Summary
The video provides a detailed insight into cutting-edge speech systems and their evolution towards generative models for creating human-like content. It delves into self-supervised learning, the transition from Phim-based to virtual cascaded systems, and the use of Wave 2 technology for converting tokens to audio. The discussion also includes the data-intensive training process, ASR pipeline, semantic representation generation, and the transformer module for text-to-speech conversion. Additionally, it explores emotional intelligence in audio, fine-tuning with adapters, and the trend towards more end-to-end models in speech systems, emphasizing the importance of open source initiatives and emotional sample training.
Chapters
Introduction to Company and Background
Challenges with Multilingual Content
Evolution of Speech Systems
Self-supervised Learning
Wave 2 and Generative Models
Data Intensive Training
Speech-to-Text Conversion
Semantic Representation and Speaker Reference
Diffusion Architecture
Transformer and Text-to-Speech
Adapters and End-to-End Systems
Open Source and Feedback Approach
Emotional Intelligence in Audio
Introduction to Company and Background
Introduction to the company Duw and the speaker's background in AI and data sciences.
Challenges with Multilingual Content
Discussion on the challenges faced with multilingual content and the need for a solution.
Evolution of Speech Systems
Exploration of the evolution of speech systems and the transition to generative models for human-like content.
Self-supervised Learning
Explanation of self-supervised learning in speech systems and the shift from Phim-based systems to virtual cascaded systems.
Wave 2 and Generative Models
Introduction to Wave 2 technology and the use of generative models to convert tokens to audio for speech systems.
Data Intensive Training
Discussion on the data-intensive training process for speech systems and the use of Phim representation.
Speech-to-Text Conversion
Explanation of the pipeline for converting speech to text using ASR and preprocessing steps.
Semantic Representation and Speaker Reference
Overview of generating semantic representations from audio and using speaker reference clips in the process.
Diffusion Architecture
Description of the diffusion architecture used to convert semantic representations to output audio.
Transformer and Text-to-Speech
Discussion on the transformer module for text-to-speech conversion and the use of llm in the system.
Adapters and End-to-End Systems
Explanation of adapters in the system for fine-tuning and the shift towards more end-to-end models in speech systems.
Open Source and Feedback Approach
Introduction to open source initiatives for early feedback and short-form generation in speech systems.
Emotional Intelligence in Audio
Exploration of emotional intelligence in audio and the training of models on emotional samples.
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