sanjidh090/moonshine-base-bn

1. This repository is not a production application; it is a research/experiment workspace for ASR adaptation to Bangla, centered on notebook-driven experimentation and paper assets.

2. The core execution path appears to live in two Jupyter notebooks:

- Vanilla_Baseline_Training.ipynb for training or reproducing a baseline ASR model.

- tokenizer_replacing_&_finetuning.ipynb for the adaptation phase, where tokenization is changed and the model is fine-tuned.

These notebooks are the main “compute” layer of the repo.

3. dataset_preparation.md is the preprocessing boundary. It likely documents how raw speech/text data is cleaned, segmented, formatted, and aligned before training. In an ASR repo like this, dataset prep is a critical upstream step feeding both baseline training and fine-tuning.

4. tokenizer_fertility_check.py is a small analysis/diagnostic script. Its role is to evaluate tokenizer suitability for Bangla, likely by measuring tokenization efficiency or “fertility” before and after tokenizer replacement. This sits between text preprocessing and model adaptation.

5. The repo suggests a two-stage workflow:

- prepare and inspect data/tokenization,

- train a vanilla baseline,

- transplant or replace the tokenizer,

- fine-tune on Bangla data.

That makes the architecture primarily pipeline-based rather than service-based.

6. assets/paper/paper.md and assets/paper/40_Tokenizer_Transplantation_M.pdf indicate a publication/reporting layer. These files are separate from the experiment code and likely capture the method, results, and conclusions for the workshop/paper.

7. index.html is probably a lightweight presentation or project landing page, not part of the training pipeline. It likely serves as a static documentation/demo surface for the paper or workshop submission.

8. assets/images/ is a supporting media boundary for figures used in the paper or HTML page. This is a documentation asset layer, not an algorithmic component.

9. assets/images/test.py is an unusual helper file in the assets directory; it may be a quick utility used to generate or validate figures. Its location suggests it is ancillary rather than part of the main ASR pipeline.

10. The main data flow is: raw/processed Bangla speech data → dataset preparation → tokenizer analysis/replacement → baseline training → fine-tuning → paper figures/results/documentation. Outputs are likely model checkpoints and evaluation metrics generated from the notebooks, then distilled into the paper assets.

11. The main architectural boundary is between reproducible experimentation code and presentation artifacts. The notebooks and script define the research workflow, while the markdown/PDF/HTML/image files package the results for sharing.

12. Because the README is minimal, the repository’s structure itself is the best guide: it is an experiment-and-publication repo for ASR adaptation, not a packaged library or deployed system.