#8764 语音识别阶段出错[faster-whisper(内置)] mkl_malloc: failed to allocate memory,Traceback (most recent call last): File

240e**8fe5 Posted at: 5 hours ago

语音识别阶段出错[faster-whisper(内置)] mkl_malloc: failed to allocate memory,Traceback (most recent call last):
File "videotrans\process\stt_faster.py", line 54, 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 54, 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 84, in faster_whisper
File "videotrans\process\stt_faster.py", line 73, in _create_model
File "videotrans\process\stt_faster.py", line 77, in _create_model
File "videotrans\process\stt_faster.py", line 54, in _create_model
File "faster_whisper\transcribe.py", line 689, in init
RuntimeError: mkl_malloc: failed to allocate memory

Traceback (most recent call last):

File "videotrans\task\job.py", line 35, in run

File "videotrans\task\job.py", line 101, in process_task

File "videotrans\task\_stage_recogn.py", line 63, in recogn

File "videotrans\recognition\__init__.py", line 141, in run

File "videotrans\recognition\_base.py", line 101, in run

File "videotrans\recognition\_whisper.py", line 39, in _exec

File "videotrans\recognition\_whisper.py", line 137, in _faster

File "videotrans\configure\base.py", line 277, 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 54, 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 54, 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 84, in faster_whisper
File "videotrans\process\stt_faster.py", line 73, in _create_model
File "videotrans\process\stt_faster.py", line 77, in _create_model
File "videotrans\process\stt_faster.py", line 54, in _create_model
File "faster_whisper\transcribe.py", line 689, in init
RuntimeError: mkl_malloc: failed to allocate memory
trk=[TransCreate]: uuid='439ecc3e50', proxy_str=None, cfg=[TaskCfgVTT]当前工作模式: 翻译视频 批量翻译模式
原始输入文件名: D:/每日播放/11.10/11.10抱手yf.mp4,
输出结果保存到文件夹: D:/翻译国家/波兰/11.10/11.10抱手yf-mp4,
临时文件夹: D:/win-pyvideotrans-v4.16/tmp/23176/439ecc3e50
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: 意大利语,
翻译渠道:DeepSeek,原始语言:意大利语,目标语言:波兰语, 已选 发送完整字幕
配音渠道:Edge-TTS(免费), 角色:Zofia(Female/PL), 配音语言:波兰语, 未选 二次语音识别
音量:+0%, 语速:+0%, 已选 音频加速, 未选 视频慢速
已选 移除每条字幕配音开头和结尾静音缓冲
静音移除力度: default
字幕: 不嵌入字幕
已选 仅输出mp4, precent=4, hasend=False, should_recogn=True, should_trans=True, should_dubbing=True, should_hebing=True, is_copy_video=False, video_codec_num=264, ignore_align=False, is_audio_trans=False, clone_ref='', should_recogn2=False, do_diarize=False


=
system:Windows-10-10.0.19045-SP0
version:v4.16
frozen:True
language:zh_CN
root_dir:D:/win-pyvideotrans-v4.16

1 Replies

#1 DeepSeek reply at 5 hours ago

这是系统内存(RAM)分配失败。日志显示 未启用CUDA加速,faster-whisper 在纯 CPU 模式下加载 large-v3-turbo 模型时,需占用大量内存(float32 下通常 6GB 以上),当可用内存不足时,Intel MKL 会直接报 mkl_malloc: failed to allocate memory,导致模型加载失败。这与显存、代理、语言设置无关。

按以下顺序处理:

  1. 降低模型尺寸(最快见效):将语音识别模型从 large-v3-turbo 换成 medium 或 small。
  2. 更改计算数据类型:菜单 → 工具/选项 → 高级选项 → 语音识别参数 → 计算数据类型 改为 int8(最省内存)或 float16。
  3. 降低并发与精度:同区域将 识别准确度beam_size、best_of 由 5 调为 1;通用设置 → CPU同时任务数 设为 1;主界面若为批量,高级选项 → 批量翻译视频时每批数量 设为 1。
  4. 释放内存:关闭浏览器等占用内存的程序;确认系统虚拟内存(页面文件)未被关闭或设置过小。
  5. 如有N卡:安装 CUDA 12.8 + cuDNN 9,主界面勾选 CUDA加速 后重新执行(并确保 高级选项 → 通用设置 → GPU同时任务数 为 1)。
  6. 重试前务必勾选 清理已生成,避免复用上次失败的残留文件。
若完成上述调整后仍出现同类报错,请提供本机物理内存容量,以及 logs/ 目录下对应日期日志文件的底部约 30 行内容。

请查阅相关文档:

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