Open-Source TTS Podcast

I'm really enjoying Google's NotebookLM, specifically it's Audio Overview feature. It makes studying for exams 100% easier, especially while working. However, there's a daily limit, so I'm wondering if there's a similar (free) alternative I can also use. Something that I can put sources into and have it create a semi-realistic podcast experience from them. ----Could you not use an open source TTS API to do so that you could host locally if that exists? It would also allow you to run it as much as you wanted. You could also use an agentic framework where you program LLM calls as classes that utilize dynamically called and adjusted database values like my personagen software does

You could indeed create a system using open-source TTS APIs combined with an agentic framework to provide a podcast-like experience. Here's how you might approach it: 1. **Open-Source TTS API**: Use tools like **Coqui TTS** or **Mozilla TTS** to generate high-quality, natural-sounding audio from text. These are fully open-source and can be hosted locally. You can even train them on your voice or other styles to make it feel more personal or engaging. 2. **LLM Integration**: Use an open-source LLM (like Vicuna, Mistral, or GPT-J) to summarize or reformat your study material into conversational formats, making it more engaging for a podcast-style delivery. 3. **Agentic Framework**: - Program agent classes to process study material dynamically. For example: - One class could summarize the text. - Another could convert the summaries into conversational scripts. - A final class could feed these scripts to the TTS API. - Use a database (like SQLite or PostgreSQL) to store and manage sources and metadata, dynamically pulling and processing content. 4. **Pipeline Workflow**: Automate the entire workflow using tools like **Airflow** or simple Python scripts: - Input study material into the system (e.g., PDFs, web pages, or notes). - Process the material into podcast-ready scripts. - Convert the scripts into audio using the TTS engine. - Organize and store the audio files for playback. This setup would give you full control, no daily limits, and the ability to tailor the experience to your preferences. Would you like me to help draft a more detailed plan or code to get started?

yes

Here’s a detailed plan to create your podcast-like study assistant using an open-source TTS API and an agentic framework. --- ## **Plan Overview** ### **1. Tech Stack** - **Programming Language**: Python - **TTS Engine**: [Coqui TTS](https://github.com/coqui-ai/TTS) or [Mozilla TTS](https://github.com/mozilla/TTS) - **LLM**: Open-source models like [Vicuna](https://lmsys.org/blog/2023-03-30-vicuna/) or [Mistral](https://mistral.ai/) via APIs like [Ollama](https://ollama.com/). - **Database**: SQLite or PostgreSQL for managing input sources, metadata, and generated content. - **Framework**: Use Python-based orchestration (e.g., a simple pipeline using `networkx` or `FastAPI` for a web-based interface). --- ### **2. Architecture** 1. **Input Sources**: - Accept various input formats (PDFs, Word Docs, web URLs, or plain text). - Use tools like `PyPDF2`, `BeautifulSoup`, or `python-docx` to extract content. 2. **LLM Agent System**: - Program multiple agents as classes: - **Agent 1: Preprocessor**: Cleans and chunks text into digestible segments. - **Agent 2: Summarizer**: Uses an LLM to summarize or simplify content. - **Agent 3: Conversationalizer**: Converts summaries into a conversational script suitable for audio narration. 