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Zero
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# -*- coding:utf-8 -*-
import argparse
import os
import traceback
from tqdm import tqdm
# from funasr.utils import version_checker
# version_checker.check_for_update = lambda: None
from funasr import AutoModel
def only_asr(input_file):
try:
text = model.generate(input=input_file)[0]["text"]
except:
text = ''
print(traceback.format_exc())
return text
def execute_asr(input_folder, output_folder, model_size, language):
input_file_names = os.listdir(input_folder)
input_file_names.sort()
output = []
output_file_name = os.path.basename(input_folder)
for file_name in tqdm(input_file_names):
try:
print(file_name)
file_path = os.path.join(input_folder, file_name)
text = model.generate(input=file_path)[0]["text"]
output.append(f"{file_path}|{output_file_name}|{language.upper()}|{text}")
except:
print(traceback.format_exc())
output_folder = output_folder or "output/asr_opt"
os.makedirs(output_folder, exist_ok=True)
output_file_path = os.path.abspath(f'{output_folder}/{output_file_name}.list')
with open(output_file_path, "w", encoding="utf-8") as f:
f.write("\n".join(output))
print(f"ASR 任务完成->标注文件路径: {output_file_path}\n")
return output_file_path
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--input_folder", type=str, required=True,
help="Path to the folder containing WAV files.")
parser.add_argument("-o", "--output_folder", type=str, required=True,
help="Output folder to store transcriptions.")
parser.add_argument("-s", "--model_size", type=str, default='large',
help="Model Size of FunASR is Large")
parser.add_argument("-l", "--language", type=str, default='zh', choices=['zh','yue','auto'],
help="Language of the audio files.")
parser.add_argument("-p", "--precision", type=str, default='float16', choices=['float16','float32'],
help="fp16 or fp32")#还没接入
cmd = parser.parse_args()
path_vad = 'tools/asr/models/speech_fsmn_vad_zh-cn-16k-common-pytorch'
path_punc = 'tools/asr/models/punc_ct-transformer_zh-cn-common-vocab272727-pytorch'
path_vad = path_vad if os.path.exists(path_vad) else "iic/speech_fsmn_vad_zh-cn-16k-common-pytorch"
path_punc = path_punc if os.path.exists(path_punc) else "iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
vad_model_revision=punc_model_revision="v2.0.4"
if(cmd.language=="zh"):
path_asr = 'tools/asr/models/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch'
path_asr = path_asr if os.path.exists(path_asr) else "iic/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch"
model_revision="v2.0.4"
else:
path_asr = 'tools/asr/models/speech_UniASR_asr_2pass-cantonese-CHS-16k-common-vocab1468-tensorflow1-online'
path_asr = path_asr if os.path.exists(path_asr) else "iic/speech_UniASR_asr_2pass-cantonese-CHS-16k-common-vocab1468-tensorflow1-online"
model_revision="master"
path_vad=path_punc=vad_model_revision=punc_model_revision=None###友情提示：粤语带VAD识别可能会有少量shape不对报错的，但是不带VAD可以.不带vad只能分阶段单独加标点。不过标点模型对粤语效果真的不行…
model = AutoModel(
model=path_asr,
model_revision=model_revision,
vad_model=path_vad,
vad_model_revision=vad_model_revision,
punc_model=path_punc,
punc_model_revision=punc_model_revision,
)
if __name__ == '__main__':
execute_asr(
input_folder = cmd.input_folder,
output_folder = cmd.output_folder,
model_size = cmd.model_size,
language = cmd.language,
)
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