easyaligner is a forced alignment library designed to synchronize text transcripts with audio recordings. It addresses the need to precisely align spoken content with corresponding text, supporting applications such as creating interactive reading experiences from audiobooks, enabling chapter navigation and keyword search in podcasts, aligning transcripts of parliamentary debates for research and accessibility, correcting or generating subtitles for videos, and building large-scale datasets for speech recognition and synthesis.
The library offers flexibility in how alignments are performed. It can handle scenarios where the transcript covers the entire audio, only a known segment, or when the relevant audio region is unknown. easyaligner supports segmentation of outputs at various granularities, such as sentences or paragraphs, while maintaining the original formatting of the text. It produces word-level timestamps, allowing for precise synchronization between text and audio. The tool is compatible with both ground-truth transcripts and outputs from automatic speech recognition (ASR) models, broadening its applicability across different workflows. Outputs are generated as JSON files for each stage of the alignment pipeline, including voice activity detection (VAD), emissions, and forced alignment.
The library can be installed via pip or uv, which automatically selects the appropriate PyTorch version depending on the user's system. The usage examples reference integration with Hugging Face models and processors, such as Wav2Vec2, and show how to tokenize and segment text for alignment. The tool also supports interaction with other libraries, such as easytranscriber, where easyaligner serves as the backend alignment engine.
easyaligner is intended for users working with audio-text alignment tasks, including those in research, accessibility, content creation, and AI model training. The documentation highlights tutorials for various alignment scenarios, emphasizing ease of use and adaptability to different project requirements.
Overview is an AI & ML product. It focuses on aligning text transcripts with audio recordings for research, accessibility, and dataset creation. It is built as an open-source project for speech researchers and developers. Overview is open source under the MIT license. It runs on the command line, and it can be self-hosted.
It is developed by kb-labb, and the product first shipped in 2025. Development happens publicly on GitHub with 19 stars and 16 commits in the last 90 days. Key capabilities include forced alignment, word-level timestamps, and ASR support.
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