blackteay/GPT-SoVITS
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damo asr funasr1.0

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游雁 2024-01-22 12:13:30 +08:00
parent 4ba412f07f
commit cbd1ce11e1
3 changed files with 15 additions and 12 deletions
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2
README.md
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@ -69,7 +69,7 @@ pip install torch numpy scipy tensorboard librosa==0.9.2 numba==0.56.4 pytorch-l
If you need Chinese ASR (supported by FunASR), install: If you need Chinese ASR (supported by FunASR), install:
```bash ```bash
pip install modelscope torchaudio sentencepiece funasr pip install modelscope torchaudio sentencepiece funasr>=1.0.0
``` ```
#### FFmpeg #### FFmpeg

2
requirements.txt
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@ -8,7 +8,7 @@ gradio==3.14.0
ffmpeg-python ffmpeg-python
onnxruntime onnxruntime
tqdm tqdm
funasr funasr>=1.0.0
cn2an cn2an
pypinyin pypinyin
pyopenjtalk pyopenjtalk

23
tools/damo_asr/cmd-asr.py
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@ -1,9 +1,11 @@
# -*- coding:utf-8 -*- # -*- coding:utf-8 -*-
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
import sys,os,traceback import sys,os,traceback
from funasr import AutoModel
dir=sys.argv[1] dir=sys.argv[1]
# opt_name=dir.split("\\")[-1].split("/")[-1] # opt_name=dir.split("\\")[-1].split("/")[-1]
opt_name=os.path.basename(dir) opt_name=os.path.basename(dir)
@ -13,17 +15,19 @@ path_punc='tools/damo_asr/models/punc_ct-transformer_zh-cn-common-vocab272727-py
path_asr=path_asr if os.path.exists(path_asr)else "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch" path_asr=path_asr if os.path.exists(path_asr)else "damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
path_vad=path_vad if os.path.exists(path_vad)else "damo/speech_fsmn_vad_zh-cn-16k-common-pytorch" path_vad=path_vad if os.path.exists(path_vad)else "damo/speech_fsmn_vad_zh-cn-16k-common-pytorch"
path_punc=path_punc if os.path.exists(path_punc)else "damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch" path_punc=path_punc if os.path.exists(path_punc)else "damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
inference_pipeline = pipeline(
task=Tasks.auto_speech_recognition, model = AutoModel(model=path_asr, model_revision="v2.0.3",
model=path_asr, vad_model=path_vad,
vad_model=path_vad, vad_model_revision="v2.0.2",
punc_model=path_punc, punc_model=path_punc,
) punc_model_revision="v2.0.3",
)
opt=[] opt=[]
for name in os.listdir(dir): for name in os.listdir(dir):
try: try:
text = inference_pipeline(audio_in="%s/%s"%(dir,name))["text"] text = model.generate(input="%s/%s"%(dir,name))[0]["text"]
opt.append("%s/%s|%s|ZH|%s"%(dir,name,opt_name,text)) opt.append("%s/%s|%s|ZH|%s"%(dir,name,opt_name,text))
except: except:
print(traceback.format_exc()) print(traceback.format_exc())
@ -31,4 +35,3 @@ for name in os.listdir(dir):
opt_dir="output/asr_opt" opt_dir="output/asr_opt"
os.makedirs(opt_dir,exist_ok=True) os.makedirs(opt_dir,exist_ok=True)
with open("%s/%s.list"%(opt_dir,opt_name),"w",encoding="utf-8")as f:f.write("\n".join(opt)) with open("%s/%s.list"%(opt_dir,opt_name),"w",encoding="utf-8")as f:f.write("\n".join(opt))