Artificial-superintelligence
commited on
Commit
•
3369106
1
Parent(s):
c56ed60
Update app.py
Browse files
app.py
CHANGED
@@ -1,299 +1,196 @@
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import streamlit as st
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from moviepy.editor import VideoFileClip, AudioFileClip,
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import whisper
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from
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from gtts import gTTS
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import tempfile
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import os
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import numpy as np
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import
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import
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# Set page configuration
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st.set_page_config(
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page_title="Tamil Movie Dubber",
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page_icon="🎬",
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layout="wide"
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)
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# Custom CSS
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st.markdown("""
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<style>
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.stButton>button {
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width: 100%;
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border-radius: 5px;
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height: 3em;
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background-color: #FF4B4B;
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color: white;
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}
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.stProgress .st-bo {
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background-color: #FF4B4B;
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}
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</style>
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""", unsafe_allow_html=True)
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# Tamil voice configurations
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TAMIL_VOICES = {
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'Female 1': {'name': 'ta-IN-PallaviNeural', 'style': 'normal'},
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'Female 2': {'name': 'ta-IN-PallaviNeural', 'style': 'formal'},
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'Male 1': {'name': 'ta-IN-ValluvarNeural', 'style': 'normal'},
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'Male 2': {'name': 'ta-IN-ValluvarNeural', 'style': 'formal'}
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}
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class TamilTextProcessor:
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@staticmethod
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def normalize_tamil_text(text):
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"""Normalize Tamil text for better pronunciation"""
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tamil_numerals = {'௦': '0', '௧': '1', '௨': '2', '௩': '3', '௪': '4',
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'௫': '5', '௬': '6', '௭': '7', '௮': '8', '௯': '9'}
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for tamil_num, eng_num in tamil_numerals.items():
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text = text.replace(tamil_num, eng_num)
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return text
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@staticmethod
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def process_for_tts(text):
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"""Process Tamil text for TTS"""
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text = ''.join(char for char in text if ord(char) < 65535)
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text = ' '.join(text.split())
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return text
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@st.cache_resource
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def
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try:
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shutil.rmtree(self.temp_dir)
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except Exception as e:
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st.warning(f"Cleanup warning: {e}")
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def transcribe_video(self, video_path):
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"""Transcribe video audio using Whisper"""
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try:
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with VideoFileClip(video_path) as video:
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# Extract audio to temporary file
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audio_path = self.create_temp_path(".wav")
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video.audio.write_audiofile(audio_path, fps=16000, verbose=False, logger=None)
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# Check if audio file is not empty
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if os.path.getsize(audio_path) == 0:
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raise ValueError("Extracted audio file is empty")
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# Transcribe using Whisper
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result = self.whisper_model.transcribe(audio_path)
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return result["segments"], video.duration
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except Exception as e:
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raise Exception(f"Transcription error: {str(e)}")
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def translate_segments(self, segments):
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"""Translate segments to Tamil"""
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translator = Translator(to_lang='ta')
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translated_segments = []
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for segment in segments:
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try:
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translated_text = translator.translate(segment["text"])
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translated_text = TamilTextProcessor.normalize_tamil_text(translated_text)
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translated_text = TamilTextProcessor.process_for_tts(translated_text)
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translated_segments.append({
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"text": translated_text,
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"start": segment["start"],
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"end": segment["end"],
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"duration": segment["end"] - segment["start"]
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})
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except Exception as e:
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st.warning(f"Translation warning for segment: {str(e)}")
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# Keep original text if translation fails
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translated_segments.append({
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"text": segment["text"],
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"start": segment["start"],
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"end": segment["end"],
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"duration": segment["end"] - segment["start"]
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})
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return translated_segments
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try:
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audio_path = self.create_temp_path(".mp3")
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tts = gTTS(text=text, lang='ta', slow=False)
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tts.save(audio_path)
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time.sleep(1) # Adding delay to avoid rate limit issues
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return audio_path
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except Exception as e:
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raise Exception(f"Audio generation error: {str(e)}")
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# Create progress tracking
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progress_text = st.empty()
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progress_bar = st.progress(0)
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# Step 1: Transcribe
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progress_text.text("Transcribing video...")
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segments, duration = processor.transcribe_video(input_path)
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progress_bar.progress(0.25)
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# Step 2: Translate
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progress_text.text("Translating to Tamil...")
