Speech and audio processing with PyTorch involves using neural networks to understand, analyze, and generate audio signals. By leveraging libraries like torchaudio, developers can load, transform, and preprocess sound data efficiently. Common tasks include automatic speech recognition (ASR), text-to-speech synthesis, speaker identification, and acoustic event detection. PyTorch’s ecosystem supports building advanced architectures, from simple convolutional models to Transformers specialized in audio tasks. This flexibility allows quick experimentation, fine-tuning on domain-specific datasets, and ultimately, the creation of responsive voice assistants, improved voice command systems, and accessible communication tools.