k.data_ptr(), v.data_ptr(), w.data_ptr(), u.data_ptr(), y.data_ptr()); 11 | } 12 | void backward(int64_t B, int64_t T, int64_t C, int64_t H, torch::Tensor &r, torch::Tensor &k, torch::Tensor &v, torch::Tensor &w, torch::Tensor &u, torch::Tensor &gy, torch::Tensor &gr, torch::Tensor &gk, torch::Tensor &gv, torch::Tensor &gw, torch::Tensor &gu) { 13 | cuda_backward(B, T, C, H, r.data_ptr(), k.data_ptr(), v.data_ptr(), w.data_ptr(), u.data_ptr(), gy.data_ptr(), gr.data_ptr(), gk.data_ptr(), gv.data_ptr(), gw.data_ptr(), gu.data_ptr()); 14 | } 15 | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { 16 | m.def("forward", &forward, "wkv6 forward"); 17 | m.def("backward", &backward, "wkv6 backward"); 18 | } 19 | 20 | TORCH_LIBRARY(wkv6, m) { 21 | m.def("forward", forward); 22 | m.def("backward", backward); 23 | } 24 | -------------------------------------------------------------------------------- /funasr/frontends/eend_ola_feature.py: -------------------------------------------------------------------------------- 1 | # Copyright 2019 Hitachi, Ltd. (author: Yusuke Fujita) 2 | # Licensed under the MIT license. 3 | # 4 | # This module is for computing audio features 5 | 6 | import librosa 7 | import numpy as np 8 | 9 | 10 | def transform(Y, dtype=np.float32): 11 | Y = np.abs(Y) 12 | n_fft = 2 * (Y.shape[1] - 1) 13 | sr = 8000 14 | n_mels = 23 15 | mel_basis = librosa.filters.mel(sr, n_fft, n_mels) 16 | Y = np.dot(Y**2, mel_basis.T) 17 | Y = np.log10(np.maximum(Y, 1e-10)) 18 | mean = np.mean(Y, axis=0) 19 | Y = Y - mean 20 | return Y.astype(dtype) 21 | 22 | 23 | def subsample(Y, T, subsampling=1): 24 | Y_ss = Y[::subsampling] 25 | T_ss = T[::subsampling] 26 | return Y_ss, T_ss 27 | 28 | 29 | def splice(Y, context_size=0): 30 | Y_pad = np.pad(Y, [(context_size, context_size), (0, 0)], "constant") 31 | Y_spliced = np.lib.stride_tricks.as_strided( 32 | np.ascontiguousarray(Y_pad), 33 | (Y.shape[0], Y.shape[1] * (2 * context_size + 1)), 34 | (Y.itemsize * Y.shape[1], Y.itemsize), 35 | writeable=False, 36 | ) 37 | return Y_spliced 38 | 39 | 40 | def stft(data, frame_size=1024, frame_shift=256): 41 | fft_size = 1 << (frame_size - 1).bit_length() 42 | if len(data) % frame_shift == 0: 43 | return librosa.stft(data, n_fft=fft_size, win_length=frame_size, hop_length=frame_shift).T[ 44 | :-1 45 | ] 46 | else: 47 | return librosa.stft(data, n_fft=fft_size, win_length=frame_size, hop_length=frame_shift).T 48 | -------------------------------------------------------------------------------- /funasr/models/mossformer/mossformer_decoder.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | 4 | 5 | class MossFormerDecoder(nn.ConvTranspose1d): 6 | """A decoder layer that consists of ConvTranspose1d. 7 | 8 | Arguments 9 | --------- 10 | kernel_size : int 11 | Length of filters. 12 | in_channels : int 13 | Number of input channels. 14 | out_channels : int 15 | Number of output channels. 16 | 17 | 18 | Example 19 | --------- 20 | >>> x = torch.randn(2, 100, 1000) 21 | >>> decoder = Decoder(kernel_size=4, in_channels=100, out_channels=1) 22 | >>> h = decoder(x) 23 | >>> h.shape 24 | torch.Size([2, 1003]) 25 | """ 26 | 27 | def __init__(self, *args, **kwargs): 28 | super(MossFormerDecoder, self).__init__(*args, **kwargs) 29 | 30 | def forward(self, x): 31 | """Return the decoded output. 32 | 33 | Arguments 34 | --------- 35 | x : torch.Tensor 36 | Input tensor with dimensionality [B, N, L]. 37 | where, B = Batchsize, 38 | N = number of filters 39 | L = time points 40 | """ 41 | 42 | if x.dim() not in [2, 3]: 43 | raise RuntimeError("{} accept 3/4D tensor as input".format(self.