#!/usr/bin/env python3
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import argparse
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import logging
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import os
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from modelscope.pipelines import pipeline
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from modelscope.utils.constant import Tasks
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(
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description="decoding configs",
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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)
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parser.add_argument("--model_name",
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type=str,
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default="speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
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help="model name in modelscope")
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parser.add_argument("--local_model_path",
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type=str,
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default=None,
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help="local model path, usually for fine-tuning")
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parser.add_argument("--wav_list",
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type=str,
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help="input wav list")
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parser.add_argument("--output_file",
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type=str,
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help="saving decoding results")
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parser.add_argument(
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"--njob",
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type=int,
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default=1,
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help="The number of jobs for each gpu",
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)
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parser.add_argument(
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"--gpuid_list",
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type=str,
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default="",
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help="The visible gpus",
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)
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parser.add_argument(
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"--ngpu",
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type=int,
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default=0,
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help="The number of gpus. 0 indicates CPU mode",
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)
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args = parser.parse_args()
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# set logging messages
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
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)
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logging.info("Decoding args: {}".format(args))
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# gpu setting
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if args.ngpu > 0:
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jobid = int(args.output_file.split(".")[-1])
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gpuid = args.gpuid_list.split(",")[(jobid - 1) // args.njob]
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os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
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os.environ["CUDA_VISIBLE_DEVICES"] = gpuid
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if args.local_model_path is None:
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inference_pipeline = pipeline(
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task=Tasks.auto_speech_recognition,
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model="damo/{}".format(args.model_name))
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else:
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inference_pipeline = pipeline(
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task=Tasks.auto_speech_recognition,
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model=args.local_model_path)
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with open(args.wav_list, 'r') as f_wav:
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wav_lines = f_wav.readlines()
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with open(args.output_file, "w") as f_out:
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for line in wav_lines:
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wav_id, wav_path = line.strip().split()
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logging.info("decoding, utt_id: ['{}']".format(wav_id))
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rec_result = inference_pipeline(audio_in=wav_path)
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text = rec_result["text"]
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f_out.write(wav_id + " " + text + "\n")
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logging.info("best hypo: {} \n".format(text))
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