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Step-by-Step Guide: How the Document-to-Podcast Blueprint Works

Transforming static documents into engaging podcast episodes involves an integration of pre-processing, LLM-powered transcript generation, and text-to-speech generation. Here's how it all works under the hood:

Overview

This system has three core stages:

📄 1. Document Pre-Processing Prepare the input document by extracting and cleaning the text.

📜 2. Podcast Script Generation Use an LLM to transform the cleaned text into a conversational podcast script.

🎙️ 3. Audio Podcast Generation Convert the script into an engaging audio podcast with distinct speaker voices.

We'll also look at how app.py brings all these steps together to build an end-to-end demo application.

First, let’s dive into each step to understand how this works in practice.

Step 1: Document Pre-Processing

The process begins with preparing the input document for AI processing. The system handles various document types while ensuring the extracted content is clean and structured.

Cleaner input data ensures that the model works with reliable and consistent information, reducing the likelihood of confusing with unexpected tokens and therefore helping it to generate better outputs.

⚙️ Key Components in this Step

1 - File Loading

  • Uses functions defined in data_loaders.py

  • Supports .html, .pdf, .txt, and .docx formats.

  • Extracts readable text from uploaded files using specialized loaders.

2 - Text Cleaning

  • Uses functions defined in data_cleaners.py

  • Removes unwanted elements like URLs, email addresses, and special characters using Python's re library, which leverages Regular Expressions (regex) to identify and manipulate specific patterns in text.

  • Ensures the document is clean and ready for the next step.

Step 2: Podcast Script Generation

In this step, the pre-processed text is transformed into a conversational podcast transcript. Using a Language Model, the system generates a dialogue that’s both informative and engaging.

⚙️ Key Components in this Step

1 - Model Loading

  • The model_loader.py script is responsible for loading GGUF-type models using the llama_cpp library.

  • The function load_llama_cpp_model takes a model ID in the format {org}/{repo}/{filename} and loads the specified model.

  • This approach of using the llama_cpp library supports efficient CPU-based inference, making language models accessible even on machines without GPUs.

2 - Text-to-Text Generation

  • The text_to_text.py script manages the interaction with the language model, converting input text into a structured conversational podcast script.

  • It uses the chat_completion function to process the input text and a customizable system prompt, guiding the language to generate a text output (e.g. a coherent podcast script between speakers).

  • The return_json parameter allows the output to be formatted as a JSON object style, which can make it easier to parse and integrate structured responses into applications.

  • Supports both single-pass outputs (text_to_text) and real-time streamed responses (text_to_text_stream), offering flexibility for different use cases.

Step 3: Audio Podcast Generation

In this final step, the generated podcast transcript is brought to life as an audio file. Using a Text-to-Speech (TTS) model, each speaker in the script is assigned a unique voice, creating an engaging and professional-sounding podcast.

⚙️ Key Components in this Step

1 - Text-to-Speech Audio Generation

  • The text_to_speech.py script converts text into audio using a specified TTS model and tokenizer.

  • A speaker profile defines the voice characteristics (e.g., tone, speed, clarity) for each speaker.

  • The function text_to_speech takes the input text (e.g podcast script) and speaker profile, generating a waveform (audio data) that represents the spoken version of the text.

2 - Parsing and Combining Voices

  • The script_to_audio.py script ensures each speaker’s dialogue is spoken in their unique voice.

  • The function parse_script_to_waveform splits the dialogue script by speakers and uses text_to_speech to generate audio for each speaker, stitching them together into a full podcast.

  • Once the podcast waveform is ready, the save_waveform_as_file function saves it as an audio file (e.g., MP3 or WAV), making it ready for distribution.

Bringing It All Together in app.py

The app.py demo app is shows you how all the components of the Document-to-Podcast Blueprint can come together. It demonstrates how you can take the individual steps—Document Pre-Processing, Podcast Script Generation, and Audio Podcast Generation—and integrate them into a functional application. This is the heart of the Blueprint in action, showing how you can build an app using the provided tools and components.

This demo uses Streamlit, an open-source Python framework for interactive apps.

Project Logo

🧠 How app.py Applies Each Step

📄 Document Upload & Pre-Processing

  • Users upload a file via the Streamlit interface (st.file_uploader), which supports .pdf, .txt, .docx, .html, and .md formats.

  • The uploaded file is passed to the File Loading and Text Cleaning modules.

  • Raw text is extracted using DATA_LOADERS, and the cleaned version is displayed alongside it using DATA_CLEANERS, and displayed to the end user.

⚙️ Loading Models

  • The script uses load_llama_cpp_model from model_loader.py to load the LLM for generating the podcast script.

  • Similarly, load_parler_tts_model_and_tokenizer is used to prepare the TTS model and tokenizer for audio generation.

  • These models are cached using @st.cache_resource to ensure fast and efficient reuse during app interactions.

📝 Podcast Script Generation

  • The cleaned text and a system-defined podcast prompt are fed into the text_to_text_stream function.

  • The PODCAST_PROMPT can be edited by the end-user to enable them to tailor their script results for their needs.

  • The script is streamed back to the user in real-time, allowing them to see the generated conversation between speakers

🎙️ Podcast Generation

  • For each speaker in the podcast script, audio is generated using the text_to_speech function with distinct speaker profiles

  • The SPEAKER_DESCRIPTION enables the user to edit the podcast speakers voices to fit their needs.

  • The generated audio is displayed with a player so users can listen directly in the app.

🎨 Customizing the Blueprint

To better understand how you can tailor this Blueprint to suit your specific needs, please visit the Customization Guide.

🤝 Contributing to the Blueprint

Want to help improve or extend this Blueprint? Check out the Future Features & Contributions Guide to see how you can contribute your ideas, code, or feedback to make this Blueprint even better!