Datasets:
license: cc-by-4.0
pretty_name: V33DA++
tags:
- animal-behavior
- bioacoustics
- bird-vocalization
- multimodal
- 3d-pose
- localization
- source-separation
- active-speaker-detection
- voice-activity-detection
- zebra-finch
task_categories:
- audio-classification
- video-classification
- other
language:
- en
configs:
- config_name: overlap
data_files:
- split: train
path: overlap/v33da_pp_overlap-*.parquet
- config_name: padded
data_files:
- split: train
path: padded/v33da_pp_padded-*.parquet
- config_name: overlap_padded
data_files:
- split: train
path: overlap_padded/v33da_pp_overlap_padded-*.parquet
V33DA++: Overlapping-Call and Long-Context Companion to V33DA
V33DA++ is the companion release to the V33DA benchmark. It is built from the same BirdPark recordings and the same 10 zebra finches, but covers data outside the strict single-vocalizer attribution clips that define V33DA. V33DA++ is intended for tasks that benefit from overlapping callers or longer temporal context: source separation, vocal activity detection, audio-visual synchronization, and active-speaker detection.
V33DA++ ships three buckets, each as a separate config:
| Bucket | Events | Description |
|---|---|---|
overlap |
3,784 | Reviewer-verified events excluded from V33DA because another bird vocalized in the same window. Each sample carries the full list of overlapping callers verified from on-body accelerometer channels. |
padded |
33,625 | ±2 s context windows around V33DA single-vocalizer events. Original event onsets and offsets are preserved inside the longer window, supplying long-range context without altering the underlying call. |
overlap_padded |
3,784 | The overlap bucket with the same ±2 s context, for long-context multi-caller tasks. |
V33DA++ is not the benchmark used in the V33DA paper's main results tables. For the single-vocalizer attribution benchmark, use V33DA.
Code: github.com/marisbasha/v33da · Main benchmark: songbirdini/V33DA
Quick start
The parquet shards are the canonical V33DA++ release and contain inline audio + accelerometer bytes for every event — they are self-sufficient. The optional flat audio/, accelerometer/, and clips/ mirrors are convenience exports (see "Release contents" below).
Load via datasets:
from datasets import load_dataset
# Pick one of: "overlap", "padded", "overlap_padded"
ds = load_dataset("songbirdini/v33da_pp", "padded", split="train", streaming=True)
for row in ds.take(1):
print(row.keys())
Or download a single bucket:
pip install huggingface_hub
# Just the parquet for a bucket (smallest, most useful)
huggingface-cli download songbirdini/v33da_pp --repo-type dataset \
--include "padded/*.parquet" --local-dir data/v33da_pp
# Full bucket (parquet + media mirrors)
huggingface-cli download songbirdini/v33da_pp --repo-type dataset \
--include "padded/*" --local-dir data/v33da_pp
Release contents
overlap/
v33da_pp_overlap-*.parquet # canonical: includes inline audio bytes
audio/<experiment>/... # convenience WAV mirror (optional)
accelerometer/<experiment>/... # convenience WAV mirror (optional)
clips/<experiment>/<session>/... # composite multi-view MP4s
metadata.json
padded/
v33da_pp_padded-*.parquet
audio/, accelerometer/, clips/
metadata.json
overlap_padded/
v33da_pp_overlap_padded-*.parquet
audio/, accelerometer/, clips/
metadata.json
The flat WAV mirrors are best-effort: HuggingFace caps each directory at 10 000 files, so for padded/audio/juvExpBP01 and padded/audio/juvExpBP02 (and the matching accelerometer/ directories) the mirror is truncated. The parquet shards are unaffected and contain every event in full.
Per-bird counts
| Experiment | Bird | overlap | padded | overlap_padded |
|---|---|---|---|---|
| BP01 | blue | 999 | 5,735 | 999 |
| BP01 | peach | 160 | 2,277 | 160 |
| BP01 | red | 404 | 3,963 | 404 |
| BP01 | white | 521 | 4,244 | 521 |
| BP02 | blue | 915 | 4,923 | 915 |
| BP02 | peach | 292 | 4,589 | 292 |
| BP02 | red | 460 | 2,065 | 460 |
| BP05 | brown | 1 | 56 | 1 |
| BP05 | purple | 20 | 2,140 | 20 |
| BP05 | yellow | 12 | 3,633 | 12 |
| Total | 3,784 | 33,625 | 3,784 |
Data fields (parquet)
Each parquet row is one vocalization event. Most binary array columns are NumPy np.save() output; load them with np.load(io.BytesIO(row[col])).
