Transformer-based dialect identification
Description:
This is an end-to-end DID model based on the transformer neural network architecture.
All the experiences are carried out on the ADI17 dataset.(http://groups.csail.mit.edu/sls/downloads/adi17/)
All the results of this experience have been summited to IALP 2020 conference. (http://www.colips.org/conferences/ialp2020/wp/)
Wanqiu Lin, Maulik Madhavi, Rohan Kumar Das and Haizhou Li, "Transformer-based Arabic Dialect Identification," International Conference on Asian Language Processing (IALP), 4-6 Dec. 2020.
Install:
Python3 (recommend Anaconda)
PyTorch 0.4.1+
Kaldi (just for feature extraction)
Work flow:
step 1: run prep_data.sh(for prepare data and shuffle)
step 2: run extract_feat.sh(for extract acoustic features)
step 3:run run_train.sh(for training model)
step 4:run base_line.py(for test model)