SRQA Audio: Synthetic Spoken Reasoning Benchmark for Audio-Language Models

SRQA Audio provides a public audio asset bundle for evaluating large audio-language models on spoken reasoning tasks. The audio files are synthetic, containing rewritten and TTS-rendered versions of established reasoning benchmarks. It was created by Oulasong and last updated on 2026-05-26.

Use Cases

• Benchmarking model performance on spoken versions of reasoning tasks based on the described synthetic audio queries.
• Evaluating how audio-language models process and reason with synthetic speech inputs.
• Training or fine-tuning models to 'think while listening' using the provided spoken question-answer pairs.

Strengths

• Specifically designed for the synthetic Spoken Reasoning Question Answering (SRQA) benchmark mentioned in a research paper.
• Provides audio files for evaluating models on spoken versions of established reasoning tasks.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count and file size are unknown, which may limit suitability assessment.

Provenance

Source

Oulasong on Hugging Face.

Collection Method

Likely contains synthetic audio files generated by rewriting and rendering established benchmark tracks with text-to-speech.

Freshness

Last updated 2026-05-26 11:11:50; freshness should be verified.