Talker-T2AV

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ACM MM 2026 Talker-T2AV Joint Talking Audio-Video Generation with Autoregressive Diffusion Modeling

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# Talker-T2AV

**Joint Talking Audio-Video Generation with Autoregressive Diffusion Modeling**

[![arXiv](https://img.shields.io/badge/arXiv-2604.23586-b31b1b.svg)](https://arxiv.org/abs/2604.23586)
[![HuggingFace](https://img.shields.io/badge/%F0%9F%A4%97%20HuggingFace-Talker--T2AV-yellow)](https://huggingface.co/HKUSTAudio/Talker-T2AV)
[![Dataset](https://img.shields.io/badge/%F0%9F%A4%97%20Dataset-Talker--T2AV--Data-yellow)](https://huggingface.co/datasets/HKUSTAudio/Talker-T2AV-Data)
[![Samples](https://img.shields.io/badge/Samples-talker--t2av.github.io-blue)](https://talker-t2av.github.io/)
[![License: Apache 2.0](https://img.shields.io/badge/License-Apache_2.0-green.svg)](https://raw.githubusercontent.com/zhenye234/Talker-T2AV/main/LICENSE)

## Highlights

- 🎤 **Unified talking audio-video generation** with a single autoregressive diffusion model supporting T2AV, A2V and V2A.
- 📦 **Open-source dataset with over 1 million high-quality talking audio-video samples**, carefully curated, cleaned and organized for large-scale training.
- 🤗 Fully open-sourced model checkpoints, training pipeline and dataset.

Talker-T2AV decouples joint talking audio-video generation into two stages:

1. **Cross-Modal Modeling** — a shared autoregressive backbone (Qwen3-0.6B)
   reasons over text + audio + video in a unified patch-level token sequence.
   Audio and video patch embeddings are summed element-wise at each position.
2. **Modality-Specific Refinement** — two lightweight diffusion transformer
   heads independently denoise the backbone hidden state into 32-d audio
   latents and 40-d motion latents via OT-CFM.

The audio decoder is **WhisperX-VAE** (32-d, 25 Hz) and the video decoder is
**LIA-X** (40-d motion, 25 Hz). A single model supports T2AV (joint
text→audio+video), A2V (audio-driven), and V2A (video dubbing) without
architectural change.

## Installation

```bash
conda create -n talker-t2av python=3.11 -y
conda activate talker-t2av

# torch first (CUDA 12.4 wheel)
pip install --index-url https://download.pytorch.org/whl/cu124 \
    torch==2.6.0 torchaudio==2.6.0 torchvision==0.21.0

# main requirements
pip install -r requirements-eval.txt

# flash-attn (5–15 min build, or grab a prebuilt wheel for torch 2.6 + cu124)
pip install flash_attn==2.7.4.post1 --no-build-isolation

# ffmpeg (LIA-X render needs it). Easiest: reuse the binary from
# the imageio-ffmpeg wheel that pip already installed:
ln -s "$(python -c 'import imageio_ffmpeg; print(imageio_ffmpeg.get_ffmpeg_exe())')" \
      "$CONDA_PREFIX/bin/ffmpeg"
```

## Download checkpoints

Inference needs three sets of pretrained weights. The Python sources for
WhisperX-VAE, LIA-X and the WavLM speaker encoder are **already vendored**
under `whisperx_vae.py`, `lia_x/`, and `speaker_verification/` — you only
need to fetch the binary `.pt`/`.ckpt` files.

**1. Talker-T2AV + WhisperX-VAE** — from
[HKUSTAudio/Talker-T2AV](https://huggingface.co/HKUSTAudio/Talker-T2AV) on
HuggingFace:

```bash
hf download HKUSTAudio/Talker-T2AV \
    --local-dir ./ckpts/hf_weights

export CHECKPOINT_DIR=$(pwd)/ckpts/hf_weights/talker-t2av
export WHISPERVAE_CKPT=$(pwd)/ckpts/hf_weights/whisperx-vae/model.ckpt
```

**2. LIA-X video autoencoder weights** — get `lia-x.pt` from
[wyhsirius/LIA-X](https://github.com/wyhsirius/LIA-X) (only the weight file
is needed; the model code is vendored under `lia_x/`):

```bash
mkdir -p ./deps/LIA-X
# place lia-x.pt here, or override LIAX_CKPT to point elsewhere
export LIAX_CKPT=$(pwd)/deps/LIA-X/lia-x.pt
```

**3. WavLM-Large fine-tuned speaker encoder** — `wavlm_large_finetune.pth`
from
[microsoft/UniSpeech speaker_verification](https://github.com/microsoft/UniSpeech/tree/main/downstreams/speaker_verification)
(again, only the weight file is needed):

```bash
export WAVLM_CKPT=/path/to/wavlm_large_finetune.pth
```

The first run will additionally fetch the upstream s3prl WavLM repo via
`torch.hub` (cached under `~/.cache/torch/hub/` or `$S3PRL_CACHE_DIR`).

