FlashVAD v0.1 model card
Summary
FlashVAD v0.1 is a 46,170-parameter causal streaming voice-activity detector. It consumes 16 kHz mono audio, produces one speech probability every 10 ms, and keeps independent convolutional, recurrent, feature, and detector state per call.
Author: Himanshu Maurya
This checkpoint is an alpha research preview for integration testing, browser demonstrations, and shadow evaluation. It is not approved for production use and is not validated as a general multilingual or India/GCC call model.
Artifacts
| Artifact | SHA-256 |
|---|---|
flashvad-v0.1.pt | ca9e35475518466b2a1f2e89b4953cd1e26e3d8c513cdcf265ab319e74e2b288 |
flashvad-stream.onnx | 9a88e34bf3118d60e25a16cb622cb394e2f3ab71445b0aa5957df6f1d5f1b6ba |
config.json | 0b1ad372808f7c67cea5a1ca4b41a817714c9a0b0cf49b3baa56fe8d5f64ad2b |
detector-calibration.json | b5d000e0406d81fbd87a9e66194a877fa8433a76783faac8275c7969c43051b4 |
The ONNX graph accepts precomputed 43-dimensional causal features. Use
src/flashvad/features.py or
report-site/src/lib/vad-features.mjs; it is not a raw-waveform graph.
The public ONNX file is self-contained and stripped of exporter stack traces, local paths, and private build metadata.
Download and source
Download the complete model repository:
hf download oss-codes/flashvad --local-dir flashvad-model
Source code and runtime integrations are published separately at
oss-codes/flashvad.
The ONNX graph does not accept raw waveform audio. It expects the causal 43-dimensional features described below, with independent feature and model state for every call.
Intended use
Appropriate current uses:
- research and architecture evaluation;
- functional integration with browser, LiveKit, Pipecat, SIP, or PSTN stacks;
- latency and concurrency measurement;
- shadow-mode comparison on consented, labelled call audio.
Do not use this checkpoint as the sole basis for:
- emergency, medical, legal, financial, or safety-critical decisions;
- call recording consent or compliance decisions;
- a production multilingual accuracy claim;
- semantic end-of-turn detection.
Reset all feature, model, resampler, and detector state when a call ends or an audio discontinuity occurs.
Architecture
- 25 ms causal analysis frame and 10 ms hop;
- 40 log-mel bands plus energy, zero-crossing rate, and spectral flatness;
- four causal depthwise temporal blocks with dilations 1, 2, 4, and 8;
- one 64-unit GRU;
- speech and auxiliary event heads;
- 184,680 bytes of FP32 parameters.
Training inputs and provenance limit
Historical training notes associated with the retained checkpoint report:
- 558 derived clips from nine FLEURS configurations: Arabic, English, Gujarati, Hindi, Kannada, Punjabi, Tamil, Telugu, and Urdu;
- 288 AMI meeting clips with meeting-family-disjoint train/validation splits;
- 64 MUSAN noise clips;
- weak frame targets from the official Silero VAD model.
FLEURS, AMI, and MUSAN attribution is in NOTICE; dataset audio is not
distributed here.
Those counts and corpus names are not embedded as complete provenance in the public checkpoint. The exact retained training manifests, their digests, source revisions, and teacher-output digest were not preserved, so the checkpoint's training run is not bit-for-bit reproducible from the public tree. This is a provenance limitation, not evidence of broader accuracy. Future release candidates must preserve those records before training begins.
Evaluation
The retained checkpoint was repeatedly inspected on TEN VAD's public 30-recording set while research candidates were compared. At the retained detector policy, the descriptive results are:
- 26,243 frames;
- ROC-AUC: 0.882;
- raw F1 at the configured threshold: 0.886;
- hysteresis-decision F1: 0.889;
- false-alarm rate: 26.3%;
- miss rate: 13.0%.
Because the public set influenced research decisions, this is an exploratory
external-set result—not an untouched test, independent benchmark, or
production generalization estimate. Language is recorded as und, and codec,
channel, device, and SNR are unknown.
The machine-readable report is
benchmarks/flashvad-v0.1/ten-public-evaluation.json. Its false-alarm rate is
too high for production.
Known limitations
- Quiet speech, music, TTS leakage, echo, television, laughter, singing, and overlapping speakers may cause misses or false triggers.
- Read speech and meetings do not cover real carrier, device, packet-loss, and room conditions.
- Language presence in training does not prove per-language performance.
- Linear 8-to-16 kHz conversion prioritizes causal speed, not audio fidelity.
- VAD cannot determine whether a speaker has semantically completed a turn.
A production candidate needs consented, human-labelled, speaker-disjoint calls with predeclared per-slice gates and a test set untouched until final evaluation.
Licences
Repository source code is MIT-licensed. The retained model artifacts are
separately available under CC BY 4.0; see
MODEL_LICENSE.md and NOTICE. Third-party datasets, models, and benchmark
materials retain their own terms.