diff --git a/.gitignore b/.gitignore index b69fe2d..2a18bcc 100755 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,353 @@ +# Created by https://www.toptal.com/developers/gitignore/api/intellij+all,python,pycharm+all,macos,windows +# Edit at https://www.toptal.com/developers/gitignore?templates=intellij+all,python,pycharm+all,macos,windows + +### Intellij+all ### +# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider +# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839 + +# User-specific stuff +.idea/**/workspace.xml +.idea/**/tasks.xml +.idea/**/usage.statistics.xml +.idea/**/dictionaries +.idea/**/shelf + +# Generated files +.idea/**/contentModel.xml + +# Sensitive or high-churn files +.idea/**/dataSources/ +.idea/**/dataSources.ids +.idea/**/dataSources.local.xml +.idea/**/sqlDataSources.xml +.idea/**/dynamic.xml +.idea/**/uiDesigner.xml +.idea/**/dbnavigator.xml + +# Gradle +.idea/**/gradle.xml +.idea/**/libraries + +# Gradle and Maven with auto-import +# When using Gradle or Maven with auto-import, you should exclude module files, +# since they will be recreated, and may cause churn. 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Uncomment if using +# auto-import. +# .idea/artifacts +# .idea/compiler.xml +# .idea/jarRepositories.xml +# .idea/modules.xml +# .idea/*.iml +# .idea/modules +# *.iml +# *.ipr + +# CMake + +# Mongo Explorer plugin + +# File-based project format + +# IntelliJ + +# mpeltonen/sbt-idea plugin + +# JIRA plugin + +# Cursive Clojure plugin + +# Crashlytics plugin (for Android Studio and IntelliJ) + +# Editor-based Rest Client + +# Android studio 3.1+ serialized cache file + +### PyCharm+all Patch ### +# Ignores the whole .idea folder and all .iml files +# See https://github.com/joeblau/gitignore.io/issues/186 and https://github.com/joeblau/gitignore.io/issues/360 + + +# Reason: https://github.com/joeblau/gitignore.io/issues/186#issuecomment-249601023 + + +# Sonarlint plugin + +### Python ### +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +pytestdebug.log + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ +doc/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +#poetry.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +# .env +.env/ +.venv/ +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ +pythonenv* + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# operating system-related files +*.DS_Store #file properties cache/storage on macOS +Thumbs.db #thumbnail cache on Windows + +# profiling data +.prof + + +### Windows ### +# Windows thumbnail cache files +Thumbs.db +Thumbs.db:encryptable +ehthumbs.db +ehthumbs_vista.db + +# Dump file +*.stackdump + +# Folder config file +[Dd]esktop.ini + +# Recycle Bin used on file shares +$RECYCLE.BIN/ + +# Windows Installer files +*.cab +*.msi +*.msix +*.msm +*.msp + +# Windows shortcuts +*.lnk + +# End of https://www.toptal.com/developers/gitignore/api/intellij+all,python,pycharm+all,macos,windows # Data&test Videos/ test/ @@ -37,4 +387,8 @@ path.txt sameSegments.txt # Ignore the cookies.json -cookies.json \ No newline at end of file +cookies.json + +# The model files exceeds the size limit 100MB of github, so download them separately. +src/subtitle/models/base.pt +src/subtitle/models/small.pt \ No newline at end of file diff --git a/README.md b/README.md index addfee8..f82a6bc 100644 --- a/README.md +++ b/README.md @@ -25,9 +25,36 @@ - **范围广**：多线程同时监听数个直播间，同时录制内容并投稿。 - **空间小**：自动删除已上传的往期直播回放，节省空间，硬盘空间可以重复利用。 - **灵活高**：模版化自定义投稿，支持自定义投稿分区，动态内容，视频描述，视频标题，视频标签等，同时支持多P上传。 -- **自动检测合并**：对于网络问题或者连线导致的视频流分段，支持自动检测并按小时合并视频片段。 +- **自动检测合并**：对于网络问题或者连线导致的视频流分段，支持自动检测并合并成为完整视频片段。 - **弹幕版视频**：录制视频同时录制弹幕文件（包含普通弹幕，付费弹幕以及礼物上舰等信息），支持自动转换xml为ass弹幕文件并且渲染到视频中形成**有弹幕版视频**，转换完成后即在上传队列中自动上传。 - **硬件要求低**：无需GPU，只需最基础的单核CPU搭配最低的运存即可完成录制，渲染，上传等等全部过程，10年前的电脑或服务器依然可以使用！ +- **(:tada:NEW)自动渲染字幕**(如需使用本功能，则需保证有 Nvidia 显卡)：采用 OpenAI 的开源模型 `whisper`，自动识别视频语音并转换为字幕渲染至视频中。 + +项目架构流程如下： + +```mermaid +graph TD + + User((用户))---->startRecord(启动录制) + startRecord(启动录制)--保存视频和字幕文件-->videoFolder[(Video 文件夹)] + + User((用户))---->startUploadNoDanmaku(启动无弹幕版视频上传) + videoFolder[(Video 文件夹)]<--实时上传-->startUploadNoDanmaku(启动无弹幕版视频上传) + + User((用户))---->startScan(启动扫描 Video 文件夹) + videoFolder[(Video 文件夹)]<--间隔两分钟扫描一次-->startScan(启动扫描 Video 文件夹) + startScan --判断是否有弹幕-->ifDanmaku{判断} + ifDanmaku -->|有弹幕| DanmakuFactory[DanmakuFactory] + ifDanmaku -->|无弹幕| ffmpeg[ffmpeg] + DanmakuFactory[DanmakuFactory] --自动转换弹幕--> ffmpeg[ffmpeg] + ffmpeg[ffmpeg] --压制视频--> whisper[whisperASR模型] + whisper[whisperASR模型] --生成字幕--> ffmpeg1[ffmpeg] + ffmpeg1[ffmpeg] --渲染字幕 --> uploadQueue[(上传队列)] + + User((用户))---->startUpload(启动有弹幕版视频上传进程) + startUpload(启动有弹幕版视频上传进程) <--扫描队列并上传视频--> uploadQueue[(上传队列)] +``` + ## 3. 测试硬件 + OS: Ubuntu 22.04.4 LTS @@ -59,12 +86,25 @@ ### 4.1 安装环境 ``` -# 安装所需依赖 +# 安装所需依赖 推荐先 conda 创建虚拟环境 pip install -r requirements.txt # 记录项目根目录 ./setRoutineTask.sh && source ~/.bashrc ``` +如果需要使用自动渲染字幕功能，模型基本参数及链接如下，注意 GPU 显存必须大于所需 VRAM： + +| Size | Parameters | Multilingual model | Required VRAM | +|:------:|:----------:|:------------------:|:-------------:| +| tiny | 39 M | [`tiny`](https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt) | ~1 GB | +| base | 74 M | [`base`](https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt) | ~1 GB | +| small | 244 M | [`small`](https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt) | ~2 GB | +| medium | 769 M | [`medium`](https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt) | ~5 GB | +| large | 1550 M | [`large`](https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt) | ~10 GB | + +> [!NOTE] +> 由于 github 单个文件限制，本仓库内只保留了 tiny 模型，如需使用其他模型，请自行下载所需模型文件，并放置在 `src/subtitle/models` 文件夹中。 + ### 4.2 biliup-rs 登录 首先按照 [biliup-rs](https://github.com/biliup/biliup-rs) 登录b站，将登录产生的`cookies.json`文件复制一份到项目根目录中。 diff --git a/requirements.txt b/requirements.txt old mode 100755 new mode 100644 index 8b76c1e..a380c2a --- a/requirements.txt +++ b/requirements.txt @@ -1,12 +1,47 @@ -certifi==2022.5.18.1 +appdirs==1.4.4 +audioread==2.1.9 +certifi==2021.10.8 +cffi==1.15.0 +chardet==4.0.0 +click==8.1.3 +decorator==5.1.1 +filelock==3.6.0 +filesplit==3.0.2 +idna==3.3 +joblib==1.1.0 +librosa==0.9.1 +llvmlite==0.38.0 +numba==0.55.1 +numpy==1.21.6 +packaging==21.3 +pooch==1.6.0 +pycparser==2.21 +pyparsing==3.0.8 +pysrt==1.1.2 +PyYAML==6.0 +regex==2022.4.24 +requests==2.27.1 +resampy==0.2.2 +sacremoses==0.0.49 +scikit-learn==1.0.2 +scipy==1.8.0 +sentencepiece==0.1.96 +six==1.16.0 +SoundFile==0.10.3.post1 +threadpoolctl==3.1.0 +tokenizers==0.12.1 +torch==1.11.0 +tqdm==4.64.0 +typing_extensions==4.2.0 +urllib3==1.26.9 +more-itertools +transformers>=4.19.0 +ffmpeg-python==0.2.0 +zhconv==1.4.3 +tiktoken==0.4.0 charset-normalizer==2.0.12 -filelock==3.7.0 greenlet==1.1.2 -idna==3.3 pyasn1==0.4.8 -requests==2.27.1 rsa==4.8 toml==0.10.2 - -blrec - +blrec \ No newline at end of file diff --git a/src/burnAndMerge.sh b/src/burnAndMerge.sh index 9ee6ff2..abd6eee 100755 --- a/src/burnAndMerge.sh +++ b/src/burnAndMerge.sh @@ -66,6 +66,12 @@ ffmpeg -f concat -safe 0 -i mergevideo.txt -use_wallclock_as_timestamps 1 -c cop rm -r $tmpDir rm mergevideo.txt -echo "==================== start upload $firstOutputFile ====================" -echo "$firstOutputFile" >> $BILIVE_PATH/src/uploadProcess/uploadVideoQueue.txt -echo "==================== OVER ====================" \ No newline at end of file +python $BILIVE_PATH/src/subtitle/generate.py $firstOutputFile > $BILIVE_PATH/logs/burningLog/subtitlesGenerate-$(date +%Y%m%d%H%M%S).log 2>&1 +srtPath=${firstOutputFile%.*}".srt" +videoUploadPath=${firstOutputFile%.*}"-s.mp4" +ffmpeg -hwaccel cuda -c:v h264_cuvid -i "$firstOutputFile" -c:v h264_nvenc -vf "subtitles=$srtPath" "$videoUploadPath" -y -nostdin > $BILIVE_PATH/logs/burningLog/subtitlesRender-$(date +%Y%m%d%H%M%S).log 2>&1 +rm $srtPath +rm $firstOutputFile + +echo "==================== add $videoUploadPath to upload queue ====================" +echo "$videoUploadPath" >> $BILIVE_PATH/src/uploadProcess/uploadVideoQueue.txt diff --git a/src/danmakuBurning.sh b/src/danmakuBurning.sh index 0c0856c..80cb09c 100755 --- a/src/danmakuBurning.sh +++ b/src/danmakuBurning.sh @@ -73,5 +73,12 @@ echo "ffmpeg successfully complete!" # Delete the original video. rm $fullPath -# Upload video. -echo "$formatVideoName" >> $BILIVE_PATH/src/uploadProcess/uploadVideoQueue.txt \ No newline at end of file +python $BILIVE_PATH/src/subtitle/generate.py $formatVideoName > $BILIVE_PATH/logs/burningLog/subtitlesGenerate-$(date +%Y%m%d%H%M%S).log 2>&1 +srtPath=${formatVideoName%.*}".srt" +videoUploadPath=${formatVideoName%.*}"-s.mp4" +ffmpeg -hwaccel cuda -c:v h264_cuvid -i "$formatVideoName" -c:v h264_nvenc -vf "subtitles=$srtPath" "$videoUploadPath" -y -nostdin > $BILIVE_PATH/logs/burningLog/subtitlesRender-$(date +%Y%m%d%H%M%S).log 2>&1 +rm $srtPath +rm $formatVideoName + +echo "==================== add $videoUploadPath to upload queue ====================" +echo "$videoUploadPath" >> $BILIVE_PATH/src/uploadProcess/uploadVideoQueue.txt diff --git a/src/scanSegments.sh b/src/scanSegments.sh index 5dd1615..b0a1452 100755 --- a/src/scanSegments.sh +++ b/src/scanSegments.sh @@ -25,7 +25,8 @@ CheckAndProcessFolder() { detectName=$(basename "$mp4File") # This length to prevent processing the burned videos again! - if [ ${#detectName} -gt 27 ]; then + # The roomid is less 10, so the length of detectName is 28. + if [ ${#detectName} -gt 28 ]; then echo $mp4File > ./src/sameSegments.txt # Find the same date and hour video diff --git a/src/settings.ini b/src/settings.ini new file mode 100644 index 0000000..69cee8d --- /dev/null +++ b/src/settings.ini @@ -0,0 +1,4 @@ +[DEFAULT] +Interface = English +Language = auto +Mode = small \ No newline at end of file diff --git a/src/subtitle/__init__.py b/src/subtitle/__init__.py new file mode 100644 index 0000000..e343384 --- /dev/null +++ b/src/subtitle/__init__.py @@ -0,0 +1,6 @@ +# -*- coding: UTF-8 -*- + +import os +import sys +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +sys.path.insert(0, os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'dependencies')) diff --git a/src/subtitle/config.py b/src/subtitle/config.py new file mode 100644 index 0000000..c71a189 --- /dev/null +++ b/src/subtitle/config.py @@ -0,0 +1,54 @@ +# -*- coding: UTF-8 -*- + +import os +from pathlib import Path +from fsplit.filesplit import Filesplit +import configparser +import platform +import stat + +def get_settings_config(): + settings_config = configparser.ConfigParser() + settings_config.read(SETTINGS_PATH, encoding='utf-8') + return settings_config + + +def get_model_path(): + model_dir = os.path.join(BASE_DIR, 'models') + model_path = os.path.join(model_dir, f'{get_settings_config()["DEFAULT"]["Mode"]}.pt') + return model_path + + +def get_interface_config(): + interface_config = configparser.ConfigParser() + interface_file = os.path.join(os.path.dirname(os.path.abspath(__file__)), f"en.ini") + interface_config.read(interface_file, encoding='utf-8') + return interface_config + + +def init_settings_config(): + if not os.path.exists(SETTINGS_PATH): + with open(os.path.join(os.path.dirname(os.path.dirname(__file__)), 'settings.ini'), mode='w', encoding='utf-8') as f: + f.write('[DEFAULT]\n') + f.write('Interface = English\n') + f.write('Language = auto\n') + f.write('Mode = small') + + +BASE_DIR = str(Path(os.path.abspath(__file__)).parent) +SETTINGS_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), 'settings.ini') + +INTERFACE_KEY_NAME_MAP = { + 'English': 'en' +} + +init_settings_config() + +ffmpeg_bin = os.path.join('linux_x64', 'ffmpeg') +FFMPEG_PATH = os.path.join(BASE_DIR, '', ffmpeg_bin) +os.chmod(FFMPEG_PATH, stat.S_IRWXU+stat.S_IRWXG+stat.S_IRWXO) + +SILENCE_THRESH = -70 # silence below -70dBFS is considered silence +MIN_SILENCE_LEN = 700 # if silence is longer than 700ms, split +LENGTH_LIMIT = 60 * 1000 # split into segments no longer than 1 minute +ABANDON_CHUNK_LEN = 500 # discard chunks shorter than 500ms \ No newline at end of file diff --git a/src/subtitle/en.ini b/src/subtitle/en.ini new file mode 100644 index 0000000..964f021 --- /dev/null +++ b/src/subtitle/en.ini @@ -0,0 +1,110 @@ +[LanguageModeGUI] +Title = Subtitle Generator +InterfaceLanguage = Choose Interface Language: +SubtitleLanguage = Choose Spoken Language: +Mode = Choose Mode: +ModeBase = fast +ModeMedium = standard +ModeLarge = accurate +InterfaceDefault = English +LanguageAUTO = Auto Detect +LanguageZH-CN = Mandarin(China, Mainland) +LanguageZH-TW = Mandarin(China, Taiwan) +LanguageZH-HK = Mandarin(China, HONG KONG) +LanguageZH-SG = Singaporean Mandarin +LanguageZH-HANS = Chinese(Simplified) +LanguageZH-HANT = Chinese(Traditional) +LanguageEN = English +LanguageJA = Japanese +LanguageKO = Korean +LanguageDE = German +LanguageES = Spanish +LanguageRU = Russian +LanguageFR = French +LanguagePT = Portuguese +LanguageVI = Vietnamese +LanguageTR = Turkish +LanguagePL = Polish +LanguageCA = Catalan +LanguageNL = Dutch +LanguageAR = Arabic +LanguageSV = Swedish +LanguageIT = Italian +LanguageID = Indonesian +LanguageHI = Hindi +LanguageFI = Finnish +LanguageHE = Hebrew +LanguageUK = Ukrainian +LanguageEL = Greek +LanguageMS = Malay +LanguageCS = Czech +LanguageRO = Romanian +LanguageDA = Danish +LanguageHU = Hungarian +LanguageTA = Tamil +LanguageNO = Norwegian +LanguageTH = Thai +LanguageUR = Urdu +LanguageHR = Croatian +LanguageBG = Bulgarian +LanguageLT = Lithuanian +LanguageLA = Latin +LanguageML = Malayalam +LanguageCY = Welsh +LanguageSK = Slovak +LanguageTE = Telugu +LanguageFA = Persian +LanguageLV = Latvian +LanguageBN = Bengali +LanguageSR = Serbian +LanguageAZ = Azerbaijani +LanguageSL = Slovenian +LanguageKN = Kannada +LanguageET = Estonian +LanguageMK = Macedonian +LanguageBR = Breton +LanguageEU = Basque +LanguageIS = Icelandic +LanguageHY = Armenian +LanguageNE = Nepali +LanguageMN = Mongolian +LanguageBS = Bosnian +LanguageKK = Kazakh +LanguageSQ = Albanian +LanguageSW = Swahili +LanguageGL = Galician +LanguageMR = Marathi +LanguagePA = Punjabi +LanguageSI = Sinhala +LanguageKM = Khmer +LanguageSN = Shona +LanguageYO = Yoruba +LanguageSO = Somali +LanguageAF = Afrikaans +LanguageOC = Occitan +LanguageKA = Georgian +LanguageBE = Belarusian +LanguageTG = Tajik +LanguageSD = Sindhi + +[SubtitleGeneratorGUI] +Title = Subtitle Generator +Open = Open +AllFile = All Files +Run = Run +Setting = Settings +OpenFileSuccess = File opened successfully +OpenVideoFirst = Please open video/audio file first + + +[Main] +RecSubLang = language +RecMode = mode +IllegalPathWarning = 【Warning】illegal file path +IllegalLanguageCode = 【Warning】Language code not support, use auto detection +LanguageDetected = 【Info】Language Detected： +StartGenerateSub = 【Processing】start to generator subs please wait for a moment... +FinishGenerateSub = 【Finished】subtitle file generated successfully +Elapse = elapse +SubLocation = Subtitle file generated at: +InputFile = Please enter the full path of video or audio file： diff --git a/src/subtitle/generate.py b/src/subtitle/generate.py new file mode 100644 index 0000000..483a7b8 --- /dev/null +++ b/src/subtitle/generate.py @@ -0,0 +1,318 @@ +# -*- coding: UTF-8 -*- + +import io +import multiprocessing +import subprocess +import tempfile +import time +import warnings +warnings.filterwarnings('ignore') +import librosa +import os +import stat +import audioop +import wave +import math +import sys +import pysrt +import six +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) +import config +import whisper +from zhconv.zhconv import convert +import argparse + +def process_line(line): + # Due to the base model's poor performance, we need to remove the lines containing below words: + if "谢谢" in line: + return None + + if "字幕" in line: + return None + + if "by" in line: + return None + + # Truncate lines longer than 40 characters + if len(line) > 40: + line = line[:40] + + # Remove characters that repeat more than 6 times consecutively + import re + line = re.sub(r'(.)\1{6,}', r'\1', line) + + return line + +def process_srt_file(input_file): + # Read the contents of the file first + with open(input_file, 'r', encoding='utf-8') as infile: + lines = infile.readlines() + + # Open the file again in write mode to overwrite it + with open(input_file, 'w', encoding='utf-8') as outfile: + for line in lines: + # Process only lines that are not timestamps or empty + if not line.strip().isdigit() and '-->' not in line and line.strip(): + processed_line = process_line(line.strip()) + if processed_line is not None: + outfile.write(processed_line + '\n') + else: + # Write timestamps and other non-text lines unchanged + outfile.write(line) + +class AudioRecogniser: + def __init__(self, language='auto'): + self.model_path = config.get_model_path() + self.model = whisper.load_model(self.model_path) + self.language = language + + def __call__(self, audio_data): + audio_data = whisper.pad_or_trim(audio_data) + mel = whisper.log_mel_spectrogram(audio_data).to(self.model.device) + + # detect audio language + _, probs = self.model.detect_language(mel) + + # decode the audio + if self.language != 'auto': + if self.language in ('zh-cn', 'zh-tw', 'zh-hk', 'zh-sg', 'zh-hans', 'zh-hant'): + options = whisper.DecodingOptions(fp16=False, language='zh') + else: + options = whisper.DecodingOptions(fp16=False, language=self.language) + else: + # if no language is set, detect language automatically + print(f"{config.get_interface_config()['Main']['LanguageDetected']}{max(probs, key=probs.get)}") + options = whisper.DecodingOptions(fp16=False) + + transcription = whisper.decode(self.model, mel, options) + zh_list = ('zh', 'zh-cn', 'zh-tw', 'zh-hk', 'zh-sg', 'zh-hans', 'zh-hant') + if self.language in zh_list: + text = convert(transcription.text, self.language) + elif max(probs, key=probs.get) in zh_list: + text = convert(transcription.text, 'zh-cn') + else: + text = transcription.text + return text + + +class FLACConverter: # pylint: disable=too-few-public-methods + """ + Class for converting a region of an input audio or video file into a FLAC audio file + """ + + def __init__(self, source_path, include_before=0.25, include_after=0.25): + self.source_path = source_path + self.include_before = include_before + self.include_after = include_after + + def __call__(self, region): + try: + start, end = region + start = max(0, start - self.include_before) + end += self.include_after + temp = tempfile.NamedTemporaryFile(suffix='.flac', delete=False) + command = [config.FFMPEG_PATH, "-ss", str(start), "-t", str(end - start), + "-y", "-i", self.source_path, + "-loglevel", "error", temp.name] + use_shell = True if os.name == "nt" else False + subprocess.check_output(command, stdin=open(os.devnull), shell=use_shell) + read_data = temp.read() + temp.close() + os.unlink(temp.name) + return read_data + + except KeyboardInterrupt: + return None + + +class SubtitleGenerator: + # if you want to change the recognition language, you can change the zh-cn to others, such as en + def __init__(self, filename, language='zh-cn'): + self.filename = filename + self.language = language + self.isFinished = False + + @staticmethod + def which(program): + """ + Return the path for a given executable. + """ + + def is_exe(file_path): + """ + Checks whether a file is executable. + """ + if not os.access(file_path, os.X_OK): + os.chmod(file_path, stat.S_IXUSR) + if not os.access(file_path, os.X_OK): + os.chmod(file_path, stat.S_IXGRP) + if not os.access(file_path, os.X_OK): + os.chmod(file_path, stat.S_IXOTH) + return os.path.isfile(file_path) and os.access(file_path, os.X_OK) + + fpath, _ = os.path.split(program) + if fpath: + if is_exe(program): + return program + else: + for path in os.environ["PATH"].split(os.pathsep): + path = path.strip('"') + exe_file = os.path.join(path, program) + if is_exe(exe_file): + return exe_file + return None + + def extract_audio(self, rate=16000): + """ + Extract audio from an input file to a temporary WAV file. + """ + temp = tempfile.NamedTemporaryFile(suffix='.wav', delete=False) + if not os.path.isfile(self.filename): + print("The given file does not exist: {}".format(self.filename)) + raise Exception("Invalid