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--- |
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configs: |
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- config_name: basic |
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data_files: |
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- split: test |
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path: |
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- "basic/test-*.parquet" |
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- "basic/test.parquet" |
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default: true |
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- config_name: advanced |
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data_files: |
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- split: test |
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path: |
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- "advanced/test-*.parquet" |
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- "advanced/test.parquet" |
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pretty_name: "Gametime" |
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tags: |
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- audio |
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- speech |
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- tts |
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- asr |
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- benchmark |
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task_categories: |
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- automatic-speech-recognition |
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- text-to-speech |
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- audio-to-audio |
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language: |
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- en |
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license: cc-by-4.0 |
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size_categories: |
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- n<100K |
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--- |
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# Gametime Benchmark |
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|
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The **Gametime** dataset provides lightweight, streaming-friendly splits for TTS/ASR/SpokenLM prototyping. |
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For full details, please refer to the paper: |
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👉 [**Game-Time: Evaluating Temporal Dynamics in Spoken Language Models**](https://arxiv.org/abs/2509.26388) |
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--- |
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## 📦 Download Options |
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### 1️⃣ Recommended — Full ZIP Download |
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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: |
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* `gametime/download/basic_instructions.zip` — unpacks to: |
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``` |
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basic_instructions/ |
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├── text/ |
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│ ├── *-dataset.json # per-dataset JSON manifest(s) |
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├── audios/ |
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│ ├── <dataset_id>/ |
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│ │ └── test/*.wav |
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├── alignments/ # per-audio alignment files |
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│ ├── <dataset_id>/ |
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│ │ ├── <stem>.jsonl |
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``` |
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* `gametime/download/advanced_instructions.zip` — unpacks to: |
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|
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``` |
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advanced_instructions/ |
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├── text/ |
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│ ├── *-dataset.json # per-dataset JSON manifest(s) with timing tokens |
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├── audios/ |
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│ ├── <dataset_id>/ |
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│ │ └── test/*.wav |
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├── alignments/ # per-audio alignment files |
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│ ├── <dataset_id>/ |
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│ │ ├── <stem>.jsonl |
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``` |
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Notes: |
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* Each ZIP in `gametime/download/` preserves the original source tree names (`basic_instructions/` or `advanced_instructions/`). |
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|
Download example (Hugging Face): |
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|
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```python |
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from huggingface_hub import hf_hub_download |
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import os |
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path = hf_hub_download( |
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repo_id="gametime-benchmark/gametime", |
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repo_type="dataset", |
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filename="download/basic_instructions.zip", |
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revision="main", |
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local_dir=".", |
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) |
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print("saved to:", path) |
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``` |
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Unzip example: |
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|
|
|
```bash |
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unzip gametime/download/basic_instructions.zip |
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``` |
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|
|
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--- |
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|
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### 2️⃣ Optional — Stream from Hugging Face |
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|
|
|
```python |
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from datasets import load_dataset |
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import io |
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import soundfile as sf |
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# Load Basic train split |
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ds_basic = load_dataset("gametime-benchmark/gametime", "basic", split="test", streaming=True) |
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ex = next(iter(ds_basic)) |
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buf = io.BytesIO(ex["audio_bytes"]) |
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wav, sr = sf.read(buf, dtype="float32") |
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print(ex["id"], sr, len(wav), ex["text"]) |
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# Load Advanced test split |
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ds_adv = load_dataset("gametime-benchmark/gametime", "advanced", split="test", streaming=True) |
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ex_adv = next(iter(ds_adv)) |
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buf_adv = io.BytesIO(ex_adv["audio_bytes"]) |
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wav_adv, sr_adv = sf.read(buf_adv, dtype="float32") |
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print(ex_adv["id"], sr_adv, len(wav_adv), ex_adv["text"]) |
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```` |
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|
* Works with **`streaming=True`** — no full download needed |
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|
* Requires only `soundfile` (libsndfile) |
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|
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--- |
|
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|
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|
## 📑 Schema |
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|
Each Parquet row has: |
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|
| Column | Type | Description | |
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|
| --------------- | ----- | -------------------------------------------------------------- | |
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|
| `id` | str | e.g. `1-a-Sequence-Number/train/1-a-Sequence-Number-01-01.wav` | |
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|
| `category` | str | `"basic"` or `"advanced"` | |
|
|
| `dataset` | str | group name (e.g. `1-a-Sequence-Number`) | |
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| `split` | str | `train` or `test` | |
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| `template_idx` | str | template index if available | |
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|
| `item_idx` | str | item index if available | |
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| `text` | str | reference transcription / prompt | |
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|
| `alignment` | str | alignment metadata | |
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|
| `audio_bytes` | bytes | raw WAV file bytes | |
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|
| `audio_format` | str | `"wav"` | |
|
|
| `sampling_rate` | int | e.g., `16000` | |
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--- |
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|
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## 📚 Citation |
|
|
|
|
|
If you use this dataset, please cite: |
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|
|
|
``` |
|
|
@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}, |
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|
year = {2025}, |
|
|
journal = {arXiv preprint arXiv:2509.26388}, |
|
|
url = {https://arxiv.org/abs/2509.26388} |
|
|
} |
|
|
``` |
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|
|
|
--- |
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|
