/urban-sound-classification

Primary LanguageJupyter Notebook

Urban-sound classification with LSTM

Dataset:

UrbanSound dataset.

Task artifacts:

Presentation with results.

Model weights.

Weights & biases report.

Training:

To replicate training process clone this repository, install the requirements.txt, update config.yaml and run command from the command line:

python3 -m src.train

Note: following script implies logging via Weights&Biases, please run: wandb loging to log into your account.

Evaluation:

As we train model using cross-validation this repository does not consist a separate script to compute accuracy on validation folder, insted we can run evaluate.py script to predict class for the audio file by running:

python3 -m src.evaluaton.evaluate -p <path to the .wav file> -w <path to the model weights>

An example of running evaluate.py script can be found in the notebook, feel free to open it in Google Colab, to listen and observe model predictions for 4 random samples from urbansound dataset.