├── 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
│
├── 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
│
│── runs <- Model Summaries
│
├── 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
│ │ ├── data_create.py
│ │ └── data_utils.py
│ │
│ │
│ ├── models <- Scripts to train models and make predictions
│ │ ├── diarize_n_cluster.py <- diraize, cluster and segment ads using diarization module
│ │ ├── encoder.py <- to train and eval the encoder for speaker diarization
│ │ └── train_model_supervised.py <- to train and eval the supervised ad detection model
│ │
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io
python setup.py install
or
pip install -r requirements.txt
or
pip install -e .
python src/data/data_create.py
-
Train and create speaker diarizations
python src/models/encoder.py --help -
Segment and Extract ads using a speaker diarization module
python src/models/diarize_n_cluster.py --help -
Train and classify ads using the supervised lstm model
python src/models/train_model_supervised.py --help
Project based on the cookiecutter data science project template. #cookiecutterdatascience