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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) |