#2030 TaskCfg(cache_folder='

2409:8a55* Posted at: 16 hours ago 👁9

语音识别阶段出错:[faster-whisper(本地)] 运行时错误：Traceback (most recent call last):
File "videotrans\process\_overall.py", line 128, in run
File "faster_whisper\transcribe.py", line 890, in transcribe
File "faster_whisper\vad.py", line 89, in get_speech_timestamps
File "faster_whisper\vad.py", line 343, in call
File "onnxruntime\capi\onnxruntime_inference_collection.py", line 287, in run

self._validate_input(list(input_dict_ort_values.keys()))

onnxruntime.capi.onnxruntime_pybind11_state.RuntimeException: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Non-zero status code returned while running Conv node. Name:'/encoder/feature_extractor/Conv' Status Message: bad allocation
:
Traceback (most recent call last):
File "videotrans\task\job.py", line 113, in run
File "videotrans\task\trans_create.py", line 365, in recogn
File "videotrans\recognition\__init__.py", line 239, in run
File "videotrans\recognition\_base.py", line 80, in run
File "videotrans\recognition\_overall.py", line 182, in _exec
RuntimeError: Traceback (most recent call last):
File "videotrans\process\_overall.py", line 128, in run
File "faster_whisper\transcribe.py", line 890, in transcribe
File "faster_whisper\vad.py", line 89, in get_speech_timestamps
File "faster_whisper\vad.py", line 343, in call
File "onnxruntime\capi\onnxruntime_inference_collection.py", line 287, in run

self._validate_input(list(input_dict_ort_values.keys()))

onnxruntime.capi.onnxruntime_pybind11_state.RuntimeException: [ONNXRuntimeError] : 6 : RUNTIME_EXCEPTION : Non-zero status code returned while running Conv node. Name:'/encoder/feature_extractor/Conv' Status Message: bad allocation

1 Replies

Floor #1 DeepSeek Posted at 16 hours ago

这个错误是 Faster-Whisper 在语音活动检测（VAD）阶段出现的 ONNX Runtime 内存分配问题。核心原因是 VAD 模型加载或运行时内存不足。

请按以下步骤解决：

  1. 更换语音识别模式：

    • 在主界面第2行“语音识别”区域，将识别模式从“整体识别”改为“批量推理”。
    • “批量推理”模式不使用 VAD 模型，可以完全避开此错误。

  2. 如果必须使用“整体识别”模式：

    • 在菜单栏选择 工具/选项 -> 高级选项。
    • 在“Faster/OpenAI/Whisper 语音识别微调”区域，将“CUDA数据类型”改为 int8 或 float16。
    • 将“识别准确度”和“最佳候选数”都改为 1。
    • 取消勾选“启用上下文感知”。
    • 这些设置能显著降低内存占用。

  3. 终极方案（如果以上无效）：

    • 更换更小的语音识别模型，例如从 large-v3 改为 medium 或 small。
    • 或者，在语音识别设置中，临时切换到其他识别引擎，如“阿里FunASR(本地)”（对中文效果很好）或任意一个在线API（如Google、OpenAI等）。

通常，执行第1步（改为“批量推理”）即可立即解决问题。

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