Text-to-Speech
Safetensors
MLX
mlx-audio
arktts
audio
tts
voice-cloning
zero-shot
multilingual
4-bit precision
Instructions to use nativ-community/Audio8-TTS-Realistic-0.6b-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use nativ-community/Audio8-TTS-Realistic-0.6b-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download nativ-community/Audio8-TTS-Realistic-0.6b-4bit --local-dir Audio8-TTS-Realistic-0.6b-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Audio8-TTS-Realistic-0.6b-4bit
MLX conversion of Edge0/Audio8-TTS-Realistic-0.6b. Audio8 generates mono 44.1 kHz speech with optional zero-shot voice cloning. The codec is included; runtime inference does not require PyTorch or remote model code.
from mlx_audio.audio_io import write
from mlx_audio.tts.utils import load
model = load("nativ-community/Audio8-TTS-Realistic-0.6b-4bit")
result = next(model.generate(text="Welcome to Audio8 on Apple Silicon."))
write("speech.wav", result.audio, result.sample_rate)
For voice cloning, pass ref_audio="reference.wav" and ref_text="Exact transcript of the reference." to generate().
python -m mlx_audio.tts.generate \
--model nativ-community/Audio8-TTS-Realistic-0.6b-4bit \
--text "Welcome to Audio8 on Apple Silicon."
The codec retains its source precision when converting or quantizing the DualAR model. Generation returns one complete waveform per call. Text, reference audio, and generated frames share a 2,048-position context budget.
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Model size
0.9B params
Tensor type
F32
路
U32 路
BF16 路
Hardware compatibility
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4-bit
Model tree for nativ-community/Audio8-TTS-Realistic-0.6b-4bit
Base model
Edge0/Audio8-TTS-Preview-0.6b Finetuned
Edge0/Audio8-TTS-Realistic-0.6b