尽管安装正确，但仍无法在 GPU 或 CUDA 上运行 WHISPER AI Transcription

Question

我有一台配备 RTX3070 卡的 Gigabyte Aorus 笔记本电脑，但无法使用 CUDA 或 GPU 运行 Whisper。我每次都会遇到同样的错误，我不知道如何解决它。任何帮助将不胜感激。 这是错误转储：

File "C:\Python\lib\runpy.py", line 196, in _run_module_as_main
return run_code(code, main_globals, None,
File "C:\Python\lib\runpy.py", line 86, in run_code
exec(code, run_globals)
File "C:\Python\Scripts\whisper.exe_main.py", line 7, in
File "C:\Python\lib\site-packages\whisper\transcribe.py", line 444, in cli
model = load_model(model_name, device=device, download_root=model_dir)
File "C:\Python\lib\site-packages\whisper_init.py", line 144, in load_model
checkpoint = torch.load(fp, map_location=device)
File "C:\Python\lib\site-packages\torch\serialization.py", line 1014, in load
return _load(opened_zipfile,
File "C:\Python\lib\site-packages\torch\serialization.py", line 1422, in _load
result = unpickler.load()
File "C:\Python\lib\site-packages\torch\serialization.py", line 1392, in persistent_load
typed_storage = load_tensor(dtype, nbytes, key, _maybe_decode_ascii(location))
File "C:\Python\lib\site-packages\torch\serialization.py", line 1366, in load_tensor
wrap_storage=restore_location(storage, location),
File "C:\Python\lib\site-packages\torch\serialization.py", line 1296, in restore_location
return default_restore_location(storage, map_location)
File "C:\Python\lib\site-packages\torch\serialization.py", line 381, in default_restore_location
result = fn(storage, location)
File "C:\Python\lib\site-packages\torch\serialization.py", line 304, in _hpu_deserialize
assert hpu is not None, "HPU device module is not loaded"
AssertionError: HPU device module is not loaded

我安装了 NVIDIA 工具包。当我测试 IsAvailable 时，CUDA 返回 true。 我什至安装了 NVIDIA 的 cuda 运行时

pip install nvidia-cuda-runtime-cu12

没有任何作用。如果我不使用设备参数，它将默认为CPU并且工作正常。但是，GPU 不断返回错误。

最后一点是，我检查是否正在使用 GPU 的方式是检查任务管理器并查看 GPU 使用情况。我还会查看仪表板中的 AORUS 控制中心来检查 GPU 负载。

感谢您的帮助。

Answer 1

我也遇到同样的问题。只是想在我的 GPU 上运行 Whisper。

这是我得到的：

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.