filepath: {}".format(self.filename)) + if not self.which(config.FFMPEG_PATH): + print("ffmpeg: Executable not found on machine.") + raise Exception("Dependency not found: ffmpeg") + command = [config.FFMPEG_PATH, "-y", "-i", self.filename, + "-ac", '1', "-ar", str(rate), + "-loglevel", "error", temp.name] + use_shell = True if os.name == "nt" else False + subprocess.check_output(command, stdin=open(os.devnull), shell=use_shell) + return temp.name, rate + + @staticmethod + def percentile(arr, percent): + """ + Calculate the given percentile of arr. + """ + arr = sorted(arr) + index = (len(arr) - 1) * percent + floor = math.floor(index) + ceil = math.ceil(index) + if floor == ceil: + return arr[int(index)] + low_value = arr[int(floor)] * (ceil - index) + high_value = arr[int(ceil)] * (index - floor) + return low_value + high_value + + def find_speech_regions(self, filename, frame_width=4096, min_region_size=0.5, + max_region_size=6): # pylint: disable=too-many-locals + """ + Perform voice activity detection on a given audio file. + """ + reader = wave.open(filename) + sample_width = reader.getsampwidth() + rate = reader.getframerate() + n_channels = reader.getnchannels() + chunk_duration = float(frame_width) / rate + + n_chunks = int(math.ceil(reader.getnframes() * 1.0 / frame_width)) + energies = [] + for _ in range(n_chunks): + chunk = reader.readframes(frame_width) + energies.append(audioop.rms(chunk, sample_width * n_channels)) + threshold = self.percentile(energies, 0.2) + elapsed_time = 0 + + regions = [] + region_start = None + + for energy in energies: + is_silence = energy <= threshold + max_exceeded = region_start and elapsed_time - region_start >= max_region_size + + if (max_exceeded or is_silence) and region_start: + if elapsed_time - region_start >= min_region_size: + regions.append((region_start, elapsed_time)) + region_start = None + + elif (not region_start) and (not is_silence): + region_start = elapsed_time + elapsed_time += chunk_duration + return regions + + @staticmethod + def srt_formatter(subtitles, padding_before=0, padding_after=0): + """ + Serialize a list of subtitles according to the SRT format, with optional time padding. + """ + sub_rip_file = pysrt.SubRipFile() + for i, ((start, end), text) in enumerate(subtitles, start=1): + item = pysrt.SubRipItem() + item.index = i + item.text = six.text_type(text) + item.start.seconds = max(0, start - padding_before) + item.end.seconds = end + padding_after + sub_rip_file.append(item) + return '\n'.join(six.text_type(item) for item in sub_rip_file) + + def run(self, output=None): + """ + Given an input audio/video file, generate subtitles in the specified language and format. + """ + audio_filename, audio_rate = self.extract_audio() + regions = self.find_speech_regions(audio_filename) + pool = multiprocessing.Pool(10) + converter = FLACConverter(source_path=audio_filename) + recognizer = AudioRecogniser(language=self.language) + transcripts = [] + print(f"{config.get_interface_config()['Main']['StartGenerateSub']}") + start_time = time.time() + + if regions: + try: + extracted_regions = [] + for i, extracted_region in enumerate(pool.imap(converter, regions)): + data, sr = librosa.load(io.BytesIO(extracted_region), sr=16000) + extracted_regions.append(data) + + for i, data in enumerate(extracted_regions): + transcript = recognizer(data) + print(transcript) + transcripts.append(transcript) + print() + + except KeyboardInterrupt: + pool.terminate() + pool.join() + print("Cancelling transcription") + raise + + timed_subtitles = [(r, t) for r, t in zip(regions, transcripts) if t] + + formatted_subtitles = self.srt_formatter(subtitles=timed_subtitles) + dest = output + if not dest: + base = os.path.splitext(self.filename)[0] + dest = "{base}.{format}".format(base=base, format="srt") + + with open(dest, 'wb') as output_file: + output_file.write(formatted_subtitles.encode("utf-8")) + process_srt_file(dest) + os.remove(audio_filename) + self.isFinished = True + elapse = time.time() - start_time + print(f"{config.get_interface_config()['Main']['FinishGenerateSub']}") + print(f"{config.get_interface_config()['Main']['SubLocation']}{dest}") + print(f"{config.get_interface_config()['Main']['Elapse']}: {elapse}s") + return dest + + +if __name__ == '__main__': + parser = argparse.ArgumentParser(description='Subtitle Generator') + + parser.add_argument('-l', '--language', help=config.get_interface_config()['LanguageModeGUI']['SubtitleLanguage'], required=False) + parser.add_argument('filename', nargs='?', help=config.get_interface_config()['Main']['InputFile']) + + args = parser.parse_args() + + if hasattr(args, 'help'): + exit() + + # 1. acquire video path + video_path = args.filename or input(f"{config.get_interface_config()['Main']['InputFile']}").strip() + # 2. create subtitle generator object, specify language + sg = SubtitleGenerator(video_path, language='zh-cn') + # 3. run program + print('Start project.') + sg.run() diff --git a/src/subtitle/linux_x64/ffmpeg b/src/subtitle/linux_x64/ffmpeg new file mode 100755 index 0000000..13e56c8 Binary files /dev/null and b/src/subtitle/linux_x64/ffmpeg differ diff --git a/src/subtitle/models/tiny.pt b/src/subtitle/models/tiny.pt new file mode 100644 index 0000000..2fe497f Binary files /dev/null and b/src/subtitle/models/tiny.pt differ diff --git a/src/subtitle/whisper/__init__.py b/src/subtitle/whisper/__init__.py new file mode 100644 index 0000000..379133b --- /dev/null +++ b/src/subtitle/whisper/__init__.py @@ -0,0 +1,154 @@ +import hashlib +import io +import os +import urllib +import warnings +from typing import List, Optional, Union + +import torch +from tqdm import tqdm + +from .audio import load_audio, log_mel_spectrogram, pad_or_trim +from .decoding import DecodingOptions, DecodingResult, decode, detect_language +from .model import ModelDimensions, Whisper +from .transcribe import transcribe +from .version import __version__ + +_MODELS = { + "tiny.en": "https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt", + "tiny": "https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt", + "base.en": "https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt", + "base": "https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt", + "small.en": "https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt", + "small": "https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt", + "medium.en": "https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt", + "medium": "https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt", + "large-v1": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large-v1.pt", + "large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt", + "large": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt", +} + +# base85-encoded (n_layers, n_heads) boolean arrays indicating the cross-attention heads that are +# highly correlated to the word-level timing, i.e. the alignment between audio and text tokens. +_ALIGNMENT_HEADS = { + "tiny.en": b"ABzY8J1N>@0{>%R00Bk>$p{7v037`oCl~+#00", + "tiny": b"ABzY8bu8Lr0{>%RKn9Fp%m@SkK7Kt=7ytkO", + "base.en": b"ABzY8;40c<0{>%RzzG;p*o+Vo09|#PsxSZm00", + "base": b"ABzY8KQ!870{>%RzyTQH3`Q^yNP!>##QT-?_)10{>%RpeA61k&I|OI3I$65C{;;pbCHh0B{qLQ;+}v00", + "small": b"ABzY8DmU6=0{>%Rpa?J`kvJ6qF(V^F86#Xh7JUGMK}P%R7%R7}kK1fFL7w6%<-Pf*t^=N)Qr&0RR9", + "large-v1": b"ABzY8r9j$a0{>%R7#4sLmoOs{s)o3~84-RPdcFk!JR%R7=D0pU<_bnWW*tkYAhobTNnu$jnkEkXqp)j;w1Tzk)UH3X%SZd&fFZ2fC2yj", + "large": b"ABzY8zd+h!0{>%R7=D0pU<_bnWW*tkYAhobTNnu$jnkEkXqp)j;w1Tzk)UH3X%SZd&fFZ2fC2yj", +} + + +def _download(url: str, root: str, in_memory: bool) -> Union[bytes, str]: + os.makedirs(root, exist_ok=True) + + expected_sha256 = url.split("/")[-2] + download_target = os.path.join(root, os.path.basename(url)) + + if os.path.exists(download_target) and not os.path.isfile(download_target): + raise RuntimeError(f"{download_target} exists and is not a regular file") + + if os.path.isfile(download_target): + with open(download_target, "rb") as f: + model_bytes = f.read() + if hashlib.sha256(model_bytes).hexdigest() == expected_sha256: + return model_bytes if in_memory else download_target + else: + warnings.warn( + f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file" + ) + + with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: + with tqdm( + total=int(source.info().get("Content-Length")), + ncols=80, + unit="iB", + unit_scale=True, + unit_divisor=1024, + ) as loop: + while True: + buffer = source.read(8192) + if not buffer: + break + + output.write(buffer) + loop.update(len(buffer)) + + model_bytes = open(download_target, "rb").read() + if hashlib.sha256(model_bytes).hexdigest() != expected_sha256: + raise RuntimeError( + "Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model." + ) + + return model_bytes if in_memory else download_target + + +def available_models() -> List[str]: + """Returns the names of available models""" + return list(_MODELS.keys()) + + +def load_model( + name: str, + device: Optional[Union[str, torch.device]] = None, + download_root: str = None, + in_memory: bool = False, +) -> Whisper: + """ + Load a Whisper ASR model + + Parameters + ---------- + name : str + one of the official model names listed by `whisper.available_models()`, or + path to a model checkpoint containing the model dimensions and the model state_dict. + device : Union[str, torch.device] + the PyTorch device to put the model into + download_root: str + path to download the model files; by default, it uses "~/.cache/whisper" + in_memory: bool + whether to preload the model weights into host memory + + Returns + ------- + model : Whisper + The Whisper ASR model instance + """ + + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + if download_root is None: + default = os.path.join(os.path.expanduser("~"), ".cache") + download_root = os.path.join(os.getenv("XDG_CACHE_HOME", default), "whisper") + + if name in _MODELS: + checkpoint_file = _download(_MODELS[name], download_root, in_memory) + alignment_heads = _ALIGNMENT_HEADS[name] + elif os.path.isfile(name): + checkpoint_file = open(name, "rb").read() if in_memory else name + alignment_heads = None + else: + raise RuntimeError( + f"Model {name} not found; available models = {available_models()}" + ) + + with ( + io.BytesIO(checkpoint_file) if in_memory else open(checkpoint_file, "rb") + ) as fp: + checkpoint = torch.load(fp, map_location=device) + del checkpoint_file + + dims = ModelDimensions(**checkpoint["dims"]) + model = Whisper(dims) + model.load_state_dict(checkpoint["model_state_dict"]) + + if alignment_heads is not None: + model.set_alignment_heads(alignment_heads) + + return model.to(device) diff --git a/src/subtitle/whisper/__main__.py b/src/subtitle/whisper/__main__.py new file mode 100644 index 0000000..d14f205 --- /dev/null +++ b/src/subtitle/whisper/__main__.py @@ -0,0 +1,3 @@ +from .transcribe import cli + +cli() diff --git a/src/subtitle/whisper/assets/gpt2.tiktoken b/src/subtitle/whisper/assets/gpt2.tiktoken new file mode 100644 index 0000000..3480e07 --- /dev/null +++ b/src/subtitle/whisper/assets/gpt2.tiktoken @@ -0,0 +1,50256 @@ +IQ== 0 +Ig== 1 +Iw== 2 +JA== 3 +JQ== 4 +Jg== 5 +Jw== 6 +KA== 7 +KQ== 8 +Kg== 9 +Kw== 10 +LA== 11 +LQ== 12 +Lg== 13 +Lw== 14 +MA== 15 +MQ== 16 +Mg== 17 +Mw== 18 +NA== 19 +NQ== 20 +Ng== 21 +Nw== 22 +OA== 23 +OQ== 24 +Og== 25 +Ow== 26 +PA== 27 +PQ== 28 +Pg== 29 +Pw== 30 +QA== 31 +QQ== 32 +Qg== 33 +Qw== 34 +RA== 35 +RQ== 36 +Rg== 37 +Rw== 38 +SA== 39 +SQ== 40 +Sg== 41 +Sw== 42 +TA== 43 +TQ== 44 +Tg== 45 +Tw== 46 +UA== 47 +UQ== 48 +Ug== 49 +Uw== 50 +VA== 51 +VQ== 52 +Vg== 53 +Vw== 54 +WA== 55 +WQ== 56 +Wg== 57 +Ww== 58 +XA== 59 +XQ== 60 +Xg== 61 +Xw== 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2 +a g +er s +k e +Ġ " +Ġ H +e m +Ġc on +Ġ W +Ġ R +he r +Ġw as +Ġ r +o d +Ġ F +u l +at e +Ġa t +r i +p p +o re +ĠT he +Ġs e +u s +Ġp ro +Ġh a +u m +Ġa re +Ġd e +a in +an d +Ġo r +ig h +es t +is t +a b +r om +Ġ N +t h +Ġc om +Ġ G +u n +o p +0 0 +Ġ L +Ġn ot +es s +Ġe x +Ġ v +re s +Ġ E +e w +it y +an t +Ġb y +e l +o s +or t +o c +q u +Ġf rom +Ġha ve +Ġs u +i ve +ou ld +Ġs h +Ġth is +n t +r a +p e +igh t +ar t +m ent +Ġa l +u st +en d +- - +al l +Ġ O +ac k +Ġc h +Ġ le +i es +re d +ar d +â Ģ +ou t +Ġ J +Ġa b +e ar +i v +al ly +ou r +o st +g h +p t +Ġp l +as t +Ġc an +a k +om e +u d +T he +Ġh is +Ġd o +Ġg o +Ġh as +g e +' t +Ġ U +r ou +Ġs a +Ġ j +Ġb ut +Ġw or +Ġa ll +e ct +Ġ k +am e +Ġw ill +o k +Ġw he +Ġthe y +id e +0 1 +f f +ic h +p l +t her +Ġt r +. . +Ġin t +i e +u re +ag e +Ġn e +i al +a p +in e +ic e +Ġm e +Ġo ut +an s +on e +on g +ion s +Ġwh o +Ġ K +Ġu p +Ġthe ir +Ġa d +Ġ 3 +Ġu s +at ed +ou s +Ġm ore +u e +o g +ĠS t +in d +i ke +Ġs o +im e +p er +. 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+Ġw e +Ġ on +er e +Ġh a +u t +a c +i d +i g +o s +k e +v er +i m +Ġ Ð +ĠT h +a m +a ll +Ġf or +e l +c h +r o +Ġth is +Ġs t +Ġ W +Ġ u +a d +ou t +i r +l d +c t +Ġ k +i f +Ġg o +. . +Ð ¾ +it h +l y +h t +q u +Ġ - +Ġd o +Ġ j +Ġha ve +Ġ B +Ġa n +Ġw ith +Ġa re +Ġ r +Ġd e +Ġs e +Ġs o +Ġ v +s t +i ll +u r +Ġl i +Ġ M +es t +o d +all y +' t +us t +Ġa s +Ġ C +c e +Ġm e +Ð ° +Ð µ +i l +Ġ H +Ġw as +t er +t h +Ġc an +an t +Ġc om +ou r +ig ht +Ġ Y +at ion +ĠA nd +o l +Ġs h +Ñ Ĥ +o p +s e +Ġn ot +ĠS o +Ġn e +u n +Ġa b +Ġli ke +Ġa t +Ġ D +i e +Ġh e +Ġc on +Ġc h +o re +Ġa l +Ġo r +Ġ qu +Ġ O +om e +r a +u l +Ġ N +p p +Ġyou r +ou ld +Ġ P +Ġf r +g e +er s +' re +Ð ¸ +Ġthe y +Ġwh at +us e +Ġa ll +ĠTh e +Ġ L +es s +e m +Ġk n +Ġj ust +ar t +Ġp ro +ver y +u m +Ġl o +Ġ ì +Ġm y +o k +Ġe x +a b +Ġth ere +Ġb ut +Ġkn ow +Ġs u +Ġ G +Ñ ģ +Ġ E +Ġm a +о Ð +Ġ en +Ġab out +ĠI t +is t +Ġw or +r i +in d +Ġon e +at e +a nd +in k +Ġl e +or t +' m +Ġ F +ic h +Ñ Ģ +id e +Ġg et +Ġ out +.. . +Ġw ill +ã ģ +i ve +Ð ½ +Ġfr om +a in +ĠW e +Ġu p +p e +re s +c a +Ġ R +Ġ if +Ġp l +Ġd on +ac k +Ġ 1 +Ġ " +Ġt r +Ġ us +ĠW h +it y +Ġ J +ĠY ou +Ġh ere +h er +Ġs ome +ou g +a k +ar d +Ġgo ing +Ġu n +m ent +Ġth ink +Ġp e +en d +Ġ ( +ca use +Ġt im +as t +à © +Ġ our +Ġw ant +am e +i es +Ġ ë +u d +in e +Ġre ally +Ġt e +Ġse e +c i +Ġb y +s o +u re +os e +Ġ [ +a re +Ġm ore +a h +on e +c k +op le +а Ð +Ġthe n +Ġth ing +Ġthe m +v en +ou nd +os t +on g +e ct +Ġr ight +a g +Ġin t +Ġpe ople +Ġwh en +ou s +p l +Ġtim e +Ġ im +Ġwh o +Ġ 2 +a p +Ġbe cause +h ing +Ġn o +ic e +Ġlo ok +Ġh as +Ġw ould +Ġh ow +ac t +Ġf e +n t +oug h +Ġp r +ĠB ut +Ġs ay +Ñ ĥ +Ġn ow +Ġm an +Ġ very +Ġwor k +i z +Ġ K +i v +it t +Ġa r +e p +Ġc l +Ġwh ich +Ġc o +an s +' ve +Ġs a +f f +' ll +Ġan y +Ġa ct +Ġy e +b er +ac h +a ge +p er +Ġal so +f er +Ġthe se +Ġa d +е Ð +th er +ac e +ic k +a ke +re at +i re +u e +Ġa g +Ġ U +u ch +ion s +r y +0 0 +n a +Ġd id +Ġqu e +Ġha d +Ġe very +ĠH e +Ġl a +Ġw ay +Ġs p +b le +ĠTh is +as s +Ġthe ir +it e +Ġne ed +Ġp art +Ġw ere +Ġb 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16014, "Ġprohib": 16015, "Ġsatellite": 16016, "é¦Ļ": 16017, "ĠIndust": 16018, "Ġtraged": 16019, "Ġtrava": 16020, "Ġihm": 16021, "Ġcruel": 16022, "ĠAgora": 16023, "ĠDoc": 16024, "Ġzones": 16025, "Ġmall": 16026, "Ġtray": 16027, "×ķ׳": 16028, "Ġirrit": 16029, "Ġkans": 16030, "ĠBeat": 16031, "udge": 16032, "ielle": 16033, "Ġtrusted": 16034, "Ġbikes": 16035, "ĠÑĥп": 16036, "ĠMember": 16037, "wick": 16038, "Ġcreators": 16039, "Ġheritage": 16040, "indistinct": 16041, "Ġresur": 16042, "ennen": 16043, "Come": 16044, "Ġfiring": 16045, "ĠBueno": 16046, "ĠТо": 16047, "ikan": 16048, "ettes": 16049, "Ġkes": 16050, "Ġtrips": 16051, "Ġdivorce": 16052, "ĠKl": 16053, "Ġconsol": 16054, "keep": 16055, "기ê°Ģ": 16056, "ĠReport": 16057, "Ġhosting": 16058, "Ġdiamond": 16059, "Ġcomplic": 16060, "Ġhelicop": 16061, "Ġdepuis": 16062, "ds": 16063, "ĠChan": 16064, "Ñıл": 16065, "Ġscissors": 16066, "ilation": 16067, "Ġproportion": 16068, "ERE": 16069, "ĠÙĪاÙĦ": 16070, "inta": 16071, "Ġmuchas": 16072, "uation": 16073, "itis": 16074, "æĬĬ": 16075, "ÑıÑī": 16076, "Ġniin": 16077, "Ġemphasize": 16078, "uela": 16079, "Ġproducers": 16080, "Ġrze": 16081, "änder": 16082, "ETH": 16083, "æº": 16084, "Ġconstitu": 16085, "åĽ½": 16086, "Ġperformances": 16087, "istle": 16088, "gov": 16089, "ĠLiter": 16090, "Ġincorporate": 16091, "Ġeducate": 16092, "ĠNin": 16093, "쪽": 16094, "ÙĩÙħ": 16095, "eleration": 16096, "×ķ×ij": 16097, "ĠyaÅŁ": 16098, "orous": 16099, "ĠCas": 16100, "Ġgrants": 16101, "ëĬ¥": 16102, "amel": 16103, "Ġê·¸ëłĩê²Į": 16104, "ĠEste": 16105, "ÑħодиÑĤ": 16106, "ĠпоÑģле": 16107, "Ġgent": 16108, "Ġfocuses": 16109, "alities": 16110, "ĠRh": 16111, "ë³´": 16112, "æ°ij": 16113, "ĠDance": 16114, "rr": 16115, "Ġamer": 16116, "Ġutilize": 16117, "ĠlÃŃ": 16118, "ĠAmong": 16119, "Ġpregnancy": 16120, "Ġloops": 16121, "алоÑģÑĮ": 16122, "ĠMoh": 16123, "Ġcatching": 16124, "Ġglob": 16125, "Ġajud": 16126, "Ġ[?": 16127, "ĠAnal": 16128, "looking": 16129, "Ġsurfaces": 16130, "Ġprogressive": 16131, "Ġviral": 16132, "08": 16133, "ξ": 16134, "KA": 16135, "Ġży": 16136, "Ġpicks": 16137, "annon": 16138, "Ġbulk": 16139, "ĠRoss": 16140, "Ġdescribing": 16141, "ĠGel": 16142, "Ġlocally": 16143, "Ġendless": 16144, "Ġmassage": 16145, "Ġcleaned": 16146, "Ġtraveled": 16147, "енÑĭ": 16148, "Ġsentiment": 16149, "igma": 16150, "ĠNas": 16151, "Ġchemicals": 16152, "Ġrighteous": 16153, "ĠMagic": 16154, "Ġrelates": 16155, "Ġtrucks": 16156, "Ġ1960": 16157, "åĪ¥": 16158, "Ġappet": 16159, "Ġsnacks": 16160, "ĠSummer": 16161, "Ġyüz": 16162, "Ġpris": 16163, "ĠMexican": 16164, "Ġtransparen": 16165, "Ġminority": 16166, "Ġverte": 16167, "Ġlassen": 16168, "46": 16169, "лек": 16170, "ép": 16171, "ĠÑĦилÑĮ": 16172, "Ġiyi": 16173, "Ġspan": 16174, "íķĺì§Ģ": 16175, "Ġindicated": 16176, "quar": 16177, "Ġscholarship": 16178, "ĠLGBT": 16179, "Ġhistorically": 16180, "óÅĤ": 16181, "Ġminist": 16182, "Ġpenet": 16183, "ĠRap": 16184, "Ġconservation": 16185, "缴": 16186, "ĠHoney": 16187, "ĠBei": 16188, "idel": 16189, "Ġresponsibilities": 16190, "Ġmessy": 16191, "ĠExcept": 16192, "ORE": 16193, "Ġinitiatives": 16194, "Ġjunior": 16195, "Ġdesigners": 16196, "Ġexploration": 16197, "Ġsponsor": 16198, "Ġmobility": 16199, "Ġinteg": 16200, "lando": 16201, "Ġbark": 16202, "Ġindicates": 16203, "à¶": 16204, "Ġemployer": 16205, "å®ī": 16206, "Ġcousin": 16207, "Ġboiling": 16208, "Ġchrom": 16209, "Ġçal": 16210, "Ġperpet": 16211, "Ġcontained": 16212, "Ġparks": 16213, "Ы": 16214, "ĠEngineering": 16215, "Please": 16216, "ĠStarting": 16217, "hero": 16218, "Ġlawyers": 16219, "西": 16220, "Ġzd": 16221, "Ġfranchise": 16222, "rage": 16223, "Ġintuit": 16224, "ĠGL": 16225, "reach": 16226, "ĠElle": 16227, "ĠnhÆ°": 16228, "ĠNord": 16229, "Ġbean": 16230, "07": 16231, "Ġpleasant": 16232, "å½ĵ": 16233, "viron": 16234, "Ġgradient": 16235, "zus": 16236, "ĠEM": 16237, "Ġessay": 16238, "ìĹIJìļĶ": 16239, "ến": 16240, "nu": 16241, "ừ": 16242, "ĠÃīs": 16243, "Ġdenomin": 16244, "ĠGirls": 16245, "Ġpersonnes": 16246, "ĠاÙĦØ£": 16247, "bild": 16248, "ĠStat": 16249, "Ġcompliment": 16250, "ĠKate": 16251, "Ġoptimal": 16252, "Ġhid": 16253, "دÙĬ": 16254, "Ġquicker": 16255, "wall": 16256, "En": 16257, "INE": 16258, "???": 16259, "ì²´": 16260, "ĠAction": 16261, "åŁ": 16262, "Ġpenalty": 16263, "ĠKaz": 16264, "'?": 16265, "Ġcried": 16266, "Ġcanvas": 16267, "fte": 16268, "Ġexclud": 16269, "¸ë¡ľ": 16270, "Ġemphasis": 16271, "Ġenzy": 16272, "ĠHou": 16273, "Ġoverseas": 16274, "ÃŃamos": 16275, "師": 16276, "öglich": 16277, "Ġheadphones": 16278, "cn": 16279, "ĠAge": 16280, "Ġakan": 16281, "Ġcharacteristic": 16282, "íķĺë©´": 16283, "gets": 16284, "Ġë¶Ī": 16285, "Ġrival": 16286, "Ġborders": 16287, "emente": 16288, "emás": 16289, "Ġyol": 16290, "Ġcompe": 16291, "enders": 16292, "ından": 16293, "Ġmöglich": 16294, "Ġbubbles": 16295, "natural": 16296, "Ġarmed": 16297, "Ġelabor": 16298, "ĠìĿ´ë²Ī": 16299, "Ġwashed": 16300, "οÏħμε": 16301, "è«ĭ": 16302, "Ġflavors": 16303, "Ġexiste": 16304, "Ġprest": 16305, "ĠThema": 16306, "опÑĢоÑģ": 16307, "eron": 16308, "UE": 16309, "eri": 16310, "Ġconcer": 16311, "Ġaixò": 16312, "åħ©": 16313, "Ġprotective": 16314, "ĠзнаÑİ": 16315, "ĠëĤł": 16316, "ĠIII": 16317, "Ġmeer": 16318, "ĠShop": 16319, "lli": 16320, "ĠOrder": 16321, "ĠMY": 16322, "ĠGhost": 16323, "ãĤĤãģĨ": 16324, "adel": 16325, "Ġstole": 16326, "Ġreleasing": 16327, "ĠComment": 16328, "Ġtrains": 16329, "ëªħ": 16330, "Ġwissen": 16331, "ensed": 16332, "Ġdescend": 16333, "Ġfier": 16334, "Ġradi": 16335, "Ġpersu": 