carlosfranzreb/accsr-daga23 ? reverse-engineered prompt

Build me a small research framework for accented speech recognition that can train an ASR model by itself, train an accent classifier on top of the encoder, or train both together either jointly or with adversarial gradient reversal.

I want to be able to run experiments from a config file, choose the language, data file, trainer settings, model weights, branching point, and whether the run is training or evaluation. It should also support analysis jobs for things like encoder representation visualizations, gradient size checks at the branching point, and WER or distance analysis.

Please wire it up so Common Voice style data can be prepared into the format the ASR toolkit expects, with seen and unseen accents handled properly. Use the existing NeMo based setup where it makes sense, and make the code easy to extend with new classifier and analysis components later. If anything is unclear, look up the current NeMo docs online and match the expected config structure as closely as possible.