arXiv:2507.13563v3 Announce Type: replace Abstract: We introduce Balalaika, an open-source, data-centric pipeline for processing audio and producing prosody-aware annotations. It combines semantic VAD for context-preserving segmentation, multi-ASR ensembling with ROVER consensus decoding, while retaining optional word-level timestamps, followed by automatic quality and speaker-purity filtering. The text is further enriched with punctuation restoration, lexical stress and "\textipa{e}/\textipa{\H{e}}" normalization, and IPA phonemes. Using Balalaika, we build a 5.1k-hour multi-source Russian co
The increasing demand for high-quality, regionally specific AI models and data infrastructure drives the development of tools like Balalaika.
This development allows for improved Russian language AI applications, potentially reducing reliance on foreign-developed speech processing solutions.
The availability of an open-source, prosody-aware annotation pipeline for Russian speech facilitates the creation of more accurate and nuanced voice AI.
- · Russian AI developers
- · Russian tech companies
- · Multilingual AI research
- · Generic speech-to-text providers (for Russian)
- · AI models lacking regional linguistic nuance
Balalaika enables the creation of higher-quality Russian speech recognition and synthesis systems.
Improved Russian AI capabilities could enhance domestic digital services and reduce external AI infrastructure dependency.
This could contribute to the broader trend of nations building out their own AI data and model sovereignty.
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