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import os
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks
from modelscope.utils.logger import get_logger
import logging
import argparse
from pydub import AudioSegment
import concurrent.futures
from tqdm import tqdm
logger = get_logger(log_level=logging.CRITICAL)
logger.setLevel(logging.CRITICAL)
os.environ["MODELSCOPE_CACHE"] = "./"
def transcribe_worker(file_path: str, inference_pipeline):
"""
Worker function for transcribing a segment of an audio file.
"""
rec_result = inference_pipeline(audio_in=file_path)
text = str(rec_result.get('text', '')).strip()
text_without_spaces = text.replace(" ", "")
logger.critical(file_path)
logger.critical("text: " + text_without_spaces)
return text_without_spaces
def transcribe_folder_parallel(folder_path, language, max_workers=4):
"""
Transcribe all .wav files in the given folder using ThreadPoolExecutor.
"""
logger.critical(f"parallel transcribe: {folder_path}|{language}|{max_workers}")
if language == "JP(日语)":
workers = [
pipeline(
task=Tasks.auto_speech_recognition,
model='damo/speech_UniASR_asr_2pass-ja-16k-common-vocab93-tensorflow1-offline'
)
for _ in range(max_workers)
]
else:
workers = [
pipeline(
task=Tasks.auto_speech_recognition,
model='damo/speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404-pytorch',
model_revision="v1.2.4"
)
for _ in range(max_workers)
]
max_duration = 60 # 最大持续时间（秒）
file_paths = []
for root, _, files in os.walk(folder_path):
for file in files:
if file.lower().endswith(".wav"):
file_path = os.path.join(root, file)
lab_file_path = os.path.splitext(file_path)[0] + ".lab"
if os.path.exists(lab_file_path):
logger.info(lab_file_path+" 已存在")
continue
audio = AudioSegment.from_wav(file_path)
duration_in_seconds = len(audio) / 1000 # 将毫秒转换为秒
if duration_in_seconds <= max_duration + 1:
file_paths.append(file_path)
all_workers = workers * (len(file_paths) // max_workers) + workers[:len(file_paths) % max_workers]
with concurrent.futures.ThreadPoolExecutor(max_workers=max_workers) as executor:
for i in tqdm(range(0, len(file_paths), max_workers), desc="转写进度: "):
l, r = i, min(i + max_workers, len(file_paths))
transcriptions = list(executor.map(
transcribe_worker,
file_paths[l:r],
all_workers[l:r]
))
for file_path, transcription in zip(file_paths[l:r], transcriptions):
if transcription:
lab_file_path = os.path.splitext(file_path)[0] + ".lab"
with open(lab_file_path, "w", encoding="utf-8") as lab_file:
lab_file.write(transcription)
logger.critical("已经将wav文件转写为同名的.lab文件, 都在raw文件夹下")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"-f", "--filepath", default='./raw/lzy_zh', help="path of your model"
)
parser.add_argument(
"-l", "--language", default='ZH(中文)', help="language"
)
parser.add_argument(
"-w", "--workers", default='4', help="trans workers"
)
args = parser.parse_args()
transcribe_folder_parallel(args.filepath, args.language, int(args.workers))
print("转写结束！")
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