__name__)) 44 | x = super().forward(x if x.dim() == 3 else torch.unsqueeze(x, 1)) 45 | 46 | if torch.squeeze(x).dim() == 1: 47 | x = torch.squeeze(x, dim=1) 48 | else: 49 | x = torch.squeeze(x) 50 | return x 51 | -------------------------------------------------------------------------------- /funasr/models/sense_voice/cuda/wkv5_op.cpp: -------------------------------------------------------------------------------- 1 | #include 2 | #include "ATen/ATen.h" 3 | typedef at::BFloat16 bf16; 4 | 5 | void cuda_forward(int B, int T, int C, int H, bf16 *r, bf16 *k, bf16 *v, float *w, bf16 *u, bf16 *y); 6 | void cuda_backward(int B, int T, int C, int H, bf16 *r, bf16 *k, bf16 *v, float *w, float *ww, bf16 *u, bf16 *gy, bf16 *gr, bf16 *gk, bf16 *gv, bf16 *gw, bf16 *gu); 7 | 8 | void forward(int64_t B, int64_t T, int64_t C, int64_t H, torch::Tensor &r, torch::Tensor &k, torch::Tensor &v, torch::Tensor &w, torch::Tensor &u, torch::Tensor &y) { 9 | cuda_forward(B, T, C, H, r.data_ptr(), k.data_ptr(), v.data_ptr(), w.data_ptr(), u.data_ptr(), y.data_ptr()); 10 | } 11 | void backward(int64_t B, int64_t T, int64_t C, int64_t H, torch::Tensor &r, torch::Tensor &k, torch::Tensor &v, torch::Tensor &w, torch::Tensor &ww, torch::Tensor &u, torch::Tensor &gy, torch::Tensor &gr, torch::Tensor &gk, torch::Tensor &gv, torch::Tensor &gw, torch::Tensor &gu) { 12 | cuda_backward(B, T, C, H, r.data_ptr(), k.data_ptr(), v.data_ptr(), w.data_ptr(), ww.data_ptr(), u.data_ptr(), gy.data_ptr(), gr.data_ptr(), gk.data_ptr(), gv.data_ptr(), gw.data_ptr(), gu.data_ptr()); 13 | } 14 | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { 15 | m.def("forward", &forward, "wkv5 forward"); 16 | m.def("backward", &backward, "wkv5 backward"); 17 | } 18 | 19 | TORCH_LIBRARY(wkv5, m) { 20 | m.def("forward", forward); 21 | m.def("backward", backward); 22 | } 23 | -------------------------------------------------------------------------------- /funasr/download/name_maps_from_hub.py: -------------------------------------------------------------------------------- 1 | name_maps_ms = { 2 | "paraformer": "iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", 3 | "paraformer-zh": "iic/speech_seaco_paraformer_large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", 4 | "paraformer-en": "iic/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020", 5 | "paraformer-en-spk": "iic/speech_paraformer-large-vad-punc_asr_nat-en-16k-common-vocab10020", 6 | "paraformer-zh-streaming": "iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online", 7 | "fsmn-vad": "iic/speech_fsmn_vad_zh-cn-16k-common-pytorch", 8 | "ct-punc": "iic/punc_ct-transformer_cn-en-common-vocab471067-large", 9 | "ct-punc-c": "iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch", 10 | "fa-zh": "iic/speech_timestamp_prediction-v1-16k-offline", 11 | "cam++": "iic/speech_campplus_sv_zh-cn_16k-common", 12 | "Whisper-large-v2": "iic/speech_whisper-large_asr_multilingual", 13 | "Whisper-large-v3": "iic/Whisper-large-v3", 14 | "Qwen-Audio": "Qwen/Qwen-Audio", 15 | } 16 | 17 | name_maps_hf = {} 18 | 19 | name_maps_openai = { 20 | "Whisper-tiny.en": "tiny.en", 21 | "Whisper-tiny": "tiny", 22 | "Whisper-base.en": "base.en", 23 | "Whisper-base": "base", 24 | "Whisper-small.en": "small.en", 25 | "Whisper-small": "small", 26 | "Whisper-medium.en": "medium.en", 27 | "Whisper-medium": "medium", 28 | "Whisper-large-v1": "large-v1", 29 | "Whisper-large-v2": "large-v2", 30 | "Whisper-large-v3": "large-v3", 31 | "Whisper-large": "large", 32 | } 33 | -------------------------------------------------------------------------------- /funasr/models/sond/sv_decoder.py: -------------------------------------------------------------------------------- 1 | import torch 2 | from torch.nn import functional as F 3 | from funasr.models.decoder.abs_decoder import AbsDecoder 4 | 5 | 6 | class DenseDecoder(AbsDecoder): 7 | def __init__( 8 | self, 9 | vocab_size, 10 | encoder_output_size, 11 | num_nodes_resnet1: int = 256, 12 | num_nodes_last_layer: int = 256, 13 | batchnorm_momentum: float = 0.5, 14 | ): 15 | super(DenseDecoder, self).