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translated_segments = processor.translate_segments(segments)
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progress_bar.progress(0.50)
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# Step 3: Generate audio
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progress_text.text("Generating Tamil audio...")
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subtitle_clips = []
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audio_clips = []
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for i, segment in enumerate(translated_segments):
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# Generate audio
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audio_path = processor.generate_tamil_audio(segment["text"])
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audio_clip = AudioFileClip(audio_path)
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audio_clips.append(audio_clip.set_start(segment["start"]))
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# Create subtitle if enabled
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if generate_subtitles:
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subtitle_clip = processor.create_subtitle_clip(
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segment["text"],
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subtitle_size,
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subtitle_color,
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(video.w, None)
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)
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subtitle_clip = (subtitle_clip
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.set_position(('center', 'bottom'))
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.set_start(segment["start"])
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.set_duration(segment["duration"]))
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subtitle_clips.append(subtitle_clip)
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progress_bar.progress(0.50 + (0.4 * (i + 1) / len(translated_segments)))
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# Step 4: Combine everything
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progress_text.text("Creating final video...")
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# Combine audio clips
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final_audio = concatenate_audioclips(audio_clips)
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# Create final video
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if generate_subtitles:
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final_video = CompositeVideoClip([video, *subtitle_clips])
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else:
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final_video = video
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# Set audio
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final_video = final_video.set_audio(final_audio)
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# Write final video
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output_path = processor.create_temp_path(".mp4")
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final_video.write_videofile(
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output_path,
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codec='libx264',
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audio_codec='aac',
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temp_audiofile=processor.create_temp_path(".m4a"),
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remove_temp=True,
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verbose=False,
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logger=None
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)
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progress_bar.progress(1.0)
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progress_text.text("Processing complete!")
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return output_path
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except Exception as e:
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raise Exception(f"Video processing error: {str(e)}")
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finally:
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# Cleanup
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processor.cleanup()
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def main():
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st.title("Tamil
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st.markdown(""
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👋 Welcome! This tool helps you:
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- 🎥 Convert English videos to Tamil
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- 🗣️ Generate Tamil voiceovers
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- 📝 Add Tamil subtitles
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""")
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# File uploader
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video_file = st.file_uploader("Upload Video File", type=['mp4', 'mov', 'avi'])
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return
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subtitle_color = st.color_picker("Subtitle Color", "#FFFFFF")
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# Process video
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if st.button("Process Video"):
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with st.spinner("Processing video..."):
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try:
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except Exception as e:
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st.error(f"
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if __name__ == "__main__":
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main()
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import streamlit as st
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from moviepy.editor import VideoFileClip, AudioFileClip, concatenate_audioclips
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import whisper
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from transformers import MBartForConditionalGeneration, MBartTokenizer
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from gtts import gTTS
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import torch
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import tempfile
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import os
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import numpy as np
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from pydub import AudioSegment
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import librosa
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import warnings
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warnings.filterwarnings('ignore')
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# Initialize models and configs
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@st.cache_resource
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def load_models():
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whisper_model = whisper.load_model("large")
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tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
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model = MBartForConditionalGeneration.from_pretrained("facebook/mbart-large-50-many-to-many-mmt")
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return whisper_model, tokenizer, model
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# Tamil language configuration
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TAMIL_CONFIG = {
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'code': 'ta',
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'whisper_code': 'tamil',
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'mbart_code': 'ta_IN',
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'gtts_code': 'ta',
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'voice_speed': 1.1, # Adjust speed for better sync
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'sample_rate': 22050
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}
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# Streamlit UI setup
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st.set_page_config(page_title="Tamil Video Dubbing AI", page_icon="🎥", layout="wide")
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def create_custom_style():
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st.markdown("""
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<style>
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.stApp {
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background-color: #f5f5f5;
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}
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.main {
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padding: 2rem;
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}
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.stButton>button {
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background-color: #FF4B4B;
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color: white;
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font-weight: bold;
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}
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</style>
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""", unsafe_allow_html=True)
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create_custom_style()
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def translate_text(text, tokenizer, model):
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"""Enhanced translation specifically for Tamil using MBart"""
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inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
