README.md

---
# Data-files config (Parquet-only)
configs:
- config_name: basic
  data_files:
  - split: test
    path:
      - "basic/test-*.parquet"
      - "basic/test.parquet"
  default: true

- config_name: advanced
  data_files:
  - split: test
    path:
      - "advanced/test-*.parquet"
      - "advanced/test.parquet"

pretty_name: "Gametime"
tags:
  - audio
  - speech
  - tts
  - asr
  - benchmark
task_categories:
  - automatic-speech-recognition
  - text-to-speech
  - audio-to-audio
language:
  - en
license: cc-by-4.0
size_categories:
  - n<100K
---

# Gametime Benchmark

The **Gametime** dataset provides lightweight, streaming-friendly splits for TTS/ASR/SpokenLM prototyping.  
For full details, please refer to the paper:  
👉 [**Game-Time: Evaluating Temporal Dynamics in Spoken Language Models**](https://arxiv.org/abs/2509.26388)

---

## 📦 Download Options

### 1️⃣ Recommended — Full ZIP Download

If you prefer the original folder layout you can download one of the ZIPs packaged in `gametime/download/`. There are two kinds available in this repository:

* `gametime/download/basic_instructions.zip` — unpacks to:

```
basic_instructions/
├── text/
│   ├── *-dataset.json         # per-dataset JSON manifest(s)
├── audios/
│   ├── <dataset_id>/
│   │   └── test/*.wav
├── alignments/                 # per-audio alignment files 
│   ├── <dataset_id>/
│   │   ├── <stem>.jsonl
```

* `gametime/download/advanced_instructions.zip` — unpacks to:

```
advanced_instructions/
├── text/
│   ├── *-dataset.json         # per-dataset JSON manifest(s) with timing tokens
├── audios/
│   ├── <dataset_id>/
│   │   └── test/*.wav
├── alignments/                 # per-audio alignment files 
│   ├── <dataset_id>/
│   │   ├── <stem>.jsonl
```

Notes:

* Each ZIP in `gametime/download/` preserves the original source tree names (`basic_instructions/` or `advanced_instructions/`).

Download example (Hugging Face):

```python
from huggingface_hub import hf_hub_download
import os

path = hf_hub_download(
    repo_id="gametime-benchmark/gametime",
    repo_type="dataset",
    filename="download/basic_instructions.zip",
    revision="main",
    local_dir=".",
)
print("saved to:", path)
```

Unzip example:

```bash
unzip gametime/download/basic_instructions.zip
```

---

### 2️⃣ Optional — Stream from Hugging Face

```python
from datasets import load_dataset
import io
import soundfile as sf

# Load Basic train split
ds_basic = load_dataset("gametime-benchmark/gametime", "basic", split="test", streaming=True)
ex = next(iter(ds_basic))
buf = io.BytesIO(ex["audio_bytes"])
wav, sr = sf.read(buf, dtype="float32")
print(ex["id"], sr, len(wav), ex["text"])

# Load Advanced test split
ds_adv = load_dataset("gametime-benchmark/gametime", "advanced", split="test", streaming=True)
ex_adv = next(iter(ds_adv))
buf_adv = io.BytesIO(ex_adv["audio_bytes"])
wav_adv, sr_adv = sf.read(buf_adv, dtype="float32")
print(ex_adv["id"], sr_adv, len(wav_adv), ex_adv["text"])
````

* Works with **`streaming=True`** — no full download needed
* Requires only `soundfile` (libsndfile)

---

## 📑 Schema

Each Parquet row has:

| Column          | Type  | Description                                                    |
| --------------- | ----- | -------------------------------------------------------------- |
| `id`            | str   | e.g. `1-a-Sequence-Number/train/1-a-Sequence-Number-01-01.wav` |
| `category`      | str   | `"basic"` or `"advanced"`                                      |
| `dataset`       | str   | group name (e.g. `1-a-Sequence-Number`)                        |
| `split`         | str   | `train` or `test`                                              |
| `template_idx`  | str   | template index if available                                    |
| `item_idx`      | str   | item index if available                                        |
| `text`          | str   | reference transcription / prompt                               |
| `alignment`     | str   | alignment metadata                                             |
| `audio_bytes`   | bytes | raw WAV file bytes                                             |
| `audio_format`  | str   | `"wav"`                                                        |
| `sampling_rate` | int   | e.g., `16000`                                                  |

---

## 📚 Citation

If you use this dataset, please cite:

```
@article{chang2025gametime,
  title   = {Game-Time: Evaluating Temporal Dynamics in Spoken Language Models},
  author  = {Kai-Wei Chang and En-Pei Hu and Chun-Yi Kuan and Wenze Ren and Wei-Chih Chen and Guan-Ting Lin and Yu Tsao and Shao-Hua Sun and Hung-yi Lee and James Glass},
  year    = {2025},
  journal = {arXiv preprint arXiv:2509.26388},
  url     = {https://arxiv.org/abs/2509.26388}
}
```

---