import argparse
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import logging
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import os
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import uuid
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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
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from modelscope.utils.logger import get_logger
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from funasr import AutoModel
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logger = get_logger(log_level=logging.INFO)
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logger.setLevel(logging.INFO)
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parser = argparse.ArgumentParser()
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parser.add_argument(
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"--host", type=str, default="0.0.0.0", required=False, help="host ip, localhost, 0.0.0.0"
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)
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parser.add_argument("--port", type=int, default=8000, required=False, help="server port")
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parser.add_argument(
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"--asr_model",
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type=str,
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default="paraformer-zh",
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help="asr model from https://github.com/alibaba-damo-academy/FunASR?tab=readme-ov-file#model-zoo",
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)
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parser.add_argument("--asr_model_revision", type=str, default="v2.0.4", help="")
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parser.add_argument(
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"--vad_model",
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type=str,
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default="fsmn-vad",
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help="vad model from https://github.com/alibaba-damo-academy/FunASR?tab=readme-ov-file#model-zoo",
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)
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parser.add_argument("--vad_model_revision", type=str, default="v2.0.4", help="")
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parser.add_argument(
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"--punc_model",
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type=str,
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default="ct-punc-c",
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help="model from https://github.com/alibaba-damo-academy/FunASR?tab=readme-ov-file#model-zoo",
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)
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parser.add_argument("--punc_model_revision", type=str, default="v2.0.4", help="")
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parser.add_argument("--ngpu", type=int, default=1, help="0 for cpu, 1 for gpu")
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parser.add_argument("--device", type=str, default="cuda", help="cuda, cpu")
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parser.add_argument("--ncpu", type=int, default=4, help="cpu cores")
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parser.add_argument(
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"--hotword_path",
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type=str,
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default="hotwords.txt",
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help="hot word txt path, only the hot word model works",
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)
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parser.add_argument("--certfile", type=str, default=None, required=False, help="certfile for ssl")
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parser.add_argument("--keyfile", type=str, default=None, required=False, help="keyfile for ssl")
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parser.add_argument("--temp_dir", type=str, default="temp_dir/", required=False, help="temp dir")
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args = parser.parse_args()
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logger.info("----------- Configuration Arguments -----------")
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for arg, value in vars(args).items():
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logger.info("%s: %s" % (arg, value))
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logger.info("------------------------------------------------")
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os.makedirs(args.temp_dir, exist_ok=True)
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logger.info("model loading")
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# load funasr model
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model = AutoModel(
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model=args.asr_model,
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model_revision=args.asr_model_revision,
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vad_model=args.vad_model,
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vad_model_revision=args.vad_model_revision,
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punc_model=args.punc_model,
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punc_model_revision=args.punc_model_revision,
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ngpu=args.ngpu,
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ncpu=args.ncpu,
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device=args.device,
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disable_pbar=True,
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disable_log=True,
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)
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logger.info("loaded models!")
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app = FastAPI(title="FunASR")
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param_dict = {"sentence_timestamp": True, "batch_size_s": 300}
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if args.hotword_path is not None and os.path.exists(args.hotword_path):
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with open(args.hotword_path, "r", encoding="utf-8") as f:
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lines = f.readlines()
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lines = [line.strip() for line in lines]
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hotword = " ".join(lines)
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logger.info(f"热词：{hotword}")
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param_dict["hotword"] = hotword
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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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suffix = audio.filename.split(".")[-1]
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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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try:
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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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except Exception as e:
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logger.error(f"读取音频文件发生错误，错误信息：{e}")
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return {"msg": "读取音频文件发生错误", "code": 1}
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rec_results = model.generate(input=audio_bytes, is_final=True, **param_dict)
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# 结果为空
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if len(rec_results[0]["text"] ) == 0:
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return {"text": "", "sentences": [], "code": 0}
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elif len(rec_results[0]["text"] ) > 0:
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# 解析识别结果
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rec_result = rec_results[0]
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text = rec_result["text"]
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sentences = []
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for sentence in rec_result["sentence_info"]:
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# 每句话的时间戳
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sentences.append(
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{"text": sentence["text"], "start": sentence["start"], "end": sentence["end"]}
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)
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ret = {"text": text, "sentences": sentences, "code": 0}
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logger.info(f"识别结果：{ret}")
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return ret
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else:
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logger.info(f"识别结果：{rec_results}")
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return {"msg": "未知错误", "code": -1}
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if __name__ == "__main__":
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uvicorn.run(
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app, host=args.host, port=args.port, ssl_keyfile=args.keyfile, ssl_certfile=args.certfile
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)
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