app.py

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

# Environment setup
HF_TOKEN = os.environ.get("HF_TOKEN")
os.environ["COQUI_TOS_AGREED"] = "1"
api = HfApi(token=HF_TOKEN)
repo_id = "artificialguybr/video-dubbing"

# Extract ffmpeg
ZipFile("ffmpeg.zip").extractall()
st = os.stat('ffmpeg')
os.chmod('ffmpeg', st.st_mode | stat.S_IEXEC)

# Initialize Whisper model
model_size = "small"
model = WhisperModel(model_size, device="cpu", compute_type="int8")

# Initialize TTS model
tts = TTS("tts_models/multilingual/multi-dataset/xtts_v2")

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 transcribe_audio(audio_path):
    segments, info = model.transcribe(audio_path, beam_size=5)
    whisper_text = " ".join(segment.text for segment in segments)
    whisper_language = info.language
    return whisper_text, whisper_language

@spaces.GPU
def generate_tts(text, speaker_wav, language_code):
    tts.tts_to_file(text, speaker_wav=speaker_wav, file_path="output_synth.wav", language=language_code)

@spaces.GPU
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."

    video_info = ffmpeg.probe(video_path)
    video_duration = float(video_info['streams'][0]['duration'])

    if video_duration > 60:
        os.remove(video_path)
        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()

    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:
        whisper_text, whisper_language = transcribe_audio(f"{run_uuid}_output_audio_final.wav")
        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 needs 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)

    generate_tts(translated_text, f"{run_uuid}_output_audio_final.wav", 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 '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.")
                cmd = f"ffmpeg -i {video_path} -i 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 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
    files_to_delete = [
        f"{run_uuid}_resized_video.mp4",
        f"{run_uuid}_output_audio.wav",
        f"{run_uuid}_output_audio_final.wav",
        "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):
    return gr.update(source="upload" if radio == "Upload" else "webcam")

# Gradio interface setup
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()