I've made an open version of Google's NotebookLM, and it shows the superiority of the open source tech task! 💪
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I've made an open version of Google's NotebookLM, and it shows the superiority of the open source tech task! 💪
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The app's workflow is simple. Given a source PDF or URL, it extracts the content from it, then tasks Meta's Llama 3.3-70B with writing the podcast script, with a good prompt crafted by 
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The app's workflow is simple. Given a source PDF or URL, it extracts the content from it, then tasks Meta's Llama 3.3-70B with writing the podcast script, with a good prompt crafted by 
@gabrielchua
mention
gabrielchua
 ("two hosts, with lively discussion, fun notes, insightful question etc.")
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 ("two hosts, with lively discussion, fun notes, insightful question etc.")
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Then it hands off the text-to-speech conversion to Kokoro-82M, and there you go, you have two hosts discussion any article.
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Then it hands off the text-to-speech conversion to Kokoro-82M, and there you go, you have two hosts discussion any article.
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The generation is nearly instant, because:
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The generation is nearly instant, because:
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> Llama 3.3 70B is running at 1,000 tokens/seconds with Cerebras inference
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> Llama 3.3 70B is running at 1,000 tokens/seconds with Cerebras inference
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> The audio is generated in streaming mode by the tiny (yet powerful) Kokoro, generating voices faster than real-time.
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> The audio is generated in streaming mode by the tiny (yet powerful) Kokoro, generating voices faster than real-time.
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And the audio generation runs for free on Zero GPUs, hosted by HF on H200s.
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And the audio generation runs for free on Zero GPUs, hosted by HF on H200s.
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Overall, open source solutions rival the quality of closed-source solutions at close to no cost!
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Overall, open source solutions rival the quality of closed-source solutions at close to no cost!
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Try it here 👉👉 
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Try it here 👉👉 
https://huggingface.co/spaces/m-ric/open-notebooklm
m-ric/open-notebooklm
space
resource
https://huggingface.co/spaces/m-ric/open-notebooklm
I've made an open version of Google's NotebookLM, and it shows the superiority of the open source tech task! 💪

The app's workflow is simple. Given a source PDF or URL, it extracts the content from it, then tasks Meta's Llama 3.3-70B with writing the podcast script, with a good prompt crafted by @gabrielchua ("two hosts, with lively discussion, fun notes, insightful question etc.")
Then it hands off the text-to-speech conversion to Kokoro-82M, and there you go, you have two hosts discussion any article.

The generation is nearly instant, because:
> Llama 3.3 70B is running at 1,000 tokens/seconds with Cerebras inference
> The audio is generated in streaming mode by the tiny (yet powerful) Kokoro, generating voices faster than real-time.

And the audio generation runs for free on Zero GPUs, hosted by HF on H200s.

Overall, open source solutions rival the quality of closed-source solutions at close to no cost!

Try it here 👉👉 https://huggingface.co/spaces/m-ric/open-notebooklm
🔥
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