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https://github.com/diovani-dupont/birdclef2023
The following code is a script developed for Kaggle’s BirdCLEEF 2023 competition, it is a machine learning script that processes, trains and evaluates an audio classification model, based on features extracted from audio files. It uses the RandomForestClassifier algorithm to train a model that identifies birdsong in audio recordings.
https://github.com/diovani-dupont/birdclef2023
Last synced: about 2 months ago
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The following code is a script developed for Kaggle’s BirdCLEEF 2023 competition, it is a machine learning script that processes, trains and evaluates an audio classification model, based on features extracted from audio files. It uses the RandomForestClassifier algorithm to train a model that identifies birdsong in audio recordings.
- Host: GitHub
- URL: https://github.com/diovani-dupont/birdclef2023
- Owner: diovani-dupont
- License: other
- Created: 2023-11-11T21:39:33.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2023-11-11T22:31:31.000Z (about 1 year ago)
- Last Synced: 2023-11-12T22:31:56.222Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 511 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
BirdCLEF
==============================
The following code is a script developed for Kaggle's BirdCLEEF 2023 competition, it is a machine learning script that processes, trains and evaluates an audio classification model, based on features extracted from audio files. It uses the RandomForestClassifier algorithm to train a model that identifies birdsong in audio recordings.
Project Organization
------------
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
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Project based on the cookiecutter data science project template. #cookiecutterdatascience