Code for the CVAE-NL model from the Accented Text-to-Speech Synthesis with a Conditional Variational Autoencoder paper, available at: https://arxiv.org/abs/2211.03316
First preprocess your data into mel spectrogram .npy arrays with the preprocess.py script Then run CUDA_VISIBLE_DEVICES=X python train.py --dataset L2Arctic
Once trained, you can run CUDA_VISIBLE_DEVICES=X python synthesize.py --dataset L2Arctic --restore_step [N] --mode [batch/single/sample] --text [TXT] --speaker_id [SPID] --accent [ACC]
Or run synthesize_debug.py in a debugger and figure out!
###Inference modes
"single": takes in a reference audio to be used for both speaker and accent branches
"batch": allows to extract mu and std values for speakers and accents from a passed dataset
"sample": take the extracted mu and std values, choose your speaker with --speaker_id, and your accent with --accent, then synthesize the speech!