mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-10-07 15:19:59 +08:00
84 lines
3.6 KiB
Python
84 lines
3.6 KiB
Python
import argparse
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import os
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import re
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import traceback
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import torch
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from tqdm import tqdm
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from funasr import AutoModel
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path_asr = "tools/asr/models/SenseVoiceSmall"
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path_vad = 'tools/asr/models/speech_fsmn_vad_zh-cn-16k-common-pytorch'
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path_punc = 'tools/asr/models/punc_ct-transformer_zh-cn-common-vocab272727-pytorch'
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path_asr = path_asr if os.path.exists(path_asr) else "iic/SenseVoiceSmall"
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path_vad = path_vad if os.path.exists(path_vad) else "iic/speech_fsmn_vad_zh-cn-16k-common-pytorch"
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path_punc = path_punc if os.path.exists(path_punc) else "iic/punc_ct-transformer_zh-cn-common-vocab272727-pytorch"
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def execute_asr(input_folder, output_folder, language, device):
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try:
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model = AutoModel(model=path_asr,
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vad_model=path_vad,
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vad_kwargs={"max_single_segment_time": 2000},
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punc_model=path_punc
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)
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except:
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return print(traceback.format_exc())
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input_file_names = os.listdir(input_folder)
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input_file_names.sort()
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output = []
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output_file_name = os.path.basename(input_folder)
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if device is None:
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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for file_name in tqdm(input_file_names):
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try:
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file_path = os.path.join(input_folder, file_name)
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res = model.generate(
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input=file_path,
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cache={},
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language=language.lower(), # "zn", "en", "yue", "ja", "ko", "nospeech"
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use_itn=False,
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batch_size_s=0,
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device = device
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)[0]['text']
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text_language = re.search(r'<([^<>]+)>', res).group(1)[1:-1].upper() if language == 'auto' else language.upper()
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text = re.sub(r'<[^<>]*>', '', res).replace(' ', '')
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if text_language != "EN":
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text = text.replace(' ', '')
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output.append(f"{file_path}|{output_file_name}|{text_language}|{text}")
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except:
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print(traceback.format_exc())
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output_folder = output_folder or "output/asr_opt"
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os.makedirs(output_folder, exist_ok=True)
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output_file_path = os.path.abspath(f'{output_folder}/{output_file_name}.list')
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with open(output_file_path, "w", encoding="utf-8") as f:
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f.write("\n".join(output))
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print(f"ASR 任务完成->标注文件路径: {output_file_path}\n")
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return output_file_path
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument("-i", "--input_folder", type=str, required=True,
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help="Path to the folder containing WAV files.")
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parser.add_argument("-o", "--output_folder", type=str, required=True,
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help="Output folder to store transcriptions.")
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parser.add_argument("-l", "--language", type=str, default='auto',
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choices=['auto','zh','en','ja'],
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help="Language of the audio files.")
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parser.add_argument("-d", "--device", type=str, default=None, choices=['cpu','cuda'],
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help="CPU or CUDA")
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parser.add_argument("-p", "--precision", type=str, default='float32', choices=['float32'],
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help="fp16 or fp32")
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parser.add_argument("-s", "--model_size", type=str, default='small',
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choices=['small'],
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help="Model Size of Faster Whisper")
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cmd = parser.parse_args()
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output_file_path = execute_asr(
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input_folder = cmd.input_folder,
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output_folder = cmd.output_folder,
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language = cmd.language,
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device = cmd.device,
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)
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