Update README.md

README.md

@@ -10,12 +10,20 @@ Samples link: https://amaai-lab.github.io/Accented-TTS-MLVAE-ADV/
 This code is built upon Comprehensive-TTS: https://github.com/keonlee9420/Comprehensive-Transformer-TTS
 
 ## Training
-First download your dataset and preprocess the audio data into mel spectrogram `.npy` arrays with the `preprocess.py script`. We used L2CMU in this paper, which stands for a combination of L2Arctic (24 speakers) and CMUArctic (4 speakers). Then run ``CUDA_VISIBLE_DEVICES=X python train.py --dataset L2CMU``
+First download your dataset (L2Arctic / and CMUArctic) and preprocess the audio data into mel spectrogram `.npy` arrays with the `preprocess.py script`. We used L2CMU in this paper, which stands for a combination of L2Arctic (24 speakers) and CMUArctic (4 speakers). Then run ``CUDA_VISIBLE_DEVICES=X python train.py --dataset L2CMU``
 
 ## Inference
 Once trained, you can run `extract_stats.py` to retrieve the accent and speaker embeddings of your evaluation set and store them. Then, you can synthesize with one of the synth scripts. :-)
 
-Once trained, you can run ``CUDA_VISIBLE_DEVICES=X python synthesize.py --dataset L2Arctic --restore_step [N] --mode [batch/single] --text [TXT] --speaker_id [SPID] --accent [ACC]``
+Once trained, to generate (accent-converted / non-converted) speech, you can run
+```bash
+CUDA_VISIBLE_DEVICES=X python synthesize.py --dataset L2Arctic --restore_step [N] --mode [batch/single] --text [TXT] --speaker_id [SPID] --accent [ACC]
+```
+SPID = ABA, ASI, NCC,... speaker ID from the L2Arctic dataset
+
+ACC = Arabic, Chinese, Hindi, Korean, Spanish, Vietnamese (accents from L2Arctic)
+
+Unfortunately, we do not provide a trained model as of now.
 
 ## BibTeX citation
 ```