#3561 TaskCfgVTT(is_cuda=True, uuid='26a05be39b', cache_folder='D:/project/Videotrans/win-pyvideotrans-v3.97-0304/tmp/13428/26

212.87* Posted at: 19 hours ago 👁15

语音识别阶段出错 [faster-whisper(本地)] 出错了,可能内存或显存不足
A child process terminated abruptly, the process pool is not usable anymore
Traceback (most recent call last):
File "videotrans\configure\_base.py", line 280, in _new_process
File "videotrans\process\signelobj.py", line 81, 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 353, in recogn
File "videotrans\recognition\__init__.py", line 265, in run
File "videotrans\recognition\_base.py", line 143, in run
File "videotrans\recognition\_overall.py", line 33, in _exec
File "videotrans\recognition\_overall.py", line 105, in _faster
File "videotrans\configure\_base.py", line 294, in _new_process
RuntimeError: 出错了,可能内存或显存不足
A child process terminated abruptly, the process pool is not usable anymore
TaskCfgVTT(is_cuda=True, uuid='26a05be39b', cache_folder='D:/project/Videotrans/win-pyvideotrans-v3.97-0304/tmp/13428/26a05be39b', target_dir='C:/Users/savior/Desktop/_video_out/p17 5. Day 1 - Deploying Fine-Tuned Models to Modal Cloud with Persistent Storage-mp4', source_language='英语', source_language_code='en', source_sub='C:/Users/savior/Desktop/_video_out/p17 5. Day 1 - Deploying Fine-Tuned Models to Modal Cloud with Persistent Storage-mp4/en.srt', source_wav='D:/project/Videotrans/win-pyvideotrans-v3.97-0304/tmp/13428/26a05be39b/en.wav', source_wav_output='C:/Users/savior/Desktop/_video_out/p17 5. Day 1 - Deploying Fine-Tuned Models to Modal Cloud with Persistent Storage-mp4/en.m4a', target_language='简体中文', target_language_code='zh-cn', target_sub='C:/Users/savior/Desktop/_video_out/p17 5. Day 1 - Deploying Fine-Tuned Models to Modal Cloud with Persistent Storage-mp4/zh-cn.srt', target_wav='D:/project/Videotrans/win-pyvideotrans-v3.97-0304/tmp/13428/26a05be39b/target.wav', target_wav_output='C:/Users/savior/Desktop/_video_out/p17 5. Day 1 - Deploying Fine-Tuned Models to Modal Cloud with Persistent Storage-mp4/zh-cn.m4a', name='C:/Users/savior/Desktop/1/p17 5. Day 1 - Deploying Fine-Tuned Models to Modal Cloud with Persistent Storage.mp4', noextname='p17 5. Day 1 - Deploying Fine-Tuned Models to Modal Cloud with Persistent Storage', basename='p17 5. Day 1 - Deploying Fine-Tuned Models to Modal Cloud with Persistent Storage.mp4', ext='mp4', dirname='C:/Users/savior/Desktop/1', shound_del_name=None, translate_type=5, tts_type=10, volume='+0%', pitch='+0Hz', voice_rate='+0%', voice_role='aozi.wav', voice_autorate=True, video_autorate=False, remove_silent_mid=False, align_sub_audio=True, detect_language='en', recogn_type=0, 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='biaozhun', subtitles='', targetdir_mp4='C:/Users/savior/Desktop/_video_out/p17 5. Day 1 - Deploying Fine-Tuned Models to Modal Cloud with Persistent Storage-mp4/p17 5. Day 1 - Deploying Fine-Tuned Models to Modal Cloud with Persistent Storage.mp4', novoice_mp4='D:/project/Videotrans/win-pyvideotrans-v3.97-0304/tmp/13428/26a05be39b/novoice.mp4', is_separate=False, instrument=None, vocal=None, back_audio='', clear_cache=False, background_music=None, subtitle_type=4, only_out_mp4=False, recogn2pass=True, output_srt=2, copysrt_rawvideo=False)


=
system:Windows-10-10.0.26200-SP0
version:v3.97
frozen:True
language:zh
root_dir:D:/project/Videotrans/win-pyvideotrans-v3.97-0304

1 Replies

这是由于显存或内存不足导致 faster-whisper 语音识别进程崩溃。

根据日志,您使用了 faster-whisper(本地)large-v3-turbo 模型,并开启了 CUDA 加速 (is_cuda=True)。错误信息 A child process terminated abruptly可能内存或显存不足 表明 GPU 显存或系统内存不足以处理该任务。

解决方案:

  1. 降低模型要求:在语音识别渠道的下拉菜单中,将模型从 large-v3-turbo 更换为更小的模型,例如 large-v3large-v2mediumlarge-v3-turbo 模型可能对显存要求更高。
  2. 优化 CUDA 设置

    • 前往 菜单 -> 工具 -> 高级选项
    • 在【语音识别参数】区域,将 CUDA数据类型defaultfloat32 改为 int8。这可以显著减少显存占用,但会略微降低识别精度。
    • 识别准确度beam_size识别准确度best_of 的值都设为 1,以降低计算负载。
    • 确保【通用设置】中的 GPU同时任务数[重启生效] 设置为 1(默认值),避免多个任务争抢显存。
  3. 调整语音识别参数

    • 在【语音识别参数】区域,适当增大 最长语音持续秒数(例如设为 10),可以减少音频分割的片段数量,降低单次处理负载。
    • 如果视频背景音复杂,可以勾选主界面上的 降噪 选项,有时能提高识别稳定性。
  4. 检查系统资源

    • 在任务管理器中查看 GPU 显存使用情况。确保没有其他大型程序(如游戏、其他AI软件)占用大量显存。
    • 关闭不必要的应用程序,释放系统内存。
  5. 终极方案(如果以上均无效)

    • 暂时取消勾选主界面上的 CUDA加速 选项,完全使用 CPU 进行识别。这会使速度变慢,但可以绕过显存问题。
    • 考虑使用对资源要求更低的语音识别渠道,例如 qwen-asr(本地)(针对中文)或 openai-whisper(本地) 搭配 small 模型。

操作顺序建议:优先尝试第1步(换模型)和第2步(改CUDA设置),这通常能解决大部分显存不足问题。

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