## Quickstart

```bash
# minimal: speak the default Chinese demo prompt with the bundled identity / voice
python infer.py

# custom: any text, any reference voice, any reference identity
python infer.py \
    --text "Hello, this is Talker-T2AV." \
    --ref-audio  path/to/voice.wav \
    --ref-motion path/to/lia-x-feature.pt \
    --ref-video  path/to/identity.mp4 \
    --output     out.mp4
```

The script writes `out.mp4` (synced video + audio).

Because Talker-T2AV encodes speech and video into temporally aligned
latent sequences at the same 25 Hz frame rate and fuses them via
element-wise summation in the AR backbone, the same checkpoint also
handles **V2A** (video dubbing — given a silent talking-head clip,
fill in the speech) and **A2V** (audio-driven — given speech, animate
a face), with no architectural change or fine-tuning.

### Two driving modes via `--gt-prefix-seconds`

The same model supports two qualitatively different generation modes,
selected purely by how much of the reference clip is fed in as a prefix:

- **`--gt-prefix-seconds 0`** — *speaker-/identity-only driving*. The model
  sees the reference audio only through the WavLM speaker embedding (timbre)
  and the reference video only through the first frame (face appearance).
  Speaking style, cadence, and head motion are sampled freely from the
  prior. Use this when you want the **voice and face** of the reference
  but a fresh delivery.

- **`--gt-prefix-seconds N>0`** — * Similar to zero-shot TTS / style cloning* (as in
  the [VALL-E](https://arxiv.org/abs/2301.02111) paper). The
  first `N` seconds of the reference audio + motion are fed through the
  AR backbone before generation starts. The model continues in the
  reference's prosody, rhythm, and head-motion style — like zero-shot
  voice cloning, but for **speech + video together**.

The prefix can play either of two roles depending on what you put in
  `--ref-audio` / `--ref-motion`:
  - *Cross-sentence* — the reference is a **different** utterance from
    the target text. The model only borrows speaker timbre, prosody,
    and head-motion style; the synthesised content comes entirely from
    `--text`.
  - *Continuation* — the reference is the **beginning of the same
    utterance** you want to produce, and the model continues from where
    the prefix leaves off. Useful for long-form synthesis or seamless
    splicing.

## Training

The minimal training entry point lives in [`training/`](training/) — see
its [README](training/README.md) for the data layout, env-var
configuration, and launch command (single- or multi-node, via
`torchrun`).

The training data is hosted on HuggingFace at
[HKUSTAudio/Talker-T2AV-Data](https://huggingface.co/datasets/HKUSTAudio/Talker-T2AV-Data).

## Repository layout

```
.
├── infer.py                  # entry point — text → talking-head mp4
├── speech_llm.py             # SpeechLLM (AR backbone + dual diffusion heads + stop predictor)
├── unified_cfm.py            # OT-CFM with CFG-zero-star + sway-sampling Euler solver
├── local_dit.py              # Diffusion Transformer Head (VoxCPMLocDiT)
├── llama4nar.py              # Patch Transformer Encoder + bidirectional NAR decoder
├── whisperx_vae.py           # vendored WhisperX-VAE audio autoencoder (encode/decode only)
├── lia_x/                    # vendored LIA-X video motion autoencoder
│   ├── motion_mean.npy / motion_std.npy   # 40-d motion normalization stats
│   └── networks/             # Encoder / Decoder / Generator + StyleGAN2 cpp_extension kernels
├── speaker_verification/     # vendored WavLM-Large ECAPA-TDNN speaker encoder
│   └── models2/ecapa_tdnn.py
├── training/                 # minimal training entry point (see training/README.md)
├── requirements-eval.txt     # Pinned package list (Python 3.11 + CUDA 12.4)
└── samples/                  # bundled demo data for `python infer.py`
    ├── reference_audio.wav
    ├── reference_motion.pt
    └── reference_video.mp4
```

## Citation

```bibtex
@misc{ye2026talkert2avjointtalkingaudiovideo,
      title={Talker-T2AV: Joint Talking Audio-Video Generation with Autoregressive Diffusion Modeling}, 
      author={Zhen Ye and Xu Tan and Aoxiong Yin and Hongzhan Lin and Guangyan Zhang and Peiwen Sun and Yiming Li and Chi-Min Chan and Wei Ye and Shikun Zhang and Wei Xue},
      year={2026},
      eprint={2604.23586},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2604.23586}, 
}
```

## License

[Apache License 2.0](LICENSE)

## Acknowledgements

- LLM backbone: [Qwen3](https://github.com/QwenLM/Qwen3)
- Audio autoencoder builds on [X-Codec-2.0](https://github.com/zhenye234/X-Codec-2.0)
- Video motion autoencoder: [LIA-X](https://github.com/wyhsirius/LIA-X)
- Speaker encoder: [Microsoft UniSpeech speaker_verification](https://github.com/microsoft/UniSpeech/tree/main/downstreams/speaker_verification) (WavLM-Large + ECAPA-TDNN)

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