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import gradio as gr
import whisper
from transformers import pipeline
import librosa
import os
# Load Whisper model
whisper_model = whisper.load_model("base")
# Load traditional summarization models
def get_summarizer(model_name):
if model_name == "BART (facebook/bart-large-cnn)":
return pipeline("summarization", model="facebook/bart-large-cnn")
elif model_name == "T5 (t5-small)":
return pipeline("summarization", model="t5-small")
elif model_name == "Pegasus (google/pegasus-xsum)":
return pipeline("summarization", model="google/pegasus-xsum")
else:
return None
# Function to transcribe audio file using Whisper
def transcribe_audio(model_size, audio_path):
# Debug: Check if the file path is correctly passed
print(f"Audio file path received: {audio_path}")
if audio_path is None or not os.path.exists(audio_path):
return "No audio file provided or file path invalid."
# Load the selected Whisper model
model = whisper.load_model(model_size)
# Load and convert audio using librosa
audio_data, sample_rate = librosa.load(audio_path, sr=16000)
# Transcribe the audio file
result = model.transcribe(audio_data)
transcription = result['text']
return transcription
# Function to summarize the transcribed text
def summarize_text(transcription, model_name):
if len(transcription.strip()) == 0:
return "No text to summarize."
summarizer = get_summarizer(model_name)
if summarizer:
summary = summarizer(transcription, max_length=150, min_length=30, do_sample=False)[0]['summary_text']
return summary
else:
return "Invalid summarization model selected."
# Create a Gradio interface that combines transcription and summarization
def combined_transcription_and_summarization(model_size, summarizer_model, audio_path):
# Step 1: Transcribe the audio using Whisper
transcription = transcribe_audio(model_size, audio_path)
# Step 2: Summarize the transcribed text using the chosen summarizer model
summary = summarize_text(transcription, summarizer_model)
return transcription, summary
# Gradio interface for transcription and summarization
iface = gr.Interface(
fn=combined_transcription_and_summarization, # The combined function
inputs=[
gr.Dropdown(label="Choose Whisper Model", choices=["tiny", "base", "small", "medium", "large"], value="base"), # Whisper model selection
gr.Dropdown(label="Choose Summarizer Model", choices=["BART (facebook/bart-large-cnn)", "T5 (t5-small)", "Pegasus (google/pegasus-xsum)"], value="BART (facebook/bart-large-cnn)"), # Summarizer model selection
gr.Audio(type="filepath") # Audio upload
],
outputs=[
gr.Textbox(label="Transcription"), # Output for the transcribed text
gr.Textbox(label="Summary") # Output for the summary
],
title="Whisper Audio Transcription and Summarization",
description="Upload an audio file, choose a Whisper model for transcription, and a summarization model to summarize the transcription."
)
# Launch the interface
iface.launch()
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