FireRedVAD -- GGUF
GGUF conversions of FireRedTeam/FireRedVAD for use with CrispStrobe/CrispASR.
Available variants
| File | Variant | Size | Params | Notes |
|---|---|---|---|---|
firered-vad.gguf | VAD | 2.4 MB | 588K | Non-streaming, lookback+lookahead |
firered-stream-vad.gguf | Stream-VAD | 2.3 MB | 568K | Streaming (no lookahead) |
firered-aed-vad.gguf | AED | 2.4 MB | 589K | Multi-label: speech/singing/music |
All variants are F32 (no quantization needed — models are already tiny).
Model details
- Architecture: DFSMN (Deep Feedforward Sequential Memory Network) — 8 blocks with depthwise lookback/lookahead convolutions (k=20)
- Parameters: ~588K (2.4 MB)
- Languages: 100+ (language-agnostic voice activity detection)
- F1 Score: 97.57% on FLEURS-VAD-102 (outperforms Silero-VAD, TEN-VAD, FunASR-VAD, WebRTC-VAD)
- License: Apache 2.0
Conversion
python models/convert-firered-vad-to-gguf.py --input FireRedTeam/FireRedVAD --variant VAD --output firered-vad.gguf
Provenance and EU AI Act Art. 53 note
- Upstream model: FireRedTeam/FireRedVAD — published by
FireRedTeam. - Upstream licence:
apache-2.0. This repository redistributes under the same terms; it grants no rights the upstream licence does not. - What was done here: format conversion and/or quantisation only (GGUF/GGML). No training, no fine-tuning, no merging, no distillation, no change to architecture, vocabulary or capability. Only the numeric representation of the upstream weights differs.
- Training data: documented — where it is documented at all — by the upstream provider; see the upstream model card. No training data was used, added or selected by this repository. No training-content summary was found on the upstream model card at the time of writing; that documentation gap is upstream's and is not filled here.
- Provider status: under Regulation (EU) 2024/1689 the upstream authors remain the provider of this model. Converting the serialisation format does not make this repository the provider of a new general-purpose AI model, and no such claim is made. Questions about training content, copyright policy or model capability belong upstream.