ajd12342/paraspeechclap-eval-combined

Combined Evaluation Dataset for ParaSpeechCLAP Models

Evaluation dataset for combined (compositional) style attributes, used in the paper:

ParaSpeechCLAP: A Dual-Encoder Speech-Text Model for Rich Stylistic Language-Audio Pretraining Anuj Diwan, Eunsol Choi, David Harwath

Overview

This dataset is the combined evaluation set for the ParaSpeechCLAP model family. It contains speech clips paired with compositional style captions that include both intrinsic and situational tags, drawn from the Expresso-EARS portion of the ParaSpeechCaps holdout set. It is used to perform retrieval evaluation. This version uses the original captions containing both intrinsic and situational tags from the ParaSpeechCaps holdout set.

Installation

Install the datasets package to load the dataset:

pip install datasets

To run retrieval evaluation with ParaSpeechCLAP, install the ParaSpeechCLAP GitHub repository:

git clone https://github.com/ajd12342/paraspeechclap.git
cd paraspeechclap
pip install -r requirements.txt

Setting up audio files

The dataset contains a relative_audio_path column but not the audio files themselves. Resolving audio paths requires specifying data.audio_root, a common root directory organized as ${audio_root}/{source}/, where {source} matches the value of the source column in the dataset.

This dataset uses the Expresso and EARS sources. Follow the ParaSpeechCaps audio setup instructions for those sources, with the following adjustment: instead of placing each source at its own root directory, place them under a common root:

• ${audio_root}/expresso/ (instead of ${expresso_root})
• ${audio_root}/ears/ (instead of ${ears_root})

Then pass data.audio_root=${audio_root} when running any ParaSpeechCLAP script.

Usage with ParaSpeechCLAP

Retrieval evaluation

python scripts/evaluate_retrieval.py \
  --config-name eval/retrieval \
  checkpoint_path=./checkpoints/paraspeechclap-combined.pth.tar \
  data.dataset_name=ajd12342/paraspeechclap-eval-combined \
  data.audio_root=/path/to/audio_root \
  meta.results=./results_retrieval/paraspeechclap-eval-combined/ajd12342-paraspeechclap-combined

Loading the dataset

from datasets import load_dataset

dataset = load_dataset("ajd12342/paraspeechclap-eval-combined", split="test")
print(f"Number of clips: {len(dataset)}")
print(f"Number of unique prompts: {len(set(dataset['text_description']))}")
print(dataset[0])

Related Resources

• GitHub Repository: https://github.com/ajd12342/paraspeechclap
• Models: ajd12342/paraspeechclap-intrinsic, ajd12342/paraspeechclap-situational and ajd12342/paraspeechclap-combined
• Parent Dataset: https://huggingface.co/datasets/ajd12342/paraspeechcaps
• Training Datasets: https://huggingface.co/datasets/ajd12342/paraspeechcaps-intrinsic-train and https://huggingface.co/datasets/ajd12342/paraspeechcaps-situational-train
• Evaluation Datasets: https://huggingface.co/datasets/ajd12342/paraspeechclap-eval-intrinsic, https://huggingface.co/datasets/ajd12342/paraspeechclap-eval-situational and https://huggingface.co/datasets/ajd12342/paraspeechclap-eval-combined

Citation

@inproceedings{diwan2026paraspeechclap,
  title={{ParaSpeechCLAP}: A Dual-Encoder Speech-Text Model for Rich Stylistic Language-Audio Pretraining},
  author={Diwan, Anuj and Choi, Eunsol and Harwath, David},
  journal={Under Review},
  year={2026}
}