LlaSE is a state-of-the-art speech enhancement model based on a speech language model architecture. It treats enhancement as a conditional generation problem: given discrete tokens of degraded speech, the model generates discrete tokens of clean speech, which are then decoded to a waveform.

Paper: LlaSE Codebase: Github Repo

Setup

git clone https://github.com/Kevin-naticl/LLaSE.git
cd LLaSE

conda create -n LLaSE python=3.10
conda activate LLaSE
pip install -r requirements.txt

cd ckpt
bash download.sh

Running Inference

The main script for inference is inference.py, which is configured via ./config/test.yml.

Step 1 — Edit ./config/test.yml:

  • Adjust chunk and overlap durations for your audio length
  • Set wav_dir to the directory where output files will be saved:
    wav_dir: /path/to/LLASE_outputs

Step 2 — Create filelist.txt in the LLaSE directory. Each line is an absolute path to a degraded audio file:

/absolute/path/to/noisy_speech_1.wav
/absolute/path/to/noisy_speech_2.wav
/absolute/path/to/noisy_speech_3.wav

Set its location in the config:

filename: filelist.txt

Step 3 — Run inference:

bash inference.sh

The enhanced .wav files will be written to wav_dir.