import spaces 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 from huggingface_hub import HfApi HF_TOKEN = os.environ.get("HF_TOKEN") os.environ["COQUI_TOS_AGREED"] = "1" api = HfApi(token=HF_TOKEN) repo_id = "artificialguybr/video-dubbing" 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="cpu", compute_type="int8") def check_for_faces(video_path): face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') cap = cv2.VideoCapture(video_path) while True: ret, frame = cap.read() if not ret: break gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) faces = face_cascade.detectMultiScale(gray, 1.1, 4) if len(faces) > 0: return True return False @spaces.GPU def process_video(radio, video, target_language, has_closeup_face): try: if target_language is None: raise ValueError("Please select a Target Language for Dubbing.") run_uuid = uuid.uuid4().hex[:6] output_filename = f"{run_uuid}_resized_video.mp4" ffmpeg.input(video).output(output_filename, vf='scale=-2:720').run() video_path = output_filename if not os.path.exists(video_path): raise FileNotFoundError(f"Error: {video_path} does not exist.") video_info = ffmpeg.probe(video_path) video_duration = float(video_info['streams'][0]['duration']) if video_duration > 60: os.remove(video_path) raise ValueError("Video duration exceeds 1 minute. Please upload a shorter video.") ffmpeg.input(video_path).output(f"{run_uuid}_output_audio.wav", acodec='pcm_s24le', ar=48000, map='a').run() 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) print("Attempting to transcribe with Whisper...") try: 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(f"Transcription successful: {whisper_text}") except RuntimeError as e: print(f"RuntimeError encountered: {str(e)}") if "CUDA failed with error device-side assert triggered" in str(e): gr.Warning("Error. Space need to restart. Please retry in a minute") api.restart_space(repo_id=repo_id) 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() translated_text = translator.translate(whisper_text, src=whisper_language, dest=target_language_code).text print(translated_text) tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2") 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 if has_closeup_face: has_face = True else: has_face = check_for_faces(video_path) if has_closeup_face: try: cmd = f"python Wav2Lip/inference.py --checkpoint_path 'Wav2Lip/checkpoints/wav2lip_gan.pth' --face {shlex.quote(video_path)} --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, check=True) except subprocess.CalledProcessError as e: if "Face not detected! Ensure the video contains a face in all the frames." in str(e.stderr): gr.Warning("Wav2lip didn't detect a face. Please try again with the option disabled.") cmd = f"ffmpeg -i {video_path} -i {run_uuid}_output_synth.wav -c:v copy -c:a aac -strict experimental -map 0:v:0 -map 1:a:0 {run_uuid}_output_video.mp4" subprocess.run(cmd, shell=True) else: cmd = f"ffmpeg -i {video_path} -i {run_uuid}_output_synth.wav -c:v copy -c:a aac -strict experimental -map 0:v:0 -map 1:a:0 {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" files_to_delete = [ f"{run_uuid}_resized_video.mp4", f"{run_uuid}_output_audio.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 except Exception as e: print(f"Error in process_video: {str(e)}") return gr.update(value=None, visible=True), f"Error: {str(e)}" 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"], value="Upload", 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", value="Spanish"), gr.Checkbox( label="Video has a close-up face. Use Wav2lip.", value=False, info="Say if video have close-up face. For Wav2lip. Will not work if checked wrongly.") ], outputs=[gr.Video(), gr.Textbox(label="Error Message")], 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. Test the [Video Transcription and Translate](https://huggingface.co/spaces/artificialguybr/VIDEO-TRANSLATION-TRANSCRIPTION) space!""", allow_flagging=False ) with gr.Blocks() as demo: iface.render() radio.change(swap, inputs=[radio], outputs=video) gr.Markdown(""" **Note:** - Video limit is 1 minute. It will dubbling all people using just one voice. - Generation may take up to 5 minutes. - By using this demo you agree to the terms of the Coqui Public Model License at https://coqui.ai/cpml - The tool uses open-source models for all models. It's a alpha version. - Quality can be improved but would require more processing time per video. For scalability and hardware limitations, speed was chosen, not just quality. - If you need more than 1 minute, duplicate the Space and change the limit on app.py. - If you incorrectly mark the 'Video has a close-up face' checkbox, the dubbing may not work as expected. """) demo.queue() demo.launch()