16336, "ç¢": 16337, "Ġмн": 16338, "ĠDest": 16339, "Ġworries": 16340, "itet": 16341, "bas": 16342, "Ġstab": 16343, "name": 16344, "oric": 16345, "ĠClose": 16346, "Ġalumni": 16347, "ĠSelf": 16348, "ffe": 16349, "itating": 16350, "atherine": 16351, "ĠRights": 16352, "Ġellos": 16353, "Ġwarrant": 16354, "Ġnerve": 16355, "Ġvegetable": 16356, "ĠTeil": 16357, "Ġê°ĻìĿ´": 16358, "RY": 16359, "Ġsustainability": 16360, "Ġsteht": 16361, "Ġbrid": 16362, "adaÅŁ": 16363, "Ġtv": 16364, "Ġduration": 16365, "Ġpessoa": 16366, "Ġmetrics": 16367, "Ġadam": 16368, "cas": 16369, "аÑĢи": 16370, "Ġevident": 16371, "Ġdisplayed": 16372, "ائ": 16373, "Ġreck": 16374, "ĠBuddha": 16375, "Ġdele": 16376, "ĠDiego": 16377, "osph": 16378, "Ġbla": 16379, "ĠMik": 16380, "ulator": 16381, "Ġ2001": 16382, "Ġpromoting": 16383, "ych": 16384, "ĠEX": 16385, "Ġlastly": 16386, "Ġoutline": 16387, "Ġspirits": 16388, "Ġveux": 16389, "Ġsubtract": 16390, "ĠÅŁimdi": 16391, "Ġpins": 16392, "Ġburger": 16393, "Ġmolto": 16394, "ĠhabÃŃa": 16395, "Ġë°ĺ": 16396, "igu": 16397, "erst": 16398, "Ġnen": 16399, "Ġbacon": 16400, "itious": 16401, "Ġcarries": 16402, "Ġpromises": 16403, "nde": 16404, "ĠLeft": 16405, "ĠLim": 16406, "æ£": 16407, "Ġ44": 16408, "Ġcareers": 16409, "Ġ주ë": 16410, "Ġspeeds": 16411, "qué": 16412, "mad": 16413, "market": 16414, "isme": 16415, "Ġ2003": 16416, "Ġrecess": 16417, "ĠJUD": 16418, "Ġracist": 16419, "ĠSchl": 16420, "Ġparler": 16421, "Ġotros": 16422, "ishes": 16423, "Ġconverted": 16424, "aaaa": 16425, "ании": 16426, "ĠArk": 16427, "ĠChance": 16428, "Ġelementary": 16429, "εν": 16430, "inks": 16431, "Interviewer": 16432, "Ġfreely": 16433, "alah": 16434, "Ġëĭ¤ë¥¸": 16435, "Ġrequested": 16436, "Ġtorque": 16437, "noÅĽci": 16438, "oured": 16439, "ĠStaff": 16440, "Ġstain": 16441, "ĠAlan": 16442, "Ġvere": 16443, "ĠWinter": 16444, "Ġdefect": 16445, "iedy": 16446, "Ġbeats": 16447, "Ġhá": 16448, "umn": 16449, "oons": 16450, "itudes": 16451, "Ġseit": 16452, "oly": 16453, "Ġreserv": 16454, "Ġextr": 16455, "Ġphysician": 16456, "visor": 16457, "Ġhandful": 16458, "ĠNations": 16459, "Ġì¢ĭìĿĢ": 16460, "uccess": 16461, "Ġupstairs": 16462, "ĠSquare": 16463, "Ġhein": 16464, "ĠSeason": 16465, "olis": 16466, "Ġprince": 16467, "Ġdefensive": 16468, "ç½": 16469, "ĠмеÑģÑĤ": 16470, "Ñĸй": 16471, "ĠاÙĨ": 16472, "umble": 16473, "ê¹ĮìļĶ": 16474, "Ġassass": 16475, "Ġcircular": 16476, "Ġqualities": 16477, "Ġhmm": 16478, "Ġblown": 16479, "ĠLiz": 16480, "ĠKur": 16481, "ĠSA": 16482, "Ġfindings": 16483, "Ġcolours": 16484, "Ġdelle": 16485, "ĠIR": 16486, "ĠAth": 16487, "ĠDub": 16488, "ĠOx": 16489, "ĠØ®": 16490, "Ġpockets": 16491, "Ġgrill": 16492, "Ġswitching": 16493, "Ġpreferred": 16494, "ĠWales": 16495, "Ġexemplo": 16496, "Ġchopped": 16497, "Ġvaccination": 16498, "Ġneuro": 16499, "Ġspecify": 16500, "ivos": 16501, "Ġserá": 16502, "Ġzie": 16503, "Ġà®®": 16504, "Ġresulting": 16505, "ĠUgh": 16506, "Ġmessed": 16507, "CD": 16508, "Ġpaar": 16509, "Ġcomer": 16510, "Ġcouch": 16511, "ĠFestival": 16512, "Ġ49": 16513, "vous": 16514, "zens": 16515, "種": 16516, "ĠKennedy": 16517, "ĠTs": 16518, "Ġë³´ìĹ": 16519, "Ġdemonstration": 16520, "Ġunto": 16521, "Ġfrustrating": 16522, "Ġlaboratory": 16523, "Ġegy": 16524, "Ġbeautifully": 16525, "Ġìŀ¬ë": 16526, "Ġalgu": 16527, "Ġöyle": 16528, "ä½łçľĭ": 16529, "ĠPH": 16530, "Ġfortune": 16531, "Ġcleaner": 16532, "ĠRobin": 16533, "Ġsaus": 16534, "ĠGeld": 16535, "Ġkat": 16536, "obs": 16537, "Ġolur": 16538, "Ġmatt": 16539, "Ġquesta": 16540, "Ġsuggestion": 16541, "encer": 16542, "оÑģÑĤ": 16543, "Ġradar": 16544, "Ġìŀ¡": 16545, 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16603, "ãĢı": 16604, "Ġunbelievable": 16605, "اء": 16606, "Ġoperational": 16607, "üs": 16608, "ĠGM": 16609, "Ġê·¸ëŁ°ëį°": 16610, "Ġgracias": 16611, "Ġentend": 16612, "ĠRegard": 16613, "rob": 16614, "ĠÑĤеÑħ": 16615, "èı": 16616, "ĠRevolution": 16617, "Ġwaar": 16618, "ĠBiz": 16619, "theless": 16620, "Ġsponsored": 16621, "quier": 16622, "ĠìĿ¼ë": 16623, "Ġtek": 16624, "ĠëIJł": 16625, "igkeit": 16626, "ĠLuck": 16627, "ĠCertainly": 16628, "Ġtoll": 16629, "ĠниÑĩего": 16630, "ĠMoney": 16631, "ĠÑģÑĤоÑĢ": 16632, "ĠDouble": 16633, "ĠWolf": 16634, "Ġchunk": 16635, "άν": 16636, "ités": 16637, "oning": 16638, "Mar": 16639, "Ġgrandes": 16640, "Ġcollections": 16641, "ĠEuropa": 16642, "ĠаÑĢ": 16643, "ĠâĢĭâĢĭâĢĭ": 16644, "Ġê·¸ëŁ¬ë©´": 16645, "ĠобÑĬ": 16646, "Ġãģª": 16647, "Ġìĭľê°Ħ": 16648, "ĠCustom": 16649, "Ġì²ĺ": 16650, "ÑĸлÑĮ": 16651, "Ġindividually": 16652, "íĹ": 16653, "Ġdozen": 16654, "Ġowe": 16655, "ĠVictoria": 16656, "åı¯èĥ½": 16657, "Ġbeet": 16658, "urb": 16659, "Ġanalog": 16660, "ição": 16661, 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"Ġdisgusting": 17552, "ĠRecent": 17553, "Ġacquired": 17554, "Ġcables": 17555, "çĶļ": 17556, "din": 17557, "Ġvisto": 17558, "Ġcommunicating": 17559, "ÑģÑĤавлÑı": 17560, "еÑģÑĤо": 17561, "ãĥ»ãĥ»ãĥ»": 17562, "Ġrég": 17563, "Ġsocks": 17564, "Ġproces": 17565, "because": 17566, "Ġutter": 17567, "Ġcolocar": 17568, "Ġnewest": 17569, "Ġgramm": 17570, "表": 17571, "ä¸įçŁ¥éģĵ": 17572, "Ġshifting": 17573, "Ġcarrier": 17574, "ĠÑģкоÑĢ": 17575, "ĠSchw": 17576, "Ġexecuted": 17577, "Ġmaintained": 17578, "ĠÏĨ": 17579, "ĠMoses": 17580, "Ġdisse": 17581, "Ġhorr": 17582, "ãĢľ": 17583, "Ġrally": 17584, "Ġallem": 17585, "ĠEventually": 17586, "Ġdiyor": 17587, "lvania": 17588, "Ġschnell": 17589, "Ġê³¼": 17590, "Ġ매": 17591, "Ġstruggles": 17592, "late": 17593, "Ġclarify": 17594, "ément": 17595, "Ġmultiplic": 17596, "ибо": 17597, "Ġjourn": 17598, "Ġfragr": 17599, "Ġsurprisingly": 17600, "Ġdesperate": 17601, "52": 17602, "Ġsul": 17603, "ĠRead": 17604, "ĠFried": 17605, "Ġmond": 17606, "woo": 17607, "Ġorganizing": 17608, "ãģĹãĤĩãģĨ": 17609, "ĠSoon": 17610, "ĠвопÑĢоÑģ": 17611, "ĠNur": 17612, "ĠÐĹд": 17613, "Ġspider": 17614, "еÑģÑı": 17615, "Ġtutorials": 17616, "Ġnutrients": 17617, "orer": 17618, "Ġcoefficient": 17619, "Ġarrangement": 17620, "Ġpricing": 17621, "nan": 17622, "yu": 17623, "BL": 17624, "Ġtribe": 17625, "ĠHoward": 17626, "unks": 17627, "Ġnewer": 17628, "Ġprovin": 17629, "Ġprediction": 17630, "hos": 17631, "Ġolsun": 17632, "ĠAround": 17633, "Ġvier": 17634, "ĠÑģÑĤоÑĢон": 17635, "Ġvalley": 17636, "ĠEla": 17637, "ifi": 17638, "Ġgalaxy": 17639, "Ġtranqu": 17640, "Ġadvers": 17641, "ĠTemple": 17642, "iffs": 17643, "igence": 17644, "èĩªå·±": 17645, "Ġkönnte": 17646, "ĠÄijó": 17647, "Did": 17648, "Ġphotographs": 17649, "ĠAWS": 17650, "ÑĨиÑı": 17651, "Ġguards": 17652, "Ġappointed": 17653, "ĠGil": 17654, "Ġмом": 17655, "Ġcod": 17656, "ĠUnlike": 17657, "Ġevenly": 17658, "isconsin": 17659, "Ġestou": 17660, "Ġmnie": 17661, "ĠExec": 17662, "ĠMV": 17663, "ĠEine": 17664, "ä¿¡": 17665, "ĠRoger": 17666, "ĠFac": 17667, "ĠList": 17668, "Ġfuer": 17669, "аеÑĤе": 17670, "omed": 17671, "Ġattraction": 17672, "èī²": 17673, "Ġterrain": 17674, "ĠDrop": 17675, "Ġcorporations": 17676, "Ġsciences": 17677, "Ġthrone": 17678, "ãģĦãģŁ": 17679, "Ġaj": 17680, "ĠRot": 17681, "çī¹": 17682, "Ġsupporters": 17683, "ĠBere": 17684, "Here": 17685, "Ġdiferentes": 17686, "Ġsignificance": 17687, "Ïĥη": 17688, "æĪij覺å¾Ĺ": 17689, "Ġclamp": 17690, "ĠëĮĢë": 17691, "Ġfabulous": 17692, "rez": 17693, "æĮģ": 17694, "Ġassumptions": 17695, "uther": 17696, "wid": 17697, "pot": 17698, "è¿İ": 17699, "Ġyan": 17700, "ulin": 17701, "ÑĢÑĭв": 17702, "ĠSlow": 17703, "ĠPennsy": 17704, "Ġíķ´ìĦľ": 17705, "Ġmeio": 17706, "Ġwealthy": 17707, "ĠEight": 17708, "Ġpulse": 17709, "Ġfriction": 17710, "idity": 17711, "ĠHoll": 17712, "iyorum": 17713, "Ġsounded": 17714, "ĠCarr": 17715, "Ġfork": 17716, "âĺ": 17717, "ĠPA": 17718, "Ġconspir": 17719, "Ġcoding": 17720, "rt": 17721, "ĠTyp": 17722, "Ġìĸij": 17723, "Ġпог": 17724, "Ġmiser": 17725, "ĠÑģмоÑĤÑĢ": 17726, "ĠSweden": 17727, "Ġolarak": 17728, "ĠZhang": 17729, "ĠChi": 17730, "ĠTitan": 17731, "Ġscreening": 17732, "ĠSpider": 17733, "ĠÅŀimdi": 17734, "Ġobstacles": 17735, "lara": 17736, "Ġchallenged": 17737, "pse": 17738, "TON": 17739, "ụ": 17740, "ĠPi": 17741, "Ġlagi": 17742, "ieurs": 17743, "Ġhurting": 17744, "Ġneglect": 17745, "Ġgenerating": 17746, "Ġyoungest": 17747, "Ġaudit": 17748, "ĠÑĢез": 17749, "Ïģά": 17750, "Ġdonate": 17751, "ĠPDF": 17752, "Ġvisits": 17753, "Ġcruise": 17754, "PP": 17755, "aser": 17756, "Ġwsp": 17757, "backs": 17758, "ivals": 17759, "ãģĨãĤĵ": 17760, "Ġdeve": 17761, "Ġproport": 17762, "Ġcath": 17763, "ĠEffect": 17764, "Ġwinds": 17765, "ĠìĻĶ": 17766, "Ġcharts": 17767, "Ġsama": 17768, "Ġautomation": 17769, "Ġпока": 17770, "Ġolan": 17771, "Ġboats": 17772, "Ġcafe": 17773, "Ġdenied": 17774, "ĠMama": 17775, "Ġblocking": 17776, "ĠThor": 17777, "Ġphenomenal": 17778, "Ġstakeholders": 17779, "Ġunos": 17780, "ÑĥеÑĤ": 17781, "ĠAbraham": 17782, "ãģ§ãĤĤ": 17783, "Ġdetection": 17784, "Ġjuris": 17785, "Ġpowered": 17786, "zial": 17787, "Ġwelfare": 17788, "Ġupgrad": 17789, "Ġmożna": 17790, "ĠCase": 17791, "cular": 17792, "ĶìĿ´": 17793, "ãĥģ": 17794, "ĠGuess": 17795, "Ġcycles": 17796, "ä¾ĭ": 17797, "給": 17798, "rock": 17799, "umi": 17800, "Ġelite": 17801, "Ġquè": 17802, "åł±": 17803, "ÑĤом": 17804, "Ġshore": 17805, "gunta": 17806, "Ġku": 17807, "Ġfaithful": 17808, "ĠJeremy": 17809, "aid": 17810, "à·": 17811, "ugal": 17812, "å°įåķĬ": 17813, "ĠVel": 17814, "Ġvrai": 17815, "stell": 17816, "¨¸": 17817, "Ġkol": 17818, "è½": 17819, "Ġquanto": 17820, "ĠзаÑĢ": 17821, "Ġ2002": 17822, "esy": 17823, "Ġreserve": 17824, "ĠмоменÑĤ": 17825, "Ġdeployed": 17826, "Ġdefining": 17827, "Ġsau": 17828, "Ġgaat": 17829, "\")": 17830, "Ġtransmit": 17831, "Ġpublishing": 17832, "Ġranking": 17833, "Ġoffense": 17834, "Ġ46": 17835, "pin": 17836, "ĠTaking": 17837, "Ġentitled": 17838, "Ġgenuinely": 17839, "Ġvariations": 17840, "Ġfinde": 17841, "Ġtau": 17842, "Ġunfortunate": 17843, "ĠRah": 17844, "ports": 17845, "ĠcÅ": 17846, "Ġmonkey": 17847, "Ġbrac": 17848, "wei": 17849, "lung": 17850, "Ġartif": 17851, "Ġsyrup": 17852, "ĠÐĶав": 17853, "Ġlifted": 17854, "Ġchez": 17855, "ĠAdvent": 17856, "ĠStock": 17857, "Ġdol": 17858, "мен": 17859, "иÑĪÑĮ": 17860, "Ġyn": 17861, "gio": 17862, "det": 17863, "Ġdesse": 17864, "Ġgri": 17865, "ĠChairman": 17866, "çħ": 17867, "Ġcuenta": 17868, "anim": 17869, "Ġcrab": 17870, "Ġescal": 17871, "Ġpremière": 17872, "ĠGef": 17873, "Ġdining": 17874, "Ġseventh": 17875, "Ġchasing": 17876, "ĠTower": 17877, "Ġbrutal": 17878, "Ġfundamentally": 17879, "ãģ¨ãģĨ": 17880, "лениÑı": 17881, "stage": 17882, "Ġacquis": 17883, "Ġcylinder": 17884, "Ġcommander": 17885, "mem": 17886, "ĠUV": 17887, "happy": 17888, "Ġepsilon": 17889, "Ġinvitation": 17890, "Ġfarmer": 17891, "chair": 17892, "Ġdestiny": 17893, "Ġsovere": 17894, "ĠHebrew": 17895, "Ġservant": 17896, "Ġbew": 17897, "Ġgast": 17898, "uties": 17899, "Ġadministrative": 17900, "ĠCommand": 17901, "éta": 17902, "Ġnitrogen": 17903, "ê·¼": 17904, "Ġabi": 17905, "Ġvillain": 17906, "Ġblanket": 17907, "ĠSend": 17908, "Ġbeaten": 17909, "²Ħ": 17910, "Ġvolunt": 17911, "Ġscholar": 17912, "ĠEmperor": 17913, "Ġ43": 17914, "vable": 17915, "ĠDus": 17916, "ĠGU": 17917, "Ġtargeting": 17918, "www": 17919, "Ġamendment": 17920, "ìĨĮë": 17921, "Ġting": 17922, "Ġnasty": 17923, "Ġgauge": 17924, "ĠÑĢод": 17925, "ĠHans": 17926, "Your": 17927, "αν": 17928, "Ġprojet": 17929, "ĠHawaii": 17930, "Ġsuspicious": 17931, "Ġschw": 17932, "Ġremoval": 17933, "Ġintrig": 17934, "ĠMU": 17935, "Ġponto": 17936, "ा": 17937, "ĠобÑĢаз": 17938, "Ġguessing": 17939, "pace": 17940, "Ġmothers": 17941, "Ġmillimeter": 17942, "ление": 17943, "没æľī": 17944, "Ġavailability": 17945, "icz": 17946, "æѤ": 17947, "Ġfract": 17948, "Ġbases": 17949, "km": 17950, "ĠBTS": 17951, "ĠField": 17952, "Ġdzie": 17953, "Ġsegundo": 17954, "ĠëĤĺëĬĶ": 17955, "Ġlegitimate": 17956, "imas": 17957, "Ġвн": 17958, "Ġcorruption": 17959, "Ġsmash": 17960, "ĠValent": 17961, "Ġaligned": 17962, "ĠPennsylvania": 17963, "Ġgab": 17964, "ĠEun": 17965, "enth": 17966, "ĠMorning": 17967, "Ġcandle": 17968, "Ġbackpack": 17969, "ĠIslamic": 17970, "ações": 17971, "Ġencry": 17972, "Ġmushrooms": 17973, "íĮĮ": 17974, "dit": 17975, "Ġtransit": 17976, "ĠWisconsin": 17977, "Ġparticipated": 17978, "ĠIls": 17979, "Ġunfold": 17980, "¶Ģë": 17981, "Ġprofits": 17982, "Ġwarming": 17983, "ĠGang": 17984, "Ġnetworking": 17985, "Ġmega": 17986, "Ġthoroughly": 17987, "lements": 17988, "ĠHm": 17989, "Ġdeciding": 17990, "Ġemotionally": 17991, "Ġexhausted": 17992, "ĠÐŁÐ¾ÑĤ": 17993, "cido": 17994, "ĠHTML": 17995, "Ġcopyright": 17996, "Ġmelody": 17997, "yim": 17998, "Ġanders": 17999, "oshop": 18000, "Ġë³¼": 18001, "Ġathlete": 18002, "ĠGE": 18003, "Ġfrequent": 18004, "Ġdesires": 18005, "Ġneeding": 18006, "ĠYun": 18007, "Ġrifle": 18008, "Ġlover": 18009, "'T": 18010, "Ġdense": 18011, "Ġtão": 18012, "Ġnotified": 18013, "Ġidi": 18014, "ìĹŃ": 18015, "íĨ": 18016, "Ġinteracting": 18017, "Ġrapport": 18018, "еÑĢи": 18019, "ski": 18020, "Ġbesser": 18021, "Ġmanufacturer": 18022, "ĠKyle": 18023, "Ġaccountable": 18024, "ĠSak": 18025, "ĠPil": 18026, "ĠDomin": 18027, "Ġpresum": 18028, "ĠÐĴÑģе": 18029, "Ġvinegar": 18030, "Ġguaranteed": 18031, "çľĭåĪ°": 18032, "Ġhandled": 18033, "éŁ³": 18034, "cat": 18035, "Ġcivilization": 18036, "Ġaccomp": 18037, "ĠVM": 18038, "émon": 18039, "Ġdeze": 18040, "Ġgrades": 18041, "Ġsollte": 18042, "Ġstaring": 18043, "×IJת": 18044, "arnt": 18045, "Ġhorizon": 18046, "Ġtravail": 18047, "hour": 18048, "第ä¸Ģ": 18049, "ĠED": 18050, "ĠDak": 18051, "Ġny": 18052, "Ġconve": 18053, "ĠCham": 18054, "Ġfirms": 18055, "ĠLiu": 18056, "ĠÑģÑĤÑĢан": 18057, "Ġlibert": 18058, "Ġlenses": 18059, "Ġintake": 18060, "ĠвÑĭб": 18061, "Ġmensen": 18062, "hel": 18063, "Ġpractition": 18064, "Ġ350": 18065, "ãĤ³": 18066, "FO": 18067, "Ġbeds": 18068, "Ġancestors": 18069, "ĠìĹĦì²Ń": 18070, "Ġdisturb": 18071, "ĠLastly": 18072, "ĠSupport": 18073, "ีà¹ī": 18074, "ĠCorona": 18075, "Ġenthusi": 18076, "Ġвозм": 18077, "ĠìĤ¬ëŀĮë": 18078, "Ġ52": 18079, "bird": 18080, "Ġreduces": 18081, "ĠìŀĪìĿĦ": 18082, "ĠGene": 18083, "êµIJ": 18084, "ÄĻp": 18085, "ĠÃľber": 18086, "Ġconcerning": 18087, "user": 18088, "Ġconcentrate": 18089, "ĠWHAT": 18090, "ishop": 18091, "onymous": 18092, "nold": 18093, "Ġsuggesting": 18094, "©°": 18095, "ĠFish": 18096, "........": 18097, "Ġvessel": 18098, "Ġtrabajo": 18099, "ãģµ": 18100, "ĠOcean": 18101, "å§IJ": 18102, "yg": 18103, "Ġtowns": 18104, "del": 18105, "Ġterrifying": 18106, "ĠçalÄ±ÅŁ": 18107, "Ġsino": 18108, "Ġeats": 18109, "Ġgez": 18110, "Ġgeme": 18111, "ĠìĻĦ": 18112, "Ġcompart": 18113, "Ġimplementing": 18114, "ĠPotter": 18115, "ĠGermans": 18116, "ĠgÅĤ": 18117, "Ġtennis": 18118, "Ġcarpet": 18119, "auer": 18120, "ĠSaudi": 18121, "yeong": 18122, "Ġcurry": 18123, "ĠForest": 18124, "Ñĭл": 18125, "Ġfifteen": 18126, "Ġbolts": 18127, "Ġ{\\": 18128, "¬´": 18129, "Ġsettlement": 18130, "Ġlange": 18131, "Ġbam": 18132, "Get": 18133, "íķĻ": 18134, "Ġswap": 18135, "ĠKhan": 18136, "Ġcommence": 18137, "Ġquarantine": 18138, "Ġscored": 18139, "çĸ": 18140, "Ġ1950": 18141, "Ġthicker": 18142, "Ġsûr": 18143, "åı£": 18144, "ĠLarry": 18145, "Ġallez": 18146, "ìĭľëĬĶ": 18147, "Ġgü": 18148, "Ġspectacular": 18149, "//": 18150, "both": 18151, "Ġstats": 18152, "妳": 18153, "ĠNancy": 18154, "Ġbunu": 18155, "Ġcrust": 18156, "Ġactivated": 18157, "Ġê·¸ëŀ": 18158, "outhe": 18159, "Ġports": 18160, "Ġneural": 18161, "Ġjaw": 18162, "Ġobservations": 18163, "Ġvoit": 18164, "aban": 18165, "ải": 18166, "¦¬ë¥¼": 18167, "omes": 18168, "à¯ĭ": 18169, "qui": 18170, "Ġkindness": 18171, "Ðij": 18172, "Ġ41": 18173, "Ġmoderate": 18174, "Ġangels": 18175, "ĠTamb": 18176, "èt": 18177, "Ġchlor": 18178, "ĠBilly": 18179, "ì²ĺë": 18180, "acon": 18181, "Ġselecting": 18182, "ĠDelta": 18183, "Ġnull": 18184, "denly": 18185, "Ġciud": 18186, "Ġtendency": 18187, "Ġbreakdown": 18188, "Ġmint": 18189, "ÑĦоÑĢм": 18190, "orph": 18191, "Ġdawn": 18192, "spr": 18193, "ĠWILL": 18194, 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"Ġì°¸": 18255, "minute": 18256, "Ġtraject": 18257, "ujÄĻ": 18258, "Ġeager": 18259, "Ġregulatory": 18260, "Ġbanking": 18261, "bling": 18262, "ÑĪÑĮ": 18263, "aż": 18264, "Ġbizarre": 18265, "itated": 18266, "dire": 18267, "Ġthreatened": 18268, "Ġshining": 18269, "Ġnesse": 18270, "Ġcorps": 18271, "ĠÑģÑĥ": 18272, "Ġteles": 18273, "Ġtemp": 18274, "tem": 18275, "Ġкан": 18276, "Ġfever": 18277, "New": 18278, "Ġheavier": 18279, "ĠSah": 18280, "bud": 18281, "Ġoutros": 18282, "Ġì°¾": 18283, "Ġëªħ": 18284, "arring": 18285, "Ġê´ľì°®": 18286, "ĠNap": 18287, "Ġsemin": 18288, "ĠThan": 18289, "ifs": 18290, "Ġdesen": 18291, "ĠÑĤакое": 18292, "Ġloses": 18293, "ĠBalt": 18294, "kon": 18295, "ĠнапÑĢ": 18296, "Ġvois": 18297, "ĠMoscow": 18298, "Ġchairs": 18299, "his": 18300, "Ġrefugees": 18301, "kg": 18302, "Ġkole": 18303, "į¨": 18304, "аÑģибо": 18305, "¦½": 18306, "ĠUniverse": 18307, "ĠDirect": 18308, "Ġcheating": 18309, "ĠCin": 18310, "Ġpatri": 18311, "Ġadvise": 18312, "ĠNether": 18313, "Ġprimeiro": 18314, 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18431, "Ġdetermination": 18432, "ĠPhill": 18433, "Ġcontributed": 18434, "ĠCit": 18435, "Ġpassengers": 18436, "Ġcôté": 18437, "Ġrever": 18438, "Ġtechnological": 18439, "Ġallen": 18440, "Ġraining": 18441, "avi": 18442, "Ġsalty": 18443, "Ġtyping": 18444, "ĠÑĤе": 18445, "Ġtilt": 18446, "Ġì¹ĺ": 18447, "ĠоÑĢ": 18448, "ĠпÑĢÑıм": 18449, "Ġrou": 18450, "Ġarena": 18451, "arat": 18452, "åĪ«": 18453, "HHHH": 18454, "Ġmanufacturers": 18455, "ĠEdward": 18456, "Ġtuck": 18457, "Ġblows": 18458, "ingo": 18459, "ĠMarc": 18460, "ìķĦìĦľ": 18461, "Mich": 18462, "ĠClean": 18463, "è´": 18464, "esto": 18465, "ĠPack": 18466, "Ġshaft": 18467, "BRUNO": 18468, "Ġaven": 18469, "uur": 18470, "ÑģколÑĮко": 18471, "ê´Ģ": 18472, "Ġautomated": 18473, "Ġventure": 18474, "Ġsurveillance": 18475, "ĠGrow": 18476, "ĠEmer": 18477, "ĠдоÑĢ": 18478, "Ġinvestor": 18479, "ĠYok": 18480, "Ġlatter": 18481, "ĠNI": 18482, "Ġfunctioning": 18483, "ĠHamilton": 18484, "Ġ51": 18485, "Ġmurdered": 18486, "Ġanchor": 18487, "Ġcuc": 18488, "ĠSCP": 18489, "ĠMadam": 18490, "Ġconstraints": 18491, "Ġbarn": 18492, "anken": 18493, "Ġë§İìĿĢ": 18494, "ĠMotor": 18495, "ĠDoing": 18496, "Ġamen": 18497, "etts": 18498, "Ġinstructor": 18499, "egt": 18500, "ako": 18501, "Ġposture": 18502, "ivia": 18503, "ĠPolish": 18504, "Ġдва": 18505, "Ġcolorful": 18506, "Ġelbow": 18507, "Ġparle": 18508, "Ġpasser": 18509, "Ġcondem": 18510, "ortal": 18511, "Ġfertil": 18512, "اد": 18513, "ĠColomb": 18514, "Ġalignment": 18515, "Ġastronaut": 18516, "ĠMut": 18517, "Ġsalmon": 18518, "Ġstructured": 18519, "ŀר": 18520, "Ġclicks": 18521, "Ġmiej": 18522, "æĶ¿": 18523, "ãģĦãĤĦ": 18524, "ĠRound": 18525, "Ġrainbow": 18526, "ĠVA": 18527, "ãģĶãģĸ": 18528, "ì§Ī": 18529, "otz": 18530, ",": 21732, "Ġchords": 21733, "ĠSanders": 21734, "Ġë¶Ħë": 21735, "Ben": 21736, "Ġdarüber": 21737, "ilians": 21738, "Ġordering": 21739, "ĠManh": 21740, "Ġkilogram": 21741, "ĠkarÅŁ": 21742, "Ġgrasp": 21743, "Ġghosts": 21744, "alen": 21745, "ĠJedi": 21746, "Ġбли": 21747, "Ġdownloaded": 21748, "Ġconducting": 21749, "ĠHak": 21750, "Ġresearcher": 21751, "ilan": 21752, "good": 21753, "ĠHannah": 21754, "ĠdÃ¼ÅŁÃ¼n": 21755, "ĠMessiah": 21756, "uity": 21757, "iona": 21758, "Ġprobable": 21759, "ĠYE": 21760, "Ġindependently": 21761, "Ġbuffer": 21762, "burn": 21763, "ourd": 21764, "ĠMcK": 21765, "Ġlingu": 21766, "ujemy": 21767, "еÑĢÑĤ": 21768, "Ġintuitive": 21769, "Ġcracks": 21770, "appropri": 21771, "nty": 21772, "Ġgeen": 21773, "Ġlend": 21774, "Ġcertification": 21775, "IDS": 21776, "unter": 21777, "pees": 21778, "Ġtrump": 21779, "Ġbankrupt": 21780, "Ġfeas": 21781, "èĹ": 21782, "Ġduż": 21783, "æ¸ħ": 21784, "Ġviruses": 21785, "Ġ58": 21786, "god": 21787, "Ġжел": 21788, "Ġstalk": 21789, "Ind": 21790, "achi": 21791, "ĠCF": 21792, "ĠCond": 21793, "Ġsanct": 21794, "Ġconten": 21795, "Ġfreed": 21796, "ĠRT": 21797, "Ġmentors": 21798, "족": 21799, "Ġportable": 21800, "ĠPaulo": 21801, "rane": 21802, "HAHA": 21803, "ĠSection": 21804, "çĨ": 21805, "hyun": 21806, "ĠÎŃÏĩ": 21807, "ĠPub": 21808, 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"ĠìĿ´ê±´": 21867, "ĠpaÅĦst": 21868, "они": 21869, "itta": 21870, "Ġconfirmation": 21871, "ĠBrooklyn": 21872, "Ġnoodle": 21873, "fund": 21874, "itud": 21875, "Ġgrandparents": 21876, "Ġbarbecue": 21877, "ειÏĤ": 21878, "Ġá": 21879, "Ġballot": 21880, "ĠVeter": 21881, "Ġpipes": 21882, "igious": 21883, "ĠGraph": 21884, "ested": 21885, "Ġë¸Įë": 21886, "ĠKE": 21887, "ãģ¡ãĤĩãģ£ãģ¨": 21888, "Ġeins": 21889, "Ġhatred": 21890, "ãģijãģ©": 21891, "Ġdang": 21892, "eeee": 21893, "Ġarchae": 