Balalaika: Data-Centric, Prosody-Aware Annotation Pipeline for Russian Speech
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866914525362520064 |
|---|---|
| author | Borodin, Kirill Vasiliev, Nikita Kudryavtsev, Vasiliy Maslov, Maxim Gorodnichev, Mikhail Mkrtchian, Grach |
| author_facet | Borodin, Kirill Vasiliev, Nikita Kudryavtsev, Vasiliy Maslov, Maxim Gorodnichev, Mikhail Mkrtchian, Grach |
| contents | 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{He}" normalization, and IPA phonemes. Using Balalaika, we build a 5.1k-hour multi-source Russian corpus with rich annotations, and show consistent gains under equalized training budgets for both speech denoising and TTS; ablations confirm complementary benefits of stress and punctuation and improved synthesis with stricter MOS filtering. The datasets are publicly available at \href{https://huggingface.co/collections/lab260/balalaika-dataset}{\underline{\textbf{HuggingFace}}} |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_13563 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Balalaika: Data-Centric, Prosody-Aware Annotation Pipeline for Russian Speech Borodin, Kirill Vasiliev, Nikita Kudryavtsev, Vasiliy Maslov, Maxim Gorodnichev, Mikhail Mkrtchian, Grach Computation and Language Sound Audio and Speech Processing 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{He}" normalization, and IPA phonemes. Using Balalaika, we build a 5.1k-hour multi-source Russian corpus with rich annotations, and show consistent gains under equalized training budgets for both speech denoising and TTS; ablations confirm complementary benefits of stress and punctuation and improved synthesis with stricter MOS filtering. The datasets are publicly available at \href{https://huggingface.co/collections/lab260/balalaika-dataset}{\underline{\textbf{HuggingFace}}} |
| title | Balalaika: Data-Centric, Prosody-Aware Annotation Pipeline for Russian Speech |
| topic | Computation and Language Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2507.13563 |