Datasets:
Improve dataset card: Update license, add explicit links, and enhance tags and structure (#1)
Browse files- Improve dataset card: Update license, add explicit links, and enhance tags and structure (cfcb1d6d64fedb6a4ecd111462f27a6a247b05f7)
Co-authored-by: Niels Rogge <[email protected]>
README.md
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license: cc-by-nc-sa-4.0
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language:
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- ru
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task_categories:
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- text-to-speech
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pretty_name: Balalaika
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---
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#
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Russian speech synthesis presents distinctive challenges, including vowel reduction, consonant devoicing, variable stress patterns, homograph ambiguity, and unnatural intonation. This paper introduces Balalaika, a novel dataset comprising more than 2,000 hours of studio-quality Russian speech with comprehensive textual annotations, including punctuation and stress markings. Experimental results show that models trained on Balalaika significantly outperform those trained on existing datasets in both speech synthesis and enhancement tasks.
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## Citation
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If you use this pipeline in your research or production, please cite:
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```
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@misc{borodin2025datacentricframeworkaddressingphonetic,
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title={A Data-Centric Framework for Addressing Phonetic and Prosodic Challenges in Russian Speech Generative Models},
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author={Kirill Borodin and Nikita Vasiliev and Vasiliy Kudryavtsev and Maxim Maslov and Mikhail Gorodnichev and Oleg Rogov and Grach Mkrtchian},
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---
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language:
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- ru
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license: cc-by-nc-nd-4.0
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task_categories:
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- text-to-speech
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pretty_name: Balalaika
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tags:
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- russian
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- audio
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- speech-synthesis
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- speech-enhancement
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# Balalaika Dataset
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This repository contains the **Balalaika** dataset, a novel dataset comprising more than 2,000 hours of studio-quality Russian speech with comprehensive textual annotations, designed for addressing phonetic and prosodic challenges in Russian speech generative models.
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**Paper:** [A Data-Centric Framework for Addressing Phonetic and Prosodic Challenges in Russian Speech Generative Models](https://huggingface.co/papers/2507.13563)
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**Code:** [GitHub Repository](https://github.com/mtuciru/balalaika)
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---
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## Abstract
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Russian speech synthesis presents distinctive challenges, including vowel reduction, consonant devoicing, variable stress patterns, homograph ambiguity, and unnatural intonation. This paper introduces Balalaika, a novel dataset comprising more than 2,000 hours of studio-quality Russian speech with comprehensive textual annotations, including punctuation and stress markings. Experimental results show that models trained on Balalaika significantly outperform those trained on existing datasets in both speech synthesis and enhancement tasks.
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## Citation
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If you use this pipeline in your research or production, please cite:
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```bibtex
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@misc{borodin2025datacentricframeworkaddressingphonetic,
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title={A Data-Centric Framework for Addressing Phonetic and Prosodic Challenges in Russian Speech Generative Models},
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author={Kirill Borodin and Nikita Vasiliev and Vasiliy Kudryavtsev and Maxim Maslov and Mikhail Gorodnichev and Oleg Rogov and Grach Mkrtchian},
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