__init__() 16 | self.resnet1_dense = torch.nn.Linear(encoder_output_size, num_nodes_resnet1) 17 | self.resnet1_bn = torch.nn.BatchNorm1d( 18 | num_nodes_resnet1, eps=1e-3, momentum=batchnorm_momentum 19 | ) 20 | 21 | self.resnet2_dense = torch.nn.Linear(num_nodes_resnet1, num_nodes_last_layer) 22 | self.resnet2_bn = torch.nn.BatchNorm1d( 23 | num_nodes_last_layer, eps=1e-3, momentum=batchnorm_momentum 24 | ) 25 | 26 | self.output_dense = torch.nn.Linear(num_nodes_last_layer, vocab_size, bias=False) 27 | 28 | def forward(self, features): 29 | embeddings = {} 30 | features = self.resnet1_dense(features) 31 | embeddings["resnet1_dense"] = features 32 | features = F.relu(features) 33 | features = self.resnet1_bn(features) 34 | 35 | features = self.resnet2_dense(features) 36 | embeddings["resnet2_dense"] = features 37 | features = F.relu(features) 38 | features = self.resnet2_bn(features) 39 | 40 | features = self.output_dense(features) 41 | return features, embeddings 42 | -------------------------------------------------------------------------------- /funasr/schedulers/warmup_lr.py: -------------------------------------------------------------------------------- 1 | """Warm up learning rate scheduler module.""" 2 | 3 | from typing import Union 4 | 5 | import torch 6 | from torch.optim.lr_scheduler import _LRScheduler 7 | 8 | from funasr.schedulers.abs_scheduler import AbsBatchStepScheduler 9 | 10 | 11 | class WarmupLR(_LRScheduler, AbsBatchStepScheduler): 12 | """The WarmupLR scheduler 13 | 14 | This scheduler is almost same as NoamLR Scheduler except for following difference: 15 | 16 | NoamLR: 17 | lr = optimizer.lr * model_size ** -0.5 18 | * min(step ** -0.5, step * warmup_step ** -1.5) 19 | WarmupLR: 20 | lr = optimizer.lr * warmup_step ** 0.5 21 | * min(step ** -0.5, step * warmup_step ** -1.5) 22 | 23 | Note that the maximum lr equals to optimizer.lr in this scheduler. 24 | 25 | """ 26 | 27 | def __init__( 28 | self, 29 | optimizer: torch.optim.Optimizer, 30 | warmup_steps: Union[int, float] = 25000, 31 | last_epoch: int = -1, 32 | ): 33 | self.warmup_steps = warmup_steps 34 | 35 | # __init__() must be invoked before setting field 36 | # because step() is also invoked in __init__() 37 | super().__init__(optimizer, last_epoch) 38 | 39 | def __repr__(self): 40 | return f"{self.__class__.__name__}(warmup_steps={self.warmup_steps})" 41 | 42 | def get_lr(self): 43 | step_num = self.last_epoch + 1 44 | return [ 45 | lr * self.warmup_steps**0.5 * min(step_num**-0.5, step_num * self.warmup_steps**-1.5) 46 | for lr in self.base_lrs 47 | ] 48 | -------------------------------------------------------------------------------- /funasr/tokenizer/whisper_tokenizer.py: -------------------------------------------------------------------------------- 1 | from funasr.register import tables 2 | 3 | 4 | @tables.register("tokenizer_classes", "WhisperTokenizer") 5 | def WhisperTokenizer(**kwargs): 6 | try: 7 | from whisper.tokenizer import get_tokenizer 8 | except: 9 | print("Notice: If you want to use whisper, please `pip install -U openai-whisper`") 10 | 11 | language = kwargs.get("language", None) 12 | task = kwargs.get("task", "transcribe") 13 | is_multilingual = kwargs.get("is_multilingual", True) 14 | num_languages = kwargs.get("num_languages", 99) 15 | tokenizer = get_tokenizer( 16 | multilingual=is_multilingual, 17 | num_languages=num_languages, 18 | language=language, 19 | task=task, 20 | ) 21 | 22 | return tokenizer 23 | 24 | 25 | @tables.register("tokenizer_classes", "SenseVoiceTokenizer") 26 | def SenseVoiceTokenizer(**kwargs): 27 | try: 28 | from funasr.models.sense_voice.whisper_lib.tokenizer import get_tokenizer 29 | except: 30 | print("Notice: If you want to use whisper, please `pip install -U openai-whisper`") 31 | 32 | language = kwargs.get("language", None) 33 | task = kwargs.get("task", None) 34 | is_multilingual = kwargs.get("is_multilingual", True) 35 | num_languages = kwargs.get("num_languages", 8749) 36 | vocab_path = kwargs.get("vocab_path", None) 37 | tokenizer = get_tokenizer( 38 | multilingual=is_multilingual, 39 | num_languages=num_languages, 40 | language=language, 41 | task=task, 42 | vocab_path=vocab_path, 43 | ) 44 | 45 | return tokenizer 46 | -------------------------------------------------------------------------------- /funasr/tokenizer/cleaner.py: -------------------------------------------------------------------------------- 1 | from typing import Collection 2 | 3 | from jaconv import jaconv 4 | 5 | # import tacotron_cleaner.cleaners 6 | 7 | try: 8 | from vietnamese_cleaner import vietnamese_cleaners 9 | except ImportError: 10 | vietnamese_cleaners = None 11 | 12 | 13 | class TextCleaner: 14 | """Text cleaner. 