omnivad 0.2.1

OmniVAD — Cross-platform Voice Activity Detection and Audio Event Detection (based on FireRedVAD)

OmniVAD

Cross-platform toolkit for FireRedVAD — SOTA voice activity detection and audio event detection.

Three models, one toolkit, runs everywhere:

Model	What it does	Output
VAD	Speech detection (non-stream)	Speech timestamps
Stream-VAD	Real-time speech detection (frame-by-frame)	Per-frame speech probability
AED	Audio event detection (non-stream)	Speech / Singing / Music timestamps

All models are based on DFSMN architecture, ~2.2MB each (~588K params), support 100+ languages.

Packages

Python (omnivad/)

PyPI package with native C bindings (ncnn). Models bundled in wheel.

pip install omnivad

CLI:

omnivad audio.wav                        # VAD + AED → audio.TextGrid
omnivad audio.wav -o out.json            # Output as JSON
omnivad audio.wav -o out.srt             # Output as SRT
omnivad audio.wav -o out.vtt             # Output as WebVTT
omnivad audio.wav -f srt                 # Format flag (textgrid/json/srt/vtt)
omnivad audio.wav -m vad                 # VAD only
omnivad audio.wav -m aed                 # AED only (speech/singing/music)
omnivad long.wav --chunk 600 --overlap 2 # Chunked processing for large audio
python -m omnivad audio.wav              # Also works

Python API:

from omnivad import OmniVAD, OmniStreamVAD, OmniAED
import numpy as np

vad = OmniVAD()

# File path — auto-loads as float32 [-1,1]
result = vad.detect("audio.wav")
# {'duration': 2.24, 'timestamps': [(0.26, 1.82)]}

# Float32 array [-1.0, 1.0] — from soundfile, torchaudio, librosa
result = vad.detect(float32_array)

# Int16 array — from raw WAV, microphone PCM
result = vad.detect(np.array([...], dtype=np.int16))

# Large audio — chunked processing with overlap
# overlap_seconds must be smaller than chunk_seconds
result = vad.detect("long.wav", chunk_seconds=600, overlap_seconds=2)

# Stream VAD — real-time, feed 160 int16 samples (10ms) at a time
svad = OmniStreamVAD()
frame = None
while frame is None:
    frame = svad.process(pcm_160_int16)
# StreamResult(time=0.420s, confidence=0.95, is_speech=True)

# AED — speech + singing + music
aed = OmniAED()
events = aed.detect("audio.wav")
# {'duration': 22.0, 'events': {'speech': [...], 'singing': [...], 'music': [...]}}

Platforms: macOS (arm64/x86_64), Linux (x86_64/aarch64), Windows (x86_64)

C/C++ Native Library (native/)

Unified C API with ncnn backend. Single header, single library.

#include "omnivad.h"

int err = OMNI_OK;

// VAD — whole audio to speech segments
OmniVadHandle vad = omni_vad_create("vad.omnivad", &err);
omni_vad_detect_int16(vad, pcm, num_samples, &config, &segments, &count);
// segments[0] = { start: 0.44, end: 1.82 }

// Stream VAD — real-time, 10ms per frame
OmniStreamVadHandle svad = omni_stream_vad_create("stream-vad.omnivad", 0.5f, &err);
omni_stream_vad_process(svad, pcm_160_samples, 160, &result);
// result.confidence = 0.95, result.is_speech = true

// AED — speech + singing + music detection
OmniAedHandle aed = omni_aed_create("aed.omnivad", &err);
omni_aed_detect_int16(aed, pcm, num_samples, &config, &segments, &count);
// segments[0] = { start: 0.09, end: 12.32, cls: OMNI_AED_MUSIC }

Build:

# Prerequisites: cmake, ncnn (brew install ncnn)
cd native
cmake -B build && cmake --build build -j$(nproc)

# Test
./build/test_all ../models/ audio.wav

Platforms: macOS (arm64/x86_64), Linux (x86_64/aarch64), Windows (x86_64), Android (armeabi-v7a/arm64-v8a)

TypeScript/JavaScript (packages/omnivad/)

Works in both browser and Node.js via ncnn WebAssembly. Zero dependencies, models bundled.

import { OmniVAD, OmniStreamVAD, OmniAED } from 'omnivad';

// Non-stream VAD — models loaded automatically from bundled WASM
const vad = await OmniVAD.create();
const result = vad.detect(audioFloat32Array);  // Float32Array [-1.0, 1.0]
// { duration: 2.32, timestamps: [[0.44, 1.82]] }

// Also accepts Int16Array (raw PCM)
const result2 = vad.detect(pcmInt16Array);

// Stream VAD — frame-by-frame or full-audio batch mode
const svad = await OmniStreamVAD.create();
const frame = svad.processFrame(pcm160);  // null until enough audio is buffered
const full = svad.detectFull(audioFloat32Array);
// { probabilities: Float32Array(...), numFrames: 98, duration: 1.0 }

// AED — speech + singing + music
const aed = await OmniAED.create();
const events = aed.detect(audioFloat32Array);
// { duration: 22.0, events: { speech: [...], singing: [...], music: [...] }, ratios: { ... } }

Build:

cd packages/omnivad
pnpm install && pnpm build
# Output: dist/index.js + dist/index.cjs + dist/index.d.ts + dist/wasm/*

Audio Input

High-level APIs accept 16kHz mono audio only.

• OmniVAD / OmniAED in Python and TypeScript accept normalized float32/Float32Array in [-1, 1] and int16 / Int16Array.
• OmniStreamVAD.process() in Python accepts int16 chunks and also converts normalized float32 chunks internally.
• OmniStreamVAD.processFrame() in TypeScript expects Int16Array chunks.
• OmniStreamVAD.detect_full() / detectFull() accept full-audio buffers and handle normalization internally.
• The C API is slightly lower-level than the Python/TypeScript wrappers. For exact input contracts, use native/include/omnivad.h as the source of truth.

