# 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}
}
```
region:us
{}
TAGS
#region-us 

# 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 URL 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:
- 'URL' 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 URL 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 'URL' (we attempted to recreate the splits from the SampleRNN work as closely as possible):

- '0.wav' to 'URL' for training
- 'URL' to 'URL' for validation
- 'URL' to 'URL' for testing

You can use the following BibTeX entries to appropriately cite prior work if you decide to use this in your research:

# 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 URL 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:
- 'URL' 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 URL 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 'URL' (we attempted to recreate the splits from the SampleRNN work as closely as possible):

- '0.wav' to 'URL' for training
- 'URL' to 'URL' for validation
- 'URL' to 'URL' for testing

You can use the following BibTeX entries to appropriately cite prior work if you decide to use this in your research:
TAGS
#region-us 
 # 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 URL 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:
- 'URL' 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 URL 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 'URL' (we attempted to recreate the splits from the SampleRNN work as closely as possible):

- '0.wav' to 'URL' for training
- 'URL' to 'URL' for validation
- 'URL' to 'URL' for testing

You can use the following BibTeX entries to appropriately cite prior work if you decide to use this in your research: