├── license
    ├── ru.pdf
    └── en_us.pdf
├── images
    └── demo.gif
├── requirements.txt
├── examples
    ├── f17a6060-6ced-4bd1-9886-8578cfbb864f.mp4
    ├── demo_video_torch.ipynb
    └── demo_video_onnx.ipynb
├── config_example.yaml
├── pyproject.toml
├── .pre-commit-config.yaml
├── .gitignore
├── README.md
├── demo.py
└── constants.py


/license/ru.pdf:
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https://raw.githubusercontent.com/hukenovs/slovo/HEAD/license/ru.pdf


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/images/demo.gif:
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https://raw.githubusercontent.com/hukenovs/slovo/HEAD/images/demo.gif


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/license/en_us.pdf:
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https://raw.githubusercontent.com/hukenovs/slovo/HEAD/license/en_us.pdf


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/requirements.txt:
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1 | loguru==0.7.0
2 | omegaconf==2.3.0
3 | onnxruntime==1.14.1
4 | opencv-contrib-python==4.7.0.72
5 | torch>=1.8
6 | 


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/examples/f17a6060-6ced-4bd1-9886-8578cfbb864f.mp4:
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https://raw.githubusercontent.com/hukenovs/slovo/HEAD/examples/f17a6060-6ced-4bd1-9886-8578cfbb864f.mp4


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/config_example.yaml:
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1 | model_path: mvit32-2.onnx
2 | frame_interval: 2 # set frame interval
3 | mean: [123.675, 116.28, 103.53]
4 | std: [58.395, 57.12, 57.375]
5 | 


--------------------------------------------------------------------------------
/pyproject.toml:
--------------------------------------------------------------------------------
 1 | # [project]
 2 | # dynamic = ["dependencies"]
 3 | #
 4 | # [tool.setuptools.dynamic]
 5 | # dependencies = requirements.txt
 6 | 
 7 | # TODO 2020/06/02: Check Flake8
 8 | # [tool.flake8]
 9 | # ignore = ['E203', 'E266', 'E501', 'W503', 'F403', 'F401']
10 | # max-line-length = 120
11 | # max-complexity = 18
12 | # select = ['B','C','E','F','W','T4','B9']
13 | 
14 | [tool.isort]
15 | # known_third_party = ["cv2", "numpy", "pandas", "qimage2ndarray", "scipy", "sklearn", "tensorflow", "torch"]
16 | multi_line_output = 3
17 | include_trailing_comma = true
18 | force_grid_wrap = 0
19 | use_parentheses = true
20 | line_length = 120
21 | 
22 | [tool.black]
23 | line-length = 120
24 | target-version = ['py39']
25 | exclude = '''
26 | /(
27 |     \.eggs
28 |   | \.git
29 |   | \.hg
30 |   | \.mypy_cache
31 |   | \.tox
32 |   | \.venv
33 |   | _build
34 |   | buck-out
35 |   | build
36 |   | dist
37 |   # The following are specific to Black, you probably don't want those.
38 |   | blib2to3
39 |   | tests/data
40 |   | profiling
41 | )/
42 | '''
43 | 


--------------------------------------------------------------------------------
/.pre-commit-config.yaml:
--------------------------------------------------------------------------------
 1 | exclude: '^$'
 2 | fail_fast: false
 3 | default_language_version:
 4 |   python: python3.9
 5 | repos:
 6 |   - repo: https://github.com/psf/black
 7 |     rev: 22.6.0
 8 |     hooks:
 9 |       - id: black
10 |         language_version: python3
11 |         args:
12 |           - "--line-length=120"
13 |   - repo: https://github.com/PyCQA/flake8
14 |     rev: 4.0.1
15 |     hooks:
16 |       - id: flake8
17 |         language_version: python3
18 |         args:
19 |           - "--max-line-length=120"
20 |           - "--ignore=E203,E265,E309,E501,E265,W503,E402"
21 |   - repo: https://github.com/pre-commit/pre-commit-hooks
22 |     rev: v3.2.0
23 |     hooks:
24 |       - id: check-docstring-first
25 |       - id: check-merge-conflict
26 |       - id: check-yaml
27 |       - id: trailing-whitespace
28 |       - id: end-of-file-fixer
29 |       - id: requirements-txt-fixer
30 | 
31 |   - repo: https://github.com/PyCQA/autoflake
32 |     rev: v1.4
33 |     hooks:
34 |       - id: autoflake
35 |         args: ['-r', '--in-place',
36 |                '--remove-all-unused-imports',
37 |                '--ignore-init-module-imports',
38 |                '--remove-unused-variables',
39 |                '--remove-duplicate-keys'
40 |               ]
41 | 
42 |   - repo: https://github.com/PyCQA/isort
43 |     rev: 5.10.1
44 |     hooks:
45 |       - id: isort
46 |         args:
47 |           - "--profile=black"
48 | 


--------------------------------------------------------------------------------
/.gitignore:
--------------------------------------------------------------------------------
  1 | # Byte-compiled / optimized / DLL files
  2 | __pycache__/
  3 | *.py[cod]
  4 | *.pyc
  5 | *$py.class
  6 | 
  7 | # C extensions
  8 | *.so
  9 | 
 10 | # Distribution / packaging
 11 | .Python
 12 | env/
 13 | build/
 14 | develop-eggs/
 15 | dist/
 16 | downloads/
 17 | eggs/
 18 | .eggs/
 19 | lib64/
 20 | /parts/
 21 | sdist/
 22 | var/
 23 | wheels/
 24 | *.egg-info/
 25 | .installed.cfg
 26 | *.egg
 27 | .DS_Store
 28 | 
 29 | # PyInstaller
 30 | #  Usually these files are written by a python script from a template
 31 | #  before PyInstaller builds the exe, so as to inject date/other infos into it.
 32 | *.manifest
 33 | *.spec
 34 | 
 35 | # Installer logs
 36 | pip-log.txt
 37 | pip-delete-this-directory.txt
 38 | 
 39 | # Unit test / coverage reports
 40 | htmlcov/
 41 | .tox/
 42 | .coverage
 43 | .coverage.*
 44 | .cache
 45 | nosetests.xml
 46 | coverage.xml
 47 | *,cover
 48 | .hypothesis/
 49 | 
 50 | # Translations
 51 | *.mo
 52 | *.pot
 53 | 
 54 | # Django stuff:
 55 | *.log
 56 | local_settings.py
 57 | 
 58 | # Flask stuff:
 59 | instance/
 60 | .webassets-cache
 61 | 
 62 | # Scrapy stuff:
 63 | .scrapy
 64 | 
 65 | # Sphinx documentation
 66 | docs/_build/
 67 | 
 68 | # PyBuilder
 69 | target/
 70 | 
 71 | # Jupyter Notebook
 72 | .ipynb_checkpoints
 73 | 
 74 | # IntelliJ IDEA
 75 | .idea/
 76 | 
 77 | #VS Code
 78 | .vscode/
 79 | 
 80 | # pyenv
 81 | .python-version
 82 | 
 83 | # celery beat schedule file
 84 | celerybeat-schedule
 85 | 
 86 | # SageMath parsed files
 87 | *.sage.py
 88 | 
 89 | # dotenv
 90 | .env
 91 | 
 92 | # virtualenv
 93 | .venv
 94 | venv/
 95 | ENV/
 96 | 
 97 | # Spyder project settings
 98 | .spyderproject
 99 | .spyproject
100 | 
101 | # Rope project settings
102 | .ropeproject
103 | 
104 | # User added:
105 | /templates
106 | *.pth
107 | *.pt
108 | *.onnx
109 | 


--------------------------------------------------------------------------------
/examples/demo_video_torch.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "id": "775d1720-0fa3-40bf-a9e5-a8cc44744dca",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "from IPython import display\n",
 11 |     "import sys\n",
 12 |     "sys.path.append(\"../\")\n",
 13 |     "\n",
 14 |     "import torch\n",
 15 |     "import numpy as np\n",
 16 |     "import cv2\n",
 17 |     "from PIL import Image\n",
 18 |     "\n",
 19 |     "from constants import classes"
 20 |    ]
 21 |   },
 22 |   {
 23 |    "cell_type": "code",
 24 |    "execution_count": 2,
 25 |    "id": "76ed828a-a15a-489c-bfd8-dcf24d3db703",
 26 |    "metadata": {},
 27 |    "outputs": [],
 28 |    "source": [
 29 |     "path_to_model = \"../mvit16-1.pt\"\n",
 30 |     "path_to_input_video = \"f17a6060-6ced-4bd1-9886-8578cfbb864f.mp4\"\n",
 31 |     "path_to_output_video = \"output_torch.mp4\""
 32 |    ]
 33 |   },
 34 |   {
 35 |    "cell_type": "code",
 36 |    "execution_count": 3,
 37 |    "id": "4a36baad-bd49-4126-b98a-4f20b7919caf",
 38 |    "metadata": {},
 39 |    "outputs": [],
 40 |    "source": [
 41 |     "model = torch.jit.load(path_to_model)\n",
 42 |     "model.eval()\n",
 43 |     "window_size = 16 # from model name\n",
 44 |     "threshold = 0.5\n",
 45 |     "frame_interval = 1\n",
 46 |     "mean = [123.675, 116.28, 103.53]\n",
 47 |     "std = [58.395, 57.12, 57.375]"
 48 |    ]
 49 |   },
 50 |   {
 51 |    "cell_type": "code",
 52 |    "execution_count": 4,
 53 |    "id": "d72fb23e-3946-4b76-ac62-cfcc325ff657",
 54 |    "metadata": {},
 55 |    "outputs": [],
 56 |    "source": [
 57 |     "def resize(im, new_shape=(224, 224)):\n",
 58 |     "    \"\"\"\n",
 59 |     "    Resize and pad image while preserving aspect ratio.\n",
 60 |     "\n",
 61 |     "    Parameters\n",
 62 |     "    ----------\n",
 63 |     "    im : np.ndarray\n",
 64 |     "        Image to be resized.\n",
 65 |     "    new_shape : Tuple[int]\n",
 66 |     "        Size of the new image.\n",
 67 |     "\n",
 68 |     "    Returns\n",
 69 |     "    -------\n",
 70 |     "    np.ndarray\n",
 71 |     "        Resized image.\n",
 72 |     "    \"\"\"\n",
 73 |     "    shape = im.shape[:2]  # current shape [height, width]\n",
 74 |     "    if isinstance(new_shape, int):\n",
 75 |     "        new_shape = (new_shape, new_shape)\n",
 76 |     "\n",
 77 |     "    # Scale ratio (new / old)\n",
 78 |     "    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])\n",
 79 |     "\n",
 80 |     "    # Compute padding\n",
 81 |     "    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))\n",
 82 |     "    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding\n",
 83 |     "\n",
 84 |     "    dw /= 2\n",
 85 |     "    dh /= 2\n",
 86 |     "\n",
 87 |     "    if shape[::-1] != new_unpad:  # resize\n",
 88 |     "        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)\n",
 89 |     "    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))\n",
 90 |     "    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))\n",
 91 |     "    im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=(114, 114, 114))  # add border\n",
 92 |     "    return im"
 93 |    ]
 94 |   },
 95 |   {
 96 |    "cell_type": "code",
 97 |    "execution_count": 5,
 98 |    "id": "184ed911-6b9b-4250-a30b-c347e3be2ed1",
 99 |    "metadata": {},
100 |    "outputs": [],
101 |    "source": [
102 |     "cap = cv2.VideoCapture(path_to_input_video)\n",
103 |     "_,frame = cap.read()\n",
104 |     "shape = frame.shape\n",
105 |     "fourcc = cv2.VideoWriter_fourcc(*'H264')\n",
106 |     "writer = cv2.VideoWriter(path_to_output_video, fourcc, 30, (frame.shape[1], frame.shape[0]+50))\n",
107 |     "\n",
108 |     "tensors_list = []\n",
109 |     "prediction_list = []\n",
110 |     "prediction_list.append(\"---\")\n",
111 |     "\n",
112 |     "frame_counter = 0\n",
113 |     "while True:\n",
114 |     "    _, frame = cap.read()\n",
115 |     "    if frame is None:\n",
116 |     "        break\n",
117 |     "    frame_counter += 1\n",
118 |     "    if frame_counter == frame_interval:\n",
119 |     "        image = cv2.cvtColor(frame.copy(), cv2.COLOR_BGR2RGB)\n",
120 |     "        image = resize(image, (224, 224))\n",
121 |     "        image = (image - mean) / std\n",
122 |     "        image = np.transpose(image, [2, 0, 1])\n",
123 |     "        tensors_list.append(image)\n",
124 |     "        if len(tensors_list) == window_size:\n",
125 |     "            input_tensor = np.stack(tensors_list[: window_size], axis=1)[None][None]\n",
126 |     "            input_tensor = input_tensor.astype(np.float32)\n",
127 |     "            input_tensor = torch.from_numpy(input_tensor)\n",
128 |     "            with torch.no_grad():\n",
129 |     "                outputs = model(input_tensor)[0]\n",
130 |     "            gloss = str(classes[outputs.argmax().item()])\n",
131 |     "            if outputs.max() > threshold:\n",
132 |     "                if gloss != prediction_list[-1] and len(prediction_list):\n",
133 |     "                    if gloss != \"---\":\n",
134 |     "                        prediction_list.append(gloss)\n",
135 |     "            tensors_list.clear()\n",
136 |     "        frame_counter = 0\n",
137 |     "\n",
138 |     "    text = \"  \".join(prediction_list)\n",
139 |     "    text_div = np.zeros((50, frame.shape[1], 3), dtype=np.uint8)\n",
140 |     "    cv2.putText(text_div, text, (10, 30), cv2.FONT_HERSHEY_COMPLEX, 0.7, (255, 255, 255), 2)\n",
141 |     "\n",
142 |     "    frame = np.concatenate((frame, text_div), axis=0)\n",
143 |     "    writer.write(frame)\n",
144 |     "writer.release()\n",
145 |     "cap.release()"
146 |    ]
147 |   },
148 |   {
149 |    "cell_type": "code",
150 |    "execution_count": 6,
151 |    "id": "3c512a02-1d2b-4603-b3cd-9801216c3bdf",
152 |    "metadata": {},
153 |    "outputs": [],
154 |    "source": [
155 |     "from IPython.display import display, HTML"
156 |    ]
157 |   },
158 |   {
159 |    "cell_type": "code",
160 |    "execution_count": 7,
161 |    "id": "53a41c5c-dcff-439b-a17a-07b9530525f8",
162 |    "metadata": {},
163 |    "outputs": [
164 |     {
165 |      "data": {
166 |       "text/html": [
167 |        "<video controls src=\"output_torch.mp4\" width=\"640\" height=\"480\" />"
168 |       ],
169 |       "text/plain": [
170 |        "<IPython.core.display.HTML object>"
171 |       ]
172 |      },
173 |      "metadata": {},
174 |      "output_type": "display_data"
175 |     }
176 |    ],
177 |    "source": [
178 |     "video_tag = f'<video controls src=\"{path_to_output_video}\" width=\"640\" height=\"480\" />'\n",
179 |     "display(HTML(video_tag))"
180 |    ]
181 |   },
182 |   {
183 |    "cell_type": "code",
184 |    "execution_count": null,
185 |    "id": "226bd051-c0f4-48eb-9555-5c24526cccf5",
186 |    "metadata": {},
187 |    "outputs": [],
188 |    "source": []
189 |   }
190 |  ],
191 |  "metadata": {
192 |   "kernelspec": {
193 |    "display_name": "Python 3 (ipykernel)",
194 |    "language": "python",
195 |    "name": "python3"
196 |   },
197 |   "language_info": {
198 |    "codemirror_mode": {
199 |     "name": "ipython",
200 |     "version": 3
201 |    },
202 |    "file_extension": ".py",
203 |    "mimetype": "text/x-python",
204 |    "name": "python",
205 |    "nbconvert_exporter": "python",
206 |    "pygments_lexer": "ipython3",
207 |    "version": "3.9.13"
208 |   }
209 |  },
210 |  "nbformat": 4,
211 |  "nbformat_minor": 5
212 | }
213 | 


--------------------------------------------------------------------------------
/examples/demo_video_onnx.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "code",
  5 |    "execution_count": 1,
  6 |    "id": "775d1720-0fa3-40bf-a9e5-a8cc44744dca",
  7 |    "metadata": {},
  8 |    "outputs": [],
  9 |    "source": [
 10 |     "from IPython import display\n",
 11 |     "import sys\n",
 12 |     "sys.path.append(\"../\")\n",
 13 |     "\n",
 14 |     "import onnxruntime as ort\n",
 15 |     "import numpy as np\n",
 16 |     "import cv2\n",
 17 |     "from PIL import Image\n",
 18 |     "\n",
 19 |     "from constants import classes"
 20 |    ]
 21 |   },
 22 |   {
 23 |    "cell_type": "code",
 24 |    "execution_count": 2,
 25 |    "id": "76ed828a-a15a-489c-bfd8-dcf24d3db703",
 26 |    "metadata": {},
 27 |    "outputs": [],
 28 |    "source": [
 29 |     "path_to_model = \"../mvit16-1.onnx\"\n",
 30 |     "path_to_input_video = \"f17a6060-6ced-4bd1-9886-8578cfbb864f.mp4\"\n",
 31 |     "path_to_output_video = \"output_onnx.mp4\""
 32 |    ]
 33 |   },
 34 |   {
 35 |    "cell_type": "code",
 36 |    "execution_count": 3,
 37 |    "id": "785fd614-df61-4bcc-90c3-2016da92fa46",
 38 |    "metadata": {},
 39 |    "outputs": [],
 40 |    "source": [
 41 |     "session = ort.InferenceSession(path_to_model)\n",
 42 |     "input_name = session.get_inputs()[0].name\n",
 43 |     "input_shape = session.get_inputs()[0].shape\n",
 44 |     "window_size = input_shape[3]\n",
 45 |     "output_names = [output.name for output in session.get_outputs()]\n",
 46 |     "\n",
 47 |     "threshold = 0.5\n",
 48 |     "frame_interval = 1\n",
 49 |     "mean = [123.675, 116.28, 103.53]\n",
 50 |     "std = [58.395, 57.12, 57.375]"
 51 |    ]
 52 |   },
 53 |   {
 54 |    "cell_type": "code",
 55 |    "execution_count": 4,
 56 |    "id": "d72fb23e-3946-4b76-ac62-cfcc325ff657",
 57 |    "metadata": {},
 58 |    "outputs": [],
 59 |    "source": [
 60 |     "def resize(im, new_shape=(224, 224)):\n",
 61 |     "    \"\"\"\n",
 62 |     "    Resize and pad image while preserving aspect ratio.\n",
 63 |     "\n",
 64 |     "    Parameters\n",
 65 |     "    ----------\n",
 66 |     "    im : np.ndarray\n",
 67 |     "        Image to be resized.\n",
 68 |     "    new_shape : Tuple[int]\n",
 69 |     "        Size of the new image.\n",
 70 |     "\n",
 71 |     "    Returns\n",
 72 |     "    -------\n",
 73 |     "    np.ndarray\n",
 74 |     "        Resized image.\n",
 75 |     "    \"\"\"\n",
 76 |     "    shape = im.shape[:2]  # current shape [height, width]\n",
 77 |     "    if isinstance(new_shape, int):\n",
 78 |     "        new_shape = (new_shape, new_shape)\n",
 79 |     "\n",
 80 |     "    # Scale ratio (new / old)\n",
 81 |     "    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])\n",
 82 |     "\n",
 83 |     "    # Compute padding\n",
 84 |     "    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))\n",
 85 |     "    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding\n",
 86 |     "\n",
 87 |     "    dw /= 2\n",
 88 |     "    dh /= 2\n",
