---
license: mit
base_model: ResembleAI/chatterbox-turbo
pipeline_tag: text-to-speech
language:
- en
tags:
- codec.cpp
- gguf
- text-to-speech
- tts
- chatterbox
- chatterbox-turbo
- voice-cloning
---

# Chatterbox Turbo — GGUF

[ResembleAI's Chatterbox Turbo](https://huggingface.co/ResembleAI/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](https://github.com/ggml-org/llama.cpp); the **codec** file holds the audio side (decoder / vocoder, speaker encoders and the adaptor between the two), run by [codec.cpp](https://github.com/mybigday/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 |

<details>
<summary>Other files</summary>

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

</details>


## Command line (codec.cpp)

With [codec.cpp](https://github.com/mybigday/codec.cpp)'s `tts-cli` ([build it](https://github.com/mybigday/codec.cpp#text-to-speech); it bundles llama.cpp), then:

```bash
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:

```bash
./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](https://github.com/mybigday/codec.cpp#text-to-speech).


## 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):

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

```python
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](https://github.com/mybigday/codec.cpp/tree/main/python).


## React Native (llama.rn)

[llama.rn](https://github.com/mybigday/llama.rn) loads the same two files on iOS and Android:

```ts
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](https://github.com/mybigday/llama.rn#text-to-speech-tts).


## 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 | 1024 (every generated token) | `--repeat-last-n 1024` |
| 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](https://huggingface.co/ResembleAI/chatterbox-turbo).