--- # 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/ │ ├── / │ │ └── test/*.wav ├── alignments/ # per-audio alignment files │ ├── / │ │ ├── .jsonl ``` * `gametime/download/advanced_instructions.zip` — unpacks to: ``` advanced_instructions/ ├── text/ │ ├── *-dataset.json # per-dataset JSON manifest(s) with timing tokens ├── audios/ │ ├── / │ │ └── test/*.wav ├── alignments/ # per-audio alignment files │ ├── / │ │ ├── .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 # Load Basic test split ds_basic = load_dataset("gametime-benchmark/gametime", "basic", split="test", streaming=True) ex = next(iter(ds_basic)) wav = ex["audio"]["array"] # numpy float array sr = ex["audio"]["sampling_rate"] # int, e.g. 24000 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)) wav_adv = ex_adv["audio"]["array"] sr_adv = ex_adv["audio"]["sampling_rate"] print(ex_adv["id"], sr_adv, len(wav_adv), ex_adv["text"]) ``` * Works with **`streaming=True`** — no full download needed * Audio is auto-decoded via the `datasets` `Audio` feature (requires `soundfile` / libsndfile under the hood) --- ## 📑 Schema Each row has the following columns (in order): | # | Column | Type | Description | | - | ----------- | -------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | 1 | `id` | `string` | e.g. `1-a-Sequence-Number/test/1-a-Sequence-Number-11-01.wav` | | 2 | `audio` | `Audio` (24 kHz) | Auto-decoded audio. Access as `ex["audio"]["array"]` (numpy float array) and `ex["audio"]["sampling_rate"]` (int). The Hugging Face dataset viewer renders this as an inline 🔊 player. | | 3 | `text` | `string` | Reference transcription / prompt. | | 4 | `alignment` | `str` | alignment metadata | | 5 | `dataset` | `string` | Group name (e.g. `1-a-Sequence-Number`). | Splits: each config (`basic`, `advanced`) provides a single `test` split. --- ## 📚 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} } ``` ---