Inspired by Google’s NotebookLM, Meta just unveiled NotebookLlama, an open-source program for converting text files into dynamic audio that sounds like podcasts. Similar to NotebookLM, NotebookLlama uses artificial intelligence (AI) to transform uploaded files like news articles or blog post PDFs into conversational scripts. A framework for producing consumable audio summaries from written content is provided by the project, which mostly uses Meta’s Llama models for processing.
A transcript is first created by NotebookLlama from an uploaded file; then it is enhanced with further dramatization and artificial pauses to give it a conversational atmosphere. Audio is then produced by running this altered transcript using open-source text-to-speech (TTS) models. But the produced audio is currently unpolished, with a distinctly artificial tone and sporadic voice overlap, which reduces its similarity to actual human interaction.
The main limitation, according to Meta’s developers, is that the TTS models are unable to produce realistic sound. On the project’s GitHub website, they mention that more realistic conversational elements could be added in the future by using sophisticated TTS models or a method where “two agents debate the topic.” The podcast outline is currently structured according to a single model, which restricts its capacity to represent a variety of viewpoints.
Although NotebookLlama is not the first project to mimic NotebookLM’s podcast feature, it does draw attention to the difficulties that all AI-generated audio projects encounter, especially the possibility of “hallucinations,” or errors, in the final product. One major obstacle is that AI models, like NotebookLlama, frequently produce information that might not be factually accurate.
Even if NotebookLM’s sound quality is superior to NotebookLlama’s, Meta’s continuous improvement suggests that more realistic and subtle AI-generated podcasts may be in the works, highlighting the quick evolution of AI-driven content creation tools.