21894, "ĠJesse": 21895, "Ġdetected": 21896, "Ġseni": 21897, "burgh": 21898, "Ġdisplacement": 21899, "Ġdop": 21900, "Ġconditioning": 21901, "ĠнеÑģколÑĮко": 21902, "Ġdisturbing": 21903, "PH": 21904, "Ġthinner": 21905, "Ġwounded": 21906, "ĠCuando": 21907, "Ġcushion": 21908, "Ġwhites": 21909, "Ġpreferences": 21910, "Ġì¤Ģë¹Ħ": 21911, "Ġkaż": 21912, "ĠGate": 21913, "ĠPath": 21914, "dles": 21915, "à¸Ħร": 21916, "imore": 21917, "Ġë³´ìŬ": 21918, "Ġdisciplines": 21919, "á»ı": 21920, "Ġmesma": 21921, "ĠìĥĪë": 21922, "Ġìĭ¬": 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"ĠgÃ¥": 22098, "試": 22099, "ת×Ļ": 22100, "ĠWayne": 22101, "Ġinstagram": 22102, "Ġrated": 22103, "paper": 22104, "Ġbild": 22105, "Ġpretending": 22106, "Ġobserving": 22107, "ĠÑģамом": 22108, "Ġtror": 22109, "Ġorganisms": 22110, "Ġfalta": 22111, "Ġhometown": 22112, "ç±": 22113, "Ġíĭ": 22114, "Ġcheg": 22115, "Ġì¡": 22116, "Ġcomma": 22117, "isé": 22118, "Ġlikelihood": 22119, "avored": 22120, "Ġgeldi": 22121, "ников": 22122, "Ġmedio": 22123, "Ġjakie": 22124, "ĠJup": 22125, "Ġgreenhouse": 22126, "Ġspit": 22127, "кое": 22128, "Ġкаж": 22129, "ĠGram": 22130, "ĠConference": 22131, "Ġdeficit": 22132, "sın": 22133, "inse": 22134, "uÄŁ": 22135, "Ġricht": 22136, "Ġcoincidence": 22137, "åıį": 22138, "Ġeurop": 22139, "Ġbutterfly": 22140, "pread": 22141, "Ġìĸ¼": 22142, "èĢ¶": 22143, "Ġwavel": 22144, "ĠInfin": 22145, "ĠPlanet": 22146, "Ġselfie": 22147, "ientras": 22148, "Ġarrog": 22149, "oser": 22150, "idal": 22151, "ł×Ĺ׳×ķ": 22152, "ütün": 22153, "Ġfreshman": 22154, "ĠMachine": 22155, "ÏĥÏĦ": 22156, 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"ospheric": 25145, "Ġflee": 25146, "Ġrất": 25147, "ĠRegardless": 25148, "Ġreluct": 25149, "Ġtimely": 25150, "ĠJulian": 25151, "GM": 25152, "éĴ": 25153, "adura": 25154, "é£Ł": 25155, "Ġdresses": 25156, "çģ£": 25157, "ĠëĶĶ": 25158, "Ġnominated": 25159, "Ġadvocates": 25160, "ymph": 25161, "Ġrecordings": 25162, "Ġdeviation": 25163, "Ġprioritize": 25164, "Ġspiral": 25165, "ĠYOUR": 25166, "Ġtranspose": 25167, "ampoo": 25168, "ĠìĽIJëŀĺ": 25169, "ĠVision": 25170, "Ġpolite": 25171, "Ġhamb": 25172, "ĠPatient": 25173, "æ¯Ķè¼ĥ": 25174, "íģ¬ë": 25175, "Ġsia": 25176, "Ġê³³": 25177, "Ġže": 25178, "è§Ģ": 25179, "Ġsupermarket": 25180, "ë¹": 25181, "ĠSierra": 25182, "Ġgrilled": 25183, "ĠUpon": 25184, "Ġabsent": 25185, "Ġmec": 25186, "ĠApollo": 25187, "Ġpunk": 25188, "ĠPaÅĦst": 25189, "ĠÑģвой": 25190, "Ġ거기": 25191, "Girl": 25192, "Ġskinny": 25193, "ĠPremier": 25194, "Ġterritories": 25195, "Ġliability": 25196, "Ġjerk": 25197, "ratic": 25198, "Ġdancers": 25199, "ĠÑĥÑĢов": 25200, "Ġê´Ģë": 25201, "only": 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26252, "Ġinterconnect": 26253, "éIJĺ": 26254, "OSH": 26255, "æŃ²": 26256, "Ġbrass": 26257, "Ġignoring": 26258, "ä»ĬæĹ¥": 26259, "infect": 26260, "Ġprojekt": 26261, "oret": 26262, "ÏĦαν": 26263, "ĠÑĤип": 26264, "Ġmutta": 26265, "Ġunboxing": 26266, "Ħ°": 26267, "å¡Ĭ": 26268, "Ġadvised": 26269, "ĠDenver": 26270, "Ġseverely": 26271, "ĠMhm": 26272, "Ġflipped": 26273, "Ġpien": 26274, "Ġkommun": 26275, "ĠFRE": 26276, "Ġà®ĩà®°": 26277, "ainted": 26278, "Ġknives": 26279, "Ġhabl": 26280, "Ġgeworden": 26281, "arettes": 26282, "CS": 26283, "ĠмаленÑĮ": 26284, "Ġgalax": 26285, "Ġninete": 26286, "ê±°ëĤĺ": 26287, "Ġsis": 26288, "Ġadvisory": 26289, "Ġdrilling": 26290, "ĠWouldn": 26291, "ünf": 26292, "gestellt": 26293, "ĠHelen": 26294, "Ġ×ŀ×IJ": 26295, "apolis": 26296, "Ġrzeczy": 26297, "Ġterra": 26298, "Ġhep": 26299, "Ġalgún": 26300, "ikk": 26301, "Ġastronom": 26302, "ĠStarbucks": 26303, "kÄħ": 26304, "Ġpatrol": 26305, "Ġì½Ķ": 26306, "Ġgon": 26307, "ĠãĢIJ": 26308, "Ġsonst": 26309, "Ġencounters": 26310, 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"ãģ¾ãģĽãĤĵ": 30250, "Ġutterly": 30251, "Ġsynthesis": 30252, "Ġlesbian": 30253, "Ġretrieve": 30254, "Ġmaneira": 30255, "Ġimpair": 30256, "Ġmentoring": 30257, "ĠSouls": 30258, "ĠGoPro": 30259, "ÑĢаÑĤÑĮ": 30260, "Ġcose": 30261, "ĠSSD": 30262, "IRE": 30263, "Ġupfront": 30264, "ĠAun": 30265, "Ġgamer": 30266, "Ġlitt": 30267, "Ġaggression": 30268, "ĠLikewise": 30269, "ĠBetty": 30270, "ĠDart": 30271, "ĠDLC": 30272, "ishment": 30273, "ìŀ¥ìĿĦ": 30274, "Ġ对": 30275, "ç»ı": 30276, "cream": 30277, "ĠBabylon": 30278, "Ġnug": 30279, "brar": 30280, "Ġaynı": 30281, "amily": 30282, "bike": 30283, "ahahaha": 30284, "loyd": 30285, "Ġmira": 30286, "Ġperme": 30287, "ĠGaming": 30288, "Ġfirmware": 30289, "Ma": 30290, "Ġassisted": 30291, "atics": 30292, "Ġìķŀìľ¼ë¡ľ": 30293, "ĠMental": 30294, "niejs": 30295, "ĠIz": 30296, "owÄħ": 30297, "Ġtougher": 30298, "Ġdeed": 30299, "èĭ¦": 30300, "Ġstylish": 30301, "ĠTools": 30302, "ĠHamp": 30303, "Ġsunscreen": 30304, "Ġarticulate": 30305, "iye": 30306, "иÑĦ": 30307, 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"ĠÑĩÑĥÑĤÑĮ": 30422, "Ġtemptation": 30423, "Ġcerteza": 30424, "Ġexempt": 30425, "ìĬ¹": 30426, "selling": 30427, "ĠTask": 30428, "hoon": 30429, "ĠCoc": 30430, "ĠParks": 30431, "Ġrepetition": 30432, "ĠÑĤÑĥда": 30433, "Ġensl": 30434, "ĠdeÄŁiÅŁ": 30435, "ĠOrlando": 30436, "ĠMainten": 30437, "æŃ¢": 30438, "ocument": 30439, "ĠHC": 30440, "Ġscooter": 30441, "ĠнапиÑģ": 30442, "Ġtighter": 30443, "Ġtease": 30444, "Ġremoves": 30445, "Ġkijken": 30446, "ĠÑģÑĥÑīеÑģÑĤв": 30447, "Ġthé": 30448, "ĠвÑĭглÑıд": 30449, "Ġrelieve": 30450, "Ġmitä": 30451, "Ġstationary": 30452, "öff": 30453, "pable": 30454, "Ġarter": 30455, "Ġdéf": 30456, "rative": 30457, "Ġconect": 30458, "Ġsaddle": 30459, "ĠDiane": 30460, "Ġcommemor": 30461, "fendim": 30462, "SÃŃ": 30463, "Ġíģ´ë": 30464, "Ġmange": 30465, "atte": 30466, "Ġarrogant": 30467, "Ġrobotic": 30468, "ĠgiÃł": 30469, "æĺ¯çļĦ": 30470, "Ġneighbourhood": 30471, "isson": 30472, "Ġдвиж": 30473, "ĠRI": 30474, "ĠNorman": 30475, "brand": 30476, "amation": 30477, "Ġrazor": 30478, "Ġmurders": 30479, "ĠÑĤÑĥ": 30480, "Ġwszystkim": 30481, "Ġutilities": 30482, "Ġmicroscop": 30483, "ê¿": 30484, "Ġdaqui": 30485, "ollar": 30486, "ĠÐĶавайÑĤе": 30487, "Ġannée": 30488, "Ġkilometres": 30489, "Ġhomosexual": 30490, "Ġarchitects": 30491, "ãģ¡ãģ¯": 30492, "Ġniye": 30493, "LER": 30494, "Ġmicrophones": 30495, "ĠStunden": 30496, "Ġconsecutive": 30497, "ienda": 30498, "vänd": 30499, "DER": 30500, "Ġlifts": 30501, "ĠMeat": 30502, "Ġsavez": 30503, "íĸĪëįĺ": 30504, "Men": 30505, "Ġdismant": 30506, "거를": 30507, "Ġinsulation": 30508, "Ġscall": 30509, "Ġspooky": 30510, "Ġparc": 30511, "Ġballet": 30512, "ĠWhatsApp": 30513, "Ġfranc": 30514, "Ġdeliberate": 30515, "ĠíħĮ": 30516, "Ġmars": 30517, "ĠZur": 30518, "Pr": 30519, "disciplinary": 30520, "Ġobsession": 30521, "ме": 30522, "Ġmarching": 30523, "ĠEmergency": 30524, "iguous": 30525, "Ġszy": 30526, "ĠLands": 30527, "Ġboarding": 30528, "ĠпоÑĩÑĤи": 30529, "Ġenvy": 30530, "Ġcompassionate": 30531, "Ġmerci": 30532, "Ġdesirable": 30533, "dale": 30534, "Ġcanım": 30535, "ĠAntar": 30536, "temps": 30537, "Ġconfigured": 30538, "ĠCompared": 30539, "neh": 30540, "icating": 30541, "Ġnickel": 30542, "ÙĪÙĤ": 30543, "ÙĥÙĪÙĨ": 30544, "opes": 30545, "Ġformulas": 30546, "ĠÐķÑģÑĤÑĮ": 30547, "Ġpobl": 30548, "ĠPJ": 30549, "ĠLud": 30550, "ä»ĬåĽŀ": 30551, "ĠBrid": 30552, "ĠHog": 30553, "ĠBris": 30554, "Jen": 30555, "Ġshading": 30556, "ĠYas": 30557, "Ġdisturbed": 30558, "Ġrecommending": 30559, "Ġcé": 30560, "ĠHOW": 30561, "ìĹĪìĸ´": 30562, "Ġreversed": 30563, "ĠInterestingly": 30564, "ioxid": 30565, "åħŃ": 30566, "Ġìĺ¤ì¼ĢìĿ´": 30567, "ếu": 30568, "xx": 30569, "Ġouais": 30570, "ĠYouTubers": 30571, "ĠRosa": 30572, "ĠHaupt": 30573, "jadi": 30574, "Ġvlogs": 30575, "Ġcultura": 30576, "ĠLeadership": 30577, "ĠHep": 30578, "Ġillum": 30579, "´ëıĻ": 30580, "Ġcustomized": 30581, "Ġmarca": 30582, "Ġquatro": 30583, "Ġнаг": 30584, "ĠSpaceX": 30585, "ĠEigen": 30586, "asting": 30587, "ĠolduÄŁu": 30588, "Ġforts": 30589, "ãģī": 30590, "riment": 30591, "iencia": 30592, "Ġtenir": 30593, "roffen": 30594, "Ġ1979": 30595, "Ġcie": 30596, "ĠëIJĺê³ł": 30597, "Ġescri": 30598, "ÏĮÏĤ": 30599, "íı¬": 30600, "uzzy": 30601, "Cong": 30602, "ìĿ¸ìĿ´": 30603, "Great": 30604, "sil": 30605, "éch": 30606, "ãģ¨ãģĭ": 30607, "Ġmultic": 30608, "ĠDisk": 30609, "²ķ": 30610, "Ġfazla": 30611, "Ġlevant": 30612, "Ġabajo": 30613, "urry": 30614, "stru": 30615, "Ġ먹ëĬĶ": 30616, "Ġaccessory": 30617, "Ġдвиг": 30618, "ĠRid": 30619, "2019": 30620, "Ġdownstream": 30621, "æķ¸": 30622, "Ġkaz": 30623, "utan": 30624, "Ġcharcoal": 30625, "Ġafect": 30626, "wu": 30627, "Ġcontexts": 30628, "Ġfeared": 30629, "ĠìĦ¤": 30630, "Ġhistories": 30631, "Ġfas": 30632, "ensible": 30633, "Ġcocoa": 30634, "illar": 30635, "geons": 30636, "Ġspirituality": 30637, "ĠPew": 30638, "Ġpharmacy": 30639, "Ġpassions": 30640, "Ġbos": 30641, "Ġallá": 30642, "Ġthriving": 30643, "ĠReact": 30644, "Ġoccupy": 30645, "Ġwithdrawal": 30646, "Ġallowance": 30647, "ĠFraktion": 30648, "Ġbuddies": 30649, "Ġidle": 30650, "Ġdissolved": 30651, "Ġprevalent": 30652, "Ġmilitar": 30653, "Ġsensing": 30654, "Ġpojaw": 30655, "Ġancora": 30656, "Ġabundant": 30657, "Ġhairst": 30658, "ãģĤãĤĮ": 30659, "Ġtwee": 30660, "Ġnächste": 30661, "ĠMöglichkeit": 30662, "Ġhoo": 30663, "ufficient": 30664, "Ġfantast": 30665, "Ġedible": 30666, "Ġëĸ¨ìĸ´ì": 30667, "ìĽĥ": 30668, "Ġvein": 30669, "ucci": 30670, "Ġdevotion": 30671, "Ġconcealer": 30672, "income": 30673, "Ġrecycled": 30674, "ĠìĬ¤íĥĢ": 30675, "Ġpontos": 30676, "Ġdessus": 30677, "Ġvérit": 30678, "Ġreflections": 30679, "ĠAA": 30680, "Ġtakeaway": 30681, "bare": 30682, "ĠContact": 30683, "eil": 30684, "ĠHear": 30685, "Ġmirac": 30686, "ĠGerilim": 30687, "ĠÑģамÑĭй": 30688, "Ġvivo": 30689, "Ġkilograms": 30690, "ĠCrim": 30691, "ût": 30692, "78": 30693, "Ġsincerely": 30694, "raz": 30695, "Ġë³µ": 30696, "Ġarriv": 30697, "Ġconception": 30698, "ĠPersian": 30699, "Ġsjäl": 30700, "Ġstarring": 30701, "ĠìķĦ무": 30702, "ĠForever": 30703, "еÑģÑĤÑĮ": 30704, "Ġveil": 30705, "Ġsubtit": 30706, "odka": 30707, "ĠоÑĤноÑĪ": 30708, "Ġcooks": 30709, "енÑı": 30710, "Kay": 30711, "Ġniños": 30712, "ĠPhone": 30713, "Ġstitching": 30714, "Ġfingerprint": 30715, "é¢ĺ": 30716, "λά": 30717, "Ġdedicate": 30718, "ĠLob": 30719, "Ġblacks": 30720, "ĠBle": 30721, "bout": 30722, "ĠÄijang": 30723, "Ġeks": 30724, "Ġsquash": 30725, "ĠKü": 30726, "odi": 30727, "ĠnÆ°á»Ľc": 30728, "Ġvoyage": 30729, "Ġplayful": 30730, "ĠØ¥ÙĦÙī": 30731, "anic": 30732, "Ġcondemn": 30733, "ĠBöyle": 30734, "ĠPolize": 30735, "ãĤ¿ãĥ¼": 30736, "Ġayuda": 30737, "Ġpam": 30738, "à¹Ħà¸Ľ": 30739, "ĠKathy": 30740, "един": 30741, "нова": 30742, "Ġbrig": 30743, "eger": 30744, "Ġeagle": 30745, "Ġvisions": 30746, "ĠíķŃìĥģ": 30747, "Ġshitty": 30748, "Ġhott": 30749, "ĠBritt": 30750, "utors": 30751, "ENTE": 30752, "æĽ²": 30753, "Ġphon": 30754, "ĠBing": 30755, "ĠподдеÑĢж": 30756, "spring": 30757, "æĸ¯": 30758, "etten": 30759, "Ġpilgr": 30760, "Ġediyor": 30761, "енÑĤÑĭ": 30762, "aggio": 30763, "Ġjul": 30764, "Ġcomprend": 30765, "teil": 30766, "Ġز": 30767, "Ġperformers": 30768, "Ġinfamous": 30769, "ĠMK": 30770, "çª": 30771, "æ³ģ": 30772, "otle": 30773, "eff": 30774, "ĠHash": 30775, "Ġcoward": 30776, "ĠBRA": 30777, "ĠDD": 30778, "Ġcomida": 30779, "Ġplata": 30780, "Ġflap": 30781, "ĠMehr": 30782, "ribution": 30783, "ĠYemen": 30784, "Ġmysteries": 30785, "ĠÄ°yi": 30786, "Ġstell": 30787, "Ġeyeliner": 30788, "Ġdeles": 30789, "Ġnailed": 30790, "Ġillnesses": 30791, "Ġstacks": 30792, "Ġtrabajar": 30793, "flower": 30794, "ciu": 30795, "Ġcrude": 30796, "Ġsubstantially": 30797, "Ġhomem": 30798, "Ġnephew": 30799, "Ġstamps": 30800, "Ġcarbs": 30801, "ÑĮÑĤе": 30802, "mooth": 30803, "Ġtunnels": 30804, "acie": 30805, "æ³¢": 30806, "ĠSeñ": 30807, "ĠHera": 30808, "ĠìķĦëĭĪìĹIJìļĶ": 30809, "ĠWyoming": 30810, "ĠHDMI": 30811, "ĠLis": 30812, "ución": 30813, "Ġsteer": 30814, "оÑİ": 30815, "иÑĤа": 30816, "NT": 30817, "Ġìĸ¼êµ´": 30818, "Ġpalms": 30819, "Ġneon": 30820, "ованиÑı": 30821, "Ġfiltering": 30822, "Ġjouer": 30823, "ĠHö": 30824, "ĠнеÑģ": 30825, "ê²łìĸ´ìļĶ": 30826, "Ġ81": 30827, "Ġstoryline": 30828, "Ġprzep": 30829, "Ġthanking": 30830, "ĠBoeing": 30831, "Ġsoftly": 30832, "jem": 30833, "алÑĮнÑĭÑħ": 30834, "Ġflashlight": 30835, "ĠпÑĥ": 30836, "ĠWOMAN": 30837, "ắc": 30838, "ÃŃch": 30839, "Ġluxurious": 30840, "Ġwün": 30841, "Ġimpactful": 30842, "Ġconson": 30843, "reu": 30844, "irring": 30845, "ifter": 30846, "Ġconstituents": 30847, "èIJ½": 30848, "Ġ94": 30849, "ĠTou": 30850, "gom": 30851, "ĠìĥĿê°ģìĿĦ": 30852, "Ġstereotypes": 30853, "Ġmożli": 30854, "åĪĨ享": 30855, "Ĥ¨": 30856, "Ġpencils": 30857, "ĠÑģлож": 30858, "Ġihrem": 30859, "ĠBesch": 30860, "ĠKoh": 30861, "ĠEntscheid": 30862, "Ġlek": 30863, "Ġförs": 30864, "Ġtotalmente": 30865, "Ġlively": 30866, "Ġentropy": 30867, "Ġdiscern": 30868, "ĠÐĹна": 30869, "Ġdov": 30870, "Ġmythology": 30871, "è¨ĺå¾Ĺ": 30872, "apanese": 30873, "Ġapproximate": 30874, "аÑĤив": 30875, "ifiable": 30876, "ĠSeo": 30877, "åĢĴ": 30878, "´ìĭ¬íŀĪ": 30879, "Ġìĺ·": 30880, "Ġtemporal": 30881, "ĠiT": 30882, "Ġestat": 30883, "ким": 30884, "Ġsprink": 30885, "Ġgrund": 30886, "Ġinfantry": 30887, "Ġschaffen": 30888, "ç´Ħ": 30889, "Ġank": 30890, "riages": 30891, "ĠYeon": 30892, "ĠMoroc": 30893, "Ġinvasive": 30894, "ģĶ": 30895, "Ġparenting": 30896, "ĠRis": 30897, "ibile": 30898, "Ġmods": 30899, "å½¢": 30900, "ĠпÑĢовеÑĢ": 30901, "ĠThing": 30902, "ĠWherever": 30903, "Ġacknowledging": 30904, "Ġpawn": 30905, "ummer": 30906, "orb": 30907, "69": 30908, "Ġretrouve": 30909, "Ġrelies": 30910, "ĠHighway": 30911, "Ġawe": 30912, "ãģ§ãģĻãģĭ": 30913, "itaire": 30914, "Ġapplicant": 30915, "Ġaisle": 30916, "worm": 30917, "Ġpayload": 30918, "Ġcarre": 30919, "ĠBach": 30920, "æł¼": 30921, "Ġì¹ľêµ¬ë": 30922, "ние": 30923, "ĠitÃŃs": 30924, "onnaise": 30925, "sol": 30926, "èı¯": 30927, "algia": 30928, "Ġrocking": 30929, "Ġbesten": 30930, "rites": 30931, "^^": 30932, "иной": 30933, "Ġbaixo": 30934, "Ġ기ìĸµ": 30935, "оÑĤÑĢи": 30936, "sim": 30937, "Ġincarn": 30938, "ëĭ¤ìĿĮ": 30939, "Ġlick": 30940, "sided": 30941, "Ġ71": 30942, "forder": 30943, "Ġresonance": 30944, "Ġtegen": 30945, "Ġmetaph": 30946, "owser": 30947, "Ġ×IJ׳×Ĺ׳×ķ": 30948, "?ãĢį": 30949, "Ġspielen": 30950, "Ġvolley": 30951, "ĶìĿ´íģ¬ìĹħ": 30952, "looked": 30953, "Ġsentenced": 30954, "Ġmultiplying": 30955, "Ġideals": 30956, "Ġwahrscheinlich": 30957, "Ġdeposits": 30958, "bilir": 30959, "Ġeffet": 30960, "illon": 30961, "Īë§Į": 30962, "Ġtestimon": 30963, "Ġzawsze": 30964, "ĠпÑĢоÑĨеÑģÑģ": 30965, "ĠLav": 30966, "ä¸įéĮ¯": 30967, "Ġtravailler": 30968, "Ġlaisse": 30969, "ĠMountains": 30970, "ĠÑĢоб": 30971, "Ġexamined": 30972, "itus": 30973, "Was": 30974, "лÑĭ": 30975, "Ġattributed": 30976, "ĠìĬ¹": 30977, "ĠBaron": 30978, "Ġgep": 30979, "Ġattent": 30980, "ĠCollection": 30981, "Ġtheat": 30982, "ĠCai": 30983, "Ġwells": 30984, "Ġhumano": 30985, "çĹħ": 30986, "ĠHast": 30987, "ĠÑħоÑĤÑı": 30988, "czas": 30989, "Ġpermits": 30990, "Ġlegg": 30991, "Ġepo": 30992, "ĠFen": 30993, "Ġthi": 30994, "ĠFoi": 30995, "Ġélect": 30996, "Ġ83": 30997, "Ġoverth": 30998, "Ġè¬Ŀè¬Ŀ": 30999, "Ġtenant": 31000, "è²·": 31001, "Next": 31002, "Ġpraised": 31003, "security": 31004, "ĠImpact": 31005, "为ä»Ģä¹Ī": 31006, "Ġvouch": 31007, "Ġnegó": 31008, "Ġunve": 31009, "Ġcriticize": 31010, "ĠKenya": 31011, "Ġtactic": 31012, "Ġlogr": 31013, "Ġpois": 31014, "Ġpapa": 31015, "speaks": 31016, "ðŁij": 31017, "ispers": 31018, "Ġsurplus": 31019, "Ġcolder": 31020, "åįĹ": 31021, "åIJ¬": 31022, "plets": 31023, "ĠVienna": 31024, "ĠLead": 31025, "Ġaerial": 31026, "ĠTah": 31027, "енÑĤов": 31028, "ĠGreeks": 31029, "Cam": 31030, "Ġmáxim": 31031, "Ġkuin": 31032, "chio": 31033, "Ġdemonstrates": 31034, "anos": 31035, "ĠCert": 31036, "ĠÑįн": 31037, "Ġblogs": 31038, "ĠìĦľìļ¸": 31039, "Ġbeams": 31040, "иков": 31041, "Ġprompted": 31042, "Ġfrightening": 31043, "ĠPorsche": 31044, "ãģĪãģ¦": 31045, "larını": 31046, "Ġchilling": 31047, "isphere": 31048, "Ġflashing": 31049, "ĠKard": 31050, "bread": 31051, "Ġexh": 31052, "Ġtycker": 31053, "Ġecological": 31054, "ĠMae": 31055, "Ġ×ŀ×IJ×ķ×ĵ": 31056, "ĠëĤĺëıĦ": 31057, "лон": 31058, "yss": 31059, "Ġpergunt": 31060, "Ġprix": 31061, "izzard": 31062, "Ġcancers": 31063, "Ġ91": 31064, "susp": 31065, "ĠItem": 31066, "ÅŁa": 31067, "Ġpest": 31068, "ĠtakÄħ": 31069, "Ġlymph": 31070, "ĠPatri": 31071, "fill": 31072, "Ġreconna": 31073, "Ġoptimism": 31074, "Ġmimic": 31075, "Ġì²ľ": 31076, "ĠMadame": 31077, "ocy": 31078, "lining": 31079, "åijĬ訴": 31080, "erme": 31081, "Ġfolders": 31082, "ĠczÅĤ": 31083, "uchar": 31084, "Ġcurso": 31085, "Ġbreach": 31086, "ниÑĤÑĮ": 31087, "ĠpamiÄĻ": 31088, "Ġelig": 31089, "Ġautop": 31090, "Flow": 31091, "Ġprogrammed": 31092, "ĠProcess": 31093, "Ġfigur": 31094, "ĠSF": 31095, "ĠEles": 31096, "Ġprogrammes": 31097, "Ġdizzy": 31098, "ìĭľê°Ħ": 31099, "Ġлибо": 31100, "Ġsniff": 31101, "ĠSebastian": 31102, "ĠHye": 31103, "Ġ4000": 31104, "Ġpermite": 31105, "æ¢Ŀ": 31106, "ĠзаÑī": 31107, "Ġguit": 31108, "ĠDais": 31109, "Ġaccordance": 31110, "Ġmodular": 31111, "ogeneous": 31112, "æĭį": 31113, "Ġpouquinho": 31114, "Ġartillery": 31115, "Ġlubric": 31116, "Ġvolcan": 31117, "ĠNH": 31118, "ðŁ¤": 31119, "Ġdean": 31120, "Rh": 31121, "Ġministre": 31122, "åĿIJ": 31123, "ĠInv": 31124, "ĠBulgar": 31125, "ĠDaten": 31126, "èİ": 31127, "Im": 31128, "Ġoriginated": 31129, "ĠNixon": 31130, "integr": 31131, "Ġlacks": 31132, "ĠNacht": 31133, "ìĸ´ëĤĺ": 31134, "camera": 31135, "Ġradish": 31136, "kiye": 31137, "Ġanges": 31138, "Ġpréf": 31139, "juk": 31140, "ĠBee": 31141, "ĠBU": 31142, "ĠвоÑģп": 31143, "ĠBT": 31144, "êmes": 31145, "ĠStück": 31146, "ĠInk": 31147, "æĪĸèĢħ": 31148, "ĠSergeant": 31149, "ĠMultip": 31150, "Ġhiçbir": 31151, "ĠСам": 31152, "ĠDé": 31153, "olph": 31154, "ìĸ¸": 31155, "Ġimpat": 31156, "ĠìķĬê³ł": 31157, "ĠÑĤакого": 31158, "ĠнавеÑĢное": 31159, "Ġunpredictable": 31160, "Ġmend": 31161, "ĠìĹĨìĸ´ìļĶ": 31162, "ĠjakieÅĽ": 31163, "Ġanni": 31164, "Ġdonné": 31165, "ĠKirsty": 31166, "Ġrectangular": 31167, "Ġempezar": 31168, "ĠExchange": 31169, "ê°Ķ": 31170, "Ġéconom": 31171, "ãģĵãĤĵ": 31172, "elin": 31173, "reibt": 31174, "Ġ×Ķפ": 31175, "Ġcemetery": 31176, "Ġespañol": 31177, "olin": 31178, "лÑİд": 31179, "Ġgrâce": 31180, "allen": 31181, "ĠPhilos": 31182, "ĠErst": 31183, "ĠìĥĪ": 31184, "ĠVid": 31185, "Give": 31186, "OH": 31187, "μο": 31188, "ĠPare": 31189, "Ġmetabolism": 31190, "Ġmaple": 31191, "Ġaxle": 31192, "ĠDy": 31193, "Ġkomme": 31194, "Ïİν": 31195, "Ġgreatness": 31196, "Ġverified": 31197, "Ġspé": 31198, "ĠFahrenheit": 31199, "ĠBren": 31200, "ĠConfeder": 31201, "Ġhistoire": 31202, "Ġeliminating": 31203, "ĠAdding": 31204, "ĠAbi": 31205, "æĿİ": 31206, "Ġhospitality": 31207, "tim": 31208, "Ġbonito": 31209, "Ġpartes": 31210, "ĠдÑĢÑĥгиÑħ": 31211, "ĠShay": 31212, "ĠSed": 31213, "Ġregrets": 31214, "Ñıми": 31215, "Ġtenants": 31216, "éĢŁ": 31217, "ĠPTS": 31218, "Ġdevi": 31219, "ĠLate": 31220, "uez": 31221, "Ġsöyl": 31222, "ãĤ»": 31223, "Ġìŀ¬ë°Į": 31224, "Ġtoggle": 31225, "Ġmasking": 31226, "алÑĮного": 31227, "Ġpersön": 31228, "Ġamerican": 31229, "fik": 31230, "ĠRGB": 31231, "enson": 31232, "ĠKA": 31233, "wwww": 31234, "ĠÑĢег": 31235, "metics": 31236, "Ġeducator": 31237, "ãĤ·ãĥ«ãĤ¯": 31238, "park": 31239, "елÑĮзÑı": 31240, "arus": 31241, "ÑĢеÑĤ": 31242, "Ġfeito": 31243, "Ġchoir": 31244, "Ġlargo": 31245, "Ġeens": 31246, "Ġwatts": 31247, "ĠSingle": 31248, "Ġsusceptible": 31249, "icer": 31250, "ĠвклÑİÑĩ": 31251, "Ġpus": 31252, "íĻĺ": 31253, "Eng": 31254, "Ġfantas": 31255, "Ġspecification": 31256, "Ġconfronted": 31257, "ĠColumbus": 31258, "ивеÑĤ": 31259, "arım": 31260, "Ġcaffeine": 31261, "munition": 31262, "Ġmigrants": 31263, "lide": 31264, "itations": 31265, "ĠGeme": 31266, "ẫ": 31267, "Ġplanner": 31268, "Ġstimulate": 31269, "Ġaproxim": 31270, "ceu": 31271, "ĠNom": 31272, "Ġvog": 31273, "ĠÑĢаÑģÑĤ": 31274, "Ġenseñ": 31275, "Ġsellers": 31276, "Ġguten": 31277, "zd": 31278, "Cal": 31279, "Ġdescript": 31280, "Ġreconciliation": 31281, "zinho": 31282, "á¹ĩa": 31283, "ãģĺãĤĥãģĤ": 31284, "acyj": 31285, "ĠCOL": 31286, "saw": 31287, "ĠíĻķìĿ¸": 31288, "Ġvarit": 31289, "Ġpartnering": 31290, "Ġdetention": 31291, "Ġbombing": 31292, "clapping": 31293, "iencies": 31294, "ondu": 31295, "AME": 31296, "Ġê°ĻìĬµëĭĪëĭ¤": 31297, "cÃŃa": 31298, "ĠпоÑģÑĤо": 31299, "ĠASMR": 31300, "Ġhomepage": 31301, "Ġsiè": 31302, "antha": 31303, "ĠPoll": 31304, "Ġigen": 31305, "cych": 31306, "Ġê°ijìŀIJ기": 31307, "Ġconsiderably": 31308, "ä»ĸçļĦ": 31309, "ĠArist": 31310, "Ġwithstand": 31311, "Ġqualitative": 31312, "ĠKraft": 31313, "ĠÑįлекÑĤ": 31314, "ĠBead": 31315, "екÑĤив": 31316, "Ġcrushing": 31317, "ì³IJ": 