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README.md

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+---
+title: Open NotebookLM
+emoji: 🎙️
+colorFrom: purple
+colorTo: red
+sdk: gradio
+sdk_version: 4.44.0
+app_file: app.py
+pinned: false
+---
+
+# Open NotebookLM
+
+## Overview
+
+This project is inspired by the NotebookLM tool, and implements it with open-source LLMs and text-to-speech models. This tool processes the content of a PDF, generates a natural dialogue suitable for an audio podcast, and outputs it as an MP3 file.
+
+## Features
+
+- **Convert PDF to Podcast:** Upload a PDF and convert its content into a podcast dialogue.
+- **Engaging Dialogue:** The generated dialogue is designed to be informative and entertaining.
+- **User-friendly Interface:** Simple interface using Gradio for easy interaction.
+
+## Installation
+
+To set up the project, follow these steps:
+
+1. **Clone the repository:**
+   ```bash
+   git clone https://github.com/gabrielchua/open-notebooklm.git
+   cd open-notebooklm
+   ```
+
+2. **Create a virtual environment and activate it:**
+   ```bash
+   python -m venv .venv
+   source .venv/bin/activate
+   ```
+
+3. **Install the required packages:**
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+## Usage
+
+1. **Set up API Key(s):**
+   For this project, I am using LLama 3.1 405B hosted on Fireworks API as its JSON Mode supports passing a pydantic object. So, please set the API key as the `FIREWORKS_API_KEY` environment variable
+
+2. **Run the application:**
+   ```bash
+   python app.py
+   ```
+   This will launch a Gradio interface in your web browser.
+
+3. **Upload a PDF:**
+   Upload the PDF document you want to convert into a podcast.
+
+4. **Generate Audio:**
+   Click the button to start the conversion process. The output will be an MP3 file containing the podcast dialogue.
+
+## Acknowledgements
+
+This project is forked from [`knowsuchagency/pdf-to-podcast`](https://github.com/knowsuchagency/pdf-to-podcast)
+
+## License
+
+This project is licensed under the Apache 2.0 License. See the [LICENSE](LICENSE) file for more information.

