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 google.generativeai as google_genai import os from dotenv import load_dotenv load_dotenv() GOOGLE_API_KEY =os.getenv("GOOGLE_API_KEY") google_genai.configure(api_key=GOOGLE_API_KEY) st.set_page_config( page_title="VidText" ) st.title('Vidtext_whisper') st.write('A web app for video/audio transcription(Youtube, mp4, mp3)') 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="openai/whisper-small") return asr_model transcriber_model = load_asr_model() def transcriber_pass(processed_audio): text_extract = transcriber_model(processed_audio) return text_extract['text'] def generate_ai_summary(transcript): model = google_genai.GenerativeModel('gemini-pro') model_response = model.generate_content([f"Give a summary of the text {transcript}"], stream=True) return model_response.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}

''', unsafe_allow_html=True) except Exception as e: st.error(e) # Video file transcription with file_select_tab: uploaded_video_file = st.file_uploader("Upload video file", type="mp4") try: if uploaded_video_file: if st.button("Transcribe", key="vidfile"): with st.spinner("Transcribing..."): with st.spinner('Extracting audio...'): audio = audio_extraction(uploaded_video_file) video_transcript = transcriber_pass(audio) st.success(f"Transcription successful") st.markdown(f'''

-> {video_transcript}

''', unsafe_allow_html=True) except Exception as e: st.error(e) # Audio transcription with audio_file_tab: audio_file = st.file_uploader("Upload audio file", type="mp3") try: if audio_file: if st.button("Transcribe", key="audiofile"): with st.spinner("Transcribing..."): processed_audio = audio_processing(audio_file) audio_transcript = transcriber_pass(processed_audio) st.success(f"Transcription successful") # st.write(audio_transcript) st.markdown(f'''

-> {audio_transcript}

''', unsafe_allow_html=True) except Exception as e: st.error(e) # Footer st.write('') st.write('') st.write('') st.markdown("""
Project by tensor_kelechi
""", unsafe_allow_html=True) # Arigato :)