31318, "Ġnavy": 31319, "ÙĪÚº": 31320, "sho": 31321, "Ġoak": 31322, "ippers": 31323, "Ġsoils": 31324, "Ġpigment": 31325, "Ġevitar": 31326, "ãĥĩ": 31327, "Ġfuse": 31328, "ĠDale": 31329, ":\"": 31330, "Ġcomplètement": 31331, "Ġkel": 31332, "à¹Ĩ": 31333, "Ġquatre": 31334, "ĠUM": 31335, "Ġë§IJë": 31336, "æł¹": 31337, "ÃŃr": 31338, "Ġleisure": 31339, "ĠHousing": 31340, "Ġfolds": 31341, "estion": 31342, "ARS": 31343, "Ġmash": 31344, "urpose": 31345, "Ġaccumulated": 31346, "ĠStuff": 31347, "èªŀ": 31348, "Ġtapes": 31349, "ĠÑģилÑĮно": 31350, "ĠLOVE": 31351, "Ġ1982": 31352, "Ġscars": 31353, "Ġcapitalist": 31354, "ĠNed": 31355, "Ġsoften": 31356, "Ġnotably": 31357, "Ġforcément": 31358, "ĠRaum": 31359, "ĠнеобÑħод": 31360, "Ġtrademark": 31361, "Ġfertig": 31362, "Ġ?!": 31363, "æĹł": 31364, "Ġreinforced": 31365, "Ġrecharge": 31366, "ĠPutting": 31367, "Ġvillains": 31368, "Ġhandic": 31369, "Ġadvertisement": 31370, "تÙĬ": 31371, "ĠÑģÑĥм": 31372, "ĠRiley": 31373, "×ķ×ij×": 31374, "京": 31375, "Os": 31376, "از": 31377, "Boy": 31378, "Ġsquish": 31379, "ocket": 31380, "Ġtestify": 31381, "æ¼Ķ": 31382, "Ġ׾×ŀ×": 31383, "ĠмаÑģÑģ": 31384, "manuel": 31385, "ĠArkansas": 31386, "iffe": 31387, "Ġanalysts": 31388, "ĠDeaf": 31389, "Ġjó": 31390, "Ġgroceries": 31391, "ĠWheel": 31392, "ĠÑĢиÑģ": 31393, "Ġcòn": 31394, "ĠCob": 31395, "Ġprisons": 31396, "ève": 31397, "ĠCabinet": 31398, "Ġposed": 31399, "Ġguerre": 31400, "ĠLloyd": 31401, "Ġclerk": 31402, "Ġcrises": 31403, "ĠSho": 31404, "ĠOre": 31405, "ĠFootball": 31406, "ĠAdvis": 31407, "ĠZheng": 31408, "èį": 31409, "ĠAMY": 31410, "Ġunfor": 31411, "Ġmonaster": 31412, "Ġcompile": 31413, "Ġimmortal": 31414, "atable": 31415, "Ġparano": 31416, "Ġtiver": 31417, "ĠSteph": 31418, "ĠFuÃŁ": 31419, "Ġdiscontin": 31420, "Ġripe": 31421, "Ġhacking": 31422, "Ġsiendo": 31423, "Ġseguro": 31424, "altres": 31425, "Ġanderes": 31426, "Ġ리ë": 31427, "Ġexports": 31428, "æŃ¥": 31429, "Ġtabii": 31430, "Ġ기ëĭ¤ë": 31431, "Ġbothering": 31432, "Ġpickle": 31433, "ĠBRIAN": 31434, "Ġaltar": 31435, "ĠпÑĢиб": 31436, "Ġtransferring": 31437, "ĠVors": 31438, "ĠÙĩÙĪ": 31439, "ĠZa": 31440, "ĠFrances": 31441, "Ġbrowse": 31442, "emit": 31443, "Ġchewing": 31444, "ĠFreddy": 31445, "Ġeditors": 31446, "älle": 31447, "ĠíĮĢ": 31448, "ĠSque": 31449, "ĠCultural": 31450, "awk": 31451, "ĠSache": 31452, "ĠCarbon": 31453, "ắt": 31454, "FL": 31455, "ĠNGO": 31456, "peÅĤ": 31457, "ĠSou": 31458, "Ġhvor": 31459, "unintelligible": 31460, "Ġë²ķ": 31461, "Ġ°": 31462, "iin": 31463, "Ġ×¢×Ŀ": 31464, "Ġderrière": 31465, "Ġczym": 31466, "ĠApost": 31467, "Ġregarder": 31468, "Ġagrade": 31469, "ĠCandy": 31470, "Ġmare": 31471, "Ġintroduces": 31472, "birds": 31473, "Ġuniquely": 31474, "Ġmuk": 31475, "Ġcooker": 31476, "Ġcrews": 31477, "Ġjeito": 31478, "ERT": 31479, "¶Ħë": 31480, "nisse": 31481, "Ġef": 31482, "Ġcarte": 31483, "ĠYak": 31484, "ĠPAT": 31485, "ино": 31486, "bokki": 31487, "Ġmates": 31488, "Ġdistint": 31489, "Ġì½Ķë¡ľëĤĺ": 31490, "Ġyıl": 31491, "Ġκάν": 31492, "Ġconfigurations": 31493, "enga": 31494, "recht": 31495, "Happy": 31496, "ãĤĦãģ£ãģ¦": 31497, "invest": 31498, "Ġreconstruct": 31499, "ĠÑįÑĤомÑĥ": 31500, "Ġmosque": 31501, "raum": 31502, "Ġvoyez": 31503, "ĠNBC": 31504, "ĠìŀIJìĭł": 31505, "Ġsturdy": 31506, "Ġкап": 31507, "Ġansch": 31508, "alid": 31509, "Ġmasih": 31510, "ĠREP": 31511, "Ġì½Ķë": 31512, "Ġdeduct": 31513, "Ġsalir": 31514, "wurf": 31515, "ilot": 31516, "ĠMutter": 31517, "olds": 31518, "ĠFEMA": 31519, "ĠBib": 31520, "Ġneighboring": 31521, "Ġbliss": 31522, "Ġíĺ¼": 31523, "лиÑģÑĮ": 31524, "ĠÑĤÑĢеб": 31525, "Ġå°±æĺ¯": 31526, "Ġgrenade": 31527, "Ġegal": 31528, "Ġfinely": 31529, "Ġpetals": 31530, "Ġkeer": 31531, "Ġchyba": 31532, "Ġskipping": 31533, "Ġthirteen": 31534, "Ġgravy": 31535, "ĠSAT": 31536, "61": 31537, "Ġног": 31538, "Ġmins": 31539, "ITE": 31540, "Ġsozial": 31541, "íķĺë©´ìĦľ": 31542, "ruktur": 31543, "Ġвозмож": 31544, "ĠопÑıÑĤÑĮ": 31545, "Ġarth": 31546, "ĠCuban": 31547, "Ġtreasures": 31548, "Ġfertilizer": 31549, "Ġawakening": 31550, "Ġë°±ìĭł": 31551, "Ġrall": 31552, "Ġdepict": 31553, "ĠPablo": 31554, "Ġnineteen": 31555, "Ġwatt": 31556, "Ġentirety": 31557, "KS": 31558, "ĠWoods": 31559, "Sch": 31560, "ĠÚ©ÙĪ": 31561, "ĠDry": 31562, "ãģŀ": 31563, "uve": 31564, "Ġreconstruction": 31565, "Ġanatomy": 31566, "Ī를": 31567, "Ġbaba": 31568, "Ġlistener": 31569, "Ġsharpen": 31570, "ĠPeru": 31571, "ĠвÑĭз": 31572, "Ġrecreation": 31573, "Ġinitiate": 31574, "Ġcalor": 31575, "ĠNaj": 31576, "gee": 31577, "ĠFeels": 31578, "ĠSnapchat": 31579, "ĠTet": 31580, "ĠNest": 31581, "ĠDaf": 31582, "ĠFinish": 31583, "ĠÑĤаким": 31584, "úc": 31585, "izens": 31586, "Ġspins": 31587, "Ġembry": 31588, "Ġpassages": 31589, "Ġcient": 31590, "Ġjustification": 31591, "ä»ĸ說": 31592, "Ġolmaz": 31593, "Ġflooded": 31594, "Ġemoji": 31595, "Ġembracing": 31596, "Ġdiscard": 31597, "ĠBasic": 31598, "agog": 31599, "ĠìľĦíķ´": 31600, "Ġasylum": 31601, "erin": 31602, "Ġfim": 31603, "Ġninja": 31604, "Ġautomate": 31605, "Ġallergic": 31606, "ÿÿÿÿ": 31607, "amam": 31608, "ĠмаÑĢ": 31609, "ĠOi": 31610, "äus": 31611, "Ġinduct": 31612, "ĠBEN": 31613, "ĠzÅĤ": 31614, "Ġkażdy": 31615, "ĠAMP": 31616, "nÄĽ": 31617, "Sure": 31618, "Ġquil": 31619, "Ġespec": 31620, "rok": 31621, "BSCRI": 31622, "Ġliebe": 31623, "pus": 31624, "achsen": 31625, "Ġcricket": 31626, "ëĬIJ": 31627, "ĠFrame": 31628, "ekkür": 31629, "arb": 31630, "ĠpÅĻ": 31631, "иÑģÑģ": 31632, "Ġzeggen": 31633, "Ġdoubles": 31634, "ĠDre": 31635, "test": 31636, "insp": 31637, "boys": 31638, "Ġmão": 31639, "ĠVerse": 31640, "Ġmuscular": 31641, "ĠMALE": 31642, "Ġdulu": 31643, "Ġoccasional": 31644, "Lo": 31645, "conomic": 31646, "Ġvak": 31647, "Ġremedy": 31648, "å¤ł": 31649, "ĠâĻªâĻªâĻª": 31650, "vem": 31651, "Ġönem": 31652, "ĠkarÅŁÄ±": 31653, "ĠSharp": 31654, "hur": 31655, "Ġë°©ë²ķ": 31656, "Ġgrandson": 31657, "Ġaktiv": 31658, "ĠThrones": 31659, "ĠìķĪìĹIJ": 31660, "Ġtots": 31661, "Ġsubd": 31662, "ĠPaula": 31663, "Ġgraves": 31664, "ĠBrent": 31665, "ĠникÑĤо": 31666, "Ġsöz": 31667, "Ġcrec": 31668, "ĠVladimir": 31669, "çĸ«": 31670, "Ġпой": 31671, "Ġ\"-": 31672, "Ġpsy": 31673, "atri": 31674, "idan": 31675, "Ġaún": 31676, "Ġstandardized": 31677, "ì¹ĺë": 31678, "ĠкÑĢов": 31679, "ĠZhu": 31680, "something": 31681, "Ġ750": 31682, "Ġmujeres": 31683, "Ġait": 31684, "éĹ´": 31685, "agu": 31686, "Ġcorrected": 31687, "ikka": 31688, "eled": 31689, "ĠCareer": 31690, "owym": 31691, "Ġroommate": 31692, "Ġdescendants": 31693, "ĠNapoleon": 31694, "ĠÐĶо": 31695, "íĸĪìĸ´ìļĶ": 31696, "Ġbunun": 31697, "ĠMicha": 31698, "ç·ļ": 31699, "Ġdescob": 31700, "PI": 31701, "Ġpalabra": 31702, "Ġtracked": 31703, "Ġdependence": 31704, "ĠBarack": 31705, "åģĩ": 31706, "Ġfertility": 31707, "ĠSouthwest": 31708, "Ġincomplete": 31709, "Ġcomunic": 31710, "Ġcompris": 31711, "ĠRestaur": 31712, "Ġacron": 31713, "κα": 31714, "Ġapprentices": 31715, "Ġmusst": 31716, "ĠAbr": 31717, "Ġpentru": 31718, "ĠConsort": 31719, "ĠAvec": 31720, "Ġdumplings": 31721, "LR": 31722, "Ġwszystkie": 31723, "Ġswamp": 31724, "нев": 31725, "uggle": 31726, "Ġwatercolor": 31727, "Ġproton": 31728, "ĠEspaña": 31729, "ocking": 31730, "овал": 31731, "Ġtakim": 31732, "Very": 31733, "Ġdementia": 31734, "ĠÅŁeyi": 31735, "Jac": 31736, "ĠMacBook": 31737, "ĠLiv": 31738, "fficients": 31739, "ĠHunt": 31740, "Ġoverlay": 31741, "æĦŁè¦º": 31742, "ĠSkype": 31743, "punkt": 31744, "Ġconfined": 31745, "ĠAdrian": 31746, "رÙĥ": 31747, "ĠJeep": 31748, "Ġenquanto": 31749, "Ġanest": 31750, "оÑĤвеÑĤ": 31751, "ĠменÑĮ": 31752, "Ġirrigation": 31753, "á»ijn": 31754, "Ġeighteen": 31755, "ĠPon": 31756, "Ġrescued": 31757, "Ġ1983": 31758, "rü": 31759, "jae": 31760, "ĠJeong": 31761, "Ġamazingly": 31762, "ĠFDP": 31763, "Ġbackstage": 31764, "cue": 31765, "ĠÏĥÏĦην": 31766, "ĠاÙĦص": 31767, "Ġlivestock": 31768, "ĠWarner": 31769, "Ġmajors": 31770, "ãĥģãĥ£": 31771, "Ġcooperative": 31772, "ĠBrady": 31773, "rained": 31774, "rieb": 31775, "Ġ×ij×ŀ×": 31776, "ĠдоволÑĮно": 31777, "ĠFE": 31778, "Ġleaked": 31779, "ĠMercury": 31780, "Ġpersuade": 31781, "Ġtransformer": 31782, "ĠNorweg": 31783, "ĠìŬ룬": 31784, "ĠzrobiÄĩ": 31785, "Ġcardiovascular": 31786, "ĠCrash": 31787, "Ġgossip": 31788, "аÑģÑĤÑĮ": 31789, "Ġ쪽": 31790, "Ġswept": 31791, "ĠHorn": 31792, "ĠAté": 31793, "Ġbukan": 31794, "ĠKaw": 31795, "KY": 31796, "ĠStories": 31797, "Gary": 31798, "Ġgardening": 31799, "ĠQuickly": 31800, "ĠFalcon": 31801, "Ġovat": 31802, "cı": 31803, "ĠComplet": 31804, "ĠDate": 31805, "ĠпÑĢим": 31806, "Ġläuft": 31807, "ĠAudrey": 31808, "ĠWent": 31809, "ĠpelÃŃcul": 31810, "Ġcarriage": 31811, "Ġunacceptable": 31812, "nymi": 31813, "ĠÑģлÑĭÑĪ": 31814, "Ġterre": 31815, "uellement": 31816, "EEEE": 31817, "Ġpharmac": 31818, "hões": 31819, "Ġzich": 31820, "Ġmigrate": 31821, "ĠFry": 31822, "ñana": 31823, "ĠMuito": 31824, "EOVER": 31825, "Ġfortress": 31826, "ĠCompan": 31827, "ĠJSON": 31828, "ordnung": 31829, "Ġwarto": 31830, "Ġungef": 31831, "ìħĶìĦľ": 31832, "ĠÑĢок": 31833, "Ġpaddle": 31834, "Jared": 31835, "Ġsubmitting": 31836, "Ġlatch": 31837, "Ġfug": 31838, "ĠкоÑģ": 31839, "ĠEf": 31840, "Ġlaunches": 31841, "Ġft": 31842, "otechn": 31843, "Ġtravelled": 31844, "اÙģ": 31845, "éģķ": 31846, "Ġproch": 31847, "Ġdedim": 31848, "83": 31849, "Ġrebound": 31850, "ĠLU": 31851, "path": 31852, "ĠÑģпÑĢав": 31853, "Ġöl": 31854, "ĠíĤ¤": 31855, "Ġprivat": 31856, "Ġtractor": 31857, "ĠAttention": 31858, "Ser": 31859, "Ġcoses": 31860, "ária": 31861, "pal": 31862, "ĠìĿĢ": 31863, "Ġsuccessor": 31864, "Ġconnectors": 31865, "ĠÑĥÑģÑĤанов": 31866, "Ġgenocide": 31867, "Ġsufficiently": 31868, "ĠAixò": 31869, "Ġstabilize": 31870, "Ġcongest": 31871, "Ġcarving": 31872, "Ġzost": 31873, "ĠбÑĭÑģÑĤÑĢо": 31874, "Ġshortest": 31875, "Ġlivel": 31876, "Ġ89": 31877, "éģĬ": 31878, "Ġerk": 31879, "Ġportraits": 31880, "à¥Ģ": 31881, "èĺ": 31882, "boat": 31883, "llah": 31884, "ANC": 31885, "Ġempirical": 31886, "ĠEcho": 31887, "ĠNederland": 31888, "è¿Ļä¹Ī": 31889, "Net": 31890, "Ġcuidado": 31891, "ĠRoma": 31892, "Ġcalf": 31893, "Ġgiants": 31894, "ĠExplorer": 31895, "ĠCollect": 31896, "alition": 31897, "ĠDestiny": 31898, "Ġausge": 31899, "ĠEdu": 31900, "ĠClo": 31901, "Ġearrings": 31902, "ĠTrack": 31903, "ĠROS": 31904, "ĠBelle": 31905, "çĻ¾": 31906, "Ġpueda": 31907, "Ġdaytime": 31908, "Ġsupplier": 31909, "ĠSV": 31910, "ĠExhale": 31911, "Ġgalera": 31912, "course": 31913, "Ġcentimeter": 31914, "ĠBast": 31915, "mud": 31916, "Ġsangat": 31917, "ĠPhysical": 31918, "Ġprivately": 31919, "Ġtrata": 31920, "lynn": 31921, "illi": 31922, "Ġë©ĶìĿ´íģ¬ìĹħ": 31923, "Ġcrystall": 31924, "Ġpods": 31925, "ản": 31926, "inator": 31927, "ĠRecords": 31928, "å®ĺ": 31929, "ÄŁimiz": 31930, "issement": 31931, "hare": 31932, "hadow": 31933, "ĠDK": 31934, "ĠìķĮê³ł": 31935, "Ġwyn": 31936, "Ġrequesting": 31937, "ĠDonna": 31938, "ĠìĹ´ìĭ¬íŀĪ": 31939, "inea": 31940, "Ġexert": 31941, "ĠDuncan": 31942, "ĠвеÑĩ": 31943, "ĠHah": 31944, "à¤Ĥ": 31945, "ĠLif": 31946, "ĠFinding": 31947, "ĠNov": 31948, "Ġзнак": 31949, "ĠоÑĦ": 31950, "ĠQuè": 31951, "Ġquarterback": 31952, "ĠÑĦак": 31953, "Ġbipartisan": 31954, "ÄŁin": 31955, "Ġnécess": 31956, "Ġreferendum": 31957, "Ġcompiler": 31958, "Ġprobabil": 31959, "еди": 31960, "Ġtrader": 31961, "æĺĵ": 31962, "ĠRum": 31963, "geme": 31964, "Ġdio": 31965, "ĠbÄĻdziemy": 31966, "ĠÏĢά": 31967, "꾸": 31968, "×ķ×ĺ": 31969, "Ġà¤ķ": 31970, "Ġблаг": 31971, "Ġscalp": 31972, "ĠPause": 31973, "Ġcaption": 31974, "Ġendanger": 31975, "Ġenlar": 31976, "Ġrotten": 31977, "ãĥĥãĥĪ": 31978, "Ġwah": 31979, "èĤī": 31980, "Ġdzi": 31981, "ĠInstall": 31982, "Ay": 31983, "Ġcrear": 31984, "енÑĤа": 31985, "Ġweighing": 31986, "Ġbutterflies": 31987, "ĠGast": 31988, "äºķ": 31989, "horn": 31990, "warz": 31991, "ICEOVER": 31992, "ĠнайÑĤи": 31993, "Ġcoefficients": 31994, "ç°¡åĸ®": 31995, "ĠSpencer": 31996, "ĠHigher": 31997, "Ġcowork": 31998, "å¨ĺ": 31999, "ĠкоÑĤоÑĢое": 32000, "Ġmonit": 32001, "Ġdysfunction": 32002, "ĠÑģÑĤанов": 32003, "Ġtournaments": 32004, "Ġoyster": 32005, "BN": 32006, "Ġtrud": 32007, "slow": 32008, "ĠPenny": 32009, "ĠOdys": 32010, "ær": 32011, "Ġfou": 32012, "Ġenjoyment": 32013, "аÑĤÑĭ": 32014, "ĠwyglÄħda": 32015, "алÑĮнаÑı": 32016, "ĠProtect": 32017, "Ġmoy": 32018, "Ġclaw": 32019, "Ġsuspicion": 32020, "Ġsacrificed": 32021, "Ġgosto": 32022, "Big": 32023, "Ġaggressively": 32024, "Ġvorne": 32025, "ãĥł": 32026, "Ġblamed": 32027, "ĠSehr": 32028, "פר": 32029, "cito": 32030, "Ġseals": 32031, "Ġmujer": 32032, "ĠWeird": 32033, "Ġforens": 32034, "Ġcontributes": 32035, "estra": 32036, "Ġpog": 32037, "LOL": 32038, "Ġhacerlo": 32039, "оÑĤÑĮ": 32040, "fiction": 32041, "79": 32042, "λο": 32043, "大æ¦Ĥ": 32044, "声": 32045, "ĠÑĤоб": 32046, "ĠGS": 32047, "ĠClara": 32048, "itez": 32049, "Ġadvocating": 32050, "ĠíĶĦë": 32051, "sung": 32052, "Ġvertices": 32053, "Ġnavigating": 32054, "Ġeuropé": 32055, "çļĨ": 32056, "Ġslowed": 32057, "Ġforeground": 32058, "ĠIndustrial": 32059, "Ġadore": 32060, "ìĭŃ": 32061, "Ġcréer": 32062, "æŀĹ": 32063, "chnitt": 32064, "Ġunaware": 32065, "Ġcurly": 32066, "entar": 32067, "Ġler": 32068, "Ġprohibited": 32069, "ĠHeroes": 32070, "ĠReed": 32071, "uca": 32072, "Ġsmok": 32073, "Ġkunna": 32074, "zeitig": 32075, "immen": 32076, "ĠLun": 32077, "ĠабÑģолÑİÑĤ": 32078, "Ġdegli": 32079, "Ġvillagers": 32080, "Ġpreset": 32081, "zept": 32082, "uds": 32083, "Ġemit": 32084, "ä½łè¦ģ": 32085, "Ġëī": 32086, "ëĬĶì§Ģ": 32087, "нако": 32088, "Ġosób": 32089, "Ġ1969": 32090, "ĠÐIJÑĢ": 32091, "Ġmanchmal": 32092, "ĠBrock": 32093, "Ġmantra": 32094, "ĠWIL": 32095, "bach": 32096, "inä": 32097, "elas": 32098, "keln": 32099, "Ġdisciple": 32100, "Ġqualc": 32101, "Ġdehyd": 32102, "ìĿ´ëĿ¼ëĬĶ": 32103, "Af": 32104, "ìĦ±ìĿ´": 32105, "Ryan": 32106, "Ġpuppet": 32107, "ĠдÑĢÑĥгие": 32108, "Ġrud": 32109, "Ġpending": 32110, "Plus": 32111, "ĠìķĬìĿĦ": 32112, "Ġbá»ĭ": 32113, "ĠSega": 32114, "çe": 32115, "Ġprogrammer": 32116, "bli": 32117, "Ġunl": 32118, "Ġenslaved": 32119, "Ġsociété": 32120, "Äģh": 32121, "Ġinheritance": 32122, "ĠBangl": 32123, "ermaid": 32124, "Ġpractitioner": 32125, "ĠStalin": 32126, "ĠUser": 32127, "cible": 32128, "Ġcardiac": 32129, "ĠKoreans": 32130, "Ġdumped": 32131, "Ġ×Ķ×Ļ×Ķ": 32132, "áis": 32133, "Ġhydraulic": 32134, "oubtedly": 32135, "ĠPit": 32136, "Ġpicnic": 32137, "Ġbehöver": 32138, "ĠÑģмог": 32139, "Ġbraking": 32140, "é»ij": 32141, "utar": 32142, "ĠìĦ¸ë": 32143, "ubl": 32144, "Ġüz": 32145, "Ġmajesty": 32146, "Ġbers": 32147, "utable": 32148, "Ġhotter": 32149, "çħ§": 32150, "ÛĮÙĨ": 32151, "Ġbiases": 32152, "Ġsubjected": 32153, "Ġnaughty": 32154, "Ġcircus": 32155, "ãģĹãģĭ": 32156, "ĠImmedi": 32157, "ĠStefan": 32158, "ĠTriple": 32159, "enk": 32160, "Ġwit": 32161, "Ġrecycle": 32162, "emie": 32163, "dated": 32164, "Ġunload": 32165, "Ġpopula": 32166, "chin": 32167, "Ġyields": 32168, "Ġenglish": 32169, "ĠBonnie": 32170, "Ġspiders": 32171, "Ãģ": 32172, "Ġerosion": 32173, "éĥ¨åĪĨ": 32174, "ĠNICK": 32175, "иÑıÑħ": 32176, "Ġimpart": 32177, "Ġкни": 32178, "Ġresolutions": 32179, "Ġlithium": 32180, "Ġconvergence": 32181, "ĠTara": 32182, "Ġдве": 32183, "ths": 32184, "ĠCindy": 32185, "æĪijè¦ģ": 32186, "幫": 32187, "ĠDIE": 32188, "Ġassurance": 32189, "ĠопиÑģ": 32190, "Ġbuckets": 32191, "Ġcues": 32192, "ĠQuiet": 32193, "Ġsimilarity": 32194, "Ġfoundational": 32195, "ĠMinist": 32196, "滿": 32197, "Ġpian": 32198, "Ġcentr": 32199, "Ġnumb": 32200, "Ġmonks": 32201, "ujourd": 32202, "enzie": 32203, "Ġskateboard": 32204, "Ġdlatego": 32205, "ĠÑģоÑĤ": 32206, "ĠAE": 32207, "Ġmasterpiece": 32208, "ĠSolomon": 32209, "ĠReddit": 32210, "Ġriot": 32211, "abl": 32212, "ĠJazz": 32213, "Ġelectromagnetic": 32214, "Ġinsecure": 32215, "ĠCompet": 32216, "geries": 32217, "обод": 32218, "ł×ķ": 32219, "ðŁĴ": 32220, "Ġsenators": 32221, "ĠBrisbane": 32222, "ĠAlb": 32223, "uttering": 32224, "ĠAllow": 32225, "zero": 32226, "Ġpai": 32227, "ĠÐIJлекÑģ": 32228, "ĠDisplay": 32229, "ĠBlade": 32230, "ĠApps": 32231, "Ġpä": 32232, "ĠдеÑģÑı": 32233, "Ġquella": 32234, "ĠGao": 32235, "еннÑĭÑħ": 32236, "Ġspoilers": 32237, "Ġgallons": 32238, "ĠÙĦÙĬ": 32239, "ĠZion": 32240, "æľīä¸Ģ": 32241, "onie": 32242, "ragt": 32243, "ĠChand": 32244, "Ġë³ij": 32245, "Ġblunt": 32246, "Ġusu": 32247, "ĠKad": 32248, "rakt": 32249, "Ġcinematic": 32250, "Ġammunition": 32251, "rene": 32252, "Ġfourteen": 32253, "ĠCarn": 32254, "crit": 32255, "Ġtenure": 32256, "vu": 32257, "Ġprincipalmente": 32258, "Ġalleen": 32259, "éĢĻä¸Ģ": 32260, "Ġkomplett": 32261, "Ġdüny": 32262, "James": 32263, "Ġreceptor": 32264, "Ġoneself": 32265, "guru": 32266, "Ġmerchant": 32267, "liness": 32268, "Ġoverlooked": 32269, "Ġharmonic": 32270, "éķ¿": 32271, "ieso": 32272, "×ķ×ŀ": 32273, "colm": 32274, "ĠпÑĢоекÑĤ": 32275, "ĠAda": 32276, "اس": 32277, "Tim": 32278, "Ġrecurring": 32279, "Ġproceeds": 32280, "ĠParticularly": 32281, "ĠDownload": 32282, "etrical": 32283, "Ġmatrices": 32284, "Ġproyecto": 32285, "ancies": 32286, "ĠUhm": 32287, "Ġcaves": 32288, "Ġìĸ´ëł¤": 32289, "ĠLeaf": 32290, "ĠобÑĭÑĩ": 32291, "ĠìĿ´ìľł": 32292, "Europe": 32293, "ĠtÄħ": 32294, "Ġpuls": 32295, "Ġtakiego": 32296, "ÐĿе": 32297, "GU": 32298, "Ġfors": 32299, "Ïģγ": 32300, "Ġfotos": 32301, "Ġ))": 32302, "Ġ멤ë": 32303, "Ġaquilo": 32304, "ĠKurd": 32305, "ï¸ı": 32306, "ptic": 32307, "ĠDort": 32308, "Ġmisery": 32309, "auso": 32310, "åĬŁ": 32311, "chuckling": 32312, "ĠRidge": 32313, "ĠíĸĪìĬµëĭĪëĭ¤": 32314, "Ġ***": 32315, "客": 32316, "ĠHmmm": 32317, "Ġgeographic": 32318, "Ġanys": 32319, "Ġtalvez": 32320, "Ġskelet": 32321, "Ġsignatures": 32322, "Ġliters": 32323, "IJë©´": 32324, "ĠÑģвоего": 32325, "Ġskiing": 32326, "ĠÐľÐ¾Ñģ": 32327, "Ġadopting": 32328, "Ġhaft": 32329, "Ġsymmetric": 32330, "ĠLiqu": 32331, "Ġthyroid": 32332, "Ġmisin": 32333, "lude": 32334, "Ġhull": 32335, "ĠXD": 32336, "ĠGust": 32337, "zeich": 32338, "Ġvibrations": 32339, "Ġesemp": 32340, "ĠвÑģÑİ": 32341, "ĠQuem": 32342, "Ġübrig": 32343, "ĠSke": 32344, "ĠLynch": 32345, "rooms": 32346, "artet": 32347, "fest": 32348, "Ġfrüher": 32349, "Ġlure": 32350, "ä¸į好æĦıæĢĿ": 32351, "ĠìķĮìķĦ": 32352, "ĠWIN": 32353, "ĠRYAN": 32354, "ĠкоÑĤоÑĢÑĥÑİ": 32355, "ĠKash": 32356, "Ġ×Ķ×ŀ": 32357, "Ġsafeg": 32358, "ĠHallelujah": 32359, "ĠдвÑĥÑħ": 32360, "Ġstaple": 32361, "Ġsediment": 32362, "ĠActs": 32363, "Ġblaming": 32364, "Ġmainland": 32365, "Ġsporting": 32366, "Ġdecorations": 32367, "Ġexecuting": 32368, "Ġparan": 32369, "ĠDollar": 32370, "Ġprojections": 32371, "Ġcommissioned": 32372, "Ġbour": 32373, "öm": 32374, "Ġsteamed": 32375, "ĠëŃĺ": 32376, "Ġpetrol": 32377, "Ġcelular": 32378, "帶": 32379, "ĠHungary": 32380, "Ġrented": 32381, "ĠваÑĢи": 32382, "bbie": 32383, "Ġsécur": 32384, "üll": 32385, "Ġswings": 32386, "between": 32387, "ĠиÑĤ": 32388, "estro": 32389, "Ġniemand": 32390, "ĠìĤ¼": 32391, "ĠPardon": 32392, "esses": 32393, "ĠMID": 32394, "Ġcentralized": 32395, "ĠAlien": 32396, "culos": 32397, "Ġcrise": 32398, "裡éĿ¢": 32399, "Ġclasse": 32400, "beitet": 32401, "iÄŁi": 32402, "Ġwhales": 32403, "Ġperimeter": 32404, "Ġtying": 32405, "Ġstrony": 32406, "Ġlikewise": 32407, "ĠPunch": 32408, "Da": 32409, "ĠBaptist": 32410, "Ġsorting": 32411, "Ġiv": 32412, "Ġíķ©": 32413, "Ġrehab": 32414, "Ġeta": 32415, "river": 32416, "Ġsai": 32417, "ãģĦãģŁãģł": 32418, "odus": 32419, "ãģĬé¡ĺãģĦãģĹãģ¾ãģĻ": 32420, "Ġessayer": 32421, "Ġturtles": 32422, "ĠHazrat": 32423, "Ġfabrics": 32424, "Ġcavity": 32425, "Ġponieważ": 32426, "Ġschlecht": 32427, "Ġsalsa": 32428, "ÅŁekkür": 32429, "Ġseating": 32430, "Ġeconomists": 32431, "Ġmang": 32432, "Ġseguinte": 32433, "Ġrang": 32434, "Ġratios": 32435, "Ġconstell": 32436, "Ġlongtemps": 32437, "uating": 32438, "Ġspoiled": 32439, "Ġrecipients": 32440, "Ġsniper": 32441, "ä¹ĭåīį": 32442, "ìĬµëĭĪê¹Į": 32443, "Ġwp": 32444, "ĠLINKE": 32445, "Ġflare": 32446, 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33436, "ĠEls": 33437, "Ġretained": 33438, "ĠLaughs": 33439, "æĢ»": 33440, "ĠSAS": 33441, "ĠколиÑĩеÑģÑĤво": 33442, "×ķתר": 33443, "Ġinnovate": 33444, "Ġkork": 33445, "ĠÑĢаÑģÑģказÑĭв": 33446, "ondere": 33447, "ivi": 33448, "aye": 33449, "ounty": 33450, "ĠполÑĥÑĩаеÑĤÑģÑı": 33451, "Ġbuns": 33452, "åħ«": 33453, "Ġyüzden": 33454, "Ġsurgeries": 33455, "Ø£ÙĨ": 33456, "Ġbankruptcy": 33457, "welt": 33458, "Ġsiamo": 33459, "Ġdarkest": 33460, "ĠHann": 33461, "gga": 33462, "Ġformas": 33463, "ĠDj": 33464, "named": 33465, "Ġshields": 33466, "ueller": 33467, "ĠFew": 33468, "Ġlace": 33469, "Ġfurious": 33470, "ĠYU": 33471, "Ġsocietal": 33472, "Ġjudgement": 33473, "ĠDos": 33474, "Ġjab": 33475, "laws": 33476, "Ġreinvent": 33477, "ĠKatherine": 33478, "ĠChoi": 33479, "adows": 