mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
113 lines
4.1 KiB
Python
113 lines
4.1 KiB
Python
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 aiofiles
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import ffmpeg
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import uvicorn
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from fastapi import FastAPI, File, UploadFile, Body
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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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parser = argparse.ArgumentParser()
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parser.add_argument("--host",
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type=str,
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default="0.0.0.0",
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required=False,
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help="host ip, localhost, 0.0.0.0")
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parser.add_argument("--port",
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type=int,
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default=8000,
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required=False,
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help="server port")
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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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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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parser.add_argument("--ngpu",
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type=int,
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default=1,
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help="0 for cpu, 1 for gpu")
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parser.add_argument("--ncpu",
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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("--certfile",
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type=str,
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default=None,
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required=False,
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help="certfile for ssl")
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parser.add_argument("--keyfile",
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type=str,
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default=None,
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required=False,
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help="keyfile for ssl")
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parser.add_argument("--temp_dir",
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type=str,
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default="temp_dir/",
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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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os.makedirs(args.temp_dir, exist_ok=True)
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print("model loading")
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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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ngpu=args.ngpu,
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ncpu=args.ncpu,
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model_revision=None)
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print(f'loaded asr models.')
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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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else:
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inference_pipeline_punc = None
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app = FastAPI(title="FunASR")
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@app.post("/recognition")
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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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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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audio_bytes, _ = (
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ffmpeg.input(audio_path, threads=0)
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.output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=16000)
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.run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
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)
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rec_result = inference_pipeline_asr(audio_in=audio_bytes, param_dict={})
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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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return ret
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if __name__ == '__main__':
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uvicorn.run(app, host=args.host, port=args.port, ssl_keyfile=args.keyfile, ssl_certfile=args.certfile)
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