Lightweight speaker anonymization [IEEE SLT2021]

This recipe optimizes parameters of voice modification modules M(*) for speaker anonymization. Given training data (speech, text, speaker label), this recipe estimates the parameters that minimize I_obj consisting of WER (word error rate) and negative EER (equal error rate).

Requirement

Python 3.6 or the later
librosa
soundfile
functools
joblib
optuna
shutil
audiotsm
scipy

Anonymization using pre-optimized parameters

You can anonymize speech using pre-optimized model parameters. For example,

python scripts/anonymize.py params/vctk/male/R.json      # resampling only
python scripts/anonymize.py params/vctk/male/R-MS.json   # resampling & MS smoothing.

This script loads data/vctk/p227_001.wav and saves the anonymized speech to anonymized.wav. The path *.json is formmatted as {dataset}/{gender}/{method}.json, where

dataset: speech dataset we used for optimization (librispeech or vctk)
gender: male or female
method: modification method. VLTN (V), resampling (R), McAdams (M), MS (MS smoothing), CL (clipping), CH (chorus).

We recommend to use */*/R.json. See reference for details.

Optimization

You can optimize model parameters using your own ASR (automatic speech recognition), ASV (automatic speaker verification), and training data (speech, text, speaker label).

python scripts/optimize.py

Before running, please revise loss_asr() and loss_asv() to use your own ASR and ASV systems for computing WER and EER. These functions implemented in this repository return dummy values. This script loads data/vctk/*.wav as training data and saves the optimized parameters to params/sample.json. The hparams variable in this script lists hyperparameters and cascaded modules. For example, when you uncomment hparams["anon_params"]["mcadas"], the McAdams transformation module will be added to the cascade. The cascade executes modules, following an order in hparams["anon_params"].

After optimization, you can drive anonymization as shown above.

Reference

Hiroto Kai, Shinnosuke Takamichi, Sayaka Shiota, Hitoshi Kiya, "Lightweight voice anonymization based on data-driven optimization of cascaded voice modification modules," Proc. IEEE SLT, pp. xxx--xxx, Shenzhen, China, 2021. (to appear)

Note