File size: 6,019 Bytes
da0e3ab
73fd4c0
 
ed64e04
80b43a8
73fd4c0
 
 
fbd6bad
73fd4c0
 
 
 
 
cf7b168
739fd69
ae3f094
 
 
 
 
9e19d29
 
 
7b3eb41
73fd4c0
2d49e86
 
 
 
 
9e19d29
fbd6bad
 
 
3364e9c
80b43a8
 
 
 
 
 
 
45a4010
80b43a8
ac401be
 
992f837
45a4010
10ac59a
 
 
233c677
 
 
80b43a8
233c677
0cab5bf
 
 
 
233c677
0cab5bf
 
 
 
233c677
0cab5bf
233c677
 
fbd6bad
 
 
902b7eb
80b43a8
233c677
 
 
4eae89a
 
9462754
4eae89a
43edaa1
 
 
233c677
80b43a8
 
233c677
 
 
 
 
 
 
 
 
80b43a8
233c677
80b43a8
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
892ff2a
 
 
3364e9c
 
 
 
 
 
 
 
73fd4c0
 
 
3364e9c
 
 
73fd4c0
3364e9c
c3f9f52
 
 
 
 
 
 
 
 
 
73fd4c0
3364e9c
 
 
45a4010
3364e9c
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
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
import threading
import time
import GPUtil

os.environ["COQUI_TOS_AGREED"] = "1"

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="int8")

def process_video(radio, video, target_language):
    # Check video duration
    video_info = ffmpeg.probe(video)
    video_duration = float(video_info['streams'][0]['duration'])
    if video_duration > 90:
        return gr.Interface.Warnings("Video duration exceeds 1 minute and 30 seconds. Please upload a shorter video.")

    run_uuid = uuid.uuid4().hex[:6]
    
    output_filename = f"{run_uuid}_resized_video.mp4"
    #ffmpeg.input(video).output(output_filename, vf='scale=-1:720:force_original_aspect_ratio=decrease').run()
    ffmpeg.input(video).output(output_filename, vf='scale=-2:720').run()

    video_path = output_filename
    
    #Time tracking
    start_time = time.time()
    if not os.path.exists(video_path):
        return f"Error: {video_path} does not exist."

    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)

    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(whisper_text)

    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()
    try:
        translated_text = translator.translate(whisper_text, src=whisper_language, dest=target_language_code).text
        print(translated_text)
    except AttributeError as e:
        print("Failed to translate text. Likely an issue with token extraction in the Google Translate API.")
        translated_text = "Translation failed due to API issue."

    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

    cmd = f"python Wav2Lip/inference.py --checkpoint_path 'Wav2Lip/checkpoints/wav2lip_gan.pth' --face {shlex.quote(video_path_fix)} --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)

    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_denoised.wav",
        f"{run_uuid}_output_audio_processed.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"], 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")
    ],
    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.

    **Note:**
    - Video limit is 1 minute.
    - Generation may take up to 5 minutes.
    - The tool uses open-source models for all operations.
    - Quality can be improved but would require more processing time per video.""",
    allow_flagging=False
)
with gr.Blocks() as demo:
    iface.render()
    radio.change(swap, inputs=[radio], outputs=video)
demo.queue(concurrency_count=2, max_size=15)
demo.launch()