baidu / NAVA

NAVA — Native Audio-Visual Alignment for Generation

State-of-the-art audio-visual synchronization with only 6.3 B parameters.

ERNIE Team · Baidu Inc. · arXiv 2026

⭐ If you find this model useful, please consider giving our GitHub repo a star! ⭐

📖 中文版 README

TL;DR

NAVA is a 6.3 B-parameter joint audio-video generator that synthesizes synchronized video and audio from a single prompt — including multi-speaker speech with reference-timbre control and image-conditioned continuations.

Instead of post-hoc-aligned dual towers or fully unified tri-modal stacks, NAVA uses an Align-then-Fuse MMDiT : a dedicated alignment space first establishes audio-video correspondence, then context (text, speaker embeddings) is fused via cross-attention. On Verse-Bench it sets new SOTA on Sync-C / Sync-D / video quality / audio WER while using 2× to 5× fewer parameters than open-source baselines.

Highlights

• 720p 1-min Fast Generation — 720p synchronized audio-video in ~1 minute via 8-GPU Ulysses sequence parallel.
• Dual-Channel Audio — stereo audio (scene + speech) jointly denoised with video, no post-hoc vocoder alignment.
• Precise Multi-Timbre Control — reference WAVs bound to <S>...<E> speech spans for per-speaker voice identity.
• Language-Described Camera Control — shot composition, motion, and pacing directly from the prompt.
• Multi-Resolution — landscape / portrait / square aspect ratios from the same checkpoint.

Model Details

Quick Facts

Architecture

NAVA instantiates Native Audio-Visual Alignment as an Align-then-Fuse MMDiT stack:

• Hierarchical Alignment Layers — 10 double-stream blocks. Video and audio keep separate QKV projections and FFNs but share a joint self-attention over concatenated [video_tokens; audio_tokens] , plus dedicated cross-attention to text. This builds an alignment space where AV correspondence is learned without semantic context interference.
• Unified Fusion Layers — 20 single-stream blocks. Video and audio share QKV/FFN; a unified joint attention treats all tokens as one stream, with a single text cross-attention path. This is where context-conditioned denoising happens.
• Backbone hyperparameters. dim=3072 , ffn_dim=14336 , 24 attention heads, 30 layers (10 double + 20 single), text_len=512 , patch size (1, 2, 2) . RMSNorm on QK; cross-attention norm; ε = 1e-6.
• Positional encoding. 3D RoPE for video (temporal + height + width), 1D RoPE for audio, applied jointly inside the joint-attention path.
• Timbre-in-Context Conditioning. Reference-WAV speaker embeddings (ReDimNet, 192-d) are injected through the context pathway and bound to <S>...<E> speech spans, enabling per-speaker timbre control in multi-speaker scenes.
• 3D cross-modal CFG. Independent classifier-free guidance scales for video, audio, and the cross-modal alignment direction ( video_align_guidance_scale , audio_align_guidance_scale ) keep AV synchronization tight at inference.

What's Different from Existing Open-Source AV Models

Components Shipped Alongside the Backbone

Evaluation

Table 1 — VerseBench (general AV capability)

NAVA achieves the best AV synchronization (Sync-C / Sync-D), video quality, and audio WER, with the smallest parameter budget.

_{↑ higher is better · ↓ lower is better · bold = best · underline = 2nd best.}

Table 2 — Seed-TTS-eval (speech quality)

Among joint AV models, NAVA delivers speech quality close to dedicated audio-only systems. Audio-only rows are listed for reference ; they are not directly comparable.

How to Use

TL;DR command. After §1 setup is complete:

bash scripts/inference.sh           # General T2AV
bash scripts/inference_timbre.sh    # I2AV + timbre control

Outputs land under eval_results/ .

1 · Setup (once)

git clone https://github.com/ernie-research/NAVA && cd NAVA

# Python deps
pip install torch torchvision torchaudio
pip install diffusers transformers accelerate safetensors einops scipy PyYAML tqdm sentencepiece
pip install flash-attn --no-build-isolation

# All weights in one shot — main checkpoint + Wan2.2 VAE + T5 + LTX audio VAE
huggingface-cli download <NAVA-repo-id> --local-dir .

