Comments (7)
davabase
commented on August 12, 2024
2
You must have ffmpeg installed on your machine.
You can install it with Chocolatey like so:
# using Chocolatey (https://chocolatey.org/)
choco install ffmpeg
or with Scoop:
# using Scoop (https://scoop.sh/)
scoop install ffmpeg
or from the ffmpeg website: https://ffmpeg.org/
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ibrahim91015
commented on August 12, 2024
Works! Thanks!
Using quite a bit of CPU, anyway to have it use the gpu instead?
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davabase
commented on August 12, 2024
To make sure you are using the CUDAversion of pytorch you can do a pip uninstall torch and then install the CUDA version of pytorch with:
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117
from the pytorch website.
If you installed via the requirements.txt this should already be true.
Make sure torch.cuda.is_available() is True in your environment.
Whisper should detect your GPU and use it automatically in that case, if it's not you can changing line 69 to:
audio_model = whisper.load_model(model, device="cuda")
to force it to use the GPU.
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ibrahim91015
commented on August 12, 2024
did that, now im gettin this error:
Traceback (most recent call last):
File "C:\Users\ibrah\Desktop\whisper_real_time-master\transcribe_demo.py", line 152, in <module>
main()
File "C:\Users\ibrah\Desktop\whisper_real_time-master\transcribe_demo.py", line 69, in main
audio_model = whisper.load_model(model, device="cuda")
File "C:\Users\ibrah\AppData\Local\Programs\Python\Python38\lib\site-packages\whisper\__init__.py", line 144, in load_model
checkpoint = torch.load(fp, map_location=device)
File "C:\Users\ibrah\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\serialization.py", line 809, in load
return _load(opened_zipfile, map_location, pickle_module, **pickle_load_args)
File "C:\Users\ibrah\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\serialization.py", line 1172, in _load
result = unpickler.load()
File "C:\Users\ibrah\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\serialization.py", line 1142, in persistent_load
typed_storage = load_tensor(dtype, nbytes, key, _maybe_decode_ascii(location))
File "C:\Users\ibrah\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\serialization.py", line 1116, in load_tensor
wrap_storage=restore_location(storage, location),
File "C:\Users\ibrah\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\serialization.py", line 1083, in restore_location
return default_restore_location(storage, map_location)
File "C:\Users\ibrah\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\serialization.py", line 217, in default_restore_location
result = fn(storage, location)
File "C:\Users\ibrah\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\serialization.py", line 182, in _cuda_deserialize
device = validate_cuda_device(location)
File "C:\Users\ibrah\AppData\Local\Programs\Python\Python38\lib\site-packages\torch\serialization.py", line 166, in validate_cuda_device
raise RuntimeError('Attempting to deserialize object on a CUDA '
RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location=torch.device('cpu') to map your storages to the CPU.
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davabase
commented on August 12, 2024
torch.cuda.is_available() is False
Unfortunately it looks like you do not have a CUDA compatible GPU.
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ibrahim91015
commented on August 12, 2024
that's weird
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joacripto
commented on August 12, 2024
Hi!, I have the same problem than @ibrahim91015. The script runs well on CPU but I force the use of the GPU, I get the same torch.cuda.is_available() is False error, but:
`❯ nvidia-smi
Sat Aug 26 18:20:07 2023
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 532.09 Driver Version: 532.09 CUDA Version: 12.1 |
|-----------------------------------------+----------------------+----------------------+
| 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 RTX 4080 L... WDDM | 00000000:01:00.0 On | N/A |
| N/A 46C P8 8W / N/A| 382MiB / 12282MiB | 0% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
+---------------------------------------------------------------------------------------+`
So, any ideas?
Thak you so much
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