BricksDisplay/BreezyVoice-GGUF

BreezyVoice — GGUF

MediaTek Research's BreezyVoice: Taiwanese-Mandarin TTS with builtin voices and voice cloning.

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.

This backbone needs the CosyVoice relative-position architecture, which upstream llama.cpp does not have yet: use the llama.cpp that codec.cpp or llama.rn builds (they carry the patch), not a stock llama.cpp.

Files

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

	--backbone	--model (codec)	Total
Recommended	breezyvoice-lm-f16.gguf	breezyvoice-codec-q8_0.gguf	840 MB
Smallest	breezyvoice-lm-q4_k_m.gguf	breezyvoice-codec-q4_k_m.gguf	470 MB
Best quality	breezyvoice-lm-f16.gguf	breezyvoice-codec-f16.gguf	1.2 GB

Other files

File	Size
breezyvoice-codec-f32.gguf	1.5 GB
breezyvoice-codec-q5_k_m.gguf	331 MB
breezyvoice-lm-q8_0.gguf	250 MB

Command line (codec.cpp)

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

hf download BricksDisplay/BreezyVoice-GGUF breezyvoice-lm-f16.gguf breezyvoice-codec-q8_0.gguf --local-dir BreezyVoice

./build/tts-cli synthesize --backbone BreezyVoice/breezyvoice-lm-f16.gguf --model BreezyVoice/breezyvoice-codec-q8_0.gguf \
    --voice zh_female --text "今天天氣真好，我們一起去公園走走吧。" --output out.wav

Clone a voice (give the clip's transcript for the closest match):

./build/tts-cli synthesize ... --ref-audio ref.wav --ref-text "參考音訊的逐字稿" --text "..." --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/BreezyVoice-GGUF", "breezyvoice-lm-f16.gguf"),
                    hf_hub_download("BricksDisplay/BreezyVoice-GGUF", "breezyvoice-codec-q8_0.gguf"))
audio, sr = tts.synthesize("今天天氣真好，我們一起去公園走走吧。", voice="zh_female")
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: 'breezyvoice-lm-f16.gguf', embedding: true })
await ctx.initVocoder({ path: 'breezyvoice-codec-q8_0.gguf' })
const { prompt, grammar, embedding, flow } = await ctx.getFormattedAudioCompletion({
  prompt: 'Hello world',
  speaker: 'zh_female',
})
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 inference code; tts-cli, tts-server, llama.rn and any llama.cpp host apply them unless you set your own.

Setting	Value	tts-cli flag
temperature	1.0	--temp 1.0
top-k	25	--top-k 25
top-p	1.0 (off)	--top-p 1.0
min-p	0 (off)	--min-p 0.0

BreezyVoice samples with plain top-k (no repetition-aware sampling).

Voices

• Builtin: zh_female (default), zh_male, ja_male, yue_female, en_female, en_male, ko_female.
• Voice cloning: --ref-audio plus --ref-text (its transcript). Without the transcript it still works, with a looser match.

Input text

• Pass one normalized sentence at a time: write numbers, dates and symbols out as words; split long text into sentences yourself.
• Plain Chinese text works. Polyphonic characters may be misread; upstream BreezyVoice adds 注音 annotations (g2pw) for those, which you can add yourself.

License

apache-2.0, the same as the original model.