laion/vocalburst-locator-synth-data

Vocal Burst Locator - Synthetic Training Data

Synthetic audio soundscapes with frame-level vocal burst annotations, used to train laion/vocalburst-locator — a Whisper-based model that detects and localizes vocal bursts (laughs, coughs, sneezes, sighs, gasps, cries, screams, etc.) in audio.

Dataset Summary

Split	Samples	Size
Train	32,712	~8.4 GB
Validation	300	~79 MB
Total	33,012	~8.5 GB

Each sample is a pair of files:

• sample_XXXXX.mp3 — a mono audio soundscape (3–30 seconds, 44.1 kHz, 96 kbps MP3)
• sample_XXXXX.json — labels with vocal burst timestamps and metadata

Label Format

{
    "events": [
        {"start_time": 3.21, "end_time": 4.85},
        {"start_time": 12.50, "end_time": 13.10}
    ],
    "duration_sec": 24.5,
    "bg_type": "music",
    "n_vocal_bursts": 2
}

Field	Type	Description
events	list	Vocal burst segments with start_time and end_time in seconds
duration_sec	float	Total audio duration in seconds
bg_type	string	Background type: "music", "sfx", "music+sfx", or "silence"
n_vocal_bursts	int	Number of vocal bursts in the clip

Samples with n_vocal_bursts: 0 have an empty events list (negative samples).

Dataset Composition

Class Balance

• 50% negative (no vocal bursts): 16,506 samples
• 50% positive (1+ vocal bursts): 16,506 samples

Distribution of vocal bursts per clip:

VBs per clip	Count
0	16,506
1	6,690
2	6,063
3	2,747
4	771
5	201
6+	34

Total vocal burst events across the entire dataset: 31,360

Background Types

Background	Count	Fraction
Music	10,464	31.7%
SFX (sound effects)	10,465	31.7%
Music + SFX	10,433	31.6%
Silence	1,650	5.0%

Duration

Clip durations follow a beta distribution biased toward longer clips: Beta(3.0, 1.2) * 27 + 3, giving a range of 3–30 seconds with most clips near 20–30 seconds.

How It Was Made

The dataset was generated synthetically by mixing vocal burst audio clips on top of background audio. The generation pipeline (generate_dataset.py in laion/vocalburst-locator) works as follows:

1. Source Audio

Four source audio collections were downloaded (using download_sources.py):

Source	Dataset	Count	Description
Vocal bursts	laion/improved_synthetic_vocal_burts	15,680	Synthetic vocal burst clips (laughs, coughs, sneezes, sighs, gasps, cries, screams, etc.)
Music	laion/captioned-ai-music-snippets	5,000	AI-generated music snippets
SFX (AudioSet)	mitermix/audioset-with-grounded-captions	5,000	Sound effects from AudioSet (filtered to exclude clips containing vocal bursts or excessive speech)
SFX (AudioSnippets)	mitermix/audiosnippets_small_with_detailed_annotation	3,000	Diverse audio snippets with annotations

2. Soundscape Generation

For each sample, the generator:

1. Picks a duration from the beta distribution (3–30s)
2. Selects a background type (music, SFX, music+SFX, or silence) based on the target distribution
3. Creates the background by randomly selecting and mixing source audio at random volumes:
   • Music: 1 track at 30–100% volume
   • SFX: 2–8 sound effects at 20–80% volume, placed at random positions
   • Music+SFX: combination of both
   • Silence: empty background
4. For positive samples (50%), places 1–5+ vocal burst clips at random positions with random volume (10–100%). Some clips may overlap (25% probability).
5. Applies optional augmentation (15% of samples): low-pass filtering and/or additive noise
6. Exports as mono MP3 (44.1 kHz, 96 kbps) with a JSON label file

Each of the 15,680 vocal burst source clips appears exactly twice across the entire dataset.

3. Train/Val Split

• 300 samples are reserved for validation
• The remaining 32,712 are used for training
• The split is deterministic (seed=2024)

Using This Dataset

Loading Samples

import json
import librosa

# Load a single sample
audio, sr = librosa.load("train/sample_00042.mp3", sr=16000)
with open("train/sample_00042.json") as f:
    labels = json.load(f)

print(f"Duration: {labels['duration_sec']:.1f}s")
print(f"Background: {labels['bg_type']}")
for ev in labels["events"]:
    print(f"  Vocal burst: {ev['start_time']:.2f}s - {ev['end_time']:.2f}s")

Converting to Frame Labels

The vocalburst-locator model operates at 50 fps (1500 frames per 30s). To convert event timestamps to frame-level binary labels:

import numpy as np

num_frames = 1500
clip_seconds = 30.0

frame_labels = np.zeros(num_frames, dtype=np.float32)
for ev in labels["events"]:
    start_frame = int(ev["start_time"] / clip_seconds * num_frames)
    end_frame = int(ev["end_time"] / clip_seconds * num_frames)
    start_frame = max(0, min(start_frame, num_frames - 1))
    end_frame = max(0, min(end_frame, num_frames))
    frame_labels[start_frame:end_frame] = 1.0

Trained Model

This dataset was used to train laion/vocalburst-locator, which achieves:

Metric	Value
Event F1	0.752
Event Precision	0.897
Event Recall	0.781

See the model card for full details on architecture, training, and inference.

Files

Training data is split into shards (subdirectories) of ~4,000 samples each due to hosting platform limits:

vocalburst-locator-synth-data/
  train/
    shard_00/               # samples 00000–03999 (4,000 samples)
      sample_00000.mp3
      sample_00000.json
      ...
    shard_01/               # samples 04000–07999
    shard_02/               # samples 08000–11999
    ...
    shard_08/               # samples 32000–32711 (712 samples)
  val/                      # 300 samples (flat directory)
    sample_00000.mp3
    sample_00000.json
    ...
  manifest.json             # Dataset generation metadata

Loading All Training Data

import os, json, glob

# Iterate across all shards
for shard_dir in sorted(glob.glob("train/shard_*")):
    for json_path in sorted(glob.glob(os.path.join(shard_dir, "*.json"))):
        mp3_path = json_path.replace(".json", ".mp3")
        with open(json_path) as f:
            labels = json.load(f)
        # Process mp3_path and labels...

Generation Parameters

Parameter	Value
Target sample rate	44,100 Hz
MP3 bitrate	96 kbps
Duration distribution	Beta(3.0, 1.2) * 27 + 3
Negative fraction	50%
VB volume range	10–100%
VB overlap probability	25%
Quality augmentation probability	15%
Random seed	2024

License

Apache 2.0