support funasr 1.0 (#1346)

夜雨飘零

2024-02-02 85c08383831ea2b7cdf4c6f863f71b20b95b6782

 runtime/python/http/server.py

@@ -4,15 +4,14 @@
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 fastapi import FastAPI, File, UploadFile
from modelscope.utils.logger import get_logger

logger = get_logger(log_level=logging.CRITICAL)
logger.setLevel(logging.CRITICAL)
from funasr import AutoModel

logger = get_logger(log_level=logging.INFO)
logger.setLevel(logging.INFO)

parser = argparse.ArgumentParser()
parser.add_argument("--host",
@@ -27,27 +26,43 @@
                    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")
                    default="paraformer-zh",
                    help="asr model from https://github.com/alibaba-damo-academy/FunASR?tab=readme-ov-file#model-zoo")
parser.add_argument("--asr_model_revision",
                    type=str,
                    default="v2.0.4",
                    help="")
parser.add_argument("--vad_model",
                    type=str,
                    default="damo/speech_fsmn_vad_zh-cn-16k-common-pytorch",
                    help="vad model from modelscope")
                    default="fsmn-vad",
                    help="vad model from https://github.com/alibaba-damo-academy/FunASR?tab=readme-ov-file#model-zoo")
parser.add_argument("--vad_model_revision",
                    type=str,
                    default="v2.0.4",
                    help="")
parser.add_argument("--punc_model",
                    type=str,
                    default="damo/punc_ct-transformer_cn-en-common-vocab471067-large",
                    help="punc model from modelscope")
                    default="ct-punc-c",
                    help="model from https://github.com/alibaba-damo-academy/FunASR?tab=readme-ov-file#model-zoo")
parser.add_argument("--punc_model_revision",
                    type=str,
                    default="v2.0.4",
                    help="")
parser.add_argument("--ngpu",
                    type=int,
                    default=1,
                    help="0 for cpu, 1 for gpu")
parser.add_argument("--device",
                    type=str,
                    default="cuda",
                    help="cuda, cpu")
parser.add_argument("--ncpu",
                    type=int,
                    default=4,
                    help="cpu cores")
parser.add_argument("--hotword_path",
                    type=str,
                    default=None,
                    default='hotwords.txt',
                    help="hot word txt path, only the hot word model works")
parser.add_argument("--certfile",
                    type=str,
@@ -65,57 +80,50 @@
                    required=False,
                    help="temp dir")
args = parser.parse_args()
print("-----------  Configuration Arguments -----------")
logger.info("-----------  Configuration Arguments -----------")
for arg, value in vars(args).items():
    print("%s: %s" % (arg, value))
print("------------------------------------------------")

    logger.info("%s: %s" % (arg, value))
logger.info("------------------------------------------------")

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,
logger.info("model loading")
# load funasr model
model = AutoModel(model=args.asr_model,
                  model_revision=args.asr_model_revision,
                                  vad_model=args.vad_model,
                  vad_model_revision=args.vad_model_revision,
                  punc_model=args.punc_model,
                  punc_model_revision=args.punc_model_revision,
                                  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
                  device=args.device,
                  disable_pbar=True,
                  disable_log=True)
logger.info("loaded models!")

app = FastAPI(title="FunASR")

param_dict = {}
if args.hotword_path is not None and os.path.exists(args.hotword_path):
    with open(args.hotword_path, 'r', encoding='utf-8') as f:
        lines = f.readlines()
        lines = [line.strip() for line in lines]
    hotword = ' '.join(lines)
    logger.info(f'热词：{hotword}')
    param_dict['hotword'] = hotword


@app.post("/recognition")
async def api_recognition(audio: UploadFile = File(..., description="audio file"),
                          add_pun: int = Body(1, description="add punctuation", embed=True)):
async def api_recognition(audio: UploadFile = File(..., description="audio file")):
    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)
    rec_result = model.generate(input=audio_path, batch_size_s=300, **param_dict)
    ret = {"result": rec_result[0]['text'], "code": 0}
    logger.info(f'识别结果：{ret}')
    return ret