movie
Video-to-fMRI alignment
Code will use the times recorded while scanning the participant, make periods with their own duration and then add 'capsules' that contain timepoints in the movie within every 1000ms elapsed since the first TTL pulse
Work in progress
Youtube API calls
You'll have to install the google API python client.
pip install --upgrade google-api-python-client
To run the code:
python YoutubeCall.py NAME OF THE SEARCH
e.g. Batman Returns scene
SpeechToText
This will get an audio file, detect speech segments, send them to the Google Cloud Speech API and get a json file back with the content and the start time in seconds.
Functions for splitting the audio into speech segments were adapted from: https://github.com/wiseman/py-webrtcvad (example.py)
Installing ffmpeg: https://github.com/adaptlearning/adapt_authoring/wiki/Installing-FFmpeg Make sure to install with ffprobe
Installing Google Cloud libraries: https://cloud.google.com/sdk/docs/quickstart-mac-os-x
Input data for SpeechToText.py
We'll need to have .wav files, with 1 audio channel and a sample rate of 8000, 16000 or 32000
Using Terminal to check if our files are alright/convert them:
ffprobe -v quiet -print_format json -show_format -show_streams 500days1.wav
ffmpeg -i 500days1.wav -ac 1 -ab 64000 -ar 16000 500days12.wav
Make sure to have the input file in the SpeechToText folder
Make sure to be authenticate with the Google Cloud if planning on transcribing the audio
Comment our the transcribe function if not needed
Run SpeechToText.py with aggressivenes as 0, 1, 2 or 3 (the more aggressive, the harsher it will differentiate between speech and non-speech):
python SpeechToText.py <agressiveness index> <input_file_name>
Useful commands for processing the video files
To concatenate mp4 files with ffmpeg
touch files.txt where you add for each row: file 'path/to/file'
cat filex.txt to check files
ffmpeg -f concat -i files.txt -c copy output_name.mp4