AudioEnhancer

A neural network capable of upsampling audio

This is the code repository for the research at https://andrewgundersen.com/audio

Usage

Clone the repo.

Download dataset at https://homepages.inf.ed.ac.uk/jyamagis/page3/page58/page58.html and place it in the data folder.

Repository Structure

• ./data: the data and the metadata
• ./main: the model and its respective utilites
• ./samples: audio files and spectrograms generated from the neural net

cd ./main python 3 main.py -h

It will then ask you if you'd like to train or evaluate. To get help for each respectivly, just type:

python 3 main.py train -h python 3 main.py eval -h

Argument Reference | train

-h, --help -i MODEL_ID an index you can add to querey the model later -c FROM_CKPT bool, begin training session from a checkpoint -k NEW_DATA bool, make new set of data -d DIM_SIZE size of each audio sample -x NUM_FILES number of files to train -e EPOCHS -b BATCH_SIZE -o CYCLE_LENGTH length of cycle in epochs (only for SGDR) -m MAX_LR max learning rate (only for SGDR) -n MIN_LR min learning rate (only for SGDR)

Example python3 main.py train \ -i 1 -c false -k True -d 8192 -x 100 -e 10 -b 32

Argument Reference | eval

-h, --help -i MODEL_ID model ID to use -n NUM_EXAMPLES number of examples/files to run -w WAVFILE_LIST list to pull examples from -r SCALE level of upsampling -s SAMPLE_RATE target sample rate -a MAKE_AUDIO bool, make audio or just spectrograms -c FROM_CKPT bool, begin eval at last checkpoint

Example | eval python3 main.py train \ -i 1 -n 10 -w ../data/val-files.txt -r 4 -s 16000 -True -c False