FireRedTeam

FireRedTeam / FireRedVAD

Public

A SOTA Industrial-Grade Voice Activity Detection & Audio Event Detection, supporting 100+ languages, outperforming Silero-VAD, TEN-VAD, FunASR-VAD and WebRTC-VAD

aed audio-event-classification audio-event-detection sound-event-detection vad
47
1
100% credibility
Found Mar 03, 2026 at 47 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

FireRedVAD is a high-performance open-source toolkit for detecting voice activity and audio events such as speech, singing, and music in audio files across over 100 languages.

How It Works

1
🔍 Discover FireRedVAD

You hear about this handy tool that spots talking, singing, or music moments in any audio recording, working great in over 100 languages.

2
💻 Set up your computer space

You create a fresh spot on your computer to play with this audio magic, keeping everything neat and simple.

3
🧠 Grab the smart detection brains

You download the ready-to-use models that power the super-accurate spotting of voices and sounds – it's quick and easy!

4
🎵 Get your audio ready

You pick an audio file like a podcast or song and tweak it to the perfect format so it works smoothly.

5
🚀 Run the audio check

You tell the tool to analyze your file, choosing to find speech, live streaming voice, or events like singing and music.

6
📊 See the magic results

Instantly, you get a clear list of exact start and end times for talking or other sounds, making editing a breeze!

🎉 Your audio is unlocked!

Now you can easily cut out silences, focus on voices, or mix tracks perfectly, saving tons of time.

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AI-Generated Review

What is FireRedVAD?

FireRedVAD is a Python library for state-of-the-art voice activity detection and audio event detection, spotting speech, singing, and music in audio files or streams across 100+ languages. It solves the pain of inaccurate VAD in multilingual ASR pipelines by delivering precise timestamps for speech segments via CLI scripts or a simple Python API. Users feed it 16kHz mono WAVs and get structured results like durations and event ratios, ready for downstream processing.

Why is it gaining traction?

This SOTA AI GitHub project crushes benchmarks like FLEURS-VAD-102 with 97.57% F1, outperforming Silero-VAD, TEN-VAD, FunASR-VAD, and WebRTC-VAD on metrics like low false alarms and miss rates. Developers dig the industrial-grade reliability for real-time streaming VAD alongside non-streaming AED, plus easy model downloads from Hugging Face or ModelScope—no fuss setup for evo SOTA GitHub audio detection akin to sota model github leaders in object detection or TTS.

Who should use this?

ASR engineers building speech-to-text services need it for cleaning noisy multilingual audio before transcription. Real-time app devs handling live calls or podcasts will value streaming VAD to segment activity without latency. Audio ML teams evaluating SOTA GitHub options for event detection in 100+ languages should benchmark it against FunASR-VAD alternatives.

Verdict

Grab FireRedVAD if you need top-tier multilingual VAD—docs and examples are solid, API is intuitive, and benchmarks prove it works. But with just 47 stars and 1.0% credibility score, it's early-stage; test thoroughly before production.

(198 words)

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