Comments (1)
Yes, our code randomly selects 2-second audio segments for training purposes, while during validation, it retains the complete audio.
If you also slice the audio during validation, it doesn't affect the training process.
However, the metrics you observe on the validation set at this point may not be the complete metrics.
Additionally, when making inferences, we recommend using the complete audio segment.
If your GPU memory is insufficient, you can slice the long audio into segments for inference and then concatenate the obtained audio segments together.
But we cannot guarantee that the final metrics will be exactly the same as before.
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Related Issues (17)
- Code Release HOT 2
- Inferior results trained from scratch HOT 4
- train HOT 1
- OutOfMemoryError: CUDA out of memory. Tried to allocate 15.25 GiB. GPU 0 has a total capacty of 23.69 GiB of which 5.50 GiB is free. HOT 15
- Result is bad HOT 3
- Fail to reproduce the paper result when training from scratch HOT 15
- Question about phase-domain loss employment HOT 1
- 作者给的模型推理结果达不到论文结果,求问 HOT 4
- 训练的话,training.txt的内容,十分困惑 HOT 1
- Training details HOT 1
- Real-time or causal system HOT 1
- Bandwidth extension checkpoint HOT 2
- CUDA memory requirement HOT 5
- torch.multiprocessing.spawn.ProcessExitedException: process 0 terminated with signal SIGSEGV HOT 3
- GPU memory HOT 5
- a question about inference HOT 1
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