ajd12342/paraspeechclap-eval-situational

Situational Evaluation Dataset for ParaSpeechCLAP Models

Evaluation dataset for situational (utterance-level) 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 Under review

This dataset is the situational evaluation dataset for the ParaSpeechCLAP model family. It contains speech clips paired with situational-tag-only style captions, drawn from the Expresso-EARS portion of the ParaSpeechCaps holdout set. It is used to evaluate both retrieval and classification evaluation. The captions are generated from solely the situational tags.

Installation

Install the datasets package to load the dataset:

pip install datasets

To run retrieval and classification 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-situational.pth.tar \
  data.dataset_name=ajd12342/paraspeechclap-eval-situational \
  data.audio_root=/path/to/audio_root \
  meta.results=./results_retrieval/paraspeechclap-eval-situational/ajd12342-paraspeechclap-situational

Classification evaluation

python scripts/evaluate_classification.py \
  --config-name eval/classification/situational \
  checkpoint_path=./checkpoints/paraspeechclap-situational.pth.tar \
  data.audio_root=/path/to/audio_root \
  meta.results=./results_classification/paraspeechclap-eval-situational/ajd12342-paraspeechclap-situational/

Loading the dataset

from datasets import load_dataset

dataset = load_dataset("ajd12342/paraspeechclap-eval-situational", 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

@misc{diwan2026paraspeechclapdualencoderspeechtextmodel,
      title={ParaSpeechCLAP: A Dual-Encoder Speech-Text Model for Rich Stylistic Language-Audio Pretraining},
      author={Anuj Diwan and Eunsol Choi and David Harwath},
      year={2026},
      eprint={2603.28737},
      archivePrefix={arXiv},
      primaryClass={eess.AS},
      url={https://arxiv.org/abs/2603.28737},
}