Voice-Privacy-Challenge/Voice-Privacy-Challenge-2024

Baseline Recipe for VoicePrivacy Challenge 2024: anonymization systems and evaluation software

Recipe for VoicePrivacy Challenge 2024

Please visit the challenge website for more information about the Challenge.

Data submission

The anonymization and evaluation scripts should have generated the files and the directories with the explained format of $anon_data_suffix suffix.
For data submission, the following command submit everything given a $anon_data_suffix argument:

VPC_DROPBOX_KEY=XXX VPC_DROPBOX_SECRET=YYY VPC_DROPBOX_REFRESHTOKEN=ZZZ VPC_TEAM=TEAM_NAME ./03_upload_submission.sh $anon_data_suffix

VPC_DROPBOX_KEY, VPC_DROPBOX_SECRET, VPC_DROPBOX_REFRESHTOKEN, and VPC_TEAM=TEAM_NAME are sent individually to each team upon receiving their system description.

Install

1. git clone https://github.com/Voice-Privacy-Challenge/Voice-Privacy-Challenge-2024.git
2. ./00_install.sh
3. source env.sh

Download data

./01_download_data_model.sh A password is required; please register to get the password.

You can modify the librispeech_corpus variable of ./01_download_data_model.sh to avoid downloading LibriSpeech 360.
You have to modify the iemocap_corpus variable of ./01_download_data_model.sh to where it is located on your server.

Important

The IEMOCAP corpus must be downloaded on your own by submitting a request at https://sail.usc.edu/iemocap/iemocap_release.htm. The waiting time may take up to 7-9 days.

Anonymization and Evaluation

There are two options:

1. Run anonymization and evaluation: ./02_run.sh configs/anon_mcadams.yaml.
   For each anonymization baseline, there is a corresponding config file:

   • Anonymization using the McAdams coefficient: B2

     configs/anon_mcadams.yaml A fast CPU-only signal processing-based system (default).

   • Anonymization using phonetic transcriptions and GAN (STTTS): B3

     configs/anon_sttts.yaml A system based on unmodified phone sequence, modified prosody, modified speaker embedding representations and speech synthesis.

   • Anonymization using neural audio codec (NAC) language modeling: B4

     configs/anon_nac.yaml

   • Anonymization using ASR-BN with vector quantization (VQ): B5 and B6

     configs/anon_asrbn.yaml A fast system based on vector quantized acoustic bottleneck, pitch, and one-hot speaker representations and a HiFi-GAN speech synthesis model.

   • ⚠ Anonymization using x-vectors and a neural source-filter model: B1

     anonymization scripts from https://github.com/Voice-Privacy-Challenge/Voice-Privacy-Challenge-2022 can be used to obtain anonymized data for B1. To perform utterance-level (in contrast to speaker-level) anonymization of the enrollment and trial data for B1, the corresponding parameters should be setup in https://github.com/Voice-Privacy-Challenge/Voice-Privacy-Challenge-2022/blob/master/baseline/config.sh: anon_level_trials=utt and anon_level_enroll=utt (https://github.com/Voice-Privacy-Challenge/Voice-Privacy-Challenge-2022/blob/d72b50c44677aa9a1ba37b7f0c383c4fde13e05f/baseline/config.sh#L59-L60)

2. Run anonymization and evaluation separately in two steps:

Step 1: Anonymization

python run_anonymization.py --config configs/anon_mcadams.yaml  #Computational time varies from 30 minutes to 10 hours, depending on the number of cores, for other methods it may be longer and depending on the available hardware. 

The anonymized audios will be saved in $data_dir=data into 9 folders corresponding to datasets. The names of the created dataset folders for anonymized audio files are appended with the suffix, i.e. $anon_data_suffix=_mcadams

data/libri_dev_enrolls${anon_data_suffix}/wav/*wav
data/libri_dev_trials_m${anon_data_suffix}/wav/*wav
data/libri_dev_trials_f${anon_data_suffix}/wav/*wav

data/libri_test_enrolls${anon_data_suffix}/wav/*wav
data/libri_test_trials_m${anon_data_suffix}/wav/*wav
data/libri_test_trials_f${anon_data_suffix}/wav/*wav

data/IEMOCAP_dev${anon_data_suffix}/wav/*wav
data/IEMOCAP_test${anon_data_suffix}/wav/*wav

data/train-clean-360${anon_data_suffix}/wav/*wav

For the next evaluation step, you should replicate the corresponding directory structure when developing your anonymization system.

Step 2: Evaluation

Evaluation metrics include:

• Privacy: Equal error rate (EER) for ignorant, lazy-informed, and semi-informed attackers (only results from the semi-informed attacker will be used in the challenge ranking)
• Utility:
  • Word Error Rate (WER) by an automatic speech recognition (ASR) model (trained on LibriSpeech)
  • Unweighted Average Recall (UAR) by a speech emotion recognition (SER) model (trained on IEMOCAP).