33480, "Ġrans": 33481, "oden": 33482, "ĠMidwest": 33483, "nın": 33484, "Ġdeport": 33485, "ĠDip": 33486, "ç´ħ": 33487, "Ġatención": 33488, "ĠCourtney": 33489, "ividad": 33490, "ĠÚ©Ûģ": 33491, "Ġefficacy": 33492, "ĠBrooks": 33493, "Ġreferral": 33494, "ĠконÑĨ": 33495, "Ġmalicious": 33496, "Ġkir": 33497, "ĠGoddess": 33498, "Ġfunky": 33499, "Ġinterim": 33500, "ĠKörper": 33501, "Ġìĸ¼ë§": 33502, "kur": 33503, "Ġкли": 33504, "Ġtrucs": 33505, "gesetz": 33506, "Ġzug": 33507, "ĠGlück": 33508, "ĠMinute": 33509, "Ġprestigious": 33510, "Ġniez": 33511, "Ġconcentrations": 33512, "лаÑģÑĤи": 33513, "ĠSis": 33514, "ĠVitamin": 33515, "kov": 33516, "ĠPBS": 33517, "Ġнее": 33518, "Ġretailers": 33519, "Ġconventions": 33520, "ĠSamantha": 33521, "Ġproudly": 33522, "Jordan": 33523, "ĠJASON": 33524, "atk": 33525, "Ġtriste": 33526, "Ġstär": 33527, "Ġreiterate": 33528, "Ġposterior": 33529, "Ġ1973": 33530, "ĠPine": 33531, "ĠJuliet": 33532, "Ġpedir": 33533, "kil": 33534, "Ġoverlapping": 33535, "Ġexclude": 33536, "Ġeconóm": 33537, "Ġaccepts": 33538, "ĠSter": 33539, "決": 33540, "Ġìļ´ëıĻ": 33541, "estab": 33542, "Ġtug": 33543, "arg": 33544, "Ġlivro": 33545, "اص": 33546, "Ġseams": 33547, "Ġburaya": 33548, "Ġello": 33549, "ĠTM": 33550, "ĠPaw": 33551, "ĠIndex": 33552, "Exc": 33553, "Ġinspirational": 33554, "Ġdunk": 33555, "è°ģ": 33556, "akter": 33557, "Ġconditioner": 33558, "ĠSalut": 33559, "ÅĤec": 33560, "Ġìī½": 33561, "ĠÑĥзна": 33562, "ĠRomeo": 33563, "fruit": 33564, "ĠYO": 33565, "Ġchá»ī": 33566, "бÑĥ": 33567, "bons": 33568, "Ġreproductive": 33569, "Ġorada": 33570, "Ġíļ¨": 33571, "Ġtentar": 33572, "Ġmañana": 33573, "ãĤ¬": 33574, "Ġsolvent": 33575, "Jessica": 33576, "ĠLegal": 33577, "Ġtua": 33578, "Ġsic": 33579, "ĠEQ": 33580, "aukee": 33581, "ìĭľëĭ¤": 33582, "ĠÅŀu": 33583, "Ġadhere": 33584, "ĠTul": 33585, "Ġà®Ĩ": 33586, "Ġtextbooks": 33587, "ĠFifth": 33588, "Ġexperi": 33589, "Ġchic": 33590, "Ġheap": 33591, "inely": 33592, "atra": 33593, "Two": 33594, "Ġhelemaal": 33595, "Ġfren": 33596, "æݨ": 33597, "Ġbisher": 33598, "اش": 33599, "ĠìĦłìĥĿ": 33600, "ĠTages": 33601, "Ġsá»±": 33602, "Ġbullied": 33603, "ؤ": 33604, "Ġbenefited": 33605, "ĠPreviously": 33606, "ĠÑįÑĦÑĦ": 33607, "Ùį": 33608, "Ġsenate": 33609, "ĠMorm": 33610, "ijke": 33611, "ĠFlu": 33612, "Ġincorporating": 33613, "jack": 33614, "ĠпиÑĤ": 33615, "Ġimply": 33616, "Ġhacks": 33617, "ĠRICH": 33618, "ĠкваÑĢ": 33619, "ĠпÑĢекÑĢаÑģ": 33620, "Ġdependency": 33621, "Ġìļ©": 33622, "Ġì±ħ": 33623, "Ġwährend": 33624, "Ġsulla": 33625, "ĠPittsburgh": 33626, "Ġesempio": 33627, "¼ë¡ľ": 33628, "prot": 33629, "ĠRosen": 33630, "ĠIndependence": 33631, "Ġparsley": 33632, "iegen": 33633, "Ġhaw": 33634, "Ġaquell": 33635, "ĠCAP": 33636, "ĠÑĢабоÑĤаÑĤÑĮ": 33637, "ĠCliff": 33638, "ionar": 33639, "Ġsecuring": 33640, "æĪijåĢijçļĦ": 33641, "νε": 33642, "Ġutilis": 33643, "Ġcoule": 33644, "ĠPing": 33645, "Ġtrek": 33646, "Ġfak": 33647, "Ġenorme": 33648, "Ġìĭ«": 33649, "让": 33650, "Ġdoubling": 33651, "ĠнÑĢавиÑĤÑģÑı": 33652, "Ġhed": 33653, "hoven": 33654, "ĠStanding": 33655, "ĠmÃŃn": 33656, "ĠJimin": 33657, "Ġmonarch": 33658, "Ġcoke": 33659, "Ġmr": 33660, "Ġclic": 33661, "Ãį": 33662, "Ġimpeachment": 33663, "Ġdurability": 33664, "Ġvarios": 33665, "Ġcommercials": 33666, "Ġgreetings": 33667, "ĠRi": 33668, "ĠAppreci": 33669, "ìŀĪëĬĶ": 33670, "Ġrésult": 33671, "ért": 33672, "Ġsalute": 33673, "Ġpoderia": 33674, "Ġsunrise": 33675, "veck": 33676, "Ġreluctant": 33677, "Ġcommissioner": 33678, "念": 33679, "âte": 33680, "ĠKenny": 33681, "ĠSiri": 33682, "ãĥĥãĥĹ": 33683, "ĠëĬĺ": 33684, "ĠEE": 33685, "Ġunch": 33686, "кон": 33687, "ĠاÙĦØ¥": 33688, "Ġbelts": 33689, "Ġhass": 33690, "ĠмоÑı": 33691, "Ġdisplaced": 33692, "Ġabra": 33693, "ÎŃλ": 33694, "Ġscratches": 33695, "Ġcomet": 33696, "Ġauthorization": 33697, "ĠLLC": 33698, "Ġproduk": 33699, "Ġrehabilitation": 33700, "åŀ": 33701, "ÑĸÑĩ": 33702, "uding": 33703, "olit": 33704, "Ġ105": 33705, "Ġexpands": 33706, "Ġaltri": 33707, "ĠKomment": 33708, "Ġanf": 33709, "Pl": 33710, "ĠMana": 33711, "fed": 33712, "Ġbri": 33713, "Ġora": 33714, "Gs": 33715, "ĠGur": 33716, "uckland": 33717, "Ġjunction": 33718, "Ġironic": 33719, "ĠFeed": 33720, "Ġprakt": 33721, "ĠHammer": 33722, "ĮëıĦ": 33723, "ĠTracy": 33724, "çµ±": 33725, "ĠAside": 33726, "него": 33727, "ĠиÑģполÑĮзоваÑĤÑĮ": 33728, "Ġzaj": 33729, "Ġequitable": 33730, "Ġcurb": 33731, "ĠãģĵãĤĮ": 33732, "Ġderivatives": 33733, "Ġpuppies": 33734, "ĠKenneth": 33735, "ĠCompl": 33736, "igram": 33737, "ĠGarcia": 33738, ")\"": 33739, "ĠHarbor": 33740, "estial": 33741, "Ġä¾Ĩ": 33742, "Ġers": 33743, "æ¹": 33744, "Ġunwanted": 33745, "Ġbelang": 33746, "аго": 33747, "emb": 33748, "dos": 33749, "ĠìĻľë": 33750, "ĠBudget": 33751, "Ġbattling": 33752, "ØŃت": 33753, "kok": 33754, "наÑĩала": 33755, "Ġplag": 33756, "Ġcantidad": 33757, "Ġgrupos": 33758, "Ġplugins": 33759, "lerini": 33760, "ĠимееÑĤ": 33761, "Ġsozusagen": 33762, "olics": 33763, "Ġpueblo": 33764, "Ġreminis": 33765, "rän": 33766, "ĠMorrison": 33767, "Ġlinha": 33768, "Ġbreaths": 33769, "ĠTaste": 33770, "Ġenfrent": 33771, "ĠDocker": 33772, "Ġден": 33773, "Ġethnicity": 33774, "Ġwob": 33775, "Ġsuffers": 33776, "Ġtransitioning": 33777, "ĠRange": 33778, "ÄĻdzy": 33779, "ĠкаÑĤ": 33780, "Ġsyner": 33781, "Ġdonut": 33782, "Ġprobabilities": 33783, "ĠOmar": 33784, "Which": 33785, "uish": 33786, "isin": 33787, "Ġdemos": 33788, "ĠìłĢ기": 33789, "Ġëĺijê°Ļ": 33790, "Ġедин": 33791, "Ġcerve": 33792, "Ġjoka": 33793, "IAN": 33794, "Ġkilometer": 33795, "Ġhorizontally": 33796, "ĠBhag": 33797, "Ġ->": 33798, "ĠMonitor": 33799, "Ġknowledgeable": 33800, "Ġfav": 33801, "Ġpinned": 33802, "ĠeBay": 33803, "icker": 33804, "Ġìŀłê¹IJë§Į": 33805, "ĠXiaomi": 33806, "Ġcapit": 33807, "Ġnp": 33808, "Ġ1965": 33809, "hoe": 33810, "Ġnok": 33811, "ĠSage": 33812, "ĠнелÑĮзÑı": 33813, "ĠTow": 33814, "gam": 33815, "Ġdicen": 33816, "ĠSUBSCRIBE": 33817, "Ġreboot": 33818, "Ġpaj": 33819, "Ġë³´ìŬë": 33820, "Ġthicken": 33821, "ĠReality": 33822, "idän": 33823, "Na": 33824, "Ġê²ĥìĿĢ": 33825, "!!)": 33826, "Ġroutines": 33827, "Ġодного": 33828, "Ġexting": 33829, "Ġì¦Ŀ": 33830, "Ġsulfur": 33831, "Ġcarve": 33832, "Ġasteroid": 33833, "ĠWarrior": 33834, "Ġphotographers": 33835, "Ġpell": 33836, "Ġcrossover": 33837, "æĪijçŁ¥éģĵ": 33838, "Ġhacemos": 33839, "ĠNej": 33840, "Ġsettling": 33841, "Ġirm": 33842, "ĠBooks": 33843, "ientôt": 33844, "Ġespacio": 33845, "ĠScholars": 33846, "Ġdoomed": 33847, "ĠIRS": 33848, "wohl": 33849, "Ġsegue": 33850, "ĠëĪĦê°Ģ": 33851, "Ġpratic": 33852, "BT": 33853, "ĠConsidering": 33854, "ĠBuffalo": 33855, "Ġtrainings": 33856, "Ġgebru": 33857, "ĠGleich": 33858, "Ġpirates": 33859, "Ġenvelop": 33860, "Ġreopen": 33861, "imat": 33862, "Ġtee": 33863, "Ġsued": 33864, "feh": 33865, "Ġ×Ķק": 33866, "Ġdiets": 33867, "Ġjuntos": 33868, "asto": 33869, "Ġmisunderstood": 33870, "Ġruim": 33871, "Ġclassify": 33872, "ĠпÑĢодÑĥк": 33873, "Ġinse": 33874, "Ġillustrated": 33875, "Ġcorrosion": 33876, "Ġaccred": 33877, "ĠAuntie": 33878, "ĠпÑĢивеÑĤ": 33879, "ĠLIVE": 33880, "Ġrek": 33881, "Ġreceipt": 33882, "åĪ°åºķ": 33883, "ĠBarbie": 33884, "ĠSnake": 33885, "turn": 33886, "Jeff": 33887, "ãģĬãģĬ": 33888, "ķĦ": 33889, "VOICEOVER": 33890, "coll": 33891, "Ġrunners": 33892, "ìłľë": 33893, "osos": 33894, "moon": 33895, "Ġkeynote": 33896, "ĠInstit": 33897, "SPEAK": 33898, "Ġplugs": 33899, "Ġcurv": 33900, "ĠYuri": 33901, "ĠTheres": 33902, "ĠPs": 33903, "ĠμÏĢο": 33904, "Ġconverter": 33905, "Ġrefine": 33906, "Ġbadass": 33907, "Ġοι": 33908, "Ġregen": 33909, "azzi": 33910, "ÙĬÙģ": 33911, "Ġseized": 33912, "Ġiçer": 33913, "ilee": 33914, "Ġupstream": 33915, "Ġbuds": 33916, "Ġpim": 33917, "Ġíķĺ루": 33918, "Ġalluded": 33919, "Ġthemed": 33920, "Ġconsisting": 33921, "Ġbons": 33922, "unuz": 33923, "ĠпÑĢовод": 33924, "ĠLovely": 33925, "à¥ĭ": 33926, "Ġparach": 33927, "ĠStaats": 33928, "éļĬ": 33929, "Ġselective": 33930, "Ġfase": 33931, "ĠGeorget": 33932, "Ġcocaine": 33933, "Ġreproduction": 33934, "ĠLara": 33935, "ĠLD": 33936, "Ġgh": 33937, "Jon": 33938, "ĠlÃ¥": 33939, "ĠëijIJë": 33940, "Ġtyped": 33941, "ĠBana": 33942, "ëĵľë": 33943, "Ġsavory": 33944, "ĠZomb": 33945, "standen": 33946, "Ġpedestrian": 33947, "Ġdifférents": 33948, "Ġìĭ¸": 33949, "èī¯": 33950, "Ġcomplained": 33951, "ç¦ı": 33952, "ĠÐļÑĤо": 33953, "Ġ׾פ": 33954, "aliÅĽmy": 33955, "Ġmortar": 33956, "Ġverdict": 33957, "Ġsuficiente": 33958, "ĠMillion": 33959, "mittel": 33960, "inals": 33961, "ĠاÙĦØ®": 33962, "аÑİÑģÑĮ": 33963, "ĠmiÄĻdzy": 33964, "ĠOle": 33965, "Ġinvert": 33966, "czyÄĩ": 33967, "озможно": 33968, "starter": 33969, "Ġauditor": 33970, "ĠScout": 33971, "chien": 33972, "ĠSverige": 33973, "uffled": 33974, "Ġzehn": 33975, "ĠAuckland": 33976, "Ġargent": 33977, "Ġ1976": 33978, "ĠHoe": 33979, "Ġbothers": 33980, "Ġsocialist": 33981, "Ġpliers": 33982, "Ġemergen": 33983, "ĠXP": 33984, "еÑĢов": 33985, "More": 33986, "ĠLevi": 33987, "ĠAnders": 33988, "ibilidad": 33989, "ĠParents": 33990, "Ġinduced": 33991, "ìĸ´ì¤": 33992, "Ġbalances": 33993, "ĠвÑĭÑĪ": 33994, "Ġsubmarine": 33995, "Start": 33996, "Ġdries": 33997, "Ġvolver": 33998, "Ġticking": 33999, "cott": 34000, "Ġfaj": 34001, "prés": 34002, "ĠSabb": 34003, "ĠзаÑĩ": 34004, "ĠпокÑĥп": 34005, "Ġbaptized": 34006, "ĠBrilliant": 34007, "ĠÐijог": 34008, "Ġmots": 34009, "bits": 34010, "Ġlattice": 34011, "æĪijè·Łä½ł": 34012, "Ġcoriander": 34013, "Ġresidency": 34014, "ync": 34015, "Ġpierwszy": 34016, "ĠKnock": 34017, "ĠZap": 34018, "ĠÐķв": 34019, "견": 34020, "å°ıå¿ĥ": 34021, "Ġuneven": 34022, "ĠJas": 34023, "odor": 34024, "ç¿Ĵ": 34025, "74": 34026, "ĠSite": 34027, "Ġaconteceu": 34028, "ympt": 34029, "Ġtrilogy": 34030, "Ġlantern": 34031, "ĠZucker": 34032, "vari": 34033, "welling": 34034, "ĠPotato": 34035, "gomery": 34036, "Ġreacted": 34037, "ĠChron": 34038, "Ġjede": 34039, "beeld": 34040, "Ġtwent": 34041, "Ġlact": 34042, "æ¨Ĥ": 34043, "Ġrése": 34044, "Ġrelent": 34045, "Ġfurnace": 34046, "Ġwidget": 34047, "Ġearthquakes": 34048, "ĠAdjust": 34049, "ilit": 34050, "ĠØ£ÙĪ": 34051, "Ġhearings": 34052, "Ġdefendant": 34053, "irsiniz": 34054, "Ġbask": 34055, "cja": 34056, "ľ¨": 34057, "Ġrifles": 34058, "Ġinstal": 34059, "ĠForgive": 34060, "pical": 34061, "ĠÐŀÑĩенÑĮ": 34062, "Ġpetites": 34063, "Ġhp": 34064, "Ġrenowned": 34065, "ĠInn": 34066, "Ġ주ìĦ¸ìļĶ": 34067, "Ġemphasized": 34068, "éĹ®é¢ĺ": 34069, "ĠìŀĪì£ł": 34070, "Ġê²ĥìľ¼ë¡ľ": 34071, "ãĤĨ": 34072, "Åĵ": 34073, "gili": 34074, "Dave": 34075, "Ġexhausting": 34076, "ÅĤug": 34077, "Ġschema": 34078, "μά": 34079, "cycl": 34080, "Ġautant": 34081, "Ġparcel": 34082, "Ġmateria": 34083, "ĠBerry": 34084, "ĠÑģами": 34085, "Ġextracted": 34086, "ĠSaying": 34087, "ismatic": 34088, "ĠпопÑĢоб": 34089, "Ġneuron": 34090, "graph": 34091, "ľë©´": 34092, "Ġenclosure": 34093, "ĠJohann": 34094, "Ġaftermath": 34095, "ÑĤоб": 34096, "Ġuży": 34097, "Ġsamp": 34098, "360": 34099, "ĠMei": 34100, "Ġtaco": 34101, "Ġreceptors": 34102, "Ġpunches": 34103, "ĠHoje": 34104, "ĠÙĩÙĨا": 34105, "=\"#": 34106, "ĠAngular": 34107, "Ġmusique": 34108, "Ġrol": 34109, "Ġñ": 34110, "sterreich": 34111, "Ġclam": 34112, "ĠTreasury": 34113, "chemical": 34114, "Ġapar": 34115, "Ġappend": 34116, "Ġforbid": 34117, "ĠHamburg": 34118, "аков": 34119, "Ġê¸Ī": 34120, "ilda": 34121, "Ġpreparations": 34122, "ĠmogÄħ": 34123, "Ġcamino": 34124, "Eric": 34125, "ĠBlind": 34126, "èĪĩ": 34127, "å¹´çļĦ": 34128, "ĠDiscovery": 34129, "ì¸ł": 34130, "çĪ¶": 34131, "Ġinterpreter": 34132, "Ġbred": 34133, "ĠPsalm": 34134, "Ġdefended": 34135, "ìī¬": 34136, "ĠErfahr": 34137, "ĠPeach": 34138, "Ġmoons": 34139, "ĠOst": 34140, "Ġspécial": 34141, "Ġarriver": 34142, "ĠWis": 34143, "uci": 34144, "Ġrobotics": 34145, "IVE": 34146, "Ġsiege": 34147, "arla": 34148, "Ġseparates": 34149, "ĠTC": 34150, "íı°": 34151, "quisite": 34152, "Ġparentheses": 34153, "ике": 34154, "ç«Ļ": 34155, "Ġtrous": 34156, "建": 34157, "ĠÑģилÑĮ": 34158, "Ġbeers": 34159, "ĠплаÑĤ": 34160, "ãģĻãģĶãģĦ": 34161, "Ġsola": 34162, "Ġdès": 34163, "mingham": 34164, "ikte": 34165, "Ġoops": 34166, "Ġtwitch": 34167, "å°ĩ": 34168, "ÏĪ": 34169, "ĠShouldn": 34170, "uvre": 34171, "Ġleer": 34172, "criptions": 34173, "Ġeyeshadow": 34174, "ĠGuo": 34175, "ĠPowell": 34176, "Ġsupuesto": 34177, "Ġana": 34178, "rals": 34179, "ĠMontreal": 34180, "Ġsurfing": 34181, "ĠÐŁÐµÑĢв": 34182, "×ŀ×ķ": 34183, "Ġmilliseconds": 34184, "Ġsuburbs": 34185, "Ġplaneta": 34186, "ÑĥÑĪка": 34187, "hrlich": 34188, "ĠHY": 34189, "ĠسÛĴ": 34190, "ĠMM": 34191, "ĠEff": 34192, "åı¯æĦĽ": 34193, "ĠHS": 34194, "anson": 34195, "Ġì§ģìłij": 34196, "Ġsuo": 34197, "Ġdeploying": 34198, "Ġkunt": 34199, "tering": 34200, "Ġerect": 34201, "ìŀ¥ìĿ´": 34202, "ĠìĿĮìĭĿ": 34203, "Ġspecimen": 34204, "!...": 34205, "æĪij說": 34206, "Ġligne": 34207, "Ġkonst": 34208, "adequ": 34209, "Ġìĥģíĥľ": 34210, "Ġaccessed": 34211, "ĠPole": 34212, "kill": 34213, "Ġë²Ħë": 34214, "Ġauthenticity": 34215, "Ġappelle": 34216, "ulle": 34217, "Ġrevision": 34218, "Ġgoats": 34219, "гли": 34220, "Ġpau": 34221, "ĠRanger": 34222, "ĠImag": 34223, "author": 34224, "Ġeve": 34225, "ĠMessenger": 34226, "Ġnay": 34227, "Ġwholes": 34228, "ätte": 34229, "Ġonwards": 34230, "ĠDepois": 34231, "ĠíijľíĺĦ": 34232, "ĠSARS": 34233, "Ġwszystkich": 34234, "Ġdestru": 34235, "umbing": 34236, "Ġcompatibility": 34237, "Ġmisinformation": 34238, "odore": 34239, "ĠFavor": 34240, "eko": 34241, "ıĮ": 34242, "waukee": 34243, "ĠTeaching": 34244, "ĠKO": 34245, "Ġbetting": 34246, "Ġquests": 34247, "Ġvivre": 34248, "ĠмÑĥзÑĭ": 34249, "Ġsaga": 34250, "Ġswell": 34251, "Ġgehe": 34252, "æĢİ麼樣": 34253, "ĠоÑĢганиз": 34254, "Ġgide": 34255, "ĠGross": 34256, "Ġdalej": 34257, "Ġclaws": 34258, "á»Ļc": 34259, "Ġprejudice": 34260, "Ġinsign": 34261, "ihood": 34262, "Ġpled": 34263, "Ġdónde": 34264, "ĠPolitical": 34265, "Ġpremises": 34266, "undert": 34267, "عت": 34268, "onnen": 34269, "Ġespaço": 34270, "Ġfé": 34271, "ĠHarrison": 34272, "ĠCensus": 34273, "Ġcardio": 34274, "Ġdiy": 34275, "Ġmilieu": 34276, "Ġjournée": 34277, "ĠRelease": 34278, "NIE": 34279, "ĠMuk": 34280, "idée": 34281, "á»įi": 34282, "Ġiçinde": 34283, "ŀĻ": 34284, "Ġresonate": 34285, "Ġmoles": 34286, "ĠFlying": 34287, "ĠGloria": 34288, "ĠPastor": 34289, "ĠArena": 34290, "好ä¸į好": 34291, "NON": 34292, "олов": 34293, "ĠallÃŃ": 34294, "omat": 34295, "ìĸ´ëıĦ": 34296, "ĠcaracterÃŃst": 34297, "Ġdeclining": 34298, "ÑĸÑı": 34299, "anco": 34300, "ĠInform": 34301, "Ġbargain": 34302, "Ġbushes": 34303, "ĠNaturally": 34304, "Ġrechts": 34305, "ĠTensor": 34306, "ĠPatricia": 34307, "Ġprincipio": 34308, "ĠMumbai": 34309, "Ġwomb": 34310, "Ġnostra": 34311, "Ġdilemma": 34312, "Ġirgendwann": 34313, "Ġ1964": 34314, "ĠenergÃŃa": 34315, "ĠнаÑĢ": 34316, "Ġsegregation": 34317, "ĠAthlet": 34318, "Ġ»,": 34319, "Ġyeni": 34320, "ĠSeit": 34321, "Ġvenom": 34322, "Ġdakika": 34323, "ĠëıĮë": 34324, "ĠÃīl": 34325, "Ġfus": 34326, "ĠMog": 34327, "¦½ëĭĪëĭ¤": 34328, "Ġremar": 34329, "ĠTeddy": 34330, "Ġbreasts": 34331, "icans": 34332, "æĶ¶çľĭ": 34333, "kap": 34334, "ĠhÆ¡n": 34335, "ĠJP": 34336, "ãĥ³ãĤ¿": 34337, "Ġresurrect": 34338, "ĠìĿ¸ë": 34339, "herical": 34340, "Ġfotograf": 34341, "ĠJosé": 34342, "Ġlivelihood": 34343, "Ġbibli": 34344, "teri": 34345, "Ġvorstellen": 34346, "ĠAAA": 34347, "Ġassessing": 34348, "YA": 34349, "Ġsplend": 34350, "Ġexcav": 34351, "Ġbaptism": 34352, "yll": 34353, "wow": 34354, "Mac": 34355, "Ġplastics": 34356, "teokbokki": 34357, "Ġintéressant": 34358, "Ġcommanded": 34359, "Ġfamously": 34360, "ĠÐĺли": 34361, "ĠManuel": 34362, "Ġsouthwest": 34363, "Ġdeformation": 34364, "ÃŃculo": 34365, "ĠнаÑħодиÑĤÑģÑı": 34366, "ĠPatter": 34367, "degree": 34368, "ĠczÄĻsto": 34369, "\"-": 34370, "Ġìħĭ": 34371, "Ġmanger": 34372, "ĠTrustee": 34373, "Ģ리": 34374, "Ġpuntos": 34375, "ivable": 34376, "Ġvolatile": 34377, "ĠëĬIJ": 34378, "Ġinstability": 34379, "Ġciel": 34380, "ciÄħ": 34381, "Ġpurity": 34382, "ноÑģÑĤ": 34383, "Sil": 34384, "edar": 34385, "åĻ¨": 34386, "NOUNCER": 34387, "Ġspelled": 34388, "GER": 34389, "Ġsanctuary": 34390, "Ġaccelerating": 34391, "Ġscout": 34392, "ĠпÑĢев": 34393, "fahren": 34394, "ãģĵãģ¡ãĤī": 34395, "ĠëĤĺìĺ¨": 34396, "ĠpoczÄħt": 34397, "ĠMeu": 34398, "kaar": 34399, "³´ê³ł": 34400, "akra": 34401, "Down": 34402, "ĠÃĦr": 34403, "ĠElite": 34404, "Ġallons": 34405, "Ġmayonnaise": 34406, "ĠSustain": 34407, "prisingly": 34408, "Ġsupervis": 34409, "Ġê·¸ëłĩì£ł": 34410, "Ġunemployed": 34411, "Ġfreshly": 34412, "Ġ×ŀ×¢": 34413, "ĠDh": 34414, "Ġtackling": 34415, "Ġogr": 34416, "Ġì´Īë": 34417, "ãĤĪãĤį": 34418, "Ġloft": 34419, "arah": 34420, "ĠAirl": 34421, "ĠDir": 34422, "ĠÐľÐ¾Ð¶Ð½Ð¾": 34423, "Ġbooking": 34424, "ĠCRA": 34425, "Ġhttps": 34426, "Ġchoke": 34427, "Ġgown": 34428, "Ġnoite": 34429, "Ġzac": 34430, "istol": 34431, "Ġsecre": 34432, "Ġresembles": 34433, "Ġcuad": 34434, "ìĤ¬ê°Ģ": 34435, "show": 34436, "Ġblanc": 34437, "Ġagu": 34438, "ĠPrint": 34439, "asted": 34440, "ĠWeather": 34441, "ipl": 34442, "Ġobscure": 34443, "Ġconte": 34444, "oughs": 34445, ");": 34446, "ĠDame": 34447, "ä¸Ģ缴": 34448, "Ġclarification": 34449, "Ġintimacy": 34450, "Ġuphold": 34451, "ĠMirror": 34452, "Ġwagon": 34453, "xide": 34454, "Ġclog": 34455, "apper": 34456, "ĠImmediately": 34457, "úde": 34458, "Ġtouchdown": 34459, "Ġrooft": 34460, "аÑĪа": 34461, "Ġçıkt": 34462, "Ġlaisser": 34463, "ĠUnreal": 34464, "ensitive": 34465, "Ġ123": 34466, "Ġplaster": 34467, "Ġducks": 34468, "Ġetme": 34469, "Ġbishop": 34470, "brevi": 34471, "Ġbic": 34472, "ä¸ĭåİ»": 34473, "Ġruntime": 34474, "Ġambitions": 34475, "маÑĤ": 34476, "ĠWein": 34477, "ĠMari": 34478, "ĠíĬ¸ë": 34479, "Ġresolver": 34480, "ĠngÃły": 34481, "ĠRise": 34482, "ãĤĪãģĨãģ«": 34483, "ĠCrus": 34484, "Ġmerchandise": 34485, "Ġeli": 34486, "Ġstatewide": 34487, "Ġowl": 34488, "éģł": 34489, "æĶ¹": 34490, "Ġtwisting": 34491, "Ġcontaminated": 34492, "ĠCommerce": 34493, "hythm": 34494, "ĠÃĪ": 34495, "Ġìĭ¤ë": 34496, "Ġmusste": 34497, "uir": 34498, "Ġsums": 34499, "ĠSomewhere": 34500, "ãĥİ": 34501, "Ġkami": 34502, "Ġaired": 34503, "ĠANDREW": 34504, "Ġêº": 34505, "Ġviendo": 34506, "Ġantibody": 34507, "Ġabsolument": 34508, "Ġprotesters": 34509, "ĠQuébec": 34510, "stadt": 34511, "Shaun": 34512, "Ġchambers": 34513, "ĠWear": 34514, "ĠEffects": 34515, "Ġhazards": 34516, "Ġnei": 34517, "Ġcorazón": 34518, "Ġá¼": 34519, "ĠSG": 34520, "Ķ©": 34521, "ĠìĹŃìĭľ": 34522, "Ġcomfy": 34523, "ĠCody": 34524, "Ġpensando": 34525, "Ġganska": 34526, "ĠAcross": 34527, "öllig": 34528, "abyte": 34529, "Ġwedge": 34530, "Ġkalian": 34531, "Ġsigue": 34532, "endes": 34533, "ĠGroÃŁ": 34534, "Ġutiliser": 34535, "Ġflown": 34536, "аниÑİ": 34537, "Ġlevar": 34538, "restrial": 34539, "Ġillustrations": 34540, "Ġaslında": 34541, "BLEEP": 34542, "ĠдоÑģÑĤ": 34543, "Ġturret": 34544, "Ġsuitcase": 34545, "ziÄĻki": 34546, "Ġsketches": 34547, "Ġacred": 34548, "ĠRei": 34549, "Ġtsun": 34550, "ĠSag": 34551, "Ġthirds": 34552, "ĠKIRBY": 34553, "rai": 34554, "Ġhumanos": 34555, "Ġrecommends": 34556, "Ġextraordinarily": 34557, "Ġcommencement": 34558, "KN": 34559, "opez": 34560, "Ġ×ijש": 34561, "Ġlethal": 34562, "ĠEstamos": 34563, "Ġinspector": 34564, "ĠSeok": 34565, "eun": 34566, "Ġoffshore": 34567, "Ġgettin": 34568, "years": 34569, "ĠSilence": 34570, "ĠNatur": 34571, "upun": 34572, "Ġtrzy": 34573, "Ġnoget": 34574, "Ġhamburger": 34575, "ĠPraise": 34576, "énd": 34577, "Ġ1971": 34578, "ylie": 34579, "krit": 34580, "ĠìĥĿê°ģìĿ´": 34581, "çļ®": 34582, "Ġmomentos": 34583, "Ġesté": 34584, "Ġdissemin": 34585, "Ġgigs": 34586, "Ġdesaf": 34587, "Ġavis": 34588, "ĠZoo": 34589, "ĠìķĬìĿĢ": 34590, "häng": 34591, "åı¥": 34592, "hake": 34593, "ĠBism": 34594, "Ġrethink": 34595, "ĠMalcolm": 34596, "Ġidentifies": 34597, "lower": 34598, "ixel": 34599, "ĠtvÃ¥": 34600, "ked": 34601, "ierz": 34602, "Ġöffentlich": 34603, "Ġproclaim": 34604, "soon": 34605, "lol": 34606, "Ġloi": 34607, "Ġbitten": 34608, "rollo": 34609, "Ġsermon": 34610, "Ġesqu": 34611, "Ġjackets": 34612, "Ġgráfic": 34613, "ĠпоказÑĭв": 34614, "Ġcabeza": 34615, "chodzi": 34616, "Ġpelvis": 34617, "Ġnostalgia": 34618, "Ġbrew": 34619, "Ġshortcuts": 34620, "ĠAdemás": 34621, "Ġsuperficial": 34622, "åħ©åĢĭ": 34623, "Ġboca": 34624, "ĠæĪijæĺ¯": 34625, "imentos": 34626, "åĽłä¸º": 34627, "Ġsprouts": 34628, "é£Ľ": 34629, "ĠJonas": 34630, "ĠFlorence": 34631, "static": 34632, "daughter": 34633, "*)": 34634, "ÅĤby": 34635, "fashion": 34636, "ĠGinger": 34637, "Ġ매ë": 34638, "Ġhustle": 34639, "utos": 34640, "ĠÑĤÑıж": 34641, "ĠLös": 34642, "ש×Ļ×Ŀ": 34643, "anych": 34644, "tuber": 34645, "Ġtidy": 34646, "Ġfrontal": 34647, "Ġwhiskey": 34648, "Ġhumid": 34649, "ĠÎŁ": 