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translated_tokens = model.generate(
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**inputs,
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forced_bos_token_id=tokenizer.lang_code_to_id["ta_IN"],
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num_beams=5,
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length_penalty=1.0,
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max_length=512,
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min_length=0,
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do_sample=True,
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temperature=0.7
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)
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return tokenizer.batch_decode(translated_tokens, skip_special_tokens=True)[0]
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def process_audio_for_sync(audio_path, target_speed=1.0):
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"""Process audio for better synchronization"""
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audio = AudioSegment.from_file(audio_path)
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# Adjust speed without changing pitch
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if target_speed != 1.0:
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sound_with_altered_frame_rate = audio._spawn(audio.raw_data, overrides={
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"frame_rate": int(audio.frame_rate * target_speed)
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})
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audio = sound_with_altered_frame_rate.set_frame_rate(audio.frame_rate)
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return audio
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def main():
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st.title("🎥 Tamil Video Dubbing AI")
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st.markdown("### Advanced Video Translation and Dubbing System")
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# Load models
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try:
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with st.spinner("Loading AI models..."):
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whisper_model, tokenizer, translation_model = load_models()
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st.success("Models loaded successfully! 🚀")
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except Exception as e:
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st.error(f"Error loading models: {e}")
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return
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# File uploader with progress
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video_file = st.file_uploader("Upload your video file", type=["mp4", "mov", "avi"])
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if video_file:
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# Video preview
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st.video(video_file)
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# Advanced settings
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with st.expander("Advanced Settings"):
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voice_speed = st.slider("Voice Speed", 0.5, 1.5, TAMIL_CONFIG['voice_speed'], 0.1)
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quality_level = st.select_slider(
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"Translation Quality",
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options=["Draft", "Standard", "High Quality"],
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value="Standard"
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)
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if st.button("Start Tamil Dubbing", key="start_dubbing"):
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try:
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with st.spinner("Processing your video..."):
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# Save uploaded video
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temp_video_path = tempfile.mktemp(suffix='.mp4')
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with open(temp_video_path, 'wb') as f:
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f.write(video_file.read())
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# Process steps with progress bar
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progress_bar = st.progress(0)
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status_text = st.empty()
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# Extract audio
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status_text.text("Extracting audio...")
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video = VideoFileClip(temp_video_path)
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audio_path = tempfile.mktemp(suffix=".wav")
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video.audio.write_audiofile(audio_path, fps=TAMIL_CONFIG['sample_rate'])
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progress_bar.progress(20)
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# Transcribe
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status_text.text("Transcribing audio...")
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result = whisper_model.transcribe(audio_path, language=TAMIL_CONFIG['whisper_code'])
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original_text = result["text"]
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progress_bar.progress(40)
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# Translate
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status_text.text("Translating to Tamil...")
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translated_text = translate_text(original_text, tokenizer, translation_model)
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progress_bar.progress(60)
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# Generate Tamil speech
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status_text.text("Generating Tamil speech...")
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tts = gTTS(text=translated_text, lang=TAMIL_CONFIG['gtts_code'])
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translated_audio_path = tempfile.mktemp(suffix=".mp3")
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tts.save(translated_audio_path)
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progress_bar.progress(80)
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# Final video creation
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status_text.text("Creating final video...")
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dubbed_audio = process_audio_for_sync(translated_audio_path, voice_speed)
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final_audio_path = tempfile.mktemp(suffix=".wav")
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dubbed_audio.export(final_audio_path, format="wav")
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# Combine video with new audio
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final_video_path = tempfile.mktemp(suffix=".mp4")
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final_audio = AudioFileClip(final_audio_path)
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final_video = video.set_audio(final_audio)
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final_video.write_videofile(final_video_path, codec='libx264', audio_codec='aac')
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progress_bar.progress(100)
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# Display results
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st.success("Video dubbed successfully! 🎉")
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st.video(final_video_path)
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# Download options
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col1, col2 = st.columns(2)
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with col1:
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with open(final_video_path, "rb") as f:
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st.download_button(
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"Download Dubbed Video",
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f,
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file_name="tamil_dubbed_video.mp4",
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mime="video/mp4"
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)
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with col2:
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st.download_button(
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"Download Tamil Script",
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translated_text,
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file_name="tamil_script.txt",
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mime="text/plain"
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)
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# Clean up
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for path in [temp_video_path, audio_path, translated_audio_path,
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final_audio_path, final_video_path]:
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if os.path.exists(path):
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os.remove(path)
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except Exception as e:
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st.error(f"An error occurred: {e}")
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st.info("Please try again with a different video or check your internet connection.")
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if __name__ == "__main__":
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main()
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