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: https://github.com/JusperLee/Falcon. 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). config.yaml is a copy of configs/Falcon.yml from the code repo; the Hub counts downloads by this file, and FalconRuntime requests it on every load.
Usage
git clone https://github.com/JusperLee/Falcon.git
cd Falcon/studio && pip install -r requirements.txt
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.