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| import streamlit as st | |
| from pytube import YouTube | |
| from moviepy.editor import * | |
| import torch | |
| from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer | |
| from transformers import pipeline | |
| import librosa | |
| # Streamlit interface setup | |
| st.title("YouTube Video Summarizer") | |
| youtube_link = st.text_input("Enter YouTube Video Link:") | |
| if st.button('Summarize'): | |
| if not youtube_link: | |
| st.warning("Please enter a valid YouTube link.") | |
| else: | |
| with st.spinner("Processing..."): | |
| try: | |
| # Download YouTube Video | |
| yt = YouTube(youtube_link) | |
| video = yt.streams.filter(only_audio=True).first() | |
| download_path = video.download() | |
| # Show progress | |
| st.progress(25) | |
| # Extract Audio | |
| video_clip = AudioFileClip(download_path) | |
| audio_path = download_path.replace('.mp4', '.wav') | |
| video_clip.write_audiofile(audio_path) | |
| # Show progress | |
| st.progress(50) | |
| # Speech to Text | |
| tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-base-960h") | |
| model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-base-960h") | |
| # Load and process the audio | |
| speech, _ = librosa.load(audio_path, sr=16000) | |
| input_values = tokenizer(speech, return_tensors="pt").input_values | |
| logits = model(input_values).logits | |
| predicted_ids = torch.argmax(logits, dim=-1) | |
| # Decode the speech | |
| transcription = tokenizer.decode(predicted_ids[0]) | |
| # Show progress | |
| st.progress(75) | |
| # Summarization | |
| summarizer = pipeline("summarization") | |
| summary = summarizer(transcription, max_length=130, min_length=30, do_sample=False) | |
| # Display the summary | |
| st.success("Done!") | |
| st.write("### Summary:") | |
| st.write(summary[0]['summary_text']) | |
| # Final progress | |
| st.progress(100) | |
| except Exception as e: | |
| st.error(f"An error occurred: {e}") | |