import os
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import logging
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import torch
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import soundfile
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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.utils.logger import get_logger
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logger = get_logger(log_level=logging.CRITICAL)
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logger.setLevel(logging.CRITICAL)
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os.environ["MODELSCOPE_CACHE"] = "./"
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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_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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model_dir = os.path.join(os.environ["MODELSCOPE_CACHE"], "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-online")
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speech, sample_rate = soundfile.read(os.path.join(model_dir, "example/asr_example.wav"))
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speech_length = speech.shape[0]
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sample_offset = 0
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chunk_size = [5, 10, 5] #[5, 10, 5] 600ms, [8, 8, 4] 480ms
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stride_size = chunk_size[1] * 960
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param_dict = {"cache": dict(), "is_final": False, "chunk_size": chunk_size}
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final_result = ""
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for sample_offset in range(0, speech_length, min(stride_size, speech_length - sample_offset)):
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if sample_offset + stride_size >= speech_length - 1:
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stride_size = speech_length - sample_offset
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param_dict["is_final"] = True
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rec_result = inference_pipeline(audio_in=speech[sample_offset: sample_offset + stride_size],
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param_dict=param_dict)
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if len(rec_result) != 0:
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final_result += rec_result['text'] + " "
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print(rec_result)
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print(final_result)
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