| # LibriSpeech-Finetuning for VALL-E | |
| Included is a dataset I've prepared for training with [my fork of a VALL-E implementation](https://git.ecker.tech/mrq/vall-e), sourced from [LibriSpeech-Finetuning](https://dl.fbaipublicfiles.com/librilight/data/librispeech_finetuning.tgz). | |
| >\> What makes this different? | |
| I've trimmed them down to better train against them, as too large of a piece of data will increase VRAM use drastically: | |
| * I re-transcribed using [m-bain/WhisperX](https://github.com/m-bain/whisperX/)'s large-v2 model and using the VAD filter to get near-perfect timestamps. | |
| * I then bias the start by -0.05 seconds, and the ends by 0.05 seconds). | |
| * very short segments are merged with preceding ones to avoid fragmenting too much | |
| * the source audio is then sliced according to each segment, and each segment gets phonemized using [bootphon/phonemizer](https://github.com/bootphon/phonemizer/) (espeak backend). | |
| * finally, the sliced audio is quantized using Encodec, for VALL-E's use. | |
| This will help alleviate problems from the default `max_phoneme` length ignoring a large chunk of the dataset, and relatively evenly distributing lengths. | |