From 0cf5dfec2c8313fc2ed2aab8d10bf3dc4b9c283f Mon Sep 17 00:00:00 2001
From: 雾聪 <wucong.lyb@alibaba-inc.com>
Date: 星期四, 14 三月 2024 14:41:49 +0800
Subject: [PATCH] update cmakelist
---
runtime/python/http/server.py | 132 +++++++++++++++++++++++++++----------------
1 files changed, 82 insertions(+), 50 deletions(-)
diff --git a/runtime/python/http/server.py b/runtime/python/http/server.py
index 19d3193..c97ac63 100644
--- a/runtime/python/http/server.py
+++ b/runtime/python/http/server.py
@@ -6,13 +6,13 @@
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 +27,43 @@
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,
@@ -65,58 +81,74 @@
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,
- 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
+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,
+ device=args.device,
+ disable_pbar=True,
+ disable_log=True)
+logger.info("loaded models!")
app = FastAPI(title="FunASR")
+param_dict = {"sentence_timestamp": True, "batch_size_s": 300}
+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'鐑瘝锛歿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)
- return ret
+ try:
+ 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)
+ )
+ except Exception as e:
+ logger.error(f'璇诲彇闊抽鏂囦欢鍙戠敓閿欒锛岄敊璇俊鎭細{e}')
+ return {"msg": "璇诲彇闊抽鏂囦欢鍙戠敓閿欒", "code": 1}
+ rec_results = model.generate(input=audio_bytes, is_final=True, **param_dict)
+ # 缁撴灉涓虹┖
+ if len(rec_results) == 0:
+ return {"text": "", "sentences": [], "code": 0}
+ elif len(rec_results) == 1:
+ # 瑙ｆ瀽璇嗗埆缁撴灉
+ rec_result = rec_results[0]
+ text = rec_result['text']
+ sentences = []
+ for sentence in rec_result['sentence_info']:
+ # 姣忓彞璇濈殑鏃堕棿鎴�
+ sentences.append({'text': sentence['text'], 'start': sentence['start'], 'end': sentence['start']})
+ ret = {"text": text, "sentences": sentences, "code": 0}
+ logger.info(f'璇嗗埆缁撴灉锛歿ret}')
+ return ret
+ else:
+ logger.info(f'璇嗗埆缁撴灉锛歿rec_results}')
+ return {"msg": "鏈煡閿欒", "code": -1}
if __name__ == '__main__':
--
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