Enable both categorical emotion recognition and dimensional affect regression on speech clips with multiple crowd-sourced annotations per instance.
Study annotator variability, label aggregation, and uncertainty.

Composition

Audio: WAV files in wavs/
Annotations:
- Raw: WHiSER/Labels/labels.txt (per-file summary + multiple worker lines)
- Aggregated: WHiSER/Labels/labels_consensus.{csv,json}
- Per-annotator: WHiSER/Labels/labels_detailed.{csv,json}

Supported Tasks

Categorical speech emotion recognition (primary label)
Dimensional affect regression (arousal/valence/dominance; 1–7 SAM scale)
Research on annotation aggregation and inter-annotator disagreement

Dataset Structure

Files

wavs/: raw audio clips (e.g., 006-040.1-2_13.wav)
WHiSER/Labels/labels.txt
- Header (one per file): <filename>; <agg_code>; A:<float>; V:<float>; D:<float>;
- Worker lines: WORKER<ID>; <primary>; <secondary_csv>; A:<float>; V:<float>; D:<float>;
WHiSER/Labels/labels_consensus.{csv,json}
- Aggregated primary emotion (and secondary/attributes when applicable), gender, speaker, split (if defined)
WHiSER/Labels/labels_detailed.{csv,json}
- Individual annotations per worker with primary, secondary (multi-label), and V-A-D

Recommended Features

id: string (e.g., 006-040.1-2_13)
file: string (e.g., wavs/006-040.1-2_13.wav)
audio: Audio feature
agg_label_code: string in {A,S,H,U,F,D,C,N,O,X}
agg_primary: normalized primary label (e.g., Happy)
vad_mean: { arousal: float32, valence: float32, dominance: float32 } (1–7)
secondary: sequence of strings (from the secondary label set)
Optional metadata: split, speaker, gender
annotations (from detailed): sequence of { worker_id: string, primary: string, secondary: sequence, vad: { arousal: float32, valence: float32, dominance: float32 } }

Label Schema

Primary consensus codes

A: Angry
S: Sad
H: Happy
U: Surprise
F: Fear
D: Disgust
C: Contempt
N: Neutral
O: Other
X: No agreement (no plurality winner)

Secondary labels (multi-select)

Angry, Sad, Happy, Amused, Neutral, Frustrated, Depressed, Surprise, Concerned, Disgust, Disappointed, Excited, Confused, Annoyed, Fear, Contempt, Other

Dimensional affect

Self-Assessment Manikin (SAM) 1–7
Valence (1 very negative; 7 very positive)
Arousal (1 very calm; 7 very active)
Dominance (1 very weak; 7 very strong)

Example Instance

id: 006-040.1-2_13
file: wavs/006-040.1-2_13.wav
agg_label_code: H
vad_mean: { arousal: 4.2, valence: 4.8, dominance: 5.0 }
annotations (subset):
- { worker_id: WORKER00014325, primary: Sad, secondary: [Sad, Concerned], vad: {A:2, V:4, D:4} }
- { worker_id: WORKER00014332, primary: Happy, secondary: [Happy, Concerned], vad: {A:4, V:6, D:6} }

Usage

Loading from Parquet

If you've converted the dataset to nested Parquet format using the provided script, you can load it with the Hugging Face datasets library:

from datasets import Dataset
import pyarrow.parquet as pq

# Load the Parquet file
table = pq.read_table("parquet/whiser_nested.parquet")
ds = Dataset(table)

print(f"Dataset size: {len(ds)}")
print(f"Features: {ds.features}")

# Access a single example
example = ds[0]
print(f"ID: {example['id']}")
print(f"Consensus label: {example['agg_primary']}")
print(f"VAD mean: A={example['vad_mean']['arousal']:.2f}, V={example['vad_mean']['valence']:.2f}, D={example['vad_mean']['dominance']:.2f}")
print(f"Secondary labels: {example['secondary']}")
print(f"Number of annotators: {len(example['annotations'])}")

# Access audio bytes (embedded in Parquet)
audio_bytes = example['audio_bytes']
sample_rate = example['sample_rate']
print(f"Audio: {len(audio_bytes)} bytes, {sample_rate} Hz")

# Iterate through per-annotator annotations
for ann in example['annotations']:
    worker = ann['worker_id']
    primary = ann['primary']
    vad = ann['vad']
    print(f"  Worker {worker}: primary={primary}, A={vad['arousal']}, V={vad['valence']}, D={vad['dominance']}")

Loading Audio

To decode audio from the embedded bytes:

import io
import soundfile as sf

example = ds[0]
audio_bytes = example['audio_bytes']

# Decode audio
audio_data, sr = sf.read(io.BytesIO(audio_bytes))
print(f"Audio shape: {audio_data.shape}, sample rate: {sr}")

Reference

@inproceedings{Naini_2024_2, 
    author={A. {Reddy Naini} and L. Goncalves and M.A. Kohler and D. Robinson and E. Richerson and C. Busso}, 
    title={{WHiSER}: {White House Tapes} Speech Emotion Recognition Corpus},
    booktitle={Interspeech 2024}, 
    volume={},
    year={2024}, 
    month={September}, 
    address =  {Kos Island, Greece},
    pages={1595-1599}, 
    doi={10.21437/Interspeech.2024-1227},
}

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