Thank you so much, that exactly answers my question with the official response (that guy works at Meta) that confirms it's the same base model!
I was concerned primarily because in the release notes it strangely didn't mention it anywhere, and I thought it would have been important enough to mention.
I followed their instructions here: https://speech.fish.audio/
I am using the locally-run API server to do inference: https://speech.fish.audio/inference/#http-api-inference
I don't know about other ways. To be clear, this is not (necessarily) an LLM, it's just for speech synthesis, so you don't run it on ollama. That said I think it does technically use Llama under the hood since there are two models, one for encoding text and the other for decoding to audio. Honestly the paper is terrible but it explains the architecture somewhat: https://arxiv.org/pdf/2411.01156
Thanks for the tips. After doing a bunch of searching, I found that what I needed was BPE, or byte-pair encoding. This allows the token set to contain sub-word sequences, which lets the tokenizer represent a unique constant like 0x0373 as ['__sow', '0x', '03', '73', '__eow'].
Thanks, the quickstart guide was straightforward to follow. Do you have any suggestions on how to do word splitting with code, if any? For example, on a test run, I found that the model was not able to synthesize unique constants correctly even though this test run consisted only of obvious "a to b" relationships.
You are right. Their description of "SOTA Open Source TTS" caused me to assume it was open source, but it's clear that
So, it's "source available" and not released under a permissive licence.