NAVA/
├── NAVA.ckpt                                                    # main checkpoint (24 GB)
├── Wan2.2-TI2V-5B/
│   ├── Wan2.2_VAE.pth                                           # 2.7 GB
│   ├── models_t5_umt5-xxl-enc-bf16.pth                          # 11 GB
│   └── google/umt5-xxl/{spiece.model, tokenizer.json}
├── params/
│   └── LTX2/
│       ├── ltx-2.3-22b-dev_audio_vae.safetensors                # 348 MB
│       └── LICENSE                                              # LTX-2 Community License
└── configs/                                                     # inference YAMLs

2 · One-command inference (recommended, 8 GPU SP)

The repo ships two end-to-end scripts that build a JSONL inline and launch SP=8 inference:

# General T2AV (text-only)
bash scripts/inference.sh

# I2AV + Timbre Control (first-frame image + reference voice)
bash scripts/inference_timbre.sh

Override defaults via env vars:

CKPT=/path/to/NAVA.ckpt OUT_DIR=eval_results/run1 bash scripts/inference.sh
TIMBRE_SCALE=3.0 SPK_WAV=/path/to/spk.wav    bash scripts/inference_timbre.sh

3 · Custom batches — write your own JSONL

Each line is one prompt:

{"prompt": "一位男子在海边奔跑，镜头跟随。背景是海浪声和风声。"}
{"prompt": "两人对话<S>Hello<E><S>Hi there<E>", "spk_wavs": ["spk1.wav", "spk2.wav"]}
{"prompt": "镜头跟随主体...", "image_path": "/abs/path/first_frame.png"}

Then launch:

SETUPTOOLS_USE_DISTUTILS=stdlib torchrun \
    --nnodes=1 --nproc_per_node=8 \
    --master_addr=127.0.0.1 --master_port=29507 \
    inference_nava.py \
    --config configs/baseline_t2av_demo_mmdit_no_split_ltx_control_unipc.yaml \
    --ckpt NAVA.ckpt \
    --out_dir ./outputs \
    --data_format json --data_file my_prompts.jsonl \
    --width 1280 --height 704 --frames 37 --fps 24 \
    --steps 50 --save_sample --gen_turn 1 --use_sp

Outputs land at outputs/{save_path}-{gen_turn}_av.mp4 . For timbre-controlled samples, also pass --timbre_cfg --timbre_align_guidance_scale 3.0 .

Mode cheatsheet

4 · Prompt rewriting (recommended for short / English inputs)

NAVA is trained on Chinese dense captions; short or English prompts benefit substantially from rewriting before inference. Three pathways are provided, all sharing the same system prompt and sampling profile (so output style stays consistent), with <S>...<E> speech spans preserved verbatim.

# Batch path: start vLLM server, then rewrite a txt of prompts
bash pe_src/start_server.sh --gpu 0 --low-footprint
python pe_src/rewrite.py -i prompts.txt -o prompts_rewritten.txt

5 · Gradio Web UI

Interactive demo with click-to-rewrite (Qwen3-4B), image upload, and reference-WAV upload:

bash gradio_demo/start_gradio.sh \
    --config configs/baseline_t2av_demo_mmdit_no_split_ltx_control_unipc.yaml \
    --ckpt NAVA.ckpt \
    --rewrite_model pe_src/Qwen3-4B-Thinking-2507 \
    --port 8000 --nproc 8

python gradio_demo/gradio_server.py --debug --port 8000

Bias, Safety, and Misuse

NAVA can synthesize video and speech conditioned on a reference image ( image_path ) and reference voice ( spk_wavs ). Using it to depict real persons without consent — including face-likeness or voice-likeness reproduction — is prohibited by the license and may also be illegal in your jurisdiction. We recommend:

1. Only use consent-approved reference media.
2. Label generated content as synthetic.
3. Apply provenance / watermarking before redistribution.

Citation

@article{nava2026,
  title   = {NAVA: Native Audio-Visual Alignment for Joint Audio-Video Generation},
  author  = {ERNIE Team},
  journal = {arXiv preprint},
  year    = {2026},
}

Acknowledgements

NAVA builds on excellent upstream work: Wan2.2-TI2V-5B (video backbone & VAE), LTX 2.3 (audio VAE + built-in vocoder), umt5-xxl (text encoder), and ReDimNet (speaker embedding). We also thank the open-source AV-generation community — Ovi, MOVA, Davinci, LTX — for releasing strong baselines that made fair benchmarking possible.

License & Contact

Released under Apache-2.0 . For research / commercial inquiries, contact the ERNIE team at Baidu Inc.