![]() ![]() The original dataset consists of over 105,000 audio files in the WAV (Waveform) audio file format of people saying 35 different words. ![]() To save time with data loading, you will be working with a smaller version of the Speech Commands dataset. # Set the seed value for experiment reproducibility. Note that you'll be using seaborn for visualization in this tutorial. Import necessary modules and dependencies. But, like image classification with the MNIST dataset, this tutorial should give you a basic understanding of the techniques involved. Real-world speech and audio recognition systems are complex. ![]() You will use a portion of the Speech Commands dataset ( Warden, 2018), which contains short (one-second or less) audio clips of commands, such as "down", "go", "left", "no", "right", "stop", "up" and "yes". This tutorial demonstrates how to preprocess audio files in the WAV format and build and train a basic automatic speech recognition (ASR) model for recognizing ten different words. ![]()
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