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FireRedVAD-GGUF

Voice Activity Detection
GGUF
multilingual
vad
audio-event-detection
aed
quantization
dfsmn
audio
speech
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metadata 
tags:
  - voice-activity-detection
  - gguf
  - multilingual
  - vad
  - audio-event-detection
  - aed
  - quantization
  - dfsmn
  - audio
  - speech
pipeline_tag: voice-activity-detection
base_model: FireRedTeam/FireRedVAD

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.

(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