Data Science Project | Part 1 | Speech to Text using TensorFlow

Описание: Welcome to Part 1 of our Data Science Project series!

In this session we explore the exciting field of speech-to-text conversion using TensorFlow, one of the most powerful deep learning frameworks. Speech-to-text systems are essential for various applications, including voice assistants, transcription services, and accessibility tools.

What You'll Learn:

Introduction to Speech-to-Text Systems: Understand the importance of converting spoken language into written text and its wide range of applications.

Overview of TensorFlow: Introduction to TensorFlow, its capabilities, and how it can be used for building speech-to-text models.

Dataset Preparation: Explore commonly used datasets for speech-to-text 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 (MFCCs) and spectrograms.

Building a Speech-to-Text Model: Step-by-step guide on building 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 speech-to-text 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'll have a solid understanding of how to preprocess audio data, build a speech-to-text model using TensorFlow, and evaluate its performance.

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