AudioEnhancer
A neural network capable of upsampling audio
This is the code repository for the research at https://crimata.com/AIPost
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