import tempfile import gradio as gr import subprocess import os, stat import uuid from googletrans import Translator from TTS.api import TTS import ffmpeg import whisper 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 os.environ["COQUI_TOS_AGREED"] = "1" ZipFile("ffmpeg.zip").extractall() st = os.stat('ffmpeg') os.chmod('ffmpeg', st.st_mode | stat.S_IEXEC) def process_video(video, high_quality, 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" if high_quality: ffmpeg.input(video).output(output_filename, vf='scale=-1:720').run() video_path = output_filename else: video_path = video 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) model = whisper.load_model("base") result = model.transcribe(f"{run_uuid}_output_audio_final.wav") whisper_text = result["text"] whisper_language = result['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 iface = gr.Interface( fn=process_video, inputs=[ gr.Video(), gr.inputs.Checkbox(label="High Quality"), gr.inputs.Dropdown(choices=["English", "Spanish", "French", "German", "Italian", "Portuguese", "Polish", "Turkish", "Russian", "Dutch", "Czech", "Arabic", "Chinese (Simplified)"], label="Target Language for Dubbing") ], outputs=gr.outputs.Video(), live=False ) iface.launch()