GPT-SoVITS/tools/asr/funasr_asr.py
SapphireLab 94e34568dd fix ASR
2024-02-15 01:39:13 +08:00

117 lines
4.8 KiB
Python

# -*- coding:utf-8 -*-
import argparse
import os
import traceback
import torch
from funasr import AutoModel
from tools.asr.config import BaseASR
from tools.my_utils import ASR_Logger
funasr_component = {
'asr': {
'name': 'Paraformer-Large',
'size': 'speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
},
'vad': {
'name': 'FSMN-Monophone VAD',
'size': 'speech_fsmn_vad_zh-cn-16k-common-pytorch',
},
'punc': {
'name': 'Controllable Time-delay Transformer',
'size': 'punc_ct-transformer_zh-cn-common-vocab272727-pytorch',
}
}
class FunASR(BaseASR):
def __init__(self, model_size='large', device="cuda", precision="float16"):
self.check_local_models()
self.model = self.load_model()
assert self.model is not None, ASR_Logger.error('模型不存在')
@classmethod
def check_local_models(self):
'''
启动时检查本地是否有 FunASR 相关模型.
'''
self.model_path_dict = funasr_component
for code, dic in self.model_path_dict.items():
model_name = dic['size']
model_path, flag = super().check_local_model(
self,
model_name = model_name,
model_file = 'model.pt',
cache_path = os.path.normpath(os.path.expanduser(f"~/.cache/modelscope/hub/"))
)
if model_path:
self.model_path_dict[code]['path'] = model_path
else:
# 没有本地路径时, 路径设置为网络链接
self.model_path_dict[code]['path'] = 'iic/' + model_name
return self.model_path_dict
def load_model(self):
try:
for code, dic in self.model_path_dict.items():
if os.path.exists(dic['path']):
ASR_Logger.info(f"加载模型: 从 {dic['path']} 加载 {dic['name']} 模型.")
if 'modelscope' in dic['path']:
ASR_Logger.warning(f"可将 {dic['path']} 移动到 tools/asr/models/ 文件夹下.")
else:
ASR_Logger.warning(f"下载模型: 从 {dic['path']} 下载 {dic['name']} 模型.")
model = AutoModel(
model = self.model_path_dict['asr']['path'],
model_revision = "v2.0.4",
vad_model = self.model_path_dict['vad']['path'],
vad_model_revision = "v2.0.4",
punc_model = self.model_path_dict['punc']['path'],
punc_model_revision = "v2.0.4",
)
ASR_Logger.propagate = False # 避免 FunASR 库导致打印重复日志
if model.kwargs['device'] != 'cpu':
device_name = torch.cuda.get_device_name(model.kwargs['device'])
else:
device_name = 'CPU'
ASR_Logger.info(f"运行设备: {device_name}, 设定精度: --.")
ASR_Logger.info(f"创建模型: FunASR 完成.\n")
return model
except:
ASR_Logger.error(traceback.format_exc())
raise ValueError(ASR_Logger.error(f"模型加载失败 or 下载失败, 可访问 https://modelscope.cn/organization/iic 自行下载, 并放置于 tools/asr/models/ 文件夹下"))
def inference(self, file_path, language='zh'):
try:
text = self.model.generate(input=file_path)[0]["text"]
return text, language
except:
ASR_Logger.error(f"当前文件 {file_path} 转写失败, 可能不是有效的音频文件.")
ASR_Logger.error(traceback.format_exc())
return '', ''
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("-i", "--input_file_or_folder", type=str, required=True,
help="Input audio file path or folder contain audio 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'],
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()
ASR = FunASR(
model_size = cmd.model_size,
precision = cmd.precision,
)
ASR.inference_file_or_folder(
input_file_or_folder = cmd.input_file_or_folder,
output_folder = cmd.output_folder,
language = cmd.language,
)