OuteTTS - an open-source text-to-speech synthesis project that generates speech based on pure language modeling methods.

What is OuteTTS?

OuteTTS is an open-source text-to-speech (TTS) project that generates speech based on pure language modeling. Built on the LLaMa architecture, OuteTTS uses the Oute3-350M-DEV base model with 350 million parameters. OuteTTS features innovative audio processing methods such as audio tokenization, CTC forced alignment, and structured cue creation. It supports voice cloning and allows users to create custom speaker voices. OuteTTS is compatible with llama.cpp and GGUF formats, making it suitable for various applications including audiobooks, intelligent customer service, and voice navigation.

Main functions of OuteTTS

• Text-to-speech synthesis: Converts text into speech output that sounds natural.
• Voice cloningUsers provide reference audio files and corresponding text to create custom voices for personalized voice applications.
• Audio tokenizationUse WavTokenizer to convert audio signals into a format that the model can process.
• CTC Forced AlignmentCreates a precise mapping between words and audio tokens, ensuring accurate correspondence between text and audio.
• Structured suggestion creationProvide clear guidance using specific formats to improve the accuracy and naturalness of speech synthesis.
• Compatible with existing technologiesCompatible with llama.cpp and GGUF formats, making it easy to integrate into different application environments.

OuteTTS Technical Principles

• Audio tokenization:OuteTTS uses WavTokenizer to tokenize audio signals, converting continuous audio waveforms into discrete audio tokens for model processing.
• CTC Forced AlignmentUsing Connected Temporal Classification (CTC) technology, OuteTTS accurately matches words in the text with audio tokens, ensuring the accuracy of speech synthesis.
• Structured suggestion creationOuteTTS, based on the structured prompt format of "[full transcription] [word] [duration token]", provides clear guidance to the model, which helps improve the accuracy and naturalness of synthesized speech.
• Model building based on the LLaMa architectureOuteTTS is based on the LLaMa architecture and uses the pre-trained Oute3-350M-DEV model. The model is pre-trained on a large amount of data and has 350 million parameters, providing strong support for speech synthesis tasks.
• Pure Language ModelingOuteTTS uses pure language modeling to achieve speech synthesis, eliminating the need for complex adapters or architectures and simplifying the TTS implementation process.
• Parameter adjustmentBy adjusting model parameters such as temperature and repetition penalty, OuteTTS achieves more stable and higher quality speech output under different conditions.

OuteTTS project address

• GitHub repository:https://github.com/edwko/OuteTTS

Application scenarios of OuteTTS

• Personalized AssistantIt provides personalized voice assistant services for smartphones, smart home devices, etc., allowing users to interact with the devices using natural language.
• audiobooks and podcastsIt converts e-books, articles, or blog content into audio, providing users with an audiobook experience, which is especially suitable for visually impaired people or those who cannot read while driving, exercising, or otherwise.
• Customer ServiceIn the field of customer service, it is used in automated voice response systems to provide natural-sounding voice replies and improve customer satisfaction.
• Language learningIt assists language learners in practicing pronunciation and listening skills, and teaches new languages using the pronunciation of native speakers.
• Navigation and GPS systemsIt provides voice navigation instructions to drivers, allowing them to focus on the road while driving and improving safety.