import os os.environ["NUMBA_DISABLE_CACHE"] = "1" import streamlit as st import whisper from gtts import gTTS from moviepy.editor import VideoFileClip, AudioFileClip from tempfile import NamedTemporaryFile import torchaudio st.set_page_config(page_title="AI Voiceover", layout="centered") st.title("🎤 AI Voiceover App") @st.cache_resource def load_whisper_model(): return whisper.load_model("small") whisper_model = load_whisper_model() video_file = st.file_uploader("Upload a short video (MP4 preferred)", type=["mp4", "mov", "avi"]) if video_file: with NamedTemporaryFile(delete=False, suffix=".mp4") as tmp_video: tmp_video.write(video_file.read()) tmp_video_path = tmp_video.name st.video(tmp_video_path) video = VideoFileClip(tmp_video_path) audio_path = tmp_video_path.replace(".mp4", ".wav") video.audio.write_audiofile(audio_path) st.info("Transcribing using Whisper...") result = whisper_model.transcribe(audio_path) st.subheader("📝 Detected Speech") st.write(result["text"]) custom_text = st.text_area("Enter your voiceover text:", result["text"]) if st.button("Generate AI Voiceover"): ai_voice_path = audio_path.replace(".wav", "_ai_voice.wav") tts = gTTS(text=custom_text) tts.save(ai_voice_path) st.audio(ai_voice_path) original_audio, sr = torchaudio.load(audio_path) ai_audio, _ = torchaudio.load(ai_voice_path) if ai_audio.shape[1] < original_audio.shape[1]: diff = original_audio.shape[1] - ai_audio.shape[1] ai_audio = torchaudio.functional.pad(ai_audio, (0, diff)) else: ai_audio = ai_audio[:, :original_audio.shape[1]] mixed_audio = (original_audio * 0.4) + (ai_audio * 0.6) mixed_path = audio_path.replace(".wav", "_mixed.wav") torchaudio.save(mixed_path, mixed_audio, sr) final_video = video.set_audio(AudioFileClip(mixed_path)) final_path = tmp_video_path.replace(".mp4", "_final_streamlit.mp4") final_video.write_videofile(final_path, codec="libx264", audio_codec="aac") with open(final_path, "rb") as f: st.download_button(label="📥 Download Final Video", data=f, file_name="final_ai_voiceover.mp4")