sonalkum/AudioSkills-Llama3

AudioSkills-XL Dataset

To promote the development of open source models, we have released AudioSkills using the exact same method generated with Llama 3.1-8B Instruct instead of GPT4o in the original.

Project page | Paper | Code

Dataset Description

AudioSkills-XL is a large-scale audio question-answering (AQA) dataset designed to develop (large) audio-language models on expert-level reasoning and problem-solving tasks over short audio clips (≤30 seconds). It expands upon the original AudioSkills collection by adding approximately 4.5 million new QA pairs, resulting in a total of ~10 million diverse examples. The release includes the full dataset, including AudioSkills and AudioSkills-XL. The dataset is partitioned into subsets based on each audio’s source dataset:

1. WavText5K (WavText5K_llama3.json)

   • Domain: Sound
   • Link to original dataset: https://github.com/microsoft/WavText5K

2. SONNISS (SONNISS_llama3.json)

   • Domain: Sound
   • Link to original dataset: https://sonniss.com/

3. MusicCaps (MusicCaps_llama3.json)

   • Domain: Sound
   • Link to original dataset: https://huggingface.co/datasets/google/MusicCaps

4. BBC Sound Effects (BBC_Sound_Effects_llama3.json)

   • Domain: Sound
   • Link to original dataset: NA

5. MusicBench (MusicBench_llama3.json)

   • Domain: Music
   • Link to original dataset: https://huggingface.co/datasets/amaai-lab/MusicBench

6. MACS (MACS_llama3.json)

   • Domain: Sound
   • Link to original dataset: https://zenodo.org/records/5114771

7. ESC-50 (ESC-50_llama3.json)

   • Domain: Sound
   • Link to original dataset: https://github.com/karolpiczak/ESC-50

8. CountingQA (CountingQA_llama3.json)

   • Domain: Sound
   • Link to original dataset: Google Drive
   • Additional Note: This split has both counting and temporal QAs.

9. MagnaTagATune (MagnaTagATune_llama3.json)

   • Domain: Music
   • Link to original dataset: http://mirg.city.ac.uk/codeapps/the-magnatagatune-dataset

10. FSD50k (FSD50k_llama3.json)

    • Domain: Sound
    • Link to original dataset: https://zenodo.org/records/4060432

11. VoxCeleb2 (VoxCeleb2_llama3.json)

    • Domain: Speech
    • Link to original dataset: https://www.robots.ox.ac.uk/~vgg/data/voxceleb/
    • Note: Audio paths follow the pattern voxceleb-2/dev/aac/id07175/GDQK8Nu5-cA/combined.wav. In each folder (voxceleb-2/dev/aac/id07175/), all WAV files were merged in chronological order to create the final combined file (combined.wav).

12. UrbanSound8K (UrbanSound8K_llama3.json)

    • Domain: Sound
    • Link to original dataset: https://urbansounddataset.weebly.com/urbansound8k.html

13. SoundDescs (SoundDescs_llama3.json)

    • Domain: Sound
    • Link to original dataset: https://github.com/akoepke/audio-retrieval-benchmark

14. Medley-solos-DB (Medley-solos-DB_llama3.json)

    • Domain: Music
    • Link to original dataset: https://zenodo.org/records/3464194

15. Medley-Pitch-DB (Medley-Pitch-DB_llama3.json)

    • Domain: Music
    • Link to original dataset: https://zenodo.org/records/3464194

16. GTZAN (GTZAN_llama3.json)

    • Domain: Music
    • Link to original dataset: https://github.com/chittalpatel/Music-Genre-Classification-GTZAN

17. Clotho-v2 (Clotho-v2_llama3.json)

    • Domain: Sound
    • Link to original dataset: https://zenodo.org/records/4783391

18. Freesound (Freesound_llama3.json)

    • Domain: Sound
    • Link to original dataset: https://freesound.org. Can also be downloaded from https://github.com/XinhaoMei/WavCaps

19. CochlScene (CochlScene_llama3.json)

    • Domain: Sound
    • Link to original dataset: https://github.com/cochlearai/cochlscene

20. WavCaps (WavCaps_llama3.json)

    • Domain: Sound
    • Link to original dataset: https://github.com/XinhaoMei/WavCaps

21. VGGSound (VGG_llama3.json)

    • Domain: Sound
    • Link to original dataset: https://github.com/amirabd/vggsound

22. TUT_Urban (TUT_Urban_llama3.json)

    • Domain: Sound
    • Link to original dataset: https://dcase-repo.github.io/dcase_datalist/datasets/scenes/tut_asc_2018_mobile_eval.html

23. SoundBible (SoundBible_llama3.json)

    • Domain: Sound
    • Link to original dataset: http://soundbible.com

24. AudioSet_SL (AudioSet_SL_llama3.json)

    • Domain: Sound
    • Link to original dataset: https://research.google.com/audioset/ Can also be downloaded from https://github.com/JishengBai/AudioSetCaps

By releasing AudioSkills-XL, researchers can train models on a broad spectrum of audio reasoning tasks. Please note that we only provide the text QA annotations. Due to licensing constraints, we do not host the original audio files. Users are responsible for retrieving the corresponding audio clips from their original sources (e.g., YouTube8M, LibriSpeech, Music4All) using the wav file name from the "sound" tag in the JSONs and dowloading the dataset from the URLs mentioned.

