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| | | # MIT License (https://opensource.org/licenses/MIT) |
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| | | from funasr import AutoModel |
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| | | wav_file = "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav" |
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| | | model = AutoModel(model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch", model_revision="v2.0.0") |
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| | | chunk_size = 60000 # ms |
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| | | model = AutoModel(model="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch", model_revision="v2.0.2") |
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| | | res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/vad_example.wav") |
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| | | print(res) |
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| | | res = model(input=wav_file, chunk_size=chunk_size, ) |
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| | | print(res) |
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| | | |
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| | | import soundfile |
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| | | import os |
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| | | wav_file = os.path.join(model.model_path, "example/vad_example.wav") |
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| | | speech, sample_rate = soundfile.read(wav_file) |
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| | | chunk_stride = int(chunk_size * sample_rate / 1000) |
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| | | |
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| | | cache = {} |
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| | | total_chunk_num = int(len((speech)-1)/chunk_stride+1) |
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| | | for i in range(total_chunk_num): |
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| | | speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride] |
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| | | is_final = i == total_chunk_num - 1 |
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| | | res = model(input=speech_chunk, |
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| | | cache=cache, |
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| | | is_final=is_final, |
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| | | chunk_size=chunk_size, |
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| | | ) |
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| | | if len(res[0]["value"]): |
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| | | print(res) |
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