AudioGPT: An Introduction to Its Capabilities
AudioGPT emerges as an innovative project designed to enhance the understanding and generation of diverse audio forms such as speech, music, sound, and facial animations or "talking head" videos. This repository hosts the open-source implementation along with pre-trained models, providing resources for developers and researchers interested in audio modelling technology.
Getting Started
To begin exploring AudioGPT's capabilities, users can follow the instructions provided in the run.md document available in the repository.
Core Capabilities
AudioGPT harnesses a variety of foundation models to power its expansive suite of tasks. Here's a glimpse into its current capabilities:
Speech
AudioGPT supports an array of speech-related tasks using foundation models:
- Text-to-Speech: Utilizing models like FastSpeech, SyntaSpeech, and VITS, AudioGPT can transform written text into spoken words. This feature is under refinement (Work in Progress, WIP).
- Style Transfer: Through the GenerSpeech model, users can alter the style of speech, maintaining content while changing expressive elements.
- Speech Recognition: Models such as Whisper and Conformer enable the conversion of spoken language into text.
- Speech Enhancement: Audio quality improvement is facilitated by models like ConvTasNet (WIP).
- Speech Separation: TF-GridNet assists in isolating individual speech sources from a mixture of sounds (WIP).
- Speech Translation: The Multi-decoder approach is in development to offer speech translation services (WIP).
- Mono-to-Binaural: NeuralWarp aids in creating spatial audio experiences by converting mono audio signals into binaural sound.
Sing
- Text-to-Sing: AudioGPT also tackles singing tasks using DiffSinger and VISinger models, transforming text into melodious singing. This functionality is currently in progress (WIP).
Audio
Beyond speech, AudioGPT extends its capabilities to general audio processing:
- Text-to-Audio: Allows the generation of audio content from text.
- Audio Inpainting and Image-to-Audio: Make-An-Audio models support these tasks, filling in missing audio segments or creating audio based on images.
- Sound Detection and Target Sound Detection: Discover and identify specific sounds within an audio segment using Audio-transformer and TSDNet models.
- Sound Extraction: LASSNet aids in pulling out particular audio components from a mix.
Talking Head
- Talking Head Synthesis: With the help of GeneFace, AudioGPT can generate facial animation videos from audio tracks. This feature is still being refined (WIP).
Acknowledgements
The development of AudioGPT stands on the shoulders of several open-source projects including ESPNet, NATSpeech, Visual ChatGPT, Hugging Face, LangChain, and Stable Diffusion. Their contributions have been foundational to the progress of AudioGPT.
In summary, AudioGPT represents a significant step forward in audio processing technologies, providing researchers and developers with a robust platform for exploring and implementing audio modelling solutions. With continuous updates and enhancements, it promises to broaden the horizons of what is possible in the realm of audio and speech technology.