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| | | # Copyright FunASR (https://github.com/alibaba-damo-academy/FunASR). All Rights Reserved. |
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| | | # MIT License (https://opensource.org/licenses/MIT) |
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| | | # from funasr import AutoModel |
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| | | # |
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| | | # model = AutoModel(model="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", model_revison="v2.0.0") |
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| | | # |
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| | | # res = model(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav") |
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| | | # print(res) |
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| | | import os |
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| | | |
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| | | from funasr import AutoModel |
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| | | from funasr import AutoFrontend |
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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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| | | model = AutoModel(model="iic/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online") |
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| | | |
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| | | frontend = AutoFrontend(model="/Users/zhifu/Downloads/modelscope_models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online", model_revison="v2.0.0") |
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| | | |
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| | | wav_file = os.path.join(model.model_path, "example/asr_example.wav") |
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| | | res = model.generate(input=wav_file, |
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| | | chunk_size=chunk_size, |
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| | | encoder_chunk_look_back=encoder_chunk_look_back, |
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| | | decoder_chunk_look_back=decoder_chunk_look_back, |
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| | | ) |
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| | | print(res) |
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| | | |
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| | | import soundfile |
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| | | speech, sample_rate = soundfile.read("/Users/zhifu/Downloads/modelscope_models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/example/asr_example.wav") |
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| | | |
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| | | chunk_size = [0, 10, 5] #[0, 10, 5] 600ms, [0, 8, 4] 480ms |
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| | | |
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| | | wav_file = os.path.join(model.model_path, "example/asr_example.wav") |
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| | | speech, sample_rate = soundfile.read(wav_file) |
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| | | |
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| | | chunk_stride = chunk_size[1] * 960 # 600ms、480ms |
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| | | # first chunk, 600ms |
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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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| | | 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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| | | fbanks = frontend(input=speech_chunk, |
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| | | batch_size=2, |
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| | | cache=cache) |
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| | | # for batch_idx, fbank_dict in enumerate(fbanks): |
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| | | # res = model(**fbank_dict) |
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| | | # print(res) |
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| | | is_final = i == total_chunk_num - 1 |
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| | | res = model.generate(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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| | | encoder_chunk_look_back=encoder_chunk_look_back, |
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| | | decoder_chunk_look_back=decoder_chunk_look_back, |
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| | | ) |
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| | | print(res) |
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