#7143 mkl_malloc: failed to allocate memory,Traceback (most recent call last): File

183.221**8 Posted at: 1 hour ago

mkl_malloc: failed to allocate memory,Traceback (most recent call last):
File "videotrans\process\stt_faster.py", line 55, in _create_model
File "faster_whisper\transcribe.py", line 689, in init
RuntimeError: mkl_malloc: failed to allocate memory

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "videotrans\process\stt_faster.py", line 55, in _create_model
File "faster_whisper\transcribe.py", line 689, in init
RuntimeError: mkl_malloc: failed to allocate memory

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "videotrans\process\stt_faster.py", line 85, in faster_whisper
File "videotrans\process\stt_faster.py", line 74, in _create_model
File "videotrans\process\stt_faster.py", line 78, in _create_model
File "videotrans\process\stt_faster.py", line 55, in _create_model
File "faster_whisper\transcribe.py", line 689, in __ini
......

File "videotrans\recognition\__init__.py", line 228, in run
File "videotrans\recognition\_base.py", line 97, in run
File "videotrans\recognition\_whisper.py", line 41, in _exec
File "videotrans\recognition\_whisper.py", line 130, in _faster
File "videotrans\configure\base.py", line 264, in _new_process
videotrans.configure.excepts.VideoTransError: mkl_malloc: failed to allocate memory,Traceback (most recent call last):
File "videotrans\process\stt_faster.py", line 55, in _create_model
File "faster_whisper\transcribe.py", line 689, in init
RuntimeError: mkl_malloc: failed to allocate memory

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "videotrans\process\stt_faster.py", line 55, in _create_model
File "faster_whisper\transcribe.py", line 689, in init
RuntimeError: mkl_malloc: failed to allocate memory

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "videotrans\process\stt_faster.py", line 85, in faster_whisper
File "videotrans\process\stt_faster.py", line 74, in _create_model
File "videotrans\process\stt_faster.py", line 78, in _create_model
File "videotrans\process\stt_faster.py", line 55, in _create_model
File "faster_whisper\transcribe.py", line 689, in init
RuntimeError: mkl_malloc: failed to allocate memory

cfg=[TaskCfgVTT]当前工作模式: 翻译视频 单视频模式
原始输入文件名: C:/Users/WIN10/Desktop/khsg35.mp4,
输出结果保存到文件夹: C:/Users/WIN10/Desktop/khsg35-mp4,
临时文件夹: D:/翻译软件/tmp/18264/13c6f2a72d
已选 清理已存在
未启用CUDA加速
未选 降噪
语音识别:faster-whisper(内置), large-v3-turbo, 发音语言: 简体中文, 断句方式:默认断句
翻译渠道:Google(免费),原始语言:简体中文,目标语言:日语, 已选 发送完整字幕
配音渠道:Edge-TTS(免费), 角色:Nanami(Female/JP), 配音语言:日语, 未选 二次语音识别
音量:+0%, 语速:+1%, 未选 音频加速, 未选 视频慢速
未选 移除字幕间空隙, 已选 强制对齐字幕和声音
字幕: 嵌入硬字幕
代理地址:http://127.0.0.1:11304


=
system:Windows-10-10.0.19045-SP0
version:v4.12
frozen:True
language:zh_CN
root_dir:D:/翻译软件

1 Replies

#1 DeepSeek reply at 1 hour ago

这是内存分配不足导致的错误。mkl_malloc: failed to allocate memory 表示在加载 faster-whisper large-v3-turbo 模型时,系统无法分配足够的内存。该模型体积较大,且你未启用 CUDA 加速,纯 CPU 运行需要大量内存。

解决方案(按优先级操作):

  1. 关闭其他占用内存的程序(尤其是浏览器、大型软件),确保有足够空闲内存。查看任务管理器内存占用,如果接近 100%,需要释放。
  2. 降低模型体积:将语音识别渠道的模型从 large-v3-turbo 改为 smallmedium。这些模型占内存小得多,速度也更快。在界面语音识别行,模型下拉框中切换。
  3. 增加虚拟内存:如果物理内存确实紧张(例如 <8GB),请手动增大 Windows 虚拟内存(页面文件)。设置方法:右键“此电脑” → 属性 → 高级系统设置 → 性能-设置 → 高级-虚拟内存-更改 → 选择“自动管理”或手动设置更大的值(如 16384MB 以上)。
  4. 如有 NVIDIA 显卡,启用 CUDA 加速:需要安装 CUDA 12.8 及以上和 cuDNN 9.11 及以上,然后在主界面勾选“启用CUDA加速”。这会将模型加载到显存中,大幅减少内存压力。
  5. 重新下载模型:如果上述操作后仍失败,可能是模型文件下载不完整。删除 软件目录/models/models--mobiuslabsgmbh--faster-whisper-large-v3-turbo 文件夹,重新尝试处理,让软件自动下载。

注意large-v3-turbo 模型在纯 CPU 模式下通常需要 6GB+ 内存,且运行速度很慢。如果你没有 N 卡或不想配置 CUDA,强烈建议改用 small 模型,内存需求约 2GB,速度更快。

Thinking...
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