CTC Forced Aligner is a forced alignment tool that synchronizes text with audio using Hugging Face's pretrained speech models.
The tool solves the problem of aligning transcribed text to audio timestamps, which is essential for creating annotated speech datasets. It uses Connectionist Temporal Classification models from Hugging Face—specifically Wav2Vec2, HuBERT, and MMS—to determine where each word or character occurs in an audio file. The approach leverages pretrained models rather than requiring model training, making alignment accessible without specialized expertise.
Developers building speech corpora, training text-to-speech systems, or creating datasets for speech research should consider this tool. It supports over 1100 languages through the MMS model family, and offers flexible alignment granularity at sentence, word, or character level. The implementation is optimized for memory efficiency, using substantially less memory than the TorchAudio forced alignment API. Users can customize alignment behavior through parameters controlling token insertion frequency and segment merging thresholds. The tool outputs results in JSON format for straightforward integration into downstream pipelines and supports GPU acceleration for faster processing. FFMPEG is required as a prerequisite dependency.
The project maintains active test coverage through continuous integration workflows. Development activity shows responsiveness to issues raised by users. The codebase is written in Python and structured for straightforward installation and command-line usage.