34650, "Ġridge": 34651, "Ġmarin": 34652, "Ġbientôt": 34653, "ĠCarrie": 34654, "chw": 34655, "Ġtahun": 34656, "ĠErgeb": 34657, "FR": 34658, "Ġìłķë¶Ģ": 34659, "ĠSoldier": 34660, "Ġenlightenment": 34661, "Ġexamining": 34662, "ĠNotre": 34663, "Ġeram": 34664, "ĠSunny": 34665, "Ġlayered": 34666, "ĠDazu": 34667, "rades": 34668, "好åIJĥ": 34669, "ĠнаÑĪей": 34670, "Ġtimber": 34671, "Ġmanners": 34672, "ĠBirmingham": 34673, "Ġminiature": 34674, "ometers": 34675, "Ġfiller": 34676, "ĠRip": 34677, "ĠKomb": 34678, "owner": 34679, "ì¿": 34680, "idian": 34681, "Ġdemás": 34682, "ĠÙĪت": 34683, "Ġprecautions": 34684, "Ġgoverno": 34685, "zelf": 34686, "ĠComplete": 34687, "å¸ĥ": 34688, "ĠPhantom": 34689, "ãģ¾ãģļ": 34690, "Ġнез": 34691, "ĠкаÑĢÑĤ": 34692, "ĠAntwort": 34693, "ĠPfizer": 34694, "ĠFranco": 34695, "ĠwÅĤ": 34696, "Ġfrig": 34697, "esper": 34698, "Ġkale": 34699, "Ġfilmmaker": 34700, "Ġkurt": 34701, "Ġinvalid": 34702, "å±Ģ": 34703, 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34819, "jaz": 34820, "plain": 34821, "Ġwrinkles": 34822, "Ġreais": 34823, "Ġpaljon": 34824, "Ġempowerment": 34825, "Ġattendees": 34826, "ppa": 34827, "Ġneden": 34828, "онÑĭ": 34829, "Ġtimeframe": 34830, "ĠCherry": 34831, "Ġidée": 34832, "Ġgag": 34833, "Ġdonkey": 34834, "Ġông": 34835, "ĠHare": 34836, "éļĽ": 34837, "ĠKara": 34838, "Ġacompan": 34839, "places": 34840, "imientos": 34841, "ĠHamm": 34842, "би": 34843, "uben": 34844, "iliyor": 34845, "Ġthirst": 34846, "Ġkry": 34847, "ĠGeorgetown": 34848, "׳×Ķ": 34849, "Ġorch": 34850, "Ġheartbeat": 34851, "Ġtransformations": 34852, "estones": 34853, "ĠKH": 34854, "Ġcartoons": 34855, "Ġanci": 34856, "Ġworthless": 34857, "Ġtailored": 34858, "pu": 34859, "Americans": 34860, "Ġpiles": 34861, "ĠMonkey": 34862, "Ġbasin": 34863, "ĠTemper": 34864, "ĠPaint": 34865, "Ġpunching": 34866, "Ġbaik": 34867, "ĠOakland": 34868, "vre": 34869, "ÅŁallah": 34870, "ydd": 34871, "Ġcasually": 34872, "odu": 34873, "Ġcoded": 34874, "ĠNorwegian": 34875, "ĠVince": 34876, 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"ÃŃveis": 34934, "acun": 34935, "Ġretaining": 34936, "Ġcontradiction": 34937, "ĠADAM": 34938, "COM": 34939, "Bryan": 34940, "ĠMonsieur": 34941, "Ġadapting": 34942, "ШÐIJ": 34943, "ĠScr": 34944, "ändert": 34945, "Ġplaus": 34946, "ä»Ĭ天çļĦ": 34947, "Ġonset": 34948, "Ġassistants": 34949, "Ġvalves": 34950, "Ġscatter": 34951, "ĠRust": 34952, "awia": 34953, "Ġreadiness": 34954, "Ġpais": 34955, "Ġbible": 34956, "Ġambiente": 34957, "ĠамеÑĢик": 34958, "Ġuncond": 34959, "Ġkalk": 34960, "åĬ¨": 34961, "Ġmoc": 34962, "unn": 34963, "Ġactu": 34964, "Ġhumming": 34965, "issimo": 34966, "ĠPatrol": 34967, "gow": 34968, "ãĥ¤": 34969, "ĠTHEY": 34970, "ĠBoden": 34971, "ĠBie": 34972, "Ġreel": 34973, "ĠÑĥÑģлов": 34974, "Ġendeavor": 34975, "ĠPeriod": 34976, "ustomed": 34977, "mals": 34978, "alon": 34979, "Box": 34980, "ĠÏĥαÏĤ": 34981, "Ġomdat": 34982, "Ġaltre": 34983, "ĠHeh": 34984, "kad": 34985, "Ġprotector": 34986, "Ġdominance": 34987, "odynamic": 34988, "Ġcommunicated": 34989, "kö": 34990, "Ġpredecessor": 34991, "ĠLuk": 34992, "ĠFlower": 34993, "Ġãģ©": 34994, "poque": 34995, "ÑĤиÑĢов": 34996, "Ġretrospect": 34997, "Ġdecisive": 34998, "Ġexempel": 34999, "{\\": 35000, "ĠRück": 35001, "rite": 35002, "ĠZeus": 35003, "Ġcalorie": 35004, "Ġattractions": 35005, "ĠHinter": 35006, "Ġuhm": 35007, "ĠíĮIJ": 35008, "Ġrulers": 35009, "Ġdiscouraged": 35010, "Ġacontecer": 35011, "Ġaccents": 35012, "ĠOptim": 35013, "ĠAlg": 35014, "kids": 35015, "2021": 35016, "ĠLindsay": 35017, "Ġfilmmakers": 35018, "prowad": 35019, "Ġterug": 35020, "ëĭ´": 35021, "ĠSommer": 35022, "2018": 35023, "Ġborrowing": 35024, "ĠTransfer": 35025, "ноп": 35026, "arias": 35027, "Ġheadphone": 35028, "ì¼ľ": 35029, "Ġtranslating": 35030, "Ġaufge": 35031, "à®ªà®Ł": 35032, "weis": 35033, "avant": 35034, "paid": 35035, "baby": 35036, "Ġtoughest": 35037, "Ġrepeats": 35038, "ĠTeresa": 35039, "Lord": 35040, "Ġacabar": 35041, "ĠRide": 35042, "dir": 35043, "Ġleng": 35044, "Ġdwa": 35045, "Ġheadaches": 35046, "Ġnữa": 35047, "ĠнаÑģÑĤоÑıÑī": 35048, "Ġboils": 35049, "Ġlonging": 35050, "rias": 35051, "ório": 35052, "ĠParadise": 35053, "ĠSeñor": 35054, "erdem": 35055, "Ġreinst": 35056, "Ġsalaries": 35057, "Ġinsecurity": 35058, "ÅĤoÅĽci": 35059, "ĠабÑģолÑİÑĤно": 35060, "inken": 35061, "ĠEddy": 35062, "udos": 35063, "Ġdummy": 35064, "Ðļак": 35065, "six": 35066, "Ġinbox": 35067, "ẩ": 35068, "People": 35069, "á»ĵng": 35070, "Ġorganizers": 35071, "find": 35072, "Ġül": 35073, "ĠCOM": 35074, "ża": 35075, "weile": 35076, "Commentary": 35077, "íĬ¸ë¥¼": 35078, "ĠMittel": 35079, "kus": 35080, "èĽĭ": 35081, "न": 35082, "iral": 35083, "Ġgarment": 35084, "ικά": 35085, "Ġstool": 35086, "payers": 35087, "Ġshimmer": 35088, "ĠOllie": 35089, "ĠJeżeli": 35090, "è¿ĺæľī": 35091, "Ġ1977": 35092, "Ġjeux": 35093, "Ġextinct": 35094, "ĠTransportation": 35095, "ĠMaker": 35096, "Ġjohn": 35097, "Ġrichest": 35098, "Ġtraumat": 35099, "Ġliegen": 35100, "´ë¥¼": 35101, "è¿ĻéĩĮ": 35102, "Ġunrest": 35103, "ĠStraw": 35104, "æĭľæĭľ": 35105, "Ġcoma": 35106, "ĠKristen": 35107, "ĠÐļонеÑĩно": 35108, "ĠBryce": 35109, "ĠÑıкÑĸ": 35110, "Ġpearls": 35111, "ĠпонимаÑİ": 35112, "Ġadditions": 35113, "Ġasympt": 35114, "ĠменÑĮÑĪе": 35115, "Ġscans": 35116, "Child": 35117, "ĠHide": 35118, "кÑĥÑİ": 35119, "etas": 35120, "Ġdank": 35121, "Ġpleas": 35122, "Ġessays": 35123, "Ġjets": 35124, "åħĴ": 35125, "Ġвед": 35126, "Ġpositives": 35127, "hof": 35128, "-)": 35129, "zzo": 35130, "Ġstarters": 35131, "Ġsmiled": 35132, "Ġ1944": 35133, "quiera": 35134, "Ġrok": 35135, "Ġpuesto": 35136, "Nico": 35137, "Ġsimulations": 35138, "Ġà¶": 35139, "Ġintrigued": 35140, "ĠOverwatch": 35141, "åĸĤ": 35142, "sigh": 35143, "bai": 35144, "Ġë§IJê³ł": 35145, "idé": 35146, "Ġcrabs": 35147, "áºŃp": 35148, "ĠIraqi": 35149, "ìĿ´ë¥¼": 35150, "ÑĤÑı": 35151, "ĠSophia": 35152, "ĠDNS": 35153, "Ġönemli": 35154, "ĠLuo": 35155, "Ŀ¤": 35156, "ĠCounsel": 35157, "ligen": 35158, "анÑĮÑĪе": 35159, "Ġtrumpet": 35160, "Ġdapat": 35161, "ĠJM": 35162, "ĠEVERY": 35163, "Ġå°įä¸įå°į": 35164, "夢": 35165, "ĠLayer": 35166, "Ġcô": 35167, "нал": 35168, "ĠJoo": 35169, "ĠHack": 35170, "Ġsunt": 35171, "ĠLeonard": 35172, "ĠFirebase": 35173, "änger": 35174, "Ġexploding": 35175, "voy": 35176, "Ġì¦IJ": 35177, "ĠÑģеÑĢÑĮ": 35178, "Ġseverity": 35179, "Ġbestimm": 35180, "çµIJæŀľ": 35181, "Ġtiring": 35182, "Ġprocurement": 35183, "Ġdiplomacy": 35184, "Ġdecorative": 35185, "ĠÙĬا": 35186, "Ġpenetration": 35187, "Õ«": 35188, "Ġoutright": 35189, "ENE": 35190, "ĠUni": 35191, "odles": 35192, "Ġzeros": 35193, "Ġdelightful": 35194, "jm": 35195, "Ġdopo": 35196, "没äºĭ": 35197, "Ġpositivity": 35198, "ĠVISTA": 35199, "ĠResource": 35200, "íĥĢë": 35201, "ÑĪие": 35202, "Carl": 35203, "Ġpiping": 35204, "Ġchopping": 35205, "ĠGanze": 35206, "üss": 35207, "ĠAo": 35208, "Ġshattered": 35209, "ĠDetective": 35210, "Ġundoubtedly": 35211, "Ġhalluc": 35212, "Ġench": 35213, "ÑĭÑĩно": 35214, "ÑĥлÑıÑĢ": 35215, "isesti": 35216, "Ġpedals": 35217, "Ġdurum": 35218, "¤íĶ": 35219, "laimer": 35220, "Ġpropre": 35221, "Cu": 35222, "Ġtranslator": 35223, "ĠcaÅĤ": 35224, "Ġ그걸": 35225, "ĠcaÅĤy": 35226, "UA": 35227, "Ġrevised": 35228, "Ġподоб": 35229, "ĠArticle": 35230, "ĠHaiti": 35231, "ĠÃĵ": 35232, "ĠCtrl": 35233, "Ġrozm": 35234, "lait": 35235, "Ġletzte": 35236, "ispering": 35237, "display": 35238, "Ġaluminium": 35239, "Ġpalabras": 35240, "Ġconocer": 35241, "Ġzitten": 35242, "Ġdirig": 35243, "åıªæľī": 35244, "Ġbrainstorm": 35245, "Ġwifi": 35246, "ĠParticip": 35247, "Ġviewpoint": 35248, "ĠQuan": 35249, "Ġhierarch": 35250, "Welcome": 35251, "対": 35252, "Ġoffen": 35253, "ĠRecovery": 35254, "gano": 35255, "Would": 35256, "Ġrepro": 35257, "Ġperceptions": 35258, "Ġdemasi": 35259, "ĠBangladesh": 35260, "ĠIncredible": 35261, "Ġletzt": 35262, "Ġbehaving": 35263, "Ġastonishing": 35264, "ĠâĨ": 35265, "ĠëĤ¨ìŀIJ": 35266, "èµ°äºĨ": 35267, "ãĥĶ": 35268, "ĠGORDON": 35269, "CAR": 35270, "?!\"": 35271, "ĠPrest": 35272, "Ġë§ŀìķĦìļĶ": 35273, "Ġtand": 35274, "Ġlash": 35275, "çĬ": 35276, "ificant": 35277, "Ġintoler": 35278, "ĠгеÑĢо": 35279, "Ġteu": 35280, "aso": 35281, "ĠÑģовеÑĤ": 35282, "Ġtravelers": 35283, "ĠSynd": 35284, "ĠвеÑĢÑģ": 35285, "Fonda": 35286, "adı": 35287, "Ġtranscription": 35288, "Ġtitanium": 35289, "Ġtwists": 35290, "Ġgearbox": 35291, "ensation": 35292, "fat": 35293, "Coll": 35294, "ĠCommonwealth": 35295, "zon": 35296, "ĠPolizei": 35297, "ĠAPPLAUSE": 35298, "fry": 35299, "ĠJuda": 35300, "esteem": 35301, "Ġsock": 35302, "ĠJugend": 35303, "ĠкÑģÑĤаÑĤи": 35304, "ĠDro": 35305, "Ġprochaine": 35306, "ãĥ¼ãĥ«": 35307, "Ġliksom": 35308, "ĠEnergie": 35309, "ĠMarina": 35310, "Ġ230": 35311, "Ġê°ĢìĦľ": 35312, "umping": 35313, "Ġlone": 35314, "ç´ļ": 35315, "Ġfonts": 35316, "Ġbusinessman": 35317, "Ġply": 35318, "Ġdoe": 35319, "grid": 35320, "ĠMilwaukee": 35321, "ĠEden": 35322, "!\".": 35323, "ĠÛĮÛģ": 35324, "ogens": 35325, "Ġteaser": 35326, "Ġquién": 35327, "Ġincentiv": 35328, "govern": 35329, "Ġchildcare": 35330, "Ġsneakers": 35331, "Ġimprisoned": 35332, "®": 35333, "иÑĤеÑģÑĮ": 35334, "anbul": 35335, "Ġregain": 35336, "Ġtranquil": 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36203, "Watch": 36204, "Ġavatar": 36205, "ĠPey": 36206, "urun": 36207, "Ġsenza": 36208, "Ġì§ĢìĹŃ": 36209, "ĠNatomiast": 36210, "Ġemergence": 36211, "rays": 36212, "Ġcrafted": 36213, "gary": 36214, "ãģłãģij": 36215, "üng": 36216, "-\"": 36217, "Ġhacked": 36218, "Ġstray": 36219, "encie": 36220, "emo": 36221, "Ġcomen": 36222, "ĠKız": 36223, "ĠJasmine": 36224, "ĠHindi": 36225, "manas": 36226, "Ġinfinitely": 36227, "emon": 36228, "ìĿ¸ëį°ìļĶ": 36229, "jak": 36230, "Ġroaring": 36231, "érique": 36232, "sweise": 36233, "ĠRolex": 36234, "åł±å°İ": 36235, "ĠStuart": 36236, "bnb": 36237, "Ġdiagnose": 36238, "Ġcoherent": 36239, "ĠMJ": 36240, "æºĸåĤĻ": 36241, "Ġpike": 36242, "lav": 36243, "Ġorchestral": 36244, "аÑģÑĤи": 36245, "Ġterminar": 36246, "Ġgatherings": 36247, "Ġcompliant": 36248, "Ġupgrading": 36249, "Ġregulator": 36250, "Ġlanç": 36251, "éĢ£": 36252, "Ġmerchants": 36253, "tawa": 36254, "Ġmonitored": 36255, "Ġrendre": 36256, "两": 36257, "Ġunterwegs": 36258, "anguard": 36259, "gard": 36260, 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"Ġflank": 36318, "ĠGol": 36319, "hã": 36320, "Ġpéri": 36321, "Ġhonorable": 36322, "ĠBreathe": 36323, "scenes": 36324, "Ġobviamente": 36325, "икÑģ": 36326, "Ġש×ŀ×": 36327, "Ġsmoothie": 36328, "ŀĪë": 36329, "Ġdime": 36330, "ĠíĸĪìĸ´ìļĶ": 36331, "Ġappel": 36332, "ĠCatholics": 36333, "Ġsingles": 36334, "Ġlaten": 36335, "Ġçünkü": 36336, "ĠVader": 36337, "æıĽ": 36338, "Ġvardı": 36339, "ĠIstanbul": 36340, "gré": 36341, "ĠElsa": 36342, "ël": 36343, "Ġinvece": 36344, "Ġcrane": 36345, "Ġobe": 36346, "ĠShark": 36347, "Ġsmack": 36348, "Ġrestoring": 36349, ".\\": 36350, "Ġë¹łë": 36351, "Ġfaded": 36352, "umbers": 36353, "Singing": 36354, "Ġdepressing": 36355, "thest": 36356, "ĠWahr": 36357, "Ġmultitude": 36358, "ÑĢавÑģÑĤвÑĥйÑĤе": 36359, "rijk": 36360, "eka": 36361, "Ġcompletes": 36362, "ĠWells": 36363, "Ġroy": 36364, "ĠPray": 36365, "ĠKalau": 36366, "izin": 36367, "iaÅĤem": 36368, "Ġlocom": 36369, "ĠNashville": 36370, "ĠPentagon": 36371, "미": 36372, "ĠNEW": 36373, "ÄħÄĩ": 36374, "ÃŃss": 36375, "Ġmarrying": 36376, "Ġfeud": 36377, "íĻķ": 36378, "æĢ¥": 36379, ")!": 36380, "ĠOperations": 36381, "ÑĥÑĶ": 36382, "Ġmoje": 36383, "Ġinstructed": 36384, "ĠëĪĦ구": 36385, "Ġ×Ķ×Ĵ": 36386, "ĠпомоÑīÑĮÑİ": 36387, "Ġsabia": 36388, "ìķĺìĸ´ìļĶ": 36389, "plane": 36390, "pri": 36391, "ĠполноÑģÑĤÑĮÑİ": 36392, "ĠKitty": 36393, "Ġpróprio": 36394, "edere": 36395, "Ġinteresante": 36396, "Ġде": 36397, "Ġcondensed": 36398, "Ġavent": 36399, "TOR": 36400, "Ġgreasy": 36401, "ARK": 36402, "orta": 36403, "AJ": 36404, "Ġdisreg": 36405, "Ġcorrections": 36406, "Ġstero": 36407, "Ġinfluenza": 36408, "Ġdesses": 36409, "Ġballots": 36410, "Ġmeget": 36411, "Ġmafia": 36412, "Ġböl": 36413, "nost": 36414, "ĠÑģÑĤаÑĤÑĮ": 36415, "Ġresponder": 36416, "Ġhinten": 36417, "grav": 36418, "à¸Ńะ": 36419, "ynchron": 36420, "Ġviens": 36421, "Ġsamo": 36422, "Ġdt": 36423, "pannt": 36424, "ĠÅĽwiat": 36425, "ĠзапиÑģ": 36426, "Ġmerged": 36427, "Ġkep": 36428, "Ġmisleading": 36429, "Ġdigamos": 36430, "Ġammon": 36431, "è¾Ľ": 36432, "chet": 36433, "Ġê°Ģìł¸": 36434, "Ġuni": 36435, "ĠëIJĺëĬĶëį°": 36436, "ĠнапÑĢав": 36437, "ĠкоÑĤоÑĢого": 36438, "Ġanimate": 36439, "×ķ×IJ×": 36440, "еÑĢв": 36441, "Ġminced": 36442, "Ġkaum": 36443, "ãģĤãģģ": 36444, "ÏĢε": 36445, "лег": 36446, "existing": 36447, "Ġplataform": 36448, "ĠKRIS": 36449, "ìĽł": 36450, "ĠFamilien": 36451, "ĠLibya": 36452, "Ġbiodiversity": 36453, "Ġidiots": 36454, "irdi": 36455, "Ġszyb": 36456, "ĠRolling": 36457, "ücht": 36458, "ĠÑĥдив": 36459, "ÑģÑĥд": 36460, "Ġrealizar": 36461, "Ġcanned": 36462, "ĠÑĢан": 36463, "Ġmetabolic": 36464, "ĠBeef": 36465, "Ġkilka": 36466, "лÑİÑģ": 36467, "Ġregistry": 36468, "моÑĤÑĢиÑĤе": 36469, "Ġvielä": 36470, "Ġodc": 36471, "Ġcondemned": 36472, "æ©ĭ": 36473, "fal": 36474, "ĠDil": 36475, "woÅĽci": 36476, "Aw": 36477, "Ġstatistically": 36478, "Ġsogen": 36479, "ĠBETH": 36480, "Ġshaving": 36481, "幸": 36482, "ocal": 36483, "ĠFunny": 36484, "Ġpeacefully": 36485, "Ġaddictive": 36486, "ĠInsert": 36487, "lauf": 36488, "Ġexperiencia": 36489, "é¦ĸåħĪ": 36490, "иÑĤелÑı": 36491, "ÃŃgen": 36492, "ágina": 36493, "Ġabdomen": 36494, "íķľëĭ¤": 36495, "icus": 36496, "imana": 36497, "ìį¨": 36498, "arching": 36499, "Ġkonkret": 36500, "ìķĺë": 36501, "ека": 36502, "oufl": 36503, "ivel": 36504, "Ġnude": 36505, "ètres": 36506, "Ġmonsieur": 36507, "Ġclash": 36508, "Ġtherapists": 36509, "Ġcubed": 36510, "Ġretrouver": 36511, "Ġwaveform": 36512, "Ġpotem": 36513, "ĠFormer": 36514, "isión": 36515, "åºľ": 36516, "Ġ×IJ×Ŀ": 36517, "undos": 36518, "ĠMeinung": 36519, "صÙĦ": 36520, "ĠJude": 36521, "ĠnÃ¥r": 36522, "ĠLeonardo": 36523, "ĠCristo": 36524, "ĠGOT": 36525, "ÑģÑĤÑĢÑĥк": 36526, "LAN": 36527, "ĠgÃ¥ng": 36528, "Ġdéb": 36529, "ĠFrankfurt": 36530, "Ġcrappy": 36531, "Ġlil": 36532, "année": 36533, "ĠмеÑģÑĤе": 36534, "RET": 36535, "ĠNer": 36536, "ĠCOSTA": 36537, "Ġjedem": 36538, "Ġcurtains": 36539, "Ġiterations": 36540, "Ġunav": 36541, "Ġplaque": 36542, "orum": 36543, "Ġζ": 36544, "Ġnúmeros": 36545, "Ġdesap": 36546, "²½": 36547, "Ġcompiled": 36548, "Ġrefle": 36549, "Ġrankings": 36550, "Ġrepaired": 36551, "ĠÐĿапÑĢ": 36552, "Ġdownloads": 36553, "Ġarmour": 36554, "Ġ×Ļ×ķתר": 36555, "Ġlongevity": 36556, "ĠTONER": 36557, "ĠкомменÑĤаÑĢ": 36558, "Ġczego": 36559, "Ġnotify": 36560, "Ġairports": 36561, "Ġenduring": 36562, "lette": 36563, "Ġapparat": 36564, "Ġhabil": 36565, "á»ĩc": 36566, "nad": 36567, "ICO": 36568, "ĠBrah": 36569, "Ġsegún": 36570, "Ġgovernors": 36571, "kaha": 36572, "ĠSchluss": 36573, "Ġodpowied": 36574, "irting": 36575, "Ġrempl": 36576, "ĠAboriginal": 36577, "identally": 36578, "Ġenhancing": 36579, "licting": 36580, "ĠHawaiian": 36581, "Ġstriving": 36582, "ĠNiet": 36583, "Ġznaczy": 36584, "Ġobedience": 36585, "ĠnÃ¥got": 36586, "Ġexpired": 36587, "Ġ1918": 36588, "presented": 36589, "Ġprowad": 36590, "ĠTerr": 36591, "ĠPrinceton": 36592, "Ġmorgen": 36593, "Ġattracting": 36594, "ĠSigma": 36595, "igner": 36596, "ĠRechts": 36597, "ĠPeki": 36598, "Ġmethy": 36599, "Ġhamm": 36600, "Ġdireito": 36601, "Ġdelegation": 36602, "иваÑİÑĤ": 36603, "Ġgin": 36604, "Young": 36605, "Ġdependencies": 36606, "ĠBradley": 36607, "buds": 36608, "Ġfis": 36609, "Ġpytanie": 36610, "Ġinterconnected": 36611, "Ġembaixo": 36612, "ĠSas": 36613, "Ġruh": 36614, "ĠSicht": 36615, "Sur": 36616, "Ġsuperb": 36617, "ĠSabbath": 36618, "ĠDanger": 36619, "kol": 36620, "Ġhou": 36621, "supp": 36622, "ĠNacional": 36623, "Ġsuccession": 36624, "Ġvá": 36625, "ĠMaÃŁnahmen": 36626, "ĠJessie": 36627, "ĠIdaho": 36628, "forest": 36629, "ħĺ": 36630, "Ġ×ŀ×ĵ": 36631, "ĠØ£ÙĬ": 36632, "Ġsweetheart": 36633, "Ġneatly": 36634, "ĠEvangel": 36635, "곡": 36636, "ĠSuite": 36637, "ública": 36638, "ĠÑĥли": 36639, "ĠAnnouncer": 36640, "ligh": 36641, "Ġsensations": 36642, "Ġshelters": 36643, "Ġhart": 36644, "Ġsqueezing": 36645, "ĠRivers": 36646, "ĠCooking": 36647, "ì±ħ": 36648, "personal": 36649, "Ġmanos": 36650, "ÑijÑĤÑģÑı": 36651, "wij": 36652, "Ġgogg": 36653, "ĠMilli": 36654, "ĠFP": 36655, "ünst": 36656, "ĠLS": 36657, "Ġspraying": 36658, "Ġfaux": 36659, "Ġautograph": 36660, "ologic": 36661, "Ġtorment": 36662, "Ġencrypted": 36663, "á»ħ": 36664, "Ġestre": 36665, "ç¹¼": 36666, "à±": 36667, "Ġstumbled": 36668, "Ġaider": 36669, "Ġsaben": 36670, "xter": 36671, "ĠCities": 36672, "ĠTürk": 36673, "ëĭ¥": 36674, "chine": 36675, "Ġtopping": 36676, "Ġpoisoned": 36677, "ĠRomania": 36678, "×ĵ×Ļ": 36679, "Ģë¡ľ": 36680, "ĠпоÑĢÑıд": 36681, "Ġchirping": 36682, "ĠìĻĦë": 36683, "×ij×¢": 36684, "Ġcuanto": 36685, "Ġdonating": 36686, "ĠRegent": 36687, "ĠBeruf": 36688, "Ġdistracting": 36689, "Ġstamina": 36690, "ĠDarren": 36691, "Ġì¶ķ": 36692, "lists": 36693, "dal": 36694, "chuss": 36695, "Ġeconomist": 36696, "ãģĪãĥ¼": 36697, "orgt": 36698, "Ġistiyorum": 36699, "è¿Ľ": 36700, "ĠSurprise": 36701, "ĠHao": 36702, "Ġìµľê³ł": 36703, "ĠGW": 36704, "ĠInner": 36705, "Ġquieren": 36706, "Ġminded": 36707, "Ġsupercomputer": 36708, "Ġdiagrams": 36709, "íĬľë": 36710, "ê²łìĸ´": 36711, "ĠобÑĬÑıÑģ": 36712, "Ġestaban": 36713, "Ġdestroys": 36714, "ĠBreaking": 36715, "ĠkarÄ±ÅŁ": 36716, "Ġrebuilding": 36717, "ľëĮĢ": 36718, "ливо": 36719, "ĠSauce": 36720, "ĠFusion": 36721, "×ķ×ŀ×": 36722, "ĠQuinn": 36723, "Ġgauche": 36724, "ĠÙĪØ£": 36725, "ĠÈ": 36726, "çĵľ": 36727, "Ġtechno": 36728, "Ġdispatch": 36729, "ĠaÅŁk": 36730, "Ġeinzel": 36731, "ĠGmail": 36732, "çŀ": 36733, "Ġê°ľìĿ¸": 36734, "ĠÑģемÑĮ": 36735, "Ġjourneys": 36736, "Ġiht": 36737, "Ġfibre": 36738, "Ġdramas": 36739, "ouched": 36740, "Ġrename": 36741, "ĠопеÑĢ": 36742, "Ġpoo": 36743, "ĠDru": 36744, "ĠиÑĤог": 36745, "Ġzast": 36746, "Ġcoz": 36747, "Ġzucch": 36748, "Ġobtaining": 36749, "Ġcommute": 36750, "Ġsubmer": 36751, "ĠVish": 36752, "ĠRabb": 36753, "ogg": 36754, "Ġhut": 36755, "íĸĪìĸ´": 36756, "æ¯Ķå¦Ĥ": 36757, "eremi": 36758, "Ġμα": 36759, "Ġdiskut": 36760, "ĠбÑĥк": 36761, "Ġimpaired": 36762, "depend": 36763, "ĠÙĪا": 36764, "ĠÑĢÑĥк": 36765, "ĠбаÑĢ": 36766, "Ġoxidation": 36767, "Ġsituação": 36768, "ÉĻn": 36769, "ução": 36770, "Ġsagte": 36771, "ĠSER": 36772, "ĠCake": 36773, "Ġturmeric": 36774, "ĠKak": 36775, "bung": 36776, "ĠKá¹Ľá¹£á¹ĩa": 36777, "Ġpoisoning": 36778, "Ġslipping": 36779, "ĠSays": 36780, "å°±åı¯ä»¥": 36781, "òng": 36782, "çŁ³": 36783, "«": 36784, "ĠClaudia": 36785, "ĠCharacter": 36786, "ниÑĨ": 36787, "coat": 36788, "Ġprogressed": 36789, "ĠFergus": 36790, "Ġìĺ¤ëĬ": 36791, "Ġoat": 36792, "ordable": 36793, "ĠLey": 36794, "ĠHeraus": 36795, "Ġresultados": 36796, "ĠKayla": 36797, "Ġriff": 36798, "Ġchegou": 36799, "Ġxi": 36800, "Ġspacious": 36801, "Ġrecognised": 36802, "Ġech": 36803, "ĠTie": 36804, "Ġlauncher": 36805, "Jim": 36806, "Ġsuppression": 36807, "ĠImpossible": 36808, "Ġguitars": 36809, "ĠFourier": 36810, "иÑĩеÑģкий": 36811, "ĠTherap": 36812, "ĠKaf": 36813, "centered": 36814, "ĠÑģооÑĤвеÑĤ": 36815, "Ġklim": 36816, "Ġcarbohydrates": 36817, "ignant": 36818, "ĠAstron": 36819, "Ġemple": 36820, "Ġdrastic": 36821, "ĠмиÑĢе": 36822, "вин": 36823, "uw": 36824, "Ġprettier": 36825, "Ġdonuts": 36826, "ĠAthena": 36827, "Ġdissert": 36828, "Ġplante": 36829, "Ġuranium": 36830, "ìĿĮë": 36831, "aré": 36832, 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"ĠLandesregierung": 37695, "ãģ£ãģ±": 37696, "ije": 37697, "Ġtrailers": 37698, "ĠTigers": 37699, "Ġgitti": 37700, "Ġforgiving": 37701, "Ġconcurrent": 37702, "ĠVu": 37703, "ĠíĬ¹íŀĪ": 37704, "ĠBROWN": 37705, "ounded": 37706, "\";": 37707, "Ġtremb": 37708, "Ġtiet": 37709, "ĠÑĢежим": 37710, "Ġnutshell": 37711, "елиÑĩ": 37712, "Ġlosers": 37713, "ricting": 37714, "Ġredeem": 37715, "defined": 37716, "Nice": 37717, "Ġbroadband": 37718, "KO": 37719, "Ġteasing": 37720, "Ġpartisan": 37721, "ıma": 37722, "Ġìŀ¬ë¯¸": 37723, "ĠJourney": 37724, "Ġslopes": 37725, "uning": 37726, "grunts": 37727, "Ġtäll": 37728, "Ġuncovered": 37729, "ĠmyÅĽlÄĻ": 37730, "ĠEsther": 37731, "äºİ": 37732, "ĠHealthy": 37733, "Ġë°ij": 37734, "rée": 37735, "Ġpolarization": 37736, "Ġflav": 37737, "Ġcambiar": 37738, "Ġyr": 37739, "ĠRanch": 37740, "Ġsplits": 37741, "Ġtrouvé": 37742, "åľĭ家": 37743, "Ġrecorder": 37744, "Ġdépart": 37745, "ÙĪب": 37746, "ĠKry": 37747, "Ġinteressant": 37748, "Ġederim": 37749, "ÅĽwiad": 37750, "ilateral": 37751, "wright": 37752, "Ġpourra": 37753, "êter": 37754, "Ġcamel": 37755, "áŀ": 