3. **TTS Engine**: - Feed the conversational scripts to the TTS engine. - Use local hosting for unlimited usage. - Output files in MP3 or WAV format. 4. **Database and Metadata Management**: - Store the source text, generated scripts, and audio metadata. - Track progress, allowing for easy replay or updates to content. 5. **Audio Output**: - Combine generated audio segments into a cohesive "podcast" using Python tools like `pydub`. 6. **Playback Interface** (Optional): - Create a simple web app (e.g., using `Flask` or `FastAPI`) to select, play, and manage generated content. --- ### **3. Detailed Implementation** #### **Step 1: Extract Content** - Use libraries to extract content: ```python from PyPDF2 import PdfReader def extract_pdf_text(file_path): reader = PdfReader(file_path) text = "" for page in reader.pages: text += page.extract_text() return text ``` #### **Step 2: LLM-Powered Summarization** - Install and set up your LLM (e.g., running Vicuna locally with Ollama): ```python import requests def summarize_text(text): url = "http://localhost:11434/api" # Ollama API endpoint payload = {"prompt": f"Summarize this: {text}"} response = requests.post(url, json=payload) return response.json()["text"] ``` #### **Step 3: TTS Generation** - Use Coqui TTS to generate audio: ```python from TTS.api import TTS def text_to_speech(script, output_file): tts = TTS(model_name="tts_models/en/ljspeech/tacotron2-DDC") tts.tts_to_file(text=script, file_path=output_file) ``` #### **Step 4: Orchestrate with Agentic Framework** - Use `networkx` for dependency graph orchestration: ```python import networkx as nx # Define agents class Preprocessor: def process(self, text): return text.replace("\n", " ").strip() class Summarizer: def process(self, text): return summarize_text(text) class Conversationalizer: def process(self, text): return f"Imagine this conversation: {text}" class TTSEngine: def process(self, script, output_file): text_to_speech(script, output_file) # Build pipeline G = nx.DiGraph() G.add_node("preprocess", agent=Preprocessor()) G.add_node("summarize", agent=Summarizer()) G.add_node("conversationalize", agent=Conversationalizer()) G.add_node("tts", agent=TTSEngine()) G.add_edges_from([("preprocess", "summarize"), ("summarize", "conversationalize"), ("conversationalize", "tts")]) def execute_pipeline(graph, input_text, output_file): current_data = input_text for node in nx.topological_sort(graph): agent = graph.nodes[node]["agent"] if node == "tts": agent.process(current_data, output_file) else: current_data = agent.process(current_data) execute_pipeline(G, "Your input text here", "output_audio.mp3") ``` #### **Step 5: Combine Audio Segments** - Merge audio for podcast format: ```python from pydub import AudioSegment def combine_audio(files, output_file): combined = AudioSegment.empty() for file in files: audio = AudioSegment.from_file(file) combined += audio combined.export(output_file, format="mp3") ``` --- ### **4. Deployment** - **Local Deployment**: Run everything on your local machine for full control. - **Web Interface**: Use Flask/FastAPI for a simple dashboard to manage inputs and playback. - **Automation**: Set up a cron job or scheduler for automatic processing. --- ### **5. Next Steps** Would you like me to help you with: - Setting up the TTS environment? - Programming the agent classes more robustly? - Deploying a local dashboard for better usability?

