Awesome GitHub RepositoriesText-To-Speech Models

Zonos is a controllable audio synthesis engine and large language model for text-to-speech. It serves as a multilingual speech generator capable of producing audio in English, Japanese, Chinese, French, and German. The system provides zero-shot voice cloning, allowing the replication of specific human voices using short audio samples. It supports the capture of nuanced behaviors, such as whispering, and provides parametric control over speaking rate, pitch, frequency, and emotional tone. The project covers a broad range of expressive speech synthesis and custom audio generation capabilities,

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

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 project distinguishes itself through text-conditioned voice control, which allows speaker attributes like gender, pitch, speaking rate, and style to be adjusted via a natural-language description. It also includes speaker embedding selection for maintaining voice identity across multiple generations, and a fine-tuning recipe system that

Este proyecto es un framework de texto a voz neuronal y modelo de PyTorch diseñado para sintetizar voz humana. Convierte texto escrito en audio sintético prediciendo espectrogramas de mel, que sirven como una representación intermedia para la generación de voz. El sistema incluye un modelo de acondicionamiento para WaveNet para asegurar una salida de audio de sonido natural. Proporciona un framework de entrenamiento distribuido que utiliza procesamiento multi-GPU y precisión mixta automática para optimizar la velocidad de entrenamiento y reducir el uso de memoria. El proyecto cubre todo el pipeline de síntesis de voz neuronal, desde el entrenamiento del modelo utilizando conjuntos de datos de texto y audio hasta la generación de voces artificiales. Emplea un codificador-decodificador convolucional y atención de secuencia a secuencia para mapear características lingüísticas a marcos acústicos.

WhisperSpeech is a multilingual speech synthesizer and neural text-to-speech system. It functions by inverting the Whisper model architecture to convert text into high-fidelity synthetic audio. The system enables voice cloning by using reference audio files to mimic specific speakers. It supports multilingual speech production, which includes the ability to generate audio across different languages and handle language switching within a single sentence. The project covers a broad range of speech capabilities, including text-to-speech generation and speech dataset preparation. It incorporates