 89 |     "\n",
 90 |     "    if shape[::-1] != new_unpad:  # resize\n",
 91 |     "        im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)\n",
 92 |     "    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))\n",
 93 |     "    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))\n",
 94 |     "    im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=(114, 114, 114))  # add border\n",
 95 |     "    return im"
 96 |    ]
 97 |   },
 98 |   {
 99 |    "cell_type": "code",
100 |    "execution_count": 5,
101 |    "id": "184ed911-6b9b-4250-a30b-c347e3be2ed1",
102 |    "metadata": {},
103 |    "outputs": [],
104 |    "source": [
105 |     "cap = cv2.VideoCapture(path_to_input_video)\n",
106 |     "_,frame = cap.read()\n",
107 |     "shape = frame.shape\n",
108 |     "fourcc = cv2.VideoWriter_fourcc(*'H264')\n",
109 |     "writer = cv2.VideoWriter(path_to_output_video, fourcc, 30, (frame.shape[1], frame.shape[0]+50))\n",
110 |     "\n",
111 |     "tensors_list = []\n",
112 |     "prediction_list = []\n",
113 |     "prediction_list.append(\"---\")\n",
114 |     "\n",
115 |     "frame_counter = 0\n",
116 |     "while True:\n",
117 |     "    _, frame = cap.read()\n",
118 |     "    if frame is None:\n",
119 |     "        break\n",
120 |     "    frame_counter += 1\n",
121 |     "    if frame_counter == frame_interval:\n",
122 |     "        image = cv2.cvtColor(frame.copy(), cv2.COLOR_BGR2RGB)\n",
123 |     "        image = resize(image, (224, 224))\n",
124 |     "        image = (image - mean) / std\n",
125 |     "        image = np.transpose(image, [2, 0, 1])\n",
126 |     "        tensors_list.append(image)\n",
127 |     "        if len(tensors_list) == window_size:\n",
128 |     "            input_tensor = np.stack(tensors_list[: window_size], axis=1)[None][None]\n",
129 |     "            outputs = session.run(output_names, {input_name: input_tensor.astype(np.float32)})[0]\n",
130 |     "            gloss = str(classes[outputs.argmax()])\n",
131 |     "            if outputs.max() > threshold:\n",
132 |     "                if gloss != prediction_list[-1] and len(prediction_list):\n",
133 |     "                    if gloss != \"---\":\n",
134 |     "                        prediction_list.append(gloss)\n",
135 |     "            tensors_list.clear()\n",
136 |     "        frame_counter = 0\n",
137 |     "\n",
138 |     "    text = \"  \".join(prediction_list)\n",
139 |     "    text_div = np.zeros((50, frame.shape[1], 3), dtype=np.uint8)\n",
140 |     "    cv2.putText(text_div, text, (10, 30), cv2.FONT_HERSHEY_COMPLEX, 0.7, (255, 255, 255), 2)\n",
141 |     "\n",
142 |     "    frame = np.concatenate((frame, text_div), axis=0)\n",
143 |     "    writer.write(frame)\n",
144 |     "writer.release()\n",
145 |     "cap.release()"
146 |    ]
147 |   },
148 |   {
149 |    "cell_type": "code",
150 |    "execution_count": 6,
151 |    "id": "3c512a02-1d2b-4603-b3cd-9801216c3bdf",
152 |    "metadata": {},
153 |    "outputs": [],
154 |    "source": [
155 |     "from IPython.display import display, HTML"
156 |    ]
157 |   },
158 |   {
159 |    "cell_type": "code",
160 |    "execution_count": 7,
161 |    "id": "53a41c5c-dcff-439b-a17a-07b9530525f8",
162 |    "metadata": {},
163 |    "outputs": [
164 |     {
165 |      "data": {
166 |       "text/html": [
167 |        "<video controls src=\"output_onnx.mp4\" width=\"640\" height=\"480\" />"
168 |       ],
169 |       "text/plain": [
170 |        "<IPython.core.display.HTML object>"
171 |       ]
172 |      },
173 |      "metadata": {},
174 |      "output_type": "display_data"
175 |     }
176 |    ],
177 |    "source": [
178 |     "video_tag = f'<video controls src=\"{path_to_output_video}\" width=\"640\" height=\"480\" />'\n",
179 |     "display(HTML(video_tag))"
180 |    ]
181 |   },
182 |   {
183 |    "cell_type": "code",
184 |    "execution_count": null,
185 |    "id": "226bd051-c0f4-48eb-9555-5c24526cccf5",
186 |    "metadata": {},
187 |    "outputs": [],
188 |    "source": []
189 |   }
190 |  ],
191 |  "metadata": {
192 |   "kernelspec": {
193 |    "display_name": "Python 3 (ipykernel)",
194 |    "language": "python",
195 |    "name": "python3"
196 |   },
197 |   "language_info": {
198 |    "codemirror_mode": {
199 |     "name": "ipython",
200 |     "version": 3
201 |    },
202 |    "file_extension": ".py",
203 |    "mimetype": "text/x-python",
204 |    "name": "python",
205 |    "nbconvert_exporter": "python",
206 |    "pygments_lexer": "ipython3",
207 |    "version": "3.9.13"
208 |   }
209 |  },
210 |  "nbformat": 4,
211 |  "nbformat_minor": 5
212 | }
213 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
  1 | # Slovo - Russian Sign Language Dataset
  2 | 
  3 | We introduce a large-scale video dataset **Slovo** for Russian Sign Language task. Slovo dataset size is about **16 GB**, and it contains **20400** RGB videos for **1000** sign language gestures from 194 singers. Each class has 20 samples. The dataset is divided into training set and test set by subject `user_id`. The training set includes 15300 videos, and the test set includes 5100 videos. The total video recording time is ~9.2 hours. About 35% of the videos are recorded in HD format, and 65% of the videos are in FullHD resolution. The average video length with gesture is 50 frames.
  4 | 
  5 | For more information see our paper - [arXiv](https://arxiv.org/abs/2305.14527).
  6 | 
  7 | ## Downloads
  8 | ### [Main download link](https://rndml-team-cv.obs.ru-moscow-1.hc.sbercloud.ru/datasets/slovo/slovo.zip)
  9 | 
 10 | |                                                                                               Downloads | Size (GB) | Comment                                                              |
 11 | |--------------------------------------------------------------------------------------------------------:|:----------|:---------------------------------------------------------------------|
 12 | |                [Slovo](https://rndml-team-cv.obs.ru-moscow-1.hc.sbercloud.ru/datasets/slovo/slovo.zip) | ~16       | Trimmed HD+ videos by `(start, end)` annotations                     |
 13 | |          [Origin](https://rndml-team-cv.obs.ru-moscow-1.hc.sbercloud.ru/datasets/slovo/slovo_full.zip) | ~105      | Original HD+ videos from mining stage                                |
 14 | |        [360p](https://rndml-team-cv.obs.ru-moscow-1.hc.sbercloud.ru/datasets/slovo/slovo_full360.zip)  | ~13       | Resized original videos by `min_side = 360`                          |
 15 | | [Landmarks](https://rndml-team-cv.obs.ru-moscow-1.hc.sbercloud.ru/datasets/slovo/slovo_mediapipe.json) | ~1.2      | Mediapipe hand landmark annotations for each frame of trimmed videos |
 16 | 
 17 | Also, you can download **Slovo** from [Kaggle](https://www.kaggle.com/datasets/kapitanov/slovo).
 18 | 
 19 | Annotation file is easy to use and contains some useful columns, see `annotations.csv` file:
 20 | 
 21 | |    | attachment_id | user_id | width | height | length |  text  | train   | begin | end |
 22 | |---:|:--------------|:--------|------:|-------:|-------:|-------:|:--------|:------|:----|
 23 | |  0 | de81cc1c-...  | 1b...   |  1440 |   1920 |     14 | привет | True    | 30    | 45  |
 24 | |  1 | 3c0cec5a-...  | 64...   |  1440 |   1920 |     32 |   утро | False   | 43    | 66  |
 25 | |  2 | d17ca986-...  | cf...   |  1920 |   1080 |     44 |  улица | False   | 12    | 31  |
 26 | 
 27 | where:
 28 | - `attachment_id` - video file name
 29 | - `user_id` - unique anonymized user ID
 30 | - `width` - video width
 31 | - `height` - video height
 32 | - `length` - video length
 33 | - `text` - gesture class in Russian Langauge
 34 | - `train` - train or test boolean flag
 35 | - `begin` - start of the gesture (for original dataset)
 36 | - `end` - end of the gesture (for original dataset)
 37 | 
 38 | For convenience, we have also prepared a compressed version of the dataset, in which all videos are processed by the minimum side `min_side = 360`. Download link - **[slovo360p](https://rndml-team-cv.obs.ru-moscow-1.hc.sbercloud.ru/datasets/slovo/slovo_full360.zip)**.
 39 | Also, we annotate trimmed videos by using **MediaPipe** and provide hand keypoints in [this annotation file](https://rndml-team-cv.obs.ru-moscow-1.hc.sbercloud.ru/datasets/slovo/slovo_mediapipe.json).
 40 | 
 41 | ## Models
 42 | We provide some pre-trained models as the baseline for Russian sign language recognition.
 43 | We tested models with frames number from [16, 32, 48], and **the best for each are below**.
 44 | The first number in the model name is frames number and the second is frame interval.
