| | |
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| | | inference_pipeline = pipeline( |
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| | | task=Tasks.auto_speech_recognition, |
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| | | model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online', |
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| | | model_revision='v1.0.6', |
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| | | model_revision='v1.0.7', |
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| | | update_model=False, |
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| | | mode='paraformer_streaming' |
|---|
| | | ) |
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| | | import soundfile |
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| | | speech, sample_rate = soundfile.read("example/asr_example.wav") |
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| | | |
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| | | chunk_size = [5, 10, 5] #[5, 10, 5] 600ms, [8, 8, 4] 480ms |
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| | | param_dict = {"cache": dict(), "is_final": False, "chunk_size": chunk_size} |
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| | | chunk_size = [0, 10, 5] #[0, 10, 5] 600ms, [0, 8, 4] 480ms |
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| | | encoder_chunk_look_back = 4 #number of chunks to lookback for encoder self-attention |
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| | | decoder_chunk_look_back = 1 #number of encoder chunks to lookback for decoder cross-attention |
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| | | param_dict = {"cache": dict(), "is_final": False, "chunk_size": chunk_size, |
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| | | "encoder_chunk_look_back": encoder_chunk_look_back, "decoder_chunk_look_back": decoder_chunk_look_back} |
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| | | chunk_stride = chunk_size[1] * 960 # 600ms、480ms |
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| | | # first chunk, 600ms |
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| | | speech_chunk = speech[0:chunk_stride] |
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| | |
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| | | inference_pipeline = pipeline( |
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| | | task=Tasks.auto_speech_recognition, |
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| | | model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online', |
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| | | model_revision='v1.0.6', |
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| | | model_revision='v1.0.7', |
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| | | update_model=False, |
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| | | mode="paraformer_fake_streaming" |
|---|
| | | ) |
|---|
| | |
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| | | print(rec_result) |
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| | | ``` |
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| | | Full code of demo, please ref to [demo](https://github.com/alibaba-damo-academy/FunASR/discussions/241) |
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| | | |
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| | | #### [Paraformer-contextual Model](https://www.modelscope.cn/models/damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404/summary) |
|---|
| | | ```python |
|---|
| | | from modelscope.pipelines import pipeline |
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| | | from modelscope.utils.constant import Tasks |
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| | | |
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| | | param_dict = dict() |
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| | | # param_dict['hotword'] = "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/hotword.txt" |
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| | | param_dict['hotword']="邓郁松 王颖春 王晔君" |
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| | | inference_pipeline = pipeline( |
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| | | task=Tasks.auto_speech_recognition, |
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| | | model="damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404", |
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| | | param_dict=param_dict) |
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| | | |
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| | | rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_hotword.wav') |
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| | | print(rec_result) |
|---|
| | | ``` |
|---|
| | | |
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| | | #### [UniASR Model](https://www.modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/summary) |
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| | | There are three decoding mode for UniASR model(`fast`、`normal`、`offline`), for more model details, please refer to [docs](https://www.modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/summary) |
|---|
| | |
|---|
| | | ``` |
|---|
| | | The decoding mode of `fast` and `normal` is fake streaming, which could be used for evaluating of recognition accuracy. |
|---|
| | | Full code of demo, please ref to [demo](https://github.com/alibaba-damo-academy/FunASR/discussions/151) |
|---|
| | | |
|---|
| | | #### [Paraformer-Spk](https://modelscope.cn/models/damo/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn/summary) |
|---|
| | | This model allows user to get recognition results which contain speaker info of each sentence. Refer to [CAM++](https://modelscope.cn/models/damo/speech_campplus_speaker-diarization_common/summary) for detailed information about speaker diarization model. |
|---|
| | | ```python |
|---|
| | | from modelscope.pipelines import pipeline |
|---|
| | | from modelscope.utils.constant import Tasks |
|---|
| | | |
|---|
| | | if __name__ == '__main__': |
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| | | audio_in = 'https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_speaker_demo.wav' |
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| | | output_dir = "./results" |
|---|
| | | inference_pipeline = pipeline( |
|---|
| | | task=Tasks.auto_speech_recognition, |
|---|
| | | model='damo/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn', |
|---|
| | | model_revision='v0.0.2', |
|---|
| | | vad_model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch', |
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| | | punc_model='damo/punc_ct-transformer_cn-en-common-vocab471067-large', |
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| | | output_dir=output_dir, |
|---|
| | | ) |
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| | | rec_result = inference_pipeline(audio_in=audio_in, batch_size_token=5000, batch_size_token_threshold_s=40, max_single_segment_time=6000) |
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| | | print(rec_result) |
|---|
| | | ``` |
|---|
| | | |
|---|
| | | #### [RNN-T-online model]() |
|---|
| | | Undo |
|---|
| | | |
|---|
| | |
|---|
| | | if __name__ == '__main__': |
|---|
| | | params = modelscope_args(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", data_path="./data") |
|---|
| | | params.output_dir = "./checkpoint" # m模型保存路径 |
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| | | params.data_path = "./example_data/" # 数据路径 |
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| | | params.data_path = "speech_asr_aishell1_trainsets" # 数据路径 |
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| | | params.dataset_type = "small" # 小数据量设置small，若数据量大于1000小时，请使用large |
|---|
| | | params.batch_bins = 2000 # batch size，如果dataset_type="small"，batch_bins单位为fbank特征帧数，如果dataset_type="large"，batch_bins单位为毫秒， |
|---|
| | | params.max_epoch = 20 # 最大训练轮数 |
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