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| | | import os |
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| | | import logging |
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| | | import torch |
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| | | import soundfile |
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| | | |
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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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| | | |
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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='v1.0.7', |
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| | | update_model=False, |
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| | | mode="paraformer_streaming" |
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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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| | | |
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| | | sample_offset = 0 |
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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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| | | 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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| | | "encoder_chunk_look_back": encoder_chunk_look_back, "decoder_chunk_look_back": decoder_chunk_look_back} |
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| | | final_result = "" |
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| | | |
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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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