Sample Usage

To download the dataset files, you can use git lfs:

git lfs install
git clone https://huggingface.co/datasets/sonalkum/AudioSkills-Llama3

Dataset Creation Date

2025/07/10

Intended Usage

AudioSkills-XL (and AudioSkills) is intended to support:

• Training and fine-tuning (large) audio-language models for expert-level reasoning over audio.

Dataset Characterization

AudioSkills-XL focuses on seven primary skills for sounds and music:

• Temporal Reasoning: Understanding temporal relationships in audio (order, attribute changes, referring, grounding).
• Attribute Identification: Recognizing specific event properties (e.g., loudness, speaker gender).
• Counting: Quantifying occurrences of target sounds at varying difficulty levels.
• Contextual Sound Event Reasoning: Inferring the purpose or cause of a sound in its acoustic context.
• Contextual Speech Event Reasoning: Explaining spoken utterances in relation to surrounding sounds or dialogue.
• Information Extraction: Pulling out detailed facts, entities, or responses from audio content.
• General Reasoning: Addressing complex questions that combine multiple reasoning skills.

and 6 primary skills for speech:

• Sarcasm Identification: Inferring sarcasm from speech by analyzing content, tone, and emotional cues.
• Emotional State Reasoning: Identifying a speaker’s emotion, reasoning about its cause, and explaining any emotion flips.
• Topic Relationship Reasoning: Determining how two ideas or topics relate within the conversation.
• Information Extraction (IE): Needle QA, Causal QA, Response QA, and Topic QA for extracting specific facts, causes, responses, or main topics.
• Summarization: Producing a concise summary of the speech content.
• Order: Temporal Order, Temporal Attribute, Temporal Referring, and Temporal Grounding to locate and sequence topics over time.

Each example is a pair of a short audio clip (≤30 s) and a corresponding QA item. Audio encompasses environmental sounds, speech (primarily English), and music. Audios are sourced from open-source datasets (see Table 6 in paper appendix). Text QA is generated using a variety of methods mentioned in the paper. Metadata from the original datasets (if available) is used to for QA generation.

Data Curation Method

• Audio is drawn from several open-source datasets. Some audios are synthetically generated.
• Available metadata (e.g., captions, transcripts, etc.) from respective datasets is curated. Additional meta-data (if required) is generated (see paper for details).
• LLMs are used to generate QA pairs from the meta-data using expert-designed reasoning prompts.
• Dataset curation had human-in-the-loop, where prompts and data sources were iteratively refined based on model outputs.

Data Collection Method

Hybrid: Human, Synthetic and Automated

Labeling Method

Synthetic

Dataset Format

• Modality: Audio (WAV/MP3/FLAC) + Text (JSON)
• JSON Schema Example:

[
  {
    "id": "ID",
    "sound": "Name of the wav file.",
    "duration": "The duration in floating point.",
    "conversations": [
      {
        "from": "human",
        "value": "<sound>
The Question."
      },
      {
        "from": "gpt",
        "value": "The Answer."
      }
    ]
  },
]

Note: While the duration field is accurate in most cases, it may be incorrect in some files and should be treated as a placeholder. If your code relies on audio durations, we recommend recalculating them. Please also note that all QA pairs are intended to correspond to the entire audio clip, not just a segment.

Reference(s):

• Audio Flamingo 3

@misc{goel2025audioflamingo3advancing,
      title={Audio Flamingo 3: Advancing Audio Intelligence with Fully Open Large Audio Language Models}, 
      author={Arushi Goel and Sreyan Ghosh and Jaehyeon Kim and Sonal Kumar and Zhifeng Kong and Sang-gil Lee and Chao-Han Huck Yang and Ramani Duraiswami and Dinesh Manocha and Rafael Valle and Bryan Catanzaro},
      year={2025},
      eprint={2507.08128},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2507.08128}, 
}

• Audio Flamingo

@inproceedings{kong2024audio,
  title={Audio Flamingo: A Novel Audio Language Model with Few-Shot Learning and Dialogue Abilities},
  author={Kong, Zhifeng and Goel, Arushi and Badlani, Rohan and Ping, Wei and Valle, Rafael and Catanzaro, Bryan},
  booktitle={International Conference on Machine Learning},
  pages={25125--25148},
  year={2024},
  organization={PMLR}
}

• Audio Flamingo 2

@article{ghosh2025audio,
  title={Audio Flamingo 2: An Audio-Language Model with Long-Audio Understanding and Expert Reasoning Abilities},
  author={Ghosh, Sreyan and Kong, Zhifeng and Kumar, Sonal and Sakshi, S and Kim, Jaehyeon and Ping, Wei and Valle, Rafael and Manocha, Dinesh and Catanzaro, Bryan},
  journal={arXiv preprint arXiv:2503.03983},
  year={2025}
}

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