Speaker Profiling

This Repository contains the code for estimating the Age and Height of a speaker with their speech signal. This repository uses s3prl library to load various upstream models like wav2vec2, CPC, TERA etc. This repository uses TIMIT dataset.

NOTE: If you want to run the single encoder model, you should checkout the singleEncoder branch and follow the README in that branch.

Installation

Use the package manager pip to install the required packages for preparing the dataset, training and testing the model.

pip install -r requirements.txt

Usage

Download the TIMIT dataset

wget https://data.deepai.org/timit.zip
unzip timit.zip -d 'path to timit data folder'

Prepare the dataset for training and testing

python TIMIT/prepare_timit_data.py --path='path to timit data folder'

Update Config and Logger

Update the config.json file to update the upstream model, batch_size, gpus, lr, etc and change the preferred logger in train_.py files. Create a folder 'checkpoints' to save the best models. If you wish to perform narrow band experiment, just set narrow_band as true in config.json file.

Training

python train_timit.py --data_path='path to final data folder' --speaker_csv_path='path to this repo/SpeakerProfiling/Dataset/data_info_height_age.csv'

Example:

python train_timit.py --data_path=/notebooks/SpeakerProfiling/TIMIT_Dataset/wav_data/ --speaker_csv_path=/notebooks/SpeakerProfiling/Dataset/data_info_height_age.csv

Testing

python test_timit.py --data_path='path to final data folder' --model_checkpoint='path to saved model checkpoint'

Example:

python test_timit.py --data_path=/notebooks/SpeakerProfiling/TIMIT_Dataset/wav_data/ --model_checkpoint=checkpoints/epoch=1-step=245-v3.ckpt

Pretrained Model

We have uploaded a pretrained model of our experiments. You can download the from Dropbox.

Download it and put it into the model_checkpoint folder.

License

MIT

Reference

• [1] S3prl: The self-supervised speech pre-training and representation learning toolkit. AT Liu, Y Shu-wen