arcada-labs/event-bench

Event Bench

29-turn multi-turn speech-to-speech benchmark for evaluating voice AI models as an event planning assistant.

Part of Audio Arena, a suite of 6 benchmarks spanning 221 turns across different domains. Built by Arcada Labs.

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Dataset Description

The model acts as an event planning assistant managing venue bookings, catering, and guest logistics. The conversation features cascading changes — a venue switch triggers catering repricing, a guest count update triggers capacity checks — along with mid-sentence self-corrections, retroactive date changes, and multi-request reversals that test whether the model can track compounding state changes.

What This Benchmark Tests

• Tool use: 6 functions — venue booking, catering quotes, guest management, availability checks, and more
• Cascading state changes: Venue switch triggers catering repricing, guest count changes trigger capacity rechecks
• Mid-sentence self-corrections: Speaker changes details partway through a request
• Vague pronoun resolution: Ambiguous references ("that one", "the other place") requiring context
• Wrong-math correction: User provides incorrect arithmetic the model must not blindly accept
• Multi-request reversals: Undoing multiple changes in a single turn
• Ambiguous add-on disambiguation: Add-ons that could apply to multiple entities
• Hypothetical reasoning: "What if" scenarios the model must handle without committing state
• Phone number swap: Contact number correction mid-conversation
• Retroactive date change: Changing the event date after downstream bookings are already set
• False memory traps: 3 turns asserting things that never happened
• Cross-entity state tracking: Keeping venue, catering, and guest details consistent

Dataset Structure

event-bench/
├── audio/                          # TTS-generated audio (1 WAV per turn)
│   ├── turn_000.wav
│   ├── turn_001.wav
│   └── ... (29 files)
├── real_audio/                     # Human-recorded audio
│   ├── person1/
│   │   └── turn_000.wav ... turn_028.wav
│   └── person2/
│       └── turn_000.wav ... turn_028.wav
├── benchmark/
│   ├── turns.json                  # Turn definitions with golden answers
│   ├── hard_turns.json             # Same as turns.json but input_text=null (audio-only)
│   ├── tool_schemas.json           # Tool/function schemas (6 tools)
│   └── knowledge_base.txt          # Event planning KB
└── metadata.jsonl                  # HF dataset viewer metadata

Metadata Fields

Field	Description
file_name	Path to the audio file
turn_id	Turn index (0–28)
speaker	tts, person1, or person2
input_text	What the user says (text transcript)
golden_text	Expected assistant response
required_function_call	Tool call the model should make (JSON, nullable)
function_call_response	Scripted tool response (JSON, nullable)
categories	Evaluation categories for this turn
subcategory	Specific sub-skill being tested
scoring_dimensions	Which judge dimensions apply

Audio Format

• Format: WAV, 16-bit PCM, mono
• TTS audio: Generated via text-to-speech
• Real audio: Human-recorded by multiple speakers, same transcript content

Usage

With Audio Arena CLI

pip install audio-arena  # or: git clone + uv sync

# Run with a text model
uv run audio-arena run event_bench --model claude-sonnet-4-5 --service anthropic

# Run with a speech-to-speech model
uv run audio-arena run event_bench --model gpt-realtime --service openai-realtime

# Judge the results
uv run audio-arena judge runs/event_bench/<run_dir>

With Hugging Face Datasets

from datasets import load_dataset

ds = load_dataset("arcada-labs/event-bench")

Evaluation

Models are judged on up to 5 dimensions per turn:

Dimension	Description
tool_use_correct	Correct function called with correct arguments
instruction_following	User's request was actually completed
kb_grounding	Claims are supported by the knowledge base or tool results
state_tracking	Consistency with earlier turns (scored on tagged turns only)
ambiguity_handling	Correct disambiguation (scored on tagged turns only)

For speech-to-speech models, a 6th turn_taking dimension evaluates audio timing correctness.

See the full methodology for details on two-phase evaluation, penalty absorption, and category-aware scoring.

Part of Audio Arena

Benchmark	Turns	Scenario
Conversation Bench	75	Conference assistant
Appointment Bench	25	Dental office scheduling
Assistant Bench	31	Personal assistant
Event Bench (this dataset)	29	Event planning
Grocery Bench	30	Grocery ordering
Product Bench	31	Laptop comparison shopping

Citation

@misc{audioarena2026,
  title={Audio Arena: Multi-Turn Speech-to-Speech Evaluation Benchmarks},
  author={Arcada Labs},
  year={2026},
  url={https://audioarena.ai}
}