import os
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import tempfile
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import codecs
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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from modelscope.msdatasets import MsDataset
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if __name__ == '__main__':
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param_dict = dict()
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param_dict['hotword'] = "https://isv-data.oss-cn-hangzhou.aliyuncs.com/ics/MaaS/ASR/test_audio/hotword.txt"
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output_dir = "./output"
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batch_size = 1
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# dataset split ['test']
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ds_dict = MsDataset.load(dataset_name='speech_asr_aishell1_hotwords_testsets', namespace='speech_asr')
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work_dir = tempfile.TemporaryDirectory().name
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if not os.path.exists(work_dir):
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os.makedirs(work_dir)
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wav_file_path = os.path.join(work_dir, "wav.scp")
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with codecs.open(wav_file_path, 'w') as fin:
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for line in ds_dict:
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wav = line["Audio:FILE"]
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idx = wav.split("/")[-1].split(".")[0]
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fin.writelines(idx + " " + wav + "\n")
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audio_in = wav_file_path
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inference_pipeline = pipeline(
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task=Tasks.auto_speech_recognition,
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model="damo/speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404",
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output_dir=output_dir,
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batch_size=batch_size,
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param_dict=param_dict)
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rec_result = inference_pipeline(audio_in=audio_in)
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