To run evaluation for arbitrary anonymized data:

1. prepare 9 anonymized folders each containing the anonymized wav files:

data/libri_dev_enrolls${anon_data_suffix}/wav/*wav
data/libri_dev_trials_m${anon_data_suffix}/wav/*wav
data/libri_dev_trials_f${anon_data_suffix}/wav/*wav

data/libri_test_enrolls${anon_data_suffix}/wav/*wav
data/libri_test_trials_m${anon_data_suffix}/wav/*wav
data/libri_test_trials_f${anon_data_suffix}/wav/*wav

data/IEMOCAP_dev${anon_data_suffix}/wav/*wav
data/IEMOCAP_test${anon_data_suffix}/wav/*wav

data/train-clean-360${anon_data_suffix}/wav/*wav

2. perform evaluations

python run_evaluation.py --config configs/eval_pre.yaml --overwrite "{\"anon_data_suffix\": \"$anon_data_suffix\"}" --force_compute True
python run_evaluation.py --config configs/eval_post.yaml --overwrite "{\"anon_data_suffix\": \"$anon_data_suffix\"}" --force_compute True

3. get the final results for ranking

results_summary_path_orig=exp/results_summary/eval_orig${anon_data_suffix}/results_orig.txt # the same value as $results_summary_path in configs/eval_pre.yaml
results_summary_path_anon=exp/results_summary/eval_anon${anon_data_suffix}/results_anon.txt # the same value as $results_summary_path in configs/eval_post.yaml
results_exp=exp/results_summary
{ cat "${results_summary_path_orig}"; echo; cat "${results_summary_path_anon}"; } > "${results_exp}/result_for_rank${anon_data_suffix}"
zip ${results_exp}/result_for_submission${anon_data_suffix}.zip -r exp/asr/*${anon_data_suffix} exp/asr/*${anon_data_suffix}.csv exp/ser/*${anon_data_suffix}.csv exp/results_summary/*${anon_data_suffix}* exp/asv_orig/*${anon_data_suffix} exp/asv_orig/*${anon_data_suffix}.csv exp/asv_anon${anon_data_suffix}

All of the above steps are automated in 02_run.sh.

Results

Note, that WER results are computed on the trials part

The result file with all the metrics and all datasets for submission will be generated in:

• Summary results: ./exp/results_summary/result_for_rank$anon_data_suffix
• Additional information for submission: ./exp/results_summary/result_for_submission${anon_data_suffix}.zip

Please see the RESULTS folder for the provided anonymization baselines:

• Results B1
• Results B2
• Results B3
• Results B4
• Results B5
• Results B6

General information

Evaluation plan

For more details about the baseline and data, please see The VoicePrivacy 2024 Challenge Evaluation Plan - Updated on 1st April 2024

Training data

Final list of models and data for training anonymization systems.

Registration

Participants are requested to register for the evaluation. Registration should be performed once only for each participating entity using the following form: Registration.

Some potential questions you may have and how to solve them:

1. $ASV_{eval}^{anon}$ training is slow

Training of the $ASV_{eval}^{anon}$ model may vary from about 2 up to 10 hours depending on the available hardware. If you have an SSD or a high-performance drive, $ASV_{eval}^{anon}$ takes ~2h, but if the drive is old and slow, in a worse case, $ASV_{eval}^{anon}$ training takes ~10h. Increasing $num_workers in config/eval_post.yaml may help to speed up the processing.

2. OOM problem when decoding by $ASR_{eval}$

Reduce the $eval_bachsize in config/eval_pre.yaml

3. The $ASR_{eval}$ is a pretrained wav2vec+ctc trained on LibriSpeech-960h

4. Error on utils.prepare_results_in_kaldi_format

means something bad happened when running the anonymization pipeline.
Remove all data/*$anon_data_suffix directories and re-run anonymization and evaluation steps (if $anon_data_suffix=suff, also remove the directories that share a matching suffix: $anon_data_suffix=something_suff). Check that your anonymization pipeline produces a wav file for each dataset entry, every original wav should have its anonymized counterpart.

Organizers (in alphabetical order)

• Pierre Champion - Inria, France
• Nicholas Evans - EURECOM, France
• Sarina Meyer - University of Stuttgart, Germany
• Xiaoxiao Miao - Singapore Institute of Technology, Singapore
• Michele Panariello - EURECOM, France
• Massimiliano Todisco - EURECOM, France
• Natalia Tomashenko - Inria, France
• Emmanuel Vincent - Inria, France
• Xin Wang - NII, Japan
• Junichi Yamagishi - NII, Japan

Contact: organisers@lists.voiceprivacychallenge.org

License

Copyright (C) 2024

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see https://www.gnu.org/licenses/.

Reference

@article{tomashenko2024voiceprivacy,
      title={The {VoicePrivacy} 2024 Challenge Evaluation Plan}, 
      author={Natalia Tomashenko and Xiaoxiao Miao and Pierre Champion and Sarina Meyer and Xin Wang and Emmanuel Vincent and Michele Panariello and Nicholas Evans and Junichi Yamagishi and Massimiliano Todisco},
      year={2024},
      eprint={2404.02677},
      archivePrefix={arXiv},
      primaryClass={eess.AS}
}

Acknowledgments

Some parts of the code are based on VoicePAT (Paper: https://ieeexplore.ieee.org/document/10365329)