metadata

license: cc-by-nc-4.0

lina-speech (beta)

Exploring "linear attention" for text-to-speech.

It predicts audio codec "à la" MusicGen : delayed residual vector quantizers so that we do not need multiple models.

Featuring RWKV, Mamba, Gated Linear Attention.

Compared to other LM TTS model :

Can be easily pretrained and finetuned on midrange GPUs.
Tiny memory footprint.
Trained on long context (up to 2000 tokens : ~27s).

Models

Model	#Params	Dataset	Checkpoint	Steps	Note
GLA	60M, 130M	Librilight-medium	Download	300k	GPU inference only
Mamba	60M	Librilight-medium	Download	300k	GPU inference only
RWKV v6	60M	LibriTTS	Download	150k	GPU inference only

Installation

Following the linear complexity LM you choose, follow respective instructions first:

For Mamba check the official repo.
For GLA/RWKV inference check flash-linear-attention.
For RWKV training check RWKV-LM

Acknowledgment

The RWKV authors and the community around for carrying high-level truly opensource research.
@SmerkyG for making my life easy at testing cutting edge language model.
@lucidrains for its huge codebase.
@sustcsonglin who made GLA and FLA.
@harrisonvanderbyl fixing RWKV inference.

Cite

@software{lemerle2024linaspeech,
  title  = {LinaSpeech: Exploring "linear attention" for text-to-speech.},
  author = {Lemerle, Théodor},
  url    = {https://github.com/theodorblackbird/lina-speech},
  month  = april,
  year   = {2024}
}

IRCAM

This work takes place at IRCAM, and is part of the following project : ANR Exovoices