limit pytorch version to cudnn8 for pip install

[NewUserHa]

Note: Version 9+ of nvidia-cudnn-cu12 appears to cause issues due its reliance on cuDNN 9 (Faster-Whisper does not currently support cuDNN 9). Ensure your version of the Python package is for cuDNN 8.

because all pytorch>=2.4 on conda are started to be compiled with cudnn9,
so this PR can avoid new users who just installed with pip install faster-whisper from getting into the issue:

>>Performing transcription...
Could not locate cudnn_ops_infer64_8.dll. Please make sure it is in your library path!

just after installing.

[BBC-Esq]

I personally don't know why faster-whisper isn't compatible with cudnn 9+ yet, but if for some reason they don't want to support it they can add something like this to their code:

1. Add Nvidia cudnn to the installation process - e.g.:

pip install nvidia-cudnn-cu12==8.9.7.29

2. In the entry point for the library add to it the following, which will add to the paths (but not replace them) :

• Tip: You can modify "CUDA_PATH_V1_1" to another version of cuda if you want as well.

def set_cuda_paths():
    try:
        venv_base = Path(sys.executable).parent
        nvidia_base_path = venv_base / 'Lib' / 'site-packages' / 'nvidia'
        for env_var in ['CUDA_PATH', 'CUDA_PATH_V12_1', 'PATH']:
            current_path = os.environ.get(env_var, '')
            os.environ[env_var] = os.pathsep.join(filter(None, [str(nvidia_base_path), current_path]))
        logging.info("CUDA paths set successfully")
    except Exception as e:
        logging.error(f"Error setting CUDA paths: {str(e)}")
        logging.debug(traceback.format_exc())

3. Take it a Step Further and add cublas, runtime or whatever else... - e.g.

pip install nvidia-cuda-runtime-cu12==12.1.105

pip install nvidia-cublas-cu12==12.1.3.1

pip install nvidia-cuda-nvrtc-cu12==12.1.105

pip install [fill in the blank with nvidia library]

That way users wouldn't have to worry about installing CUDA/CUDNN globally at all.

Again, not sure why faster-whisper chooses not to update compatibility - I know it's a hassle - but perhaps this is a more elegant solution that would work regardless of whether

[NewUserHa]

according to https://www.github.com/pytorch/pytorch/issues/100974, pip install torch will automatically install cuda/cudnn. (I tested it failed to though on windows, but it may work on linux)

therefore before faster-whisper is compatible with cudnn9, this PR should be a not-bad workaround

[aligokalppeker]

@BBC-Esq faster-whisper is not compliant with cuddn 9 as ctranslate2 does not. It can not be compliant without a custom build of ctranslate2 as ctranslate2 is the core for all the cuda stuff.

[jhj0517]

I wish this would be merged as a workaround until OpenNMT/CTranslate2#1780 is fixed.

When you install faster-whisper in a completely new environment using Just By:

pip install faster-whisper torch --index-url https://download.pytorch.org/whl/cu121

torch will be installed as the latest and faster-whisper will not be usable because of this bug.

I hope this workaround will be merged for now until CTranslate2 really supports the cuDNN 9 build.