Introducing the fine-tuning process for Whisper

Realizing Whisper’s full potential requires moving beyond out-of-the-box offerings through purposeful fine-tuning – configuring and enhancing the model to capture precise niche needs. This specialized optimization journey traverses nine key milestones:

1. Preparing robust Python environments with essential libraries such as Transformers and datasets that empower rigorous experimentation.
2. Incorporating diverse, multilingual datasets, including Common Voice, for expanding linguistic breadth.
3. Setting up Whisper pipeline components such as tokenizers for easier pre/post-processing.
4. Transforming raw speech data into model-digestible log-Mel spectrogram features.
5. Defining training parameters and hardware configurations aligned to target model size.
6. Establishing standardized test sets and metrics for reliable performance benchmarking.
7. Executing training loops that meld configured hyperparameters, data...