Notes

This project is cloned from https://github.com/kuleshov/audio-super-res, the credits belongs to V. Kuleshov et. al.
Ziqi is working to make it adaptive to Tensorflow 2.0. The final goal is make the model applicable to his "UWB Audio Retrival Project".

The following readme is adapted from that of the original author:

Audio Super Resolution Using Neural Networks

This repository implements the audio super-resolution model proposed in:

V. Kuleshov, Z. Enam, and S. Ermon. Audio Super Resolution Using Neural Networks. ICLR 2017 (Workshop track)
V. Kuleshov, Z. Enam, P. W. Koh, and S. Ermon. Deep Convolutional Time Series Translation, ArXiv 2017

Installation

Requirements

The model is implemented in Tensorflow and Keras and uses several additional libraries. Specifically, we used:

• tensorflow=2.1.0
• keras==2.3.1
• numpy==1.18.1
• scipy==1.4.1
• librosa==0.7.2
• h5py==2.10.0
• matplotlib==3.1.3

Note: the contents below to be updated

Running the model

Contents

The repository is structured as follows.

• ./samples: audio samples from the model
• ./src: model source code
• ./docs: html source code for the project webpage
• ./data: code to download the model data

Retrieving data

The ./data subfolder contains code for preparing the VCTK speech dataset. Make sure you have enough disk space and bandwidth (the dataset is over 18G, uncompressed). You need to type:

cd ./data/vctk;
make;

Next, you must prepare the dataset for training: you will need to create pairs of high and low resolution sound patches (typically, about 0.5s in length). We have included a script called prep_vctk.py that does that, which works as follows.

usage: prep_vctk.py [-h] [--file-list FILE_LIST] [--in-dir IN_DIR] [--out OUT]
                    [--scale SCALE] [--dimension DIMENSION] [--stride STRIDE]
                    [--interpolate] [--low-pass] [--batch-size BATCH_SIZE]
                    [--sr SR] [--sam SAM]

optional arguments:
  -h, --help            show this help message and exit
  --file-list FILE_LIST
                        list of input wav files to process
  --in-dir IN_DIR       folder where input files are located
  --out OUT             path to output h5 archive
  --scale SCALE         scaling factor
  --dimension DIMENSION
                        dimension of patches
  --stride STRIDE       stride when extracting patches
  --interpolate         interpolate low-res patches with cubic splines
  --low-pass            apply low-pass filter when generating low-res patches
  --batch-size BATCH_SIZE
                        we produce # of patches that is a multiple of batch
                        size
  --sr SR               audio sampling rate
  --sam SAM             subsampling factor for the data

The output of the data preparation step are two .h5 archives containing, respectively, the training and validation pairs of high/low resolution sound patches. You can also generate these by running make in the corresponding directory, e.g.

cd ./speaker1;
make;

This will use a set of default parameters.

Audio super resolution tasks

We have included code to prepare two datasets.

• The single-speaker dataset consists only of VCTK speaker #1; it is relatively quick to train a model (a few hours).
• The multi-speaker dataset uses the last 8 VCTK speakers for evaluation, and the rest for training; it takes several days to train the model, and several hours to prepare the data.

We suggest starting with the single-speaker dataset.

Training the model

Running the model is handled by the src/run.py script.

usage: run.py train [-h] --train TRAIN --val VAL [-e EPOCHS]
                    [--batch-size BATCH_SIZE] [--logname LOGNAME]
                    [--layers LAYERS] [--alg ALG] [--lr LR]

optional arguments:
  -h, --help            show this help message and exit
  --train TRAIN         path to h5 archive of training patches
  --val VAL             path to h5 archive of validation set patches
  -e EPOCHS, --epochs EPOCHS
                        number of epochs to train
  --batch-size BATCH_SIZE
                        training batch size
  --logname LOGNAME     folder where logs will be stored
  --layers LAYERS       number of layers in each of the D and U halves of the
                        network
  --alg ALG             optimization algorithm
  --lr LR               learning rate

For example, to run the model on data prepared for the single speaker dataset, you would type:

python run.py train \
  --train ../data/vctk/speaker1/vctk-speaker1-train.4.16000.8192.4096.h5 \
  --val ../data/vctk/speaker1/vctk-speaker1-val.4.16000.8192.4096.h5 \
  -e 120 \
  --batch-size 64 \
  --lr 3e-4 \
  --logname singlespeaker

The above run will store checkpoints in ./singlespeaker.lr0.000300.1.g4.b64.

Testing the model

The run.py command may be also used to evaluate the model on new audio samples.

usage: run.py eval [-h] --logname LOGNAME [--out-label OUT_LABEL]
                   [--wav-file-list WAV_FILE_LIST] [--r R] [--sr SR]

optional arguments:
  -h, --help            show this help message and exit
  --logname LOGNAME     path to training checkpoint
  --out-label OUT_LABEL
                        append label to output samples
  --wav-file-list WAV_FILE_LIST
                        list of audio files for evaluation
  --r R                 upscaling factor
  --sr SR               high-res sampling rate

In the above example, we would type:

python run.py eval \
  --logname ./singlespeaker.lr0.000300.1.g4.b64/model.ckpt-20101 \
  --out-label singlespeaker-out \
  --wav-file-list ../data/vctk/speaker1/speaker1-val-files.txt \
  --r 4

This will look at each file specified via the --wav-file-list argument (these must be high-resolution samples), and create for each file f.wav three audio samples:

• f.singlespeaker-out.hr.wav: the high resolution version
• f.singlespeaker-out.lr.wav: the low resolution version processed by the model
• f.singlespeaker-out.sr.wav: the super-resolved version

These will be found in the same folder as f.wav. Because of how our model is defined, the number of samples in the input must be a multiple of 2**downscaling_layers; if that's not the case, we will clip the input file (potentially shortening it by a fraction of a second).

Remarks

We would like to emphasize a few points.

• Machine learning algorithms are only as good as their training data. If you want to apply our method to your personal recordings, you will most likely need to collect additional labeled examples.
• You will need a very large model to fit large and diverse datasets (such as the 1M Songs Dataset)
• Interestingly, super-resolution works better on aliased input (no low-pass filter). This is not reflected well in objective benchmarks, but is noticeable when listening to the samples. For applications like compression (where you control the low-res signal), this may be important.
• More generally, the model is very sensitive to how low resolution samples are generated. Even the type of low-pass filter (Butterworth, Chebyshev) will affect performance.

Extensions

The same architecture can be used on many time series tasks outside the audio domain. We have successfully used it to impute functional genomics data and denoise EEG recordings. Stay tuned for more updates!