The text discusses the development of a universal audio generation model called UniAudio. It aims to handle various audio-generating tasks, such as speech synthesis and music production, using a single unified model. The model utilizes Large Language Models (LLMs) and tokenization techniques to generate audio based on different input modalities. UniAudio has been shown to achieve competitive performance across multiple audio tasks and has the potential to become a foundation model for universal audio generation.
A New Universal Audio Generation System: UniAudio
Introduction
Generative AI, specifically audio generation, has become increasingly popular in recent years. The need for audio production that includes speech synthesis, voice conversion, singing voice synthesis, and more has grown. However, existing solutions are often limited to specific tasks and configurations. This study aims to create a universal audio generation model, UniAudio, which can handle various audio-generating jobs with a single unified model.
The UniAudio Approach
UniAudio utilizes Large Language Models (LLMs) to generate a variety of audio genres, including speech, noises, music, and singing. It tokenizes all audio formats and input modalities as discrete sequences using a universal neural codec model. The source-target pairs are combined into single sequences, and LLM conducts next-token prediction. To handle the complexity of tokenization, a multi-scale Transformer architecture is used, with a global Transformer module representing inter-frame correlation and a local Transformer module modeling intra-frame correlation.
Scalability and Performance
UniAudio is trained on multiple audio-generating tasks simultaneously to provide the model with previous knowledge and relationships between audio and other input modalities. It supports 11 audio-generating tasks and consistently achieves competitive performance compared to task-specific models. UniAudio can also adapt quickly to new audio-generating workloads.
Key Contributions
The key contributions of UniAudio are as follows:
1. UniAudio is a single solution for 11 audio-generating jobs, surpassing previous efforts.
2. It introduces fresh ideas for representing audio and other input modalities and offers an effective model architecture for audio generation.
3. Extensive testing confirms UniAudio’s performance and highlights the advantages of a flexible audio-generating paradigm.
4. UniAudio’s demo and source code are publicly available, providing a foundation model for future audio production studies.
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