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