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Transcribe crash jupyter notebook kernels #820
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I am experiencing the same issue. Have you found any effective workaround or root cause? |
Have you solved this? I have been suffering from this recently. |
I dont know the underlying cause, but the LD_LIBRARY_PATH env had become unset, and setting it before starting the jupyter server stopped the crashes. I never had explicitly set it before and faster-whisper was working previously, but this has fixed my problem for now |
Thanks for your response. I also fix this but by uninstalling cuda 12, then installing cuda 11 (specifically 11.8) and downgrade the faster-whisper. The latest faster-whisper requires cuda 12 but cudnn for cuda 11. I guess most crash issues are caused by this. This is really weird, anyway. See #717 |
Appartently once the jupyter kernel is running changing the LD_LIBRARY_PATH has no effect. I am using vscode with the Jupyter extension for running the jupyter notebook (actually on a AWS Sagemaker Code Editor instance). I was able to setting the LD_LIBRARY_PATH by:
Apparently changing the original kernel.json is not used when starting up the vscode jupyter kernel corresponding to the conda environment |
Actually faster-whisper==1.0.2 does not require any CUDA 11 dependences. This issue has been solved |
Thanks for sharing your solution. My current model in running normally and I'm exhausted by solving this problem : ( |
The execution of the transcribe methods within a .ipynb jupyter notebook results in crashing the kernel (despite setting KMP_DUPLICATE_LIB_OK to True).
However, if I running the same exact code as a .py python script it works perfectly fine.
Here is a test code
The file "stereo_diarization.wav" is the one presented in the tests of this package faster-whisper/tests/data/stereo_diarization.wav
I used an the environment with Python 3.11.9 with the following package installed (full requirements here
requirements.txt)
I tested the notebook (always crashing) and the script (always OK) on a cloud instance with Ubuntu 22.04 (AWS EC2 ml.g4dn.xlarge) with NVIDIA Driver Version: 535.129.03 and CUDA Toolkit Version: 12.2
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