README.md

metadata

title: VoxSplit
emoji: 🎙️
colorFrom: purple
colorTo: pink
sdk: docker
app_port: 7860
pinned: false

Audio Transcription & Gender Detection (POC)

Upload a .wav file and get a diarized, timestamped transcript (via the Sarvam AI saaras:v3 batch API) plus a per-speaker gender estimate. The transcript is synced to audio playback — the active segment highlights as it plays, and clicking a segment seeks to it.

How it works

1. The WAV is uploaded to a small FastAPI backend.
2. The backend runs a Sarvam batch STT job with with_diarization=True, which returns speaker-labelled segments with start/end timestamps.
3. For each speaker, the backend pools all of their audio and runs the prithivMLmods/Common-Voice-Gender-Detection wav2vec2 classifier, returning softmax female / male probabilities. Below a 0.6 confidence floor (or with too little audio) the speaker is marked uncertain.
4. The frontend renders the audio player, a speaker legend, and the synced transcript.

The gender model is downloaded from HuggingFace on first run (360 MB) and cached. It's a trained classifier (98% reported accuracy) but can still err on children, atypical voices, or noisy/short audio.

Setup

cd audio-gender-detection
python3 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt

cp .env.example .env   # then add your Sarvam API key

.env:

SARVAM_API_KEY=your_sarvam_api_key_here
NUM_SPEAKERS=2

(You can also paste the key directly into the UI instead of using .env.)

Run

uvicorn backend.main:app --reload --port 8000

Open http://localhost:8000

Notes

• Diarization is only available through Sarvam's Batch API, so processing is asynchronous — longer files take longer.
• Uploaded files land in uploads/ (gitignored). Clean it up periodically.
• librosa/soundfile need a working audio backend; on macOS these install cleanly via pip. On Linux you may need libsndfile1 (apt install libsndfile1).