Introducing AudioGPT: The Multi-Modal AI System That’s Redefining Audio Modality
Large language models are emerging as the true superpowers. They can read flawlessly, they can write immaculately, and they can talk like humans. Their transformative powers are surreal. While they have been quite successful in text processing and generation, LLMs have not exactly explored audio modality, music, and sound. And so, the next logical step for LLMs is to understand and produce talking heads, voices, and music.
Audio modality has its own benefits, yet, training LLMs that support audio processing can be challenging.
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Why is Training LLMs Difficult?
The reasons can vary from limitations in sourcing human-labeled speech data (which can also be expensive), to the requirement of multilingual conversational speech data (because data is limited), besides the fact that training them from scratch is time-consuming. Researchers from Zhejiang University, Peking University, Carnegie Mellon University, and Remin University have found an ideal solution.
Meet AudioGPT – a system that has remarkable capabilities in producing audio modality in spoken dialogues. One distinctive aspect of AudioGPT is the bots’ ability to process speech input in addition to text by first converting the audio to text.
- Expensive and time-consuming.
- Limited resources.
AudioChatGPT – Basic Processes and Features
AudioGPT has four basic functions – task analysis, response generation, modality transformation, and model assignment.
- As opposed to being trained from the start, multi-modal LLMs use a variety of audio foundation models to comprehend sophisticated audio input.
- For speech conversations, they connect LLMs with input/output interfaces instead of training a spoken language model.
- They employ LLMs as the all-purpose interface that enables AudioGPT to handle a variety of audio-generating and interpretation tasks.
Besides, it would be entirely pointless to train from the beginning as audio foundation models are skilled to recognize and generate talking heads, sound, audio, and speech. AudioGPT uses the above-mentioned basic functions.
- Modality transformation: With the help of input/output interfaces, spoken language, and ChatGPT and by converting speech-to-text, LLMs can successfully communicate.
- Task analysis – Prompt manager and conversation engine help ChatGPT decipher a user’s intent when processing audio data.
- ChatGPT delivers the audio foundation models for understanding and generation following the receipt of the ordered arguments for prosody, timbre, and language management.
- Producing and giving users a final response after the audio foundation model has been executed.
Assessing how well multi-modal LLMs understand the human intention and coordinate the cooperation of diverse foundation models is a common issue researchers encounter.
A few experiments concluded that AudioGPT is capable of processing multi-round chat with complicated audio data for many AI applications which includes the production and comprehension of talking heads, music, and sound.
The paper explains the design principles and testing process used to determine the uniformity, reliability, capability, and sturdiness of AudioGPT in this work. With multiple phases of discussion, AudioGPT efficiently comprehends and creates audio, allowing users to easily create vivid audio content.
The code is available on GitHub.
In recent times, LLMs have demonstrated amazing abilities in a wide range of areas and challenges that test our theories of cognition and learning. But it’s a known fact that language models are continuously (and consistently) evolving, and along with this, we can expect a lot of unexpected developments. Since AudioGPPT is still a work in progress; it is likely that the lives and work of musicians will change in the near future.
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