The dataset here contains Obama's speeches from The Obama White House YouTube channel.
To run the script, first install FFmpeg on your operating system. Then run
pip3 install -r requirements.txt
python process.py
In total, there's around 28GB of speech data spread out across 30K wav files that are 1-7 seconds long.
The script iterates through obama_speeches.csv, fetches each YouTube video, uses FFmpeg to convert it to audio. It then fetches the corresponding timestamps. Both are stored in the input_data directory.
Later, it creates a folder with the VIDEO_ID as the directory name in output_data and splices the data into a bunch of wav files. Each wav file is written into data.txt in the form output_data/video_id/\d+.wav|text of the wav.
Run trim.py to create train/val files from data.txt.