Full Stack Text-to-Speech (TTS) Career Path
About This Course
- Understanding Text-to-Speech Technology: Grasp foundational concepts and applications of TTS in various industries..
- Speech Synthesis Techniques:Learn different methods of speech synthesis, including concatenative, formant, parametric, and neural network-based synthesis.
- Working with TTS Libraries and Tools:Get hands-on experience with popular TTS libraries like Google Text-to-Speech and Mozilla TTS, including setup and implementation.
- Building and Training TTS Systems:Implement and train TTS models, such as Tacotron, to generate high-quality speech from text.
- Enhancing TTS Output Quality:Apply post-processing techniques and control speech characteristics to improve the naturalness and engagement of synthesized speech.
- Voice Cloning and Personalization:Explore techniques for cloning voices and customizing TTS systems to match specific user needs or preference.
- Google Text-to-Speech: A popular TTS library for converting text into speech.
- Mozilla TTS: An open-source TTS library for high-quality speech synthesis
- Tacotron: Deep learning architectures (Tacotron and Tacotron 2) for advanced TTS applications.
- TensorFlow: A powerful framework for training and deploying deep learning models, including TTS systems.
- Keras: A high-level neural networks API for building and training deep learning models.
- PyTorch: An open-source machine learning library used for applications such as computer vision and natural language processing, including TTS.
- Librosa: A Python package for analyzing and processing audio and music.
- WaveGlow: A flow-based model for generating high-quality speech.
- Festival: A general multi-lingual speech synthesis system offering various options for TTS.
- SoX (Sound eXchange): A cross-platform command-line utility for processing audio files.
- Stakeholder Communication: Clearly articulate the benefits, capabilities, and potential limitations of TTS technology to stakeholders, ensuring alignment with business objectives and user needs.
- Technical Documentation: Develop thorough and accessible documentation for TTS models, processes, and implementation details to support team understanding and collaboration.
- Project Management: Plan, execute, and monitor TTS projects efficiently, ensuring they are delivered on time, within scope, and meet business goals.
- Data Presentation: Present TTS outputs and performance metrics in a clear and compelling manner to non-technical stakeholders, aiding in decision-making and showcasing value.
- Cross-Functional Collaboration: Work effectively with linguists, audio engineers, data scientists, and business teams to integrate TTS solutions seamlessly into products and services.
- Analyze and define user requirements to tailor TTS solutions for specific applications, such as accessibility tools and customer service bots.
- Architect scalable TTS solutions that can handle varying loads and growing demands using cloud-based services and advanced TTS models.
- Implement strategies to enhance the performance and naturalness of synthesized speech, focusing on prosody, intonation, and clarity.
- Develop secure TTS systems by protecting sensitive user data and ensuring compliance with privacy regulations.
- Apply design thinking principles to create innovative and user-centric TTS applications, incorporating feedback loops for continuous improvement.
Gain a foundational understanding of text-to-speech (TTS) technology and its applications in various industries. Learn the basic terminologies and concepts essential for working with TTS systems.
- 1.1 What is Text-to-Speech?:
- Understand the core concepts and objectives of TTS, including applications in accessibility (e.g., screen readers), automotive (e.g., voice navigation), and customer service (e.g., interactive voice response systems).
- 1.2 Basic Terminologies:
- learn about synthesis, prosody, phonemes, and how they form the basis of TTS tasks.
Explore the essential components and techniques for converting text into speech.
- 1.1 Text Processing:
- Dive into text processing techniques such as tokenization and normalization, which are crucial for accurate speech synthesis.
- 1.2 Phonetics and Phonology:
- Understand the basics of phonetics and phonology to effectively translate text into spoken words.
- 1.3 Prosody and Intonation:
- Learn about the importance of prosody and intonation in making synthesized speech sound natural and human-like.
- Learn about the importance of prosody and intonation in making synthesized speech sound natural and human-like.
Learn various methods of speech synthesis and their applications.
- 1.1 Concatenative Synthesis:
- Explore how pre-recorded speech units are concatenated to form complete utterances.
- 1.2 Formant Synthesis:
- Understand how speech is generated by modeling the human vocal tract.
- 1.3 Parametric Synthesis:
- Learn about the statistical modeling of speech sounds.
- 1.4 Neural Network-Based Synthesis:
- Delve into modern techniques like neural TTS models for high-quality speech synthesis.
- Delve into modern techniques like neural TTS models for high-quality speech synthesis.
Get hands-on with popular TTS libraries and tools.
- 1.1 Overview of TTS Libraries:
- Learn about Google Text-to-Speech, Mozilla TTS, and other popular libraries.
- 1.2 Installation and Setup:
- Set up your development environment to start working with TTS tools.
Implement a basic TTS system using the knowledge and tools acquired.
- 1.1 Text Preprocessing:
- Prepare text data for synthesis.
- 1.2 Selecting Synthesis Technique:
- Choose the appropriate synthesis method for your project.
- 1.3 Generating Speech from Text:
- Combine all components to create a functional TTS system.
Explore different types of voice models and their training processes.
- 1.1 Types of Voice Models:
- Learn about male, female, robotic, and other voice models.
- 1.2 Training Voice Models:
- Understand the process of training voice models for high-quality speech synthesis.
- 1.3 Voice Quality and Naturalness:
- Learn techniques to improve the naturalness of synthesized speech.
Utilize deep learning techniques to enhance TTS systems.
- 1.1 Introduction to Deep Learning for TTS:
- Understand the basics of using deep learning in TTS.
- 1.2 Sequence-to-Sequence Models:
- Learn about models that convert sequences of text to sequences of speech.
- 1.3 Tacotron and Tacotron 2 Architectures:
- Dive into advanced architecture for high-quality speech synthesis.
Implement a Tacotron model for a practical TTS project.
- 1.1Implementing Tacotron Model:
- Set up and implement a Tacotron model.
- 1.2 Training a TTS Model:
- Train the model on a dataset to produce speech.
- 1.3 Generating Speech:
- Use the trained model to convert text inputs into speech outputs.
- Use the trained model to convert text inputs into speech outputs.
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POPULAR
Live Interaction
Self paced
Fee Structure
$599
$299
Curriculum & Course Materials
Live coding environment
AI-based learning platform
100+ hours of instruction
20+ assignments
10+ banking & finance case studies
Banking & finance domain focused curriculum
Capstone projects
Live Classes
Flexible study options
Cancel anytime in first 7 days, full refund
Mentors
15+ hours of sessions with industry veterans & experts
Personalized mentorship by course instructors
Unlimited 1:1 doubt solving sessions
Career Support
Personalized placement assistance
1:1 mock interviews with industry experts
Soft-skills training module
Essential digital tools for digital workplace module
Interview preparation module
Masterclass on resume building & LinkedIn
Access to curated companies & jobs