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| import spaces | |
| import tempfile | |
| import gradio as gr | |
| import subprocess | |
| import os, stat | |
| import uuid | |
| from googletrans import Translator | |
| from TTS.api import TTS | |
| from faster_whisper import WhisperModel | |
| import soundfile as sf | |
| import numpy as np | |
| import cv2 | |
| from huggingface_hub import HfApi | |
| import shlex | |
| HF_TOKEN = os.environ.get("HF_TOKEN") | |
| os.environ["COQUI_TOS_AGREED"] = "1" | |
| api = HfApi(token=HF_TOKEN) | |
| repo_id = "artificialguybr/video-dubbing" | |
| # 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 | |
| def process_video(radio, video, target_language, has_closeup_face): | |
| if target_language is None: | |
| return gr.Error("Please select a Target Language for Dubbing.") | |
| run_uuid = uuid.uuid4().hex[:6] | |
| output_filename = f"{run_uuid}_resized_video.mp4" | |
| # Use subprocess for ffmpeg operations | |
| subprocess.run(["ffmpeg", "-i", video, "-vf", "scale=-2:720", output_filename]) | |
| video_path = output_filename | |
| if not os.path.exists(video_path): | |
| return f"Error: {video_path} does not exist." | |
| # Check video duration | |
| video_info = subprocess.check_output(["ffprobe", "-v", "error", "-show_entries", "format=duration", "-of", "default=noprint_wrappers=1:nokey=1", video_path]) | |
| video_duration = float(video_info) | |
| if video_duration > 60: | |
| os.remove(video_path) | |
| return gr.Error("Video duration exceeds 1 minute. Please upload a shorter video.") | |
| subprocess.run(["ffmpeg", "-i", video_path, "-acodec", "pcm_s24le", "-ar", "48000", "-map", "a", f"{run_uuid}_output_audio.wav"]) | |
| subprocess.run(["ffmpeg", "-y", "-i", f"{run_uuid}_output_audio.wav", "-af", "lowpass=3000,highpass=100", f"{run_uuid}_output_audio_final.wav"]) | |
| 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.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) | |
| 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 0 15 0 0 --resize_factor 1 --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.") | |
| subprocess.run(["ffmpeg", "-i", video_path, "-i", f"{run_uuid}_output_synth.wav", "-c:v", "copy", "-c:a", "aac", "-strict", "experimental", "-map", "0:v:0", "-map", "1:a:0", f"{run_uuid}_output_video.mp4"]) | |
| else: | |
| subprocess.run(["ffmpeg", "-i", video_path, "-i", f"{run_uuid}_output_synth.wav", "-c:v", "copy", "-c:a", "aac", "-strict", "experimental", "-map", "0:v:0", "-map", "1:a:0", f"{run_uuid}_output_video.mp4"]) | |
| 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 | |
| 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 | |
| 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(), | |
| 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 dubbing 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 an 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(concurrency_count=1, max_size=15) | |
| demo.launch() | |