Comments (1)
It was the same as LibriTTS-460, so the OOD data is the same as itself, although the speakers are different. This is because the dataset is already large enough so we don't need an extra OOD dataset (though it helps if you can get more).
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Related Issues (20)
- weird pulse at the end of the model HOT 3
- Low GPU Utilization during training HOT 10
- Small bug in train_finetune
- Very high GPU memory usage in voice cloning after 10-15 runs. HOT 1
- Strange Loss Behavior During Stage Two Training - Not Decreasing after Diff Epoch HOT 2
- Finetune on ljspeech or libritts? HOT 1
- Better LJSpeech or LibriTTS for finetuning a single speaker voice? Or training from scratch with not so much data? HOT 3
- SLM Adversarial Training did not start when finetuning HOT 10
- Second stage training with smaller window size HOT 1
- Possible Bug in Style Diffusion Inference Code
- Issue with impropper pauses and random bursts of noise
- Cannot Convert float NaN to integer HOT 1
- HELP WANTED!!!!!!!!!!! HOT 3
- asr negative loss
- Resuming finetuning uses second to last epoch
- Help Wanted For Stage-1 HOT 1
- Inference with multilingual PL-BERT Model HOT 4
- During training, the graphics memory has been continuously increasing
- May be a bug? input parameters for model.predictor_encoder and model.style_encoder in train_finetune.py
- S_loss = 0 ... why?
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from styletts2.