| | |
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| | | ``` |
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| | | 演示代码完整版本，请参考[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) |
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| | | ```python |
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| | | 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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| | | ``` |
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| | | |
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| | | #### [UniASR 模型](https://www.modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/summary) |
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| | | UniASR 模型有三种解码模式(fast、normal、offline)，更多模型细节请参考[文档](https://www.modelscope.cn/models/damo/speech_UniASR_asr_2pass-zh-cn-8k-common-vocab3445-pytorch-online/summary) |
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| | | ```python |
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| | |
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| | | fast 和 normal 的解码模式是假流式解码，可用于评估识别准确性。 |
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| | | 演示的完整代码，请参见 [demo](https://github.com/alibaba-damo-academy/FunASR/discussions/151) |
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| | | |
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| | | #### [Paraformer-Spk model](https://modelscope.cn/models/damo/speech_paraformer-large-vad-punc-spk_asr_nat-zh-cn/summary) |
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| | | 返回识别结果的同时返回每个子句的说话人分类结果。关于说话人日志模型的详情请见[CAM++](https://modelscope.cn/models/damo/speech_campplus_speaker-diarization_common/summary)。 |
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| | | |
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| | | ```python |
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| | | from modelscope.pipelines import pipeline |
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| | | from modelscope.utils.constant import Tasks |
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| | | |
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| | | 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" |
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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-vad-punc-spk_asr_nat-zh-cn', |
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| | | model_revision='v0.0.2', |
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| | | 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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| | | ) |
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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) |
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| | | ``` |
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| | | |
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| | | |
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| | | #### [RNN-T-online 模型]() |
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| | | Undo |
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| | | |
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