mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
861147c730
* sensevoice finetune * sensevoice finetune * sensevoice finetune * sensevoice finetune * sensevoice finetune * sensevoice finetune * sensevoice finetune * sensevoice finetune * sensevoice finetune * sensevoice finetune * bugfix * update with main (#1631) * update seaco finetune * v1.0.24 --------- Co-authored-by: 维石 <shixian.shi@alibaba-inc.com> * sensevoice * sensevoice * sensevoice * update with main (#1638) * update seaco finetune * v1.0.24 * update rwkv template --------- Co-authored-by: 维石 <shixian.shi@alibaba-inc.com> * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sensevoice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * sense voice * whisper * whisper * update style * update style --------- Co-authored-by: 维石 <shixian.shi@alibaba-inc.com>
47 lines
1.5 KiB
Python
47 lines
1.5 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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import os
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from funasr import AutoModel
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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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wav_file = os.path.join(model.model_path, "example/asr_example.wav")
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res = model.generate(
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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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import soundfile
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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(
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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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