Orpheus TTS is an open-source text-to-speech system based on the Llama-3b model, aiming to provide more natural human speech synthesis. It boasts strong voice cloning and emotional expression capabilities, suitable for various real-time applications. This product is free and aims to provide developers and researchers with a convenient speech synthesis tool.

CSM 1B is a speech generation model based on the Llama architecture, capable of generating RVQ audio codes from text and audio input. The model is primarily used in speech synthesis and boasts high-quality speech generation capabilities. Its advantages include the ability to handle multi-speaker dialogue scenarios and generate natural and fluent speech through contextual information. This open-source model is intended to support research and educational purposes but is explicitly prohibited from being used for impersonation, fraud, or illegal activities.

CSM is a conversational speech generation model developed by Sesame. It can generate high-quality speech from text and audio input. The model is based on the Llama architecture and uses the Mimi audio encoder. It is mainly used for speech synthesis and interactive voice applications, such as voice assistants and educational tools. The main advantages of CSM are its ability to generate natural and fluent speech and its ability to optimize speech output through contextual information. The model is currently open-source and suitable for research and educational purposes.

Spark-TTS is a highly efficient text-to-speech synthesis model based on large language models, featuring single-stream decoupled speech tokens. Leveraging the power of large language models, it directly reconstructs audio predicted from code, omitting the additional acoustic feature generation model, thus improving efficiency and reducing complexity. This model supports zero-shot text-to-speech synthesis, enabling cross-lingual and code-switching scenarios, making it ideal for speech synthesis applications requiring high naturalness and accuracy. It also supports virtual voice creation; users can generate different voices by adjusting parameters such as gender, pitch, and speaking rate. The model aims to address the inefficiencies and complexities of traditional speech synthesis systems, providing a highly efficient, flexible, and powerful solution for research and production. Currently, the model is primarily intended for academic research and legitimate applications such as personalized speech synthesis, assistive technologies, and language research.

Llasa is a text-to-speech (TTS) base model based on the Llama framework, designed for large-scale speech synthesis tasks. The model is trained using 160,000 hours of tokenized speech data and has efficient language generation capabilities and multilingual support. Its main advantages include powerful speech synthesis capabilities, low inference costs, and flexible framework compatibility. This model is suitable for education, entertainment, and commercial scenarios, providing users with high-quality speech synthesis solutions. This model is currently freely available on Hugging Face, aiming to promote the development and application of speech synthesis technology.

IndexTTS is a GPT-style text-to-speech (TTS) model primarily developed based on XTTS and Tortoise. It can correct Chinese pronunciation using pinyin and control pauses using punctuation marks. This system introduces a character-pinyin mixed modeling method in Chinese scenarios, significantly improving training stability, timbre similarity, and audio quality. Furthermore, it integrates BigVGAN2 to optimize audio quality. The model is trained on tens of thousands of hours of data and outperforms current popular TTS systems such as XTTS, CosyVoice2, and F5-TTS. IndexTTS is suitable for scenarios requiring high-quality speech synthesis, such as voice assistants and audiobooks, and its open-source nature makes it suitable for academic research and commercial applications.

Zonos is an advanced text-to-speech model that supports multiple languages and can generate natural speech based on text prompts along with speaker embeddings or audio prefixes. It also features voice cloning, allowing for accurate replication of a speaker's voice with just a few seconds of reference audio. The model delivers high-quality speech output (44kHz) and allows fine control over speech rate, pitch variation, audio quality, and emotional tone (such as happiness, fear, sadness, and anger). Zonos offers Python and Gradio interfaces for easy user onboarding and supports deployment through Docker. The model achieves a real-time factor of approximately 2 times on an RTX 4090, making it suitable for applications that require high-quality speech synthesis.

Developed by Zyphra, Zonos-v0.1-hybrid is an open-source text-to-speech model capable of generating highly natural speech based on text prompts. The model is trained on extensive English voice data, employing eSpeak for text normalization and phoneme processing, and predicting DAC tokens via a transformer or hybrid backbone network. It supports multiple languages, including English, Japanese, Chinese, French, and German, and allows for fine control over speech speed, pitch, audio quality, and emotion. Additionally, it features zero-shot voice cloning, requiring only 5 to 30 seconds of speech samples to achieve high-fidelity voice replication. The model operates with a real-time factor of about 2x on an RTX 4090, offering fast performance. It is equipped with an easy-to-use gradio interface and can be easily installed and deployed using Docker. Currently, the model is available on Hugging Face for free, but users need to deploy it themselves.

