README.md

---
license: cc-by-nc-4.0
task_categories:
- audio-classification
- automatic-speech-recognition
- question-answering
language:
- en
tags:
- Audio
modalities:
- audio
- text
size_categories:
- 1K<n<10K
---
# AudioMarathon: A Comprehensive Long-Form Audio Understanding Benchmark

## Abstract

**AudioMarathon** is a large-scale, multi-task audio understanding benchmark designed to systematically evaluate audio language models' capabilities in processing and comprehending long-form audio content. It provides a diverse set of **10** tasks built upon three pillars:
long-context audio inputs with durations ranging from **90.0 to 300.0** seconds, which
correspond to encoded sequences of 2,250 to 7,500 audio tokens, respectively, full
domain coverage across speech, sound, and music, and complex reasoning that
requires multi-hop inference.


## 📊 Task Taxonomy & Statistics

### Task Categories

AudioMarathon organizes tasks into four primary categories:

1. **Speech Content Extraction** -
2. **Audio Classification** 
3. **Speaker Information Modeling** 

### Dataset Statistics

| Task ID | Dataset                                 | Task Type                          | # Samples | Duration | Format     | License       | Status      |
| ------- | --------------------------------------- | ---------------------------------- | --------- | ------------ | ---------- | ------------- | ----------- |
| 1       | [LibriSpeech-long](#1-librispeech-long) | Automatic Speech Recognition (ASR) | 204       | 1-4min       | FLAC 16kHz | CC BY 4.0     | ✅ Full      |
| 2       | [RACE](#2-race)                         | Speech Content Reasoning (SCR)     | 820       | 2-4.22min    | WAV 16kHz  | Apache-2.0    | ✅ Full      |
| 3       | [HAD](#3-had)                           | Speech Detection (SD)              | 776       | 3~5min       | WAV 16kHz  | CC BY 4.0     | ✅ Full      |
| 4       | [GTZAN](#4-gtzan)                       | Music classifier (MC)              | 120       | 4min         | WAV 22kHz  | Research Only | ✅ Full      |
| 5       | [TAU](#5-tau)                           | Audio scene classifier (ASC)       | 1145      | 1.5-3.5min   | WAV 16kHz  | CC BY 4.0     | ✅ Full      |
| 6       | [VESUS](#6-vesus)                       | Emotion Recognition (ER)           | 185       | 1.5-2min     | WAV 16kHz  | Academic Only | ✅ Full      |
| 7       | [SLUE](#7-slue)                         | Speech Entity Recognition (SER)    | 490       | 2.75~5min    | WAV 16kHz  | CC BY 4.0     | ✅ Full      |
| 8       | [DESED](#8-desed)                       | Sound event detection (SED)        | 254       | 4.5-5min     | WAV 16kHz  | Mixed CC*     | ✅ Full      |
| 9       | [VoxCeleb-Gender](#9-voxceleb-gender)   | Speaker Gender Recognition (SGR)   | 1614      | 1.5-3.5min   | WAV 16kHz  | CC BY 4.0     | ✅ Full      |
| 10      | [VoxCeleb-Age](#10-voxceleb-age)        | Speaker Age Recognition (SAR)      | 959       | 1.5-3.5min   | WAV 16kHz  | CC BY 4.0     | ✅ Full      |

**Total**: 6567 samples | ~60GB 

\* *DESED requires per-clip Freesound attribution (CC0/CC BY 3.0/4.0)*

---

## 🎯 Benchmark Objectives

AudioMarathon is designed to evaluate:

1. **Long-Audio Processing**: Ability to maintain coherence across extended audio sequences
2. **Multi-Domain Generalization**: Performance across diverse acoustic environments and tasks
3. **Semantic Understanding**: Comprehension of spoken content, not just acoustic patterns
4. **Efficiency**: Computational requirements for long-form audio processing

