Finetune SSL models for MOS prediction

This is code for our paper under review for ICASSP 2022:

"Generalization Ability of MOS Prediction Networks" Erica Cooper, Wen-Chin Huang, Tomoki Toda, Junichi Yamagishi https://arxiv.org/abs/2110.02635

Please cite this preprint if you use this code.

Dependencies:

• Fairseq toolkit: https://github.com/pytorch/fairseq Make sure you can import fairseq in Python.
• torch, numpy, scipy, torchaudio
• I have exported my conda environment for this project to environment.yml
• You also need to download a pretrained wav2vec2 model checkpoint. These can be obtained here: https://github.com/pytorch/fairseq/tree/main/examples/wav2vec Please choose wav2vec_small.pt, w2v_large_lv_fsh_swbd_cv.pt, or xlsr_53_56k.pt.
• You also need to have a MOS dataset. Datasets for the MOS prediction challenge will be released once the challenge starts. TODO update with a link.

How to use

• Modify the paths in mos_fairseq.py to point to your own data and SSL checkpoints.
• Run python mos_fairseq.py to finetune an SSL model on the data.
• Modify variables in predict.py to point to your favorite checkpoint.
• run predict.py to run inference using that checkpoint.

Acknowledgments

This study is supported by JST CREST grants JP- MJCR18A6, JPMJCR20D3, and JPMJCR19A3, and by MEXT KAKENHI grants 21K11951 and 21K19808. Thanks to the organizers of the Blizzard Challenge and Voice Conversion Challenge, and to Zhenhua Ling, Zhihang Xie, and Zhizheng Wu for answering our questions about past challenges. Thanks also to the Fairseq team for making their code and models available.

License

BSD 3-Clause License

Copyright (c) 2021, Yamagishi Laboratory, National Institute of Informatics All rights reserved.

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

• Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

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