About the dataset

LibriMix is an open source dataset for source separation in noisy environments. It is derived from LibriSpeech signals (clean subset) and WHAM noise. It offers a free alternative to the WHAM dataset and complements it. It will also enable cross-dataset experiments.

Generating LibriMix

To generate LibriMix, clone the repo and run the main script : generate_librimix.sh

git clone https://github.com/JorisCos/LibriMix
cd LibriMix 
./generate_librimix.sh storage_dir

Make sure that SoX is installed on your machine.

For windows :

conda install -c groakat sox

For Linux :

conda install -c conda-forge sox

You can either change storage_dir and n_src by hand in the script or use the command line.
By default, LibriMix will be generated for 2 and 3 speakers, at both 16Khz and 8kHz, for min max modes, and all mixture types will be saved (mix_clean, mix_both and mix_single). This represents around 430GB of data for Libri2Mix and 332GB for Libri3Mix. You will also need to store LibriSpeech and wham_noise_augmented during generation for an additional 30GB and 50GB. Please refer to this section if you want to generate less data. You will also find a detailed storage usage description in each metadata folder.

Features

In LibriMix you can choose :

• The number of sources in the mixtures.
• The sample rate of the dataset from 16 KHz to any frequency below.
• The mode of mixtures : min (the mixture ends when the shortest source ends) or max (the mixtures ends with the longest source)
• The type of mixture : mix_clean (utterances only) mix_both (utterances + noise) mix_single (1 utterance + noise)

You can customize the generation by editing generate_librimix.sh.

Note on scripts

For the sake of transparency, we have released the metadata generation scripts. However, we wish to avoid any changes to the dataset, especially to the test subset that shouldn't be changed under any circumstance.

Why use LibriMix

More than just an open source dataset, LibriMix aims towards generalizable speech separation. You can checkout section 3.3 of our paper here for more details.

Related work

If you wish to implement models based on LibriMix you can checkout Asteroid and the recipe associated to LibriMix for reproducibility.

Along with LibriMix, SparseLibriMix a dataset aiming towards more realistic, conversation-like scenarios has been released here.

(contributors: @JorisCos, @mpariente and @popcornell )

Citing Librimix

@misc{cosentino2020librimix,
    title={LibriMix: An Open-Source Dataset for Generalizable Speech Separation},
    author={Joris Cosentino and Manuel Pariente and Samuele Cornell and Antoine Deleforge and Emmanuel Vincent},
    year={2020},
    eprint={2005.11262},
    archivePrefix={arXiv},
    primaryClass={eess.AS}
}