Streaming Neural VAD — Causal CRNN (47,873 params)
A small-footprint, causal, streaming-oriented Voice Activity Detector built for an on-device far-field target. Four causal depthwise-separable Conv1D residual blocks (kernel 5, dilations {1,2,4,8}) followed by a unidirectional GRU (hidden 64) and a per-frame linear head, over 64-bin log-mel features at a 10 ms frame rate.
- Parameters: 47,873 (~0.19 MB FP32)
- Look-ahead: 0 ms in the network; 12.5 ms from the centred 25 ms analysis window
- CPU end-to-end RTF: 0.0035 on 60 s inputs (single thread, Xeon Gold 6248)
- Framework: PyTorch 2.8
Intended use
Frame-level speech/non-speech decisions upstream of wake-word / ASR gating. Trained on clean read English (LibriSpeech-style) with additive MUSAN noise; it is a research prototype, not validated on real far-field, multi-speaker, or reverberant audio (see Limitations).
Results
Held-out, speaker-disjoint clean test partition (93 utterances, 4 speakers). All operating thresholds are selected on validation only, then frozen.
| System | Precision | Recall | F1 | FA | Miss | Collared DER |
|---|---|---|---|---|---|---|
| Energy (calibrated) | 0.945 | 0.941 | 0.943 | 0.294 | 0.059 | 7.07% |
| Silero (calibrated) | 0.976 | 0.976 | 0.976 | 0.126 | 0.024 | — |
| CRNN raw | 0.986 | 0.966 | 0.976 | 0.073 | 0.034 | 1.15% |
| CRNN + post | 0.987 | 0.970 | 0.978 | 0.069 | 0.030 | 0.64% |
Continuous-score: CRNN AUROC 0.984 / AUPRC 0.997; Silero 0.982 / 0.996. A paired 10k-utterance bootstrap gives a 95% CI of [0.0009, 0.0035] for the clean F1 difference (CRNN − calibrated Silero).
Noise robustness (frame F1, held-out MUSAN, global-RMS SNR):
| SNR (dB) | −5 | 0 | 5 | 10 | 15 | 20 |
|---|---|---|---|---|---|---|
| CRNN + post | 0.932 | 0.952 | 0.961 | 0.969 | 0.972 | 0.974 |
| Silero | 0.913 | 0.950 | 0.966 | 0.969 | 0.972 | 0.974 |
| Energy | 0.916 | 0.917 | 0.917 | 0.918 | 0.919 | 0.924 |
Full analysis, curves, and caveats are in the accompanying technical report.
Usage
from inference import VAD
vad = VAD("vad_crnn_export") # dir with model.safetensors + config.json
probs, mask, times = vad("clip.wav") # frame probs, binary mask, frame centres (s)
vad_model.py must contain your real architecture — see its docstring.
Files
vad_crnn_export/model.safetensors,vad_crnn_export/config.json— weights + configinference.py— standalone loader / predictorvad_model.py— architecture factory (you paste your crnn.py here)export_checkpoint.py— regenerates the export from the training checkpoint
Limitations
Clean read English only; additive-noise robustness under a global-RMS SNR definition; reverberation untested in the released model; four test speakers; single training seed; server-CPU (not target-device) latency; no stateful chunked-streaming benchmark. Do not treat in-domain F1 as deployed performance.
Training data & attribution
Provided challenge corpus (LibriSpeech-derived) + MUSAN music/noise for augmentation. The corpus and any derived mixtures are not redistributed here. LibriSpeech (Panayotov et al., 2015), MUSAN (Snyder et al., 2015).