# palak-463/tablataalrecognitionsystem architecture

> Architecture diagram of the GitHub repository palak-463/tablataalrecognitionsystem, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-06-22). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/palak-463/tablataalrecognitionsystem
- Repository: https://github.com/palak-463/tablataalrecognitionsystem

## Overview

1. **Repository purpose**
   - This is a small Python-based **Tabla Taal recognition** project.
   - The architecture is centered on **offline model training / serialization** plus a **runtime inference GUI** that classifies an uploaded Tabla audio clip into one of the supported Taals.

2. **Main execution boundary**
   - The most likely user-facing entrypoint is **`app.py`**.
   - It acts as the boundary between the user and the ML pipeline: it receives an audio file, triggers feature extraction and prediction, and displays the recognized Taal.

3. **ML model artifacts**
   - The deployed inference path depends on three persisted artifacts:
     - **`model.pkl`**: the trained classifier
     - **`scaler.pkl`**: feature normalization / standardization state
     - **`encoder.pkl`**: label encoding for mapping model outputs to Taal names
   - These files indicate the system is designed to load a pre-trained model rather than train on every run.

4. **Data and preprocessing layer**
   - **`data.csv`** is the structured dataset used for training or experimentation.
   - The pipeline likely converts audio into numerical features, stores/uses them in tabular form, and then applies the scaler and encoder before/after prediction.
   - This forms the key preprocessing boundary between raw audio and the ML model.

5. **Audio input**
   - **`xyz.wav`** is a sample audio file included in the repo.
   - It likely serves as a test input for validating the recognition pipeline and demonstrates the expected input format: a Tabla performance in WAV audio.

6. **Model/training logic**
   - **`Model.py`** is the code boundary for ML-related logic.
   - Based on the README, it likely contains the training workflow, feature engineering, model selection, and serialization of the trained artifacts.
   - The README mentions both **FNN** and **CNN**, but the file set suggests the deployed version may be a serialized trained model with supporting preprocessing assets rather than multiple live model implementations.

7. **Inference flow**
   - The runtime path is:
     - user selects an audio file in the GUI
     - the app processes the audio into features
     - the features are scaled
     - the classifier predicts a class
     - the label encoder converts the class index to a Taal name
     - the GUI presents the result
   - This is the core data flow to reflect in an architecture diagram.

8. **Testing / validation**
   - **`test.py`** is a separate validation script.
   - It likely exercises the prediction pipeline against sample audio or known data to confirm the trained artifacts and preprocessing steps work correctly outside the GUI.

9. **System boundary and dependencies**
   - The only explicitly stated technology is **Python**.
   - The architectural dependencies that matter are the serialized ML assets and whatever audio-processing stack the Python code uses internally.
   - From a diagram standpoint, the important external inputs are **audio files** and **trained model artifacts**.

10. **What is not in the repo**
   - There is no evidence of a server, database, or cloud service layer.
   - There is also no sign of a multi-service architecture; this looks like a **single-process local application** with bundled model files.
   - So the diagram should be drawn as a compact desktop/inference system rather than a distributed application.

11. **Recommended diagram shape**
   - Show three zones:
     - **User/UI**: the GUI in `app.py`
     - **ML pipeline**: preprocessing, scaling, model inference, label decoding
     - **Artifacts/data**: `data.csv`, `model.pkl`, `scaler.pkl`, `encoder.pkl`, sample WAV input
   - The key arrows should go from **audio upload → feature extraction → scaling/model prediction → label decoding → displayed Taal**.

12. **High-level interpretation**
   - This repo is primarily a **packaged recognition workflow** for classical Tabla Taals.
   - The architecture is built around **pretrained ML inference**, not continuous learning or online serving.

Main entry points: [`app.py`](https://github.com/palak-463/tablataalrecognitionsystem/blob/main/app.py) (App), [`Model.py`](https://github.com/palak-463/tablataalrecognitionsystem/blob/main/Model.py) (Model Code), [`model.pkl`](https://github.com/palak-463/tablataalrecognitionsystem/blob/main/model.pkl) (Model Artifact), [`test.py`](https://github.com/palak-463/tablataalrecognitionsystem/blob/main/test.py) (Test Script)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_ui["User Interface"]
  node_app["App<br/>gui orchestrator<br/>[app.py]"]
  node_show_result["Result<br/>ui output"]
end

subgraph group_inference["Inference Pipeline"]
  node_feature_extract{{"Features<br/>audio preprocessing"}}
  node_scale_features["Scale<br/>normalization"]
  node_predict["Predict<br/>classifier inference"]
  node_decode_label["Decode<br/>label mapping"]
end

subgraph group_training["Training Assets"]
  node_sample_audio["Sample WAV<br/>audio input<br/>[xyz.wav]"]
  node_model_py["Model Code<br/>training workflow<br/>[Model.py]"]
  node_data_csv["Dataset<br/>training data<br/>[data.csv]"]
  node_model_pkl[("Model Artifact<br/>serialized classifier<br/>[model.pkl]")]
  node_scaler_pkl[("Scaler Artifact<br/>serialized scaler<br/>[scaler.pkl]")]
  node_encoder_pkl[("Encoder Artifact<br/>serialized encoder<br/>[encoder.pkl]")]
  node_test_py["Test Script<br/>validation script<br/>[test.py]"]
end

