Repository exploring the SpecTNT architecture applied to:

• music tagging as described in the paper "SpecTNT: a time-frequency transformer for music audio"
• beats and downbeats estimation as described in the paper "Modeling beats and downbeats with a time-frequency transformer"

This is by no mean an official implementation. Any comments and suggestions regarding fixes and enhancements are highly encouraged.

Datasets constitution and their associated class implementation are excluded from this repo. Dummy classes are presented instead.

⚠️ as is, the implentation of validation steps in models entails that validation datamodules should stack entire tracks by batch of 1.

List of datasets used in the paper associated to each task for training, validation and testing:

• music tagging: a Million Song Dataset subset split this way and intersected with the LastFM dataset for tags
• beats and downbeats estimation: Beatles, Ballroom, SMC, Hainsworth, Simac, HJDB, RWC-Popular, Harmonix Set

• input_length: models are trained on audio chunks which length is defined here in seconds.
• hop_length: number of samples between successive frames used to constructs features (melspectrograms or harmonic filters).
• time_shrinking (specific to beat estimation): time pooling entails dimension shrinking along the time axis after inference through the front-end model (see below for more details). Target labels tensors should be constructed taking this shrinking into account. Has a crutial impact on inference durations but is not clearly specified in the paper.

• freq_pooling, time_pooling (specific to beat estimation): pooling along the frequency and the time axis that occur at the end of the front-end module. Have a crutial impact on inference durations but are not clearly specified in the paper.

• n_channels, n_frequencies, n_times: shape of tensors that input the SpecTNT. Should be consistent with audio chunk shape and feature extractor parameters.
• use_tct: whether to use Temporal Class Tokens, which act as aggregators along the temporal axis, or not. Set to true in case of a track-wise prediction task (ex: tagging) and to false in case of a frame-wise prediction task (ex: beat estimation).
• n_classes: number of output classes.

Inference functions are presented in inference.py and should guide users unfamiliar with pytorch-lightning and hydra libraries.

⚠️ Real training should be preceded with a dataset constitution following guidelines presented in each paper.

Users can though test training pipelines with the dummy dataset classes presented in this repo using the following commands in terminal:

• beats and downbeats estimation:

python train.py --config-name beats

• music tagging:

python train.py --config-name tagging