Build a Speech Recognition Model
.MP4, AVC, 1280x720, 30 fps | English, AAC, 2 Ch | 1h 13m | 193 MB
Instructor: Anthony Alampi
This course will teach you to collect and preprocess audio, extract spectrograms/MFCCs, train a deep learning ASR model, decode sequences, and evaluate performance against existing platforms like OpenAI's Whisper.
What you'll learn
Developers and teams increasingly need to integrate speech recognition into applications. In this course, Build a Speech Recognition Model, you'll gain the ability to develop a complete, end-to-end speech recognition system capable of converting spoken audio into accurate text.
First, you'll explore how to collect, preprocess, and clean audio data, learning techniques such as normalization, noise reduction, and segmenting recordings to prepare high-quality input for a model. Next, you'll discover how to extract meaningful features from raw audio and structure datasets for training deep learning models. Finally, you'll learn how to train, decode, and evaluate a custom speech-to-text model using modern architectures.
When you're finished with this course, you'll have the skills and knowledge needed to confidently build speech recognition models and make informed decisions about deploying deep learning-based voice technologies in real-world applications.
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