Identity & metadata
| Field | Type | Description |
|---|---|---|
id |
string | Globally unique row identifier: "{experiment}/{video_stem}/{mid_frame}/{bird_name}" |
experiment |
string | One of juvExpBP01, juvExpBP02, juvExpBP05 |
date |
string | Recording date YYYY-MM-DD |
video_stem |
string | Source recording stem (e.g. BP_2021-08-05_08-40-49_416952_0460000) |
bird_name |
string | Internal vocalizer ID (per-experiment, e.g. b14o14_j) |
bird_color |
string | Vocalizer's backpack color (per-experiment identifier; not global) |
vocalizer_idx |
int32 | Vocalizer's index within this experiment's bird set (0-based) |
Event timing (this row's vocalization)
| Field | Type | Description |
|---|---|---|
event_onset_sec |
float32 | Voc onset in seconds, relative to the source recording |
event_offset_sec |
float32 | Voc offset in seconds, relative to the source recording |
event_onset_frame |
int32 | Voc onset in 47.68 fps video frames |
event_offset_frame |
int32 | Voc offset in 47.68 fps video frames |
context_pad_sec |
float32 | Audio/video pad on each side of the event (≈0.01 for overlap, 2.0 for the *_padded buckets) |
n_frames |
int32 | Number of video frames covered by the keypoint window |
Audio & accelerometer
| Field | Type | Description |
|---|---|---|
audio |
bytes | NumPy-serialized (5, T) float32 array — 5 cage microphones at 24,414 Hz, padded by context_pad_sec on each side |
audio_path |
string | Path to a separately exported multichannel WAV mirror (best-effort, see Release contents) |
accelerometer |
bytes | NumPy-serialized (N_birds, T) float32 array — per-bird on-body vibration channel at 24,414 Hz, used as the verification oracle |
accelerometer_path |
string | Path to a separately exported multichannel WAV mirror (best-effort) |
video_path |
string | Path to a composite multi-view MP4 (top, back, side) — null if not available |
3D pose (every visible bird, every frame)
5 keypoints per bird: beak, head, backpack, tail base, tail tip.
| Field | Type | Description |
|---|---|---|
keypoints_3d |
bytes | NumPy (T_frames, N_birds, 5, 3) float32 — 3D positions triangulated from top + back views, in BirdPark cage coordinates (mm) |
keypoints_2d_top |
bytes | NumPy (T_frames, N_birds, 5, 2) float32 — 2D keypoints in the top-camera image (pixels) |
keypoints_2d_back |
bytes | NumPy (T_frames, N_birds, 5, 2) float32 — 2D keypoints in the back-camera image (pixels) |
Per-frame indexing: bird i's keypoint k at frame t is kp3d[t, i, k] (and similarly for the 2D arrays). NaN values may appear in rows that were extracted under a lenient filter (see Lenient extraction, below). Bird index → bird color/name mapping is fixed within an experiment (see metadata.json).
Radio telemetry (every visible bird, every frame)
Per-bird FM radio signals from a 4-antenna receiver array (4 antennas: A, B, C, D; M = mean over antennas). Each is a NumPy (N_birds, T_radio) float32 byte blob.
| Field | Description |
|---|---|
radio_frq |
Per-bird FM carrier frequency offset (Hz) |
radio_thetaS |
Source-side phase used for triangulation |
radio_thetaA / B / C / D |
Per-antenna received phase (rad) |
radio_phiA / B / C / D / M |
Per-antenna and mean phase difference (rad) |
radio_powRA / B / C / D / M |
Per-antenna and mean received power (linear) |
radio_powNA / B / C / D / M |
Per-antenna and mean noise floor (linear) |
Overlap-event metadata (overlap and overlap_padded only)
These rows belong to multi-caller events: the audio window contains vocalizations from ≥2 birds at once. The fields below describe every concurrent vocalization that overlapped with this row's bird in any +/-PAD_SEC sliding window (including the row's own voc), enabling per-caller source separation, per-caller vocal-activity-detection, etc.
