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# samplernn-pytorch
A PyTorch implementation of [SampleRNN: An Unconditional End-to-End Neural Audio Generation Model](https://arxiv.org/abs/1612.07837).
It's based on the reference implementation in Theano: https://github.com/soroushmehr/sampleRNN_ICLR2017. Unlike the Theano version, our code allows training models with arbitrary number of tiers, whereas the original implementation allows maximum 3 tiers. However it doesn't have weight normalization and doesn't allow using LSTM units (only GRU). For more details and motivation behind rewriting this model to PyTorch, see our blog post: http://deepsound.io/samplernn_pytorch.html.
## Dependencies
This code requires Python 3.5+ and PyTorch 0.1.12+. Installation instructions for PyTorch are available on their website: http://pytorch.org/. You can install the rest of the dependencies by running `pip install -r requirements.txt`.
## Datasets
We provide a script for creating datasets from YouTube single-video mixes. It downloads a mix, converts it to wav and splits it into equal-length chunks. To run it you need youtube-dl (a recent version; the latest version from pip should be okay) and ffmpeg. To create an example dataset - 4 hours of piano music split into 8 second chunks, run:
```
cd datasets
./download-from-youtube.sh "https://www.youtube.com/watch?v=EhO_MrRfftU" 8 piano
```
You can also prepare a dataset yourself. It should be a directory in `datasets/` filled with equal-length wav files. Or you can create your own dataset format by subclassing `torch.utils.data.Dataset`. It's easy, take a look at `dataset.FolderDataset` in this repo for an example.
## Training
To train the model you need to run `train.py`. All model hyperparameters are settable in the command line. Most hyperparameters have sensible default values, so you don't need to provide all of them. Run `python train.py -h` for details. To train on the `piano` dataset using the best hyperparameters we've found, run:
```
python train.py --exp TEST --frame_sizes 16 4 --n_rnn 2 --dataset piano
```
The results - training log, loss plots, model checkpoints and generated samples will be saved in `results/`.
We also have an option to monitor the metrics using [CometML](https://www.comet.ml/). To use it, just pass your API key as `--comet_key` parameter to `train.py`.
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