Comments (3)
yl4579
commented on July 30, 2024
1
We did this in our most recent work on vocoder: https://github.com/yl4579/HiFTNet
Basically, we could achieve close-to-ground truth performance with HiFiGAN architecture on LibriTTS but not with iSTFTNet. However TTS is harder than vocoding so you should expect more degradation than mel-to-waveform synthesis, so ground truth level performance on LibriTTS is still hard for TTS models.
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stevenhillis
commented on July 30, 2024
Awesome, thanks for the pointer! Any particular reason for the switch from the C8C8 architecture in the paper to the C10C6 setup in this repo?
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yl4579
commented on July 30, 2024
Because this repo is 24 kHz while the original one is 22.5 kHz.
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Related Issues (20)
- 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 11
- 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 2
- 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? HOT 2
- Inference Error: context_features exists but no features provided HOT 1
- Speech conditioning like tortoise TTS HOT 1
- FP8 Fine Tuning Crashes HOT 1
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