In this session, we explore the exciting area of speech-to-text conversion using TensorFlow, one of the most powerful deep learning frameworks. Text-to-speech systems are essential for a variety of applications, including voice assistants, transcription services, and accessibility tools.
What you will learn:
– Introduction to speech synthesis systems: understand the importance of converting spoken language into written text and its wide range of applications.
– TensorFlow Overview: Introduction to TensorFlow, its features, and how it can be used to create text-to-speech models.
– Dataset preparation: Explore commonly used datasets for text-to-speech tasks, such as the LibriSpeech dataset, and learn how to preprocess audio data for model training.
– Feature Extraction: Techniques for extracting features from audio data, including Mel Frequency Cepstral Coefficients (MFCC) and spectrograms.
– Creating a speech-to-text model: Step-by-step guide on creating and training a speech-to-text model using TensorFlow, including model architecture and training strategies.
– Model Evaluation: Learn how to evaluate the performance of your text-to-speech model using metrics such as word error rate (WER) and character error rate (CER).
Why watch this session?
This session is essential for data scientists and developers interested in mastering speech recognition technology. By the end of this session, you will have a solid understanding of how to preprocess audio data, create a text-to-speech model using TensorFlow, and evaluate its performance.
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