 45 | 
 46 | | Model Name        | Model Size (MB) | Metric | ONNX                                                                                                            | TorchScript                                                                                                 |
 47 | |-------------------|-----------------|--------|-----------------------------------------------------------------------------------------------------------------|-------------------------------------------------------------------------------------------------------------|
 48 | | MViTv2-small-16-4 | 140.51          | 58.35  | [weights](https://rndml-team-cv.obs.ru-moscow-1.hc.sbercloud.ru/datasets/slovo/models/mvit/onnx/mvit16-4.onnx) | [weights](https://rndml-team-cv.obs.ru-moscow-1.hc.sbercloud.ru/datasets/slovo/models/mvit/pt/mvit16-4.pt) |
 49 | | MViTv2-small-32-2 | 140.79          | 64.09  | [weights](https://rndml-team-cv.obs.ru-moscow-1.hc.sbercloud.ru/datasets/slovo/models/mvit/onnx/mvit32-2.onnx) | [weights](https://rndml-team-cv.obs.ru-moscow-1.hc.sbercloud.ru/datasets/slovo/models/mvit/pt/mvit32-2.pt) |
 50 | | MViTv2-small-48-2 | 141.05          | 62.18  | [weights](https://rndml-team-cv.obs.ru-moscow-1.hc.sbercloud.ru/datasets/slovo/models/mvit/onnx/mvit48-2.onnx) | [weights](https://rndml-team-cv.obs.ru-moscow-1.hc.sbercloud.ru/datasets/slovo/models/mvit/pt/mvit48-2.pt) |
 51 | | Swin-large-16-3   | 821.65          | 48.04  | [weights](https://rndml-team-cv.obs.ru-moscow-1.hc.sbercloud.ru/datasets/slovo/models/swin/onnx/swin16-3.onnx) | [weights](https://rndml-team-cv.obs.ru-moscow-1.hc.sbercloud.ru/datasets/slovo/models/swin/pt/swin16-3.pt) |
 52 | | Swin-large-32-2   | 821.74          | 54.84  | [weights](https://rndml-team-cv.obs.ru-moscow-1.hc.sbercloud.ru/datasets/slovo/models/swin/onnx/swin32-2.onnx) | [weights](https://rndml-team-cv.obs.ru-moscow-1.hc.sbercloud.ru/datasets/slovo/models/swin/pt/swin32-2.pt) |
 53 | | Swin-large-48-1   | 821.78          | 55.66  | [weights](https://rndml-team-cv.obs.ru-moscow-1.hc.sbercloud.ru/datasets/slovo/models/swin/onnx/swin48-1.onnx) | [weights](https://rndml-team-cv.obs.ru-moscow-1.hc.sbercloud.ru/datasets/slovo/models/swin/pt/swin48-1.pt) |
 54 | | ResNet-i3d-16-3   | 146.43          | 32.86  | [weights](https://rndml-team-cv.obs.ru-moscow-1.hc.sbercloud.ru/datasets/slovo/models/resnet/onnx/resnet16-3.onnx) | [weights](https://rndml-team-cv.obs.ru-moscow-1.hc.sbercloud.ru/datasets/slovo/models/resnet/pt/resnet16-3.pt) |
 55 | | ResNet-i3d-32-2   | 146.43          | 38.38  | [weights](https://rndml-team-cv.obs.ru-moscow-1.hc.sbercloud.ru/datasets/slovo/models/resnet/onnx/resnet32-2.onnx) | [weights](https://rndml-team-cv.obs.ru-moscow-1.hc.sbercloud.ru/datasets/slovo/models/resnet/pt/resnet32-2.pt) |
 56 | | ResNet-i3d-48-1   | 146.43          | 43.91  | [weights](https://rndml-team-cv.obs.ru-moscow-1.hc.sbercloud.ru/datasets/slovo/models/resnet/onnx/resnet48-1.onnx) | [weights](https://rndml-team-cv.obs.ru-moscow-1.hc.sbercloud.ru/datasets/slovo/models/resnet/pt/resnet48-1.pt) |
 57 | 
 58 | ## SignFlow models
 59 | 
 60 | | Model Name | Desc                                                                                                                | ONNX                                                                                                    | Params |
 61 | |------------|---------------------------------------------------------------------------------------------------------------------|---------------------------------------------------------------------------------------------------------|--------|
 62 | | SignFlow-A | **63.3 Top-1** Acc on  [WLASL-2000](https://paperswithcode.com/sota/sign-language-recognition-on-wlasl-2000) (SOTA) | [weights](https://rndml-team-cv.obs.ru-moscow-1.hc.sbercloud.ru/datasets/slovo/models/SignFlow-A.onnx) | 36M    |
 63 | | SignFlow-R | Pre-trained on **~50000** samples, has **267** classes, tested with GigaChat (as-is and context-based modes)        | [weights](https://rndml-team-cv.obs.ru-moscow-1.hc.sbercloud.ru/datasets/slovo/models/SignFlow-R.onnx) | 37M    |
 64 | 
 65 | 
 66 | ## Demo
 67 | ```console
 68 | usage: demo.py [-h] -p CONFIG [--mp] [-v] [-l LENGTH]
 69 | 
 70 | optional arguments:
 71 |   -h, --help            show this help message and exit
 72 |   -p CONFIG, --config CONFIG
 73 |                         Path to config
 74 |   --mp                  Enable multiprocessing
 75 |   -v, --verbose         Enable logging
 76 |   -l LENGTH, --length LENGTH
 77 |                         Deque length for predictions
 78 | 
 79 | 
 80 | python demo.py -p <PATH_TO_CONFIG>
 81 | ```
 82 | 
 83 | ![demo](images/demo.gif)
 84 | 
 85 | ## Authors and Credits
 86 | - [Kapitanov Alexander](https://www.linkedin.com/in/hukenovs)
 87 | - [Kvanchiani Karina](https://www.linkedin.com/in/kvanchiani)
 88 | - [Nagaev Alexander](https://www.linkedin.com/in/nagadit/)
 89 | - [Petrova Elizaveta](https://www.linkedin.com/in/elizaveta-petrova-248135263/)
 90 | 
 91 | ## Citation
 92 | You can cite the paper using the following BibTeX entry:
 93 | 
 94 |     @inproceedings{kapitanov2023slovo,
 95 |         title={Slovo: Russian Sign Language Dataset},
 96 |         author={Kapitanov, Alexander and Karina, Kvanchiani and Nagaev, Alexander and Elizaveta, Petrova},
 97 |         booktitle={International Conference on Computer Vision Systems},
 98 |         pages={63--73},
 99 |         year={2023},
100 |         organization={Springer}
101 |     }
102 | 
103 | ## Links
104 | - [arXiv](https://arxiv.org/abs/2305.14527)
105 | - [Kaggle](https://www.kaggle.com/datasets/kapitanov/slovo)
106 | - [Habr](https://habr.com/ru/companies/sberdevices/articles/737018/)
107 | - [Medium](https://medium.com/@nagadit/slovo-russian-sign-language-dataset-a8a8bd6fa17d)
108 | - [Github](https://github.com/hukenovs/slovo)
109 | - [Gitlab](https://gitlab.aicloud.sbercloud.ru/rndcv/slovo)
110 | - [Paperswithcode](https://paperswithcode.com/paper/slovo-russian-sign-language-dataset)
111 | 
112 | ## License
113 | <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a><br />This work is licensed under a variant of <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
114 | 
115 | Please see the specific [license](https://github.com/hukenovs/slovo/blob/master/license/en_us.pdf).
116 | 


--------------------------------------------------------------------------------
/demo.py:
--------------------------------------------------------------------------------
  1 | import argparse
  2 | import sys
  3 | import time
  4 | from collections import deque
  5 | from multiprocessing import Manager, Process, Value
  6 | from typing import Optional, Tuple
  7 | 
  8 | import onnxruntime as ort
  9 | from loguru import logger
 10 | 
 11 | ort.set_default_logger_severity(4)  # NOQA
 12 | logger.add(sys.stdout, format="{level} | {message}")  # NOQA
 13 | logger.remove(0)  # NOQA
 14 | import cv2
 15 | import numpy as np
 16 | from omegaconf import OmegaConf
 17 | 
 18 | from constants import classes
 19 | 
 20 | 
 21 | class BaseRecognition:
 22 |     def __init__(self, model_path: str, tensors_list, prediction_list, verbose):
 23 |         self.verbose = verbose
 24 |         self.started = None
 25 |         self.output_names = None
 26 |         self.input_shape = None
 27 |         self.input_name = None
 28 |         self.session = None
 29 |         self.model_path = model_path
 30 |         self.window_size = None
 31 |         self.tensors_list = tensors_list
 32 |         self.prediction_list = prediction_list
 33 | 
 34 |     def clear_tensors(self):
 35 |         """
 36 |         Clear the list of tensors.
 37 |         """
 38 |         for _ in range(self.window_size):
 39 |             self.tensors_list.pop(0)
 40 | 
 41 |     def run(self):
 42 |         """
 43 |         Run the recognition model.
 44 |         """
 45 |         if self.session is None:
 46 |             self.session = ort.InferenceSession(self.model_path)
 47 |             self.input_name = self.session.get_inputs()[0].name
 48 |             self.input_shape = self.session.get_inputs()[0].shape
 49 |             self.window_size = self.input_shape[3]
 50 |             self.output_names = [output.name for output in self.session.get_outputs()]
 51 | 
 52 |         if len(self.tensors_list) >= self.input_shape[3]:
 53 |             input_tensor = np.stack(self.tensors_list[: self.window_size], axis=1)[None][None]
 54 |             st = time.time()
 55 |             outputs = self.session.run(self.output_names, {self.input_name: input_tensor.astype(np.float32)})[0]
 56 |             et = round(time.time() - st, 3)
 57 |             gloss = str(classes[outputs.argmax()])
 58 |             if gloss != self.prediction_list[-1] and len(self.prediction_list):
 59 |                 if gloss != "---":
 60 |                     self.prediction_list.append(gloss)
 61 |             self.clear_tensors()
 62 |             if self.verbose:
 63 |                 logger.info(f"- Prediction time {et}, new gloss: {gloss}")
 64 |                 logger.info(f" --- {len(self.tensors_list)} frames in queue")
 65 | 
 66 |     def kill(self):
 67 |         pass
 68 | 
 69 | 
 70 | class Recognition(BaseRecognition):
 71 |     def __init__(self, model_path: str, tensors_list: list, prediction_list: list, verbose: bool):
 72 |         """
 73 |         Initialize recognition model.
 74 | 
 75 |         Parameters
 76 |         ----------
 77 |         model_path : str
 78 |             Path to the model.
 79 |         tensors_list : List
 80 |             List of tensors to be used for prediction.
 81 |         prediction_list : List
 82 |             List of predictions.
