WhisperX is an automated speech recognition toolkit designed to convert spoken audio into text while maintaining precise synchronization with the original media. It functions as an integrated pipeline that combines transcription, phoneme-based alignment, and speaker diarization to produce structured, attributed transcripts.
The project distinguishes itself through its use of forced alignment, which matches existing text to audio signals at the phoneme level to generate accurate word-level timestamps. It also incorporates speaker diarization to identify and label unique voices within a recording, allowing for the creation of transcripts that attribute specific segments to individual speakers.
The system supports multilingual transcription and automated caption generation by sequencing multiple machine learning models, including transformer-based recognition and voice activity detection. These processes are optimized through GPU-accelerated tensor computation to handle large audio files and complex neural network operations.
