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import streamlit as st
import torch
from transformers import pipeline
from moviepy.editor import *
from moviepy.video.tools.subtitles import SubtitlesClip
from TTS.api import TTS
import tempfile
import os
# Initialize Hugging Face models
@st.cache_resource
def load_models():
video_gen = pipeline('text-to-video-generation', model='cerspense/zeroscope_v2_576w')
tts_model = TTS(model_name="tts_models/multilingual/multi-dataset/xtts_v2", progress_bar=False)
return video_gen, tts_model
video_gen, tts_model = load_models()
# Streamlit app
st.title("Text-to-Video with Voice Cloning")
# User input
input_text = st.text_area("Enter text to generate video:", height=150)
voice_file = st.file_uploader("Upload your voice sample (WAV format):", type=["wav"])
if st.button("Generate Video") and input_text and voice_file:
with st.spinner("Generating video..."):
# Generate video frames
video_output = video_gen(input_text, num_frames=30)
video_tensor = video_output["video"]
video_np = (video_tensor * 255).astype('uint8')
# Save video
video_filename = tempfile.mktemp(suffix=".mp4")
clips = [ImageClip(frame).set_duration(0.1) for frame in video_np]
video_clip = concatenate_videoclips(clips, method="compose")
video_clip.write_videofile(video_filename, fps=10)
# Generate cloned voice audio
audio_filename = tempfile.mktemp(suffix=".wav")
voice_path = tempfile.mktemp(suffix=".wav")
with open(voice_path, 'wb') as f:
f.write(voice_file.getvalue())
tts_model.tts_to_file(text=input_text, speaker_wav=voice_path, language='en', file_path=audio_filename)
# Combine audio and video
final_video_path = tempfile.mktemp(suffix=".mp4")
video_clip = VideoFileClip(video_filename)
audio_clip = AudioFileClip(audio_filename)
video_clip = video_clip.set_audio(audio_clip)
video_clip.write_videofile(final_video_path, fps=10)
# Display video
st.video(final_video_path)
# Cleanup
os.remove(video_filename)
os.remove(audio_filename)
os.remove(voice_path)
os.remove(final_video_path)
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