 83 | 
 84 |         Notes
 85 |         -----
 86 |         The recognition model is run in a separate process.
 87 |         """
 88 |         super().__init__(
 89 |             model_path=model_path, tensors_list=tensors_list, prediction_list=prediction_list, verbose=verbose
 90 |         )
 91 |         self.started = True
 92 | 
 93 |     def start(self):
 94 |         self.run()
 95 | 
 96 | 
 97 | class RecognitionMP(Process, BaseRecognition):
 98 |     def __init__(self, model_path: str, tensors_list, prediction_list, verbose):
 99 |         """
100 |         Initialize recognition model.
101 | 
102 |         Parameters
103 |         ----------
104 |         model_path : str
105 |             Path to the model.
106 |         tensors_list : Manager.list
107 |             List of tensors to be used for prediction.
108 |         prediction_list : Manager.list
109 |             List of predictions.
110 | 
111 |         Notes
112 |         -----
113 |         The recognition model is run in a separate process.
114 |         """
115 |         super().__init__()
116 |         BaseRecognition.__init__(
117 |             self, model_path=model_path, tensors_list=tensors_list, prediction_list=prediction_list, verbose=verbose
118 |         )
119 |         self.started = Value("i", False)
120 | 
121 |     def run(self):
122 |         while True:
123 |             BaseRecognition.run(self)
124 |             self.started = True
125 | 
126 | 
127 | class Runner:
128 |     STACK_SIZE = 6
129 | 
130 |     def __init__(
131 |             self,
132 |             model_path: str,
133 |             config: OmegaConf = None,
134 |             mp: bool = False,
135 |             verbose: bool = False,
136 |             length: int = STACK_SIZE,
137 |     ) -> None:
138 |         """
139 |         Initialize runner.
140 | 
141 |         Parameters
142 |         ----------
143 |         model_path : str
144 |             Path to the model.
145 |         config : OmegaConf
146 |             Configuration file.
147 |         length : int
148 |             Deque length for predictions
149 | 
150 |         Notes
151 |         -----
152 |         The runner uses multiprocessing to run the recognition model in a separate process.
153 | 
154 |         """
155 |         self.multiprocess = mp
156 |         self.cap = cv2.VideoCapture(0)
157 |         self.manager = Manager() if self.multiprocess else None
158 |         self.tensors_list = self.manager.list() if self.multiprocess else []
159 |         self.prediction_list = self.manager.list() if self.multiprocess else []
160 |         self.prediction_list.append("---")
161 |         self.frame_counter = 0
162 |         self.frame_interval = config.frame_interval
163 |         self.length = length
164 |         self.prediction_classes = deque(maxlen=length)
165 |         self.mean = config.mean
166 |         self.std = config.std
167 |         if self.multiprocess:
168 |             self.recognizer = RecognitionMP(model_path, self.tensors_list, self.prediction_list, verbose)
169 |         else:
170 |             self.recognizer = Recognition(model_path, self.tensors_list, self.prediction_list, verbose)
171 | 
172 |     def add_frame(self, image):
173 |         """
174 |         Add frame to queue.
175 | 
176 |         Parameters
177 |         ----------
178 |         image : np.ndarray
179 |             Frame to be added.
180 |         """
181 |         self.frame_counter += 1
182 |         if self.frame_counter == self.frame_interval:
183 |             image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
184 |             image = self.resize(image, (224, 224))
185 |             image = (image - self.mean) / self.std
186 |             image = np.transpose(image, [2, 0, 1])
187 |             self.tensors_list.append(image)
188 |             self.frame_counter = 0
189 | 
190 |     @staticmethod
191 |     def resize(im, new_shape=(224, 224)):
192 |         """
193 |         Resize and pad image while preserving aspect ratio.
194 | 
195 |         Parameters
196 |         ----------
197 |         im : np.ndarray
198 |             Image to be resized.
199 |         new_shape : Tuple[int]
200 |             Size of the new image.
201 | 
202 |         Returns
203 |         -------
204 |         np.ndarray
205 |             Resized image.
206 |         """
207 |         shape = im.shape[:2]  # current shape [height, width]
208 |         if isinstance(new_shape, int):
209 |             new_shape = (new_shape, new_shape)
210 | 
211 |         # Scale ratio (new / old)
212 |         r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
213 | 
214 |         # Compute padding
215 |         new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
216 |         dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
217 | 
218 |         dw /= 2
219 |         dh /= 2
220 | 
221 |         if shape[::-1] != new_unpad:  # resize
222 |             im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
223 |         top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
224 |         left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
225 |         im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=(114, 114, 114))  # add border
226 |         return im
227 | 
228 |     def run(self):
229 |         """
230 |         Run the runner.
231 | 
232 |         Notes
233 |         -----
234 |         The runner will run until the user presses 'q'.
235 |         """
236 |         if self.multiprocess:
237 |             self.recognizer.start()
238 | 
239 |         while self.cap.isOpened():
240 |             if self.recognizer.started:
241 |                 _, frame = self.cap.read()
242 |                 text_div = np.zeros((50, frame.shape[1], 3), dtype=np.uint8)
243 |                 self.add_frame(frame)
244 | 
245 |                 if not self.multiprocess:
246 |                     self.recognizer.start()
247 | 
248 |                 if self.prediction_list:
249 |                     text = "  ".join(self.prediction_list)
250 |                     cv2.putText(text_div, text, (10, 30), cv2.FONT_HERSHEY_COMPLEX, 0.7, (255, 255, 255), 2)
251 | 
252 |                 if len(self.prediction_list) > self.length:
253 |                     self.prediction_list.pop(0)
254 | 
255 |                 frame = np.concatenate((frame, text_div), axis=0)
256 |                 cv2.imshow("frame", frame)
257 |                 condition = cv2.waitKey(10) & 0xFF
258 |                 if condition in {ord("q"), ord("Q"), 27}:
259 |                     if self.multiprocess:
260 |                         self.recognizer.kill()
261 |                     self.cap.release()
262 |                     cv2.destroyAllWindows()
263 |                     break
264 | 
265 | 
266 | def parse_arguments(params: Optional[Tuple] = None) -> argparse.Namespace:
267 |     parser = argparse.ArgumentParser(description="Demo full frame classification...")
268 | 
269 |     parser.add_argument("-p", "--config", required=True, type=str, help="Path to config")
270 |     parser.add_argument("--mp", required=False, action="store_true", help="Enable multiprocessing")
271 |     parser.add_argument("-v", "--verbose", required=False, action="store_true", help="Enable logging")
272 |     parser.add_argument("-l", "--length", required=False, type=int, default=4, help="Deque length for predictions")
273 | 
274 |     known_args, _ = parser.parse_known_args(params)
275 |     return known_args
276 | 
277 | 
278 | if __name__ == "__main__":
279 |     args = parse_arguments()
280 |     conf = OmegaConf.load(args.config)
281 |     runner = Runner(conf.model_path, conf, args.mp, args.verbose, args.length)
282 |     runner.run()
283 | 


--------------------------------------------------------------------------------
/constants.py:
--------------------------------------------------------------------------------
   1 | classes = {
   2 |     0: "Ё",
   3 |     1: "А",
   4 |     2: "Р",
   5 |     3: "Е",
   6 |     4: "Ч",
   7 |     5: "Л",
   8 |     6: "Ц",
   9 |     7: "С",
  10 |     8: "Й",
  11 |     9: "З",
  12 |     10: "Ь",
  13 |     11: "Я",
  14 |     12: "Б",
  15 |     13: "Щ",
  16 |     14: "У",
  17 |     15: "Ы",
  18 |     16: "Д",
  19 |     17: "Ф",
  20 |     18: "Т",
  21 |     19: "Ъ",
  22 |     20: "О",
  23 |     21: "М",
  24 |     22: "П",
  25 |     23: "Х",
  26 |     24: "Э",
  27 |     25: "Н",
  28 |     26: "В",
  29 |     27: "Ю",
  30 |     28: "Ш",
  31 |     29: "Ж",
  32 |     30: "Г",
  33 |     31: "К",
  34 |     32: "И",
  35 |     33: "козел",
  36 |     34: "гнездо",
  37 |     35: "вой",
  38 |     36: "креветка",
  39 |     37: "бегемот",
  40 |     38: "нашествие",
  41 |     39: "лиса",
  42 |     40: "динозавр",
  43 |     41: "перо",
  44 |     42: "крокодил",
