---
title: NAVA Audio-Video Generator
emoji: 🎬
colorFrom: purple
colorTo: blue
sdk: gradio
sdk_version: 6.16.0
app_file: app.py
models:
  - ernie-research/NAVA
  - Qwen/Qwen3-4B-Instruct-2507
  - Qwen/Qwen3-VL-4B-Instruct
short_description: Native AV alignment — joint video + audio generation
license: apache-2.0
---

# NAVA on ZeroGPU

[Project Page](https://ernie-research.github.io/NAVA) · [arXiv](https://arxiv.org/abs/2605.30073) · [GitHub](https://github.com/ernie-research/NAVA) · [Models](https://huggingface.co/ernie-research/NAVA)

NAVA is a Native Audio-Visual Alignment framework for **joint** audio + video generation:
text → synchronized 24fps video + stereo audio in one shot, image-to-AV with reference-voice timbre control included. This Space runs the FP8 checkpoint (~13 GB) on a single H200 via ZeroGPU.

## How it works

1. **(Optional) Upload a first-frame image** — the aspect ratio dropdown auto-detects portrait/landscape/square.
2. **Type a short prompt** — Chinese or English, brief is fine.
3. **Click "Rewrite Prompt"** — Qwen3-VL captions the image (if any), composes with your prompt, and Qwen3 rewrites it into the long, dense Chinese caption NAVA was trained on. Auto-retries on `<S>...<E>` mismatch.
4. **Click "Generate"** — diffusion runs in FP8 (~5 minutes per 4s clip on H200).

## Speech tags

Wrap any spoken line in `<S>...<E>`. The rewriter preserves these verbatim (auto-retried until the count matches your input). Add reference WAVs to bind voice identity to each speech span.

## Hardware

ZeroGPU H200 (80 GB). Each `Generate` call is allocated up to 5 minutes of GPU time. If the queue is busy, you'll wait. The model + Qwen3 weights stay resident in CPU RAM between calls; only the active stage moves to GPU.