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| title: Audio Abstract42 | |
| emoji: 😻 | |
| colorFrom: blue | |
| colorTo: green | |
| sdk: gradio | |
| sdk_version: 4.7.1 | |
| app_file: app.py | |
| pinned: false | |
| # PDF Audio Summarizer | |
| This application summarizes PDF documents and converts the summary to audio. | |
| ## How it works | |
| The core logic is in the `audio_pdf` function. It: | |
| 1. Extracts raw text from the uploaded PDF using `PyPDF2` | |
| 2. Summarizes the text using [LED-Based Summarization](https://huggingface.co/pszemraj/led-base-book-summary) Model from HuggingFace Transformers. This uses `AutoTokenizer` and `AutoModelForSeq2SeqLM` to load the model and generate a summary | |
| 3. Converts the text summary to an audio file using `gTTS` (Google Text-to-Speech) | |
| The summary and audio file are returned and displayed in the Gradio web interface. | |
| ## Interface | |
| The interface is created using Gradio. The key components are: | |
| - `File` input to upload a PDF | |
| - `Text` output to display the text summary | |
| - `Audio` output to play the audio file | |
| The interface is launched via `iface.launch()` | |
| ## Dependencies | |
| - PyPDF2 | |
| - Transformers | |
| - gTTS | |
| - Gradio | |
| - torch | |
| - numpy | |
| - scipy | |
| - io | |
| Additional dependencies: | |
| - `torch`: For neural network computations in Transformers | |
| - `numpy`: For numerical processing | |
| - `scipy`: For scientific computing | |
| - `io`: To buffer the audio data | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference |