3 repositorios
Generative models using flow matching to transform noise into continuous audio latents.
Distinct from Flow-Matching Frameworks: Distinct from Flow-Matching Frameworks: focuses on audio signal latents rather than image diffusion.
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Audiocraft is a deep learning audio library and machine learning framework designed for training, fine-tuning, and evaluating generative models for music and sound effects. It functions as a text-to-music generative model and a neural audio codec, providing the tools necessary to compress audio signals into discrete representations and synthesize high-fidelity waveforms from textual descriptions. The framework is distinguished by its ability to combine multiple conditioning signals, allowing for the generation of audio based on text prompts, melodic excerpts, or style-based audio clips. It al
Implements a flow matching objective to train models on continuous latents extracted from audio compressors.
F5-TTS is a text-to-speech system that utilizes a flow matching engine and diffusion transformers to generate fluent synthetic speech. It functions as a multilingual speech synthesizer and neural training framework, providing tools for voice cloning and high-performance inference serving. The project distinguishes itself through a voice cloning toolkit capable of mimicking specific speaker characteristics and tones from reference audio clips. It supports cross-lingual generation, allowing for the synthesis of audio across various global languages or the mixing of multiple languages within a s
Uses a flow matching engine and diffusion transformers to generate fluent synthetic speech.
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.
Provides a comprehensive neural engine for training speech models and generating synthetic audio.