Update README.md

README.md

@@ -5,7 +5,8 @@ This is the repo of the manuscript "Dual-branch Attention-In-Attention Transform
 Abstract：Curriculum learning begins to thrive in the speech enhancement area, which decouples the original spectrum estimation task into multiple easier sub-tasks to achieve better performance. Motivated by that, we propose a dual-branch attention-in-attention transformer-based module dubbed DB-AIAT to handle both coarse- and fine-grained regions of spectrum in parallel. From a complementary perspective, a magnitude masking branch is proposed to estimate the overall spectral magnitude, while a complex refining branch is designed to compensate for the missing complex spectral details and implicitly derive phase information. Within each branch, we propose a novel attention-in-attention transformer-based module to replace the conventional RNNs and temporal convolutional network for temporal sequence modeling. Specifically, the proposed attention-in-attention transformer consists of adaptive temporal-frequency attention transformer blocks and an adaptive hierarchical attention module, which can capture long-term time-frequency dependencies and further aggregate global hierarchical contextual information. The experimental results on VoiceBank + Demand dataset show that DB-AIAT yields state-of-the-art performance (e.g., 3.31 PESQ, 95.6% STOI and 10.79dB SSNR) over previous advanced systems with a relatively light model size (2.81M).
 
 ### Code:
-The code for network architecture is privoided now, while the trainer is released soon. You can use dual_aia_trans_merge_crm() in aia_trans.py for dual-branch SE, while aia_complex_trans_mag() and aia_complex_trans_ri() are single-branch aprroaches.
+	The code for network architecture is privoided now, while the trainer is released soon. You can use dual_aia_trans_merge_crm() in aia_trans.py for dual-branch SE, while aia_complex_trans_mag() and aia_complex_trans_ri() are single-branch aprroaches.
+	The trained weights on VB dataset is also provided. You can directly perform inference or finetune the model by using vb_aia_merge_new.pth.tar. The code for inference will be released soon.
 
 ### requirements: