Instructions to use fluxions/vui with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Vui
How to use fluxions/vui with Vui:
# !pip install git+https://github.com/fluxions-ai/vui import torchaudio from vui.inference import render from vui.model import Vui, model = Vui.from_pretrained().cuda() waveform = render( model, "Hey, here is some random stuff, usually something quite long as the shorter the text the less likely the model can cope!", ) print(waveform.shape) torchaudio.save("out.opus", waveform[0], 22050) - Notebooks
- Google Colab
- Kaggle
| { | |
| "model_type": "vui", | |
| "library_name": "vui", | |
| "pipeline_tag": "text-to-speech", | |
| "license": "mit", | |
| "language": ["en"], | |
| "architectures": ["VuiForConditionalGeneration"], | |
| "model_files": { | |
| "base": "vui-nano.safetensors", | |
| "tokenizer": "HuggingFaceTB/SmolLM2-135M" | |
| }, | |
| "model_variants": { | |
| "vui-nano": { | |
| "description": "Streaming conversational TTS — Llama-style backbone with an RQ-Transformer acoustic head, paired with the Qwen3-TTS codec.", | |
| "file": "vui-nano.safetensors", | |
| "params": 305356033, | |
| "size_mb": 1166 | |
| } | |
| }, | |
| "model_config": { | |
| "d_model": 768, | |
| "n_layers": 22, | |
| "n_heads": 8, | |
| "n_kv_heads": null, | |
| "bias": false, | |
| "use_rotary_emb": true, | |
| "rope_theta": 10000.0, | |
| "use_rq_transformer": true, | |
| "rq_n_layers": 5, | |
| "rq_d_model": 768, | |
| "rq_n_heads": 8, | |
| "codebook_size": 2048, | |
| "n_quantizers": 16, | |
| "codec_hz": 12.5, | |
| "spk_emb_dim": 1024, | |
| "max_text_tokens": 100, | |
| "max_audio_tokens": 100 | |
| }, | |
| "tokenizer_config": { | |
| "base_tokenizer": "HuggingFaceTB/SmolLM2-135M", | |
| "add_time_tokens": false | |
| }, | |
| "audio_codec": { | |
| "name": "qwen3-tts", | |
| "native_sample_rate": 24000 | |
| }, | |
| "capabilities": [ | |
| "text-to-speech", | |
| "conversational-speech", | |
| "voice-cloning", | |
| "streaming", | |
| "on-device-inference" | |
| ], | |
| "torch_dtype": "float32", | |
| "framework": "pytorch" | |
| } | |