Train a Convolutional Neural Network from Scratch: PyTorch, Next.js, React, Tailwind, Python (2025)
Source Code & Drawings: https://github.com/Andreaswt/audio-cnn
Discord & More: https://andreastrolle.com
Modal: https://modal.com/andreas
Hi 🤙 In this video, you'll learn to train and deploy an audio classification CNN from scratch with PyTorch. I'll cover all the required concepts, so no prior experience is needed. The model will classify sounds like a dog barking or birds chirping from an audio file. You'll work with advanced techniques like Residual Networks (ResNet), data mixing, and Mel Spectrograms to build a robust training pipeline. Afterwards, we'll build a dashboard using Next.js and React to upload audio and visualize the model's internal layers to see what it "sees". The project uses Python, PyTorch, Next.js, React, and Tailwind, based on the T3 Stack. You can build along with me from start to finish. All services used are 100% free for you to use.
Features
🧠 Deep Audio CNN for sound classification
🧱 ResNet-style architecture with residual blocks
🎼 Mel Spectrogram audio-to-image conversion
🎛️ Data augmentation with Mixup & Time/Frequency Masking
⚡ Serverless GPU inference with Modal
📊 Interactive Next.js & React dashboard
👁️ Visualization of internal CNN feature maps
📈 Real-time audio classification with confidence scores
🌊 Waveform and Spectrogram visualization
🚀 FastAPI inference endpoint
⚙️ Optimized training with AdamW & OneCycleLR scheduler
📈 TensorBoard integration for training analysis
🛡️ Batch Normalization for stable & fast training
🎨 Modern UI with Tailwind CSS & Shadcn UI
✅ Pydantic data validation for robust API requests
Dataset: https://github.com/karolpiczak/ESC-50
Timestamps
0:00 Demo
2:08 Neural Networks
28:21 CNNs
01:17:28 CNN hyperparameters
01:28:43 Audio in CNNs
01:39:06 Model architecture
01:54:36 Implementing network
02:19:46 Training program
03:52:12 Training
03:56:09 Tensorboard
04:00:10 Inference endpoint
04:59:22 Frontend
06:27:24 Visualization discussion
06:35:26 Results
06:36:27 Exercises
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