---
license: cc-by-4.0
task_categories:
- text-to-audio
language:
- en
pretty_name: MMAG
tags:
- audio-generation
- mixed-audio
- benchmark
- multi-control
- voice-cloning
- temporal-control
configs:
  - config_name: main_set
    default: true
    data_files:
      - split: test
        path: "main_set/metadata.jsonl"
  - config_name: voice_cloning_set
    data_files:
      - split: test
        path: "voice_cloning_set/metadata.jsonl"
  - config_name: timestamp_set
    data_files:
      - split: test
        path: "timestamp_set/metadata.jsonl"
---

# MMAG: A Multi‑Control Mixed Audio Generation Benchmark

MMAG is a comprehensive benchmark for evaluating mixed audio generation under multiple control conditions. It assesses a model's ability to generate coherent acoustic scenes containing speech, music, and sound effects simultaneously, while supporting fine-grained control over speaker identity and temporal alignment.

## Dataset Structure

The dataset is organized into three subsets, each targeting a specific evaluation dimension:

| Config Name | Clips | Description |
|-------------|-------|-------------|
| main_set | ~4,000 | Primary evaluation set for holistic generation quality |
| voice_cloning_set | ~690 | Speaker identity preservation from short voice prompts |
| timestamp_set | ~1,800 | Temporal alignment and event timing control |

Each subset is provided as a separate Hugging Face configuration.

## File Structure

```
MMAG/
├── README.md
├── dataset_infos.json
├── audio_files/
│   └── *.wav
├── prompt_audio/
│   └── *.wav
├── main_set/
│   └── metadata.jsonl
├── voice_cloning_set/
│   └── metadata.jsonl
└── timestamp_set/
    └── metadata.jsonl
```
## Loading the Dataset
### Option 1 Soundfile
Install the required dependencies:
```bash
pip install datasets soundfile huggingface_hub  
```
Load any subset via Hugging Face datasets:
```python
from huggingface_hub import snapshot_download
import soundfile as sf
from datasets import load_dataset

# Step 1: Download the entire dataset repository
snapshot_download(
    repo_id="anonymous2026082026/MMAG-Benchmark",
    repo_type="dataset",
    local_dir="./mmag_data"
)

# Step 2: Load the metadata
dataset = load_dataset("anonymous2026082026/MMAG-Benchmark", "main_set", split="test")

# Step 3: Access audio files from local directory
example = dataset[0]
audio, sr = sf.read(f"./mmag_data/{example['file_name']}")
print(f"Audio shape: {audio.shape}, Sample rate: {sr}")
```
For the voice cloning subset, the corresponding voice prompt is loaded from the voice_prompt field:
```python
prompt_audio, sr = sf.read(f"./mmag_data/{example['voice_prompt']}")
```
### Option 2 TorchCodec
```bash
pip install datasets torchcodec
```
```python
from datasets import load_dataset, Audio

dataset = load_dataset("anonymous2026082026/MMAG-Benchmark", "main_set", split="test")
dataset = dataset.cast_column("file_name", Audio())

example = dataset[0]
audio = example["file_name"]["array"]
sr = example["file_name"]["sampling_rate"]
```
## Annotation Fields

Each subset's metadata file (metadata.jsonl) contains the following core fields:

### Main Set (main_set/metadata.jsonl)

| Field | Description |
|-------|-------------|
| file_name | Audio file path (relative to root) |
| id | Unique identifier for the clip |
| caption | Overall caption describing the full acoustic scene (see Caption Annotation Coverage below) |

### Voice Cloning Set (voice_cloning_set/metadata.jsonl)

| Field | Description |
|-------|-------------|
| file_name | Target audio file path (relative to root) |
| voice_prompt | Voice prompt file path (relative to root) |
| caption | Overall caption |
| id | Unique identifier for the clip |

### Timestamp Set (timestamp_set/metadata.jsonl)

| Field | Description |
|-------|-------------|
| file_name | Audio file path (relative to root) |
| id | Unique identifier for the clip |
| caption | Overall caption |
| timestamped_caption | Caption with precise temporal boundaries |

## Caption Annotation Coverage

The caption field in each subset is not a simple description; it is a richly structured annotation that covers multiple acoustic and semantic dimensions. The following table summarizes the types of information encoded in the captions:

| Category | Attributes Covered in Caption |
|----------|-------------------------------|
| Speech | Transcription, speaker gender, age, accent, emotion |
| Music | Instrumentation, genre, mood |
| Sound Events | Foreground and background sound description, ambient acoustic context |
| Temporal | Relative ordering of events (e.g., "before", "after", "throughout"), fine-grained timestamps |

All captions are generated through a multi-expert annotation pipeline and verified by human quality control to ensure factual accuracy and consistency.

## License

This dataset is released under the CC BY 4.0 license for non-commercial research use. All source audio materials are sourced from publicly available datasets and are used in accordance with their original licenses.