Comments (2)
ivanvovk
commented on June 19, 2024
@ErfolgreichCharismatisch modern TTS models consist of 2 parts: feature generator and vocoder. Feature generator produces low-dimensional time-frequency acoustic features from text, while vocoder reconstructs raw waveform from these features. Each model trains separately. WaveGrad corresponds to the second part, vocoder. It takes acoustic features (mel-spectrograms) as input, not text. And it can be trained on arbitrary dataset.
from wavegrad.
ErfolgreichCharismatisch
commented on June 19, 2024
Interesting. So which feature generator(s) does it work out of the box with?
from wavegrad.
Related Issues (20)
- ValueError: low >= high HOT 2
- inference.py seems not loading the specified checkpoint HOT 1
- Exponents calculation in positional encoding HOT 9
- Were your `generated_samples` generated using a model trained with AMP? HOT 2
- predict_start_from_noise HOT 2
- best noise schedule HOT 1
- schedules model for other dataset and different sample rate HOT 4
- Training so slow HOT 3
- How to make it work with TPU? HOT 2
- Audio quality improvements HOT 6
- The order of upsampling_dilations HOT 1
- Interpolation and Conv order in Upsample module HOT 1
- slow training in single GPU HOT 1
- Using NVIDIA RTX 3090 GPU?
- Static Noise with f_max = 10000
- Poor Synthesis Quality on 44k Sample Rate HOT 1
- Evaluation tools
- Unable to load the pre-trained parameters for inference HOT 2
- Matplotlib API change & NaNs for short clips & new hop_length HOT 27
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from wavegrad.