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--- |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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dataset_info: |
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features: |
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- name: chosen |
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dtype: |
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audio: |
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sampling_rate: 44100 |
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|
- name: reject |
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dtype: |
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audio: |
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|
sampling_rate: 44100 |
|
|
- name: captions |
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|
dtype: string |
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|
- name: duration |
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|
dtype: int32 |
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|
- name: iteration |
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|
dtype: int32 |
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|
splits: |
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|
- name: train |
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|
num_bytes: 180239660645 |
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|
num_examples: 100000 |
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|
download_size: 172620977911 |
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|
dataset_size: 180239660645 |
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|
task_categories: |
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|
- text-to-audio |
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|
tags: |
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|
- DPO |
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|
- text-to-audio |
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|
--- |
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|
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|
### Dataset Description |
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|
<!-- Provide a longer summary of what this dataset is. --> |
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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. |
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Since every iteration contains 20k prompts sampled from audiocaps prompts, some prompts are the same across iterations. |
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### Dataset Sources |
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|
<!-- Provide the basic links for the dataset. --> |
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- **Repository:** https://github.com/declare-lab/TangoFlux |
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- **Paper :** https://arxiv.org/abs/2412.21037 |
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- **Demo :** https://huggingface.co/spaces/declare-lab/TangoFlux |
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## Uses |
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|
<!-- Address questions around how the dataset is intended to be used. --> |
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You can directly download the dataset and use them for preference optimization in text-to-audio. |
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## Citation |
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|
If you find our dataset useful, please cite us! Thanks! |
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|
**BibTeX:** |
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|
|
|
``` |
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|
@misc{hung2024tangofluxsuperfastfaithful, |
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|
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}, |
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|
year={2024}, |
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|
eprint={2412.21037}, |
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|
archivePrefix={arXiv}, |
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|
primaryClass={cs.SD}, |
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|
url={https://arxiv.org/abs/2412.21037}, |
|
|
} |
|
|
``` |