app.py

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+"""
+main.py
+"""
+
+# Standard library imports
+import glob
+import os
+import time
+from pathlib import Path
+from tempfile import NamedTemporaryFile
+from typing import List, Literal, Tuple, Optional
+
+# Third-party imports
+import gradio as gr
+from loguru import logger
+from pydantic import BaseModel, Field
+from pypdf import PdfReader
+from pydub import AudioSegment
+
+# Local imports
+from prompts import SYSTEM_PROMPT
+from utils import generate_script, generate_podcast_audio, parse_url
+
+
+class DialogueItem(BaseModel):
+    """A single dialogue item."""
+
+    speaker: Literal["Host (Jane)", "Guest"]
+    text: str
+
+
+class ShortDialogue(BaseModel):
+    """The dialogue between the host and guest."""
+
+    scratchpad: str
+    name_of_guest: str
+    dialogue: List[DialogueItem] = Field(..., description="A list of dialogue items, typically between 5 to 9 items")
+
+
+class MediumDialogue(BaseModel):
+    """The dialogue between the host and guest."""
+
+    scratchpad: str
+    name_of_guest: str
+    dialogue: List[DialogueItem] = Field(..., description="A list of dialogue items, typically between 8 to 13 items")
+
+
+LANGUAGE_MAPPING = {
+    "English": "en",
+    "Chinese": "zh",
+    "French": "fr",
+    "German": "de",
+    "Hindi": "hi",
+    "Italian": "it",
+    "Japanese": "ja",
+    "Korean": "ko",
+    "Polish": "pl",
+    "Portuguese": "pt",
+    "Russian": "ru",
+    "Spanish": "es",
+    "Turkish": "tr"
+}
+
+MELO_TTS_LANGUAGE_MAPPING = {
+    "en": "EN",
+    "es": "ES",
+    "fr": "FR",
+    "zh": "ZJ",
+    "ja": "JP",
+    "ko": "KR",
+}
+
+
+
+
+def generate_podcast(
+    files: List[str],
+    url: Optional[str],
+    question: Optional[str],
+    tone: Optional[str],
+    length: Optional[str],
+    language: str,
+    use_advanced_audio: bool,
+) -> Tuple[str, str]:
+    """Generate the audio and transcript from the PDFs and/or URL."""
+
+
+
+    text = ""
+
+    # Check if the selected language is supported by MeloTTS when not using advanced audio
+    if not use_advanced_audio and language in ['German', 'Hindi', 'Italian', 'Polish', 'Portuguese', 'Russian', 'Turkish']:
+        raise gr.Error(f"The selected language '{language}' is not supported without advanced audio generation. Please enable advanced audio generation or choose a supported language.")
+
+    # Check if at least one input is provided
+    if not files and not url:
+        raise gr.Error("Please provide at least one PDF file or a URL.")
+
+    # Process PDFs if any
+    if files:
+        for file in files:
+            if not file.lower().endswith(".pdf"):
+                raise gr.Error(
+                    f"File {file} is not a PDF. Please upload only PDF files."
+                )
+
+            try:
+                with Path(file).open("rb") as f:
+                    reader = PdfReader(f)
+                    text += "\n\n".join([page.extract_text() for page in reader.pages])
+            except Exception as e:
+                raise gr.Error(f"Error reading the PDF file {file}: {str(e)}")
+
+    # Process URL if provided
+    if url:
+        try:
+            url_text = parse_url(url)
+            text += "\n\n" + url_text
+        except ValueError as e:
+            raise gr.Error(str(e))
+
+    # Check total character count
+    if len(text) > 100000:
+        raise gr.Error(
+            "The total content is too long. Please ensure the combined text from PDFs and URL is fewer than ~100,000 characters."
+        )
+    
+
+    # Modify the system prompt based on the user input
+    modified_system_prompt = SYSTEM_PROMPT
+    if question:
+        modified_system_prompt += f"\n\PLEASE ANSWER THE FOLLOWING QN: {question}"
+    if tone:
+        modified_system_prompt += f"\n\nTONE: The tone of the podcast should be {tone}."
+    if length:
+        length_instructions = {
+            "Short (1-2 min)": "Keep the podcast brief, around 1-2 minutes long.",
+            "Medium (3-5 min)": "Aim for a moderate length, about 3-5 minutes.",
+        }
+        modified_system_prompt += f"\n\nLENGTH: {length_instructions[length]}"
+    if language:
+        modified_system_prompt += (
+            f"\n\nOUTPUT LANGUAGE <IMPORTANT>: The the podcast should be {language}."
+        )
+
+    # Call the LLM
+    if length == "Short (1-2 min)":
+        llm_output = generate_script(modified_system_prompt, text, ShortDialogue)
+    else:
+        llm_output = generate_script(modified_system_prompt, text, MediumDialogue)
+    logger.info(f"Generated dialogue: {llm_output}")
+
+    # Process the dialogue
+    audio_segments = []
+    transcript = ""
+    total_characters = 0
+
