LibriSpeech-Finetuning for VALL-E

Included is a dataset I've prepared for training with my fork of a VALL-E implementation, sourced from LibriSpeech-Finetuning.

> 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'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 (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.