  45 |     43: "летучая мышь",
  46 |     44: "дикое животное",
  47 |     45: "попугай",
  48 |     46: "ежик",
  49 |     47: "волк",
  50 |     48: "овца",
  51 |     49: "петух",
  52 |     50: "белка",
  53 |     51: "пингвин",
  54 |     52: "помесь",
  55 |     53: "осел",
  56 |     54: "дятел",
  57 |     55: "воротник",
  58 |     56: "аллигатор",
  59 |     57: "кошка",
  60 |     58: "рога",
  61 |     59: "лошадь",
  62 |     60: "жираф",
  63 |     61: "вол",
  64 |     62: "населять",
  65 |     63: "верблюд",
  66 |     64: "коза",
  67 |     65: "ползанье",
  68 |     66: "бабочка",
  69 |     67: "клюв",
  70 |     68: "собака",
  71 |     69: "кролик",
  72 |     70: "паук",
  73 |     71: "водоплавающее",
  74 |     72: "носорог",
  75 |     73: "мышь",
  76 |     74: "зверь",
  77 |     75: "плавник",
  78 |     76: "паучья сеть",
  79 |     77: "животное",
  80 |     78: "лев",
  81 |     79: "тигр",
  82 |     80: "миграция",
  83 |     81: "копыто",
  84 |     82: "зубы",
  85 |     83: "коготь",
  86 |     84: "рыба",
  87 |     85: "лось",
  88 |     86: "пчела",
  89 |     87: "птица",
  90 |     88: "комар",
  91 |     89: "змея",
  92 |     90: "гавканье",
  93 |     91: "бык",
  94 |     92: "зоопарк",
  95 |     93: "клевать",
  96 |     94: "акула",
  97 |     95: "рычать",
  98 |     96: "панда",
  99 |     97: "крыса",
 100 |     98: "олень",
 101 |     99: "делать выводы",
 102 |     100: "орел",
 103 |     101: "лебедь",
 104 |     102: "рычание",
 105 |     103: "медведь",
 106 |     104: "слон",
 107 |     105: "дельфин",
 108 |     106: "павлин",
 109 |     107: "вилять",
 110 |     108: "собачий домик",
 111 |     109: "домашнее животное",
 112 |     110: "курица",
 113 |     111: "медуза",
 114 |     112: "клетка",
 115 |     113: "свинья",
 116 |     114: "лягушка",
 117 |     115: "голова",
 118 |     116: "корм для домашних животных",
 119 |     117: "корова",
 120 |     118: "обезьяна",
 121 |     119: "соты",
 122 |     120: "крыло",
 123 |     121: "заяц",
 124 |     122: "прирученный",
 125 |     123: "поверхностный",
 126 |     124: "интеллектуальный",
 127 |     125: "застенчивый",
 128 |     126: "добрый",
 129 |     127: "истерик",
 130 |     128: "аниматор",
 131 |     129: "кротость",
 132 |     130: "адаптивный",
 133 |     131: "амбициозный",
 134 |     132: "проныра",
 135 |     133: "скучный",
 136 |     134: "гордый",
 137 |     135: "одаренный",
 138 |     136: "прагматичный",
 139 |     137: "испорченный",
 140 |     138: "нежный",
 141 |     139: "любопытный",
 142 |     140: "гений",
 143 |     141: "рассеянный",
 144 |     142: "непослушный",
 145 |     143: "наивный",
 146 |     144: "острый",
 147 |     145: "вежливый",
 148 |     146: "отличный",
 149 |     147: "смелость",
 150 |     148: "правша",
 151 |     149: "разумный",
 152 |     150: "ленивый",
 153 |     151: "детский",
 154 |     152: "ребяческий",
 155 |     153: "стойкость",
 156 |     154: "уверенный",
 157 |     155: "обычный",
 158 |     156: "миленький",
 159 |     157: "остроумие",
 160 |     158: "послушный",
 161 |     159: "невиновный",
 162 |     160: "опытный",
 163 |     161: "инициатива",
 164 |     162: "тщеславный",
 165 |     163: "доминантный",
 166 |     164: "наблюдательность",
 167 |     165: "сильный",
 168 |     166: "вредный; озорной",
 169 |     167: "неловкий; неуклюжий",
 170 |     168: "верный",
 171 |     169: "особенный",
 172 |     170: "мятежный",
 173 |     171: "жестокий",
 174 |     172: "зрелищный",
 175 |     173: "строгий",
 176 |     174: "злобный",
 177 |     175: "нерадивый",
 178 |     176: "логичный",
 179 |     177: "торжественный",
 180 |     178: "враждебный",
 181 |     179: "получающий удовольствие",
 182 |     180: "потрясающий",
 183 |     181: "доминирующий",
 184 |     182: "жадный",
 185 |     183: "угрюмый",
 186 |     184: "трус",
 187 |     185: "жёсткий",
 188 |     186: "подлинный",
 189 |     187: "милосердный",
 190 |     188: "модный",
 191 |     189: "осторожный",
 192 |     190: "неудобно",
 193 |     191: "недобрый",
 194 |     192: "красивый",
 195 |     193: "жадность",
 196 |     194: "высокомерный",
 197 |     195: "приличный",
 198 |     196: "искренний",
 199 |     197: "аккуратный",
 200 |     198: "лицемерный",
 201 |     199: "положительный",
 202 |     200: "грубый",
 203 |     201: "ответственный",
 204 |     202: "отвратительный",
 205 |     203: "странный",
 206 |     204: "глупый",
 207 |     205: "адаптивное поведение",
 208 |     206: "отрицательный",
 209 |     207: "оживленный",
 210 |     208: "адаптивность",
 211 |     209: "объективный",
 212 |     210: "агрессивный",
 213 |     211: "предусмотрительный",
 214 |     212: "недовольный",
 215 |     213: "мудрый",
 216 |     214: "причинять резкую боль",
 217 |     215: "талант",
 218 |     216: "мужественность",
 219 |     217: "неприятный",
 220 |     218: "смешной",
 221 |     219: "запасливый",
 222 |     220: "милый",
 223 |     221: "трусливо",
 224 |     222: "образованный",
 225 |     223: "веселый",
 226 |     224: "дружелюбный",
 227 |     225: "нахальный",
 228 |     226: "личные качества",
 229 |     227: "беспокоящий",
 230 |     228: "наглость",
 231 |     229: "зло",
 232 |     230: "продуманный",
 233 |     231: "активный",
 234 |     232: "брюзжать",
 235 |     233: "скромный",
 236 |     234: "оранжевый",
 237 |     235: "основной цвет",
 238 |     236: "бледный",
 239 |     237: "тёмный",
 240 |     238: "серебро",
 241 |     239: "нераскрашенный",
 242 |     240: "розовый",
 243 |     241: "цветовой оттенок",
 244 |     242: "прозрачный",
 245 |     243: "оттенок",
 246 |     244: "коричневый",
 247 |     245: "бежевый",
 248 |     246: "непрозрачность",
 249 |     247: "цвет",
 250 |     248: "красноватый",
 251 |     249: "фиолетовый",
 252 |     250: "желтый",
 253 |     251: "темнота",
 254 |     252: "красный",
 255 |     253: "золотой",
 256 |     254: "черный",
 257 |     255: "розовато-лиловый",
 258 |     256: "серый",
 259 |     257: "синий",
 260 |     258: "зелёный",
 261 |     259: "белый",
 262 |     260: "грустный",
 263 |     261: "подавленный",
 264 |     262: "ошеломить",
 265 |     263: "нервный",
 266 |     264: "напряжение",
 267 |     265: "преданный",
 268 |     266: "мурашки",
 269 |     267: "облегчение",
 270 |     268: "ожидать",
 271 |     269: "зависть",
 272 |     270: "жажда",
 273 |     271: "слабый",
 274 |     272: "отношение",
 275 |     273: "отчаяться",
 276 |     274: "печаль",
 277 |     275: "озабоченный",
 278 |     276: "страх",
 279 |     277: "испуганный",
 280 |     278: "напуганный",
 281 |     279: "разрыдаться",
 282 |     280: "злость",
 283 |     281: "отвращение",
 284 |     282: "сочувствие",
 285 |     283: "неудобство",
 286 |     284: "самопроверка",
 287 |     285: "быть потрясенным",
 288 |     286: "доверять",
 289 |     287: "скорбеть",
 290 |     288: "смущенный",
 291 |     289: "желать",
 292 |     290: "вскружить",
 293 |     291: "самоанализ",
 294 |     292: "желание",
 295 |     293: "импульс",
 296 |     294: "презрение",
 297 |     295: "разъяренный",
 298 |     296: "самовосприятие",
 299 |     297: "беспокоиться",
 300 |     298: "возрадоваться",
 301 |     299: "подозревать",
 302 |     300: "терпеть",
 303 |     301: "влюбиться",
 304 |     302: "восхищаться",
 305 |     303: "нравиться",
 306 |     304: "злой",
 307 |     305: "расстроенный",
 308 |     306: "расслабление",
 309 |     307: "жуткий",
 310 |     308: "не выносить",
 311 |     309: "спокойный",
 312 |     310: "гнев",
 313 |     311: "сарказм",
 314 |     312: "отважиться",
 315 |     313: "сожалеть",
 316 |     314: "ленность",
 317 |     315: "расслабиться",
 318 |     316: "интересный",
 319 |     317: "самоосознание",
 320 |     318: "весенняя",
 321 |     319: "ненавидеть",
 322 |     320: "доброта",
 323 |     321: "отчаянный",
 324 |     322: "сомнение",
 325 |     323: "страшный",
 326 |     324: "покаяние",
 327 |     325: "испытывать",
 328 |     326: "счастливый",
 329 |     327: "испуг",
 330 |     328: "предпочитать",
 331 |     329: "дистресс",
 332 |     330: "возбуждение",
 333 |     331: "несчастье",
 334 |     332: "довольный",
 335 |     333: "благодарность",
 336 |     334: "голод",
 337 |     335: "изношенный",
 338 |     336: "счастье",
 339 |     337: "радость",
 340 |     338: "страдать",
 341 |     339: "мотивировать",
 342 |     340: "вдохновлять",
 343 |     341: "непреклонный",
 344 |     342: "рассердиться",
 345 |     343: "жалость",
 346 |     344: "безумно",
 347 |     345: "безразличие",
 348 |     346: "доставлять удовольствие",
 349 |     347: "изможденный",
 350 |     348: "добропорядочность",
 351 |     349: "любовный",
 352 |     350: "позор",
 353 |     351: "забота",
 354 |     352: "горе",
 355 |     353: "влюбленный",