Audio Pipeline

16kHz PCM → Fbank (80-dim, 25ms window, 10ms shift) → CMVN → DFSMN → Sigmoid → Post-processing → Segments
                     Povey window                        μ/σ    ~2.2MB   [0,1]    4-state machine
                     pre-emphasis 0.97                                            merge/split/extend

Model Files

Prebuilt .omnivad bundles used by the Python package, TypeScript package, and local examples are already included in this repo under models/.

You only need to download upstream FireRedVAD checkpoints if you want to re-export ONNX or regenerate the native assets yourself.

# Download upstream PyTorch models + export to ONNX
pip install fireredvad
python -m fireredvad.bin.export_onnx --all

# Or download pre-exported ONNX models directly
# fireredvad_vad.onnx              — Non-stream VAD (2.3MB)
# fireredvad_aed.onnx              — Non-stream AED (2.3MB)
# fireredvad_stream_vad_with_cache.onnx — Stream VAD (2.2MB)

# For C/ncnn: convert ONNX → ncnn with pnnx
pip install pnnx
pnnx fireredvad_vad.onnx "inputshape=[1,100,80]"

Testing

# Run the full Python test suite
pip install -e ".[dev]"
pytest tests -v

# Utility scripts (not pytest — require external FireRedVAD models)
python tests/generate_reference.py            # Generate Python reference data
python tests/check_timestamp_accuracy.py      # Strict C vs Python comparison
python tests/vad_to_textgrid.py audio.wav     # Audio → TextGrid + RTF benchmark

Accuracy (C/ncnn vs Python, 5 audio files × 3 models):

Model	Timestamp Δ	Probability Δ	Status
VAD	≤ 0.020s	≤ 0.001	Exact match
AED (singing/music)	≤ 0.010s	≤ 0.013	Exact match
AED (speech)	≤ 0.030s	≤ 0.015	Match (ncnn fp16 edge cases on event.wav)
Stream-VAD (detect_full)	≤ 0.010s	≤ 0.001	Exact match

Project Structure

omnivad/
├── omnivad/                         # Python PyPI package
│   ├── __init__.py                  #   Public API: OmniVAD, OmniStreamVAD, OmniAED
│   ├── cli.py                       #   CLI entry point (omnivad command)
│   ├── _binding.py                  #   ctypes bindings to libomnivad
│   ├── vad.py                       #   OmniVAD (non-stream)
│   ├── stream_vad.py                #   OmniStreamVAD (real-time)
│   └── aed.py                       #   OmniAED (3-class)
├── native/                          # C/C++ library (ncnn backend)
│   ├── include/omnivad.h            #   Unified C API header
│   ├── src/omnivad.cpp              #   Core implementation
│   ├── frontend/                    #   Fbank/FFT/WAV (from FireRedVAD)
│   ├── test/                        #   4 test programs
│   └── CMakeLists.txt
├── packages/omnivad/                # TypeScript npm package
│   ├── src/
│   │   ├── vad.ts                   #   OmniVAD (non-stream)
│   │   ├── stream-vad.ts            #   OmniStreamVAD (real-time)
│   │   ├── aed.ts                   #   OmniAED (3-class)
│   │   ├── wasm-binding.ts          #   Emscripten/WASM bindings
│   │   ├── types.ts                 #   Public TypeScript types
│   │   ├── index.ts                 #   Package exports
│   │   └── wasm.d.ts                #   WASM module declarations
│   ├── package.json
│   └── tsconfig.json
└── tests/                           # Test suite
    ├── test_c_vs_python.py          #   Accuracy: omnivad vs Python reference
    ├── test_determinism.py          #   Repeated-run determinism
    ├── test_edge_cases.py           #   Edge cases: tiny/empty/silence inputs
    ├── smoke_test.py                #   CI smoke test (import + detect)
    ├── test_memory.sh               #   Native memory/leak checks
    ├── check_timestamp_accuracy.py  #   Strict C vs Python comparison (manual)
    ├── check_native.py              #   Native C binary validation (manual)
    ├── generate_reference.py        #   Generate Python reference data
    ├── vad_to_textgrid.py           #   Audio → TextGrid + RTF benchmark
    └── data/                        #   5 test audio files + reference JSON

Performance

RTF (Real-Time Factor) on Apple M-series, lower = faster:

Model	RTF	Speed
VAD	~0.003	~330x real-time
Stream-VAD	~0.002	~500x real-time
AED	~0.002	~500x real-time

Origin & Attribution

OmniVAD is a cross-platform deployment toolkit built on top of FireRedVAD, developed by Xiaohongshu (小红书). FireRedVAD provides high-quality Voice Activity Detection models and a lightweight Audio Event Detection model that can distinguish speech, singing, and music.

Original paper: FireRedVAD (arXiv:2603.10420)

What FireRedVAD provides: DFSMN-based models (~2.2MB each), Python inference code, PyTorch training, strong VAD benchmark results (FLEURS-VAD-102 F1: 97.57%).

What OmniVAD adds: Unified C API (ncnn backend) for native deployment, TypeScript/JavaScript npm package (ncnn WebAssembly) for browser and Node.js, cross-platform build system, comprehensive test suite with accuracy validation.

License

Apache-2.0 — same as the upstream FireRedVAD.

Credits

• FireRedVAD — Kaituo Xu, Wenpeng Li, Kai Huang, Kun Liu (Xiaohongshu)
• ncnn — Tencent
• Emscripten — WebAssembly toolchain