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Notebookllama
Overview :
NotebookLlama is an open-source project designed to guide users in building workflows from PDF to Podcast through a series of tutorials and notebooks. It covers the entire process, from text preprocessing to the utilization of text-to-speech models, making it accessible for users with no prior knowledge of large language models (LLMs), prompts, or audio models. The main advantages of NotebookLlama include user-friendliness, educational value, and experimental nature, as it not only provides a reference implementation but also encourages users to optimize results through experimenting with different models and prompts.
Target Users :
NotebookLlama's target audience includes developers, researchers, and educators interested in natural language processing, machine learning, and audio generation. It is particularly suited for users looking to explore and experiment with the applications of large language models in audio content creation.
Use Cases
Use NotebookLlama to convert academic paper PDFs into podcast formats for easier public understanding of research findings.
Transform technical document PDFs into podcasts to help developers learn new technologies during commutes or leisure time.
Leverage NotebookLlama to create audio content based on historical literature, providing audiences with an immersive historical experience.
Features
Preprocess PDFs using the Llama-3.2-1B-Instruct model and save them as .txt files.
Convert text into podcast scripts using the Llama-3.1-70B-Instruct model.
Enhance the dramatic quality of scripts with the Llama-3.1-8B-Instruct model.
Generate conversational podcasts using parler-tts/parler-tts-mini-v1 and bark/suno models.
Support the extension of methods using different TTS models.
Encourage users to experiment with different models and prompts to optimize results.
How to Use
1. Ensure you have access to a GPU server or an API provider to utilize the Llama models: 70B, 8B, and 1B.
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2. Log in using the huggingface CLI and start a Jupyter notebook server to facilitate the downloading of the Llama model.
3. Clone the NotebookLlama GitHub repository and navigate to the corresponding directory.
4. Install the dependencies listed in requirements.txt.
5. Follow the guidelines to run the four notebooks, each with specific tasks and prompts.
6. During the execution process, try modifying the model prompts to improve the results.
7. Upon completing all steps, you will generate a podcast file derived from the text.
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