Instructions to use majentik/Audio8-TTS-Preview-0.6b-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use majentik/Audio8-TTS-Preview-0.6b-MLX-8bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Audio8-TTS-Preview-0.6b-MLX-8bit majentik/Audio8-TTS-Preview-0.6b-MLX-8bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Audio8-TTS-Preview-0.6b-MLX-8bit
MLX 8-bit (group size 64) quantization of
Edge0/Audio8-TTS-Preview-0.6b, a 0.6B DualAR
text-to-speech model with zero-shot voice cloning across 11 languages
(Cantonese, Chinese, Dutch, English, French, German, Italian, Japanese, Korean, Polish, Spanish). Runs on Apple Silicon with
mlx-audio (arktts model class).
Provenance
- Source: Edge0/Audio8-TTS-Preview-0.6b @ revision
f07040f3d151f1ba0253bfb92cb2f5dd38b44594(Apache-2.0). - Only the language model's linear layers are quantized to 8-bit affine (gs64). The 44.1 kHz codec is kept in fp32 and the tied token-embedding table in bf16 (it doubles as the output head), so audio fidelity is bounded by the LM tier, not the vocoder.
- The upstream
codec.pth(torch pickle) was re-serialised to safetensors with keys unchanged;auto_mapwas dropped fromconfig.jsonso loading never asks for remote code. - Built with the standalone lane
pipelines/audio8_tts_mlx_release.pyin ajentik/majek.
Smoke gate
Before upload this tier synthesised one sentence per language below with the default voice and was transcribed back by majentik/Qwen3-ASR-1.7B-MLX-8bit. A tier ships only if every sample is finite, > 1 s, non-silent and has character error rate <= 0.35.
| Lang | Audio | RTF | CER | Verdict |
|---|---|---|---|---|
| en | 3.9 s | 0.96 | 0.000 | pass |
| zh | 2.9 s | 0.32 | 0.000 | pass |
| ko | 4.2 s | 0.32 | 0.000 | pass |
| ja | 4.9 s | 0.33 | 0.000 | pass |
| yue | 4.0 s | 0.33 | 0.250 | pass |
Peak unified memory during synthesis: 6.8 GB
(mlx 0.32.2, mlx-audio 0.5.4, Apple M5 Max 128GB).
Gate record: .sisyphus/evidence/audio8-tts/8bit/smoke.json in the repo above.
Usage
pip install -U mlx-audio soundfile
python -m mlx_audio.tts.generate --model majentik/Audio8-TTS-Preview-0.6b-MLX-8bit \
--text "The quick brown fox jumps over the lazy dog." --output_path out
import mlx.core as mx, numpy as np, soundfile as sf
from mlx_audio.tts.utils import load
model = load("majentik/Audio8-TTS-Preview-0.6b-MLX-8bit")
for r in model.generate(
text="今天天气很好,我们一起去公园散步吧。",
# optional zero-shot cloning:
# ref_audio="speaker.wav", ref_text="transcript of speaker.wav",
):
sf.write("out.wav", np.asarray(r.audio.astype(mx.float32)), 44100)
Evaluation
Only the multilingual smoke gate above is run at publish time; no MOS or speaker-similarity study has been done on this tier. Upstream reports token-exact greedy parity between the MLX port and the reference implementation for the unquantized model.
Available tiers
| Repo | Bits | Group size |
|---|---|---|
| majentik/Audio8-TTS-Preview-0.6b-MLX-8bit | 8-bit | gs64 |
| majentik/Audio8-TTS-Preview-0.6b-MLX-4bit | 4-bit | gs32 |
No bf16 tier is published here: the source is already bf16 and mlx-community/Audio8-TTS-Preview-0.6b-bf16 covers it.
License
Apache-2.0, inherited from the source model. See the source card for the model's intended use and limitations.
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8-bit
Model tree for majentik/Audio8-TTS-Preview-0.6b-MLX-8bit
Base model
Edge0/Audio8-TTS-Preview-0.6b