vall-e/docs

What is VALL-E?

VALL-E describes how treating text-to-speech synthesis as a language problem can easily be solved with a language model. The original paper utilizes a basic transformer as the underlying architecture to perform zero-shot text-to-speech synthesis using a short audio prompt as reference.

Why VALL-E?

At the time, state-of-the-art neural-based TTS solutions were sparing. TorToiSe had a similar approach to treating TTS as a language problem, but required a ton of additional cruft on top of its ensemble. Thus, when VALL-E's paper released, it was simple yet effective with it requiring, at the time, just an AR and a NAR model, and leaving EnCodec to handle the rest (feature extraction, encoding audio, decoding audio). Vocos then improves upon EnCodec's decoding to produce better quality audio.

Why this VALL-E?

Unlike the paper, this VALL-E aims to:

• be lightweight as possible, only requiring one model to load and use (and EnCodec/Vocos).
  • Even the original VALL-E requires a separate AR and a NAR.
• keep training and finetuning (be it the base model or through LoRAs) accessible to anyone.
  • Bark was needlessly complex in providing even additional voices to use.
  • Current SoTA such as F5-TTS supports it, but seems to have a rather high ceiling to finetune it.
• provide decent zero-shot text-to-speech synthesis, both without requiring sampling adjustments and providing thorough sampler settings.
• provide additional, easy to use functionality, that other solutions don't offer.

Caveats

Despite how lightweight it is in comparison to other TTS's I've meddled with, there are still some caveats, be it with the implementation or model weights:

• the audio embeddings have some quirks to having the AR's RVQ level 0 separate from the NAR's RVQ level 0 (sharing them caused some problems in testing)
• the trainer / dataloader assumes there are zero variations between a speaker's utterances, and thus it can extract the basics of a speaker's features rather than deeper features (like prosidy, tone, etc.) when performing inferences.
  • however, trying to work around this would require training under tts-c (VALL-E continuous) mode or modifying an input prompt enough to where its quantized representation differs enough from the output response the prompt derives from.
  • to remedy this, training benefits from calculating the most similar utterances for each utterance, and using that as the input prompt for training.
• the trainer's default RVQ level distribution prioritizes lower RVQ levels over higher RVQ levels, as the lower levels contribute to the final waveform more; however, this leaves some minor artifacting that rises in the higher RVQ levels due to inaccuracy issues.
  • summing the audio embeddings for later RVQ levels seems to help?
  • model.experimental.p_rvq_levels: [0,0,0,0,0,0,0,1,2,3,4,5,6,7] seems to help?
• speakers that aren't similar to an audiobook narrator voice has similarity issues due to the majority of training used path-based dataloader sampling instead of speaker-based (or group-based) dataloader sampling.
  • although LoRAs help a ton for fixing results for a single voice.
  • a diverse dataset in prosidy and speaker (such as a corpus sourced from dramatic media like video games) helps a ton, but still has issues for speakers not similar to any seen speakers.

To-Do

• train and release a serviceable model for finetuning against.
• train and release a good zero-shot model.
  • for what it's worth it's decent enough for me to finally be happy with it.
• well-integrated training through the Web UI (without the kludge from ai-voice-cloning)
• explore alternative setups, like a NAR-only model or Descript-Audio-Codec
  • the current experiment of an AR length-predictor + NAR for the rest seems to fall apart...
  • Descript-Audio-Codec 44KHz has NAR issues, but this might be user error.
• explore better sampling techniques
  • the AR doesn't need exotic sampling techniques, as they're bandaids for a bad AR.
  • the NAR benefits from greedy sampling, and anything else just harms output quality.
• clean up the README, and document, document, document.
• extend to multiple languages (VALL-E X).
  • reference model is trained against English, Japanese, French, and German.
• extend to addditional tasks (SpeechX).
  • stt (Speech-to-Text) seems to be working fine for the most part.
  • other tasks seem to require a ton of VRAM......
• extend using VALL-E 2's features (grouped code modeling + repetition aware sampling)
  • desu these don't seem to be worthwhile improvements, as inferencing is already rather fast, and RAS is just a fancy sampler.
• audio streaming
  • this technically can work without any additional architecture changes, just clever tricks with sampling-then-decoding-to-audio.
  • something similar to HiFiGAN (or the one for TorToiSe) trained on the last hidden states of the AR might also enable an alternate way for streaming.
• speed up inferencing
  • KV caching both yields broken output and quadratically slow output, unless I'm doing something grossly wrong.
    • A pure HF model is the only way to fix this, but converting the model to one is a bit of a chore.
  • Speculative sampling seems overkill for small models (and in reality seems like it's better to just train a larger model).
  • Self-speculation through layer-skipping doesn't offer any tangible speedups, sadly.
• replace the phonemizer with something that doesn't depend on espeak
  • train the model to handle text => phoneme (without a hit to the rest of the model)
    • ...and phonemes => text
    • allow raw text as input instead
  • espeak is nice, but I can only really put my whole trust with phonemizing English.
  • a small model trained to handle converting text to phonemes might work, but has it's own problems (another model to carry around, as accurate as the dataset it was trained against, requires training for each language... etc).
• smarter/clever inferencing, such as:
  • "rolling" context, where the last generated sentence is the prefix for the next sentence.
• explore exotic features like:
  • using a pure text vocab rather than IPA phonemes (as a transformer should be "smart" enough to map text tokens)
  • interleaving by using summed embedding tokens:
    • for example, <RVQ 0-7><RVQ 0> => <RVQ 0-7><RVQ 0-1> => <RVQ 0-7><RVQ 0-2> (etc.)
    • however, I imagine the sequences to train for this are too exotic.
  • mixing multiple speakers through summing input prompt embeddings
    • I do not expect this to work, but you never know...

Notices and Citations

Unless otherwise credited/noted in this repo or within the designated Python file, this repository is licensed under AGPLv3.

• EnCodec is licensed under CC-BY-NC 4.0. If you use the code to generate audio quantization or perform decoding, it is important to adhere to the terms of their license.

• This implementation was originally based on enhuiz/vall-e, but has been heavily, heavily modified over time. Without it I would not have had a good basis to muck around and learn.

@article{wang2023neural,
  title={Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers},
  author={Wang, Chengyi and Chen, Sanyuan and Wu, Yu and Zhang, Ziqiang and Zhou, Long and Liu, Shujie and Chen, Zhuo and Liu, Yanqing and Wang, Huaming and Li, Jinyu and others},
  journal={arXiv preprint arXiv:2301.02111},
  year={2023}
}

@article{defossez2022highfi,
  title={High Fidelity Neural Audio Compression},
  author={Défossez, Alexandre and Copet, Jade and Synnaeve, Gabriel and Adi, Yossi},
  journal={arXiv preprint arXiv:2210.13438},
  year={2022}
}