Files
Eric BuehlerandGitHub cb2231c749 Add the Dia 1.6b TTS model! (#1304)
* Add loading

* Add rope, mlp, most of attn

* Add encoder + encoder layer, decoder layer forwards

* Add decoder forwards

* Add prepare_audio_prompt

* prepare_generation mostly done

* Add a proper dia kvcache

* Add most of decoder_step

* Add the sampler

* Add the generation loop

* Wire things up

* Add speech pipeline

* Fixes

* Loads

* Some fixes

* f32

* Some progress

* Ok it runs upto dac decoding

* Add dac part loading

* Loads and runs at least

* Remove encodec

* Debugging

* Debugging

* Huh

* Complete merge

* Interactive

* Confirmed dac works at least

* Looks like encoder works

* Much progress

* Hmm

* Sampling

* Almost there

* Sampler

* Sampler

* Bf16 support

* Response

* Use it in interactive mode

* Fix oneshot

* Add openai api

* Add openai api

* Refactor loading

* Use naive sdpa for inplace

* Factor out

* Clippy

* Clippy

* Config

* Refactor config

* Metal clippy

* Fix t/s

* ISQ support

* Some fixes, nits

* Fix cuda

* Clippy

* Inhibit cublaslt for cuda

* Add server example

* Add python example

* Add rust api

* Add docs

* Update config.toml

* Fix .pyi

* Update readme

* config.toml tweak

* config.toml tweak

* config.toml tweak

* config.toml tweak

* config.toml tweak

* config.toml tweak

* config.toml tweak

* config.toml tweak

* config.toml tweak
2025-05-16 17:19:49 -04:00

32 lines
1.2 KiB
Rust

use std::time::Instant;
use anyhow::Result;
use mistralrs::{speech_utils, SpeechLoaderType, SpeechModelBuilder};
#[tokio::main]
async fn main() -> Result<()> {
let model = SpeechModelBuilder::new("nari-labs/Dia-1.6B", SpeechLoaderType::Dia)
.with_logging()
.build()
.await?;
let start = Instant::now();
// let text_to_speak = "[S1] Dia is an open weights text to dialogue model. [S2] You get full control over scripts and voices. [S1] Wow. Amazing. (laughs) [S2] Try it now on Git hub or Hugging Face.";
let text_to_speak = "[S1] mistral r s is a local LLM inference engine. [S2] You can run text and vision models, and also image generation and speech generation. [S1] There is agentic web search, tool calling, and a convenient Python API. [S2] Check it out on github.";
let (pcm, rate, channels) = model.generate_speech(text_to_speak).await?;
let finished = Instant::now();
let mut output = std::fs::File::create("out.wav").unwrap();
speech_utils::write_pcm_as_wav(&mut output, &pcm, rate as u32, channels as u16).unwrap();
println!(
"Done! Took {} s. Audio saved at `out.wav`.",
finished.duration_since(start).as_secs_f32(),
);
Ok(())
}