#!/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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#
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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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from funasr import AutoFrontend
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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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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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chunk_size = [0, 10, 5] #[0, 10, 5] 600ms, [0, 8, 4] 480ms
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chunk_stride = chunk_size[1] * 960 # 600ms、480ms
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# first chunk, 600ms
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cache = {}
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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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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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