VoxHumana (VxH) is a web-based pipeline for processing sociolinguistic speech recordings. With just a few clicks, VxH will transcribe your audio, force-align it, and extract vowel formant measurements automatically. In a short time, you can go from raw audio to a spreadsheet of formant measurements without having to download software or code anything yourself.
Every recording submitted to VxH goes through three steps:
- Transcription — Whisper converts your audio to text with utterance-level timestamps.
- Forced alignment — Montreal Forced Aligner (MFA) gets the precise start and end time of every word and phone.
- Formant extraction — new-fave measures F1, F2, F3, and F4 for every vowel token.
Results are delivered as a zip file containing:
| File | Contents |
|---|---|
*_points.csv |
One row per vowel token — the main file for most analyses |
*_tracks.csv |
Formant trajectories across each vowel (multiple time points per token) |
*_param.csv |
DCT coefficients of the formant tracks (Hz scale) |
*_logparam.csv |
DCT coefficients of the formant tracks (log Hz scale) |
*_recoded.TextGrid |
Praat TextGrid with vowels relabeled in Labov vowel-class notation |
processing_log.txt |
Full record of every parameter used and how to replicate the run offline |
oovs_found.txt |
Words not found in MFA's dictionary (included only if any were found) |
Output files are organized into subdirectories by pipeline step (whisper_output/, mfa_output/, newfave_output/).
All processing happens entirely on BYU's server — your audio is never sent to OpenAI or any other external service. Whisper, MFA, and new-fave all run locally. Uploaded audio is deleted from the server as soon as processing finishes. Result files are available for download for 72 hours, then deleted automatically. No audio or transcripts are retained, shared, or used for any other purpose.
VxH is a spiritual successor to DARLA. It is clearly inspired by DARLA and seeks to replicate much of its functionality. However, the creators of DARLA were not involved in the development of VxH (outside of a small consulting role), and it is an independent development by its creator, Joey Stanley.
Key differences:
- Transcriptions are done using Whisper instead of an in-house system, YouTube, or BedWord.
- Formant extractions are done using new-fave, the modern successor to FAVE-Extract.
- VxH is fully open-source; everything is available on GitHub, which means you can download it and run it offline if you have the necessary software.
VoxHumana runs as a local web server. You will need:
- Python 3.13+
- ffmpeg (
brew install ffmpegon Mac,apt install ffmpegon Linux) - Montreal Forced Aligner (installed separately via conda — see MFA docs)
Accepted audio formats: .wav, .mp3, .flac, .ogg, .m4a, .aiff. Maximum file size: 1 GB.
git clone https://github.com/JoeyStanley/VoxHumana.git
cd VoxHumana
pip install -e .
uvicorn web.app:app --reloadThen open http://localhost:8000 in your browser.
Command-line use. You can also run the pipeline directly on a file without the web interface:
python main.py path/to/audio.wav path/to/output/dirVox Humana is Latin for "human voice" (Modern Ecclesiastical pronunciation: [ˈvɔks uˈmaː.na]). It is also the name of a classic pipe organ stop. This organ motif appears in several places in VxH — the color palette is inspired by the façade of the Salt Lake Tabernacle organ. This theme is shared with Pipeline, another web-based tool that takes the output of VxH and creates interactive vowel plots.
I chose the organ theme in part because of the play on words with pipeline, but also because I play the organ and wanted to add a personal flair to this tool. Vox Humana was a natural choice for a project that intersects nerdy organ knowledge with the human voice; you can learn more about the Vox Humana organ stop here.
Much of VxH was written using Claude by Anthropic. This includes pretty much all of the back-end and the UI. The documentation is my own.
I used AI to write VxH because I felt that there was great need for a replacement for DARLA and that it would be best written in Python. But my Python skills are extremely limited. Ideally, I would learn Python well enough to code everything by hand or hire someone else to do so. But I do not have the time or resources for those options, and the growing pressure for a tool like this means the field couldn't wait.
I acknowledge that some users may feel uncomfortable with this use of AI; I recognize your concerns. Please make the best choice for you and your data.
VoxHumana is developed and maintained by Joey Stanley, associate professor in the linguistics department at Brigham Young University. Please send questions and bug reports to joey_stanley@byu.edu.
MIT — see LICENSE