https://github.com/richardassar/SampleRNN_torch

Torch implementation of SampleRNN: An Unconditional End-to-End Neural Audio Generation Model
https://github.com/richardassar/SampleRNN_torch

audio deep-learning generative neural-network torch

Last synced: about 2 months ago
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Torch implementation of SampleRNN: An Unconditional End-to-End Neural Audio Generation Model

• Host: GitHub
• URL: https://github.com/richardassar/SampleRNN_torch
• Owner: richardassar
• Created: 2017-04-06T22:08:37.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2017-05-21T22:22:43.000Z (over 7 years ago)
• Last Synced: 2024-07-05T21:09:43.485Z (3 months ago)
• Topics: audio, deep-learning, generative, neural-network, torch
• Language: Lua
• Homepage:
• Size: 54.7 KB
• Stars: 156
• Watchers: 8
• Forks: 25
• Open Issues: 5
• Metadata Files:
  • Readme: README.md

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README

        
# SampleRNN_torch

A Torch implementation of [SampleRNN: An Unconditional End-to-End Neural Audio Generation Model](https://openreview.net/forum?id=SkxKPDv5xl).

![A visual representation of the SampleRNN architecture](http://deepsound.io/images/samplernn.png)

## Samples

Listen to a selection of generated output at the following links:

- [piano](https://soundcloud.com/psylent-v/sets/samplernn_torch)

- [mozart](https://soundcloud.com/psylent-v/sets/samplernn_torch-mozart)

Feel free to submit links to any interesting output you generate or dataset creation scripts as a pull request.

## Dependencies

The following packages are required to run SampleRNN_torch:

- nn

- cunn

- cudnn

- rnn

- optim

- audio

- xlua

- gnuplot

**NOTE**: Update `nn` and `cudnn` even if they were already installed as fixes have been submitted which affect this project.

## Datasets

To retrieve and prepare the *piano* dataset, as used in the reference implementation, run:

```

cd datasets/piano/

./create_dataset.sh

```

Other dataset preparation scripts may be found under `datasets/`.

Custom datasets may be created by using `scripts/generate_dataset.lua` to slice multiple audio files into segments for training, audio must be placed in `datasets/[dataset]/data/`.

## Training

To start a training session run `th train.lua -dataset piano`. To view a description of all accepted arguments run `th train.lua -help`.

To view the progress of training run `th generate_plots`, the loss and gradient norm curve will be saved in `sessions/[session]/plots/`.

## Sampling

By default samples are generated at the end of every training epoch but they can also be generated separately using `th train.lua -generate_samples` with the `session` parameter to specify the model.

Multiple samples are generated in batch mode for efficiency, however generating a single audio sample is faster with `th fast_sample.lua`. See `-help` for a description of the arguments.

## Models

A pretrained model of the *piano* dataset is available [here](https://drive.google.com/uc?id=0B5pXFO5X-KJ9Mko3MUZuLUpEQVU&export=download). Download and copy it into your `sessions/` directory and then extract it in place.

More models will be uploaded soon.

## Theano version

This code is based on the reference implementation in Theano.

https://github.com/soroushmehr/sampleRNN_ICLR2017