# sanjidh090/moonshine-base-bn architecture

> Architecture diagram of the GitHub repository sanjidh090/moonshine-base-bn, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-06-27). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/sanjidh090/moonshine-base-bn
- Repository: https://github.com/sanjidh090/moonshine-base-bn

## Overview

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.

Main entry points: [`tokenizer_replacing_&_finetuning.ipynb`](https://github.com/sanjidh090/moonshine-base-bn/blob/main/tokenizer_replacing_&_finetuning.ipynb) (Adapt finetune), [`assets/paper/paper.md`](https://github.com/sanjidh090/moonshine-base-bn/blob/main/assets/paper/paper.md) (Paper draft), [`index.html`](https://github.com/sanjidh090/moonshine-base-bn/blob/main/index.html) (Landing page), [`dataset_preparation.md`](https://github.com/sanjidh090/moonshine-base-bn/blob/main/dataset_preparation.md) (Data prep)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_grp_workflow["Experiment workflow"]
  node_n_fertility["Fertility check<br/>analysis script"]
  node_n_baseline_nb["Baseline train<br/>notebook"]
  node_n_finetune_nb["Adapt finetune<br/>notebook"]
end

subgraph group_grp_docs["Documentation"]
  node_n_dataset_prep["Data prep<br/>prep guide"]
  node_n_paper_md["Paper draft<br/>paper markdown<br/>[paper.md]"]
  node_n_paper_pdf["Paper PDF<br/>published paper"]
  node_n_readme["Overview<br/>repo intro<br/>[README.md]"]
  node_n_license["License"]
end

subgraph group_grp_assets["Presentation assets"]
  node_n_index["Landing page<br/>static page<br/>[index.html]"]
  node_n_images["Figures<br/>image assets"]
  node_n_image_test["Image helper<br/>utility script<br/>[test.py]"]
end

node_n_dataset_prep -->|"prepared text"| node_n_fertility
node_n_dataset_prep -->|"training data"| node_n_baseline_nb
node_n_dataset_prep -->|"adaptation data"| node_n_finetune_nb
node_n_fertility -->|"token insights"| node_n_finetune_nb
node_n_baseline_nb -->|"baseline checkpoint"| node_n_finetune_nb
node_n_finetune_nb -->|"results"| node_n_paper_md
node_n_paper_md -->|"compiled as"| node_n_paper_pdf
node_n_baseline_nb -->|"showcase"| node_n_index
node_n_paper_pdf -->|"displayed via"| node_n_index
node_n_images -->|"figures"| node_n_paper_md
node_n_images -->|"media"| node_n_index
node_n_image_test -->|"maintains"| node_n_images
node_n_readme -->|"orients"| node_n_index
node_n_readme -->|"describes"| node_n_paper_md

