README.md

---
language:
  - en
license: mit
pretty_name: Assistant Bench
tags:
  - audio
  - benchmark
  - speech-to-speech
  - voice-ai
  - multi-turn
  - tool-use
  - evaluation
  - state-tracking
  - function-calling
task_categories:
  - automatic-speech-recognition
  - text-generation
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: train
        path: metadata.jsonl
---

# Assistant Bench

**31-turn multi-turn speech-to-speech benchmark** for evaluating voice AI models as a personal assistant handling flights, email, calendar, and reminders.

Part of [Audio Arena](https://audioarena.ai), a suite of 6 benchmarks spanning 221 turns across different domains. Built by [Arcada Labs](https://arcada.dev).

[Leaderboard](https://audioarena.ai/leaderboard) | [GitHub](https://github.com/Design-Arena/audio-arena-bench) | [All Benchmarks](#part-of-audio-arena)

## Dataset Description

The model acts as a personal assistant managing flight bookings, email composition, calendar events, and reminders. Turns include dual requests packed into a single utterance, mid-conversation topic switching, late references back to early topics, and correction chains that the model must track across the full session.

## What This Benchmark Tests

- **Tool use**: 7 functions — flight booking, email, calendar, reminders, and more
- **Dual requests in single turns**: Two distinct tasks in one spoken utterance
- **Topic switching**: Abrupt mid-conversation jumps between domains (flights, email, calendar)
- **Late references to early topics**: Callbacks to details from 20+ turns earlier
- **Intent segmentation**: Parsing multi-intent speech into separate tool calls
- **Mid-sentence self-correction**: Speaker changes their mind partway through a request
- **Retroactive email correction**: Changing details of a previously composed email
- **Correction-chain recall**: Tracking a value through multiple sequential corrections
- **False memory traps**: 3 false memory traps plus a correction-chain trap
- **Audio traps**: Name spelling, airport codes, dates, and times that are ambiguous in speech

## Dataset Structure

```
assistant-bench/
├── audio/                          # TTS-generated audio (1 WAV per turn)
│   ├── turn_000.wav
│   ├── turn_001.wav
│   └── ... (31 files)
├── real_audio/                     # Human-recorded audio
│   ├── person1/
│   │   └── turn_000.wav ... turn_030.wav
│   └── person2/
│       └── turn_000.wav ... turn_030.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 (7 tools)
│   └── knowledge_base.txt          # Assistant KB
└── metadata.jsonl                  # HF dataset viewer metadata
```

### Metadata Fields

| Field | Description |
|-------|-------------|
| `file_name` | Path to the audio file |
| `turn_id` | Turn index (0–30) |
| `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

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

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

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

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

### With Hugging Face Datasets

```python
from datasets import load_dataset

ds = load_dataset("arcada-labs/assistant-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](https://github.com/Design-Arena/audio-arena-bench#methodology) for details on two-phase evaluation, penalty absorption, and category-aware scoring.

## Part of Audio Arena

| Benchmark | Turns | Scenario |
|-----------|-------|----------|
| [Conversation Bench](https://huggingface.co/datasets/arcada-labs/conversation-bench) | 75 | Conference assistant |
| [Appointment Bench](https://huggingface.co/datasets/arcada-labs/appointment-bench) | 25 | Dental office scheduling |
| **Assistant Bench** (this dataset) | 31 | Personal assistant |
| [Event Bench](https://huggingface.co/datasets/arcada-labs/event-bench) | 29 | Event planning |
| [Grocery Bench](https://huggingface.co/datasets/arcada-labs/grocery-bench) | 30 | Grocery ordering |
| [Product Bench](https://huggingface.co/datasets/arcada-labs/product-bench) | 31 | Laptop comparison shopping |

## Citation

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