 356 |     354: "рыдать",
 357 |     355: "вздрагивать",
 358 |     356: "самоконтроль",
 359 |     357: "ошарашенный",
 360 |     358: "неверие",
 361 |     359: "одержимый",
 362 |     360: "вдохновение",
 363 |     361: "самообман",
 364 |     362: "наслаждайся",
 365 |     363: "возбужденный",
 366 |     364: "любить",
 367 |     365: "терпение",
 368 |     366: "обнимать",
 369 |     367: "горький",
 370 |     368: "презирать",
 371 |     369: "беспокойство",
 372 |     370: "расстройство",
 373 |     371: "надоело",
 374 |     372: "слабость",
 375 |     373: "покраснеть",
 376 |     374: "голодный",
 377 |     375: "разгневанный",
 378 |     376: "ревнивый",
 379 |     377: "раздражать",
 380 |     378: "смириться",
 381 |     379: "обманчивость",
 382 |     380: "самомнение",
 383 |     381: "жаждущий",
 384 |     382: "возбуждать",
 385 |     383: "надеяться",
 386 |     384: "беспокойный",
 387 |     385: "мотивация",
 388 |     386: "пренебрежение",
 389 |     387: "благоговение",
 390 |     388: "суматоха",
 391 |     389: "горевать",
 392 |     390: "неуважение",
 393 |     391: "сомневаться",
 394 |     392: "нравственность",
 395 |     393: "горюющий",
 396 |     394: "надежда",
 397 |     395: "одинокий",
 398 |     396: "довольство",
 399 |     397: "агрессия",
 400 |     398: "страдание",
 401 |     399: "благополучие",
 402 |     400: "любовь",
 403 |     401: "боль",
 404 |     402: "скучающий",
 405 |     403: "домогаться",
 406 |     404: "разочарованный",
 407 |     405: "дрожать",
 408 |     406: "так себе",
 409 |     407: "стыдно",
 410 |     408: "вина",
 411 |     409: "весёлый",
 412 |     410: "страсть",
 413 |     411: "радостный",
 414 |     412: "объятие",
 415 |     413: "сожаление",
 416 |     414: "обеспокоенный",
 417 |     415: "рвеность",
 418 |     416: "гордость",
 419 |     417: "внутреннее",
 420 |     418: "одновременность",
 421 |     419: "восхищение",
 422 |     420: "поднос",
 423 |     421: "кафе",
 424 |     422: "лить",
 425 |     423: "откупоривать",
 426 |     424: "есть",
 427 |     425: "закуска перед едой",
 428 |     426: "кормить",
 429 |     427: "окунать",
 430 |     428: "бутылка",
 431 |     429: "жевать",
 432 |     430: "вкусный",
 433 |     431: "жадно есть",
 434 |     432: "переваривать",
 435 |     433: "MakDonalds",
 436 |     434: "предложить тост",
 437 |     435: "аллергия на еду",
 438 |     436: "откусывание",
 439 |     437: "ланч",
 440 |     438: "помощник повара",
 441 |     439: "кубок",
 442 |     440: "плата за вход",
 443 |     441: "резервирование; бронирование",
 444 |     442: "аппетит",
 445 |     443: "кафетерий",
 446 |     444: "нежный, мягкий",
 447 |     445: "пир",
 448 |     446: "прием пищи",
 449 |     447: "обедать",
 450 |     448: "кусок; тонкими слоями",
 451 |     449: "пить",
 452 |     450: "кружка",
 453 |     451: "кусать",
 454 |     452: "вилка",
 455 |     453: "делать мелкий глоток",
 456 |     454: "ароматный",
 457 |     455: "банкет",
 458 |     456: "заварочный чайник",
 459 |     457: "глотать",
 460 |     458: "кувшин",
 461 |     459: "ресторан",
 462 |     460: "переваривание",
 463 |     461: "глотать жадно",
 464 |     462: "официант",
 465 |     463: "заказывать",
 466 |     464: "женский туалет",
 467 |     465: "повар",
 468 |     466: "мужской туалет",
 469 |     467: "накрывать на стол",
 470 |     468: "вкус",
 471 |     469: "место для пикника",
 472 |     470: "диета",
 473 |     471: "питьевая вода",
 474 |     472: "Прощание",
 475 |     473: "С днем рождения",
 476 |     474: "Пока",
 477 |     475: "Позвонить на сервис",
 478 |     476: "Привет!",
 479 |     477: "Добро пожаловать!",
 480 |     478: "слово",
 481 |     479: "пока (что)",
 482 |     480: "верить",
 483 |     481: "иначе",
 484 |     482: "искать",
 485 |     483: "чемодан",
 486 |     484: "слышать",
 487 |     485: "вред",
 488 |     486: "десять",
 489 |     487: "почва",
 490 |     488: "сто",
 491 |     489: "достаточно",
 492 |     490: "пустой",
 493 |     491: "охотно",
 494 |     492: "значить",
 495 |     493: "собирать",
 496 |     494: "такой",
 497 |     495: "между",
 498 |     496: "карандаш",
 499 |     497: "маленький",
 500 |     498: "готовый",
 501 |     499: "восемь",
 502 |     500: "сказать",
 503 |     501: "восток",
 504 |     502: "иностранный",
 505 |     503: "платить",
 506 |     504: "дочь",
 507 |     505: "неделя",
 508 |     506: "семнадцать",
 509 |     507: "не",
 510 |     508: "двенадцатый",
 511 |     509: "писать",
 512 |     510: "целый",
 513 |     511: "часы",
 514 |     512: "конечно",
 515 |     513: "звук",
 516 |     514: "использовать",
 517 |     515: "быстрый",
 518 |     516: "семья",
 519 |     517: "четверг",
 520 |     518: "мужчина",
 521 |     519: "потом",
 522 |     520: "длинный",
 523 |     521: "отец",
 524 |     522: "ручка",
 525 |     523: "оба",
 526 |     524: "там",
 527 |     525: "тоже",
 528 |     526: "часть",
 529 |     527: "десятый",
 530 |     528: "дом",
 531 |     529: "новости",
 532 |     530: "лежать",
 533 |     531: "еще",
 534 |     532: "медленный",
 535 |     533: "семь",
 536 |     534: "возможный",
 537 |     535: "женщина",
 538 |     536: "рот",
 539 |     537: "муж",
 540 |     538: "жёлтый",
 541 |     539: "среда",
 542 |     540: "хорошо",
 543 |     541: "налог",
 544 |     542: "смеяться",
 545 |     543: "магазин",
 546 |     544: "другой",
 547 |     545: "путешествовать",
 548 |     546: "причина",
 549 |     547: "дешёвый",
 550 |     548: "книга",
 551 |     549: "еда",
 552 |     550: "шесть",
 553 |     551: "направление",
 554 |     552: "идти",
 555 |     553: "комната",
 556 |     554: "вчера",
 557 |     555: "один",
 558 |     556: "срочный",
 559 |     557: "известный",
 560 |     558: "двенадцать",
 561 |     559: "низкий",
 562 |     560: "пятый",
 563 |     561: "везде",
 564 |     562: "мы",
 565 |     563: "двадцать",
 566 |     564: "ноль",
 567 |     565: "должен",
 568 |     566: "почему",
 569 |     567: "девочка",
 570 |     568: "мир",
 571 |     569: "буду",
 572 |     570: "пятьдесят",
 573 |     571: "смотреть",
 574 |     572: "месяц",
 575 |     573: "короткий",
 576 |     574: "необходимый",
 577 |     575: "с",
 578 |     576: "если",
 579 |     577: "полный",
 580 |     578: "важный",
 581 |     579: "без",
 582 |     580: "человек",
 583 |     581: "немного",
 584 |     582: "завтра",
 585 |     583: "большой",
 586 |     584: "всегда",
 587 |     585: "молодой",
 588 |     586: "вниз",
 589 |     587: "стоять",
 590 |     588: "сестра",
 591 |     589: "ночь",
 592 |     590: "билет",
 593 |     591: "плакать",
 594 |     592: "имя",
 595 |     593: "или",
 596 |     594: "свободный",
 597 |     595: "мальчик",
 598 |     596: "говорить",
 599 |     597: "утро",
 600 |     598: "лето",
 601 |     599: "погода",
 602 |     600: "путешествие",
 603 |     601: "обучать",
 604 |     602: "пять",
 605 |     603: "сумка",
 606 |     604: "ребёнок",
 607 |     605: "север",
 608 |     606: "час",
 609 |     607: "очень",
 610 |     608: "место",
 611 |     609: "все",
 612 |     610: "ужасный",
 613 |     611: "держать",
 614 |     612: "одежда",
 615 |     613: "точно",
 616 |     614: "опасный",
 617 |     615: "седьмой",
 618 |     616: "много",
 619 |     617: "второй",
 620 |     618: "различный",
 621 |     619: "тридцать",
 622 |     620: "трудный",
 623 |     621: "два",
 624 |     622: "лучший",
 625 |     623: "одиннадцатый",
 626 |     624: "девятый",
 627 |     625: "быстро",
 628 |     626: "люди",
 629 |     627: "чувствовать",
 630 |     628: "письмо",
 631 |     629: "мочь",
 632 |     630: "друг",
 633 |     631: "назад",
 634 |     632: "шестьдесят",
 635 |     633: "улица",
 636 |     634: "уже",
 637 |     635: "правый",
 638 |     636: "сердце",
 639 |     637: "дорога",
 640 |     638: "высокий",
 641 |     639: "слушать",
 642 |     640: "кроме",
 643 |     641: "цена",
 644 |     642: "восьмой",
 645 |     643: "народ",
 646 |     644: "твердый",
 647 |     645: "мне",
 648 |     646: "тебе",
 649 |     647: "мертвый",
 650 |     648: "твой",
 651 |     649: "сын",
 652 |     650: "девяносто",
 653 |     651: "хлеб",
 654 |     652: "четвертый",
 655 |     653: "сегодня",
 656 |     654: "брат",
 657 |     655: "наконец",
 658 |     656: "постепенно",
 659 |     657: "рука",
 660 |     658: "весна",
 661 |     659: "хороший",
 662 |     660: "тот",
 663 |     661: "шум",
 664 |     662: "вода",
 665 |     663: "следующий",
 666 |     664: "шестнадцать",
 667 |     665: "безопасный",
 668 |     666: "несколько",
 669 |     667: "тысяча",
 670 |     668: "нас",
 671 |     669: "только",
 672 |     670: "воздух",
 673 |     671: "одиннадцать",
 674 |     672: "восемьдесят",
 675 |     673: "последний",
 676 |     674: "картина",
