Comments (5)
mpariente
commented on July 22, 2024
Yeah, that's a memory problem on GPU and a nn.Conv1d problem on CPU.
We should foward in chunks and combine them.
Would you like to give it a try?
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tux-coder
commented on July 22, 2024
I don't know pytorch, I can try but not sure to succeed.
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mpariente
commented on July 22, 2024
It's not really pytorch related, it's signal processing. It would be kind of an chunk, enhance, overlap add strategy but with very long frames.
You can have a look at torch.fold and torch.unfold, I guess they would help !
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mpariente
commented on July 22, 2024
Refs #193
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popcornell
commented on July 22, 2024
We added an overlap add wrapper which makes it possible to do inference on long files without going OOM.
See: #193. Once merged this issue will close.
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Related Issues (20)
- Legacy Install Failure HOT 1
- X-UMX: lr_scheduler_step() missing 1 required positional argument: 'metric' HOT 4
- About the usage HOT 1
- torchmetrics>=0.11.0 HOT 2
- HOW TO USE OTHER DATASET TO TRAIN Multi-Decoder-DPRNN MODEL HOT 3
- val loss in distribute training HOT 2
- refer to asteroid/dsp/overlap_add.py:31 LambdaOverlapAdd model use file_separate output audio file is All noise HOT 2
- KeyError while evaluating musdb18 (X-UMX) HOT 2
- RunTimeError related with NCCL when training librimix recipe HOT 3
- Input and output mean scales updated during training (X-UMX) HOT 5
- how to train the dataset HOT 3
- Transfer learning of pretrained model on custom dataset HOT 2
- Inference script to hear samples from AVSpeech Model ?
- Support for mobile development (Android)? HOT 2
- PIT Loss for multichannel audio for speech separation HOT 1
- test model
- mossFormer2 & sepTDA models HOT 2
- KeyError: 'source_2_path' when training ConvTasNet using enh_single mode HOT 3
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