This is an implementation of the papers:
(1) Language-independent speaker anonymization approach using self-supervised pre-trained models
(2) Analyzing Language-Independent Speaker Anonymization Framework under Unseen Conditions
The authors are Xiaoxiao Miao, Xin Wang, Erica Cooper, Junichi Yamagishi, Natalia Tomashenko.
Audio samples can be found here: https://nii-yamagishilab.github.io/SAS-audio-samples/
Please cite these papers if you use this code.
git clone https://github.com/nii-yamagishilab/SSL-SAS.git
cd SSL-SAS
bash scripts/install.sh
Make sure sox and parallel are installed.
If not:
source env.sh
conda install -c conda-forge sox
conda install -c conda-forge parallel
-
Try pre-trained model
- Download English development and evaluation data provided by the VoicePrivacy2020 Challenge: VCTK-subsets (vctk_dev and vctk_test) and LibriSpeech-subsets (libri_dev and libri_test). Just run
bash adapted_from_vpc/00_download_testdata.sh. The user will be requested the password, please contact VoicePrivacy2020 Challenge organizers. - Generate anonymized speech:
bash scripts/engl_scripts/01_demo.sh. - Following the VoicePrivacy2020 Challenge to compute the performance.
- Download English development and evaluation data provided by the VoicePrivacy2020 Challenge: VCTK-subsets (vctk_dev and vctk_test) and LibriSpeech-subsets (libri_dev and libri_test). Just run
-
Train a HiFi-GAN using LibriTTS-100h on your own:
bash scripts/engl_scripts/02_train.sh
Mandarin models and speaker vectors are available for internal academic and research use only. If users would like to reproduce Mandarin anonymization experiments, please contact xiaoxiaomiao@nii.ac.jp.
This study is supported by JST CREST Grants (JPMJCR18A6 and JPMJCR20D3), MEXT KAKENHI Grants (21K17775, 21H04906, 21K11951, 18H04112), and the VoicePersonal project (ANR-18-JSTS-0001)
The adapted_from_facebookreaserch subfolder has Attribution-NonCommercial 4.0 International License. The adapted_from_speechbrain subfolder has Apache License. They were created by the facebookreasearch and speechbrain orgnization, respectively. The scripts subfolder has the MIT license.
Because this source code was adapted from the facebookresearch and speechbrain, the whole project follows
the Attribution-NonCommercial 4.0 International License.
Copyright (c) 2022, Yamagishi Laboratory, National Institute of Informatics. All rights reserved.
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