license: apache-2.0 base_model: k2-fsa/ZipVoice library_name: ggml pipeline_tag: text-to-speech language:

zh
en tags:
tts
voice-cloning
zero-shot
gguf
ggml
flow-matching

ZipVoice-Distill GGUF for audio.cpp

Ready-to-run GGUF packages of ZipVoice-Distill (k2-fsa), a zero-shot voice-cloning TTS with a TTSZipformer flow-matching backbone and a Vocos 24 kHz vocoder, packaged for the audio.cpp engine.

Each file is fully self-contained: flow-matching model + bundled Vocos vocoder + embedded text-frontend sidecars (tokens.txt, model.json, zh tables, jieba dictionaries). Output: 24 kHz mono WAV.

Files

File	Precision	Size	Recommended
`zipvoice-distill-q8_0.gguf`	Q8_0 (weight-only)	158 MB	✅ default
`zipvoice-distill-orig.gguf`	F32 (original)	553 MB	bit-exact reference

Q8_0 is the recommended download. Weights are stored as int8 blocks (one F16 scale per 32 weights); activations stay F32 and the kernels dequantize each block on the fly — no full-model dequantization at load, no int8 arithmetic.

Q8_0 vs F32 (Apple M3, Metal, 34 s utterance)

Metric	F32	Q8_0
File size	553 MB	158 MB (3.5× smaller)
Runtime memory	1310 MB	971 MB (−26%)
RTF (lower is better)	0.217	0.204
Log-mel cosine vs F32	1.0000	0.9987 (male) / 0.9997 (female)
Velocity field cosine vs PyTorch	1.0000	0.9993

Quality is transparent to the ear; 8-step distilled sampling, guidance scale 3, time shift 0.5. Lower-bit variants (q6_k … q4_0) are deliberately not provided — they trade audible quality for size on this model.

Usage

Install with the audio.cpp model manager

# from an audio.cpp checkout
python3 tools/model_manager_v2.py install zipvoice_distill_q8_0
# → models/ZipVoice-Distill-GGUF/zipvoice-distill-q8_0.gguf

Direct synthesis (CLI)

audiocpp_cli --task clon --family zipvoice \
    --model models/ZipVoice-Distill-GGUF/zipvoice-distill-q8_0.gguf \
    --voice-ref prompt.wav --reference-text "Reference transcript." \
    --text "你好，这是ZipVoice，支持中文和English两种语言。" --out out.wav

--reference-text must transcribe --voice-ref. Chinese, English and mixed input are handled by the built-in Emilia frontend (jieba + pypinyin tables + espeak-ng; the English path needs the espeak-ng dylib shown above). Python API, server and streaming options are documented in docs/community_models/zipvoice.md.

Provenance & conversion

Upstream weights: k2-fsa/ZipVoice (zipvoice_distill, Apache-2.0)
Conversion: tools/community_models/convert_zipvoice.py (torch → safetensors → GGUF with model.* / vocos.* namespaces)
Quantization: audiocpp_gguf --type q8_0 (weight-only, per-block scales)
Parity: text encoder (per-layer taps), flow decoder, Vocos and full synthesis verified against the PyTorch reference; see the zipvoice doc in audio.cpp for the full report

License

Apache-2.0, inherited from the upstream ZipVoice release.

zipvoice-gguf