singaligner

A compact audio-to-phoneme aligner for singing voice.

The available datasets in our experiments are: Opencpop, NamineRitsu. One can experiment on your own datasets.

Once the data is prepared, you should just do:

1. Create a virtual environment.
2. Define the dataloder and the collate function in utils/data_utils.py. You can inherit the existing classes.
3. Import your dataloader to train.py and change trainset, valset and collate_fn in prepare_dataloaders function.
4. Prepare a file named phone_set.json which contains the phone set of your dataset and put it at root of data_dir.
5. Change the data_dir to your data path in hparams.py
6. Run this command to start training:

   CUDA_VISIBLE_DEVICES=0 python train.py --output_directory experiments/exp_name/ --log_directory tensorboard_logs

7. Run this command to start inferring:

   CUDA_VISIBLE_DEVICES=0 python infer_prob.py --checkpoint_path experiments/exp_name/checkpoint_name \
   --output_dir experiments/exp_name/

   Citation

   @inproceedings{zheng2023compact,
   title={A Compact Phoneme-To-Audio Aligner for Singing Voice},
   author={Zheng, Meizhen and Bai, Peng and Shi, Xiaodong},
   booktitle={International Conference on Advanced Data Mining and Applications},
   pages={183--197},
   year={2023},
   organization={Springer}
   }