In the area of AI, Meta has made remarkable progress. To compete with OpenAI, Google, and Microsoft, the Mark Zuckerberg-owned social media behemoth launched its own “open-source Large Language Model” dubbed LlaMa 2. Now, to step things up a notch, Meta has unveiled its very own text-to-voice-based generative AI model dubbed AudioCraft. To learn more about AudioCraft, keep reading.

By employing straightforward text-based cues, Meta’s AudioCraft generative AI model may assist you in producing high-quality music and audio. The main USP of AudioCraft is that it uses RAW audio signals for training in order to provide a genuine and realistic experience. This is comparable to MusicLM, Google’s audio AI technology.

MusicGen, AudioGen, and EnCodec are three different AI models that form the foundation of AudioCraft. The goal of MusicGen is to create “music from text-based inputs” utilizing music samples that are owned and authorized by Meta. On the other hand, AudioGen uses freely accessible sound effects to create “audio from text-based inputs.” The EnCodec decoder creates audio outputs that are accurate and, in Meta’s words, “with fewer artifacts.”

This means that you can quickly create various scenes with parts that are each independently focused and that sync up in the finished product. For instance, if you use the command “Jazz music from the 1980s with a dog barking in the background,” AudioCraft will use its MusicGen to play your jazz portion while AudioGen will smoothly insert and merge the dog’s background barking. And the EnCodec’s sophisticated decoding abilities will be used to convey all of this to you.

Although you may believe that AudioCraft’s generative AI skills are its strongest suit, this is untrue. Additionally open-source, AudioCraft. In order to better comprehend this technology and develop their own datasets to aid in its improvement, researchers can see the AudioCraft model’s source code. GitHub provides access to AudioCraft’s source code.

You can quickly produce compression and generation using AudioCraft, as well as music and sound. Because users can improve upon the existing code base to produce better sound generators and compression techniques, AudioCraft is flexible. You do not have to start from scratch, to put it briefly. Your foundation will be based on the dataset’s current ceiling.