---

## 📁 Directory Structure

```
Dataset/
├── librispeech-long/        # Automatic Speech Recognition
│   ├── README.md
│   ├── test-clean/          # Clean test set
│   ├── test-other/          # Noisy test set
│   ├── dev-clean/           # Clean dev set
│   └── dev-other/           # Noisy dev set
│
├── race_audio/              # Reading Comprehension
│   ├── race_benchmark.json  # Task metadata
│   └── test/                # Audio articles
│       └── article_*/
│
├── HAD/                     # Half-truth Audio Detection
│   └── concatenated_audio/
│       ├── had_audio_classification_task.json
│       ├── real/            # Authentic audio
│       └── fake/            # Synthesized audio
│
├── GTZAN/                   # Music Genre Classification
│   └── concatenated_audio/
│       ├── music_genre_classification_meta.json
│       └── wav/             # Genre-labeled music clips
│
├── TAU/                     # Acoustic Scene Classification
│   ├── acoustic_scene_task_meta.json
│   ├── LICENSE
│   ├── README.md
│   └── concatenated_resampled/
│
├── VESUS/                   # Emotion Recognition
│   ├── audio_emotion_dataset.json
│   └── [1-10]/              # Speaker directories
│
├── SLUE/                    # Named Entity Recognition
│   ├── merged_audio_data.json
│   ├── dev/
│   ├── test/
│   └── fine-tune/
│
├── DESED/                   # Sound Event Detection
│   └── DESED_dataset/
│       ├── license_public_eval.tsv
│       └── concatenated_audio/
│
├── VoxCeleb/                # Speaker Recognition
│   ├── concatenated_audio/
│   │   └── gender_id_task_meta.json
│   ├── concatenated_audio_age/
│   │   └── age_classification_task_meta.json
│   └── txt/
│
└── README.md                # This file
```

---

## 🎯 Dataset Details

### 1. LibriSpeech-long

**Task**: Automatic Speech Recognition (ASR)  
**Description**: Long-form English speech from audiobooks  
**Format**: FLAC files with `.trans.txt` transcriptions  
**Splits**: test-clean, test-other, dev-clean, dev-other  
**License**: CC BY 4.0  
**Source**: https://github.com/google-deepmind/librispeech-long

**Structure**:
```
librispeech-long/
  test-clean/
    <speaker_id>/
      <chapter_id>/
        <speaker>-<chapter>-<utterance>.flac
        <speaker>-<chapter>.trans.txt
```

**Citation**:
```bibtex
@article{kahn2020libri,
  title={Libri-light: A benchmark for asr with limited or no supervision},
  author={Kahn, Jacob and Rivière, Morgane and Zheng, Weiyi and Khudanpur, Sanjeev and others},
  journal={ICASSP 2020},
  year={2020}
}
```

---

### 2. RACE

**Task**: Reading Comprehension from Audio  
**Description**: Multiple-choice questions based on audio passages  
**Format**: WAV files + JSON metadata  
**Sample Count**: ~200 articles  
**License**: Apache-2.0 (verify)  
**Source**: https://huggingface.co/datasets/ehovy/race

**JSON Format**:
```json
{
  "article_id": 7870154,
  "audio_path": "test/article_7870154/audio.wav",
  "question": "What did the author do...?",
  "options": ["A", "B", "C", "D"],
  "answer": "A"
}
```

**Citation**:
```bibtex
@inproceedings{lai2017race,
  title={RACE: Large-scale reading comprehension dataset from examinations},
  author={Lai, Guokun and Xie, Qizhe and Liu, Hanxiao and Yang, Yiming and Hovy, Eduard},
  booktitle={EMNLP},
  year={2017}
}
```

---

### 3. HAD

**Task**: Half-truth Audio Detection  
**Description**: Classify audio as real or containing synthesized segments  
**License**: CC BY 4.0  
**Source**: https://zenodo.org/records/10377492

**JSON Format**:
```json
{
  "path": "real/HAD_train_real_249.wav",
  "question": "Is this audio authentic or fake?",
  "choice_a": "Real",
  "choice_b": "Fake",
  "answer_gt": "Real",
  "duration_seconds": 297.78
}
```

---

### 4. GTZAN

**Task**: Music Genre Classification  
**Description**: 10-genre music classification dataset  
**Genres**: blues, classical, country, disco, hiphop, jazz, metal, pop, reggae, rock  
**⚠️ License**: Research Use Only 
**Source**: https://www.kaggle.com/datasets/andradaolteanu/gtzan-dataset-music-genre-classification


