FireRedVAD GGUF Models
Pre-converted FireRedVAD models in GGUF format for cross-platform, high-performance C++ inference. Includes Standard VAD, Streaming VAD, and Audio Event Detection (AED) with multiple quantization options.
- Original Models: FireRedTeam/FireRedVAD
- C++ Inference Engine & Conversion Tools: https://github.com/Strg-Alt-Entf-0x00/firered-vad
(Update 2026-08-30: Models re-uploaded to fix a mathematical transposition bug in the FSMN lookahead filters. These models now have 100% exact parity with the original PyTorch implementation).
⚡ Quick Start (Python / Hub)
pip install huggingface-hub
from huggingface_hub import hf_hub_download
# Download streaming VAD (INT8-CH, recommended)
model_path = hf_hub_download(
repo_id="Strg-Alt-Entf-0x00/FireRedVAD-GGUF",
filename="stream-vad/int8-ch/firered-stream-vad-int8-ch.gguf"
)
For the highly-optimized Native C++ Engine (no PyTorch required, up to 53x realtime), see the Integration Repository.
📁 Available Models
All models follow this structure: {model_type}/{quantization}/{filename}.gguf
stream-vad/ — Real-time Streaming VAD (Causal, Low Latency)
Best for real-time voice activity detection. Fully causal — no future context needed. Optimized for streaming applications with 10ms frame processing. Achieves up to 53.6x realtime performance on a single CPU core.
| Quantization | Path | Size | MAE vs FP32 | SQNR | Notes |
|---|---|---|---|---|---|
| INT8-CH | stream-vad/int8-ch/ | 601 KB | 0.000985 | 59.4 dB | Recommended for Edge / IoT. Per-channel quantization, near-FP32 accuracy |
| INT8 | stream-vad/int8/ | 574 KB | 0.001918 | 50.5 dB | Per-tensor quantization, good accuracy |
| INT16 | stream-vad/int16/ | 1.14 MB | 0.000077 | 94.2 dB | High precision, 2x memory |
| FP32 | stream-vad/fp32/ | 2.28 MB | 0.0 | ∞ | Recommended for Desktop / Server. Reference quality, 100% precision |
vad/ — Standard VAD (Bidirectional, High Accuracy)
Uses bidirectional context (lookback + lookahead). Higher accuracy than stream-vad, but requires buffering full audio segments. Best for offline/batch processing.
Performance: 97.57% F1 on FLEURS-VAD-102 dataset. Runs at 31.3x realtime.
| Quantization | Path | Size | MAE vs FP32 | SQNR | Notes |
|---|---|---|---|---|---|
| INT8-CH | vad/int8-ch/ | 627 KB | 0.000985 | 59.4 dB | Recommended for Edge / IoT |
| INT8 | vad/int8/ | 595 KB | 0.001957 | 50.4 dB | Good accuracy, minimal size |
| INT16 | vad/int16/ | 1.18 MB | 0.000079 | 94.2 dB | High precision |
| FP32 | vad/fp32/ | 2.36 MB | 0.0 | ∞ | Recommended for Desktop / Server |
aed/ — Audio Event Detection (Multi-class)
Simultaneous detection of speech, music, and singing in 100+ languages. Runs at 31.7x realtime.
| Quantization | Path | Size | MAE vs FP32 | SQNR | Notes |
|---|---|---|---|---|---|
| INT8-CH | aed/int8-ch/ | 628 KB | 0.000985 | 59.4 dB | Recommended for Edge / IoT |
| INT8 | aed/int8/ | 596 KB | 0.001957 | 50.4 dB | Good accuracy |
| INT16 | aed/int16/ | 1.18 MB | 0.000079 | 94.2 dB | High precision |
| FP32 | aed/fp32/ | 2.36 MB | 0.0 | ∞ | Recommended for Desktop / Server |
🔍 Quantization Details
Why INT8-CH (Per-Channel) is Recommended
Problem with standard INT8 (per-tensor): DFSMN architectures have wide variance in weight distribution across output channels. A single global scale factor per tensor cannot capture this range accurately, causing silent accuracy degradation.
Solution: INT8 Per-Channel (int8-ch): Assigns one scale factor per output channel. This preserves near-FP32 accuracy (MAE < 0.001, SQNR > 59 dB) while maintaining INT8 speed and memory efficiency.
Quantization Quality Metrics (Per Model)
Each model includes a -debug.json file with per-tensor quantization statistics:
- MAE (Mean Absolute Error): Average difference from FP32
- SQNR (Signal-to-Quantization-Noise Ratio): Quality metric in dB
- Per-channel scale factors: Min/max/mean scales per layer
⚙️ Hardware Requirements
Minimum (INT8-CH models)
- RAM: ~5 MB for model + ~2 MB for inference buffers
- CPU: Any modern x86_64, ARM, or RISC-V processor
- Storage: ~600 KB per model
Recommended (FP32 models)
- RAM: ~10 MB for model + ~3 MB for inference buffers
- CPU: SIMD support (SSE, AVX, NEON) for faster inference
- Storage: ~2.3 MB per model
📜 License & Attribution
- FireRedVAD by Xiaohongshu (FireRedTeam) — Apache 2.0
Source: https://github.com/FireRedTeam/FireRedVAD - FireRedVAD-GGUF by Strg-Alt-Entf-0x00 — Apache 2.0
Repository: https://github.com/Strg-Alt-Entf-0x00/firered-vad