import argparse
|
import logging
|
import os
|
import uuid
|
|
import aiofiles
|
import ffmpeg
|
import uvicorn
|
from fastapi import FastAPI, File, UploadFile, Body
|
from modelscope.pipelines import pipeline
|
from modelscope.utils.constant import Tasks
|
from modelscope.utils.logger import get_logger
|
|
logger = get_logger(log_level=logging.CRITICAL)
|
logger.setLevel(logging.CRITICAL)
|
|
parser = argparse.ArgumentParser()
|
parser.add_argument("--host",
|
type=str,
|
default="0.0.0.0",
|
required=False,
|
help="host ip, localhost, 0.0.0.0")
|
parser.add_argument("--port",
|
type=int,
|
default=8000,
|
required=False,
|
help="server port")
|
parser.add_argument("--asr_model",
|
type=str,
|
default="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch",
|
help="offline asr model from modelscope")
|
parser.add_argument("--vad_model",
|
type=str,
|
default="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
|
help="vad model from modelscope")
|
parser.add_argument("--punc_model",
|
type=str,
|
default="damo/punc_ct-transformer_cn-en-common-vocab471067-large",
|
help="punc model from modelscope")
|
parser.add_argument("--ngpu",
|
type=int,
|
default=1,
|
help="0 for cpu, 1 for gpu")
|
parser.add_argument("--ncpu",
|
type=int,
|
default=4,
|
help="cpu cores")
|
parser.add_argument("--hotword_path",
|
type=str,
|
default=None,
|
help="hot word txt path, only the hot word model works")
|
parser.add_argument("--certfile",
|
type=str,
|
default=None,
|
required=False,
|
help="certfile for ssl")
|
parser.add_argument("--keyfile",
|
type=str,
|
default=None,
|
required=False,
|
help="keyfile for ssl")
|
parser.add_argument("--temp_dir",
|
type=str,
|
default="temp_dir/",
|
required=False,
|
help="temp dir")
|
args = parser.parse_args()
|
print("----------- Configuration Arguments -----------")
|
for arg, value in vars(args).items():
|
print("%s: %s" % (arg, value))
|
print("------------------------------------------------")
|
|
|
os.makedirs(args.temp_dir, exist_ok=True)
|
|
print("model loading")
|
param_dict = {}
|
if args.hotword_path is not None and os.path.exists(args.hotword_path):
|
param_dict['hotword'] = args.hotword_path
|
# asr
|
inference_pipeline_asr = pipeline(task=Tasks.auto_speech_recognition,
|
model=args.asr_model,
|
vad_model=args.vad_model,
|
ngpu=args.ngpu,
|
ncpu=args.ncpu,
|
param_dict=param_dict)
|
print(f'loaded asr models.')
|
|
if args.punc_model != "":
|
inference_pipeline_punc = pipeline(task=Tasks.punctuation,
|
model=args.punc_model,
|
ngpu=args.ngpu,
|
ncpu=args.ncpu)
|
print(f'loaded pun models.')
|
else:
|
inference_pipeline_punc = None
|
|
app = FastAPI(title="FunASR")
|
|
|
@app.post("/recognition")
|
async def api_recognition(audio: UploadFile = File(..., description="audio file"),
|
add_pun: int = Body(1, description="add punctuation", embed=True)):
|
suffix = audio.filename.split('.')[-1]
|
audio_path = f'{args.temp_dir}/{str(uuid.uuid1())}.{suffix}'
|
async with aiofiles.open(audio_path, 'wb') as out_file:
|
content = await audio.read()
|
await out_file.write(content)
|
audio_bytes, _ = (
|
ffmpeg.input(audio_path, threads=0)
|
.output("-", format="s16le", acodec="pcm_s16le", ac=1, ar=16000)
|
.run(cmd=["ffmpeg", "-nostdin"], capture_stdout=True, capture_stderr=True)
|
)
|
rec_result = inference_pipeline_asr(audio_in=audio_bytes, param_dict={})
|
if add_pun:
|
rec_result = inference_pipeline_punc(text_in=rec_result['text'], param_dict={'cache': list()})
|
ret = {"results": rec_result['text'], "code": 0}
|
print(ret)
|
return ret
|
|
|
if __name__ == '__main__':
|
uvicorn.run(app, host=args.host, port=args.port, ssl_keyfile=args.keyfile, ssl_certfile=args.certfile)
|
