FireRedVAD-CoreML

Core ML conversion of FireRedVAD Stream-VAD for real-time voice activity detection on Apple platforms (iOS 16+ / macOS 13+). Converted from the original PyTorch model by FireRedTeam/FireRedVAD.

Model Description

Original model: FireRedVAD by Xiaohongshu (小红书) FireRedTeam
Architecture: DFSMN (Deep Feedforward Sequential Memory Network) — 8 DFSMN blocks + 1 DNN layer
Variant: Stream-VAD (causal, lookahead=0), suitable for real-time streaming
Parameters: ~568K (extremely lightweight)
Model size: 2.2 MB (FP32)
Input: 80-dim log-Mel filterbank features (16kHz, 25ms frame, 10ms shift)
Output: Speech probability [0, 1] per frame
Language support: 100+ languages, 20+ Chinese dialects

Performance

Results from the FLEURS-VAD-102 benchmark (102 languages, 9,443 audio clips):

Metric	FireRedVAD	Silero-VAD	TEN-VAD	FunASR-VAD	WebRTC-VAD
AUC-ROC	99.60	97.99	97.81	-	-
F1 Score	97.57	95.95	95.19	90.91	52.30
False Alarm	2.69%	9.41%	15.47%	44.03%	2.83%
Miss Rate	3.62%	3.95%	2.95%	0.42%	64.15%

Core ML Model Specification

Inputs

Name	Shape	Type	Description
`feat`	`[1, 1..512, 80]`	Float32	Log-Mel filterbank features (dynamic time axis)
`cache_0` ~ `cache_7`	`[1, 128, 19]`	Float32	FSMN lookback cache for each of the 8 layers

Outputs

Name	Type	Description
`probs`	Float32	Speech probability, shape `[1, T, 1]`
`new_cache_0` ~ `new_cache_7`	Float32	Updated lookback cache

Minimum deployment target: iOS 16 / macOS 13
Compute units: CPU + Neural Engine

Conversion

Converted from PyTorch using coremltools via the export script in FireRedASR2S. The Stream-VAD variant was selected for its causal (no lookahead) property, making it suitable for real-time streaming applications.

Usage

import CoreML

// Load model
let model = try FireRedVAD(configuration: .init())

// Initialize caches (8 layers x [1, 128, 19])
var caches = (0..<8).map { _ in
    try! MLMultiArray(shape: [1, 128, 19], dataType: .float32)
}

// Process audio frame by frame
let input = FireRedVADInput(
    feat: fbankFeatures,       // [1, T, 80]
    cache_0: caches[0], cache_1: caches[1],
    cache_2: caches[2], cache_3: caches[3],
    cache_4: caches[4], cache_5: caches[5],
    cache_6: caches[6], cache_7: caches[7]
)
let output = try model.prediction(input: input)
let speechProb = output.probs  // [1, T, 1]

// Update caches for next frame
caches = [
    output.new_cache_0, output.new_cache_1,
    output.new_cache_2, output.new_cache_3,
    output.new_cache_4, output.new_cache_5,
    output.new_cache_6, output.new_cache_7
]