Speech Activity Detection (SAD) in movie audio.

The details of the Subtitle-Aligned Movie (SAM) Corpus are provided in the wiki. In order to train an SAD model from scratch using this data, the following steps have to be performed after cloning the directory:

1. Download the features from here.
2. Extract the features : tar -xvzf movie_features.tar.gz /path/to/download/
3. In the file SAD_parameters.py, edit DATA_PATH to reflect complete path to where the data is stored, and LOG_DIR to reflect directory in which to save logs and model file.
4. Additionally, training parameters can be modified.

To train a model, execute the training script by running : python train.py

Since we use log-mel filterbank features extracted using Kaldi, a Kaldi installation is necessary to run the SAD inference script. Kaldi can be installed by following the instructions here. Once Kaldi is installed, make sure to edit the KALDI_ROOT variable in the 'path.sh' file to reflect the complete path to the installation directory.

SAD can be performed on any media file compatible with ffmpeg (recommended types .mp4/.mkv/.wav) by executing the script 'perform_SAD.sh'.

Usage: bash perform_SAD.sh [-h] [-w y/n] [-f y/n] [-j num_jobs] movie_paths.txt (out_dir)
e.g.: bash perform_SAD.sh -w y -f y -nj 8 demo.txt DEMO
where:
-h : Show help
-w : Store wav files after processing (default: n)
-f : Store feature files after processing (default: n)
-j : Number of parallel jobs to run (default: 16)
movie_paths.txt : Text file consisting of complete paths to media files (eg, .mp4/.mkv) on each line
out_dir : Directory in which to store all output files (default: SAD_out_dir)