+    for line in llm_output.dialogue:
+        logger.info(f"Generating audio for {line.speaker}: {line.text}")
+        if line.speaker == "Host (Jane)":
+            speaker = f"**Jane**: {line.text}"
+        else:
+            speaker = f"**{llm_output.name_of_guest}**: {line.text}"
+        transcript += speaker + "\n\n"
+        total_characters += len(line.text)
+
+        language_for_tts = LANGUAGE_MAPPING[language]
+
+        if not use_advanced_audio:
+            language_for_tts = MELO_TTS_LANGUAGE_MAPPING[language_for_tts]
+
+        # Get audio file path
+        audio_file_path = generate_podcast_audio(
+            line.text, line.speaker, language_for_tts, use_advanced_audio
+        )
+        # Read the audio file into an AudioSegment
+        audio_segment = AudioSegment.from_file(audio_file_path)
+        audio_segments.append(audio_segment)
+
+    # Concatenate all audio segments
+    combined_audio = sum(audio_segments)
+
+    # Export the combined audio to a temporary file
+    temporary_directory = "./gradio_cached_examples/tmp/"
+    os.makedirs(temporary_directory, exist_ok=True)
+
+    temporary_file = NamedTemporaryFile(
+        dir=temporary_directory,
+        delete=False,
+        suffix=".mp3",
+    )
+    combined_audio.export(temporary_file.name, format="mp3")
+
+    # Delete any files in the temp directory that end with .mp3 and are over a day old
+    for file in glob.glob(f"{temporary_directory}*.mp3"):
+        if os.path.isfile(file) and time.time() - os.path.getmtime(file) > 24 * 60 * 60:
+            os.remove(file)
+
+    logger.info(f"Generated {total_characters} characters of audio")
+
+    return temporary_file.name, transcript
+
+
+demo = gr.Interface(
+    title="Open NotebookLM",
+    description="""
+
+<table style="border-collapse: collapse; border: none; padding: 20px;">
+  <tr style="border: none;">
+    <td style="border: none; vertical-align: top; padding-right: 30px; padding-left: 30px;">
+      <img src="https://raw.githubusercontent.com/gabrielchua/daily-ai-papers/main/_includes/icon.png" alt="Open NotebookLM" width="120" style="margin-bottom: 10px;">
+    </td>
+    <td style="border: none; vertical-align: top; padding: 10px;">
+      <p style="margin-bottom: 15px;"><strong>Convert</strong> your PDFs into podcasts with open-source AI models (Llama 3.1 405B and MeloTTS).</p>
+      <p style="margin-top: 15px;">Note: Only the text content of the PDFs will be processed. Images and tables are not included. The total content should be no more than 100,000 characters due to the context length of Llama 3.1 405B.</p>
+    </td>
+  </tr>
+</table>
+""",
+    fn=generate_podcast,
+    inputs=[
+        gr.File(
+            label="1. 📄 Upload your PDF(s)", file_types=[".pdf"], file_count="multiple"
+        ),
+        gr.Textbox(
+            label="2. 🔗 Paste a URL (optional)",
+            placeholder="Enter a URL to include its content",
+        ),
+        gr.Textbox(label="3. 🤔 Do you have a specific question or topic in mind?"),
+        gr.Dropdown(
+            choices=["Fun", "Formal"],
+            label="4. 🎭 Choose the tone",
+            value="Fun"
+        ),
+        gr.Dropdown(
+            choices=["Short (1-2 min)", "Medium (3-5 min)"],
+            label="5. ⏱️ Choose the length",
+            value="Medium (3-5 min)"
+        ),
+        gr.Dropdown(
+            choices=list(LANGUAGE_MAPPING.keys()),
+            value="English",
+            label="6. 🌐 Choose the language"
+        ),
+        gr.Checkbox(
+            label="7. 🔄 Use advanced audio generation? (Experimental)",
+            value=False
+        )
+    ],
+    outputs=[
+        gr.Audio(label="Podcast", format="mp3"),
+        gr.Markdown(label="Transcript"),
+    ],
+    allow_flagging="never",
+    api_name="generate_podcast",
+    theme=gr.themes.Soft(),
+    concurrency_limit=3,
+    examples=[
+        [
+            [str(Path("examples/1310.4546v1.pdf"))],
+            "",
+            "Explain this paper to me like I'm 5 years old",
+            "Fun",
+            "Short (1-2 min)",
+            "English",
+            True
+        ],
+        [
+            [],
+            "https://en.wikipedia.org/wiki/Hugging_Face",
+            "How did Hugging Face become so successful?",
+            "Fun",
+            "Short (1-2 min)",
+            "English",
+            False
+        ],
+        [
+            [],
+            "https://simple.wikipedia.org/wiki/Taylor_Swift",
+            "Why is Taylor Swift so popular?",
+            "Fun",
+            "Short (1-2 min)",
+            "English",
+            False
+        ],
+    ],
+    cache_examples=True,
+)
+
+if __name__ == "__main__":
+    demo.launch(show_api=True)

