This repository contains a minimal wrapper around Maad where the user only has to input a list of files and the script outputs a dataframe of indices for each acoustic file analyzed.
Note that the code makes use of fsspec so that the analysis can be done on files from a remote server.
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
If you want to obtain the acoustic indices for your files:
python3 compute_indices.py filecache::ssh://user:password@host/device/2022-10-24T19_36_53.317Z.mp3
If you want to compute the VGGish features for your audio files:
python3 vggish_embeddings.py filecache::ssh://user:password@host/device/2022-10-24T19_36_53.317Z.mp3
-
- Install GNU Parallel:
sudo apt-get install parallel
-
- List the files you want to analyze in a
files_to_analyze.csv
file in the form:
- List the files you want to analyze in a
filename |
---|
filecache::ssh://user:password@host/device/2022-10-24T19_36_53.317Z.mp3 |
filecache::ssh://user:password@host/device/2022-10-24T19_01_36.412Z.mp3 |
filecache::ssh://user:password@host/device/2022-10-24T11_07_50.161Z.mp3 |
-
- Run the
run_parallel_indices.sh
or therun_parallel_embeddings.sh
:
- Run the
./run_parallel_indices.sh
-
- Get the results
Using the run_parallel_indices.sh
you should find the results stored in an output.txt file
⭐ Note if the computation crashes, GNU parallel
is able to resume the computation where it stopped.