Finetune SSL models for MOS prediction

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Overview

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

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.
Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

Comments

How to get the label s of BVCC test wav?

How can I get BVCC test dataset? I only found data and label of train and val in VoiceMOS codalab, but I don't know how to get the label s of test wav. Is it opensource?

opened by jiusansan222 3
Loading pre-trained wav2vec2.0 model error

Hi, have you encountered the error of loading pre-trained wav2vec2.0 models?

the code is as follows: cp_path = './ssl/xlsr_53_56k.pt' model, cfg, task = fairseq.checkpoint_utils.load_model_ensemble_and_task([cp_path]) model = model[0] model.eval()

it works with hubert_large_ll60k.pt, but not with xlsr_53_56k.pt.

the error code is omegaconf.errors.ConfigKeyError: Key 'eval_wer' not in 'AudioPretrainingConfig' full_key: eval_wer reference_type=Optional[AudioPretrainingConfig] object_type=AudioPretrainingConfig

Thank you!

opened by Kristopher-Chen 2
Val loss vray greatly during training

When I used this code and bvcc dataset to trian a mos predict model, I found that the results of serval times were different. And I plot the train loss and val loss picture, it shows that val loss vray greatly from the vary begining of training. Do you have the same findings? Is it overfitting? Thanks!!

opened by jiusansan222 0

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Finetune SSL models for MOS prediction

Related tags

Overview

Finetune SSL models for MOS prediction

Dependencies:

How to use

Acknowledgments

License

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Comments

How to get the label s of BVCC test wav?

Loading pre-trained wav2vec2.0 model error

Val loss vray greatly during training

Owner

Yamagishi and Echizen Laboratories, National Institute of Informatics

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