How srihari1306/temp2 works
This repository combines a music-genre model-training and analysis script with a React dashboard for exploring its dataset and reported results. The user-facing workflow is to open the dashboard, select a tab, and inspect dataset overview, signal quality, spectral statistics, training curves, or model comparisons. The sampled Python source shows audio preparation and dataset/model code, while the dashboard’s metrics are hardcoded. Training-script excerpts are incomplete, so the graph avoids asserting how its stages are wired together.
Main entry points: App.jsx Dashboard app · metrics.js Hardcoded metrics · train.py Audio preparation · main.jsx React entry
Components 16
Analytics dashboard
- React entry
frontend/src/main.jsx - Dashboard app
frontend/src/App.jsx - Tab controls
frontend/src/components/Tabs.jsx - Dataset overview
frontend/src/components/OverviewTab.jsx - Signal quality EDA
frontend/src/components/EDATab.jsx - Spectral analysis
frontend/src/components/SpectralTab.jsx - Training curves
frontend/src/components/ModelTrainingTab.jsx - Model comparison
frontend/src/components/ModelComparisonTab.jsx
Reported metrics
- Hardcoded metrics
frontend/src/data/metrics.js
Model workflow
- Audio preparation
train.py - Mel dataset
train.py - AST dataset
train.py - CNN model
train.py - AST model
train.py - Model training
train.py
Other components
- Dashboard user
Connections 17
- Dashboard user → React entry: opens
- React entry → Dashboard app: renders
- Dashboard app → Tab controls: renders controls
- Dashboard app → Dataset overview: selects view
- Dashboard app → Signal quality EDA: selects view
- Dashboard app → Spectral analysis: selects view
- Dashboard app → Training curves: selects view
- Dashboard app → Model comparison: selects view
- Dataset overview → Hardcoded metrics: reads
- Signal quality EDA → Hardcoded metrics: reads
- Spectral analysis → Hardcoded metrics: reads
- Training curves → Hardcoded metrics: reads
- Model comparison → Hardcoded metrics: reads
- Audio preparation → Mel dataset: prepares audio for
- Audio preparation → AST dataset: prepares audio for
- Model training → CNN model: trains
- Model training → AST model: trains