File size: 9,025 Bytes
6ff2047 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 |
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"machine_shape": "hm"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU",
"gpuClass": "standard"
},
"cells": [
{
"cell_type": "markdown",
"source": [
"# Super Wav2Lip\n",
"\n",
"This Colab project is based on [Wav2Lip-GFPGAN](https://github.com/ajay-sainy/Wav2Lip-GFPGAN), but updates the requirements.txt (to function properly) and updates Colab file for ease of use."
],
"metadata": {
"id": "JHXiEHrtVVxl"
}
},
{
"cell_type": "markdown",
"source": [
"## 1. Installation\n",
"\n",
"Run this block to install the necessary dependencies."
],
"metadata": {
"id": "VBrG_nOFpmWj"
}
},
{
"cell_type": "code",
"source": [
"!git clone https://github.com/indianajson/wav2lip-HD.git\n",
"basePath = \"/content/wav2lip-HD\"\n",
"%cd {basePath}\n",
"\n",
"wav2lipFolderName = 'Wav2Lip-master'\n",
"gfpganFolderName = 'GFPGAN-master'\n",
"wav2lipPath = basePath + '/' + wav2lipFolderName\n",
"gfpganPath = basePath + '/' + gfpganFolderName\n",
"\n",
"!wget 'https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth' -O {wav2lipPath}'/face_detection/detection/sfd/s3fd.pth'\n",
"\n",
"!wget 'https://iiitaphyd-my.sharepoint.com/personal/radrabha_m_research_iiit_ac_in/_layouts/15/download.aspx?share=EdjI7bZlgApMqsVoEUUXpLsBxqXbn5z8VTmoxp55YNDcIA' -O {wav2lipPath}'/checkpoints/wav2lip_gan.pth'\n",
"#!wget 'https://iiitaphyd-my.sharepoint.com/:u:/g/personal/radrabha_m_research_iiit_ac_in/Eb3LEzbfuKlJiR600lQWRxgBIY27JZg80f7V9jtMfbNDaQ?e=TBFBVW' -O {wav2lipPath}'/checkpoints/wav2lip.pth'\n",
"\n",
"!gdown https://drive.google.com/uc?id=1fQtBSYEyuai9MjBOF8j7zZ4oQ9W2N64q --output {wav2lipPath}'/checkpoints/'\n",
"\n",
"!pip install -r requirements.txt\n",
"!pip install -U librosa==0.8.1 # The process will fail without downgrading librosa\n",
"!mkdir inputs\n",
"\n",
"!cd $gfpganFolderName && python setup.py develop\n",
"!wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P {gfpganFolderName}'/experiments/pretrained_models'\n",
"\n",
"%cd {basePath}\n",
"\n",
"from IPython.display import clear_output\n",
"clear_output()\n",
"\n",
"print(\"Installation complete.\")"
],
"metadata": {
"id": "YhFe3CJGAIiV"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## 2. Synchronize Video and Speech"
],
"metadata": {
"id": "G0BMWoIyX2dc"
}
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "EqX_2YtkUjRI",
"cellView": "form"
},
"outputs": [],
"source": [
"#@markdown Adjust the parameters below then run the code block to synthesize the speech onto your video file:\n",
"\n",
"import os\n",
"outputPath = basePath+'/outputs' \n",
"inputAudio = 'audio.mp3' #@param{type:\"string\"}\n",
"inputAudioPath = basePath + '/inputs/' + inputAudio \n",
"inputVideo = 'video.mov' #@param{type:\"string\"}\n",
"inputVideoPath = basePath + '/inputs/'+inputVideo\n",
"lipSyncedOutputPath = basePath + '/outputs/result.mp4' \n",
"model = \"wav2lip\" #@param [\"wav2lip\", \"wav2lip_gan\"] {type:\"string\"}\n",
"\n",
"\n",
"if not os.path.exists(outputPath):\n",
" os.makedirs(outputPath)\n",
"\n",
"from IPython.display import clear_output\n",
"clear_output()\n",
"\n",
"!cd $wav2lipFolderName && python inference.py \\\n",
"--checkpoint_path checkpoints/{model}.pth \\\n",
"--face {inputVideoPath} \\\n",
"--audio {inputAudioPath} \\\n",
"--outfile {lipSyncedOutputPath}\n",
"\n",
"\n",
"\n",
"#print(\"Video synthesis complete.\")"
]
},
{
"cell_type": "markdown",
"source": [
