---
language:
- en
- hi
pretty_name: DECIDE — 20 Public Samples
task_categories:
- audio-classification
- question-answering
size_categories:
- n<1K
tags:
- audio
- speech
- voice-agents
- decision-making
- multiple-choice
- benchmark-preview
configs:
- config_name: default
  data_files:
  - split: preview
    path: data/preview-00000-of-00001.parquet
---
# DECIDE: 20 public samples

**Decision Evaluation in Calls: Intent, Dialogue and Emotion**

[Website](https://decide-benchmark.vercel.app/) · [GitHub](https://github.com/tazolabs/DECIDE) · [Full benchmark evaluation](https://decide-benchmark.vercel.app/evaluate)

This public preview contains **exactly 20 labelled decision questions**, using
**six audio clips** already published on the DECIDE website. A clip can support
multiple different questions. The examples span **14 task types across
all six decision families**. Audio comes from production voice-agent calls
supplied by [Bolna](https://www.bolna.ai/).

The full DECIDE v1.0 benchmark contains 4,867 questions over 1,081 clips and
32 tasks and remains private. This repository is a small, deliberately selected
demonstration of question types, not the full benchmark or a representative test
set. Use the hosted evaluator to obtain full-benchmark results.

## Contents

| Decision family | Rows |
| --- | ---: |
| Business and conversation | 7 |
| Graph routing | 4 |
| Language switching | 1 |
| Tool decisions | 1 |
| Turn taking | 3 |
| Voice properties | 4 |
| **Total** | **20** |

Examples include lead qualification, caller availability and callback requests,
information delivery, caller responses, graph routing and next-node selection,
external-tool selection, next-turn prediction, caller emotion, speaker count,
audio disturbance and language switching.

Each row contains:

- `audio`: a playable mono 16 kHz PCM16 WAV clip, embedded in the Parquet file.
- `question`, `context`, `options`: the decision question, permitted context and
  allowed option IDs with descriptions.
- `answer`: the existing human reference label from the frozen DECIDE v1.0 data.
- `task`, `family`: the task identifier and decision family.
- `id`, `clip_id`: public sample identifiers. Repeated `clip_id` values mean the
  same audio is being used for different questions.
- `current_node`: the node at the clip start for graph questions; empty for other
  questions. The graph specification is included in that row's context.
- `duration_seconds`, `duration_bucket`: audio duration and duration group.

## Load the preview

```python
from datasets import Audio, load_dataset

samples = load_dataset("tazolabs/DECIDE-samples", split="preview")
assert len(samples) == 20

# Read encoded WAV bytes without installing an audio decoder.
samples = samples.cast_column("audio", Audio(decode=False))
row = samples[0]
print(row["question"])
print(row["options"])
print(row["answer"])
wav_bytes = row["audio"]["bytes"]
```

The Hugging Face dataset viewer provides an audio player for each row. For local
decoded-audio playback, use the audio dependencies supported by your version of
`datasets`.

## Interpretation

The task is to choose one allowed decision at the **end of the supplied clip**.
Use only the audio and permitted context. Future conversation is not provided.
`agent` means the AI assistant; `caller` means the human/user. Graphs and tools are
reconstructed benchmark specifications, not production execution logs. Choosing
a tool does not execute it.

Reference answers are copied unchanged from existing human annotations. Context
and question wording are shortened for public inspection; full-call histories,
private source identifiers, reviewer notes and model predictions are excluded.
Consequently, preview scores must not be reported as full DECIDE benchmark scores.

Only these 20 question rows are published here. No new call recordings beyond
the six existing public website previews are released. The code repository's
license does not grant a license to the underlying call recordings; no separate
open-data license is specified for this preview.

## Attribution

DECIDE is created by Tazo Labs using production call recordings supplied by Bolna.
Authors: Harsh Pare, Naman Jain, Sachin Kumar Singh, Jayakrishna Sahit,
Siddharth Batra, Sanket Bhat and Surya Vamsee Ayyagari.
Paper/arXiv link: forthcoming. See the website for updates.