click node_n_dataset_prep "https://github.com/sanjidh090/moonshine-base-bn/blob/main/dataset_preparation.md"
click node_n_fertility "https://github.com/sanjidh090/moonshine-base-bn/blob/main/tokenizer_fertility_check.py"
click node_n_baseline_nb "https://github.com/sanjidh090/moonshine-base-bn/blob/main/Vanilla_Baseline_Training.ipynb"
click node_n_finetune_nb "https://github.com/sanjidh090/moonshine-base-bn/blob/main/tokenizer_replacing_&_finetuning.ipynb"
click node_n_paper_md "https://github.com/sanjidh090/moonshine-base-bn/blob/main/assets/paper/paper.md"
click node_n_paper_pdf "https://github.com/sanjidh090/moonshine-base-bn/blob/main/assets/paper/40_Tokenizer_Transplantation_M.pdf"
click node_n_index "https://github.com/sanjidh090/moonshine-base-bn/blob/main/index.html"
click node_n_images "https://github.com/sanjidh090/moonshine-base-bn/tree/main/assets/images"
click node_n_image_test "https://github.com/sanjidh090/moonshine-base-bn/blob/main/assets/images/test.py"
click node_n_readme "https://github.com/sanjidh090/moonshine-base-bn/blob/main/README.md"
click node_n_license "https://github.com/sanjidh090/moonshine-base-bn/tree/main/LICENSE"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_n_fertility,node_n_baseline_nb,node_n_finetune_nb toneBlue
class node_n_dataset_prep,node_n_paper_md,node_n_paper_pdf,node_n_readme,node_n_license toneAmber
class node_n_index,node_n_images,node_n_image_test toneMint
```

## Components

### Experiment workflow

Notebook-driven ASR adaptation and analysis path.

- [Fertility check](https://github.com/sanjidh090/moonshine-base-bn/blob/main/tokenizer_fertility_check.py): analysis script · Diagnostic script for assessing tokenizer suitability and tokenization efficiency. · `tokenizer_fertility_check.py`
- [Baseline train](https://github.com/sanjidh090/moonshine-base-bn/blob/main/Vanilla_Baseline_Training.ipynb): notebook · Notebook for reproducing or training the vanilla ASR baseline. · `Vanilla_Baseline_Training.ipynb`
- [Adapt finetune](https://github.com/sanjidh090/moonshine-base-bn/blob/main/tokenizer_replacing_&_finetuning.ipynb): notebook · Notebook for tokenizer replacement and downstream fine-tuning on Bangla data. · `tokenizer_replacing_&_finetuning.ipynb`

### Documentation

Preprocessing notes and paper/reporting materials.

- [Data prep](https://github.com/sanjidh090/moonshine-base-bn/blob/main/dataset_preparation.md): prep guide · Defines how Bangla speech/text data is cleaned, segmented, aligned, and formatted for training. · `dataset_preparation.md`
- [Paper draft](https://github.com/sanjidh090/moonshine-base-bn/blob/main/assets/paper/paper.md): paper markdown · Markdown source for the accompanying paper or workshop writeup. · `assets/paper/paper.md`
- [Paper PDF](https://github.com/sanjidh090/moonshine-base-bn/blob/main/assets/paper/40_Tokenizer_Transplantation_M.pdf): published paper · Compiled paper artifact for sharing or submission. · `assets/paper/40_Tokenizer_Transplantation_M.pdf`
- [Overview](https://github.com/sanjidh090/moonshine-base-bn/blob/main/README.md): repo intro · Minimal repository description and entry point for readers. · `README.md`
- [License](https://github.com/sanjidh090/moonshine-base-bn/tree/main/LICENSE): license · Repository licensing boundary. · `LICENSE`

### Presentation assets

Static media and page used to present the work.

- [Landing page](https://github.com/sanjidh090/moonshine-base-bn/blob/main/index.html): static page · Lightweight HTML presentation surface for the project or paper. · `index.html`
- [Figures](https://github.com/sanjidh090/moonshine-base-bn/tree/main/assets/images): image assets · Supporting images used in the paper and/or landing page. · `assets/images`
- [Image helper](https://github.com/sanjidh090/moonshine-base-bn/blob/main/assets/images/test.py): utility script · Small ancillary Python script located with images, likely for figure generation or validation. · `assets/images/test.py`

## Connections

- Data prep → Fertility check: prepared text · Prepared data informs tokenizer suitability analysis.
- Data prep → Baseline train: training data · Preprocessed speech/text feeds baseline training.
- Data prep → Adapt finetune: adaptation data · Prepared data is reused for fine-tuning after tokenizer changes.
- Fertility check → Adapt finetune: token insights · Tokenizer diagnostics guide replacement and adaptation decisions.
- Baseline train → Adapt finetune: baseline checkpoint · Baseline model training provides the starting point for adaptation.
- Adapt finetune → Paper draft: results · Notebook outputs are summarized into the paper draft.
- Paper draft → Paper PDF: compiled as · Markdown paper source becomes the distributable PDF.
- Baseline train → Landing page: showcase · Baseline/adaptation work is presented through the static page.
- Paper PDF → Landing page: displayed via · The landing page likely surfaces the paper artifact.
- Figures → Paper draft: figures · Paper draft consumes figures from the image asset boundary.
- Figures → Landing page: media · Images support the project landing page.
- Image helper → Figures: maintains · Utility script appears to support image/figure assets.
- Overview → Landing page: orients · Repository overview points readers toward the project entry surface.
- Overview → Paper draft: describes · README frames the research and paper context.