AudioQA-1M

AudioQA-1M is a dataset containing over one and a half million examples composed of audio excerpts accompanied by corresponding questions. This massive corpus is intended to train models capable of interpreting, understanding and automatically answering questions asked about various audio content.

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Size

Approximately 1.7 million audio-question-answer examples, audio files, and associated metadata

Licence

Apache 2.0

Description

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The dataset AudioQA-1M contains over 1.7 million sample audio with associated questions, designed to assess and train audio analysis and comprehension models. Each entry consists of an audio file and a question about that sound content, which encourages the development of systems that can answer questions based on real auditory data.

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What is this dataset for?

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Train audio question-answer models for various applications (voice assistants, multimedia analysis)
Evaluate the fine audio comprehension of machine learning models
Enabling research on the understanding of audio content in a variety of contexts

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Can it be enriched or improved?

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It is possible to enrich this dataset by annotating audio metadata in more detail (duration, type of sound), adding transcripts, or even integrating new languages or audio domains. The dataset can also be remixed with other audio games to increase diversity.

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🔎 In summary

Criterion	Evaluation
🧩 Ease of use	⭐⭐⭐✩✩ (Standard API, requires audio processing skills)
🧼 Need for cleaning	⭐⭐⭐⭐✩ (Moderate – metadata to check, potential noise in audio)
🏷️ Annotation richness	⭐⭐⭐⭐✩ (Good – questions associated with audios, but limited annotations)
📜 Commercial license	✅ Yes (Apache 2.0)
👨‍💻 Beginner friendly	⚠️ Moderate – large format, audio learning required
🔁 Fine-tuning ready	🎯 Perfect for fine-tuning audio QA models
🌍 Cultural diversity	⚡ Undetermined – depends on audio sources, but large volume favors diversity