AudioGPT is a cutting-edge technology that is changing the way we create, edit, and consume audio content. Based on the GPT (Generative Pre-trained Transformer) architecture, AudioGPT is an AI-powered system that can generate and manipulate audio content, ranging from music to speech and sound effects. With the ability to learn from large amounts of data, AudioGPT can produce high-quality audio outputs that are indistinguishable from those created by humans. In this article, we will explore the capabilities and potential applications of AudioGPT, and discuss the impact it could have on the future of audio production and consumption.

What is AudioGPT?

AudioGPT is a generative model that can generate realistic audio samples from text or other audio inputs. It is built on the Transformer architecture and use self-attention to understand long-term relationships in audio data. AudioGPT may be used for a variety of activities including voice synthesis, speech recognition, audio style transfer, and audio super-resolution.

AudioGPT is a Dialogue Assistant

 AudioGPT may be utilized in a chatbot interface similar to ChatGPT. In reality, it functions similarly to ChatGPT in most conversational applications. One distinguishing characteristic of Audio GPT is that, in addition to text, the chatbot can handle speech as input by first transcribing the audio to text. As a result, this is a true conversation helper with whom you may converse or write, depending on your needs.

AudioGPT can perform various audio tasks

AudioGPT’s dialogue capabilities are only a support function. Its primary objective is to give a unified experience for tackling a wide range of audio analysis and creation jobs. Here are a few examples of the tasks it can handle.

Audio-to-Audio

• Style Transfer: Generate human speech with styles derived from a reference
• Speech Enhancement: Improve the speech quality by reducing background noise.
• Speech Separation: Separate mix-speech of different speakers
• Mono-to-Binaural: Generate binaural audio given mono.

Audio-to-Event

• Sound Extraction: Selectively extract a part of audio based on description.
• Sound Detection: Predict the event timelines in audio.

Audio-to-Video

• Talking Head Synthesis: Generate a talking human portrait video given input audio.

Text-to-Audio

• Text-to-Speech: Generate human speech given user-input text
• Text-to-Audio: Generate general audio given a user description.

Image-to-Audio

• Image-to-Audio: Generate audio from images.

Score-to-Audio

• Singing Synthesis: Generate singing voice given input text, note, and duration sequence.

The nice thing about AudioGPT is that, unlike ChatGPT, it can receive and transfer audio files. When I asked AudioGPT to make a certain sound for me, it did so, exported it as a wav file, and shared with me the location of the output file.

Also Read Bark-Text to speech AI.

How was AudioGPT implemented?

While AudioGPT might seem like a typical AI chatbot to the user, there is actually a lot more going on under the hood. In fact, the chatbot AI (ChatGPT) is only used as a translator between the user request and other AI models. Such approaches already exist for other domains like image (TaskMatrix) or text (LangChain). Let us look at the illustration of AudioGPTs workflow provided by the authors in their paper.

Modality transformation

AudioGPT is designed to accept both speech and text input. As a result, the initial step is to determine if the user is texting or speaking to the system. If the input is spoken, a speech recognition system similar to Alexa or Siri transcribes and converts it to text. This conversion procedure should feel smooth to the user.

Task analysis

ChatGPT takes over with this text input and attempts to interpret the user’s request. Whether you say, “Generate a wav file of a thunder sound effect” or “Give me a thunder sound”: ChatGPT is expert at understanding alternative formulations of the same problem and mapping the request to a specific job. in this example, text-to-audio sound production.

Model assignment

Once ChatGPT understands the request, it chooses an appropriate AI model from the system’s current set of 17 models. Each of these 17 is responsible for one unique duty in a very precise manner. As a result, it is critical that ChatGPT understands the request, locates the appropriate model, and delivers the user request in such a way that the model can handle it.

Response generation

When an acceptable model is found and executed, it produces an output. This output can be in a variety of formats (audio, text, image, video). That’s where ChatGPT comes in once more. It gathers model output and delivers it to the user in an understandable and interpretable format. A text output, for example, may be sent directly to the user, but an audio output will be exported and the user will be given a file path to the produced audio.

Memory & Chat History

It’s fantastic to do only one assignment. What actually distinguishes this chatbot technique is AudioGPT’s ability to examine the complete conversation history. This means you can always refer back to past requests, inquiries, or outputs and ask AudioGPT to do anything with them. It’s similar to ChatGPT in some ways, but with the ability to receive and distribute audio files.

What is AudioGPT capable of?

In this section, we’d want to show you some instances of what AudioGPT can achieve from the article. This is not a full list, but rather some interesting highlights.

Image to audio generation

In this example, AudioGPT is requested to produce sounds that corresponds to a cat picture. The system then returns the location of an exported audio file as well as a visual representation of the audio waveform. We can’t hear the answer in this paper example, but it’s most likely a cat sound like a hiss or a purr. Under the hood, the image is captioned first, and then the image caption is synthesised to an audio signal. This might be really useful for artists who want to make samples for their music by just uploading an image of what they want.

Singing voice generation

This one is for musicians! When we feed the model a sentence together with information on notes and note durations, it synthesises a singing voice and transmits the audio back to you. State-of-the-art voice synthesis models (DiffSinger [2], VISinger [3]) are used under the hood. It’s simple to conceive how this technique may be used directly in a DAW, for example, to make singing samples for hip-hop beats or even backing voices.

Sound extraction

AudioGPT determines when a specified event happens in an audio signal based on a written prompt and cuts off the irrelevant part of the audio for the user. Cutting samples or sounds with only vocal cues might be extremely beneficial for musicians. We may soon be directing our DAW to “retrieve the most emotional part of this sample and cut it down to one bar” without having to perform any of the technical work ourselves.

Source separation

In this case, AudioGPT is asked to extract two speakers from an audio signal and return them individually. This system currently does not contain a music source separation tool. However, we can easily imagine extracting specific instruments or instrument groups from an audio signal right inside our DAW via a chatbot interface in the near future.

limitations of AudioGPT

It was not designed for music.

It is important to point out in the context of this post that AudioGPT is not currently a fantastic tool for music analysis or production. The singing voice synthesis model is the only true devoted music model. Other models can produce musical sounds, but they are designed primarily for speech and sounds, not music.

However, this is not a system limitation in and of itself. This is partly due to the creators’ decision not to incorporate more specialized music AI models in this application. With AudioGPT as a foundation, it is possible to incorporate more and more audio models into this system or to build a separate, music-specific system.

It is still a work in progress.

I can tell from my short experience with AudioGPT that the job assignment procedure does not operate as well as I would want. My request is frequently misinterpreted, and the incorrect model is invoked, resulting in utterly worthless results. It appears that further optimization is still required to make this system more capable of comprehending the user’s wants.

Furthermore, the state of audio AI as a whole lag far behind that of text AI, for example. The majority of the 17 models contained in Audio GPT perform rather well but have apparent limits. As a result, even if Audio GPT’s job assignment performed flawlessly, the systems would be constrained by the capabilities of the underlying models.

How can I use AudioGPT?

As a programmer

Simply clone the AudioGPT GitHub repository, install all of the models used, enter your OpenAI API key, and get started as a programmer. This will enable you to use ALL of the features described in the article.

As a non-technologist

If you are not a coder, you may still utilize AudioGPT in this HuggingFace web app, albeit to a limited extent. You will need an OpenAI API key to utilize the system. Here’s a guide on how to obtain it. To use the token, you may need to provide your credit card information, depending on OpenAI’s current terms of service. Because Audio GPT uses ChatGPT in the background, this key is required. ChatGPT is not costly to use (0.002$ cents for 700 words as of April 23, see docs). Still, if you decide to use this key for AudioGPT, I recommend keeping an eye on the system costs in your OpenAI account.

Unfortunately, this HuggingFace web app has not been working all that well, for me. When I upload files, there is usually an error. The audio outputs are sometimes completely wrong, although my request seems to have been understood… If you already have an OpenAI API key, you should definitely try it out. If not, I am not sure if this web app is worth going through the effort of creating the account and key.

AudioGPT FAQs

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