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--- |
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license: mit |
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task_categories: |
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- audio-to-audio |
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language: |
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- en |
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--- |
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# Libri Conversation FLAC Dataset |
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This dataset accompanies the paper: |
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**[Proactive Hearing Assistants that Isolate Egocentric Conversations](https://www.arxiv.org/abs/2511.11473)** |
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*Hu et al., 2025* |
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It contains **~234 hours of conversational-style audio** derived from LibriSpeech-like sources, processed into 60-second multispeaker segments under different experimental conditions: |
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- **libri_leaving** — scenarios where one speaker intermittently leaves the conversation |
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- **libri_multi** — 3-speaker conversational segments |
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All audio is stored in **FLAC** format. Metadata files (JSON) are preserved exactly as in the original directory structure. |
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--- |
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## Dataset Structure |
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The dataset is organized into two main components: |
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libri_leaving/ |
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train/ |
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val/ |
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test/ |
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libri_multi/ |
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train/ |
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val/ |
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test/ |
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Because of Hugging Face API request limits, the dataset is packaged into `.tar` archives. |
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Each archive mirrors the original folder structure: |
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libri_leaving_train.tar |
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libri_leaving_val.tar |
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libri_leaving_test.tar |
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libri_multi_train.tar |
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libri_multi_val.tar |
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libri_multi_test.tar |
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--- |
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## Citation |
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If you use this dataset, please cite the following paper: |
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@inproceedings{hu2025proactive, |
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title={Proactive Hearing Assistants that Isolate Egocentric Conversations}, |
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author={Hu, Guilin and Itani, Malek and Chen, Tuochao and Gollakota, Shyamnath}, |
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booktitle={Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing}, |
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pages={25377--25394}, |
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year={2025} |
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} |
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