Free Spoken Digit Dataset (FSDD) is a simple audio/speech dataset consisting of recordings of spoken digits in wav files. The dataset contains approximately 20 MB of 1,500 recordings of spoken digits from 0 to 9. Each digit was spoken by 50 different speakers, and each speaker spoke each digit five times. The recordings are trimmed so that they have near minimal silence at the beginnings and ends.

FSDD is an open dataset, which means it will grow over time as data is contributed. It is a useful dataset for speech recognition tasks and can be thought of as an audio version of the popular MNIST dataset which consists of hand-written digits.

Notebook

• Imput data: Mel spectograms (2400 train, 600 test)
• Model: Modified ResNet18 model
• Total epochs: 300
• LR: 0.001, step size 20, gamma 0.9
• Loss: 1.4690
• Val Loss: 1.5069
• Accuracy: 0.9571
• Val Acc: 0.9696

Best epoch (highest validation accuracy):

• Epoch: 271
• Precision: 0.9691420399131312
• Recall: 0.9694164524957861
• F1 score: 0.9690412348965143

You can use the trained model to run inference on a single mel spectogram image using:
python inference.py Data\Mel\0\0_george_0.png

Output:
Prediction: 0 (Confidence: 0.9999996423721313)

• Any empty signal at the beginning or end of each clip is removed, however, this does not mean noise before and after the section of the desired signal is removed. To fix, the data could be passed through a noise gate to remove noise before and after the desired signal while not affecting it.
• The model should be modified to allow input of different lengths without having to resize them.