"## 3. Boost the Resolution of the Synthesized Video\n",
"\n"
],
"metadata": {
"id": "oAIIanxYqFcE"
}
},
{
"cell_type": "code",
"source": [
"import cv2\n",
"from tqdm import tqdm\n",
"from os import path\n",
"\n",
"import os\n",
"\n",
"inputVideoPath = outputPath+'/result.mp4'\n",
"unProcessedFramesFolderPath = outputPath+'/frames'\n",
"\n",
"if not os.path.exists(unProcessedFramesFolderPath):\n",
" os.makedirs(unProcessedFramesFolderPath)\n",
"\n",
"vidcap = cv2.VideoCapture(inputVideoPath)\n",
"numberOfFrames = int(vidcap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
"fps = vidcap.get(cv2.CAP_PROP_FPS)\n",
"print(\"FPS: \", fps, \"Frames: \", numberOfFrames)\n",
"\n",
"for frameNumber in tqdm(range(numberOfFrames)):\n",
" _,image = vidcap.read()\n",
" cv2.imwrite(path.join(unProcessedFramesFolderPath, str(frameNumber).zfill(4)+'.jpg'), image)\n",
"\n",
"\n",
"!cd $gfpganFolderName && \\\n",
" python inference_gfpgan.py -i $unProcessedFramesFolderPath -o $outputPath -v 1.3 -s 2 --only_center_face --bg_upsampler None\n",
"\n",
"import os\n",
"restoredFramesPath = outputPath + '/restored_imgs/'\n",
"processedVideoOutputPath = outputPath\n",
"\n",
"dir_list = os.listdir(restoredFramesPath)\n",
"dir_list.sort()\n",
"\n",
"import cv2\n",
"import numpy as np\n",
"\n",
"#Get FPS of original video for writer\n",
"inputVideoPath = outputPath+'/result.mp4'\n",
"vidcap = cv2.VideoCapture(inputVideoPath)\n",
"fps = vidcap.get(cv2.CAP_PROP_FPS)\n",
"print(\"The video is \"+str(fps)+\" FPS.\")\n",
"\n",
"batch = 0\n",
"batchSize = 1300\n",
"from tqdm import tqdm\n",
"for i in tqdm(range(0, len(dir_list), batchSize)):\n",
" img_array = []\n",
" start, end = i, i+batchSize\n",
" print(\"processing \", start, end, end=\"\\r\")\n",
" for filename in tqdm(dir_list[start:end]):\n",
" filename = restoredFramesPath+filename;\n",
" img = cv2.imread(filename)\n",
" if img is None:\n",
" continue\n",
" height, width, layers = img.shape\n",
" size = (width,height)\n",
" img_array.append(img)\n",
" out = cv2.VideoWriter(processedVideoOutputPath+'/output_'+str(batch).zfill(4)+'.mp4',cv2.VideoWriter_fourcc(*'DIVX'), fps, size)\n",
" batch = batch + 1\n",
" \n",
" for i in range(len(img_array)):\n",
" out.write(img_array[i])\n",
" out.release()\n",
"\n",
"from IPython.display import clear_output\n",
"clear_output()\n",
"\n",
"print(\"Video upscaling complete.\")"
],
"metadata": {
"id": "XibzGPIVJfvP"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## 4. Clear Cached Files\n",
"\n",
"Run this block once you've downloaded your final video file. This will empty /inputs and /outputs, so you can start again, fresh.\n"
],
"metadata": {
"id": "ajuOpF9t0psE"
}
},
{
"cell_type": "markdown",
"source": [],
"metadata": {
"id": "J8JhzA9t_xrS"
}
},
{
"cell_type": "code",
"source": [
"%cd /content/wav2lip-HD/\n",
"\n",
"#@markdown Choose whether to remove both inputs and outputs, or just one of the two. You may want to preserve inputs if you are only changing one of the two inputs. \n",
"\n",
"removeInputs = True #@param {type:\"boolean\"}\n",
"removeOutputs = True #@param {type:\"boolean\"}\n",
"\n",
"if removeInputs == True:\n",
" %rm inputs/*\n",
"if removeOutputs == True:\n",
" %rm outputs/frames/*\n",
" %rm outputs/restored_imgs/*\n",
" %rm outputs/*\n",
"\n",
"\n",
"from IPython.display import clear_output\n",
"clear_output()\n",
"\n",
"print(\"Cleared cached files.\")\n"
],
"metadata": {
"id": "o2trJyiC0mRs",
"cellView": "form"
},
"execution_count": null,
"outputs": []
}
]
} |