DmitryUlyanov / Neural Style Audio Torch

Audio texture synthesis (and a bit of stylization)

This is an extension of texture synthesis and style transfer method of Leon Gatys et al. based on Justin Johnson's code for neural style transfer.

To listen to examples go to the blog post. Almost identical Lasagne implementation by Vadim Lebedev can be found here. Also check out TensorFlow implementation.

Examples

As there is no way to embed an audio player with github markdown please follow this link for the examples of texture synthesis and style transfer.

Prerequisites

• Torch7
  • With npy4th, cudnn.torch
• Python (tested with 2.7)
  • numpy, librosa, torchfile
• Download pretrained Network and mean file

wget https://www.dropbox.com/s/xpyoehayuhxvibq/net.t7?dl=1 -O data/net.t7
wget https://www.dropbox.com/s/dwsq33r5bsgy9cd/mean.t7?dl=1 -O data/mean.t7

Usage

1. First convert raw audio to spectrogram

python get_spectrogram.py --in_audio <path to audio file> --out_npy <where to save spectrogram>

Additional arguments:

• -offset and -duration control boundaries of a segment which will be converted to spectrogram. [By default first 10 seconds are used].
• -sr: sample rate (samples), [default = 44100].
• -n_fft: FFT window size (samples) [default = 1024].

2. Run texture synthesis or style transfer

This is the cmd used for all the texture synthesis examples:

th neural_style_audio.lua -content <path to content.npy> -style <path to style.npy> -content_layers <indices of content layers> -style_layers <indices of style layers> -style_weight <number> -content_weight <number>

Parameters:

• -content, -style are paths to .npy spectrogram files generated previously.
• -content_layers, -style_layers: comma separated layer indices (the provided net has about 50 layers and of encoder-decoder type).
• -style_weight, -content_weight: set content_weight to zero for texture synthesis.
• -lowres: the higher receptive field the larger textures network captures. The easiest way to increase a receptive field is to drop every other column from an input spectrogram (kind of downscaling). Use this flag to process spectrogram both in original and low resolution at the same time.
• -how_div, -normalize_gradients, -loss change some details of loss calculation.
• -save indicates folder to save intermediate results.

3. Convert spectrogram back to audio file

python invert_spectrogram.py --spectrogram_t7 <path to synthesized spectrogram> --out_audio keyboard2_texture.wav

Parameters:

Command-line used for texture examples

python get_spectrogram.py --out_npy data/inputs/keyboard2.npy --in_audio data/inputs/keyboard2.mp3
th neural_style_audio.lua -style data/inputs/keyboard2.npy -content_layers '' -style_layers 1,5,10,15,20,25 -style_weight 10000000 -optimizer lbfgs -learning_rate 1e-1 -num_iterations 5000 -lowres
python invert_spectrogram.py --spectrogram_t7 data/out/out.png.t7 --out_audio data/outputs/keyboard2_texture.wav

Command-line used for style transfer

We have also implemented minimalistic script identical to TensorFlow and Lasagne scripts. Here is example how to use it:

python get_spectrogram.py --out_npy data/inputs/usa.npy --in_audio data/inputs/usa.mp3 --n_fft 2048 --sr 22050
python get_spectrogram.py --out_npy data/inputs/imperial.npy --in_audio data/inputs/imperial.mp3 --n_fft 2048 --sr 22050
th neural_style_audio_random.lua -alpha 1e-2
python invert_spectrogram.py --spectrogram_t7 data/out/out.t7 --out_audio out.wav --n_iter 500 --n_fft 2048 --sr 22050