Llasa-1B is a text-to-speech model developed by the Audio Lab at the Hong Kong University of Science and Technology. Based on the LLaMA architecture and integrated with speech tokens from the XCodec2 codec, it converts text into natural and fluent speech. The model has been trained on 250,000 hours of Chinese and English speech data and supports generating speech from plain text, as well as utilizing given voice prompts for synthesis. Its main advantage is the ability to produce high-quality multilingual speech, making it suitable for various applications such as audiobooks and voice assistants. The model is licensed under CC BY-NC-ND 4.0, prohibiting commercial use.

Text to Bark is the first AI-powered text-to-speech model developed by ElevenLabs, designed to help people communicate more effectively with their dogs. This technology not only demonstrates high-quality speech synthesis but also simulates dog sounds naturally, creating a communication method suitable for dogs to understand. The launch of this innovative product elevates the interaction between humans and pets to a new level, making communication between owners and their dogs more interesting and effective. Users can generate corresponding "dog language" through simple text input, thereby better understanding and interacting with their pets.

This is a powerful text-to-speech generator with over 1000 high-quality AI voices. Suitable for various use cases such as podcasts, education, and business content creation. Users can leverage this platform to generate clear, natural-sounding voice content, supporting voice cloning and audio/video editing. Reasonably priced at only $39.99 per month, it's suitable for both individuals and businesses.

Orpheus TTS is an open-source text-to-speech system based on the Llama-3b model, aiming to provide more natural human speech synthesis. It boasts strong voice cloning and emotional expression capabilities, suitable for various real-time applications. This product is free and aims to provide developers and researchers with a convenient speech synthesis tool.

CSM 1B is a speech generation model based on the Llama architecture, capable of generating RVQ audio codes from text and audio input. The model is primarily used in speech synthesis and boasts high-quality speech generation capabilities. Its advantages include the ability to handle multi-speaker dialogue scenarios and generate natural and fluent speech through contextual information. This open-source model is intended to support research and educational purposes but is explicitly prohibited from being used for impersonation, fraud, or illegal activities.

CSM is a conversational speech generation model developed by Sesame. It can generate high-quality speech from text and audio input. The model is based on the Llama architecture and uses the Mimi audio encoder. It is mainly used for speech synthesis and interactive voice applications, such as voice assistants and educational tools. The main advantages of CSM are its ability to generate natural and fluent speech and its ability to optimize speech output through contextual information. The model is currently open-source and suitable for research and educational purposes.

Zonos TTS is an advanced AI text-to-speech technology supporting multiple languages, emotion control, and zero-shot voice cloning. It generates natural, expressive speech suitable for various scenarios, including education, audiobooks, video games, and voice assistants. The technology provides users with an efficient and personalized speech generation solution through high-quality audio output (44kHz) and fast real-time processing capabilities. While not entirely free, it offers flexible pricing plans to meet the needs of different users.

Spark-TTS is a highly efficient text-to-speech synthesis model based on large language models, featuring single-stream decoupled speech tokens. Leveraging the power of large language models, it directly reconstructs audio predicted from code, omitting the additional acoustic feature generation model, thus improving efficiency and reducing complexity. This model supports zero-shot text-to-speech synthesis, enabling cross-lingual and code-switching scenarios, making it ideal for speech synthesis applications requiring high naturalness and accuracy. It also supports virtual voice creation; users can generate different voices by adjusting parameters such as gender, pitch, and speaking rate. The model aims to address the inefficiencies and complexities of traditional speech synthesis systems, providing a highly efficient, flexible, and powerful solution for research and production. Currently, the model is primarily intended for academic research and legitimate applications such as personalized speech synthesis, assistive technologies, and language research.

Llasa is a text-to-speech (TTS) base model based on the Llama framework, designed for large-scale speech synthesis tasks. The model is trained using 160,000 hours of tokenized speech data and has efficient language generation capabilities and multilingual support. Its main advantages include powerful speech synthesis capabilities, low inference costs, and flexible framework compatibility. This model is suitable for education, entertainment, and commercial scenarios, providing users with high-quality speech synthesis solutions. This model is currently freely available on Hugging Face, aiming to promote the development and application of speech synthesis technology.

Octave TTS is a next-generation speech synthesis model developed by Hume AI. It not only converts text to speech but also understands the semantics and emotions of the text to generate expressive speech output. The core advantage of this technology lies in its deep understanding of language, allowing it to generate natural and vivid speech based on context. It is suitable for various application scenarios, including audiobooks, virtual assistants, and expressive voice interaction. The emergence of Octave TTS marks the development of speech synthesis technology from simple text reading to a more expressive and interactive direction, providing users with a more personalized and emotional voice experience. Currently, this product is primarily aimed at developers and creators, providing services through APIs and platforms. Future expansion to more languages and application scenarios is expected.