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 โ€” models are already tiny).

Model details

  • Architecture: DFSMN (Deep Feedforward Sequential Memory Network) โ€” 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 โ€” 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 โ€” where it is documented at all โ€” 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.
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