Comments (4)
You also need to change the decoder type to HiFiGAN and make sure the data file contains the speaker ids for the second stage training. Iโm currently testing the code on LibriTTS and it seems to work for the first stage, though itโs not done yet.
The code is exactly same as single speakers, the only difference is that multispeaker models use HiFiGAN decoder and the style diffuser takes one extra speaker embedding as the input.
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I found a few bugs in the multispeaker settings. I have fixed them, but there might still be some other problems. I'm still testing everything so it may take another one to two weeks.
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I found a few other bugs in the second stage as I have finished the first stage training in evaluations. Hopefully Iโll finish the LibriTTS model by next week.
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I think I have fixed all the bugs in the code for training multispeaker models. I just need to wait for the last phase of second stage to be done for the LibriTTS model.
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Related Issues (20)
- 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
- Error Message After Using a fine tuned ASR Model
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