构建5.1小时俄语语音数据集，自动标注韵律特征提升语音合成质量。
Balalaika: Data-Centric, Prosody-Aware Annotation Pipeline for Russian Speech
- 基于语义语音活动检测与多语音识别集成，实现上下文保留的精准分段
- 构建5.1小时多源俄语语料库，支持音调、重音、音标等丰富标注
- 适用于语音降噪与语音合成任务，适合关注俄语语音处理的研究者
我们提出Balalaika，一个开源的数据驱动型语音处理流水线，用于音频处理与韵律感知标注。该流程结合语义语音活动检测（VAD）实现上下文保留的分段，采用多语音识别器集成与ROVER共识解码，并可选择性保留词级时间戳，随后进行自动质量与说话人纯净度过滤。文本进一步增强标点恢复、词汇重音与"\textipa{e}/\textipa{He}"归一化及国际音标（IPA）音素标注。使用Balalaika，我们构建了5.1小时多源俄语语料库，包含丰富标注。在同等训练预算下，该语料库在语音降噪与语音合成任务中均表现出一致提升；消融实验验证了重音与标点的互补收益，且更严格的MOS过滤可进一步提升合成质量。数据集已公开于HuggingFace。
原文摘要 · Abstract (English)
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}}}
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