Python version 3.9 is used for this project
The directory boeck is from CPJKU/onset_detection.
The rest of this repo is licensed under LGPL 3.0
To analyze audio, first load a model and specify the features (ODFs) and network configurations. Then specify input audios (can be multiple files separated by a space). The output will be two files -- a .txt file with all onset timestamps one per line like
0.15
0.36
0.45
0.61
and a .wav file with onset clicks on the original audio (for sonic evaluation).
python training.py --load-model-file best_model.pt --feature superflux wpd --num-layer-units 4 --num-layers 3 --input-audio "./demo_audio_content/ENERGY SYNERGY MATRIX.wav"
To train a model, specify the training parameters by providing features (ODFs), network layout,
targeting delta (in frames), and specify a filename to save the model. If you also need to evaluate
the model, use --evaluate
python training.py --evaluate --feature superflux wpd --num-layer-units 4 --num-layers 3 --targeting-delta-frame 2 --model-filename saved_model.pt
The model weights and the command line args will be saved as separate files for later reference. The training record will also be saved as a text file.
You still need to specify features and network layout when loading a model.
python training.py --evaluate --load-model-file saved_model.pt --feature superflux wpd --num-layer-units 4 --num-layers 3
- Prepare the Boeck dataset in
../Datasets/Boeck/ - Install PyTorch (1.9+Optional CUDA) for your platform
- Install requirements by
pip3 install -r requirements.txt - run
training.py
See python training.py --help
-h, --help show this help message and exit
--load-model-file LOAD_MODEL_FILE
Load a model from a specified saved checkpoint
filepath. If not specified, a new one is trained
--save-model-file SAVE_MODEL_FILE
Save the model checkpoint into a filename.Enter ONLY
the filename. The file will be created in 'run
xxx\'.If not specified, the model WON'T be saved
--evaluate evaluate the trained or loaded model on the test set
(see --test-set-index)
--input-audio [INPUT_AUDIO ...]
If specified, one or more input audio file is used for
onset detection
--speed-test Test the processing speed of the current setup.
Results will only be shown in console. Make sure if
you want to use --cpu-only. Note: Speed test evaluates
the test set one by one (no concurrency).
--cpu-only Use CPU instead of GPU
--threads THREADS Manually set the number of threads for concurrency
--no-cached-preprocessing
If cache is enabled, all the odf preprocessing result
will be cached in MEMORY,so use this if you have less
than 8GB of RAM and suffer from crashing
--feature {superflux,rcd,wpd} [{superflux,rcd,wpd} ...]
List of features (ODFs)
--rcd-log RCD_LOG
--superflux-log SUPERFLUX_LOG
--sampling-rate SAMPLING_RATE
--n-fft N_FFT The size of the FFT window
--hop-length HOP_LENGTH
--superflux-lag SUPERFLUX_LAG
The order to which the difference is calculated for
SuperFlux
--no-normalize-wave
--no-normalize-odf
--targeting-delta-frames TARGETING_DELTA_FRAMES
The delta value for triangular targeting, in
frames.Default 1. Set 0 for single targeting
--onset-merge-interval ONSET_MERGE_INTERVAL
In interval within which onsets are merged into one,
in seconds. This is used for both training and
evaluation. Default 0.03.
--num-layer-units NUM_LAYER_UNITS
The number of units in each hidden layer. Default 4
--num-layers NUM_LAYERS
The number of hidden layers. Default 2.
--nonlinearity NONLINEARITY
The activation function of hidden layers. Default tanh
--bidirectional BIDIRECTIONAL
--weight WEIGHT
--optimizer {adam,sgd}
--lr LR
--early-stop-patience EARLY_STOP_PATIENCE
--early-stop-min-epochs EARLY_STOP_MIN_EPOCHS
--batch-size BATCH_SIZE
--model-filename MODEL_FILENAME
The filename to save the model used in the current
run.
--no-revert-to-best Use this arg if you do not want the training process
to revert to the best weights after stopping the
training.
--test-set-index TEST_SET_INDEX
The index of the test set of Boeck Dataset. Default 0.
--max-epochs MAX_EPOCHS
--save-checkpoint-to-file CHECKPOINT_FILE
Specify the filename for saving checkpoints at each
epoch. This lowers the performance of training.
--peak-picking-threshold PEAK_PICKING_THRESHOLD
The threshold used for final peak-picking.This is used
for analyzing audio, instead of evaluating the model.
Default 0.35.
--peak-picking-smooth-window-second PEAK_PICKING_SMOOTH_WINDOW_SECOND
The length of the smoothing window for peak-picking in
seconds. Default 0.05.
--peak-picking-threshold-lower PEAK_PICKING_THRESHOLD_LOWER
For evaluating the model, the lower boundary of a best
threshold search. Default 0.05.
--peak-picking-threshold-upper PEAK_PICKING_THRESHOLD_UPPER
For evaluating the model, the upper boundary of a best
threshold search. Default 0.8.
--peak-picking-threshold-step PEAK_PICKING_THRESHOLD_STEP
For evaluating the model, search step of a best
threshold search. Default 0.05.
--evaluation-window EVALUATION_WINDOW
The window within which an onset candidate is counted
as a true positive, in seconds. Default 0.025.
--evaluation-delay EVALUATION_DELAY
Add an delay to all detections when evaluating
(seconds). Default 0.
--no-concurrent-testing
For test purpose, disable multi-threading.
Process finished with exit code 0
Entrance of the program. Contains manipulations of the model, i/o processing, args parsing.
Definitions of the RNN model and some pytorch settings.
Calculation of onset detection functions. Includes Wave to STFT frames, and tonal filters.
Helper functions for onset manipulation and peak-picking.
Definitions of datasets.
Utilities.