---
tags:
- DeSTA
- Audio-Understanding
size_categories:
- 1M<n<10M
---

[📑 Paper](https://arxiv.org/abs/2507.02768) | [👩‍💻 Github](https://github.com/kehanlu/DeSTA2.5-Audio) | [🤗 Model](https://huggingface.co/collections/DeSTA-ntu/desta25-audio-686a6b9e71afd92e1dd87486) | [🤗 Dataset](https://huggingface.co/datasets/DeSTA-ntu/DeSTA-AQA5M-FROM-Llama3.1-8B-Instruct)

DeSTA-AQA5M comprises 50 speech, environmental sound, and music datasets, totaling over 7,000 hours of audio.
Our training framework centers on self-generated response for efficient cross-modal alignment. ([see our paper!](https://arxiv.org/abs/2507.02768)). In DeSTA, each audio clip is first transformed into a textual description using its metadata. A Large Language Model (LLM) is then prompted with this description to self-generate a response.
Ultimately, we construct a large-scale dataset of 5 million audio–instruction–response triplets, enabling effective finetuning of a Large Audio Language Model (LALM)—without the need for task-specific instruction data.







|         | Response Generated From | HuggingFace ID                                                                                                                     | Preview                                                                                      |
| ----------- | ----------------------- | ---------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------- |
| DeSTA-AQA5M | Llama3.1-8B-Instruct    | [DeSTA-ntu/DeSTA-AQA5M-FROM-Llama3.1-8B-Instruct](https://huggingface.co/datasets/DeSTA-ntu/DeSTA-AQA5M-FROM-Llama3.1-8B-Instruct) | [🔍](https://huggingface.co/datasets/DeSTA-ntu/DeSTA-AQA5M-FROM-Llama3.1-8B-Instruct/viewer) |

Check [GitHub dataset document](https://github.com/kehanlu/DeSTA2.5-Audio/blob/main/docs/dataset.md) for more details!

### Load Dataset

```python
from datasets import load_dataset

dataset = load_dataset("DeSTA-ntu/DeSTA-AQA5M-FROM-Llama3.1-8B-Instruct")

# Load from chunked data files
# dataset = load_dataset("DeSTA-ntu/DeSTA-AQA5M-FROM-Llama3.1-8B-Instruct", data_files=["audio.0.jsonl", "audio.1.jsonl", "speech.0.jsonl", "speech.1.jsonl"])
```

```
DatasetDict({
    train: Dataset({
        features: ['id', 'dataset', 'seed_description', 'prompt', 'response', 'messages'],
        num_rows: 4963845
    })
})
```

- **Core fields (used for dataset generation and training):**
  - `messages`: The input messages used for data generation and model training.
  - `response`: The model-generated response (used as the training target).

- **Auxiliary fields (for display or metadata purposes):**
  - `id`: Audio file ID(relative audio filepath)
  - `dataset`: The source dataset.
  - `seed_description`: The textual description constructed from the audio metadata.
  - `prompt`: The sampled prompt from the instruction pool.