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no_burst. Multi-label (independent
sigmoid per class) — a clip can contain several bursts. Works on pure bursts and bursts embedded in speech.laion/voiceclap-commercial (768-d, frozen) → MLP 2048-wide × 4 deep (LayerNorm+GELU+dropout) → 83 sigmoids. ~14.4M trainable params.laion/vocalburst-locator.v2 (current): retrained with 9,000 multilingual burst-free negatives (FLEURS: Chinese, Hindi, Bengali, Arabic, Persian, Urdu, Tamil, Telugu, Vietnamese, Thai, Indonesian, Japanese, Korean, Swahili, Yoruba, Zulu, Turkish, Russian) — fixes hallucinated bursts on non-European speech.
| metric | fine (82) | coarse (16 families) |
|---|---|---|
| macro mAP | 0.39 | 0.64 |
| top-1 exact | 49% | – |
| a true label in top-3 | 77% | – |
| Best when you take the top-1 prediction (which the two-stage combo demo uses). Much stronger at the coarse "which family of sound" level than the exact fine subtype. |
laion/voiceclap-commercial audio encoder turns a clip into a 768-d embedding
(encode_waveform, auto-downloaded — the repo needs no extra setup), then this small trained MLP head
maps it to 83 outputs = 82 VocalBurst classes + no_burst (taxonomy: LAION-AI/voice-taxonomies · vocalburst).
A no-burst gate: if P(no_burst) ≥ 0.5 the clip is declared burst-free (no false alarm); otherwise the top classes are returned. Clips are truncated to the first 30 s (the encoder's window).1from inference import VocalBurstClassifier
2clf = VocalBurstClassifier("laion/vocalburst-classifier-multilabel") # HF repo id, or a local checkout dir
3print(clf.predict("clip.wav")) # -> {no_burst, p_no_burst, top1, predictions:[(class,prob)], group}model.pt (MLP weights) · config.json (arch) · classes.json (83 labels, index order) ·
class_to_group.json (fine→16 coarse families) · inference.py · example.py · requirements.txt.