 677 |     675: "газета",
 678 |     676: "сторона",
 679 |     677: "способ",
 680 |     678: "переводить",
 681 |     679: "ждать",
 682 |     680: "особенно",
 683 |     681: "довольно",
 684 |     682: "бумага",
 685 |     683: "вероятно",
 686 |     684: "шестой",
 687 |     685: "обычно",
 688 |     686: "пятница",
 689 |     687: "под",
 690 |     688: "тот же самый (одинаковый)",
 691 |     689: "посылать",
 692 |     690: "четырнадцать",
 693 |     691: "с тех пор как",
 694 |     692: "вещь",
 695 |     693: "понять",
 696 |     694: "девятнадцать",
 697 |     695: "читать",
 698 |     696: "невозможный",
 699 |     697: "юг",
 700 |     698: "делать",
 701 |     699: "перед",
 702 |     700: "возможно",
 703 |     701: "понедельник",
 704 |     702: "жизнь",
 705 |     703: "голос",
 706 |     704: "грязный",
 707 |     705: "опять",
 708 |     706: "чистый",
 709 |     707: "три",
 710 |     708: "вопрос",
 711 |     709: "ничего",
 712 |     710: "город",
 713 |     711: "ты",
 714 |     712: "часто",
 715 |     713: "против",
 716 |     714: "их",
 717 |     715: "вечер",
 718 |     716: "ошибка",
 719 |     717: "прибывать",
 720 |     718: "я",
 721 |     719: "это",
 722 |     720: "осень",
 723 |     721: "наш",
 724 |     722: "школа",
 725 |     723: "вторник",
 726 |     724: "здесь",
 727 |     725: "веселиться",
 728 |     726: "глаз",
 729 |     727: "ключ",
 730 |     728: "сзади",
 731 |     729: "старый",
 732 |     730: "первый",
 733 |     731: "дорогой",
 734 |     732: "солнце",
 735 |     733: "что",
 736 |     734: "когда",
 737 |     735: "мой",
 738 |     736: "плохой",
 739 |     737: "четыре",
 740 |     738: "язык",
 741 |     739: "левый",
 742 |     740: "третий",
 743 |     741: "лучше",
 744 |     742: "видеть",
 745 |     743: "скоро",
 746 |     744: "деньги",
 747 |     745: "жена",
 748 |     746: "сверху",
 749 |     747: "худший",
 750 |     748: "суббота",
 751 |     749: "никогда",
 752 |     750: "воскресенье",
 753 |     751: "год",
 754 |     752: "новый",
 755 |     753: "зима",
 756 |     754: "никто",
 757 |     755: "время",
 758 |     756: "работать",
 759 |     757: "восемнадцать",
 760 |     758: "иногда",
 761 |     759: "однажды",
 762 |     760: "кто?",
 763 |     761: "до",
 764 |     762: "пятнадцать",
 765 |     763: "после",
 766 |     764: "семьдесят",
 767 |     765: "минута",
 768 |     766: "Плохо",
 769 |     767: "Даже",
 770 |     768: "Забрать",
 771 |     769: "Лицо",
 772 |     770: "Память",
 773 |     771: "Лопата",
 774 |     772: "Смех",
 775 |     773: "Футбол",
 776 |     774: "Выручить",
 777 |     775: "Соль",
 778 |     776: "Чашка",
 779 |     777: "Подробно",
 780 |     778: "Корона",
 781 |     779: "Рубль",
 782 |     780: "Крест",
 783 |     781: "Цемент",
 784 |     782: "Выигрыш",
 785 |     783: "Золото",
 786 |     784: "Жарить",
 787 |     785: "Пылесос",
 788 |     786: "Почки",
 789 |     787: "Свисток",
 790 |     788: "Чужой",
 791 |     789: "Религия",
 792 |     790: "Сразу",
 793 |     791: "Обладать",
 794 |     792: "Борода",
 795 |     793: "Витамин",
 796 |     794: "Раб",
 797 |     795: "остановить",
 798 |     796: "из-за",
 799 |     797: "чудесный",
 800 |     798: "боюсь",
 801 |     799: "закончить",
 802 |     800: "им",
 803 |     801: "эти",
 804 |     802: "ваш",
 805 |     803: "который",
 806 |     804: "немедленно",
 807 |     805: "нога",
 808 |     806: "ранний",
 809 |     807: "следовало",
 810 |     808: "остановиться",
 811 |     809: "поздний",
 812 |     810: "этот",
 813 |     811: "значит",
 814 |     812: "верх",
 815 |     813: "одолжить",
 816 |     814: "получить",
 817 |     815: "проверить",
 818 |     816: "замужем",
 819 |     817: "нуждаться",
 820 |     818: "открыть",
 821 |     819: "подруга",
 822 |     820: "остаться",
 823 |     821: "ясный",
 824 |     822: "минимум",
 825 |     823: "тогда",
 826 |     824: "над",
 827 |     825: "ей",
 828 |     826: "близко",
 829 |     827: "сюрприз",
 830 |     828: "изучить",
 831 |     829: "предложить",
 832 |     830: "терять",
 833 |     831: "теперь",
 834 |     832: "прекрасный",
 835 |     833: "около",
 836 |     834: "что-нибудь",
 837 |     835: "взять",
 838 |     836: "нам",
 839 |     837: "дать",
 840 |     838: "положить",
 841 |     839: "оставить",
 842 |     840: "сколько",
 843 |     841: "вас",
 844 |     842: "жаркий",
 845 |     843: "уронить",
 846 |     844: "слишком",
 847 |     845: "вы",
 848 |     846: "позволить",
 849 |     847: "привезти",
 850 |     848: "петь",
 851 |     849: "вам",
 852 |     850: "наверху",
 853 |     851: "наружу",
 854 |     852: "ехать",
 855 |     853: "те",
 856 |     854: "от",
 857 |     855: "прийти",
 858 |     856: "закрыть",
 859 |     857: "её",
 860 |     858: "всё",
 861 |     859: "женат",
 862 |     860: "бы",
 863 |     861: "показать",
 864 |     862: "весь",
 865 |     863: "сорок",
 866 |     864: "восемьсот",
 867 |     865: "пять тысяч",
 868 |     866: "три тысячи",
 869 |     867: "пятьсот",
 870 |     868: "двести",
 871 |     869: "четыреста",
 872 |     870: "семьсот",
 873 |     871: "тринадцать",
 874 |     872: "девять",
 875 |     873: "девятьсот",
 876 |     874: "четыре тысячи",
 877 |     875: "две тысячи",
 878 |     876: "триста",
 879 |     877: "миллион",
 880 |     878: "время от 0 ночи до 12 дня",
 881 |     879: "вспомнить",
 882 |     880: "бояться",
 883 |     881: "мыть",
 884 |     882: "свет",
 885 |     883: "найти",
 886 |     884: "обеспокоен",
 887 |     885: "запад",
 888 |     886: "сердитый",
 889 |     887: "жить",
 890 |     888: "шутить",
 891 |     889: "думать",
 892 |     890: "великий",
 893 |     891: "сухой",
 894 |     892: "спросить",
 895 |     893: "спать",
 896 |     894: "сначала",
 897 |     895: "хотеть",
 898 |     896: "знать",
 899 |     897: "цвета",
 900 |     898: "продолжить",
 901 |     899: "холодный",
 902 |     900: "да",
 903 |     901: "мать",
 904 |     902: "доволен",
 905 |     903: "легкий",
 906 |     904: "привык",
 907 |     905: "быть",
 908 |     906: "удивлён",
 909 |     907: "внизу",
 910 |     908: "рад",
 911 |     909: "болит",
 912 |     910: "согласно",
 913 |     911: "купить",
 914 |     912: "играть",
 915 |     913: "изменить",
 916 |     914: "ответить",
 917 |     915: "начать",
 918 |     916: "сквозь",
 919 |     917: "больше",
 920 |     918: "упасть",
 921 |     919: "вверх",
 922 |     920: "приятный",
 923 |     921: "тёплый",
 924 |     922: "уверен",
 925 |     923: "лечь",
 926 |     924: "забыть",
 927 |     925: "меня",
 928 |     926: "принести",
 929 |     927: "тебя",
 930 |     928: "встретить",
 931 |     929: "тяжёлый",
 932 |     930: "обещать",
 933 |     931: "день",
 934 |     932: "кусок",
 935 |     933: "помочь",
 936 |     934: "продать",
 937 |     935: "иметь",
 938 |     936: "назначенное время",
 939 |     937: "октябрь",
 940 |     938: "редко",
 941 |     939: "постоянный",
 942 |     940: "задерживать",
 943 |     941: "канун",
 944 |     942: "затем; потом",
 945 |     943: "ожидающий",
 946 |     944: "запоздалый",
 947 |     945: "6 часов",
 948 |     946: "поздно",
 949 |     947: "недавний",
 950 |     948: "в спешке",
 951 |     949: "распределять",
 952 |     950: "продлевать",
 953 |     951: "расписание",
 954 |     952: "откладывать",
 955 |     953: "немедленный",
 956 |     954: "время по Гринвичу",
 957 |     955: "день и ночь",
 958 |     956: "между тем",
 959 |     957: "ноябрь",
 960 |     958: "ранее",
 961 |     959: "продолжительность",
 962 |     960: "ежедневный",
 963 |     961: "будущее",
 964 |     962: "днем",
 965 |     963: "по часам",
 966 |     964: "две недели",
 967 |     965: "начинать",
 968 |     966: "вечный",
 969 |     967: "затягивать",
 970 |     968: "выходные",
 971 |     969: "бывший",
 972 |     970: "наконец-то",
 973 |     971: "временный",
 974 |     972: "восход солнца",
 975 |     973: "время для измерений",
 976 |     974: "в настоящее время",
 977 |     975: "в этом году",
 978 |     976: "краткий",
 979 |     977: "декабрь",
 980 |     978: "календарь",
 981 |     979: "7:45",
 982 |     980: "еще нет",
 983 |     981: "рано",
 984 |     982: "август",
 985 |     983: "в прошлом году",
 986 |     984: "отложить",
 987 |     985: "2 часа",
 988 |     986: "вечность",
 989 |     987: "дважды",
 990 |     988: "занятый",
 991 |     989: "начало",
 992 |     990: "отложенный",
 993 |     991: "мгновение",
 994 |     992: "неожиданный",
 995 |     993: "предыдущий",
 996 |     994: "в восемь пятнадцать",
 997 |     995: "продление",
 998 |     996: "башенные часы",
 999 |     997: "в то время как",
1000 |     998: "время 11 часов",
1001 |     999: "навсегда",
1002 |     1000: "---",
1003 | }
1004 | 


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