Text-to-Audio
LTX.io
English
ltx-audio
audio
audio-generation
speech
reference-conditioning
multi-speaker
flow-matching
diffusion
Instructions to use suryatmodulus/scena with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- LTX.io
How to use suryatmodulus/scena with LTX.io:
# Install the LTX-2 pipelines git clone https://github.com/Lightricks/LTX-2.git cd LTX-2 uv sync --frozen
# Download the weights from this repo, plus the Gemma text encoder hf download suryatmodulus/scena --local-dir models/scena hf download google/gemma-3-12b-it-qat-q4_0-unquantized --local-dir models/gemma-3-12b
# Fast pipeline (distilled model, no distilled LoRA needed) uv run python -m ltx_pipelines.distilled \ --distilled-checkpoint-path models/scena/<distilled-checkpoint>.safetensors \ --spatial-upsampler-path models/scena/<spatial-upsampler>.safetensors \ --gemma-root models/gemma-3-12b \ --prompt "A beautiful sunset over the ocean" \ --output-path output.mp4 # For image-to-video, add: --image path/to/image.jpg 0 0.8# HQ pipeline (two-stage, higher quality) uv run python -m ltx_pipelines.ti2vid_two_stages_hq \ --checkpoint-path models/scena/<checkpoint>.safetensors \ --distilled-lora models/scena/<distilled-lora>.safetensors 0.8 \ --spatial-upsampler-path models/scena/<spatial-upsampler>.safetensors \ --gemma-root models/gemma-3-12b \ --prompt "A beautiful sunset over the ocean" \ --output-path output.mp4 # For image-to-video, add: --image path/to/image.jpg 0 0.8 - Notebooks
- Google Colab
- Kaggle
File size: 4,146 Bytes
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license: other
license_name: ltx-2-community-license
license_link: https://huggingface.co/Lightricks/LTX-2/blob/main/LICENSE
language:
- en
tags:
- audio
- text-to-audio
- audio-generation
- speech
- reference-conditioning
- multi-speaker
- flow-matching
- diffusion
- ltx
library_name: ltx-audio
pipeline_tag: text-to-audio
---
# ScenA — Reference-Driven Multi-Speaker Audio Scene Generation
**ScenA** generates multi-speaker audio *scenes* (dialogue, conversation) from a text
prompt, conditioned on one or more **reference-audio** clips that set the speakers'
voices. It is an audio-only, reference-conditioned text-to-audio flow-matching DiT
built on the LTX-2 architecture (~4B parameters, 48 layers).
- 📄 **Paper:** [arXiv:2606.19325](https://arxiv.org/abs/2606.19325)
- 🌐 **Project page:** https://finmickey.github.io/scena/
- 💻 **Code:** https://github.com/finmickey/scena
- 🏢 Lightricks · Tel Aviv University
## Files
| file | size | contents |
|---|---|---|
| `scena.safetensors` | ~8.2 GB | the ScenA transformer + Gemma text-projection weights |
| `audio_vae.safetensors` | ~365 MB | the audio VAE (encoder+decoder) + vocoder (bundled so you don't need the full LTX-2 model) |
You also need the **Gemma-3-12B-IT** text encoder (`google/gemma-3-12b-it`, gated on HF).
## Quickstart
```bash
git clone https://github.com/finmickey/scena
cd scena
pip install -e . # or: uv sync
# download the checkpoint + audio VAE bundle
huggingface-cli download mifinkelson/scena scena.safetensors audio_vae.safetensors --local-dir ./checkpoints
```
Two example reference voices ship with the repo under `examples/references/` — the same two voices
drive both examples below, so this runs out of the box:
```python
from ltx_pipelines.t2aud_ref_cond import T2AudRefCondPipeline
pipe = T2AudRefCondPipeline(
checkpoint_path="checkpoints/scena.safetensors",
audio_vae_path="checkpoints/audio_vae.safetensors",
gemma_root="/path/to/google/gemma-3-12b-it",
)
refs = ["examples/references/reference_1.wav", "examples/references/reference_2.wav"]
# A simple two-speaker dialogue
pipe(
prompt='The speaker from reference 1 says: "The taxi drivers are on strike again." The speaker from reference 2 says: "What for?" The speaker from reference 1 says: "They want the government to reduce the price of the gasoline." The speaker from reference 2 says: "It is really a hot potato."',
ref_audio_paths=refs,
duration=7.0,
seed=1,
).save("dialogue.wav")
# Farm at sunrise — the same two voices in a scene with sound effects
pipe(
prompt='A farm at sunrise: a rooster crows. Chickens cluck softly throughout. The speaker from reference 1 says with a yawn: "Way too early for this." The speaker from reference 2 chuckles: "Welcome to country life." The rooster crows again.',
ref_audio_paths=refs,
duration=8.0,
seed=1,
).save("farm.wav")
```
### Prompting guide
- Refer to speakers as **"the speaker from reference 1"**, **"reference 2"**, … matching the
order of `ref_audio_paths`.
- Put the spoken words **in quotes**; describe sound effects / ambience in plain prose
(e.g. *"rain drums steadily on a tin roof"*, *"a roaring stadium crowd cheers"*).
- Reference clips: clean single-speaker speech, up to ~20 s each — the more the better (any sample rate; mono or stereo).
- You don't have to follow a strict turn-taking pattern; see the [demo page](https://finmickey.github.io/scena/) for more varied examples.
- 60 inference steps, guidance ~7, and output durations up to ~20 s work best (the model was trained on ≤20 s scenes).
## Citation
```bibtex
@article{finkelson2026scena,
title = {Reference-Driven Multi-Speaker Audio Scene Generation from In-the-Wild Priors},
author = {Finkelson, Michael and Segal, Daniel and Richardson, Eitan and Armon, Shahar and Goldring, Nani and Panet, Poriya and Zabari, Nir and Brazowski, Benjamin and Patashnik, Or and HaCohen, Yoav},
journal = {arXiv preprint arXiv:2606.19325},
year = {2026}
}
```
## License
Released under the [LTX-2 Community License](https://huggingface.co/Lightricks/LTX-2/blob/main/LICENSE).
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