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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"
)
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, output_format):
audio = extract_audio(
input_path=os.fspath(video_file), output_path=f"{str(video_file)[:-4]}.mp3", output_format=f"{output_format}"
)
return audio
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-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']
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
file_select_tab, audio_file_tab = st.tabs([ "Video file", "Audio file"])
# with youtube_url_tab:
# url = st.text_input("Enter the Youtube url")
# yt_video, title = youtube_video_downloader(url)
# if url:
# if st.button("Transcribe", key="yturl"):
# with st.spinner("Transcribing..."):
# audio = audio_extraction(yt_video, "mp3")
# audio = audio_processing(audio)
# ytvideo_transcript = transcriber_pass(audio)
# st.success(f"Transcription successful")
# st.write(ytvideo_transcript)
# # st.write(f'Completed in {run_time}')
# if st.button("Generate Summary"):
# summary = generate_ai_summary(ytvideo_transcript)
# st.write(summary)
# Video file transcription
with file_select_tab:
uploaded_video_file = st.file_uploader("Upload video file", type="mp4")
if uploaded_video_file:
video_file = uploaded_video_file.read()
if st.button("Transcribe", key="vidfile"):
with st.spinner("Transcribing..."):
audio = audio_extraction(os.fspath(video_file), "mp3")
audio = audio_processing(audio)
video_transcript = transcriber_pass(audio)
st.success(f"Transcription successful")
st.write(video_transcript)
if st.button("Generate Summary", key="ti2"):
summary = generate_ai_summary(video_transcript)
st.write(summary)
# Audio transcription
with audio_file_tab:
audio_file = st.file_uploader("Upload audio file", type="mp3")
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)
if st.button("Generate Summary", key="ti1"):
summary = generate_ai_summary(audio_transcript)
st.write(summary)
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