m-bain
RuntimeError: parallel_for failed: cudaErrorNoKernelImageForDevice: no kernel image is available for execution on the device
I have been trying to get whisperX to work on my GTX 970, but have been running into a myriad of problems. Please bear with me as I’m a beginner in all things programming.
I followed all the installation instructions to the letter and then ran the following command:
whisperx.exe "C:\Users\Justin\Music\Kraft Punk Soundbites\WAV\Hey what's up_ I'm Kraft Punk.wav" --model large-v2 --device cuda --batch_size 1 --compute_type float32 --output_dir "C:\Users\Justin\Desktop\" --language en --diarize --min_speakers 1 --max_speakers 1 --hf_token XXXXXXXXXXXXXXXXX
I then ran into the following error:
torchvision is not available - cannot save figures
Lightning automatically upgraded your loaded checkpoint from v1.5.4 to v2.2.3. To apply the upgrade to your files permanently, run `python -m pytorch_lightning.utilities.upgrade_checkpoint C:\Users\Justin\.cache\torch\whisperx-vad-segmentation.bin`
Model was trained with pyannote.audio 0.0.1, yours is 3.1.1. Bad things might happen unless you revert pyannote.audio to 0.x.
Model was trained with torch 1.10.0+cu102, yours is 2.0.0. Bad things might happen unless you revert torch to 1.x.
>>Performing transcription...
Traceback (most recent call last):
File "C:\Users\Justin\miniconda3\envs\whisperx\lib\runpy.py", line 196, in _run_module_as_main
return _run_code(code, main_globals, None,
File "C:\Users\Justin\miniconda3\envs\whisperx\lib\runpy.py", line 86, in _run_code
exec(code, run_globals)
File "C:\Users\Justin\miniconda3\envs\whisperx\Scripts\whisperx.exe\__main__.py", line 7, in <module>
File "C:\Users\Justin\miniconda3\envs\whisperx\lib\site-packages\whisperx\transcribe.py", line 176, in cli
result = model.transcribe(audio, batch_size=batch_size, chunk_size=chunk_size, print_progress=print_progress)
File "C:\Users\Justin\miniconda3\envs\whisperx\lib\site-packages\whisperx\asr.py", line 218, in transcribe
for idx, out in enumerate(self.__call__(data(audio, vad_segments), batch_size=batch_size, num_workers=num_workers)):
File "C:\Users\Justin\miniconda3\envs\whisperx\lib\site-packages\transformers\pipelines\pt_utils.py", line 124, in __next__
item = next(self.iterator)
File "C:\Users\Justin\miniconda3\envs\whisperx\lib\site-packages\transformers\pipelines\pt_utils.py", line 125, in __next__
processed = self.infer(item, **self.params)
File "C:\Users\Justin\miniconda3\envs\whisperx\lib\site-packages\transformers\pipelines\base.py", line 1112, in forward
model_outputs = self._forward(model_inputs, **forward_params)
File "C:\Users\Justin\miniconda3\envs\whisperx\lib\site-packages\whisperx\asr.py", line 152, in _forward
outputs = self.model.generate_segment_batched(model_inputs['inputs'], self.tokenizer, self.options)
File "C:\Users\Justin\miniconda3\envs\whisperx\lib\site-packages\whisperx\asr.py", line 47, in generate_segment_batched
encoder_output = self.encode(features)
File "C:\Users\Justin\miniconda3\envs\whisperx\lib\site-packages\whisperx\asr.py", line 86, in encode
return self.model.encode(features, to_cpu=to_cpu)
RuntimeError: parallel_for failed: cudaErrorNoKernelImageForDevice: no kernel image is available for execution on the device
I did run some basic diagnostics to check if CUDA is available:
>>> import torch
>>> import sys
>>> print('A', sys.version)
A 3.10.14 | packaged by Anaconda, Inc. | (main, Mar 21 2024, 16:20:14) [MSC v.1916 64 bit (AMD64)]
>>> print('B', torch.__version__)
B 2.3.0
>>> print('C', torch.cuda.is_available())
C True
>>> print('D', torch.backends.cudnn.enabled)
D True
>>> device = torch.device('cuda')
>>> print('E', torch.cuda.get_device_properties(device))
E _CudaDeviceProperties(name='NVIDIA GeForce GTX 970', major=5, minor=2, total_memory=4095MB, multi_processor_count=13)
>>> print('F', torch.tensor([1.0, 2.0]).cuda())
F tensor([1., 2.], device='cuda:0')
>>> import torch
>>> x = torch.rand(5, 3)
>>> print(x)
tensor([[0.2926, 0.4866, 0.1281],
[0.6154, 0.8456, 0.5436],
[0.4880, 0.7883, 0.2404],
[0.6841, 0.2353, 0.2622],
[0.9875, 0.0566, 0.4680]])
Also ran nvidia-smi:
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 551.23 Driver Version: 551.23 CUDA Version: 12.4 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA GeForce GTX 970 WDDM | 00000000:01:00.0 On | N/A |
| 47% 30C P2 50W / 250W | 980MiB / 4096MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
I have asked the NVIDIA Developer Forum, but they say it's not a CUDA error:
One or more of the software stacks (perhaps the whisperx.exe executable) you are using have not been compiled to support a GTX 970. This isn’t a CUDA setup issue (which is what this forum is about) but rather a problem with the software stack.
