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import gradio as gr
import moviepy.editor as mp
from transformers import pipeline
import time
# Load models
whisper = pipeline("automatic-speech-recognition", model="openai/whisper-base") # Use a smaller model
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
def process_video(video_path):
try:
# Extract audio from video
start_time = time.time()
video_clip = mp.VideoFileClip(video_path)
audio_path = "extracted_audio.wav"
video_clip.audio.write_audiofile(audio_path, codec='pcm_s16le')
print(f"Audio extraction took {time.time() - start_time:.2f} seconds")
# Transcribe audio to text
start_time = time.time()
transcription = whisper(audio_path)
text = transcription['text']
print(f"Transcription took {time.time() - start_time:.2f} seconds")
if not text:
raise ValueError("Transcription returned empty text.")
# Summarize text
start_time = time.time()
summary = summarizer(text, max_length=150, min_length=40, do_sample=False)
summary_text = summary[0]['summary_text']
print(f"Summarization took {time.time() - start_time:.2f} seconds")
if not summary_text:
raise ValueError("Summarization returned empty text.")
return text, summary_text
except Exception as e:
return str(e), ""
# Gradio Interface
iface = gr.Interface(
fn=process_video,
inputs=gr.Video(label="Upload Video"),
outputs=[gr.Textbox(label="Transcription"), gr.Textbox(label="Summarization")], # Corrected output specification
title="Video Transcription and Summarization",
description="Upload a video to extract audio, transcribe it to text, and summarize the content."
)
iface.launch(share=True)
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