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import streamlit as st
import youtube_dl
from moviepy.editor import VideoFileClip
import speech_recognition as sr
from transformers import pipeline
# Initialize the Hugging Face text generation pipeline (using GPT-Neo or GPT-2)
generator = pipeline("text-generation", model="EleutherAI/gpt-neo-2.7B")
def download_video(url):
ydl_opts = {
'format': 'bestvideo+bestaudio/best',
'outtmpl': 'downloaded_video.mp4'
}
with youtube_dl.YoutubeDL(ydl_opts) as ydl:
ydl.download([url])
def trim_video(video_path, start_time, end_time):
video = VideoFileClip(video_path)
trimmed_video = video.subclip(start_time, end_time)
trimmed_video.write_videofile("trimmed_video.mp4")
def transcribe_audio(video_path):
recognizer = sr.Recognizer()
with sr.AudioFile(video_path) as source:
audio = recognizer.record(source)
try:
text = recognizer.recognize_google(audio) # This uses Google's free Web Speech API
return text
except sr.UnknownValueError:
return "Sorry, I could not understand the audio."
except sr.RequestError:
return "Error with the API request."
def generate_subtitles(text):
# Use Hugging Face's GPT-Neo model for text generation
generated_text = generator(text, max_length=150, num_return_sequences=1)
return generated_text[0]['generated_text']
# Streamlit Interface
st.title("YouTube Video Trimmer and Subtitle Generator")
url = st.text_input("Enter YouTube URL")
start_time = st.selectbox("Select Start Time", [0, 15, 30, 60])
end_time = st.selectbox("Select End Time", [15, 30, 60, 90])
font_style = st.selectbox("Select Font Style", ["Arial", "Helvetica", "Times New Roman", "Courier"])
if st.button("Generate"):
# Download and trim the video
download_video(url)
trim_video("downloaded_video.mp4", start_time, end_time)
# Transcribe audio to text
transcription = transcribe_audio("trimmed_video.mp4")
# Generate subtitles using Hugging Face's GPT model
subtitles = generate_subtitles(transcription)
# Show trimmed video and generated subtitles
st.video("trimmed_video.mp4")
st.subheader("Subtitles:")
st.write(subtitles)
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