There are two views of the same multi-caller event, kept separate for compatibility:
Color-set view (existing fields, unique colors)
| Field | Type | Description |
|---|---|---|
overlap_callers |
list[string] | Backpack colors of all concurrent callers, deduplicated and sorted alphabetically (e.g. ['blue', 'white']) |
overlap_count |
int32 | len(overlap_callers) — number of unique caller-birds in the event |
Vocalization-list view (new fields, one entry per voc)
These six lists are parallel (same length, same index order — sorted by bird_color, ties broken by onset). Length is overlap_voc_count, which can be larger than overlap_count if the same bird vocalized more than once in the active window or if the row participated in two adjacent overlap windows that share a partner.
| Field | Type | Description |
|---|---|---|
overlap_caller_bird_names |
list[string] | Internal bird names (e.g. 'b14o14_j') — one per voc |
overlap_caller_bird_colors |
list[string] | Backpack colors (may contain duplicates), same order as bird_names |
overlap_caller_onset_sec |
list[float32] | Each voc's onset in seconds (same time frame as event_onset_sec) |
overlap_caller_offset_sec |
list[float32] | Each voc's offset in seconds |
overlap_caller_onset_frame |
list[int32] | Each voc's onset in 47.68 fps video frames |
overlap_caller_offset_frame |
list[int32] | Each voc's offset in 47.68 fps video frames |
overlap_voc_count |
int32 | len(overlap_caller_bird_names) |
Tip. To find when caller
'white'is vocalizing inside this event, doi = overlap_caller_bird_colors.index('white'); on, off = overlap_caller_onset_sec[i], overlap_caller_offset_sec[i]. If'white'appears multiple times (multiple vocs from the same bird), iterate through all matches.
Lenient extraction
A small fraction of rows (~6% of overlap and overlap_padded) are extracted under a lenient policy: the per-frame motion-displacement filter is bypassed and any NaN keypoints are zero-filled. These rows have valid audio, radio, accelerometer, and event-timing fields but possibly noisier 3D pose. They are included so the bucket size matches the paper's event count.
You can recognise them by checking for any zeros (or large displacements) in keypoints_3d. Strictly extracted rows (under V33DA's normal filter) have all-finite keypoints with frame-to-frame displacement ≤ 40 px.
Per-bucket metadata
Each bucket ships a metadata.json listing exact row counts, per-bird counts, schema, filter settings, frame rate, and pad-frame counts.
Note on bird identity. Backpack color codes are experiment-local identifiers, not global bird IDs. A color reused across experiments (e.g.
redin BP01 and BP02) refers to two different physical individuals; all 10 released birds are distinct.
Suggested tasks
- Source separation — given multichannel audio with 2+ concurrent callers, recover per-bird streams (
overlapbucket;overlap_callersprovides the speaker set). - Voice activity detection — given padded multichannel audio, predict per-frame call/non-call (
paddedandoverlap_paddedbuckets;event_onset/offset_secprovide reference boundaries). - Active-speaker detection / audio-visual sync — given multi-view video and multichannel audio in a long window, predict who is calling when (
overlap_paddedbucket).
License
CC-BY-4.0. External tools used during preprocessing (e.g., Whisper / WhisperSeg, SAM2, ByteTrack, YOLOX-Pose) retain their original licenses.
Citation
If you use V33DA or V33DA++, please cite the V33DA paper:
@unpublished{basha2026v33da,
title = {Who Called? V33DA: A Physically Verified Multimodal Benchmark for Vocal Attribution in Zebra Finch Groups},
author = {Basha, Maris and Wang, Yuhang and Chen, Xiaoran and Cheng, Longbiao and Yapura, Luca and Zai, Anja T. and Salzmann, Mathieu and Hahnloser, Richard},
year = {2026},
note = {Under review},
}