mirror of
https://github.com/modelscope/FunASR
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b28f3c9da9
* funasr1.0 funetine * funasr1.0 pbar * update with main (#1260) * Update websocket_protocol_zh.md * update --------- Co-authored-by: Yabin Li <wucong.lyb@alibaba-inc.com> Co-authored-by: shixian.shi <shixian.shi@alibaba-inc.com> * update with main (#1264) * Funasr1.0 (#1261) * funasr1.0 funetine * funasr1.0 pbar * update with main (#1260) * Update websocket_protocol_zh.md * update --------- Co-authored-by: Yabin Li <wucong.lyb@alibaba-inc.com> Co-authored-by: shixian.shi <shixian.shi@alibaba-inc.com> --------- Co-authored-by: Yabin Li <wucong.lyb@alibaba-inc.com> Co-authored-by: shixian.shi <shixian.shi@alibaba-inc.com> * bug fix --------- Co-authored-by: Yabin Li <wucong.lyb@alibaba-inc.com> Co-authored-by: shixian.shi <shixian.shi@alibaba-inc.com> * funasr1.0 sanm scama * funasr1.0 infer_after_finetune * funasr1.0 fsmn-vad bug fix * funasr1.0 fsmn-vad bug fix --------- Co-authored-by: Yabin Li <wucong.lyb@alibaba-inc.com> Co-authored-by: shixian.shi <shixian.shi@alibaba-inc.com>
43 lines
1.6 KiB
Python
43 lines
1.6 KiB
Python
#!/usr/bin/env python3
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# -*- encoding: utf-8 -*-
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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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chunk_size = [5, 10, 5] #[0, 10, 5] 600ms, [0, 8, 4] 480ms
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encoder_chunk_look_back = 0 #number of chunks to lookback for encoder self-attention
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decoder_chunk_look_back = 0 #number of encoder chunks to lookback for decoder cross-attention
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model = AutoModel(model="/Users/zhifu/Downloads/modelscope_models/speech_SCAMA_asr-zh-cn-16k-common-vocab8358-streaming", model_revision="v2.0.2")
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cache = {}
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res = model.generate(input="https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav",
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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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import soundfile
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import os
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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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chunk_stride = chunk_size[1] * 960 # 600ms、480ms
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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.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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