This repository is the official implementation of the ICASSP2024 paper: GTCRN: A Speech Enhancement Model Requiring Ultralow Computational Resources.
Audio examples are available at Audio examples of GTCRN.
Grouped Temporal Convolutional Recurrent Network (GTCRN) is a speech enhancement model requiring ultralow computational resources, featuring only 23.7 K parameters and 39.6 MMACs per second. Experimental results show that our proposed model not only surpasses RNNoise, a typical lightweight model with similar computational burden, but also achieves competitive performance when compared to recent baseline models with significantly higher computational resources requirements.
Pre-trained models are provided in checkpoints folder, which were trained on DNS3 and VCTK-DEMAND datasets, respectively.
The inference procedure is presented in infer.py.
A streaming GTCRN is provided in stream folder, which demonstrates an impressive real-time factor (RTF) of 0.07 on the 12th Gen Intel(R) Core(TM) i5-12400 CPU @ 2.50 GHz.
SEtrain: A training code template for DNN-based speech enhancement.
TRT-SE: An example of how to convert a speech enhancement model into a streaming format and deploy it using ONNX or TensorRT.