yes

Let’s get started! Here's a step-by-step guide to set up your local podcast-like assistant: --- ## **Step 1: Install Required Tools** Make sure you have the following installed: 1. **Python Environment**: Install Python (version 3.8+ recommended). 2. **Required Libraries**: Install the libraries via `pip`: ```bash pip install TTS networkx pydub requests PyPDF2 ``` 3. **FFmpeg for Audio Processing**: Required for `pydub` to work. - Install via your package manager: - On Ubuntu/Debian: `sudo apt install ffmpeg` - On MacOS: `brew install ffmpeg` - On Windows: Download from [FFmpeg's website](https://ffmpeg.org/). 4. **Coqui TTS**: - Follow [Coqui TTS setup guide](https://github.com/coqui-ai/TTS) to install and test models locally. 5. **LLM API (Optional)**: - Install and run [Ollama](https://ollama.com/) for local LLM access. --- ## **Step 2: Set Up Project Structure** Organize your project directory like this: ``` podcast_assistant/ ├── input/ # Source files (PDFs, text, etc.) ├── output/ # Generated audio files ├── scripts/ # Python scripts for processing └── requirements.txt # List of dependencies ``` --- ## **Step 3: Build the System** ### **Script 1: Content Extraction** Save this as `scripts/extract_content.py`: ```python from PyPDF2 import PdfReader import os def extract_pdf_text(file_path): reader = PdfReader(file_path) text = "" for page in reader.pages: text += page.extract_text() return text if __name__ == "__main__": input_path = "../input/sample.pdf" output_text = extract_pdf_text(input_path) with open("../output/extracted_text.txt", "w") as f: f.write(output_text) print("Text extracted successfully!") ``` --- ### **Script 2: Summarization with LLM** Save this as `scripts/summarize_text.py`: ```python import requests def summarize_text(text): url = "http://localhost:11434/api" # Ollama API endpoint payload = {"prompt": f"Summarize this: {text}"} response = requests.post(url, json=payload) return response.json().get("text", "Error in summarization") if __name__ == "__main__": with open("../output/extracted_text.txt", "r") as f: content = f.read() summary = summarize_text(content) with open("../output/summarized_text.txt", "w") as f: f.write(summary) print("Summarization completed!") ``` --- ### **Script 3: Generate Audio** Save this as `scripts/generate_audio.py`: ```python from TTS.api import TTS def text_to_speech(script, output_file): tts = TTS(model_name="tts_models/en/ljspeech/tacotron2-DDC") tts.tts_to_file(text=script, file_path=output_file) if __name__ == "__main__": with open("../output/summarized_text.txt", "r") as f: script = f.read() output_path = "../output/audio_output.mp3" text_to_speech(script, output_path) print("Audio generation completed!") ``` --- ### **Script 4: Combine Audio Segments** Save this as `scripts/combine_audio.py`: ```python from pydub import AudioSegment import os def combine_audio(files, output_file): combined = AudioSegment.empty() for file in files: audio = AudioSegment.from_file(file) combined += audio combined.export(output_file, format="mp3") if __name__ == "__main__": audio_files = ["../output/audio_part1.mp3", "../output/audio_part2.mp3"] output_path = "../output/combined_podcast.mp3" combine_audio(audio_files, output_path) print("Podcast combined successfully!") ``` --- ### **Script 5: Pipeline Orchestration** Save this as `scripts/pipeline.py`: ```python import os from extract_content import extract_pdf_text from summarize_text import summarize_text from generate_audio import text_to_speech def execute_pipeline(input_pdf, output_audio): # Step 1: Extract content text = extract_pdf_text(input_pdf) print("Text extracted.") # Step 2: Summarize content summary = summarize_text(text) print("Text summarized.") # Step 3: Generate audio text_to_speech(summary, output_audio) print("Audio generated.") if __name__ == "__main__": input_pdf = "../input/sample.pdf" output_audio = "../output/final_podcast.mp3" execute_pipeline(input_pdf, output_audio) print("Pipeline executed successfully!") ``` --- ## **Step 4: Run the Pipeline** 1. Place your input PDF in the `input/` directory. 2. Run the pipeline script: ```bash python scripts/pipeline.py ``` 3. Your podcast audio will be generated in the `output/` directory. --- ## **Next Steps** - **Enhancements**: - Add error handling for better robustness. - Build a web interface using Flask or FastAPI for uploading files and playing audio. - **Customization**: - Train the TTS engine for custom voices. - Extend the LLM prompts for better conversational tone. Would you like help building the web interface or adding specific features?

write a prompt to be used with Sora that will tell this story or at least the first ten seconds of it

Here is a prompt designed for Sora to narrate the story's opening, focusing on the intense and reflective tone of "The Way of the Roach and Cat": --- **Prompt for Sora:** "Begin with a vivid and introspective tone. Narrate the opening of a story where the protagonist, fresh from a hospital stay in Austin, finds themselves handcuffed in the back of a police cruiser. The car drives through the shifting landscapes of Texas, from the urban sprawl of Austin to the contrasting conservative air of Georgetown. Introduce the setting of Rock Springs, a psychiatric facility, as a place of stark contrasts compared to the warmth and charity of Shoal Creek hospital. Dive into the protagonist's swirling thoughts—a mix of calm, introspection, and unease—as they recall their experiences in mental health facilities. Set the tone with a mix of raw emotion and philosophical reflection, hinting at the journey that is to follow." --- This should capture the first ten seconds of the narration effectively. Would you like me to refine it further or adjust for a different perspective?

now without people in any of the shots

Here’s a revised prompt for Sora, focusing entirely on vivid descriptions of settings and mood without involving people: --- **Prompt for Sora:** "Paint the scene with evocative imagery. Describe the journey of a police cruiser leaving Austin, the cityscape fading into the vast Texas countryside under a blue-gray sky. Focus on the transition from the liberal bustle of Austin to the quieter, more restrained air of Georgetown. Introduce Rock Springs, a psychiatric facility set amidst a mix of modern design and sterile functionality. Capture the mood with contrasts: the warmth of sunlight on the building's exterior against the cold, clinical atmosphere inside. Layer the narrative with the weight of anticipation and introspection, hinting at the dichotomy of hope and uncertainty that surrounds this place." --- This focuses solely on the environment, mood, and atmosphere without any direct mention of people. Would you like me to adjust further or expand on specific settings?