Multimodal data building creates training datasets that pair text with images, audio, video, or other modalities. The builder handles the complex task of aligning content across modalities, ensuring that paired samples are semantically consistent and properly synchronized.
Image-text alignment uses a combination of metadata matching, OCR extraction, caption analysis, and visual-semantic similarity scoring to create high-quality pairings. The builder supports both natural image-text pairs found in web data and synthetic pairs generated through captioning models.
Audio-text alignment processes speech recordings with automatic speech recognition and forced alignment to produce timestamped transcripts. The builder handles multiple speakers, background noise, and code-switching between languages.
Cross-modal quality validation ensures that paired samples are semantically consistent. Misaligned pairs, where the text does not accurately describe the visual or audio content, are flagged for review or removal.
The builder outputs datasets in standard formats compatible with major multimodal training frameworks including WebDataset, TFDS, and HuggingFace Datasets.