---
license: apache-2.0
library_name: pytorch
pipeline_tag: audio-to-audio
tags:
- audio-visual
- target-speaker-extraction
- speech-separation
- streaming
datasets:
- JusperLee/RealSSA-Real
- JusperLee/RealSSA-Sim
---
# Falcon
Causal audio-visual target speaker extraction model from the paper: a single-pass multi-scale separator trained with an encoder-space speaker contrastive loss (0.5 M trainable parameters).
Code, training and evaluation: . Paper: *Efficient Streaming Audio-Visual Target Speaker Extraction for Real-World Acoustic Scenes* (NeurIPS 2026), Wendi Sang\*, Kai Li\*, Yifan Li, Jianqiang Huang, Xiaolin Hu (\*equal contribution).
| File | Config in the code repo | Audio network | Total params |
|---|---|---|---|
| `falcon.pt` | `configs/Falcon.yml` | `FalconSpeakerContrastive` | 11.72 M |
Total parameters include the frozen 11.2 M-parameter FRCNN lip-reading encoder. The file holds only `state_dict` (`audio_model.*`, `video_model.*`), the architecture name and the training epoch; load it with `torch.load(..., weights_only=True)`.
## Usage
```bash
git clone https://github.com/JusperLee/Falcon.git
cd Falcon/studio && pip install -r requirements.txt
```
```python
import numpy as np
from falcon_runtime import FalconRuntime
model = FalconRuntime.from_pretrained("JusperLee/Falcon") # downloads falcon.pt on first use
audio = np.zeros(32000, dtype=np.float32) # 2 s mono 16 kHz mixture
mouth = np.zeros((50, 96, 96), dtype=np.uint8) # 25 fps grayscale mouth crops of the target speaker
target = model.infer(audio, mouth) # (32000,) target speech
```
Whole clips: `python infer_file.py --audio mix.wav --mouth mouth.npy --out target.wav --model JusperLee/Falcon`. Live separation in the browser and in Zoom / Tencent Meeting: see `studio/README.md` in the code repo.
Input: mono 16 kHz audio and synchronized 25 fps grayscale 96×96 mouth crops (center-cropped to 88×88 and normalized inside the runtime).
## License
Apache License 2.0, the same as the code repository.