15 | 16 | Examples: 17 | >>> cleaner = TextCleaner("tacotron") 18 | >>> cleaner("(Hello-World); & jr. & dr.") 19 | 'HELLO WORLD, AND JUNIOR AND DOCTOR' 20 | 21 | """ 22 | 23 | def __init__(self, cleaner_types: Collection[str] = None): 24 | 25 | if cleaner_types is None: 26 | self.cleaner_types = [] 27 | elif isinstance(cleaner_types, str): 28 | self.cleaner_types = [cleaner_types] 29 | else: 30 | self.cleaner_types = list(cleaner_types) 31 | 32 | def __call__(self, text: str) -> str: 33 | for t in self.cleaner_types: 34 | if t == "tacotron": 35 | # text = tacotron_cleaner.cleaners.custom_english_cleaners(text) 36 | pass 37 | elif t == "jaconv": 38 | text = jaconv.normalize(text) 39 | elif t == "vietnamese": 40 | if vietnamese_cleaners is None: 41 | raise RuntimeError("Please install underthesea") 42 | text = vietnamese_cleaners.vietnamese_cleaner(text) 43 | elif t == "korean_cleaner": 44 | text = KoreanCleaner.normalize_text(text) 45 | else: 46 | raise RuntimeError(f"Not supported: type={t}") 47 | 48 | return text 49 | -------------------------------------------------------------------------------- /funasr/models/transformer/positionwise_feed_forward.py: -------------------------------------------------------------------------------- 1 | #!/usr/bin/env python3 2 | # -*- coding: utf-8 -*- 3 | 4 | # Copyright 2019 Shigeki Karita 5 | # Apache 2.0 (http://www.apache.org/licenses/LICENSE-2.0) 6 | 7 | """Positionwise feed forward layer definition.""" 8 | 9 | import torch 10 | 11 | from funasr.models.transformer.layer_norm import LayerNorm 12 | 13 | 14 | class PositionwiseFeedForward(torch.nn.Module): 15 | """Positionwise feed forward layer. 16 | 17 | Args: 18 | idim (int): Input dimenstion. 19 | hidden_units (int): The number of hidden units. 20 | dropout_rate (float): Dropout rate. 21 | 22 | """ 23 | 24 | def __init__(self, idim, hidden_units, dropout_rate, activation=torch.nn.ReLU()): 25 | """Construct an PositionwiseFeedForward object.""" 26 | super(PositionwiseFeedForward, self).__init__() 27 | self.w_1 = torch.nn.Linear(idim, hidden_units) 28 | self.w_2 = torch.nn.Linear(hidden_units, idim) 29 | self.dropout = torch.nn.Dropout(dropout_rate) 30 | self.activation = activation 31 | 32 | def forward(self, x): 33 | """Forward function.""" 34 | return self.w_2(self.dropout(self.activation(self.w_1(x)))) 35 | 36 | 37 | class PositionwiseFeedForwardDecoderSANMExport(torch.nn.Module): 38 | def __init__(self, model): 39 | super().__init__() 40 | self.w_1 = model.w_1 41 | self.w_2 = model.w_2 42 | self.activation = model.activation 43 | self.norm = model.norm 44 | 45 | def forward(self, x): 46 | x = self.activation(self.w_1(x)) 47 | x = self.w_2(self.norm(x)) 48 | return x 49 | -------------------------------------------------------------------------------- /funasr/datasets/large_datasets/utils/clipping.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import torch 3 | 4 | from funasr.datasets.large_datasets.collate_fn import crop_to_max_size 5 | 6 | 7 | def clipping(data): 8 | assert isinstance(data, list) 9 | assert "key" in data[0] 10 | 11 | keys = [x["key"] for x in data] 12 | 13 | batch = {} 14 | data_names = data[0].keys() 15 | for data_name in data_names: 16 | if data_name == "key": 17 | continue 18 | else: 19 | if data[0][data_name].dtype.kind == "i": 20 | tensor_type = torch.int64 21 | else: 22 | tensor_type = torch.float32 23 | 24 | tensor_list = [torch.tensor(np.copy(d[data_name]), dtype=tensor_type) for d in data] 25 | tensor_lengths = torch.tensor([len(d[data_name]) for d in data], dtype=torch.int32) 26 | 27 | length_clip = min(tensor_lengths) 28 | tensor_clip = tensor_list[0].new_zeros( 29 | len(tensor_list), length_clip, tensor_list[0].shape[1] 30 | ) 31 | for i, (tensor, length) in enumerate(zip(tensor_list, tensor_lengths)): 32 | diff = length - length_clip 