Comments (1)
Yes it can, and it can be more easily adapted to other datasets, but in the original StyleTTS paper we showed that using a pretrained text aligner helps with the alignment accuracy and hence helps the performance. The alignment problem is one of the reasons VITS sometimes generates unnatural prosody (the duration is incorrect). Simply using TMA (pretrained aligner finetuned for TTS task) helps the naturalness of VITS.
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Related Issues (20)
- 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
- Error Message After Using a fine tuned ASR Model
- Stage 2 Training Fails with NaN Loss on Single GPU Due to Inconsistent Checkpoint Keys
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