bnovikov/gemma-4-e4b-audio-qa

Gemma-4 E4B Audio-QA Training Mix

A 91k-row audio question-answering dataset assembled from four public upstream datasets, formatted as ChatML-style conversations for instruction-tuning an audio-language model. This is the exact training data used for bnovikov/gemma-4-e4b-audio-v3.

Important: this repository contains only the metadata and prompts/answers. The audio files are NOT hosted here. Each audio_path is a source-tagged ID like librispeech/3664-11714-0019.wav — the prefix identifies the upstream dataset, the remainder is the upstream filename. Download the four upstream datasets yourself and prepend your local root for each source in your loader. See "Using the dataset" below.

Why JSONL-only (no audio bundled)

Two upstream sources have ambiguous redistribution rights (ClothoAQA's audio from Freesound has per-clip licensing; MusicQA's music is derived from pipelines that pull from sources without bulk-redistribution grants). Rather than ship a partial audio bundle that crosses licensing boundaries, this dataset ships the textual contribution only — questions, answers, source tags, and source-relative audio IDs. See the upstream datasets' own pages for their distribution terms.

Data

split	rows	sources
train.jsonl	91,196	MusicQA 47,495 · ClothoAQA 19,947 · FSD50K-QA 14,246 · LibriSpeech-QA 9,508
val.jsonl	4,804	MusicQA 2,505 · ClothoAQA 1,053 · FSD50K-QA 754 · LibriSpeech-QA 492

Train and val are split by audio clip (not by row), so no audio appears in both. Some clips answer multiple questions (~7k in train, ~400 in val) — this is standard for QA datasets.

Row schema

{
  "audio_path": "clotho_aqa/14430.wav",
  "messages": [
    {"role": "system",    "content": "You are an audio understanding assistant..."},
    {"role": "user",      "content": "how many steps are taken?"},
    {"role": "assistant", "content": "five"}
  ],
  "source": "clotho_aqa"
}

The audio_path prefix determines which upstream source the file comes from: librispeech/, clotho_aqa/, musicqa/, fsd50k/.

The system prompt is the same for every row.

How each source was built

source field	upstream	Q/A provenance
clotho_aqa	ClothoAQA	Q&A taken from upstream, used as-is
musicqa	MusicQA	Q&A taken from upstream, with a light quality filter (rows whose answers contain "not specified", "unknown", "unclear", "likely ", or attribution-style phrases like "hit song", "famous artist" were dropped)
librispeech_qa	LibriSpeech (train-clean-100)	Five transcription-style templates applied to each clip; answers are the LibriSpeech transcripts with light sentence-casing normalization
fsd50k_qa	FSD50K (dev set)	Four sound-identification templates; answers are constructed from FSD50K multi-label tags (e.g., ["Harmonica", "Musical instrument", "Music"] → "Harmonica, Musical instrument, and Music")

Using the dataset

1. Download each upstream dataset (one-time, external):

   • LibriSpeech train-clean-100: https://www.openslr.org/12/
   • ClothoAQA: https://zenodo.org/record/6473207
   • MusicQA: https://huggingface.co/datasets/mu-llama/MusicQA
   • FSD50K: https://zenodo.org/record/4060432

2. In your data loader, map the audio_path prefix to your local root for that source. A minimal example:

   import json, os
   
   ROOTS = {
       "librispeech": "/abs/path/to/LibriSpeech/train-clean-100",  # searched recursively
       "clotho_aqa":  "/abs/path/to/ClothoAQA/audio",
       "musicqa":     "/abs/path/to/MusicQA/audio",
       "fsd50k":      "/abs/path/to/FSD50K/dev_audio",
   }
   
   def resolve(audio_path: str) -> str:
       prefix, filename = audio_path.split("/", 1)
       return os.path.join(ROOTS[prefix], filename)
   
   for line in open("train.jsonl"):
       row = json.loads(line)
       abs_path = resolve(row["audio_path"])
       # ... load audio, feed to model ...
   

   LibriSpeech's default layout nests audio files under speaker/chapter subfolders, so resolving there typically needs a recursive basename index rather than a flat join. The other three sources are flat.

Licenses

• This repository (JSONL): CC-BY 4.0 for the metadata, structure, system prompt, and the LibriSpeech-QA / FSD50K-QA question templates we authored. The answer text in clotho_aqa and musicqa rows is copied (with light filtering for MusicQA) from those upstream datasets and remains subject to their source licenses.
• Audio (not included): each upstream dataset has its own terms. Summary:
  • LibriSpeech — CC BY 4.0
  • FSD50K — CC-BY 4.0 or CC0 per sample
  • ClothoAQA audio — per-clip Creative Commons variants from Freesound
  • MusicQA — check the upstream repo's terms before redistributing

Users should not consider the audio as under this dataset's license.

Citation

If you use this mix, please also cite the four upstream datasets:

@inproceedings{panayotov2015librispeech,
  title={Librispeech: an ASR corpus based on public domain audio books},
  author={Panayotov, Vassil and Chen, Guoguo and Povey, Daniel and Khudanpur, Sanjeev},
  booktitle={ICASSP}, year={2015}
}

@inproceedings{lipping2022clothoaqa,
  title={Clotho-{AQA}: A Crowdsourced Dataset for Audio Question Answering},
  author={Lipping, Samuel and Sudarsanam, Parthasaarathy and Drossos, Konstantinos and Virtanen, Tuomas},
  booktitle={EUSIPCO}, year={2022}
}

@article{liu2024musicqa,
  title={Music Understanding {LLaMA}: Advancing Text-to-Music Generation with Question Answering and Caption},
  author={Liu, Shansong and Hussain, Atin Sakkeer and Sun, Chenshuo and Shan, Ying},
  year={2024}
}

@article{fonseca2021fsd50k,
  title={{FSD50K}: An Open Dataset of Human-Labeled Sound Events},
  author={Fonseca, Eduardo and Favory, Xavier and Pons, Jordi and Font, Frederic and Serra, Xavier},
  journal={IEEE/ACM TASLP}, year={2021}
}