A local AI-powered tool that converts PDF documents into engaging audio—such as podcasts or custom audio content—using local LLMs and TTS models.

• PDF text extraction and processing
• Customizable audio generation (podcasts, summaries, interviews, and more) with different styles and lengths
• Support for various LLM providers (OpenAI, Groq, LMStudio, Ollama, Azure)
• Text-to-Speech conversion with voice selection
• Flexible pipeline with many options for content, style, and voices
• Programmatic API for integration in other projects
• FastAPI server for web-based access
• Example podcast included for demonstration

If your browser still blocks embedded playback on GitHub, use direct links:

• Casual example (.wav)
• Gen-Z example (.wav)

• Python 3.9+
• Local LLM server (optional, for local inference)
• Local TTS server (optional, for local audio generation)
• At least 8GB RAM (32GB+ recommended for local models)
• 10GB+ free disk space

pip install local-notebooklm

1. Clone the repository:

git clone https://github.com/Goekdeniz-Guelmez/Local-NotebookLM.git
cd Local-NotebookLM

2. Create and activate a virtual environment (conda works too):

python -m venv venv
source venv/bin/activate  # On Windows, use: venv\Scripts\activate

3. Install the required packages:

pip install -r requirements.txt

You can run Local-NotebookLM using Docker for both the Web UI and API modes.

• Docker installed on your system

1. Build the Docker image:

   docker build -t local-notebooklm-ui .

2. Run the Gradio Web UI:

   docker run -p 7860:7860 local-notebooklm-ui

   The Web UI will be available at http://localhost:7860.

3. Run the FastAPI API server:

   docker run -e APP_MODE=api -p 8000:8000 local-notebooklm-ui

   The API server will be available at http://localhost:8000.

• Follow one installation type (docker, docker-compose, uv) at https://github.com/remsky/Kokoro-FastAPI
• Test in your browser that http://localhost:8880/v1 return the json: {"detail":"Not Found"}

The repository includes an example podcast in examples/podcast.wav to demonstrate the quality and format of the output. The models used are: gpt4o and Mini with tts-hs on Azure. You can listen to this example to get a sense of what Local-NotebookLM can produce before running it on your own PDFs.

Run the script with the following command:

python -m local_notebooklm.make_audio --pdf PATH_TO_PDF [options]

Local-NotebookLM supports both single-speaker and multi-speaker formats:

Single-Speaker Formats:

• summary
• narration
• storytelling
• explainer
• lecture
• tutorial
• news-report
• executive-brief
• analysis

Two-Speaker Formats:

• podcast
• interview
• panel-discussion
• debate
• q-and-a
• meeting

Multi-Speaker Formats:

• panel-discussion (3, 4, or 5 speakers)
• debate (3, 4, or 5 speakers)

Basic usage:

python -m local_notebooklm.make_audio --pdf documents/research_paper.pdf

Customized podcast:

python -m local_notebooklm.make_audio --pdf documents/research_paper.pdf --format_type podcast --length long --style casual

With custom preferences:

python -m local_notebooklm.make_audio --pdf documents/research_paper.pdf --custom_preferences "Focus on practical applications and real-world examples"

Specify number of speakers:

python -m local_notebooklm.make_audio --pdf documents/research_paper.pdf --format_type panel-discussion --num_speakers 3

Enable multimodal transcript generation (text + PDF images):

python -m local_notebooklm.make_audio --pdf documents/research_paper.pdf --is-vlm

You can also use Local-NotebookLM programmatically in your Python code:

from local_notebooklm.processor import generate_audio

generate_audio(
    pdf_path="documents/research_paper.pdf",
    output_dir="./test_output",
    llm_model="qwen3:30b-a3b-instruct-2507-q4_K_M",
    language="english",
    format_type="interview",
    style="professional",
    length="long",
    num_speakers=2,
    custom_preferences="Focus on the key technical aspects"
)

Local-NotebookLM now includes a user-friendly Gradio web interface that makes it easy to use the tool without command line knowledge:

python -m local_notebooklm.web_ui

By default, the web UI runs locally on http://localhost:7860. You can access it from your browser.

The main interface of the Local-NotebookLM web UI

Basic local usage:

python -m local_notebooklm.web_ui

Share with others on your network:

python -m local_notebooklm.web_ui --share

Use a custom port:

python -m local_notebooklm.web_ui --port 8080

The web interface provides all the same options as the command line tool in an intuitive UI, making it easier for non-technical users to generate audio content from PDFs.

Start the FastAPI server to access the functionality via a web API:

 python -m local_notebooklm.server

By default, the server runs on http://localhost:8000. You can access the API documentation at http://localhost:8000/docs.

1. PDF Extraction
   • Extracts and cleans text from the provided PDF.
2. Transcript Generation
   • Generates a transcript or script based on the extracted content and user options.
3. Audio Generation
   • Converts the optimized transcript to audio using the specified TTS model and outputs the final audio file.

flowchart TD
    subgraph "Main Controller"
        generate_audio["generate_audio()"]
    end

    subgraph "Pipeline Steps"
        extractPDF["PDF Extraction"]
        transcript["Transcript Generation"]
        ttsOpt["TTS Optimization"]
        audioGen["Audio Generation"]
    end

    generate_audio --> extractPDF
    extractPDF --> transcript
    transcript --> ttsOpt
    ttsOpt --> audioGen
    audioGen --> outputFile["Audio File"]

Local-NotebookLM now supports multiple languages. You can specify the language when using the programmatic API or through the command line.

Important Note: When using a non-English language, ensure that both your selected LLM and TTS models support the desired language. Language support varies significantly between different models and providers. For optimal results, verify that your chosen models have strong capabilities in your target language before processing.

The pipeline generates the following files:

• segments/podcast_segment_*.wav: Individual audio segments
• podcast.wav: Final concatenated podcast audio file

1. PDF Extraction Fails

   • Try a different PDF file
   • Check if the PDF is password-protected
   • Ensure the PDF contains extractable text (not just images)

2. API Connection Errors

   • Verify your API keys are correct
   • Check your internet connection
   • Ensure the API endpoints are accessible

3. Out of Memory Errors

   • Reduce the chunk size in the configuration
   • Use a smaller model
   • Close other memory-intensive applications

4. Audio Quality Issues

   • Try different TTS voices
   • Adjust the sample rate in the configuration
   • Check if the TTS server is running correctly

If you encounter issues not covered here, please:

1. Check the logs for detailed error messages
2. Open an issue on the GitHub repository with details about your problem
3. Include the error message and steps to reproduce the issue

• Python 3.9+
• PyPDF2
• tqdm
• numpy
• soundfile
• requests
• pathlib
• fastapi
• uvicorn

Full requirements are listed in requirements.txt.

• This project uses various open-source libraries and models
• Special thanks to the developers of LLaMA, OpenAI, and other AI models that make this possible

Best Gökdeniz Gülmez

The Local-NotebookLM software suite was developed by Gökdeniz Gülmez. If you find Local-NotebookLM useful in your research and wish to cite it, please use the following BibTex entry:

@software{
  Local-NotebookLM,
  author = {Gökdeniz Gülmez},
  title = {{Local-NotebookLM}: A Local-NotebookLM to convert PDFs into Audio.},
  url = {https://github.com/Goekdeniz-Guelmez/Local-NotebookLM},
  version = {0.1.5},
  year = {2025},
}