What is OuteTTS
OuteTTS is an open-source text-to-speech (TTS) project that generates speech based on a pure language modeling approach. The OuteTTS project is based on the LLaMa architecture, uses the Oute3-350M-DEV base model, and has 350 million parameters. OuteTTS features innovative audio processing methods such as audio tokenization, CTC forced alignment technology, and structured prompt creation. It supports voice cloning functionality, allowing users to create custom speaker voices. OuteTTS is compatible with llama.cpp and the GGUF format, making it suitable for various application scenarios such as audiobooks, intelligent customer service, and voice navigation.
Main Features of OuteTTS
- Text-to-Speech Synthesis: Converts text into natural-sounding speech output.
- Voice Cloning: Users provide a reference audio file and the corresponding text to create a custom voice for personalized voice applications.
- Audio Tokenization: Uses WavTokenizer to convert audio signals into a format that can be processed by the model.
- CTC Forced Alignment: Creates precise mappings between words and audio tokens, ensuring accurate correspondence between text and audio.
- Structured Prompt Creation: Provides clear guidance in a specific format to improve the accuracy and naturalness of speech synthesis.
- Compatibility with Existing Technologies: Compatible with llama.cpp and the GGUF format, making it easy to integrate into different application environments.
Technical Principles of OuteTTS
- Audio Tokenization：OuteTTS uses WavTokenizer to tokenize audio signals, converting continuous audio waveforms into discrete audio tokens for the model to process.
- CTC Forced Alignment: Using connectionist temporal classification (CTC) technology, OuteTTS precisely maps words in the text to audio tokens, ensuring the accuracy of speech synthesis.
- Structured Prompt Creation: OuteTTS is based on the structured prompt format of "[full transcription] [word] [duration token]", providing clear guidance to the model, which helps improve the accuracy and naturalness of synthesized speech.
- Model Construction Based on the LLaMa Architecture: OuteTTS 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, has 350 million parameters, and provides strong support for speech synthesis tasks.
- Pure Language Modeling: OuteTTS implements speech synthesis using pure language modeling, without the need for complex adapters or architectures, simplifying the TTS implementation process.
- Parameter Adjustment: By adjusting model parameters such as temperature and repetition penalty, OuteTTS achieves more stable and high-quality speech output under different conditions.
OuteTTS Project Address
- GitHub Repository：https://github.com/edwko/OuteTTS
Application Scenarios of OuteTTS
- Personalized Assistant: Provides personalized voice assistant services for smartphones, smart home devices, etc., allowing users to interact with devices using natural language.
- Audiobooks and Podcasts: Converts e-books, articles, or blog content into speech, providing users with an audiobook experience, especially suitable for visually impaired individuals or when reading is inconvenient, such as while driving or exercising.
- Customer Service: In the customer service field, it is used in automated voice response systems to provide natural-sounding voice replies and improve customer satisfaction.
- Language Learning: Assists language learners in practicing pronunciation and listening, teaching new languages with simulated native speaker speech.
- Navigation and GPS Systems: Provides voice navigation instructions for drivers, allowing drivers to focus on the road while driving, improving safety.