video-dubbing / app.py
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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="cuda", compute_type="float16")
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"
ffmpeg.input(video).output(output_filename, vf='scale=-2:720').run()
video_path = output_filename
if not os.path.exists(video_path):
return f"Error: {video_path} does not exist."
# Move the duration check here
video_info = ffmpeg.probe(video_path)
video_duration = float(video_info['streams'][0]['duration'])
if video_duration > 60:
os.remove(video_path) # Delete the resized video
return gr.Error("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()
#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)
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")
# Restart the script
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_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
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):
# Fallback to FFmpeg merge
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:
# Merge audio with the original video without running Wav2Lip
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"
# 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_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 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(concurrency_count=1, max_size=15)
demo.launch()