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Running
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Zero
import os | |
import uuid | |
import asyncio | |
import subprocess | |
import json | |
from zipfile import ZipFile | |
import stat | |
import gradio as gr | |
import ffmpeg | |
import cv2 | |
import edge_tts | |
from googletrans import Translator | |
from huggingface_hub import HfApi | |
import moviepy.editor as mp | |
import spaces | |
# Constants and initialization | |
HF_TOKEN = os.environ.get("HF_TOKEN") | |
REPO_ID = "artificialguybr/video-dubbing" | |
MAX_VIDEO_DURATION = 60 # seconds | |
api = HfApi(token=HF_TOKEN) | |
# Extract and set permissions for ffmpeg | |
ZipFile("ffmpeg.zip").extractall() | |
st = os.stat('ffmpeg') | |
os.chmod('ffmpeg', st.st_mode | stat.S_IEXEC) | |
language_mapping = { | |
'English': ('en', 'en-US-EricNeural'), | |
'Spanish': ('es', 'es-ES-AlvaroNeural'), | |
'French': ('fr', 'fr-FR-HenriNeural'), | |
'German': ('de', 'de-DE-ConradNeural'), | |
'Italian': ('it', 'it-IT-DiegoNeural'), | |
'Portuguese': ('pt', 'pt-PT-DuarteNeural'), | |
'Polish': ('pl', 'pl-PL-MarekNeural'), | |
'Turkish': ('tr', 'tr-TR-AhmetNeural'), | |
'Russian': ('ru', 'ru-RU-DmitryNeural'), | |
'Dutch': ('nl', 'nl-NL-MaartenNeural'), | |
'Czech': ('cs', 'cs-CZ-AntoninNeural'), | |
'Arabic': ('ar', 'ar-SA-HamedNeural'), | |
'Chinese (Simplified)': ('zh-CN', 'zh-CN-YunxiNeural'), | |
'Japanese': ('ja', 'ja-JP-KeitaNeural'), | |
'Korean': ('ko', 'ko-KR-InJoonNeural'), | |
'Hindi': ('hi', 'hi-IN-MadhurNeural'), | |
'Swedish': ('sv', 'sv-SE-MattiasNeural'), | |
'Danish': ('da', 'da-DK-JeppeNeural'), | |
'Finnish': ('fi', 'fi-FI-HarriNeural'), | |
'Greek': ('el', 'el-GR-NestorasNeural') | |
} | |
print("Starting the program...") | |
def generate_unique_filename(extension): | |
return f"{uuid.uuid4()}{extension}" | |
def cleanup_files(*files): | |
for file in files: | |
if file and os.path.exists(file): | |
os.remove(file) | |
print(f"Removed file: {file}") | |
def transcribe_audio(file_path): | |
print(f"Starting transcription of file: {file_path}") | |
temp_audio = None | |
if file_path.endswith(('.mp4', '.avi', '.mov', '.flv')): | |
print("Video file detected. Extracting audio...") | |
try: | |
video = mp.VideoFileClip(file_path) | |
temp_audio = generate_unique_filename(".wav") | |
video.audio.write_audiofile(temp_audio) | |
file_path = temp_audio | |
except Exception as e: | |
print(f"Error extracting audio from video: {e}") | |
raise | |
output_file = generate_unique_filename(".json") | |
command = [ | |
"insanely-fast-whisper", | |
"--file-name", file_path, | |
"--device-id", "0", | |
"--model-name", "openai/whisper-large-v3", | |
"--task", "transcribe", | |
"--timestamp", "chunk", | |
"--transcript-path", output_file | |
] | |
try: | |
result = subprocess.run(command, check=True, capture_output=True, text=True) | |
print(f"Transcription output: {result.stdout}") | |
except subprocess.CalledProcessError as e: | |
print(f"Error running insanely-fast-whisper: {e}") | |
raise | |
try: | |
with open(output_file, "r") as f: | |
transcription = json.load(f) | |
except json.JSONDecodeError as e: | |
print(f"Error decoding JSON: {e}") | |
raise | |
result = transcription.get("text", " ".join([chunk["text"] for chunk in transcription.get("chunks", [])])) | |
cleanup_files(output_file, temp_audio) | |
return result | |
async def text_to_speech(text, voice, output_file): | |
communicate = edge_tts.Communicate(text, voice) | |
await communicate.save(output_file) | |
def process_video(video, target_language, use_wav2lip): | |
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 > MAX_VIDEO_DURATION: | |
cleanup_files(video_path) | |
raise ValueError(f"Video duration exceeds {MAX_VIDEO_DURATION} seconds. Please upload a shorter video.") | |
