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| | | import argparse |
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| | | import logging |
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| | | import os |
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| | | import random |
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| | | import time |
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| | | import uuid |
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| | | |
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| | | import aiofiles |
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| | | import ffmpeg |
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| | |
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| | | parser.add_argument("--asr_model", |
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| | | type=str, |
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| | | default="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch", |
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| | | help="model from modelscope") |
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| | | help="offline asr model from modelscope") |
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| | | parser.add_argument("--vad_model", |
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| | | type=str, |
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| | | default="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch", |
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| | | help="vad model from modelscope") |
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| | | parser.add_argument("--punc_model", |
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| | | type=str, |
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| | | default="damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727", |
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| | | help="model from modelscope") |
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| | | default="damo/punc_ct-transformer_cn-en-common-vocab471067-large", |
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| | | help="punc model from modelscope") |
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| | | parser.add_argument("--ngpu", |
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| | | type=int, |
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| | | default=1, |
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| | |
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| | | type=int, |
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| | | default=4, |
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| | | help="cpu cores") |
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| | | parser.add_argument("--hotword_path", |
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| | | type=str, |
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| | | default=None, |
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| | | help="hot word txt path, only the hot word model works") |
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| | | parser.add_argument("--certfile", |
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| | | type=str, |
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| | | default=None, |
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| | |
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| | | required=False, |
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| | | help="temp dir") |
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| | | args = parser.parse_args() |
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| | | print("----------- Configuration Arguments -----------") |
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| | | for arg, value in vars(args).items(): |
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| | | print("%s: %s" % (arg, value)) |
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| | | print("------------------------------------------------") |
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| | | |
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| | | |
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| | | os.makedirs(args.temp_dir, exist_ok=True) |
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| | | |
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| | | print("model loading") |
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| | | param_dict = {} |
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| | | if args.hotword_path is not None and os.path.exists(args.hotword_path): |
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| | | param_dict['hotword'] = args.hotword_path |
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| | | # asr |
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| | | inference_pipeline_asr = pipeline(task=Tasks.auto_speech_recognition, |
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| | | model=args.asr_model, |
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| | | vad_model=args.vad_model, |
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| | | ngpu=args.ngpu, |
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| | | ncpu=args.ncpu, |
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| | | model_revision=None) |
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| | | param_dict=param_dict) |
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| | | print(f'loaded asr models.') |
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| | | |
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| | | if args.punc_model != "": |
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| | | inference_pipeline_punc = pipeline(task=Tasks.punctuation, |
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| | | model=args.punc_model, |
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| | | model_revision="v1.0.2", |
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| | | ngpu=args.ngpu, |
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| | | ncpu=args.ncpu) |
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| | | print(f'loaded pun models.') |
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| | |
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| | | async def api_recognition(audio: UploadFile = File(..., description="audio file"), |
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| | | add_pun: int = Body(1, description="add punctuation", embed=True)): |
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| | | suffix = audio.filename.split('.')[-1] |
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| | | audio_path = f'{args.temp_dir}/{int(time.time() * 1000)}_{random.randint(100, 999)}.{suffix}' |
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| | | audio_path = f'{args.temp_dir}/{str(uuid.uuid1())}.{suffix}' |
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| | | async with aiofiles.open(audio_path, 'wb') as out_file: |
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| | | content = await audio.read() |
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| | | await out_file.write(content) |
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| | |
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| | | if add_pun: |
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| | | rec_result = inference_pipeline_punc(text_in=rec_result['text'], param_dict={'cache': list()}) |
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| | | ret = {"results": rec_result['text'], "code": 0} |
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| | | print(ret) |
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| | | return ret |
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