Whisper Data Preparation and Fine-Tuning with Unsloth

Learn to prepare audio data and fine-tune OpenAI's Whisper speech recognition model using Unsloth's optimization framework in this comprehensive 41-minute tutorial. Set up a one-click GPU environment with Jupyter notebooks and explore the differences between Whisper, Voxtral, and Kyutai models before deciding between Whisper Large and Turbo variants. Master the installation process for Unsloth and Whisper Timestamped tools, then dive into practical audio data preparation techniques including recording, transcription, and the critical differences between standard Whisper and Whisper-Timestamped for obtaining word-level timestamps. Discover how to create precise text and audio segments using word-timestamped transcripts, understand the challenges of chunking audio segments under 30 seconds, and implement both automated and manual transcript cleanup techniques to improve data quality. Build datasets from processed audio and text segments, then proceed with fine-tuning using Unsloth's efficient training methods. Evaluate model performance using Word Error Rate metrics, comparing teacher forcing versus predict_with_generate approaches, and analyze training hyperparameters, losses, and results. Compare base model performance against your fine-tuned version, then learn to merge models, push them to Hugging Face Hub, and prepare them for inference deployment.