**Citation**:
```bibtex
@inproceedings{tzanetakis2002musical,
  title={Musical genre classification of audio signals},
  author={Tzanetakis, George and Cook, Perry},
  booktitle={IEEE Transactions on Speech and Audio Processing},
  year={2002}
}
```

---

### 5. TAU

**Task**: Acoustic Scene Classification  
**Description**: Urban sound scene recognition  
**Scenes**: airport, bus, metro, park, public_square, shopping_mall, street_pedestrian, street_traffic, tram  
**License**: CC BY 4.0  
**Source**: https://zenodo.org/records/7870258

**Files**:
- `acoustic_scene_task_meta.json`: Task metadata
- `LICENSE`: Original license text
- `concatenated_resampled/`: Resampled audio files

**Citation**:
```bibtex
@article{mesaros2018detection,
  title={Detection and classification of acoustic scenes and events: Outcome of the DCASE 2016 challenge},
  author={Mesaros, Annamaria and Heittola, Toni and Virtanen, Tuomas},
  journal={IEEE/ACM TASLP},
  year={2018}
}
```

---

### 6. VESUS

**Task**: Emotion Recognition from Speech  
**Description**: Actors reading neutral script with emotional inflections  
**Emotions**: neutral, angry, happy, sad, fearful  
**Actors**: 10 (5 male, 5 female)  
**⚠️ License**: Academic Use Only (access by request)  
**Source**: https://engineering.jhu.edu/nsa/vesus/

**Citation**:
```bibtex
@inproceedings{sager2019vesus,
  title={VESUS: A crowd-annotated database to study emotion production and perception in spoken English},
  author={Sager, Jennifer and Shankar, Raghav and Reinhold, Jacob and Venkataraman, Archana},
  booktitle={Interspeech},
  year={2019}
}
```

---

### 7. SLUE

**Task**: Named Entity Recognition (NER) from Speech  
**Description**: Count named entities in audio segments  
**Entity Types**: LAW, NORP, ORG, PLACE, QUANT, WHEN  
**License**: CC BY 4.0 (VoxPopuli-derived)  
**Source**: https://arxiv.org/abs/2111.10367

**JSON Format**:
```json
{
  "path": "dev/concatenated_audio_with/concatenated_audio_0000.wav",
  "question": "How many named entities appear?",
  "options": ["49 entities", "51 entities", "52 entities", "46 entities"],
  "answer_gt": "D",
  "entity_count": 49
}
```

**Citation**:
```bibtex
@article{shon2022slue,
  title={SLUE: New benchmark tasks for spoken language understanding evaluation on natural speech},
  author={Shon, Suwon and Pasad, Ankita and Wu, Felix and others},
  journal={ICASSP 2022},
  year={2022}
}
```

---

### 8. DESED

**Task**: Sound Event Detection  
**Description**: Detect domestic sound events  
**Events**: Alarm bell, Blender, Cat, Dishes, Dog, Electric shaver, Frying, Running water, Speech, Vacuum cleaner  
**License**: Mixed CC (Freesound sources: CC0, CC BY 3.0/4.0)  
**Source**: https://github.com/turpaultn/DESED

**⚠️ ATTRIBUTION REQUIRED**: 
- Audio clips sourced from Freesound.org
- Each clip has individual CC license
- Must maintain attribution when redistributing
- See `license_public_eval.tsv` for per-file credits

**Citation**:
```bibtex
@inproceedings{turpault2019sound,
  title={Sound event detection in domestic environments with weakly labeled data and soundscape synthesis},
  author={Turpault, Nicolas and Serizel, Romain and Salamon, Justin and Shah, Ankit Parag},
  booktitle={DCASE Workshop},
  year={2019}
}
```

---

### 9. VoxCeleb-Gender

**Task**: Speaker Gender Identification  
**Description**: Binary classification (male/female)  
**License**: CC BY 4.0  
**Source**: https://www.robots.ox.ac.uk/~vgg/data/voxceleb/

**JSON Format**:
```json
{
  "path": "concatenated_audio/speaker_001.wav",
  "question": "What is the gender of the speaker?",
  "choice_a": "Male",
  "choice_b": "Female",
  "answer_gt": "A"
}
```

