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gametime-benchmark/gametime

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 📦 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:… See the full description on the dataset page: https://huggingface.co/datasets/gametime-benchmark/gametime.

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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**

📦 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

# 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}
}