seed-vc is a voice conversion model based on the SEED-TTS architecture, capable of zero-shot voice conversion, meaning it can convert voices without requiring specific voice samples from individuals. This technology excels in audio quality and tonal similarity, holding substantial research and application value.

OptiSpeech is an efficient, lightweight, and fast text-to-speech model specifically designed for device-side text-to-speech conversion. Leveraging advanced deep learning techniques, it converts text into naturally sounding speech, making it suitable for applications that require speech synthesis on mobile devices or embedded systems. The development of OptiSpeech was significantly accelerated by GPU resources provided by Pneuma Solutions.

speech-to-speech is an open-source modular GPT4-o project that achieves speech-to-speech conversion through sequential components such as voice activity detection, speech-to-text, language modeling, and text-to-speech synthesis. It leverages the Transformers library and models available on the Hugging Face hub, providing a high degree of modularity and flexibility.

Pandrator is a tool based on open-source software that converts text, PDF, EPUB, and SRT files into voice audio in multiple languages. It includes features for voice cloning, LLM-based text preprocessing, and directly saving generated audio subtitles into video files, blending them with original audio tracks. It is designed for ease of use and installation, featuring a one-click installer and a graphical user interface.

FunAudioLLM is a framework aimed at enhancing natural voice interaction between humans and Large Language Models (LLMs). It comprises two innovative models: SenseVoice, responsible for high-precision multi-lingual speech recognition, emotion recognition, and audio event detection; and CosyVoice, responsible for natural voice generation, supporting multi-lingual, timbre, and emotion control. SenseVoice supports over 50 languages with extremely low latency; CosyVoice excels in multi-lingual voice generation, zero-shot context generation, cross-lingual voice cloning, and instruction following capabilities. Relevant models are open-sourced on Modelscope and Huggingface, and corresponding training, inference, and fine-tuning codes are released on GitHub.

ChatTTS-Forge is a project built around the ChatTTS text-to-speech generation model. It provides a comprehensive API service and a Gradio-based WebUI, enabling the generation of long texts exceeding 1000 words while maintaining consistency. The platform boasts built-in style management with 32 distinct styles.

ChatTTS-ui is a web interface and API interface for the ChatTTS project. It allows users to perform text-to-speech operations through a webpage and remotely call the service through an API interface. It supports multiple voice options, and users can customize the text-to-speech parameters, such as adding laughter or pauses. This project provides an easy-to-use interface for text-to-speech technology, lowering the technical barrier and making text-to-speech more convenient.

ChatTTS is an open-source text-to-speech (TTS) model that allows users to convert text into speech. This model is primarily aimed at academic research and educational purposes and is not suitable for commercial or legal applications. It utilizes deep learning techniques to generate natural and fluent speech output, making it suitable for individuals involved in speech synthesis research and development.

OpenVoice V2 is a text-to-speech (TTS) model released in April 2024, which includes all the features of V1 and has been improved. It employs a distinct training strategy to deliver superior sound quality, supporting English, Spanish, French, Chinese, Japanese, and Korean, among other languages. Additionally, it provides free usage for commercial purposes. OpenVoice V2 can precisely clone reference pitch coloration and generate speech in various languages and accents. It also supports zero-shot cross-language cloning, meaning the language of the generated speech and the reference speech do not need to be present in a large-scale multilingual training dataset.

FineVoice is a multifunctional AI voiceover platform that utilizes advanced artificial intelligence technology to provide users with realistic and personalized voice services. This platform not only converts text into natural-sounding voices but also offers speech-to-text and voice-changing capabilities, greatly enriching the possibilities for content creation. FineVoice's primary advantages include high efficiency, low cost, multilingual support, and user-friendliness, making it especially suitable for individuals and businesses that need to generate large volumes of voice content quickly.

seed-vc is a voice conversion model based on the SEED-TTS architecture, capable of zero-shot voice conversion, meaning it can convert voices without requiring specific voice samples from individuals. This technology excels in audio quality and tonal similarity, holding substantial research and application value.

OptiSpeech is an efficient, lightweight, and fast text-to-speech model specifically designed for device-side text-to-speech conversion. Leveraging advanced deep learning techniques, it converts text into naturally sounding speech, making it suitable for applications that require speech synthesis on mobile devices or embedded systems. The development of OptiSpeech was significantly accelerated by GPU resources provided by Pneuma Solutions.

speech-to-speech is an open-source modular GPT4-o project that achieves speech-to-speech conversion through sequential components such as voice activity detection, speech-to-text, language modeling, and text-to-speech synthesis. It leverages the Transformers library and models available on the Hugging Face hub, providing a high degree of modularity and flexibility.

Pandrator is a tool based on open-source software that converts text, PDF, EPUB, and SRT files into voice audio in multiple languages. It includes features for voice cloning, LLM-based text preprocessing, and directly saving generated audio subtitles into video files, blending them with original audio tracks. It is designed for ease of use and installation, featuring a one-click installer and a graphical user interface.

FunAudioLLM is a framework aimed at enhancing natural voice interaction between humans and Large Language Models (LLMs). It comprises two innovative models: SenseVoice, responsible for high-precision multi-lingual speech recognition, emotion recognition, and audio event detection; and CosyVoice, responsible for natural voice generation, supporting multi-lingual, timbre, and emotion control. SenseVoice supports over 50 languages with extremely low latency; CosyVoice excels in multi-lingual voice generation, zero-shot context generation, cross-lingual voice cloning, and instruction following capabilities. Relevant models are open-sourced on Modelscope and Huggingface, and corresponding training, inference, and fine-tuning codes are released on GitHub.

ChatTTS-Forge is a project built around the ChatTTS text-to-speech generation model. It provides a comprehensive API service and a Gradio-based WebUI, enabling the generation of long texts exceeding 1000 words while maintaining consistency. The platform boasts built-in style management with 32 distinct styles.

ChatTTS-ui is a web interface and API interface for the ChatTTS project. It allows users to perform text-to-speech operations through a webpage and remotely call the service through an API interface. It supports multiple voice options, and users can customize the text-to-speech parameters, such as adding laughter or pauses. This project provides an easy-to-use interface for text-to-speech technology, lowering the technical barrier and making text-to-speech more convenient.

ChatTTS is an open-source text-to-speech (TTS) model that allows users to convert text into speech. This model is primarily aimed at academic research and educational purposes and is not suitable for commercial or legal applications. It utilizes deep learning techniques to generate natural and fluent speech output, making it suitable for individuals involved in speech synthesis research and development.