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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 = "/Users/zhifu/funasr_github/test_local/asr_example.wav" |
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| | | chunk_size = 60000 # ms |
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| | | model = AutoModel(model="/Users/zhifu/Downloads/modelscope_models/speech_fsmn_vad_zh-cn-16k-common-streaming", model_revision="v2.0.0") |
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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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| | | res = model(input=wav_file, |
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| | | chunk_size=chunk_size, |
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| | | ) |
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| | | print(res) |
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| | | |
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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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| | | |
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| | | # |
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| | | # import soundfile |
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| | | # import os |
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| | | # |
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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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| | | # |
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| | | # chunk_stride = int(chunk_size * 16000 / 1000) |
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| | | # |
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| | | # cache = {} |
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| | | # |
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| | | # for i in range(int(len((speech)-1)/chunk_stride+1)): |
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| | | # speech_chunk = speech[i*chunk_stride:(i+1)*chunk_stride] |
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| | | # is_final = i == int(len((speech)-1)/chunk_stride+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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| | | # print(res) |
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