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Generative Pre-trained Transformer (GPT) models have achieved remarkableperformance on various natural language processing tasks. However, there hasbeen limited research on applying similar frameworks to audio tasks. Previouslyproposed large language models for audio tasks either lack sufficientquantitative evaluations, or are limited to tasks for recognizing andunderstanding audio content, or significantly underperform existingstate-of-the-art (SOTA) models. In this paper, we propose LauraGPT, a unifiedGPT model for audio recognition, understanding, and generation. LauraGPT is aversatile language model that can process both audio and text inputs andgenerate outputs in either modalities. It can perform a wide range of tasksrelated to content, semantics, paralinguistics, and audio-signal analysis. Someof its noteworthy tasks include automatic speech recognition, speech-to-texttranslation, text-to-speech synthesis, machine translation, speech enhancement,automated audio captioning, speech emotion recognition, and spoken languageunderstanding. To achieve this goal, we use a combination of continuous anddiscrete features for audio. We encode input audio into continuousrepresentations using an audio encoder and decode output audio from discretecodec codes. We then fine-tune a large decoder-only Transformer-based languagemodel on multiple audio-to-text, text-to-audio, audio-to-audio, andtext-to-text tasks using a supervised multitask learning approach. Extensiveexperiments show that LauraGPT achieves competitive or superior performancecompared to existing SOTA models on various audio processing benchmarks.

LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT