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README.md

@@ -1,4 +1,23 @@
 ---
+language:
+  - en
+license: mit
+pretty_name: Grocery 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:
@@ -6,6 +25,136 @@ configs:
         path: metadata.jsonl
 ---
 
-# grocery_bench
+# Grocery Bench
 
-30-turn grocery ordering benchmark with 15 difficulty enhancements: 3-item turn, relative-math quantity, conditional addition, chained corrections, ambiguous 'both', revert removal, second subtotal after mods, 'same as first' recall, partial name reference, phone number correction, audio false start, homophone collision (flower/flour), fifteen/fifty audio confusion, conditional removal by price threshold, plus vague pronoun, mid-sentence self-correction, false memory trap, item removal, swap operation, retroactive qty change, full order reconciliation
+**30-turn multi-turn speech-to-speech benchmark** for evaluating voice AI models as a grocery ordering assistant.
+
+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) | [All Benchmarks](#part-of-audio-arena)
+
+## Dataset Description
+
+The model acts as a grocery ordering assistant helping a customer build, modify, and finalize an order. The conversation is designed around 15 difficulty enhancements that stress-test item lookup, quantity math, chained corrections, and order reconciliation — culminating in a full order summary the model must compute correctly.
+
+## What This Benchmark Tests
+
+- **Tool use**: 5 functions — item lookup, add to cart, remove from cart, modify quantity, order summary
+- **3-item turns**: Multiple items added in a single spoken request
+- **Relative-math quantity**: "Double the bananas", "add three more"
+- **Conditional addition/removal**: "If X costs more than $5, remove it"
+- **Chained corrections**: Multiple sequential edits to the same item
+- **Homophone collisions**: flower vs flour — ambiguous in speech
+- **Fifteen/fifty audio confusion**: Quantities that sound alike over audio
+- **Ambiguous "both"**: References to multiple items where "both" is under-specified
+- **Revert removal**: Undoing a previously removed item
+- **Swap operations**: Replace one item with another in a single turn
+- **Retroactive quantity change**: Changing a quantity set many turns earlier
+- **Mid-sentence self-correction**: Speaker changes their mind partway through
+- **False memory traps**: Assertions about items never added
+- **Full order reconciliation**: Final order summary requiring correct math across all modifications
+
+## Dataset Structure
+
+```
+grocery-bench/
+├── audio/                          # TTS-generated audio (1 WAV per turn)
+│   ├── turn_000.wav
+│   ├── turn_001.wav
+│   └── ... (30 files)
+├── real_audio/                     # Human-recorded audio
+│   ├── person1/
+│   │   └── turn_000.wav ... turn_029.wav
+│   └── person2/
+│       └── turn_000.wav ... turn_029.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 (5 tools)
+│   └── knowledge_base.txt          # Grocery store KB (products, policies, delivery)
+└── metadata.jsonl                  # HF dataset viewer metadata
+```
+
+### Metadata Fields
+
+| Field | Description |
+|-------|-------------|
+| `file_name` | Path to the audio file |
+| `turn_id` | Turn index (0–29) |
+| `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 grocery_bench --model claude-sonnet-4-5 --service anthropic
+
+# Run with a speech-to-speech model
+uv run audio-arena run grocery_bench --model gpt-realtime --service openai-realtime
+
+# Judge the results
+uv run audio-arena judge runs/grocery_bench/<run_dir>
+```
+
+### With Hugging Face Datasets
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("arcada-labs/grocery-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#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](https://huggingface.co/datasets/arcada-labs/assistant-bench) | 31 | Personal assistant |
+| [Event Bench](https://huggingface.co/datasets/arcada-labs/event-bench) | 29 | Event planning |
+| **Grocery Bench** (this dataset) | 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}
+}
+```