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import streamlit as st
from moviepy.editor import VideoFileClip, AudioFileClip, concatenate_audioclips
import whisper
from translate import Translator
from gtts import gTTS
import tempfile
import os
import numpy as np
# Initialize Whisper model
try:
whisper_model = whisper.load_model("base")
except Exception as e:
st.error(f"Error loading Whisper model: {e}")
# Language options
LANGUAGES = {
'English': 'en',
'Tamil': 'ta',
'Sinhala': 'si',
'French': 'fr', # Add more languages as needed
}
st.title("AI Video Translator with Whisper and GTTS")
# Step 1: Upload video file
video_file = st.file_uploader("Upload a video file", type=["mp4", "mov", "avi", "mkv"])
if video_file:
# Step 2: Select translation language
target_language = st.selectbox("Select the target language for translation", list(LANGUAGES.keys()))
# Process when user clicks translate
if st.button("Translate Video"):
# Save video to a temporary file
with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as temp_video:
temp_video.write(video_file.read())
temp_video_path = temp_video.name
# Extract audio from video
try:
video = VideoFileClip(temp_video_path)
audio_path = tempfile.mktemp(suffix=".wav")
video.audio.write_audiofile(audio_path)
except Exception as e:
st.error(f"Error extracting audio from video: {e}")
os.remove(temp_video_path)
st.stop()
# Function to transcribe audio in chunks
def transcribe_audio_in_chunks(audio_path, model, chunk_length=30):
audio_clip = whisper.load_audio(audio_path)
audio_duration = len(audio_clip) / whisper.audio.SAMPLE_RATE # Calculate duration in seconds
segments = []
for start in np.arange(0, audio_duration, chunk_length):
end = min(start + chunk_length, audio_duration)
segment = audio_clip[int(start * whisper.audio.SAMPLE_RATE):int(end * whisper.audio.SAMPLE_RATE)]
result = model.transcribe(segment)
segments.append(result['text'])
return ' '.join(segments)
# Translate text function with debug info
def translate_text(original_text, translator):
translated_text = translator.translate(original_text)
# Debugging: Check translation results
st.write(f"Translated Text Debug: {translated_text}")
if translated_text.strip() == original_text.strip():
st.warning("The translated text is the same as the original. Check if the target language is appropriate.")
return translated_text
# Transcribe audio using Whisper
try:
original_text = transcribe_audio_in_chunks(audio_path, whisper_model)
st.write("Original Transcription:", original_text)
# Translate text to the target language
translator = Translator(to_lang=LANGUAGES[target_language])
translated_text = translate_text(original_text, translator)
st.write(f"Translated Text ({target_language}):", translated_text)
# Convert translated text to speech
tts_audio_path = tempfile.mktemp(suffix=".mp3")
tts = gTTS(text=translated_text, lang=LANGUAGES[target_language])
tts.save(tts_audio_path)
# Merge translated audio with the original video
final_video_path = tempfile.mktemp(suffix=".mp4")
original_video = VideoFileClip(temp_video_path)
final_audio = AudioFileClip(tts_audio_path)
final_video = original_video.set_audio(final_audio)
final_video.write_videofile(final_video_path, codec='libx264', audio_codec='aac')
# Display success message and provide download link
st.success("Translation successful! Download your translated video below:")
st.video(final_video_path)
# Provide download link
with open(final_video_path, "rb") as f:
st.download_button("Download Translated Video", f, file_name="translated_video.mp4")
except Exception as e:
st.error(f"Error during transcription/translation: {e}")
# Clean up temporary files
os.remove(temp_video_path)
os.remove(audio_path)
os.remove(tts_audio_path)
if 'final_video_path' in locals(): # Check if final_video_path exists
os.remove(final_video_path)
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