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import whisper
from pytube import YouTube
import requests, io
from urllib.request import urlopen
from PIL import Image
import time
import streamlit as st
from streamlit_lottie import st_lottie
import numpy as np
import os
st.set_page_config(page_title="Youtube Transcriber", page_icon="🗣", layout="wide")
# Define a function that we can use to load lottie files from a link.
@st.cache(allow_output_mutation=True)
def load_lottieurl(url: str):
r = requests.get(url)
if r.status_code != 200:
return None
return r.json()
col1, col2 = st.columns([1, 3])
with col1:
lottie = load_lottieurl("https://assets9.lottiefiles.com/private_files/lf30_bntlaz7t.json")
st_lottie(lottie, speed=1, height=200, width=200)
with col2:
st.write("""
## Youtube Transcriber
##### This is an app that transcribes YouTube videos into text.""")
#def load_model(size):
#default_size = size
#if size == default_size:
#return None
#else:
#loaded_model = whisper.load_model(size)
#return loaded_model
@st.cache(allow_output_mutation=True)
def populate_metadata(link):
yt = YouTube(link)
author = yt.author
title = yt.title
description = yt.description
thumbnail = yt.thumbnail_url
length = yt.length
views = yt.views
#return author, title, description, thumbnail, length, views
# Uncomment if you want to fetch the thumbnails as well.
#def fetch_thumbnail(thumbnail):
#tnail = urlopen(thumbnail)
#raw_data = tnail.read()
#image = Image.open(io.BytesIO(raw_data))
#st.image(image, use_column_width=True)
def convert(seconds):
#return time.strftime("%H:%M:%S", time.gmtime(seconds))
loaded_model = whisper.load_model("small")
#current_size = "None"
#size = st.selectbox("Model Size", ["tiny", "base", "small", "medium", "large"], index=1)
def change_model(current_size, size):
if current_size != size:
loaded_model = whisper.load_model(size)
st.write(f"Model is {'multilingual' if loaded_model.is_multilingual else 'English-only'} "
f"and has {sum(np.prod(p.shape) for p in loaded_model.parameters()):,} parameters.")
return loaded_model
else:
return None
@st.cache(allow_output_mutation=True)
def inference(link):
yt = YouTube(link)
path = yt.streams.filter(only_audio=True)[0].download(filename="audio.mp4")
results = loaded_model.transcribe(path)
return results["text"]
def main():
change_model(current_size, size)
link = st.text_input("YouTube Link")
if st.button("Transcribe"):
author, title, description, thumbnail, length, views = populate_metadata(link)
results = inference(link)
col3, col4 = st.columns(2)
with col3:
#fetch_thumbnail(thumbnail)
st.video(link)
st.markdown(f"**Channel**: {author}")
st.markdown(f"**Title**: {title}")
st.markdown(f"**Length**: {convert(length)}")
st.markdown(f"**Views**: {views:,}")
with col4:
with st.expander("Video Description"):
st.write(description)
#st.markdown(f"**Video Description**: {description}")
with st.expander("Video Transcript"):
st.write(results)
# Write the results to a .txt file and download it.
with open("transcript.txt", "w+") as f:
f.writelines(results)
f.close()
with open(os.path.join(os.getcwd(), "transcript.txt"), "rb") as f:
data = f.read()
if st.download_button(label="Download Transcript",
data=data,
file_name="transcript.txt"):
st.success("Downloaded Successfully!")
if __name__ == "__main__":
main() |