mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-04-05 19:41:56 +08:00
108 lines
3.9 KiB
Python
108 lines
3.9 KiB
Python
import argparse
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import os
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os.environ["HF_ENDPOINT"]="https://hf-mirror.com"
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import traceback
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import requests
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from glob import glob
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from faster_whisper import WhisperModel
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from tqdm import tqdm
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from tools.asr.config import check_fw_local_models
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from tools.asr.funasr_asr import only_asr
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os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
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language_code_list = [
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"af", "am", "ar", "as", "az",
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"ba", "be", "bg", "bn", "bo",
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"br", "bs", "ca", "cs", "cy",
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"da", "de", "el", "en", "es",
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"et", "eu", "fa", "fi", "fo",
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"fr", "gl", "gu", "ha", "haw",
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"he", "hi", "hr", "ht", "hu",
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"hy", "id", "is", "it", "ja",
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"jw", "ka", "kk", "km", "kn",
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"ko", "la", "lb", "ln", "lo",
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"lt", "lv", "mg", "mi", "mk",
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"ml", "mn", "mr", "ms", "mt",
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"my", "ne", "nl", "nn", "no",
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"oc", "pa", "pl", "ps", "pt",
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"ro", "ru", "sa", "sd", "si",
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"sk", "sl", "sn", "so", "sq",
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"sr", "su", "sv", "sw", "ta",
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"te", "tg", "th", "tk", "tl",
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"tr", "tt", "uk", "ur", "uz",
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"vi", "yi", "yo", "zh", "yue",
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"auto"]
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def execute_asr(input_folder, output_folder, model_size, language,precision):
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if '-local' in model_size:
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model_size = model_size[:-6]
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model_path = f'tools/asr/models/faster-whisper-{model_size}'
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else:
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model_path = model_size
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if language == 'auto':
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language = None #不设置语种由模型自动输出概率最高的语种
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print("loading faster whisper model:",model_size,model_path)
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try:
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model = WhisperModel(model_path, device="cuda", compute_type=precision)
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except:
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return print(traceback.format_exc())
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output = []
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output_file_name = os.path.basename(input_folder)
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output_file_path = os.path.abspath(f'{output_folder}/{output_file_name}.list')
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if not os.path.exists(output_folder):
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os.makedirs(output_folder)
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for file in tqdm(glob(os.path.join(input_folder, '**/*.wav'), recursive=True)):
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try:
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segments, info = model.transcribe(
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audio = file,
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beam_size = 5,
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vad_filter = True,
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vad_parameters = dict(min_silence_duration_ms=700),
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language = language)
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text = ''
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if info.language == "zh":
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print("检测为中文文本,转funasr处理")
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text = only_asr(file)
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if text == '':
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for segment in segments:
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text += segment.text
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output.append(f"{file}|{output_file_name}|{info.language.upper()}|{text}")
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except:
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return print(traceback.format_exc())
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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("-s", "--model_size", type=str, default='large-v3',
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choices=check_fw_local_models(),
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help="Model Size of Faster Whisper")
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parser.add_argument("-l", "--language", type=str, default='ja',
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choices=language_code_list,
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help="Language of the audio files.")
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parser.add_argument("-p", "--precision", type=str, default='float16', choices=['float16','float32'],
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help="fp16 or fp32")
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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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model_size = cmd.model_size,
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language = cmd.language,
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precision = cmd.precision,
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)
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