video-dubbing / app.py
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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}")
@spaces.GPU(duration=90)
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)
@spaces.GPU
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()