b2aiprep

https://github.com/sensein/b2aiprep

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Repository

Basic Info

Host: GitHub
Owner: sensein
License: apache-2.0
Language: Python
Default Branch: main
Size: 80.9 MB

Statistics

Stars: 17
Watchers: 7
Forks: 6
Open Issues: 47
Releases: 39

Created almost 2 years ago · Last pushed 6 months ago

Metadata Files

Readme Changelog License Citation

B2AI Prep

A simple Python package to prepare acoustic data for the Bridge2AI voice project.

Caution: this package is under active development and interfaces may change rapidly.

Installation

MacOS with Mx (ARM) chip only: Make sure that you have the ARM version of python installed. Run this to check: [path/to/your/python] -c "import platform; print(platform.machine())

Requires a Python >= 3.10, <3.12 environment

pip install b2aiprep

Usage

See commands available through the CLI:

bash b2aiprep-cli --help

Convert an audio file to features:

The simplest form takes an audio file, a subject id, and a task name.

bash b2aiprep-cli convert test_audio.wav s1 mpt

It will save a pytorch .pt file with a dictionary of features. This can be loaded by torch.load(). The file is named following a simple convention: sub-<subject_id>_task-<task_name>_md5-<checksum>_features.pt

To enable speech to text transcription, specify the --speech2text flag.
Batch process audio files

This requires a CSV file, where there is a column header called filename and then each subsequent line is of the form: path/to/audio.wav

This also supports a CSV file where each line is of the form: path/to/audio.wav,subjectid,taskname

To generate this csv file from the Production directory pulled from wasabi, use command 3.

bash b2aiprep-cli batchconvert filelist.csv --plugin cf n_procs=2 --outdir out --save_figures

The above command uses pydra under the hood to parallel process the audio files. All outputs are currently stored in a single directory specified by the --outdir flag.

One can also generate a hugging face dataset in the output directory by specifying the --dataset flag.

To enable speech to text transcription, specify the --speech2text flag.
Generate csv file to feed to batchconvert

bash b2aiprep-cli createbatchcsv input_dir outfile

The input directory should point to the location of the Production directory pulled from Wasabi e.g. /Users/b2ai/production.

This directory can have subfolders for each institution, (e.g. production/MIT), and each subdirectory is expected to have all the .wav files from each institution.

Outfile is the path to and name of the csv file to be generated, e.g. audiofiles.csv

The csv file will have a header named filename with all the filenames listed under.

Verify if two audio files are from the same speaker

bash b2aiprep-cli test_audio1.wav test_audio2.wav --model 'speechbrain/spkrec-ecapa-voxceleb'

This will use the speechbrain speaker recognition model to verify that the two audio files are from the same speaker.

There is a notebook in the docs directory that can be used to interact with the library programmatically.
Convert the speaker in the source audio file (1st argument) into the speaker of the target audio file (2nd argument) and save the result in the output file (3rd argument)

bash b2aiprep-cli convert-voice data/vc_source.wav data/vc_target.wav data/vc_output.wav
Transcribe the audio

bash b2aiprep-cli transcribe data/vc_source.wav

Or use a different model. Note that the large model may take some time to download.

bash b2aiprep-cli transcribe data/vc_source.wav --model_id 'openai/whisper-large-v3' --return_timestamps true

BIDS-like data

This package provides conversion from a RedCap/file-based custom structure into a BIDS-like structure for downstream analysis. In addition, utilities are provided for working with the BIDS-like structured data and to support data analysis of voice and questionnaire data.

Convert a RedCap CSV and a folder of audio files into the BIDS format:

sh b2aiprep-cli redcap2bids bridge2ai_voice_data.csv --outdir output --audiodir audio

The audiodir option above can be omitted, in which case no audio data is reorganized.

See the tutorial.ipynb for a few use examples of data in the BIDS-like format.

Summer School Data Preparation

This command organizes the data with the BIDS-like conversion tool, extracts audio features, and saves the whole thing as a .tar file for easy distribution for the Bridge2AI Summer School:

sh b2aiprep-cli prepsummerdata \ [path to RedCap CSV] \ [path to Wasabi export directory] \ [desired path to BIDS output] \ [desired output path for .tar file]

Streamlit dashboard

A dashboard is provided to help navigate the data in the BIDS format. Launch the dashboard from the repository folder with:

sh streamlit run src/b2aiprep/app/Dashboard.py [path to BIDS directory] After the Streamlit dashboard opens, please wait for the BIDS data to be loaded while on the Dashboard page, as an error regarding a non-initialized bids_dir directory may pop up if the BIDS data has not been fully loaded.

Owner

Name: Senseable Intelligence Group
Login: sensein
Kind: organization

Website: https://sensein.group/
Twitter: sensein_
Repositories: 36
Profile: https://github.com/sensein

Our group uses neuroimaging, speech communication, and machine learning to improve assessments and treatments for mental health.

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Bevers"
  given-names: "Isaac"
  orcid: "https://orcid.org/0009-0002-0074-0087"
- family-names: "Ghosh"
  given-names: "Satrajit"
  orcid: "https://orcid.org/0000-0002-5312-6729"
- family-names: "Johnson"
  given-names: "Alistair"
  orcid: "https://orcid.org/0000-0002-8735-3014"
- family-names: "Brito"
  given-names: "Rahul"
  orcid: "https://orcid.org/0000-0003-0343-0455"
- family-names: "Bedrick"
  given-names: "Steven"
  orcid: "https://orcid.org/0000-0002-0163-9397"
- family-names: "Catania"
  given-names: "Fabio"
  orcid: "https://orcid.org/0000-0002-5403-9002"
- family-names: "Ng"
  given-names: "Evan"
  orcid: "https://orcid.org/0009-0006-8554-5414"
title: "Bridge2AI Data Processing Library"
version: 0.21.0
date-released: 2024-11-27
url: "https://github.com/sensein/b2aiprep"

GitHub Events

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Last Year

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Total pull requests: 35
Average time to close issues: 10 days
Average time to close pull requests: 7 days
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Average comments per issue: 0.38
Average comments per pull request: 0.91
Merged pull requests: 20
Bot issues: 0
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Past Year

Issues: 34
Pull requests: 35
Average time to close issues: 10 days
Average time to close pull requests: 7 days
Issue authors: 4
Pull request authors: 3
Average comments per issue: 0.38
Average comments per pull request: 0.91
Merged pull requests: 20
Bot issues: 0
Bot pull requests: 0

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Total versions: 40
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pypi.org: b2aiprep

A small package to generate features from acoustic

Homepage: https://github.com/sensein/b2aiprep
Documentation: https://b2aiprep.readthedocs.io/
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Latest release: 2.7.0
published 6 months ago

Versions: 40
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