Text-to-Speech
Safetensors
MLX
Zonos
mlx-audio
zonos2
tts
voice-cloning
quantized
4-bit precision
apple-silicon
Instructions to use amal-david/Zyphra-ZONOS2-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use amal-david/Zyphra-ZONOS2-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir Zyphra-ZONOS2-4bit amal-david/Zyphra-ZONOS2-4bit
- Zonos
How to use amal-david/Zyphra-ZONOS2-4bit with Zonos:
# pip install git+https://github.com/Zyphra/Zonos.git import torchaudio from zonos.model import Zonos from zonos.conditioning import make_cond_dict model = Zonos.from_pretrained("amal-david/Zyphra-ZONOS2-4bit", device="cuda") wav, sr = torchaudio.load("speaker.wav") # 5-10s reference clip speaker = model.make_speaker_embedding(wav, sr) cond = make_cond_dict(text="Hello, world!", speaker=speaker, language="en-us") codes = model.generate(model.prepare_conditioning(cond)) audio = model.autoencoder.decode(codes)[0].cpu() torchaudio.save("sample.wav", audio, model.autoencoder.sampling_rate) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio