Amphion is an audio generation toolkit designed for the research and development of models that synthesize speech, music, and environmental sound effects. It provides a standardized framework for reproducible audio synthesis, incorporating a text-to-speech engine and a voice conversion framework. The project specializes in transforming audio identities, allowing for the modification of speaker accents and voice identities while preserving original rhythm and style. It also includes capabilities for singing voice synthesis and the generation of environmental soundscapes from text descriptions
TTS-WebUI is a web interface and speech synthesis manager designed to convert written text into spoken audio files. It serves as a self-hosted audio AI suite that allows users to configure speech synthesis models, manage speaker profiles, and generate audio through a graphical dashboard. The system functions as both a visual manager and a generative audio API, providing standardized endpoints and OpenAI-compatible request formats for external applications to trigger synthesis programmatically. It includes a plugin-based extension system that allows new tools and models to be added via externa
KittenTTS is a neural text-to-speech engine and text-to-audio synthesis tool that converts written text into spoken audio using lightweight neural network models. It functions as both a speech synthesizer and an audio file generator, producing spoken audio for offline playback. The system includes a text normalization processor that expands numbers and abbreviations into full spoken words to improve the naturalness of the synthesized speech. It supports diverse voice options and provides the ability to adjust playback speed.
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
Audio generation using diffusion models, in PyTorch.
The main features of archinetai/audio-diffusion-pytorch are: Audio Generation.
Open-source alternatives to archinetai/audio-diffusion-pytorch include: open-mmlab/amphion — Amphion is an audio generation toolkit designed for the research and development of models that synthesize speech,… kittenml/kittentts — KittenTTS is a neural text-to-speech engine and text-to-audio synthesis tool that converts written text into spoken… rsxdalv/tts-webui — TTS-WebUI is a web interface and speech synthesis manager designed to convert written text into spoken audio files. It… aigc-audio/audiogpt — AudioGPT is an LLM-driven audio framework and processing suite that uses large language models to orchestrate neural… magenta/magenta — Magenta is a comprehensive toolkit for training, synthesizing, and performing music through neural models and… mubertai/mubert-text-to-music — A simple notebook demonstrating prompt-based music generation via Mubert API.