ffmpeg.input(video_path).output(f"{run_uuid}_output_audio.wav", acodec='pcm_s24le', ar=48000, map='a').run() | |
subprocess.run(f"ffmpeg -y -i {run_uuid}_output_audio.wav -af lowpass=3000,highpass=100 {run_uuid}_output_audio_final.wav", shell=True, check=True) | |
whisper_text = transcribe_audio(f"{run_uuid}_output_audio_final.wav") | |
print(f"Transcription successful: {whisper_text}") | |
target_language_code, voice = language_mapping[target_language] | |
translator = Translator() | |
translated_text = translator.translate(whisper_text, dest=target_language_code).text | |
print(f"Translated text: {translated_text}") | |
asyncio.run(text_to_speech(translated_text, voice, f"{run_uuid}_output_synth.wav")) | |
if use_wav2lip: | |
try: | |
subprocess.run(f"python Wav2Lip/inference.py --checkpoint_path 'Wav2Lip/checkpoints/wav2lip_gan.pth' --face '{video_path}' --audio '{run_uuid}_output_synth.wav' --pads 0 15 0 0 --resize_factor 1 --nosmooth --outfile '{run_uuid}_output_video.mp4'", shell=True, check=True) | |
except subprocess.CalledProcessError as e: | |
print(f"Wav2Lip error: {str(e)}") | |
gr.Warning("Wav2lip encountered an error. Falling back to simple audio replacement.") | |
subprocess.run(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", shell=True, check=True) | |
else: | |
subprocess.run(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", shell=True, check=True) | |
output_video_path = f"{run_uuid}_output_video.mp4" | |
if not os.path.exists(output_video_path): | |
raise FileNotFoundError(f"Error: {output_video_path} was not generated.") | |
cleanup_files( | |
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" | |
) | |
return output_video_path, "" | |
except Exception as e: | |
print(f"Error in process_video: {str(e)}") | |
return None, f"Error: {str(e)}" | |
# Gradio interface setup | |
with gr.Blocks(theme=gr.themes.Soft()) as demo: | |
gr.Markdown("# AI Video Dubbing") | |
gr.Markdown("This tool uses AI to dub videos into different languages. Upload a video, choose a target language, and get a dubbed version!") | |
with gr.Row(): | |
with gr.Column(scale=2): | |
video_input = gr.Video(label="Upload Video") | |
target_language = gr.Dropdown( | |
choices=list(language_mapping.keys()), | |
label="Target Language for Dubbing", | |
value="Spanish" | |
) | |
use_wav2lip = gr.Checkbox( | |
label="Use Wav2Lip for lip sync", | |
value=False, | |
info="Enable this if the video has close-up faces. May not work for all videos." | |
) | |
submit_button = gr.Button("Process Video", variant="primary") | |
with gr.Column(scale=2): | |
output_video = gr.Video(label="Processed Video") | |
error_message = gr.Textbox(label="Status/Error Message") | |
submit_button.click( | |
process_video, | |
inputs=[video_input, target_language, use_wav2lip], | |
outputs=[output_video, error_message] | |
) | |
gr.Markdown(""" | |
## Notes: | |
- Video limit is 1 minute. The tool will dub all speakers using a single voice. | |
- Processing may take up to 5 minutes. | |
- This is an alpha version using open-source models. | |
- Quality vs. speed trade-off was made for scalability and hardware limitations. | |
- For videos longer than 1 minute, please duplicate this Space and adjust the limit in the code. | |
""") | |
gr.Markdown(""" | |
--- | |
Developed by [@artificialguybr](https://twitter.com/artificialguybr) using open-source tools. | |
Special thanks to Hugging Face for GPU support and [@yeswondwer](https://twitter.com/@yeswondwerr) for the original code. | |
Try our [Video Transcription and Translation](https://huggingface.co/spaces/artificialguybr/VIDEO-TRANSLATION-TRANSCRIPTION) tool! | |
""") | |
print("Launching Gradio interface...") | |
demo.queue() | |
demo.launch() |