| | |
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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_zh.wav') |
|---|
| | | print(rec_result) |
|---|
| | | ``` |
|---|
| | | #### [Paraformer-long Model](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/summary) |
|---|
| | | ```python |
|---|
| | | from modelscope.pipelines import pipeline |
|---|
| | | from modelscope.utils.constant import Tasks |
|---|
| | | |
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| | | inference_pipeline = pipeline( |
|---|
| | | task=Tasks.auto_speech_recognition, |
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| | | model='damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch', |
|---|
| | | vad_model='damo/speech_fsmn_vad_zh-cn-16k-common-pytorch', |
|---|
| | | #punc_model='damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch', |
|---|
| | | punc_model='damo/punc_ct-transformer_cn-en-common-vocab471067-large', |
|---|
| | | ) |
|---|
| | | |
|---|
| | | rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav', |
|---|
| | | batch_size_token=5000, batch_size_token_threshold_s=40, max_single_segment_time=6000) |
|---|
| | | print(rec_result) |
|---|
| | | ``` |
|---|
| | | |
|---|
| | | Where, |
|---|
| | | - `batch_size_token` refs to dynamic batch_size and the total tokens of batch is `batch_size_token`, 1 token = 60 ms. |
|---|
| | | - `batch_size_token_threshold_s`: The batch_size is set to 1, when the audio duration exceeds the threshold value of `batch_size_token_threshold_s`, specified in `s`. |
|---|
| | | - `max_single_segment_time`: The maximum length for audio segmentation in VAD, specified in `ms`. |
|---|
| | | |
|---|
| | | Suggestion: When encountering OOM (Out of Memory) issues with long audio inputs, as the GPU memory usage increases with the square of the audio duration, there are three possible scenarios: |
|---|
| | | |
|---|
| | | a) In the initial inference stage, GPU memory usage primarily depends on `batch_size_token`. Reducing this value appropriately can help reduce memory usage. |
|---|
| | | b) In the middle of the inference process, when encountering long audio segments segmented by VAD, if the total number of tokens is still smaller than `batch_size_token` but OOM issues persist, reducing `batch_size_token_threshold_s` can help. If the threshold is exceeded, forcing the batch size to 1 can be considered. |
|---|
| | | c) Towards the end of the inference process, when encountering long audio segments segmented by VAD and the total number of tokens is smaller than `batch_size_token` but exceeds the threshold `batch_size_token_threshold_s`, forcing the batch size to 1 may still result in OOM errors. In such cases, reducing `max_single_segment_time` can be considered to shorten the duration of audio segments generated by VAD. |
|---|
| | | |
|---|
| | | #### [Paraformer-online Model](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/summary) |
|---|
| | | ##### Streaming Decoding |
|---|
| | | ```python |
|---|
| | |
|---|
| | | ``` |
|---|
| | | Full code of demo, please ref to [demo](https://github.com/alibaba-damo-academy/FunASR/discussions/241) |
|---|
| | | |
|---|
| | | #### [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 |
|---|
| | | from modelscope.utils.constant import Tasks |
|---|
| | | |
|---|
| | | param_dict = dict() |
|---|
| | | # param_dict['hotword'] = "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/hotword.txt" |
|---|
| | | param_dict['hotword']="邓郁松 王颖春 王晔君" |
|---|
| | | inference_pipeline = pipeline( |
|---|
| | | task=Tasks.auto_speech_recognition, |
|---|
| | | model="damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404", |
|---|
| | | param_dict=param_dict) |
|---|
| | | |
|---|
| | | rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_hotword.wav') |
|---|
| | | print(rec_result) |
|---|
| | | ``` |
|---|
| | | |
|---|
| | | #### [UniASR Model](https://www.modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/summary) |
|---|
| | | 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) |
|---|
| | | ```python |
|---|
| | |
|---|
| | | ``` |
|---|
| | | 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__': |
|---|
| | | audio_in = 'https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_speaker_demo.wav' |
|---|
| | | 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', |
|---|
| | | punc_model='damo/punc_ct-transformer_cn-en-common-vocab471067-large', |
|---|
| | | output_dir=output_dir, |
|---|
| | | ) |
|---|
| | | rec_result = inference_pipeline(audio_in=audio_in, batch_size_token=5000, batch_size_token_threshold_s=40, max_single_segment_time=6000) |
|---|
| | | 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 |
|---|
| | | params.data_path = "./example_data/" # 数据路径 |
|---|
| | | params.data_path = "speech_asr_aishell1_trainsets" # 数据路径 |
|---|
| | | 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 |
|---|