33 | assert diff >= 0 34 | if diff == 0: 35 | tensor_clip[i] = tensor 36 | else: 37 | tensor_clip[i] = crop_to_max_size(tensor, length_clip) 38 | 39 | batch[data_name] = tensor_clip 40 | batch[data_name + "_lengths"] = torch.tensor( 41 | [tensor.shape[0] for tensor in tensor_clip], dtype=torch.long 42 | ) 43 | 44 | return keys, batch 45 | -------------------------------------------------------------------------------- /funasr/datasets/large_datasets/utils/hotword_utils.py: -------------------------------------------------------------------------------- 1 | import random 2 | 3 | 4 | def sample_hotword( 5 | length, 6 | hotword_min_length, 7 | hotword_max_length, 8 | sample_rate, 9 | double_rate, 10 | pre_prob, 11 | pre_index=None, 12 | pre_hwlist=None, 13 | ): 14 | if length < hotword_min_length: 15 | return [-1] 16 | if random.random() < sample_rate: 17 | if pre_prob > 0 and random.random() < pre_prob and pre_index is not None: 18 | return pre_index 19 | if length == hotword_min_length: 20 | return [0, length - 1] 21 | elif random.random() < double_rate and length > hotword_max_length + hotword_min_length + 2: 22 | # sample two hotwords in a sentence 23 | _max_hw_length = min(hotword_max_length, length // 2) 24 | # first hotword 25 | start1 = random.randint(0, length // 3) 26 | end1 = random.randint(start1 + hotword_min_length - 1, start1 + _max_hw_length - 1) 27 | # second hotword 28 | start2 = random.randint(end1 + 1, length - hotword_min_length) 29 | end2 = random.randint( 30 | min(length - 1, start2 + hotword_min_length - 1), 31 | min(length - 1, start2 + hotword_max_length - 1), 32 | ) 33 | return [start1, end1, start2, end2] 34 | else: # single hotword 35 | start = random.randint(0, length - hotword_min_length) 36 | end = random.randint( 37 | min(length - 1, start + hotword_min_length - 1), 38 | min(length - 1, start + hotword_max_length - 1), 39 | ) 40 | return [start, end] 41 | else: 42 | return [-1] 43 | -------------------------------------------------------------------------------- /funasr/models/eend/utils/losses.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import torch 3 | import torch.nn.functional as F 4 | from scipy.optimize import linear_sum_assignment 5 | 6 | 7 | def standard_loss(ys, ts): 8 | losses = [F.binary_cross_entropy(torch.sigmoid(y), t) * len(y) for y, t in zip(ys, ts)] 9 | loss = torch.sum(torch.stack(losses)) 10 | n_frames = ( 11 | torch.from_numpy(np.array(np.sum([t.shape[0] for t in ts]))) 12 | .to(torch.float32) 13 | .to(ys[0].device) 14 | ) 15 | loss = loss / n_frames 16 | return loss 17 | 18 | 19 | def fast_batch_pit_n_speaker_loss(ys, ts): 20 | with torch.no_grad(): 21 | bs = len(ys) 22 | indices = [] 23 | for b in range(bs): 24 | y = ys[b].transpose(0, 1) 25 | t = ts[b].transpose(0, 1) 26 | C, _ = t.shape 27 | y = y[:, None, :].repeat(1, C, 1) 28 | t = t[None, :, :].repeat(C, 1, 1) 29 | bce_loss = F.binary_cross_entropy(torch.sigmoid(y), t, reduction="none").mean(-1) 30 | C = bce_loss.cpu() 31 | indices.append(linear_sum_assignment(C)) 32 | labels_perm = [t[:, idx[1]] for t, idx in zip(ts, indices)] 33 | 34 | return labels_perm 35 | 36 | 37 | def cal_power_loss(logits, power_ts): 38 | losses = [ 39 | F.cross_entropy(input=logit, target=power_t.to(torch.long)) * len(logit) 40 | for logit, power_t in zip(logits, power_ts) 41 | ] 42 | loss = torch.sum(torch.stack(losses)) 43 | n_frames = ( 44 | torch.from_numpy(np.array(np.sum([power_t.shape[0] for power_t in power_ts]))) 45 | .to(torch.float32) 46 | .to(power_ts[0].device) 47 | ) 48 | loss = loss / n_frames 49 | return loss 50 | -------------------------------------------------------------------------------- /funasr/models/fsmn_vad_streaming/template.yaml: -------------------------------------------------------------------------------- 1 | # This is an example that demonstrates how to configure a model file. 2 | # You can modify the configuration according to your own requirements. 