Recently, Meta has introduced a new tool called NotebookLlama, which can be considered as an open-source version of the popular podcast generation feature in Google's NotebookLM.
NotebookLlama relies on Meta's own Llama model to process text, enabling users to convert uploaded files into interactive podcast-style summaries, which sounds quite impressive.
Specifically, NotebookLlama first converts uploaded files, such as PDF news articles or blog posts, into text. It then adds dramatic elements and dialogue inserts to the text before reading it aloud through an open text-to-speech model. Although the process sounds fun, some examples I've heard show that the generated voices still have a distinct mechanical feel and occasionally overlap, sounding somewhat unnatural.
However, the research team behind NotebookLlama believes that voice quality will improve with the development of more powerful models. They mentioned on the project's GitHub page: "The text-to-speech model is a limiting factor for naturalness of voice." Additionally, the team has proposed a new concept of creating podcast outlines by having two characters debate a topic, as opposed to using a single model for the task.
It's worth noting that NotebookLlama is not the first project attempting to replicate the podcast feature of NotebookLM; there have been similar attempts with varying results. Nevertheless, no project, including NotebookLM itself, has fully addressed the "hallucination" problem in AI-generated content, meaning that these podcasts may still contain some false information.
The introduction of NotebookLlama opens up new possibilities for open-source podcast generation, despite current technical challenges, with significant potential for future development.
Project link: https://github.com/meta-llama/llama-recipes/tree/main/recipes/quickstart/NotebookLlama
Key Points:
🎧 NotebookLlama is Meta's open-source podcast generation tool, utilizing the Llama model to process user-uploaded files.
🤖 The tool converts text into podcast-style summaries, but currently suffers from low voice quality, including mechanical tones and overlapping voices.
📉 AI-generated podcasts may still contain false information, a common challenge across all AI projects.
