🎙️ Voice Search Intent Classifier (with GPU Profiling Lab)

This project trains an artificial neural network to classify voice search queries by intent—such as information, command, shopping, or entertainment. Built for clarity, modularity, and performance, the lab includes full GPU profiling using NVIDIA Nsight Systems and NVTX markers.

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🚀 Project Structure

ml-training-lab/ ├── train_model.py # Training script with NVTX profiling ├── predict_intent.py # Inference script for new voice queries ├── utils.py # Shared preprocessing and encoding functions ├── assets/ # .png files for viewing ├── traces/ # Nsight Systems .nsys-rep files └── README.md # Project overview and instructions

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🧠 What This Project Demonstrates

• ✅ Real-world NLP pipeline using voice search data
• ✅ Intent classification with TensorFlow/Keras
• ✅ Modular design for training, inference, and preprocessing
• ✅ GPU profiling with Nsight Systems + NVTX annotations
• ✅ Interview-ready explanations of model behavior and performance

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📊 Dataset

• Source: voice_search_query_captures.csv
• Fields used:
  • query_text: Input to the model
  • intent: Target label
• Preprocessing:
  • Lowercasing, punctuation removal
  • Tokenization + padding
  • Label encoding

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🧪 Model Architecture

• Embedding layer (vocab size: 1000, dim: 16)
• GlobalAveragePooling1D
• Dense (24 units, ReLU)
• Dense (softmax output for multi-class classification)

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🧭 Architecture Overview

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🔍 Profiling with Nsight Systems

Training is wrapped with NVTX markers for each epoch:

with nvtx.annotate("Epoch 1", color="blue"):
    model.fit(...)

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To generate a trace:

nsys profile -o traces/nn_training_trace --trace=cuda,nvtx python train_model.py

Open the .nsys-rep file in Nsight Systems to visualize:

• CUDA kernel launches
• Memory transfers
• NVTX-labeled epochs
• CPU orchestration

🔮 Inference Example

python predict_intent.py

Sample output:

Query: 'Play jazz music on Spotify' → Predicted Intent: 'entertainment' Query: 'Turn off the living room lights' → Predicted Intent: 'command'

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📓 Simulation Notebook

Explore the full training and testing workflow in the interactive notebook:

👉 ml_training_lab_SIM.ipynb

This notebook walks through:

• Data loading and preprocessing
• Model architecture and training
• NVTX-wrapped profiling setup
• Inference on new voice queries
• Commentary and visual outputs

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🧰 Requirements

• Python 3.8+
• TensorFlow
• scikit-learn
• NVIDIA Nsight Systems
• NVTX (pip install nvtx)

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🎯 Future Enhancements

• Add confusion matrix and intent distribution visualizations
• Integrate real-time voice input via microphone
• Expand to multilingual intent classification
• Profile inference latency and memory footprint

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🧠 Author

Dartayous — Creative Technologist & AI/ML Engineer Focused on cinematic, modular GPU workflows and explainable AI. Built this lab to showcase real-world ML engineering and profiling mastery.

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📎 License

MIT License — feel free to fork, remix, and build upon it.