3 | 4 | # to print the register_table: 5 | # from funasr.register import tables 6 | # tables.print() 7 | 8 | # network architecture 9 | model: FsmnVADStreaming 10 | model_conf: 11 | sample_rate: 16000 12 | detect_mode: 1 13 | snr_mode: 0 14 | max_end_silence_time: 800 15 | max_start_silence_time: 3000 16 | do_start_point_detection: True 17 | do_end_point_detection: True 18 | window_size_ms: 200 19 | sil_to_speech_time_thres: 150 20 | speech_to_sil_time_thres: 150 21 | speech_2_noise_ratio: 1.0 22 | do_extend: 1 23 | lookback_time_start_point: 200 24 | lookahead_time_end_point: 100 25 | max_single_segment_time: 60000 26 | snr_thres: -100.0 27 | noise_frame_num_used_for_snr: 100 28 | decibel_thres: -100.0 29 | speech_noise_thres: 0.6 30 | fe_prior_thres: 0.0001 31 | silence_pdf_num: 1 32 | sil_pdf_ids: [0] 33 | speech_noise_thresh_low: -0.1 34 | speech_noise_thresh_high: 0.3 35 | output_frame_probs: False 36 | frame_in_ms: 10 37 | frame_length_ms: 25 38 | 39 | encoder: FSMN 40 | encoder_conf: 41 | input_dim: 400 42 | input_affine_dim: 140 43 | fsmn_layers: 4 44 | linear_dim: 250 45 | proj_dim: 128 46 | lorder: 20 47 | rorder: 0 48 | lstride: 1 49 | rstride: 0 50 | output_affine_dim: 140 51 | output_dim: 248 52 | 53 | frontend: WavFrontend 54 | frontend_conf: 55 | fs: 16000 56 | window: hamming 57 | n_mels: 80 58 | frame_length: 25 59 | frame_shift: 10 60 | dither: 0.0 61 | lfr_m: 5 62 | lfr_n: 1 63 | -------------------------------------------------------------------------------- /funasr/models/rwkv_bat/cuda_decoder/wkv_op.cpp: -------------------------------------------------------------------------------- 1 | /* 2 | * Bsed on https://github.com/BlinkDL/RWKV-LM/blob/main/RWKV-v4/cuda/wkv_op.cpp 3 | Function signatures were modified based on https://github.com/huggingface/transformers/blob/main/src/transformers/kernels/rwkv/wkv_op.cpp 4 | 5 | */ 6 | 7 | #include 8 | 9 | void cuda_forward(int B, int T, int C, float *w, float *u, float *k, float *v, float *y); 10 | 11 | void cuda_backward(int B, int T, int C, float *w, float *u, float *k, float *v, float *y, float *gy, float *gw, float *gu, float *gk, float *gv); 12 | 13 | void forward(torch::Tensor &w, torch::Tensor &u, torch::Tensor &k, torch::Tensor &v, torch::Tensor &y) { 14 | const int B = k.size(0); 15 | const int T = k.size(1); 16 | const int C = k.size(2); 17 | 18 | cuda_forward(B, T, C, w.data_ptr(), u.data_ptr(), k.data_ptr(), v.data_ptr(), y.data_ptr()); 19 | } 20 | 21 | void backward(torch::Tensor &w, torch::Tensor &u, torch::Tensor &k, torch::Tensor &v, torch::Tensor &y, torch::Tensor &gy, torch::Tensor &gw, torch::Tensor &gu, torch::Tensor &gk, torch::Tensor &gv) { 22 | const int B = k.size(0); 23 | const int T = k.size(1); 24 | const int C = k.size(2); 25 | 26 | cuda_backward(B, T, C, w.data_ptr(), u.data_ptr(), k.data_ptr(), v.data_ptr(), y.data_ptr(), gy.data_ptr(), gw.data_ptr(), gu.data_ptr(), gk.data_ptr(), gv.data_ptr()); 27 | } 28 | 29 | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { 30 | m.def("forward", &forward, "wkv forward"); 31 | m.def("backward", &backward, "wkv backward"); 32 | } 33 | 34 | TORCH_LIBRARY(wkv_decoder, m) { 35 | m.def("forward", forward); 36 | m.def("backward", backward); 37 | } 38 | -------------------------------------------------------------------------------- /funasr/models/rwkv_bat/cuda_encoder/wkv_op.cpp: -------------------------------------------------------------------------------- 1 | /* 2 | * Bsed on https://github.com/BlinkDL/RWKV-LM/blob/main/RWKV-v4/cuda/wkv_op.cpp 3 | Function signatures were modified based on https://github.com/huggingface/transformers/blob/main/src/transformers/kernels/rwkv/wkv_op.cpp 4 | 5 | */ 6 | 7 | #include 8 | 9 | void cuda_forward(int B, int T, int C, float *w, float *u, float *k, float *v, float *y); 10 | 11 | void cuda_backward(int B, int T, int C, float *w, float *u, float *k, float *v, float *y, float *gy, float *gw, float *gu, float *gk, float *gv); 12 | 13 | void forward(torch::Tensor &w, torch::Tensor &u, torch::Tensor &k, torch::Tensor &v, torch::Tensor &y) { 14 | const int B = k.size(0); 15 | const int T = k.size(1); 16 | const int C = k.size(2); 17 | 18 | cuda_forward(B, T, C, w.data_ptr(), u.data_ptr(), k.data_ptr(), v.data_ptr(), y.data_ptr()); 19 | } 20 | 21 | void backward(torch::Tensor &w, torch::Tensor &u, torch::Tensor &k, torch::Tensor &v, torch::Tensor &y, torch::Tensor &gy, torch::Tensor &gw, torch::Tensor &gu, torch::Tensor &gk, torch::Tensor &gv) { 22 | const int B = k.size(0); 23 | const int T = k.size(1); 24 | const int C = k.size(2); 25 | 26 | cuda_backward(B, T, C, w.data_ptr(), u.data_ptr(), k.data_ptr(), v.data_ptr(), y.data_ptr(), gy.data_ptr(), gw.data_ptr(), gu.data_ptr(), gk.data_ptr(), gv.data_ptr()); 27 | } 28 | 29 | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { 30 | m.def("forward", &forward, "wkv forward"); 31 | m.def("backward", &backward, "wkv backward"); 32 | } 33 | 34 | TORCH_LIBRARY(wkv_encoder, m) { 35 | m.def("forward", forward); 36 | m.def("backward", backward); 37 | } 38 | -------------------------------------------------------------------------------- /funasr/models/sense_voice/encoder.py: -------------------------------------------------------------------------------- 1 | import copy 2 | from typing import Optional, Tuple, Union 3 | 4 | import torch 5 | import torch.nn as nn 6 | import torch.nn.functional as F 7 | from funasr.models.transformer.utils.nets_utils import make_pad_mask 8 | 9 | 10 | def sense_voice_encode_forward( 11 | self, 12 | x: torch.Tensor, 13 | ilens: torch.Tensor = None, 14 | **kwargs, 15 | ): 16 | use_padmask = self.use_padmask 17 | x = F.gelu(self.conv1(x)) 18 | x = F.gelu(self.conv2(x)) 19 | x = x.permute(0, 2, 1) 20 | 21 | n_frames = x.size(1) 22 | max_pos = self.positional_embedding.size(0) 23 | max_pos = n_frames if n_frames < max_pos else max_pos 24 | x = (x[:, :max_pos, :] + self.positional_embedding[None, :max_pos, :]).to(x.dtype) 25 | 26 | if ilens is not None: 27 | if self.downsample_rate == 4: 28 | olens = ( 29 | 1 30 | + (ilens - self.conv1.kernel_size[0] + 2 * self.conv1.padding[0]) 31 | // self.conv1.stride[0] 32 | ) 33 | else: 34 | olens = ilens 35 | olens = ( 36 | 1 37 | + (olens - self.conv2.kernel_size[0] + 2 * self.conv2.padding[0]) 38 | // self.conv2.stride[0] 39 | ) 40 | olens = torch.clamp(olens, max=max_pos) 41 | else: 42 | olens = None 43 | 44 | if use_padmask and olens is not None: 45 | padding_mask = (~make_pad_mask(olens)[:, None, :]).to(x.device) 46 | else: 47 | padding_mask = None 48 | 49 | for layer, block in enumerate(self.blocks): 50 | x = block(x, mask=padding_mask, is_pad_mask=True) 51 | 52 | x = self.ln_post(x) 53 | 54 | if ilens is None: 55 | return x 56 | else: 57 | return x, olens 58 | -------------------------------------------------------------------------------- /funasr/datasets/audio_datasets/preprocessor.py: -------------------------------------------------------------------------------- 1 | import os 2 | import json 3 | import torch 4 | import logging 5 | import concurrent.futures 6 | import librosa 7 | import torch.distributed as dist 8 | from typing import Collection 9 | import torch 10 | import torchaudio 11 | from torch import nn 12 | import random 13 | import re 14 | from funasr.tokenizer.cleaner import TextCleaner 15 | from funasr.register import tables 16 | 17 | 18 | @tables.register("preprocessor_classes", "SpeechPreprocessSpeedPerturb") 19 | class SpeechPreprocessSpeedPerturb(nn.Module): 20 | def __init__(self, speed_perturb: list = None, **kwargs): 21 | super().__init__() 22 | self.speed_perturb = speed_perturb 23 | 24 | def forward(self, waveform, fs, **kwargs): 25 | if self.speed_perturb is None: 26 | return waveform 27 | speed = random.choice(self.speed_perturb) 28 | if speed != 1.0: 29 | if not isinstance(waveform, torch.Tensor): 30 | waveform = torch.tensor(waveform) 31 | waveform, _ = torchaudio.sox_effects.apply_effects_tensor( 32 | waveform.view(1, -1), fs, [["speed", str(speed)], ["rate", str(fs)]] 33 | ) 34 | waveform = waveform.view(-1) 35 | 36 | return waveform 37 | 38 | 39 | @tables.register("preprocessor_classes", "TextPreprocessSegDict") 40 | class TextPreprocessSegDict(nn.Module): 41 | def __init__( 42 | self, 43 | seg_dict: str = None, 44 | text_cleaner: Collection[str] = None, 45 | split_with_space: bool = False, 46 | **kwargs 47 | ): 48 | super().