How palak-463/tablataalrecognitionsystem works
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 App · Model.py Model Code · model.pkl Model Artifact · test.py Test Script
Components 13
User Interface
Local GUI boundary for uploading audio and showing the recognized Taal.
- App · User-facing entrypoint that accepts an audio file and presents the predicted Taal.
app.py - Result · Displays the recognized Taal to the user in the GUI.
Inference Pipeline
Runtime audio-processing and prediction flow used by the app.
- Features · Converts raw Tabla audio into numeric features for downstream classification.
- Scale · Applies the fitted scaler before prediction.
- Predict · Loads the trained model and produces a class index from the scaled features.
- Decode · Maps predicted class indices back to human-readable Taal names.
Training Assets
Offline training, persisted artifacts, and validation inputs.
- Sample WAV · Example Tabla audio clip used to exercise the recognition workflow.
xyz.wav - Model Code · Offline ML logic for feature engineering, model fitting, and artifact creation.
Model.py - Dataset · Structured data used for experimentation and model training.
data.csv - Model Artifact · Persisted trained classifier loaded at runtime for inference.
model.pkl - Scaler Artifact · Persisted feature scaling state reused during inference.
scaler.pkl - Encoder Artifact · Persisted label encoder used to translate model outputs into Taal names.
encoder.pkl - Test Script · Separate script that exercises the recognition pipeline outside the GUI.
test.py
Connections 15
- 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.