import tempfile import gradio as gr import subprocess import os, stat import uuid from googletrans import Translator from TTS.api import TTS import ffmpeg from faster_whisper import WhisperModel from scipy.signal import wiener import soundfile as sf from pydub import AudioSegment import numpy as np import librosa from zipfile import ZipFile import shlex import cv2 import torch import torchvision from tqdm import tqdm from numba import jit import threading import time import GPUtil os.environ["COQUI_TOS_AGREED"] = "1" ZipFile("ffmpeg.zip").extractall() st = os.stat('ffmpeg') os.chmod('ffmpeg', st.st_mode | stat.S_IEXEC) #Whisper model_size = "small" model = WhisperModel(model_size, device="cuda", compute_type="int8") def process_video(radio, video, target_language): # Check video duration video_info = ffmpeg.probe(video) video_duration = float(video_info['streams'][0]['duration']) if video_duration > 90: return gr.Interface.Warnings("Video duration exceeds 1 minute and 30 seconds. Please upload a shorter video.") run_uuid = uuid.uuid4().hex[:6] output_filename = f"{run_uuid}_resized_video.mp4" #ffmpeg.input(video).output(output_filename, vf='scale=-1:720:force_original_aspect_ratio=decrease').run() ffmpeg.input(video).output(output_filename, vf='scale=-2:720').run() video_path = output_filename #Time tracking start_time = time.time() if not os.path.exists(video_path): return f"Error: {video_path} does not exist." ffmpeg.input(video_path).output(f"{run_uuid}_output_audio.wav", acodec='pcm_s24le', ar=48000, map='a').run() #y, sr = sf.read(f"{run_uuid}_output_audio.wav") #y = y.astype(np.float32) #y_denoised = wiener(y) #sf.write(f"{run_uuid}_output_audio_denoised.wav", y_denoised, sr) #sound = AudioSegment.from_file(f"{run_uuid}_output_audio_denoised.wav", format="wav") #sound = sound.apply_gain(0) #sound = sound.low_pass_filter(3000).high_pass_filter(100) #sound.export(f"{run_uuid}_output_audio_processed.wav", format="wav") shell_command = f"ffmpeg -y -i {run_uuid}_output_audio.wav -af lowpass=3000,highpass=100 {run_uuid}_output_audio_final.wav".split(" ") subprocess.run([item for item in shell_command], capture_output=False, text=True, check=True) segments, info = model.transcribe(f"{run_uuid}_output_audio_final.wav", beam_size=5) whisper_text = " ".join(segment.text for segment in segments) whisper_language = info.language print(whisper_text) language_mapping = {'English': 'en', 'Spanish': 'es', 'French': 'fr', 'German': 'de', 'Italian': 'it', 'Portuguese': 'pt', 'Polish': 'pl', 'Turkish': 'tr', 'Russian': 'ru', 'Dutch': 'nl', 'Czech': 'cs', 'Arabic': 'ar', 'Chinese (Simplified)': 'zh-cn'} target_language_code = language_mapping[target_language] translator = Translator() try: translated_text = translator.translate(whisper_text, src=whisper_language, dest=target_language_code).text print(translated_text) except AttributeError as e: print("Failed to translate text. Likely an issue with token extraction in the Google Translate API.") translated_text = "Translation failed due to API issue." tts = TTS("tts_models/multilingual/multi-dataset/xtts_v1") tts.to('cuda') tts.tts_to_file(translated_text, speaker_wav=f"{run_uuid}_output_audio_final.wav", file_path=f"{run_uuid}_output_synth.wav", language=target_language_code) pad_top = 0 pad_bottom = 15 pad_left = 0 pad_right = 0 rescaleFactor = 1 video_path_fix = video_path cmd = f"python Wav2Lip/inference.py --checkpoint_path 'Wav2Lip/checkpoints/wav2lip_gan.pth' --face {shlex.quote(video_path_fix)} --audio '{run_uuid}_output_synth.wav' --pads {pad_top} {pad_bottom} {pad_left} {pad_right} --resize_factor {rescaleFactor} --nosmooth --outfile '{run_uuid}_output_video.mp4'" subprocess.run(cmd, shell=True) if not os.path.exists(f"{run_uuid}_output_video.mp4"): raise FileNotFoundError(f"Error: {run_uuid}_output_video.mp4 was not generated.") output_video_path = f"{run_uuid}_output_video.mp4" # Cleanup: Delete all generated files except the final output video files_to_delete = [ f"{run_uuid}_resized_video.mp4", f"{run_uuid}_output_audio.wav", f"{run_uuid}_output_audio_denoised.wav", f"{run_uuid}_output_audio_processed.wav", f"{run_uuid}_output_audio_final.wav", f"{run_uuid}_output_synth.wav" ] for file in files_to_delete: try: os.remove(file) except FileNotFoundError: print(f"File {file} not found for deletion.") return output_video_path def swap(radio): if(radio == "Upload"): return gr.update(source="upload") else: return gr.update(source="webcam") video = gr.Video() radio = gr.Radio(["Upload", "Record"], show_label=False) iface = gr.Interface( fn=process_video, inputs=[ radio, video, gr.Dropdown(choices=["English", "Spanish", "French", "German", "Italian", "Portuguese", "Polish", "Turkish", "Russian", "Dutch", "Czech", "Arabic", "Chinese (Simplified)"], label="Target Language for Dubbing") ], outputs=gr.Video(), live=False, title="AI Video Dubbing", description="""This tool was developed by [@artificialguybr](https://twitter.com/artificialguybr) using entirely open-source tools. Special thanks to Hugging Face for the GPU support. Thanks [@yeswondwer](https://twitter.com/@yeswondwerr) for original code. **Note:** - Video limit is 1 minute. - Generation may take up to 5 minutes. - The tool uses open-source models for all operations. - Quality can be improved but would require more processing time per video.""", allow_flagging=False ) with gr.Blocks() as demo: iface.render() radio.change(swap, inputs=[radio], outputs=video) demo.queue(concurrency_count=2, max_size=15) demo.launch()