prompts.py

@@ -0,0 +1,55 @@
+"""
+prompts.py
+"""
+
+SYSTEM_PROMPT = """
+You are a world-class podcast producer tasked with transforming the provided input text into an engaging and informative podcast script. The input may be unstructured or messy, sourced from PDFs or web pages. Your goal is to extract the most interesting and insightful content for a compelling podcast discussion.
+
+# Steps to Follow:
+
+1. **Analyze the Input:**
+   Carefully examine the text, identifying key topics, points, and interesting facts or anecdotes that could drive an engaging podcast conversation. Disregard irrelevant information or formatting issues.
+
+2. **Brainstorm Ideas:**
+   In the `<scratchpad>`, creatively brainstorm ways to present the key points engagingly. Consider:
+   - Analogies, storytelling techniques, or hypothetical scenarios to make content relatable
+   - Ways to make complex topics accessible to a general audience
+   - Thought-provoking questions to explore during the podcast
+   - Creative approaches to fill any gaps in the information
+
+3. **Craft the Dialogue:**
+   Develop a natural, conversational flow between the host (Jane) and the guest speaker (the author or an expert on the topic). Incorporate:
+   - The best ideas from your brainstorming session
+   - Clear explanations of complex topics
+   - An engaging and lively tone to captivate listeners
+   - A balance of information and entertainment
+
+   Rules for the dialogue:
+   - The host (Jane) always initiates the conversation and interviews the guest
+   - Include thoughtful questions from the host to guide the discussion
+   - Incorporate natural speech patterns, including occasional verbal fillers (e.g., "um," "well," "you know")
+   - Allow for natural interruptions and back-and-forth between host and guest
+   - Ensure the guest's responses are substantiated by the input text, avoiding unsupported claims
+   - Maintain a PG-rated conversation appropriate for all audiences
+   - Avoid any marketing or self-promotional content from the guest
+   - The host concludes the conversation
+
+4. **Summarize Key Insights:**
+   Naturally weave a summary of key points into the closing part of the dialogue. This should feel like a casual conversation rather than a formal recap, reinforcing the main takeaways before signing off.
+
+5. **Maintain Authenticity:**
+   Throughout the script, strive for authenticity in the conversation. Include:
+   - Moments of genuine curiosity or surprise from the host
+   - Instances where the guest might briefly struggle to articulate a complex idea
+   - Light-hearted moments or humor when appropriate
+   - Brief personal anecdotes or examples that relate to the topic (within the bounds of the input text)
+
+6. **Consider Pacing and Structure:**
+   Ensure the dialogue has a natural ebb and flow:
+   - Start with a strong hook to grab the listener's attention
+   - Gradually build complexity as the conversation progresses
+   - Include brief "breather" moments for listeners to absorb complex information
+   - End on a high note, perhaps with a thought-provoking question or a call-to-action for listeners
+
+Remember: Always reply in valid JSON format, without code blocks. Begin directly with the JSON output.
+"""

requirements.txt

@@ -0,0 +1,6 @@
+
+gradio==3.14.0
+gitpython==3.1.30
+transformers==4.22.0
+scipy==1.9.3
+bark

utils.py

@@ -0,0 +1,32 @@
+
+import os
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+from bark import SAMPLE_RATE, generate_audio, preload_models
+from scipy.io.wavfile import write as write_wav
+
+# Load local models for LLM and TTS
+preload_models()
+
+# Load the Hugging Face model locally
+tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B")
+model = AutoModelForCausalLM.from_pretrained("EleutherAI/gpt-neo-2.7B")
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+def generate_script(system_prompt: str, input_text: str, output_model=None):
+    """Generate dialogue script using local GPT-Neo model."""
+    try:
+        # Generate text using the local Hugging Face model
+        response = generator(system_prompt + input_text, max_length=1000, do_sample=True)
+        dialogue = response[0]["generated_text"]
+        return dialogue
+    except Exception as e:
+        return f"Error generating script: {str(e)}"
+
+def generate_podcast_audio(text: str, output_file="podcast_audio.wav"):
+    """Generate audio from text using Bark (local TTS)."""
+    try:
+        audio_array = generate_audio(text)
+        write_wav(output_file, SAMPLE_RATE, audio_array)
+        return output_file
+    except Exception as e:
+        return f"Error generating audio: {str(e)}"