CNN implementation in Python with PyTorch, on audio (.wav) files (94+ on test).
Implementation of a neural network on the audio files. using gcommand_dataset.py that converts the .wav files into a 2D matrix (of 161 x 101).
The audio files in this dataset are ~ 1sec long, and there are 30 optional commands that can be heard in the files.
In short, the model has 5 convolutional layers, with Batch Normalize, ReLU and Max Pooling after each one. Then 2 more Fully Connected layers. The output of the neural network is 30.
In more detail:
- First layer: Convolutional layer, kernel size = 5, stride = 2, padding = 2, batch norm = 16 => [1,16]
- Second layer: Convolutional layer, kernel size = 3, stride = 1, padding = 1, batch norm = 32 => [16,32]
- Third layer: Convolutional layer, kernel size = 3, stride = 1, padding = 1, batch norm = 64 => [32,64]
- Fourth layer: Convolutional layer, kernel size = 3, stride = 1, padding = 1, batch norm = 128 => [64,128]
- Fifth lyaer: Convolutional layer, kernel size = 3, stride = 1, padding = 1, batch norm = 256 => [128,256]
- Sixth layer: Fully Connected layer, batch norm = 128 => [512, 128]
- Seventh layer: Fully Connected layer => [128,30]
Throughout the model building, I monitored the loss and accuracy values so that I could get value of the accuracy of the model, and how And where can I improve it. This can be seen under the section called: ๐๐๐๐๐๐ ๐ฃ๐๐๐๐๐๐ก๐๐๐ ๐๐ข๐๐๐๐๐๐๐ in the attached code.
The program code exports a total of 2 files:
- A
test_yfile that contains the predictions for the test. - The
BestModelcpu.pngorBestModelcuda.pngfile (based on the device on which the code runs), which contains a graph of the accuracy percentage and loss values of the training and the validation depending on the epochs.
Note that for using the dataset given in this repo, you need to download the dataset (about 1GB). You can also use google colab for running this program.
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