#3945 TaskCfgVTT(is_cuda=True, uuid='83b594a021', cache_folder='D:/pyvideotran/tmp/23044/83b594a021', target_dir='E:/1/_video_

117.81* Posted at: 2 days ago 👁22

语音识别阶段出错 [openai-whisper(本地)] 出错了,可能内存或显存不足 Model:large-v3-turbo GPU0
Traceback (most recent call last):
File "videotrans\configure\_base.py", line 281, in _new_process
File "videotrans\process\signelobj.py", line 80, in submit_task_gpu
File "concurrent\futures\process.py", line 720, in submit
concurrent.futures.process.BrokenProcessPool: A child process terminated abruptly, the process pool is not usable anymore

Traceback (most recent call last):
File "videotrans\configure\_base.py", line 281, in _new_process
File "videotrans\process\signelobj.py", line 80, in submit_task_gpu
File "concurrent\futures\process.py", line 720, in submit
concurrent.futures.process.BrokenProcessPool: A child process terminated abruptly, the process pool is not usable anymore

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "videotrans\task\job.py", line 105, in run
File "videotrans\task\trans_create.py", line 360, in recogn
File "videotrans\recognition\__init__.py", line 292, in run
File "videotrans\recognition\_base.py", line 143, in run
File "videotrans\recognition\_overall.py", line 31, in _exec
File "videotrans\recognition\_overall.py", line 73, in _openai
File "videotrans\configure\_base.py", line 303, in _new_process
RuntimeError: 出错了,可能内存或显存不足 Model:large-v3-turbo GPU0
Traceback (most recent call last):
File "videotrans\configure\_base.py", line 281, in _new_process
File "videotrans\process\signelobj.py", line 80, in submit_task_gpu
File "concurrent\futures\process.py", line 720, in submit
concurrent.futures.process.BrokenProcessPool: A child process terminated abruptly, the process pool is not usable anymore
TaskCfgVTT(is_cuda=True, uuid='83b594a021', cache_folder='D:/pyvideotran/tmp/23044/83b594a021', target_dir='E:/1/_video_out/NACT-055-mp4', source_language='日语', source_language_code='ja', source_sub='E:/1/_video_out/NACT-055-mp4/ja.srt', source_wav='D:/pyvideotran/tmp/23044/83b594a021/ja.wav', source_wav_output='E:/1/_video_out/NACT-055-mp4/ja.m4a', target_language='简体中文', target_language_code='zh-cn', target_sub='E:/1/_video_out/NACT-055-mp4/zh-cn.srt', target_wav='D:/pyvideotran/tmp/23044/83b594a021/target.wav', target_wav_output='E:/1/_video_out/NACT-055-mp4/zh-cn.m4a', name='E:/1/NACT-055.mp4', noextname='NACT-055', basename='NACT-055.mp4', ext='mp4', dirname='E:/1', shound_del_name=None, translate_type=0, tts_type=0, volume='+0%', pitch='+0Hz', voice_rate='+0%', voice_role='No', voice_autorate=False, video_autorate=False, remove_silent_mid=False, align_sub_audio=False, detect_language='ja', recogn_type=1, model_name='large-v3-turbo', shibie_audio=None, remove_noise=False, enable_diariz=False, nums_diariz=0, rephrase=0, fix_punc=False, subtitle_language=None, app_mode='tiqu', subtitles='', targetdir_mp4='E:/1/_video_out/NACT-055-mp4/NACT-055.mp4', novoice_mp4='D:/pyvideotran/tmp/23044/83b594a021/novoice.mp4', is_separate=False, embed_bgm=True, instrument=None, vocal=None, back_audio='', clear_cache=False, background_music=None, subtitle_type=0, only_out_mp4=False, recogn2pass=True, output_srt=0, copysrt_rawvideo=False)


=
system:Windows-10-10.0.26200-SP0
version:v3.98
frozen:True
language:zh
root_dir:D:/pyvideotran

1 Replies

这是由于显存或内存不足导致语音识别进程崩溃。

根据错误信息,你使用的是 openai-whisper(本地)large-v3-turbo 模型,并启用了 CUDA 加速。该模型对显存和内存要求较高,当资源不足时,子进程会异常终止,导致 BrokenProcessPool 错误。

请按以下步骤尝试解决:

  1. 降低模型大小:将语音识别模型从 large-v3-turbo 更换为更小的模型,例如 mediumsmalllarge-v3-turbo 模型最低需要约 10GB 显存。
  2. 调整高级设置:在菜单栏 工具 -> 高级选项 中,找到 【语音识别参数】 区域,进行如下修改以降低资源消耗:

    • CUDA数据类型:将 float32 改为 float16int8
    • 识别准确度beam_size:将 5 改为 1
    • 识别准确度best_of:将 5 改为 1
    • 启用上下文感知:确保此项为 false(未勾选)。
  3. 关闭二次识别:从你的配置 recogn2pass=True 可以看出你启用了二次识别。在处理多个视频时,二次识别会额外占用大量显存。请在主界面取消勾选 二次识别 选项后重试。
  4. 检查任务并行度:如果你在批量处理多个视频,请在 工具 -> 高级选项 -> 【通用设置】 中,确保 GPU同时任务数[重启生效] 设置为 1(除非你的单卡显存大于20GB)。同时可以勾选 批量翻译时强制串行,改为顺序处理视频。
  5. 释放系统资源:关闭其他占用大量显存或内存的应用程序(如游戏、浏览器、其他AI软件)。

如果以上调整后问题依旧,可能是你的硬件确实无法承载 large-v3-turbo 模型,请务必降级到 mediumsmall 模型。

请查阅相关文档:

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