USEF-Laura-TSE

USEF-Laura-TSE

This repository provides the official model checkpoints for Discriminative–Generative Target Speaker Extraction with Decoder-Only Language Models.

The paper is currently under review. At this stage, we release the model checkpoints and inference code only. The training code will be made publicly available after the review process is complete.

Model Description

USEF-Laura-TSE is a two-stage target speaker extraction system that combines:

• a discriminative USEF-TFGridNet front-end for extracting the target speaker from a speech mixture; and
• a Laura-based generative back-end built with a decoder-only language model for improving perceptual quality and speech naturalness.

The generative back-end supports both autoregressive (AR) and non-autoregressive (NAR) inference. NAR inference additionally supports a configurable ratio between 0.0 and 1.0.

Model Files

The FunCodec model and configuration required by the generative back-end are downloaded separately by the inference release.

Installation

Clone the official inference repository and install its dependencies:

git clone https://github.com/ZBang/USEF-Laura-TSE.git
cd USEF-Laura-TSE
python -m pip install -r requirements.txt

Download the released checkpoints:

python scripts/download_models.py

Inference

Autoregressive inference:

bash infer.sh --mode ar --output both

Non-autoregressive inference:

bash infer.sh --mode nar --ratio 1.0 --output both

The --output option supports front, generative, and both. See the GitHub repository for manifest formats, checkpoint paths, and complete usage instructions.

License

This model is released under the Creative Commons Attribution-NonCommercial 4.0 International License. Third-party components and models remain subject to their original licenses.