#voice cloning

Explore an innovative open source text-to-speech system designed for flexibility and commercial use. Currently supporting English with plans for multilingual compatibility, recent updates enhance performance and introduce voice cloning. Test its capabilities on Google Colab with models leveraging Whisper, EnCodec, and Vocos.

Experience comprehensive text-to-speech capabilities with the Python library by ElevenLabs. This API is intended for developers and content creators, offering vibrant, realistic voices across numerous languages and accents efficiently. Featuring advanced models such as Eleven Multilingual v2 and Eleven Turbo v2.5, the library ensures consistent performance with a focus on diversity and speed. Installation and integration are straightforward, allowing users to generate audio, clone voices, and adjust settings to meet various project needs. This makes it suitable for anyone in search of professional-quality audio tools.

The Multi-Tacotron Voice Cloning project is a multilingual phonemic implementation for Russian and English, built on a deep learning framework. The project, an extension of Real-Time-Voice-Cloning, facilitates the creation of numeric voice representations from brief audio samples. It includes pre-trained models and necessary datasets, providing efficient pathways for text-to-speech conversion. The diverse datasets and neural networks such as Tacotron 2 and WaveRNN enable seamless multilingual capabilities, suited for advanced TTS synthesis requirements.

Discover the capabilities of a leading Text-to-Speech library supporting 16 languages and delivering efficient performance with latency below 200ms. The library includes models such as Tacotron, Glow-TTS, and VITS, with options for fine-tuning and multi-speaker TTS support. Utilize over 1100 Fairseq models for various linguistic needs and access numerous tools for training and refining speech models. Designed for a diverse range of applications, this library offers developers a flexible solution for generating high-quality speech.

AI-powered software for multi-lingual dubbing, enhancing accessibility in media through speech synthesis and voice cloning. Addresses accessibility gaps in dubbing for visual impairments and dyslexia, compatible with Windows and Linux.

The project utilizes HuBERT and custom quantizers to support developers in implementing voice cloning. It includes code samples, pretrained models, and input preparation guidelines. Available tools like audio-webui and community contributions in German and Polish are included. Designed for Python 3.10, it also offers resources for training custom models with semantic data, aiming for precise and realistic voice cloning.

The Unet-TTS project facilitates improved speaker and style transfer for one-shot voice cloning by utilizing the Unet network and AdaIN layer. This open-source project offers inferencing code and pre-trained models for generating diverse text-based audio. It addresses the complexity of out-of-domain style transfer using a neutral emotion corpus. The project automates duration statistics and supports multi-speaker TTS with a pre-trained Content Encoder, along with a detailed setup guide. Compatible with Linux and certain TensorFlow versions, implementation is accessible through both Python scripts and Colab notebooks.