__init__() 49 | 50 | self.text_cleaner = TextCleaner(text_cleaner) 51 | 52 | def forward(self, text, **kwargs): 53 | text = self.text_cleaner(text) 54 | 55 | return text 56 | -------------------------------------------------------------------------------- /funasr/train_utils/recursive_op.py: -------------------------------------------------------------------------------- 1 | """Torch utility module.""" 2 | 3 | import torch 4 | 5 | if torch.distributed.is_available(): 6 | from torch.distributed import ReduceOp 7 | 8 | 9 | def recursive_sum(obj, weight: torch.Tensor, distributed: bool = False): 10 | assert weight.dim() == 1, weight.size() 11 | if isinstance(obj, (tuple, list)): 12 | return type(obj)(recursive_sum(v, weight, distributed) for v in obj) 13 | elif isinstance(obj, dict): 14 | return {k: recursive_sum(v, weight, distributed) for k, v in obj.items()} 15 | elif isinstance(obj, torch.Tensor): 16 | assert obj.size() == weight.size(), (obj.size(), weight.size()) 17 | obj = (obj * weight.type(obj.dtype)).sum() 18 | if distributed: 19 | torch.distributed.all_reduce(obj, op=ReduceOp.SUM) 20 | return obj 21 | elif obj is None: 22 | return None 23 | else: 24 | raise ValueError(type(obj)) 25 | 26 | 27 | def recursive_divide(a, b: torch.Tensor): 28 | if isinstance(a, (tuple, list)): 29 | return type(a)(recursive_divide(v, b) for v in a) 30 | elif isinstance(a, dict): 31 | return {k: recursive_divide(v, b) for k, v in a.items()} 32 | elif isinstance(a, torch.Tensor): 33 | assert a.size() == b.size(), (a.size(), b.size()) 34 | return a / b.type(a.dtype) 35 | elif a is None: 36 | return None 37 | else: 38 | raise ValueError(type(a)) 39 | 40 | 41 | def recursive_average(obj, weight: torch.Tensor, distributed: bool = False): 42 | obj = recursive_sum(obj, weight, distributed) 43 | weight = weight.sum() 44 | if distributed: 45 | torch.distributed.all_reduce(weight, op=ReduceOp.SUM) 46 | # Normalize weight to be sum-to-1 47 | obj = recursive_divide(obj, weight) 48 | return obj, weight 49 | -------------------------------------------------------------------------------- /funasr/tokenizer/sentencepiece_tokenizer.py: -------------------------------------------------------------------------------- 1 | from pathlib import Path 2 | from typing import Iterable 3 | from typing import List 4 | from typing import Union 5 | 6 | import sentencepiece as spm 7 | 8 | from funasr.tokenizer.abs_tokenizer import BaseTokenizer 9 | from funasr.register import tables 10 | 11 | 12 | @tables.register("tokenizer_classes", "SentencepiecesTokenizer") 13 | class SentencepiecesTokenizer(BaseTokenizer): 14 | def __init__(self, bpemodel: Union[Path, str], **kwargs): 15 | super().__init__(**kwargs) 16 | self.bpemodel = str(bpemodel) 17 | # NOTE(kamo): 18 | # Don't build SentencePieceProcessor in __init__() 19 | # because it's not picklable and it may cause following error, 20 | # "TypeError: can't pickle SwigPyObject objects", 21 | # when giving it as argument of "multiprocessing.Process()". 22 | self.sp = None 23 | 24 | def __repr__(self): 25 | return f'{self.__class__.__name__}(model="{self.bpemodel}")' 26 | 27 | def _build_sentence_piece_processor(self): 28 | # Build SentencePieceProcessor lazily. 29 | if self.sp is None: 30 | self.sp = spm.SentencePieceProcessor() 31 | self.sp.load(self.bpemodel) 32 | 33 | def text2tokens(self, line: str) -> List[str]: 34 | self._build_sentence_piece_processor() 35 | return self.sp.EncodeAsPieces(line) 36 | 37 | def tokens2text(self, tokens: Iterable[str]) -> str: 38 | self._build_sentence_piece_processor() 39 | return self.sp.DecodePieces(list(tokens)) 40 | 41 | def encode(self, line: str) -> List[int]: 42 | self._build_sentence_piece_processor() 43 | return self.sp.EncodeAsIds(line) 44 | 45 | def decode(self, line: List[int]): 46 | self._build_sentence_piece_processor() 47 | return self.sp.DecodeIds(line) 48 | 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