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StyleTTS2 is an adversarial text-to-speech model that uses style diffusion and large speech language models to generate natural-sounding speech from text input. It combines adversarial training with large pre-trained speech models to improve speech quality and reduce artifacts, while employing a style diffusion process that extracts prosodic and timbral features from reference audio to guide speech generation. The model supports multi-speaker voice synthesis by conditioning the diffusion process on speaker-specific embeddings derived from reference utterances, enabling voice cloning and adapt
This project is a deep learning text-to-speech toolkit used for training and deploying neural speech synthesis models. It provides a comprehensive framework for converting written text into spoken audio, utilizing neural vocoders to transform synthesized spectrograms into high-fidelity audio waveforms. The toolkit includes a voice cloning system that replicates specific human voices by extracting speaker embeddings from short audio samples. It also supports multi-speaker audio synthesis, allowing the generation of speech across different vocal identities using specialized model architectures.
ChatTTS is a conversational text-to-speech generative model designed to convert written dialogue into natural sounding audio. It functions as a multilingual speech synthesis framework capable of producing human-like audio across different languages and speaker profiles. The system is distinguished by its ability to generate interactive dialogue with realistic vocal nuances. It utilizes a speech nuance controller to insert specific tokens that trigger non-verbal elements, such as laughter, pauses, and interjections, during the synthesis process. The project includes a streaming audio generato
PaddleSpeech is a comprehensive toolkit of neural models for speech recognition, synthesis, and translation built on the PaddlePaddle deep learning framework. It provides a collection of frameworks and tools for converting spoken audio into written text, synthesizing natural audio from text, and performing direct speech translation. The toolkit includes specialized capabilities for keyword spotting to detect trigger words and speaker verification systems that extract unique voiceprints to identify and distinguish between individuals. It also features end-to-end translation tools that map audi
Parler-TTS is a library for generating high-quality speech from text, supporting both inference and model training. It combines a transformer-based text-to-speech generator with a mel-spectrogram decoder to convert written text into natural-sounding audio.
The main features of huggingface/parler-tts are: Text-to-Speech, Neural Decoders, Natural Language Conditioners, Fine-tuning Recipes, Attribute Descriptions, Speaker Embeddings, Libraries, Transformer Decoders.
Projects with overlapping indexed features include: coqui-ai/tts — This project is a deep learning text-to-speech toolkit used for training and deploying neural speech synthesis models.… yl4579/styletts2 — StyleTTS2 is an adversarial text-to-speech model that uses style diffusion and large speech language models to… 2noise/chattts — ChatTTS is a conversational text-to-speech generative model designed to convert written dialogue into natural sounding… paddlepaddle/paddlespeech — PaddleSpeech is a comprehensive toolkit of neural models for speech recognition, synthesis, and translation built on… rsxdalv/tts-webui — TTS-WebUI is a web interface and speech synthesis manager designed to convert written text into spoken audio files. It… rhasspy/piper — Piper is a local neural text-to-speech engine designed to convert written text into natural human speech entirely on…