| # Beethoven Sonatas Dataset | |
| Beethoven is a raw audio waveform dataset used in the paper "It's Raw! Audio Generation with State-Space Models". It has been used primarily as a source of single instrument piano music for training music generation models at a small scale. | |
| The dataset was originally introduced in the SampleRNN paper by Mehri et al. (2017) and download details from the original paper can be found at https://github.com/soroushmehr/sampleRNN_ICLR2017/tree/master/datasets/music. Here, we provide a more convenient download of a processed version of the dataset in order to standardize future use. | |
| We include two versions of the dataset: | |
| - `beethoven.zip` is a zip file containing 4328 8-second audio clips sampled at 16kHz. These were generated by first joining all the piano sonatas, and then splitting the track into 8-second chunks. This data can also be used with the https://github.com/HazyResearch/state-spaces repository to reproduce SaShiMi results, and was the dataset used in the paper. | |
| - `beethoven_raw.zip` contains the raw audio tracks, sampled at 16kHz. | |
| We recommend (and follow) the following train-validation-test split for the audio files in `beethoven.zip` (we attempted to recreate the splits from the SampleRNN work as closely as possible): | |
| - `0.wav` to `3807.wav` for training | |
| - `3808.wav` to `4067.wav` for validation | |
| - `4068.wav` to `4327.wav` for testing | |
| You can use the following BibTeX entries to appropriately cite prior work if you decide to use this in your research: | |
| ``` | |
| @article{goel2022sashimi, | |
| title={It's Raw! Audio Generation with State-Space Models}, | |
| author={Goel, Karan and Gu, Albert and Donahue, Chris and R\'{e}, Christopher}, | |
| journal={arXiv preprint arXiv:2202.09729}, | |
| year={2022} | |
| } | |
| @inproceedings{mehri2017samplernn, | |
| title={SampleRNN: An Unconditional End-to-End Neural Audio Generation Model}, | |
| author={Mehri, Soroush and Kumar, Kundan and Gulrajani, Ishaan and Kumar, Rithesh and Jain, Shubham and Sotelo, Jose and Courville, Aaron and Bengio, Yoshua}, | |
| booktitle={International Conference on Learning Representations}, | |
| year={2017} | |
| } | |
| ``` |