README.md

Ukulele Chords Detector Ludwig edition

Based on https://uber.github.io/ludwig/examples/#spoken-digit-speech-recognition

For docs see https://uber.github.io/ludwig/user_guide/#audio-features

For the origins of the project and sounds data see https://github.com/alexander-rykhlitskiy/ukulele_chords_detector

docker build -t test_ludwig .
docker run -v $(pwd):/app -it --rm test_ludwig /bin/bash

Running

Prepare csv files

git submodule update --init --recursive
find . -type f -name '*.wav' -exec sox {} {}_1_channel.wav remix 1 \;
find . -type f -name '*_1_channel.wav' | grep "/train/" | (echo "audio_path,label"; ruby -e 'puts STDIN.read.split("\n").map { |l| "#{l},#{l.match(/samples\/([^\/]+)\//)[1]}" }.join("\n")') > train_notes.csv
(echo "audio_path"; find . -type f -name '*_1_channel.wav' | grep "/test/") > test_notes.csv

Run training and prediction

rm train_notes.hdf5 train_notes.json # strange, but sometimes it increases accuracy
ludwig train --data_csv train_notes.csv --model_definition_file notes_model_definition.yaml
ludwig predict --data_csv test_notes.csv -m results/experiment_run_/model

Results comparing to pure TensorFlow implementation

+ Ludwig automatically calculates number of epochs (seems to be pretty accurate)
+ Allows to configure many nifty sound processing parameters in model yaml file
- Accuracy seems to be lower
- Less control, trickier errors (surprise!)

Conclusion

Examples from docs with prepared datasets run pretty smoothly, though you still need to read more, e.g. to find out you need ludwig[audio] package to be installed.

But when you prepare your own data, most likely you will face tricky obscure erros like ValueError: could not broadcast input array from shape (2400,2) into shape (2400). This error means you need wav file with 1 channel instead of 2. It can be easily achieved with sox {} {}.wav remix 1, but still it creates some difficulties with getting started.

Maybe in version 1.0 they will improve docs and error messages, and then it will be more convenient. But for now I would prefer to write python script based on pure TensorFlow.