import streamlit as st import time from transformers import pipeline from pytube import YouTube from pydub import AudioSegment from audio_extract import extract_audio import os from dotenv import load_dotenv load_dotenv() st.set_page_config( page_title="VidText_distilled" ) st.title('Vidtext_distilwhisper') st.write('A web app for video/audio transcription(Youtube, mp4, mp3). Using distilled Whisper') def youtube_video_downloader(url): yt_vid = YouTube(url) title = yt_vid.title vid_dld = ( yt_vid.streams.filter(progressive=True, file_extension="mp4") .order_by("resolution") .desc() .first() ) vid_dld = vid_dld.download() return vid_dld, title def audio_extraction(video_file): audio = AudioSegment.from_file(video_file, format="mp4") audio_path = 'audio.wav' audio.export(audio_path, format="wav") return audio_path def audio_processing(mp3_audio): audio = AudioSegment.from_file(mp3_audio, format="mp3") wav_file = "audio_file.wav" audio = audio.export(wav_file, format="wav") return wav_file @st.cache_resource def load_asr_model(): asr_model = pipeline(task="automatic-speech-recognition", model="distil-whisper/distil-large-v3") return asr_model transcriber_model = load_asr_model() def transcriber_pass(processed_audio): text_extract = transcriber_model(processed_audio) return text_extract['text'] # Streamlit UI youtube_url_tab, file_select_tab, audio_file_tab = st.tabs(["Youtube URL","Video file", "Audio file"]) with youtube_url_tab: url = st.text_input("Enter the Youtube url") try: yt_video, title = youtube_video_downloader(url) if url: if st.button("Transcribe", key="yturl"): with st.spinner("Transcribing..."): with st.spinner('Extracting audio...'): audio = audio_extraction(yt_video) ytvideo_transcript = transcriber_pass(audio) st.success(f"Transcription successful") st.write(f'Video title: {title}') st.write('___') # st.write(ytvideo_transcript) st.markdown(f'''
-> {ytvideo_transcript}
-> {video_transcript}
-> {audio_transcript}