👧Modeling methods

General Introduction

Text-to-speech (TTS) AI is a technology that enables machines to convert written text into speech. TTS systems use machine learning algorithms and natural language processing techniques to generate human-like speech that can be used in various applications such as voice assistants, digital books, and other multimedia content. TTS systems typically consist of two main components: a front-end that processes the text input and a back-end that generates the speech output.

Inference refers to the process of using a trained TTS model to generate speech for a new text input. During inference, the TTS model takes in a text input, processes it using the learned representations, and generates a speech output. Inference is a crucial component of TTS systems as it determines the quality of the speech output generated by the model.

Contrastive Language-Voice Pretraining (CLVP) is a method of pretraining TTS systems on large amounts of data to improve their ability to produce high-quality speech. The idea behind CLVP is to train the AI model on a contrastive objective, where it must distinguish between different speech samples and identify the correct speech output for a given text input. By training on a diverse set of data, CLVP can improve the TTS system's ability to generalize to new data and produce speech that is robust to interference.

There are different types of TTS models, including feedforward models, recurrent models, and transformers. These models use different AI architectures and algorithms to generate speech, and each has its own advantages and disadvantages. For example, feedforward models are fast and efficient, but may not be able to capture the dependencies between different parts of the speech output. Recurrent models, on the other hand, can capture these dependencies, but may be slower and more computationally expensive.

AI models used in speech synthesis include WaveNet, Tacotron, and DeepVoice. These models use different architectures and algorithms to generate speech, and each has its own strengths and weaknesses. For example, WaveNet is known for its high-quality speech output, but it is computationally expensive and may be slow during inference. Tacotron is a more efficient model that uses attention-based mechanisms to generate speech, but its output quality may not be as high as WaveNet.

GPUs (graphics processing units) play a critical role in the training and inference of AI models for TTS. GPUs have parallel processing capabilities that allow AI models to perform complex computations faster and more efficiently. By using GPUs, TTS models can be trained on large amounts of data in a shorter amount of time, and speech output can be generated quickly during inference. This makes TTS systems more practical and usable in real-world applications.