---

### 10. VoxCeleb-Age

**Task**: Speaker Age Classification  
**Description**: Multi-class age group classification  
**Age Groups**: 20s, 30s, 40s, 50s, 60s, 70s  
**License**: CC BY 4.0  
**Source**: VoxCeleb + https://github.com/hechmik/voxceleb_enrichment_age_gender

**Note**: Age/gender labels are derivative annotations on VoxCeleb corpus

**Citation**:
```bibtex
@inproceedings{nagrani2017voxceleb,
  title={VoxCeleb: a large-scale speaker identification dataset},
  author={Nagrani, Arsha and Chung, Joon Son and Zisserman, Andrew},
  booktitle={Interspeech},
  year={2017}
}
```

---

## 🔧 Usage Guidelines

You can load the dataset via Hugging Face datasets:

from datasets import load_dataset
ds = load_dataset("Hezep/AudioMarathon")

---



### Special Requirements

### Disclaimer

This benchmark is provided "AS IS" without warranty. Users bear sole responsibility for:
- License compliance verification
- Obtaining restricted datasets independently  
- Proper attribution maintenance
- Determining fitness for specific use cases

---

## 📊 Benchmark Statistics

### Overview

| Metric         | Value                                    |
| -------------- | ---------------------------------------- |
| Total Tasks    | 10                                       |
| Total Samples  | 6567                                     |
| Total Duration | 392h                                     |
| Total Size     | ~60 GB                                   |
| Languages      | English                                  |
| Domains        | Speech, Music, Soundscape, Environmental |
| Task Types     | Classification (7), QA (2), ASR (1)      |

### Audio Characteristics

| Property | Range | Predominant |
|----------|-------|-------------|
| Sampling Rate | 16 kHz - 22.05 kHz | 16 kHz (90%) |
| Duration | 30s - 5+ min | 2-3 min (avg) |
| Channels | Mono | Mono (100%) |
| Format | FLAC, WAV | WAV (80%) |
| Bit Depth | 16-bit | 16-bit (100%) |

### Task Distribution

| Category                 | # Tasks | # Samples | % of Total |
| ------------------------ | ------- | --------- | ---------- |
| Speech Understanding     | 3       | 1514      | 23%        |
| Acoustic Analysis        | 3       | 1519      | 23%        |
| Speaker Characterization | 3       | 2758      | 42%        |
| Content Authenticity     | 1       | 776       | 12%        |

---

## 🔗 Related Resources

- **GitHub Repository**: https://github.com/DabDans/AudioMarathon
- **Paper**: []

---

## 📝 Citation

If you use AudioMarathon in your research, please cite:



### Citing Component Datasets

When using specific tasks, please also cite the original datasets (see individual task documentation above for BibTeX entries):

- **LibriSpeech**: Panayotov et al. (2015)
- **RACE**: Lai et al. (2017)  
- **HAD**: Zenodo record 10377492
- **GTZAN**: Tzanetakis & Cook (2002)
- **TAU**: DCASE Challenge (Mesaros et al., 2018)
- **VESUS**: Sager et al. (2019)
- **SLUE**: Shon et al. (2022)
- **DESED**: Turpault et al. (2019)
- **VoxCeleb**: Nagrani et al. (2017, 2018)

Full BibTeX entries available in individual task sections.

---

## 🤝 Contributing & Support



## 📧 Contact


- **GitHub Issues**: https://github.com/DabDans/AudioMarathon/issues

---




## 🙏 Acknowledgments

AudioMarathon builds upon the pioneering work of numerous research teams. We gratefully acknowledge the creators of:

- LibriSpeech (Panayotov et al.)
- RACE (Lai et al.)
- HAD (Zenodo contributors)
- GTZAN (Tzanetakis & Cook)
- TAU/DCASE (Mesaros et al.)
- VESUS (Sager et al., JHU)
- SLUE (Shon et al.)
- DESED (Turpault et al. & Freesound community)
- VoxCeleb (Nagrani et al., Oxford VGG)

Their datasets enable comprehensive audio understanding research.

---

<p align="center">
  <b>AudioMarathon</b><br>
  A Comprehensive Long-Form Audio Understanding Benchmark<br>
  <i>Version 1.0.0 | October 2025</i>
</p>