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Learn how to leverage PyTorch and torchaudio for deep learning applications in audio and music processing through this comprehensive tutorial. Master the fundamentals of implementing and training neural networks with PyTorch, then advance to making accurate predictions with your trained models. Discover how to create custom audio datasets using PyTorch's framework and efficiently extract mel spectrograms using both PyTorch and torchaudio libraries. Explore essential preprocessing techniques for handling audio files of varying durations and optimize your audio preprocessing workflows for GPU acceleration. Build practical skills by implementing convolutional neural networks specifically designed for sound classification tasks, then train your own sound classifier using PyTorch's powerful training capabilities. Complete your learning journey by mastering the prediction process with your trained sound classification models, gaining hands-on experience with real-world audio processing scenarios throughout the 2 hours and 55 minutes of instruction.
