Out of Memory when inferencing with 60secs file

[HimaJyothi17]

torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 137.36 GiB. GPU 0 has a total capacity of 47.54 GiB of which 44.17 GiB is free. Process 1932274 has 3.36 GiB memory in use. Of the allocated memory 1.89 GiB is allocated by PyTorch, and 23.82 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

Why it is expecting 137 GB to infer just 60 sec file? Is this model only for real time purpose?

[yxlu-0102]

Current mainstream speech enhancement models do indeed occupy a large amount of memory.

You can try enhancing the speech by dividing it into segments like this:

[HimaJyothi17]

What are the 8 type of noises used for training in DEMAND dataset?