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| # -*- coding: utf-8 -*- | |
| """Q_A_Whisper_summarize.ipynb | |
| Automatically generated by Colab. | |
| Original file is located at | |
| https://colab.research.google.com/drive/1uchqsFgN6JyJX5rYfyMy3H__ThfIRGXJ | |
| **Question:** | |
| "How can you create a Gradio app that allows users to upload an audio file, transcribe it using the Whisper tiny model, and then summarize the transcription using Hugging Face's BART summarization model?" | |
| This question prompts the user to build the Gradio app using both Whisper for transcription and the BART model for summarization. | |
|  | |
| """ | |
| !pip install gradio | |
| !pip install git+https://github.com/openai/whisper.git | |
| !pip install transformers | |
| import gradio as gr | |
| import whisper | |
| from transformers import pipeline | |
| # Load the tiny Whisper model | |
| whisper_model = whisper.load_model("tiny") | |
| model = whisper.load_model("base") | |
| # Load the text summarization model from Hugging Face | |
| summarizer = pipeline(task="summarization", model="facebook/bart-large-cnn") | |
| # Function to transcribe and summarize the audio file | |
| def transcribe_and_summarize(audio): | |
| # Step 1: Transcribe the audio using Whisper | |
| transcription_result = whisper_model.transcribe(audio) | |
| transcription = transcription_result['text'] | |
| # Step 2: Summarize the transcription | |
| summary = summarizer(transcription, min_length=10, max_length=100) | |
| summary_text = summary[0]['summary_text'] | |
| return transcription, summary_text | |
| # Define the Gradio interface | |
| interface = gr.Interface( | |
| fn=transcribe_and_summarize, # Function to run | |
| inputs=gr.Audio(type="filepath", label="Upload your audio file"), # Input audio field | |
| outputs=[gr.Textbox(label="Transcription"), gr.Textbox(label="Summary")], # Output fields | |
| title="Whisper Tiny Transcription and Summarization", | |
| description="Upload an audio file, get the transcription from Whisper tiny model and the summarized version using Hugging Face." | |
| ) | |
| # Launch the Gradio app | |
| interface.launch(debug=True) |