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@@ -19,22 +19,24 @@ inference_pipeline = pipeline(
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rec_result = inference_pipeline(audio_in='https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/asr_example_zh.wav')
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print(rec_result)
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```
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-#### [Paraformer-online Model](https://www.modelscope.cn/models/damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online/summary)
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+#### [Paraformer-online Model](https://www.modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online/summary)
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```python
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inference_pipeline = pipeline(
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task=Tasks.auto_speech_recognition,
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- model='damo/speech_paraformer_asr_nat-zh-cn-16k-common-vocab8404-online',
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+ model='damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online',
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+ model_revision='v1.0.4'
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)
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import soundfile
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speech, sample_rate = soundfile.read("example/asr_example.wav")
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-param_dict = {"cache": dict(), "is_final": False}
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-chunk_stride = 7680# 480ms
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-# first chunk, 480ms
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+chunk_size = [5, 10, 5] #[5, 10, 5] 600ms, [8, 8, 4] 480ms
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+param_dict = {"cache": dict(), "is_final": False, "chunk_size": chunk_size}
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+chunk_stride = chunk_size[1] * 960 # 600ms、480ms
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+# first chunk, 600ms
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speech_chunk = speech[0:chunk_stride]
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rec_result = inference_pipeline(audio_in=speech_chunk, param_dict=param_dict)
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print(rec_result)
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-# next chunk, 480ms
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+# next chunk, 600ms
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speech_chunk = speech[chunk_stride:chunk_stride+chunk_stride]
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rec_result = inference_pipeline(audio_in=speech_chunk, param_dict=param_dict)
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print(rec_result)
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hnluo