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Llasa: Llama-Based Speech Synthesis

llasatts.github.io

1–10 of 23 posts

Re: Llasa: Llama-Based Speech Synthesis

#3

LLaSA is a simple framework for speech synthesis that employs a single-layer vector quantizer (VQ) codec and a single Transformer architecture to fully align with standard LLMs such as LLaMA.

Probably the title should have the correct capitalization then. Cause I was fully expecting a speech synthesis tool that sounded like llamas talking human language and now I'm bummed out!

Re: Llasa: Llama-Based Speech Synthesis

#7
> employs a single-layer vector quantizer (VQ) codec and a single Transformer architecture to fully align

I really wish when new models were released that they would draw a diagram of all the layers and the tensor input and output sizes at each layer, with zoom in/out capabilities if needed using D3.js or whatever visualization framework if needed. Every single layer should be on there with its input and output sizes.

These one-sentence descriptions, and approximate block diagrams with arrows pointing at each other are never enough to understand how something is actually implemented.

Re: Llasa: Llama-Based Speech Synthesis

#9

the long 'uuuuhhhhhhh' from some of the lesser models is killing me.

based on the samples, it really seams like anything smaller than 3B is pretty useless.

If you're doing a home lab voice assistant 1B is nice, because on a 12gb gpu you can run a moderately competent 7b LLM and two 1b models; 1 for speech to text and also text to speech, plus some for the wake word monitor. Maybe in a couple of years we can combine all this into a single ~8b model that runs efficiently on 12gb gpu. Nvidia doesn't seem very incentivized right now to sell consumer GPUs that can run all this on a single consumer grade chip when they're making so much money selling commercial grade 48gb cards.

Re: Llasa: Llama-Based Speech Synthesis

#10
post #7

> employs a single-layer vector quantizer (VQ) codec and a single Transformer architecture to fully align I really wish when new models were released that they would draw a diagram of all the layers and the tensor input and output sizes at each layer, with zoom in/out capabilities if needed using D3.js or whatever visualization framework if needed. Every single layer should be on there with its input and output sizes…

Sounds like a solid SaaS business plan!
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