README.md

---
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
dataset_info:
  features:
  - name: chosen
    dtype:
      audio:
        sampling_rate: 44100
  - name: reject
    dtype:
      audio:
        sampling_rate: 44100
  - name: captions
    dtype: string
  - name: duration
    dtype: int32
  - name: iteration
    dtype: int32
  splits:
  - name: train
    num_bytes: 180239660645
    num_examples: 100000
  download_size: 172620977911
  dataset_size: 180239660645
task_categories:
- text-to-audio
tags:
- DPO
- text-to-audio
---


### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->

This dataset consists of 100k audio preference pairs generated by TangoFlux during the CRPO stage. Specifically, TangoFlux performed five iterations of CRPO. In each iteration, 20k prompts were sampled from a prompt bank. For each prompt, audio samples with the highest and lowest CLAP scores were selected to form the "chosen" and "rejected" pairs, respectively. This process resulted in a total of 100k preference pairs.


Since every iteration contains 20k prompts sampled from audiocaps prompts, some prompts are the same across iterations.

### Dataset Sources 

<!-- Provide the basic links for the dataset. -->

- **Repository:** https://github.com/declare-lab/TangoFlux
- **Paper :** https://arxiv.org/abs/2412.21037
- **Demo :** https://huggingface.co/spaces/declare-lab/TangoFlux

## Uses

<!-- Address questions around how the dataset is intended to be used. -->

You can directly download the dataset and use them for preference optimization in text-to-audio.




## Citation 

If you find our dataset useful, please cite us! Thanks!

**BibTeX:**

```
@misc{hung2024tangofluxsuperfastfaithful,
      title={TangoFlux: Super Fast and Faithful Text to Audio Generation with Flow Matching and Clap-Ranked Preference Optimization}, 
      author={Chia-Yu Hung and Navonil Majumder and Zhifeng Kong and Ambuj Mehrish and Rafael Valle and Bryan Catanzaro and Soujanya Poria},
      year={2024},
      eprint={2412.21037},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2412.21037}, 
}
```