Align Audio and Text for Speech Recognition Model Training

Learn how to align audio and text data for effective speech recognition model training in this comprehensive 27-minute tutorial. Explore the critical process of creating clean, properly segmented training datasets by aligning audio recordings with their corresponding text transcriptions. Discover two powerful alignment models: the MMS-FA model and a commercially-licensed CTC aligner alternative that overcomes licensing restrictions found in other tools. Master the multi-step alignment workflow including text normalization, emissions generation, character probability calculation, and the Viterbi algorithm for determining optimal alignment paths. Understand how word timestamps enable automatic sentence boundary detection, allowing you to create clean 20-30 second audio chunks ideal for model training. Examine the Trelis Studio data preparation interface and see how realignment processes transform raw audio-text pairs into structured datasets with precise word-level timestamps. Learn why fine-tuning models like Whisper without proper timestamps leads to catastrophic forgetting and how proper alignment prevents this issue. Dive deep into the technical foundations including Wave2Vec models, emissions as character probabilities per audio frame, forced alignment algorithms, and masked audio prediction techniques used in pre-training. Gain practical insights into handling licensing restrictions, implementing Viterbi methods for mapping ground truth text to audio windows, and training emissions models using unlabeled data approaches.