node_app -->|"selects"| node_sample_audio
node_app -->|"routes audio"| node_feature_extract
node_feature_extract -->|"feature vector"| node_scale_features
node_scale_features -->|"scaled features"| node_predict
node_predict -->|"class id"| node_decode_label
node_decode_label -->|"display"| node_show_result
node_model_py -->|"trains on"| node_data_csv
node_model_py -->|"serializes"| node_model_pkl
node_model_py -->|"serializes"| node_scaler_pkl
node_model_py -->|"serializes"| node_encoder_pkl
node_app -->|"loads"| node_model_pkl
node_app -->|"loads"| node_scaler_pkl
node_app -->|"loads"| node_encoder_pkl
node_test_py -.->|"verifies with"| node_sample_audio
node_test_py -.->|"checks"| node_model_pkl

click node_app "https://github.com/palak-463/tablataalrecognitionsystem/blob/main/app.py"
click node_sample_audio "https://github.com/palak-463/tablataalrecognitionsystem/blob/main/xyz.wav"
click node_model_py "https://github.com/palak-463/tablataalrecognitionsystem/blob/main/Model.py"
click node_data_csv "https://github.com/palak-463/tablataalrecognitionsystem/blob/main/data.csv"
click node_model_pkl "https://github.com/palak-463/tablataalrecognitionsystem/blob/main/model.pkl"
click node_scaler_pkl "https://github.com/palak-463/tablataalrecognitionsystem/blob/main/scaler.pkl"
click node_encoder_pkl "https://github.com/palak-463/tablataalrecognitionsystem/blob/main/encoder.pkl"
click node_test_py "https://github.com/palak-463/tablataalrecognitionsystem/blob/main/test.py"

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_app,node_show_result toneBlue
class node_feature_extract,node_scale_features,node_predict,node_decode_label toneAmber
class node_sample_audio,node_model_py,node_data_csv,node_model_pkl,node_scaler_pkl,node_encoder_pkl,node_test_py toneMint
```

## Components

### User Interface

Local GUI boundary for uploading audio and showing the recognized Taal.

- [App](https://github.com/palak-463/tablataalrecognitionsystem/blob/main/app.py): gui orchestrator · User-facing entrypoint that accepts an audio file and presents the predicted Taal. · `app.py`
- **Result**: ui output · Displays the recognized Taal to the user in the GUI.

### Inference Pipeline

Runtime audio-processing and prediction flow used by the app.

- **Features**: audio preprocessing · Converts raw Tabla audio into numeric features for downstream classification.
- **Scale**: normalization · Applies the fitted scaler before prediction.
- **Predict**: classifier inference · Loads the trained model and produces a class index from the scaled features.
- **Decode**: label mapping · Maps predicted class indices back to human-readable Taal names.

### Training Assets

Offline training, persisted artifacts, and validation inputs.

- [Sample WAV](https://github.com/palak-463/tablataalrecognitionsystem/blob/main/xyz.wav): audio input · Example Tabla audio clip used to exercise the recognition workflow. · `xyz.wav`
- [Model Code](https://github.com/palak-463/tablataalrecognitionsystem/blob/main/Model.py): training workflow · Offline ML logic for feature engineering, model fitting, and artifact creation. · `Model.py`
- [Dataset](https://github.com/palak-463/tablataalrecognitionsystem/blob/main/data.csv): training data · Structured data used for experimentation and model training. · `data.csv`
- [Model Artifact](https://github.com/palak-463/tablataalrecognitionsystem/blob/main/model.pkl): serialized classifier · Persisted trained classifier loaded at runtime for inference. · `model.pkl`
- [Scaler Artifact](https://github.com/palak-463/tablataalrecognitionsystem/blob/main/scaler.pkl): serialized scaler · Persisted feature scaling state reused during inference. · `scaler.pkl`
- [Encoder Artifact](https://github.com/palak-463/tablataalrecognitionsystem/blob/main/encoder.pkl): serialized encoder · Persisted label encoder used to translate model outputs into Taal names. · `encoder.pkl`
- [Test Script](https://github.com/palak-463/tablataalrecognitionsystem/blob/main/test.py): validation script · Separate script that exercises the recognition pipeline outside the GUI. · `test.py`

## Connections

- App → Sample WAV: selects · The GUI takes a user-provided audio clip or sample WAV as input.
- App → Features: routes audio · App hands the chosen audio to preprocessing.
- Features → Scale: feature vector · Extracted features are normalized before classification.
- Scale → Predict: scaled features · Model consumes standardized input.
- Predict → Decode: class id · Predicted numeric class is mapped to a Taal label.
- Decode → Result: display · Final label is shown in the GUI.
- Model Code → Dataset: trains on · Training logic consumes the tabular dataset.
- Model Code → Model Artifact: serializes · Training produces the persisted classifier artifact.
- Model Code → Scaler Artifact: serializes · Training saves the fitted scaler for later reuse.
- Model Code → Encoder Artifact: serializes · Training saves the label encoder used at inference time.
- App → Model Artifact: loads · Runtime inference uses the trained classifier artifact.
- App → Scaler Artifact: loads · Runtime inference uses the saved scaler.
- App → Encoder Artifact: loads · Runtime inference uses the saved label encoder.
- Test Script → Sample WAV: verifies with · Validation script can run against the sample clip.
- Test Script → Model Artifact: checks · Tests the persisted classifier outside the GUI.