I'm at a loss and would really appreciate any help :)
I met the same issue, and my GPU is GTX 950M.
My CUDA version:
nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2021 NVIDIA Corporation Built on Thu_Nov_18_09:45:30_PST_2021 Cuda compilation tools, release 11.5, V11.5.119 Build cuda_11.5.r11.5/compiler.30672275_0
Also ran nvidia-smi:
NVIDIA-SMI 535.171.04 Driver Version: 535.171.04 CUDA Version: 12.2
I think you can refer to this link：https://github.com/m-bain/whisperX/issues/720 I encountered the same error message as yours , and solved by the solutions in this ref (pip install ctranslate2==3.24.0)
I think you can refer to this link：#720 I encountered the same error message as yours , and solved by the solutions in this ref (pip install ctranslate2==3.24.0)
Thx. I tried it, but it didn't work. It showed another error: "Segmentation fault "
I think you can refer to this link：#720 I encountered the same error message as yours , and solved by the solutions in this ref (pip install ctranslate2==3.24.0)
This worked for me. Hopefully this is resolved at some point to take advantage of newer versions.
I think you can refer to this link：#720 I encountered the same error message as yours , and solved by the solutions in this ref (pip install ctranslate2==3.24.0)
This worked for me. Hopefully this is resolved at some point to take advantage of newer versions.
Glad to hear that. My system is Ubuntu, maybe the solution of it can be different.
I think you can refer to this link：#720 I encountered the same error message as yours , and solved by the solutions in this ref (pip install ctranslate2==3.24.0)
This worked for me. Hopefully this is resolved at some point to take advantage of newer versions.
@JusticeGH
Not sure how this can work, at least with regard to this repo (whisperx) when faster-whisper requires ctranslate2 >4.0.0. Can you explain how you got past that requirement?
I think you can refer to this link：#720 I encountered the same error message as yours , and solved by the solutions in this ref (pip install ctranslate2==3.24.0)
I'm astounded... This worked for me as well.
@JusticeGH
Not sure how this can work, at least with regard to this repo (whisperx) when faster-whisper requires ctranslate2 >4.0.0. Can you explain how you got past that requirement?
You have to install it after installing everything else. pip will install the conflicting requirement, but it will complain (see the ERROR followed by the SUCCESS):
Using cached ctranslate2-3.24.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (36.8 MB)
Installing collected packages: ctranslate2
Attempting uninstall: ctranslate2
Found existing installation: ctranslate2 4.3.1
Uninstalling ctranslate2-4.3.1:
Successfully uninstalled ctranslate2-4.3.1
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
faster-whisper 1.0.0 requires ctranslate2<5,>=4.0, but you have ctranslate2 3.24.0 which is incompatible.
Successfully installed ctranslate2-3.24.0
Basically, you're forcing something that should not be compatible to install anyway, at least as a workaround until this issue can be resolved some other way (likely in the CTranslate package).