VoiceTrace-Bench

VoiceTrace is a benchmark and unified framework for who-said-what speech retrieval: given a natural-language query about a speaker's identity or what they said, retrieve the matching audio document. Unlike conventional speaker verification or diarization benchmarks, VoiceTrace evaluates retrieval jointly over who is speaking and what is being said, across both single-speaker and multi-speaker conversational recordings.

This repository hosts the VoiceTrace-Bench evaluation dataset, split into two subsets:

• single — single-speaker query/document audio pairs.
• multi — multi-speaker conversational recordings with diarization annotations.

Each example pairs a query (natural-language question, optionally with a query audio clip) against a document audio clip, along with speaker identity labels and transcript text used for evaluation.

Dataset Structure

Each split is stored as data.parquet with associated audio files. An example record:

{
  "id": 1,
  "source": "benchmark_single_sample",
  "query_text": "In which segment does this speaker repeatedly ask others to be quiet?",
  "query_audio": "audio/001_query.wav",
  "document_audio": "audio/001_document.wav",
  "document_audio_text": "please stop talking stop",
  "query_audio_speaker_ids": "['seamless_0092']",
  "document_audio_speaker_ids": "['seamless_0092']"
}

Usage

Download the dataset with the Hugging Face CLI:

hf download cara-ai/VoiceTrace-Bench --local-dir benchmark

Then evaluate a baseline speaker retrieval model against it using the VoiceTrace evaluation script:

git clone git@github.com:caml-labs/VoiceTrace.git
cd VoiceTrace
conda create -n eval python=3.12
conda activate eval
pip install -r requirements.txt
python evaluate_baseline.py --single-dataset-path benchmark/single --multi-dataset-path benchmark/multi

Citation

If you find this work useful, please consider citing:

@misc{yee2026voicetrace,
      title={VoiceTrace: A Benchmark and Retrieval Framework for Who-Said-What Speech Retrieval}, 
      author={Aaron Yee and Fengjie Lu and Jiarui Hai and Chenang Jiang and Helin Wang and Siwei Tu and Weitao You and Lingyun Sun},
      year={2026},
      eprint={2609.18521},
      archivePrefix={arXiv},
      primaryClass={cs.SD},
      url={https://arxiv.org/abs/2609.18521}, 
}

License

This dataset is licensed under CC-BY-NC 4.0 (Creative Commons Attribution-NonCommercial 4.0 International).

Acknowledgements

• VoxCeleb / VoxCeleb2 for providing large-scale benchmark datasets for speaker-related research.
• VoxConverse for providing multi-speaker conversational recordings with diarization annotations.
• Seamless Interaction for open-source resources that support speech and multimodal interaction research.