The AI community is now significantly impacted by large language models, and the introduction of ChatGPT and GPT-4 has advanced natural language processing. Thanks to vast web-text data and robust architecture, LLMs can read, write, and converse like humans. Despite the successful applications in text processing and generation, the success of audio modality, music, sound, and talking head) is limited, even though it is highly advantageous because: 1) In real-world scenarios, humans communicate using spoken language throughout daily conversations, and they use spoken assistant to make life more convenient; 2) Processing audio modality information is required to achieve artificial generation success.
The crucial step for LLMs towards more sophisticated AI systems is understanding and producing voice, music, sound, and talking heads. Despite the advantages of audio modality, it is still difficult to train LLMs that support audio processing because of the following problems: 1) Data: Very few sources offer real-world spoken conversations, and obtaining human-labeled speech data is an expensive and time-consuming operation. Additionally, there is a need for multilingual conversational speech data compared to the vast corpora of web-text data, and the amount of data is limited. 2) Computational resources: Training multi-modal LLMs from scratch is computationally demanding and time-consuming.
Researchers from Zhejiang University, Peking University, Carnegie Mellon University, and the Remin University of China present “AudioGPT” in this work, a system made to be excellent in comprehending and producing audio modality in spoken dialogues. In particular:
They use a variety of audio foundation models to process complex audio information instead of training multi-modal LLMs from scratch.
They connect LLMs with input/output interfaces for speech conversations rather than training a spoken language model.
They use LLMs as the general-purpose interface that enables AudioGPT to solve numerous audio understanding and generation tasks.
It would be useless to begin training from scratch since audio foundation models can already comprehend and produce speech, music, sound, and talking heads.
Using input/output interfaces, ChatGPT, and spoken language, LLMs can communicate more effectively by converting speech to text. ChatGPT uses the conversation engine and prompt manager to determine a user’s intent when processing audio data. The AudioGPT process may be separated into four parts, as shown in Figure 1:
• Transformation of modality: Using input/output interfaces, ChatGPT, and spoken language LLMs can communicate more effectively by converting speech to text.
• Analysis of tasks: ChatGPT uses the conversation engine and prompt manager to determine a user’s intent when processing audio data.
• Assignment of a model: ChatGPT allocates the audio foundation models for comprehension and generation after receiving the structured arguments for prosody, timbre, and language control.
• Response Design: Generating and providing consumers with a final answer following audio foundation model execution.
Figure 1: A general overview of AudioGPT. Modality transformation, task analysis, model assignment, and response generation are the four processes that make-up AudioGPT. In order to handle difficult audio jobs, it provides ChatGPT with audio foundation models. Additionally, it connects to a modalities transformation interface to enable spoken communication. We develop design guidelines to assess the consistency, capacity, and robustness of multi-modal LLMs.
Evaluating the effectiveness of multi-modal LLMs in comprehending human intention and orchestrating the collaboration of various foundation models is becoming an increasingly popular research issue. Results from experiments show that AudioGPT can process complex audio data in multi-round dialogue for different AI applications, including creating and comprehending speech, music, sound, and talking heads. They describe the design concepts and evaluation procedure for AudioGPT’s consistency, capacity, and robustness in this study.
They suggest AudioGPT, which provides ChatGPT with audio foundation models for sophisticated audio jobs.
This is one of the paper’s major contributions. A modalities transformation interface is coupled to ChatGPT as a general-purpose interface to enable spoken communication. They describe the design concepts and evaluation procedure for multi-modal LLMs and assess the consistency, capacity, and robustness of AudioGPT. AudioGPT effectively understands and produces audio with numerous rounds of discussion, enabling people to produce rich and varied audio material with previously unheard-of simplicity. The code has been open-sourced on GitHub.
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The AI community is now significantly impacted by large language models, and the introduction of ChatGPT and GPT-4 has advanced natural language processing. Thanks to vast web-text data and robust architecture, LLMs can read, write, and converse like humans. Despite the successful applications in text processing and generation, the success of audio modality, music, sound,
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