Link to article: https://betterprogramming.pub/text-to-audio-generation-with-bark-clearly-explained-4ee300a3713a

• Amidst the transformative surge of generative AI, text-to-audio models are emerging as one of the most promising frontiers.
• These advances are not just about converting text to speech, but also about crafting audio experiences that are indistinguishable from human-produced content.
• From audiobooks narrated in any voice imaginable to dynamic music compositions prompted by mere sentences, the potential applications are vast and captivating.
• In this article, we delve into the capabilities and technical intricacies of Bark, an open-source text-prompted audio generation model in Python.

Bark is a transformer-based text-to-audio model capable of generating realistic multilingual speech, music, and sound effects. It is created by Suno, a research-driven company that develops cutting-edge audio AI. As Bark was developed for research purposes, its pre-trained model checkpoints have been made open-source and available for commercial use, which is a valuable contribution to the generative AI community.

• https://github.com/suno-ai/bark
• https://audiocraft.metademolab.com/encodec.html
• https://www.streamingmedia.com/Articles/ReadArticle.aspx?ArticleID=74487
• https://towardsdatascience.com/optimizing-vector-quantization-methods-by-machine-learning-algorithms-77c436d0749d
• https://www.assemblyai.com/blog/what-is-residual-vector-quantization/
• https://github.com/facebookresearch/encodec
• https://ai.meta.com/blog/ai-powered-audio-compression-technique/
• https://arxiv.org/abs/2210.13438
• https://github.com/facebookresearch/encodec#extracting-discrete-representations
• https://paperswithcode.com/paper/speaker-anonymization-using-neural-audio
• https://huggingface.co/suno/bark/tree/main/speaker_embeddings/v2