BricksDisplay/Chatterbox-Turbo-GGUF

Chatterbox Turbo — GGUF

ResembleAI's Chatterbox Turbo: fast English TTS with voice cloning and paralinguistic tags such as [laugh].

Two files make one model, as with llama.cpp's multimodal projectors: the model file is the text-to-speech LM backbone, run by llama.cpp; the codec file holds the audio side (decoder / vocoder, speaker encoders and the adaptor between the two), run by codec.cpp. The runtimes below wire the two graphs together, on CPU or GPU (CUDA, Vulkan, Metal), fully offline.

Files

Every run needs one model file + one codec file from this repo.

	--backbone	--model (codec)	Total
Recommended	chatterbox-turbo-t3-q8_0.gguf	chatterbox-turbo-codec-q8_0.gguf	768 MB
Smallest	chatterbox-turbo-t3-q4_k_m.gguf	chatterbox-turbo-codec-q4_k_m.gguf	499 MB
Best quality	chatterbox-turbo-t3-f16.gguf	chatterbox-turbo-codec-f16.gguf	1.4 GB

Other files

File	Size
chatterbox-turbo-codec-f32.gguf	1.3 GB
chatterbox-turbo-codec-q5_k_m.gguf	260 MB

Command line (codec.cpp)

With codec.cpp's tts-cli (build it; it bundles llama.cpp), then:

hf download BricksDisplay/Chatterbox-Turbo-GGUF chatterbox-turbo-t3-q8_0.gguf chatterbox-turbo-codec-q8_0.gguf --local-dir Chatterbox-Turbo

./build/tts-cli synthesize --backbone Chatterbox-Turbo/chatterbox-turbo-t3-q8_0.gguf --model Chatterbox-Turbo/chatterbox-turbo-codec-q8_0.gguf \
    --text "Hello, this is Chatterbox Turbo. [laugh]" --output out.wav

Clone a voice from a few seconds of clean speech:

./build/tts-cli synthesize ... --ref-audio my_voice.wav --text "Hello there." --output out.wav

All tts-cli options (voices, voice cloning, sampling, GPU) are described in the codec.cpp README.

Python

The codec_cpp package runs the same pipeline (it builds codec.cpp with its bundled llama.cpp, so it needs CMake and a C++ compiler):

pip install "codec-cpp @ git+https://github.com/mybigday/codec.cpp" huggingface_hub soundfile

import codec_cpp, soundfile
from huggingface_hub import hf_hub_download

tts = codec_cpp.TTS(hf_hub_download("BricksDisplay/Chatterbox-Turbo-GGUF", "chatterbox-turbo-t3-q8_0.gguf"),
                    hf_hub_download("BricksDisplay/Chatterbox-Turbo-GGUF", "chatterbox-turbo-codec-q8_0.gguf"))
audio, sr = tts.synthesize("Hello, this is Chatterbox Turbo. [laugh]")
soundfile.write("out.wav", audio, sr)

tts.voices() lists the builtin voices; ref_audio= clones a voice where the model supports it; tts.stream(text) yields audio as it is generated. More in the Python docs.

React Native (llama.rn)

llama.rn loads the same two files on iOS and Android:

const ctx = await initLlama({ model: 'chatterbox-turbo-t3-q8_0.gguf', embedding: true })
await ctx.initVocoder({ path: 'chatterbox-turbo-codec-q8_0.gguf' })
const { prompt, grammar, embedding, flow } = await ctx.getFormattedAudioCompletion({
  prompt: 'Hello world',
})
const result = await ctx.completion({ prompt, grammar, embedding })
const pcm = flow === 'continuous_embd'
  ? await ctx.decodeAudioEmbeddings(result.embeddings, result.embedding_dim)
  : await ctx.decodeAudioTokens(result.audio_tokens)

Voice cloning, saved voices and per-model notes: llama.rn TTS docs.

Sampling

The model files carry the recommended sampling as llama.cpp's general.sampling.* metadata, the settings of the original ChatterboxTurboTTS.generate(); tts-cli, tts-server, llama.rn and any llama.cpp host apply them unless you set your own.

Setting	Value	tts-cli flag
temperature	0.8	--temp 0.8
top-k	1000	--top-k 1000
top-p	0.95	--top-p 0.95
min-p	0 (off)	--min-p 0.0
repetition penalty	1.2	--repeat-penalty 1.2
repetition window	-1 (all generated tokens)	--repeat-last-n -1
sampler order	temperature → top-k → top-p → penalties	--samplers "temperature;top_k;top_p;penalties"

The order is the original's (Hugging Face warpers: temperature, top-k, top-p, then the repetition penalty).

Voices

• Builtin voice: default.
• Voice cloning: --ref-audio with any short, clean clip. Save it with --save-speaker to reuse it without the clip.

License

mit, the same as the original model.