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This project aims to classify urban sounds from the UrbanSound8K dataset into 10 different classes using Convolutional Neural Networks.
Urban sound classification involves categorizing various urban sounds into distinct classes, such as sirens, car horns, street music, and more. This project uses machine learning to automate the classification process based on audio data.
The UrbanSound8K dataset is a compilation of urban sound recordings, classified in 10 categories according to the paper "A Dataset and Taxonomy for Urban Sound Research", which proposes a taxonomical categorization to describe different environmental sound types.
The UrbanSound8K dataset contains 8732 labeled sound slices of varying duration up to 4 seconds. The categorization labels being:
Note that the dataset comes already organized in 10 validation folds. In the case we want to compare our results with other we should stick with this schema.
Dataset metadata
The included metadata file (audio_data/metadata/UrbanSound8K.csv
) provides all the required information about each audio file:
The project directory is organized as follows:
.
├── README.md
├── audio_data.dvc
├── dvc.lock
├── dvc.yaml
├── metrics.csv
├── params.yml
├── requirements.txt
├── audio_data
├── audio
├── fold1
├── .wav
├── .wav
├── fold2
...
├── fold10
├── metadata
├── UrbanSound8K.csv
└── src
├── __init__.py
├── const.py
├── data
│ ├── __init__.py
│ └── generator.py
├── model
│ ├── __init__.py
│ └── train.py
└── notebook
└── Urban_Audio_Classifier_.ipynb
Ensure you have the following dependencies installed:
git clone https://dagshub.com/nirbarazida/urban-audio-classifier.git
cd urban-audio-classifier
pip install -r requirements.txt
Training (The training process will be logged using DagsHub and MLflow)
MLFLOW_TRACKING_URI=https://dagshub.com/{DAGSHUB_REPO_OWNER}/{DAGSHUB_REPO_NAME}.mlflow \
MLFLOW_TRACKING_USERNAME={DAGSHUB_USER_NAME} \
MLFLOW_TRACKING_PASSWORD={DAGSHUB_TOKEN} \
dvc repro
MLFLOW_TRACKING_URI=https://dagshub.com/{DAGSHUB_REPO_OWNER}/{DAGSHUB_REPO_NAME}.mlflow \
MLFLOW_TRACKING_USERNAME={DAGSHUB_USER_NAME} \
MLFLOW_TRACKING_PASSWORD={DAGSHUB_TOKEN} \
python -m src.model.train
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commented in commit80bc30793bon branch main
3 months agohello, i am giving this a try but i am stuck at getting my
DAGSHUB_TOKEN
, please point me in directions on how to have one