LongSpeech: Benchmark Dataset for Long-Form Speech Understanding
LongSpeech is a new benchmark dataset for evaluating audio LLMs on long-form speech understanding. Contains 100,000+ segments averaging ~10 minutes each, spanning 8 evaluation tasks: ASR, translation, summarization, speaker counting, QA, and emotion analysis. Released by AIDC-AI, with associated paper and Hugging Face hosting. Addresses the gap where most existing audio LLMs are limited to short audio inputs and lack benchmarks for long-form recordings.
LongSpeech: Benchmark Dataset for Long-Form Speech Understanding
LongSpeech is a new open-source benchmark dataset featuring 100,000+ audio segments averaging ~10 minutes each, designed to evaluate audio LLMs on 8 long-form speech understanding tasks. It addresses the critical gap where most existing audio LLMs are optimized for short audio inputs but lack standardized evaluation for extended recordings.
Integration Strategy
When to Use This?
LongSpeech is purpose-built for evaluating audio LLM capabilities in scenarios involving extended speech recordings:
- Enterprise meeting transcription: Multi-speaker discussions exceeding 30 minutes
- Podcast and broadcast analysis: Long-form content summarization and topic extraction
- Educational content processing: Lecture transcription, key point extraction, and QA generation
- Customer service analytics: Call center recording analysis with emotion detection
- Accessibility tools: Long-document audio-to-text workflows for visually impaired users
How to Integrate?
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Dataset Access: Direct download from Hugging Face Hub using the
datasetslibrary:from datasets import load_dataset dataset = load_dataset("AIDC-AI/Marco_Longspeech") -
Evaluation Pipeline: The paper (arXiv:2601.13539) presumably outlines evaluation protocols for each task. Developers should reference the official documentation for metric definitions and scoring methodology.
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Baseline Comparison: Compare your audio LLM's performance against any published benchmarks or leaderboard results referenced in the ICASSP 2026 paper.
Compatibility
Framework Requirements: Standard Hugging Face dataset compatibility—works with PyTorch, TensorFlow, and JAX ecosystems via the datasets library.
Model Requirements: Audio LLMs must demonstrate:
- Extended context window handling (minimum 10-minute audio = ~2,400 tokens at 16kHz)
- Multi-speaker disambiguation capabilities
- Long-range dependency tracking
Inferred Requirements (based on benchmark scope): Models should support incremental processing or chunked inference to handle 10-minute segments without context loss.
Source: @ChenyangLyu Reference: AIDC-AI/Marco_Longspeech Dataset | arXiv Paper Published: 2026 (Conference: ICASSP 2026) DevRadar Analysis Date: 2026-04-21