37756, "Ġrapidement": 37757, "Ġmej": 37758, "Ġstiffness": 37759, "ADAS": 37760, "Ġdiffers": 37761, "Ġalot": 37762, "ĠSig": 37763, "ÑıÑĤелÑĮ": 37764, "Ġabstraction": 37765, "åľĺ": 37766, "Ġkeiner": 37767, "grupp": 37768, "ĠSherlock": 37769, "íĺĶ": 37770, "Ġcite": 37771, "Ġoverflow": 37772, "Ġtại": 37773, "úcar": 37774, "bula": 37775, "Ġconjunto": 37776, "ĠCI": 37777, "Ġmoderator": 37778, "Ġindirectly": 37779, "Ġalleine": 37780, "âĤ": 37781, "ÑĪиб": 37782, "Ġбаб": 37783, "Ġdanach": 37784, "Ġ1939": 37785, "Ġpromet": 37786, "Ġdestinations": 37787, "ĠIllust": 37788, "ικÏĮ": 37789, "Ġsabes": 37790, "Ġheh": 37791, "ĠGesetzent": 37792, "ĠMiz": 37793, "енко": 37794, "ĠMys": 37795, "Ь": 37796, "ĠJudaism": 37797, "Ġmustache": 37798, "Ġstimmt": 37799, "ĠGaza": 37800, "Ġvolte": 37801, "Ġnuo": 37802, "Ġmón": 37803, "ĠComput": 37804, "ูà¹Ī": 37805, "ĠRadi": 37806, "Ġexceptionally": 37807, "Ġassumes": 37808, "éĸĭå¿ĥ": 37809, "ãģĪãģ°": 37810, "inform": 37811, "Ġshrine": 37812, "æĵĬ": 37813, "Ġimplication": 37814, "ĠFitz": 37815, "æ²ĴéĹľä¿Ĥ": 37816, "!.": 37817, "Ġlt": 37818, "Ġalloy": 37819, "Ġethic": 37820, "Ġmonastery": 37821, "ìĭľì£ł": 37822, "icação": 37823, "Ġcoordinating": 37824, "ĠMoto": 37825, "Ġoverlook": 37826, "Ġchois": 37827, "Ġantibiotic": 37828, "ĠMinne": 37829, "ĠBJ": 37830, "ĠApa": 37831, "orian": 37832, "Ġspilled": 37833, "Jam": 37834, "Ġhusbands": 37835, "Ġcreations": 37836, "Ġañ": 37837, "üssel": 37838, "ĠìĿ´ìļ©": 37839, "Ġanalyse": 37840, "rose": 37841, "Ġpunched": 37842, "Ġpresque": 37843, "Ġastronomy": 37844, "Ġschwierig": 37845, "ĠEbola": 37846, "Ġcis": 37847, "Ġacet": 37848, "ĠFX": 37849, "endre": 37850, "ĠìĿĮìķħ": 37851, "Ġwebpage": 37852, "Ġfreaked": 37853, "Ġlatte": 37854, "Ġì¿ł": 37855, "Ġ머ë": 37856, "Never": 37857, "Gra": 37858, "íĻĶ를": 37859, "eyed": 37860, "Ġë°ľëĿ¼": 37861, "Ġespera": 37862, "Ġaparece": 37863, "ração": 37864, "Ġdisruptive": 37865, "ĠJoint": 37866, "urous": 37867, "reas": 37868, "ĠquerÃŃa": 37869, "Ġdistributions": 37870, "Ġexponent": 37871, "ì¹ĺ를": 37872, "Ġdl": 37873, "zhou": 37874, "ĠHearing": 37875, "å·®ä¸įå¤ļ": 37876, "ĠCraw": 37877, "Ġfloats": 37878, "ounced": 37879, "Lab": 37880, "World": 37881, "Ġburdens": 37882, "Ġauthoritarian": 37883, "ĠBolt": 37884, "ĠоднÑĥ": 37885, "Ġpigeon": 37886, "Ġdistractions": 37887, "ĠHerausforder": 37888, "Ġzest": 37889, "esc": 37890, "Ġshakes": 37891, "atas": 37892, "ĠÙħØ´": 37893, "holes": 37894, "Ġthinkers": 37895, "alta": 37896, "Ġarche": 37897, "ĠSuk": 37898, "anha": 37899, "Ġtempting": 37900, "Ġyoutuber": 37901, "Ġvì": 37902, "ĠdziaÅĤa": 37903, "ĠVatican": 37904, "Park": 37905, "Ġsupers": 37906, "ĠNikki": 37907, "ëĬIJë": 37908, "orang": 37909, "ramient": 37910, "鬼": 37911, "Ġê°ĸê³ł": 37912, "Ġdesserts": 37913, "Ġavere": 37914, "ĠGregory": 37915, "Ġëĵ¤ìĸ´ìĺ": 37916, "Ġcosting": 37917, "ĠClinic": 37918, "Ġrebels": 37919, "ĠMob": 37920, "Ġbunlar": 37921, "ĠYours": 37922, "ertime": 37923, "Ġretali": 37924, "mara": 37925, "atus": 37926, "alles": 37927, "ĠдÑĢ": 37928, "ĠдиÑģ": 37929, "Ġdiscounts": 37930, "ĠGUY": 37931, "Ġкакое": 37932, "ĠExperiment": 37933, "rement": 37934, "ĠXiang": 37935, "Ġbate": 37936, "WE": 37937, "Ġspecialize": 37938, "Ġdeity": 37939, "ĠLoki": 37940, "mag": 37941, "ĠNit": 37942, "West": 37943, "Ġmaternal": 37944, "Ġquis": 37945, "åŁºæľ¬": 37946, "broken": 37947, "Ġlasers": 37948, "Ġhakk": 37949, "ĠAngels": 37950, "Ġmastery": 37951, "antis": 37952, "Tiffany": 37953, "eee": 37954, "çij": 37955, "orem": 37956, "Ġinacc": 37957, "Ġjurisdictions": 37958, "ĠKardash": 37959, "æľº": 37960, "Il": 37961, "ĠSinn": 37962, "åĭķçĶ»": 37963, "Ġathletics": 37964, "cÄĻ": 37965, "Ġloosely": 37966, "Ġdieta": 37967, "Ag": 37968, "Ġ??": 37969, "ĠëĮĢíijľ": 37970, "Ġsuperv": 37971, "Ġnutrit": 37972, "Ġdrifting": 37973, "ĠìĦłìĥĿëĭĺ": 37974, "ĠпонÑıл": 37975, "ĠVictory": 37976, "ÙĦØ©": 37977, "×ķ׳×Ķ": 37978, "ĠпиÑĪ": 37979, "Ġshaved": 37980, "Ġmesure": 37981, "onden": 37982, "Ùĥر": 37983, "Ġexile": 37984, "ĠDesde": 37985, "ĠPinterest": 37986, "Ġattachments": 37987, "Ġhombres": 37988, "Ġfines": 37989, "ĠìĦ¸ìĥģ": 37990, "Ġsleeps": 37991, "ĠTaco": 37992, "ĠIRA": 37993, "rios": 37994, "Ġoll": 37995, "etes": 37996, "Ġunut": 37997, "fashioned": 37998, "Ġtreball": 37999, "ĠNearly": 38000, "ĠÑĢеалÑĮно": 38001, "Ġchil": 38002, "éĢ±": 38003, "ÄŁa": 38004, "ĠMEL": 38005, "roscop": 38006, "ĠCG": 38007, "Ġvenge": 38008, "Ġdishwasher": 38009, "algic": 38010, "Ġmodifier": 38011, "Ġembassy": 38012, "timer": 38013, "emics": 38014, "Ġintricate": 38015, "Ġevet": 38016, "ĠëĮĢë°ķ": 38017, "Ġisot": 38018, "ĠнаÑĥÑĩ": 38019, "ĠQuiz": 38020, "reso": 38021, "δÏİ": 38022, "Ġyelled": 38023, "Ġfeder": 38024, "ELLER": 38025, "Ġexceeded": 38026, "onas": 38027, "icano": 38028, "ĠживоÑĤ": 38029, "ĠMao": 38030, "ĠKazuto": 38031, "Ġãħĭãħĭãħĭãħĭ": 38032, "Ġfrontline": 38033, "ĠHungarian": 38034, "Ġüberall": 38035, "awat": 38036, "Ġgrips": 38037, "ições": 38038, "arnya": 38039, "ĠÍ¡": 38040, "Ġseid": 38041, "Ġanak": 38042, "Ġacabou": 38043, "íķij": 38044, "Ġnotorious": 38045, "ĠGodzilla": 38046, "Ġovercoming": 38047, "ĠPend": 38048, "Ġolabilir": 38049, "ülme": 38050, "Ġerhalten": 38051, "ãĤīãģĦ": 38052, "ê·¹": 38053, "ĠMeter": 38054, "Ġstaan": 38055, "Ol": 38056, "Ġchats": 38057, "ĠBuenos": 38058, "ÃŃve": 38059, "aluable": 38060, "Ġstrategically": 38061, "Ġcomprised": 38062, "ĠпеÑĢÑģонаж": 38063, "Ġwann": 38064, "ĠCen": 38065, "ниÑĤе": 38066, "Łģ": 38067, "ĠÑĤобой": 38068, "iad": 38069, "ĠkardeÅŁim": 38070, "ĠCongressman": 38071, "reaming": 38072, "homme": 38073, "Ġcommunaut": 38074, "Ġalcoholic": 38075, "Ġpickled": 38076, "Ġacord": 38077, "position": 38078, "egól": 38079, "Ġtroubling": 38080, "ĠMarcheg": 38081, "Ġzumindest": 38082, "Ġseamlessly": 38083, "Ġolun": 38084, "ĠTVs": 38085, "ĠпÑĢакÑĤиÑĩеÑģки": 38086, "Ġbackend": 38087, "ãģĵãĤĵãģ«ãģ¡ãģ¯": 38088, "idable": 38089, "Ġgadget": 38090, "Ġfaço": 38091, "ĠMarchegiani": 38092, "Ġë°¤": 38093, "Ġaccidental": 38094, "ĠLP": 38095, "Ġeldest": 38096, "ĠAdmiral": 38097, "ĠnÄĥm": 38098, "lever": 38099, "Ġpastel": 38100, "Ġfondo": 38101, "Connie": 38102, "Ġtercer": 38103, "Ġpact": 38104, "ĠMonte": 38105, "Ġmeats": 38106, "ĠSMS": 38107, "ĠAustralians": 38108, "ç¼": 38109, "Rhett": 38110, "Ġexactement": 38111, "Ġë¹¼": 38112, "ĠMOD": 38113, "ç¡": 38114, "ĠRapt": 38115, "ĠNoch": 38116, "Ġabort": 38117, "ĠNaval": 38118, "ĠFuji": 38119, "INTER": 38120, "ĠновÑĭй": 38121, "Ġmiejsce": 38122, "ĠICU": 38123, "ĠGraduate": 38124, "ĠGlen": 38125, "ardi": 38126, "ĠÈĺ": 38127, "Ġsolder": 38128, "Ġprofessions": 38129, "Ġorthog": 38130, "omn": 38131, "introdu": 38132, "ĠDenise": 38133, "ìŀIJ를": 38134, "Ġcorrespondence": 38135, "AMA": 38136, "Ġinflict": 38137, "Ġfand": 38138, "ĠGü": 38139, "ĠÑĩеÑĤ": 38140, "Ġtraced": 38141, "Ġpatents": 38142, "Ġambush": 38143, "Ġlotta": 38144, "ffer": 38145, "ĠWagner": 38146, "Ġimperson": 38147, "Ġextrêmement": 38148, "ÙĤت": 38149, "conduct": 38150, "Att": 38151, "ĠMueller": 38152, "ĠAlicia": 38153, "Ġcyc": 38154, "Ġhacker": 38155, "Ġtys": 38156, "Ġhail": 38157, "ĠзаÑıв": 38158, "Ġpasso": 38159, "Ġì¶Ķê°Ģ": 38160, "ĠÎĪ": 38161, "Ġpackaged": 38162, "ĠCynthia": 38163, "heet": 38164, "ä¸ŃåĽ½": 38165, "ĠNissan": 38166, "ĠQuesto": 38167, "é¨": 38168, "did": 38169, "Ġμια": 38170, "ĠEllis": 38171, "ĠAnalysis": 38172, "cemos": 38173, "Ġaseg": 38174, "ĠMyster": 38175, "ĠCao": 38176, "Ġtuv": 38177, "ĠIndustry": 38178, "ì£¼ê³ł": 38179, "otal": 38180, "Ġpequeño": 38181, "bras": 38182, "Ġcomprehend": 38183, "ĠSimpson": 38184, "ÑģÑĤвие": 38185, "ocracy": 38186, "иÑĩеÑģки": 38187, "ĠMush": 38188, "ĠLaurie": 38189, "Ġtriangular": 38190, "ĠPresents": 38191, "ĠKunden": 38192, "ç´¹": 38193, "æѦ": 38194, "ĠIss": 38195, "ĠDeck": 38196, "á»ĥn": 38197, "ĠDarkness": 38198, "Ġinflammatory": 38199, "eremiah": 38200, "Ġwarmed": 38201, "veyard": 38202, "ĠMemory": 38203, "etty": 38204, "Ġtaxpayers": 38205, "à¸ĵ": 38206, "Ø¡": 38207, "Ġpractise": 38208, "ëĭ¬ë": 38209, "Ġdrilled": 38210, "mÃ¼ÅŁ": 38211, "logo": 38212, "ĠFach": 38213, "¤ë¡ľ": 38214, "Ġübrigens": 38215, "Ġkonnten": 38216, "Ġnormalmente": 38217, "Ġargues": 38218, "ilingual": 38219, "°ë¥¼": 38220, "egal": 38221, "Ġtravaill": 38222, "ovy": 38223, "аÑĤо": 38224, "Ġruth": 38225, "ĠLights": 38226, "Ġconsisted": 38227, "×ijר×Ļ×Ŀ": 38228, "Ġstereotype": 38229, "Ġpayer": 38230, "ĠRee": 38231, "ĠAirbnb": 38232, "Ġdrowned": 38233, "ĠZoe": 38234, "Ġcanopy": 38235, "Ġbarr": 38236, "ĠноÑĩ": 38237, "Ġpagan": 38238, "Ġjars": 38239, "Ġrê": 38240, "erver": 38241, "æĪ¿": 38242, "ieben": 38243, "Ġespect": 38244, "ĠFi": 38245, "Ġunwilling": 38246, "Ġtechnician": 38247, "ặt": 38248, "member": 38249, "ĠCanal": 38250, "سÙħ": 38251, "Ġlieber": 38252, "Ġinference": 38253, "Ġhonoring": 38254, "åijµ": 38255, "ĠCampaign": 38256, "Ġlineage": 38257, "ĠStress": 38258, "Ġvictories": 38259, "Ġdeja": 38260, "×£": 38261, "êtes": 38262, "blick": 38263, "Ġменее": 38264, "oths": 38265, "ĠCouple": 38266, "Jason": 38267, "ĠNicolas": 38268, "екÑģ": 38269, "lib": 38270, "Ġherramient": 38271, "Ġ×IJ×ķ×ŀר": 38272, "Ġвидим": 38273, "millimeter": 38274, "Ġsilhouette": 38275, "Ġdriveway": 38276, "Ġcherish": 38277, "ãħłãħł": 38278, "Ġransom": 38279, "Ġinterdisciplinary": 38280, "ĠPortal": 38281, "Ġtrag": 38282, "thood": 38283, "Ġtedious": 38284, "Ġglossy": 38285, "Ġprépar": 38286, "ĠCay": 38287, "ĠTook": 38288, "ĠBottom": 38289, "Ġzig": 38290, "å«": 38291, "åį±": 38292, "represented": 38293, "à¹Ģลย": 38294, "Ġdesarrollo": 38295, "ìĦľë": 38296, "Ġviscos": 38297, "Ġmilligram": 38298, "ĠGund": 38299, "Ġferment": 38300, "drum": 38301, "Ġdrawers": 38302, "Laugh": 38303, "Ġpelos": 38304, "Ġpavement": 38305, "Ġmemoir": 38306, "avait": 38307, "Ġ2050": 38308, "¤ë¥¼": 38309, "Ġrazón": 38310, "Ġflourish": 38311, "Ġstern": 38312, "ä¸Ī": 38313, "ĠChung": 38314, "Ġserpent": 38315, "ĠGentlemen": 38316, "羣çļĦå¾Ī": 38317, "kook": 38318, "Ġlut": 38319, "importe": 38320, "parent": 38321, "Ġwsz": 38322, "Ġscree": 38323, "ĠMitarbeiter": 38324, "å·´": 38325, "mut": 38326, "Ġìĸĺ기를": 38327, "Ġsemble": 38328, "ĠOW": 38329, "Ġinvestigator": 38330, "ĠCheryl": 38331, "ĠGerald": 38332, "Ġprere": 38333, "Ġcompares": 38334, "nyt": 38335, "Ġdiferença": 38336, "?-": 38337, "Ġquá": 38338, "ר×Ļ": 38339, "Sen": 38340, "Ġheps": 38341, "Ġgratuit": 38342, "Ġconsort": 38343, "ĠSTOP": 38344, "ĠProtestant": 38345, "Ġelectrode": 38346, "âĹ": 38347, "Ġsecurely": 38348, "иÑĩеÑģкой": 38349, "Ġtää": 38350, "Ġregisters": 38351, "ĠHeavenly": 38352, "ogly": 38353, "issä": 38354, "ĠPhysics": 38355, "ĠMerkel": 38356, "Ġrév": 38357, "éĻ¢": 38358, "Ġerased": 38359, "ĠSacramento": 38360, "Ġcoffin": 38361, "Ġexacer": 38362, "Ġlanz": 38363, "Ġpoets": 38364, "ulif": 38365, "Ġì¹ĺë": 38366, "ĠNerd": 38367, "ĠNCT": 38368, "ĠHour": 38369, "nehmer": 38370, "ŀĺëıĦ": 38371, "ĠPrinci": 38372, "Sw": 38373, "mies": 38374, "armed": 38375, "ĠBeatles": 38376, "Ġpropagation": 38377, "Ġexchanged": 38378, "Ġcumulative": 38379, "Ġì§ijìĹIJ": 38380, "Ġdefeating": 38381, "æĬ±": 38382, "bels": 38383, "Ġwes": 38384, "ĠOdyssey": 38385, "ä½łæĥ³": 38386, "avior": 38387, "ĠìľĦìĹIJ": 38388, "Ġbrit": 38389, "Ġhijo": 38390, "DAY": 38391, "ĠاÙĦتÙĬ": 38392, "ĠСеÑĢг": 38393, "Ñĥка": 38394, "edsiÄĻ": 38395, "Ġimpos": 38396, "Ġellas": 38397, "Ġfirearms": 38398, "ĠNR": 38399, "Ġ×ij×IJ": 38400, "ĠÐŁÐ¾ÐºÐ°": 38401, "awi": 38402, "ĠìĦ±ê³µ": 38403, "Ġpupils": 38404, "ĠTack": 38405, "Ġfrase": 38406, "ĠShip": 38407, "Ġstad": 38408, "举": 38409, "ĠGreater": 38410, "unun": 38411, "immung": 38412, "grown": 38413, "ĠNXT": 38414, "ĠAmericas": 38415, "fox": 38416, "Ġmanten": 38417, "éłIJåĤĻ": 38418, "ĠÑģок": 38419, "Ġrikt": 38420, "lectric": 38421, "deep": 38422, "ĠзнаеÑĪÑĮ": 38423, "Ġbenut": 38424, "ĠInfrast": 38425, "ĠEmir": 38426, "ĠоÑĤпÑĢав": 38427, "ĠKimchi": 38428, "ĠFinnish": 38429, "´ìłģ": 38430, "inaire": 38431, "Ġoike": 38432, "æ¸ħæ¥ļ": 38433, "Ġhostage": 38434, "ĠButton": 38435, "ÙĤÙĬ": 38436, "eking": 38437, "ĠKazakh": 38438, "Ġcomforting": 38439, "Ġsog": 38440, "Ġgreeted": 38441, "guitar": 38442, "payer": 38443, "Ġrelational": 38444, "Ġconstruir": 38445, "çī¹åĪ¥": 38446, "opian": 38447, "ĠVolume": 38448, "ieth": 38449, "ÑģÑĤвом": 38450, "urrection": 38451, "liÅĽmy": 38452, "Ġhemisphere": 38453, "ĠBean": 38454, "IGN": 38455, "Ġkötü": 38456, "ĠFallout": 38457, "Ġbrace": 38458, "ç¹¼çºĮ": 38459, "ÏĢά": 38460, "ĠHAS": 38461, "Ġgé": 38462, "Ġcharacterize": 38463, "ặc": 38464, "ĠMilky": 38465, "Ġtumors": 38466, "Ġnuit": 38467, "ĠGaz": 38468, "ĠìŀĪëĭ¤ëĬĶ": 38469, "ĠгаÑĢ": 38470, "essment": 38471, "ĠAbe": 38472, "Ġë½ij": 38473, "ĠEinsatz": 38474, "JIN": 38475, "jä": 38476, "Cry": 38477, "ĠPromised": 38478, "ĠÑģеÑĢд": 38479, "okus": 38480, "Ġscalable": 38481, "ĠпоÑģмоÑĤÑĢеÑĤÑĮ": 38482, "ücklich": 38483, "Ġrealism": 38484, "Ġmayo": 38485, "Ġjuvenile": 38486, "Ġheadlights": 38487, "ĠgörÃ¼ÅŁ": 38488, "ĠReform": 38489, "Ġhalves": 38490, "czne": 38491, "Ġbreakup": 38492, "żej": 38493, "Ġrätt": 38494, "Day": 38495, "ĠìĿ¼ë³¸": 38496, "Ġmuerte": 38497, "Ġtunes": 38498, "ĠSmile": 38499, "record": 38500, "Ġrecherche": 38501, "atisfied": 38502, "Ġpozi": 38503, "Ġcelebrations": 38504, "isexual": 38505, "ĠROB": 38506, "thirds": 38507, "ĠFortune": 38508, "ĠÑĤой": 38509, "Ġbranded": 38510, "loo": 38511, "Ġdud": 38512, "Ġrandomized": 38513, "Ġcombin": 38514, "ä¸ĢäºĽ": 38515, "ieran": 38516, "czenia": 38517, "įãĥ«": 38518, "Ġcurator": 38519, "Ġartery": 38520, "ĠÑĥÑĪ": 38521, "ĠÑĩиÑĤ": 38522, "Ġsubsidies": 38523, "Ġblossom": 38524, "ĠTwilight": 38525, "Ġhyvä": 38526, "ĠPompe": 38527, "ĠCisco": 38528, "ĠÐŁÑĢо": 38529, "Ġbiri": 38530, "Ġgern": 38531, "Ġrebuilt": 38532, "Ġwcze": 38533, "Ġbenefici": 38534, "Ġdrummer": 38535, "Ġsolids": 38536, "Ġdiyorsun": 38537, "ãģĤãĤĬãģĮãģ¨ãģĨãģĶãģĸãģĦãģ¾ãģĹãģŁ": 38538, "lated": 38539, "Ġmuddy": 38540, "Ġholog": 38541, "Ġclaps": 38542, "ĠRings": 38543, "ĠOkey": 38544, "ĠBrave": 38545, "Ġvaluation": 38546, "Ġmigrant": 38547, "Ġintermitt": 38548, "Ġeigene": 38549, "iliary": 38550, "ãĥ¼ãĥĪ": 38551, "markt": 38552, "kr": 38553, "ĠRib": 38554, "á»Ļi": 38555, "Ġaccusations": 38556, "Ġarab": 38557, "wash": 38558, "ĠBardzo": 38559, "Ġugh": 38560, "esters": 38561, "ophren": 38562, "Ġalimentos": 38563, "ĠUz": 38564, "ÖĤ": 38565, "Ġ650": 38566, "ĠпÑĢиеÑħ": 38567, "FI": 38568, "Ġsampai": 38569, "Ġparlé": 38570, "hesion": 38571, "Ġsır": 38572, "Ġapparatus": 38573, "Ġcorrelated": 38574, "ĠPrincipal": 38575, "Ġcorr": 38576, "ĠOfficial": 38577, "иÑĩеÑģкие": 38578, "Ġterminals": 38579, "Should": 38580, "Ġvacun": 38581, "Ġstellt": 38582, "Ġmooi": 38583, "etzung": 38584, "ĠкÑĢа": 38585, "Ġdai": 38586, "Ġпож": 38587, "Team": 38588, "ĠPPE": 38589, "ĠÐŀÑģ": 38590, "ĠLeah": 38591, "ĠIvy": 38592, "yst": 38593, "Ġuhhh": 38594, "Ġnighttime": 38595, "Ġtrendy": 38596, "Ġsecurities": 38597, "Ġcontinents": 38598, "Ġfirsthand": 38599, "ĠVeron": 38600, "ĠëĤ®": 38601, "Ġbrowsing": 38602, "ĠCada": 38603, "tro": 38604, "Ġtramp": 38605, "reib": 38606, "Ġerstmal": 38607, "irler": 38608, "Ġpsic": 38609, "Ġgetir": 38610, "ĠNP": 38611, "Ġdzieci": 38612, "обÑĢаз": 38613, "Ġmagician": 38614, "Ġscrutiny": 38615, "Ġslab": 38616, "ĠOT": 38617, "isty": 38618, "iries": 38619, "orest": 38620, "Ġtasked": 38621, "Ġmorally": 38622, "ìķ¼ì§Ģ": 38623, "ustered": 38624, "Ġfools": 38625, "Ġirrespons": 38626, "Ġeinf": 38627, "Ġviá»ĩc": 38628, "Ġscor": 38629, "Ġpillows": 38630, "ĠGegen": 38631, "Ġtutte": 38632, "Ġquarterly": 38633, "Ġdidnt": 38634, "ĠGym": 38635, "ĠEther": 38636, "ĠØ«": 38637, "лиÑĪком": 38638, "Ġsignaling": 38639, "ĠNode": 38640, "ĠDoncs": 38641, "Ġyah": 38642, "ĠKanal": 38643, "Ġfading": 38644, "etin": 38645, "Ġinfluencers": 38646, "Ġmedals": 38647, "Ġengineered": 38648, "Ġfermented": 38649, "ê²łì§Ģë§Į": 38650, "ĠBeethoven": 38651, "×ŀש": 38652, "inental": 38653, "ĠìķĮ볤": 38654, "ütfen": 38655, "alnya": 38656, "Ġovere": 38657, "Ġdenkt": 38658, "акÑĤеÑĢ": 38659, "Ġâĺ": 38660, "Ġnecesit": 38661, "Ġgenerators": 38662, "grass": 38663, "ĠподÑĥм": 38664, "lieÃŁen": 38665, "Bar": 38666, "ľëıĻ": 38667, "ĠдеÑĤей": 38668, "Ġsucking": 38669, "Ġstencil": 38670, "Ġprimo": 38671, "ĠBreath": 38672, "strom": 38673, "Ġimmensely": 38674, "Ġappreh": 38675, "ìłķìĿ´": 38676, "Pop": 38677, "Ġjong": 38678, "ĠGiul": 38679, "ĠADHD": 38680, "Ġhören": 38681, "Ġelo": 38682, "ivent": 38683, "Ġrus": 38684, "Ġoutrageous": 38685, "Ġmastered": 38686, "Ġ커": 38687, "ÙĪÙģ": 38688, "ipes": 38689, "ĠRudy": 38690, "Jacob": 38691, "Ġbullish": 38692, "Ġtapped": 38693, "Ġfaud": 38694, "izophren": 38695, "ĠÑģоÑħ": 38696, "ĠDarling": 38697, "Ġ1963": 38698, "ĠPrevention": 38699, "²Ķ": 38700, "Ġabdominal": 38701, "stones": 38702, "Ġavaient": 38703, "á»ķi": 38704, "make": 38705, "Ġsare": 38706, "ĠInstant": 38707, "кам": 38708, "Ġkeeper": 38709, "Ġblankets": 38710, "ãģ§ãģĹãĤĩãģĨ": 38711, "Ġsweats": 38712, "ĠMinneapolis": 38713, "åħ¨éĥ¨": 38714, "Ġgenommen": 38715, "Ġfasten": 38716, "ĠBrussels": 38717, "åij¼": 38718, "Ġcafeter": 38719, "Ġabsorbing": 38720, "Ġhago": 38721, "ĠElmo": 38722, "Ġgusto": 38723, "ĠYap": 38724, "Música": 38725, "Ġtert": 38726, "Ġbanda": 38727, "Ġmily": 38728, "Ġthereafter": 38729, "ĠStockholm": 38730, "ĠCarson": 38731, "Ġcalibration": 38732, "avaÅŁ": 38733, "ansa": 38734, "ikke": 38735, "Ġforesee": 38736, "Ġqualche": 38737, "Ġdeste": 38738, "æ¤": 38739, "ünüz": 38740, "Ġforge": 38741, "Dis": 38742, "esten": 38743, "Ġδια": 38744, "Ġencaps": 38745, "ĠGespr": 38746, "Ġchercher": 38747, "ickets": 38748, "ÑĤоÑĢÑĭ": 38749, "Cr": 38750, "ĠТакже": 38751, "Ġrabbits": 38752, "ĠDot": 38753, "heiten": 38754, "Ġcausal": 38755, "ĠFoster": 38756, "ajÄħc": 38757, "Ġbereit": 38758, "Ġayudar": 38759, "é«Ļ": 38760, "ãģ³": 38761, "song": 38762, "comb": 38763, "Ġfringe": 38764, "Ġcybersecurity": 38765, "Ġ뾨": 38766, "Ġkier": 38767, "Ġbeschäft": 38768, "ĠконÑĨе": 38769, "Ġfacilit": 38770, "ĠNamen": 38771, "Ġbilateral": 38772, "tx": 38773, "ĠWissenschaft": 38774, "Ġnuances": 38775, "Ġripping": 38776, "Ġfy": 38777, "ĠSicherheit": 38778, "ĠGhana": 38779, "olon": 38780, "Ġtopped": 38781, "ĠMorocco": 38782, "Ġradial": 38783, "ĠLEE": 38784, "ĠAndreas": 38785, "edd": 38786, "ĠìĹ´ë": 38787, "ĠAirlines": 38788, "ãģĵãĤį": 38789, "Ġvalores": 38790, "ê·ľ": 38791, "Hy": 38792, "ĠзадаÑĩ": 38793, "ĠKendall": 38794, "ĠÑħаÑĢ": 38795, "ĠVamp": 38796, "Ġpython": 38797, "Ġmanageable": 38798, "ĠGente": 38799, "oise": 38800, "iciary": 38801, "Ġimposs": 38802, "ĠBunny": 38803, "iesta": 38804, "Andrew": 38805, "Ġsert": 38806, "ĠCec": 38807, "zzarella": 38808, "Ġautomobile": 38809, "ĠTiere": 38810, "allows": 38811, "åĨĨ": 38812, "Ġë°Ģ": 38813, "ĠScorp": 38814, "ĠJelly": 38815, "agara": 38816, "ĠStretch": 38817, "Ġredef": 38818, "Ġexacerb": 38819, "ĠSHA": 38820, "éf": 38821, "orsa": 38822, "Ġflawed": 38823, "ĠNoel": 38824, "?!?": 38825, "Ġprocent": 38826, "Ġmenstru": 38827, "ĠпÑĢоÑĩ": 38828, "Ġinfants": 38829, "ðŁİµ": 38830, "pause": 38831, "ĠRacing": 38832, "Ġ1948": 38833, "Ġsuperintendent": 38834, "idores": 38835, "idy": 38836, "brahim": 38837, "Ġunlucky": 38838, "Ġperk": 38839, "anci": 38840, "Ġë§ĮëĤĺ": 38841, "ĠÐľÐ¾Ñģкв": 38842, "Ġfinans": 38843, "Ġdiferencia": 38844, "łĪìĿ´": 38845, "éħį": 38846, "ORY": 38847, "ĠTac": 38848, "ÛĮا": 38849, "Ġdesem": 38850, "Ġважно": 38851, "ĠJU": 38852, "ĠìŀĪìŀĸìķĦìļĶ": 38853, "ĠÎĿ": 38854, "Ġinformations": 38855, "ĠHEL": 38856, "hst": 38857, "ĠпоговоÑĢ": 38858, "Ġvoiture": 38859, "Ġreus": 38860, "ändig": 38861, "ĠпоÑħож": 38862, "jing": 38863, "Ġdru": 38864, "altra": 38865, "Ġproduits": 38866, "Ġkite": 38867, "Ġeyeball": 38868, "ĠBelt": 38869, "ĠRestaurant": 38870, "Ġgamb": 38871, "Ġporridge": 38872, "itters": 38873, "Ġconverts": 38874, "Ġyardım": 38875, "Ġmáximo": 38876, "wirtschaft": 38877, "ĠíķĺëĤĺë": 38878, "Ġì¤Ģ": 38879, "Ġiceberg": 38880, "Ġvorbei": 38881, "Ġ256": 38882, "ocratic": 38883, "Ġreckless": 38884, "onner": 38885, "Ġmús": 38886, "Ġlogically": 38887, "ĠPrison": 38888, "ĠNetz": 38889, "Ġvacant": 38890, "Ġnimmt": 38891, "ĠHARR": 38892, "Ġзов": 38893, "ĠDee": 38894, "ringe": 38895, "niest": 38896, "ĠRules": 38897, "ìĬ¤ëŁ½": 38898, "cussions": 38899, "Ġfloral": 38900, "Ġconstrained": 38901, "Ġdifferentiation": 38902, "ĠQuebec": 38903, "ĠÛģÛĮÚº": 38904, "Ġpública": 38905, "itel": 38906, "Ġaccommodations": 38907, "ĠGrü": 38908, "íľ": 38909, "Ġpickles": 38910, "иÑĩеÑģкиÑħ": 38911, "Ġcommissions": 38912, "ĠBaek": 38913, "ĠçocuÄŁ": 38914, "ĠMedium": 38915, "Ġperiodically": 38916, "Ġwonderfully": 38917, "Ġstaffing": 38918, "ìĽIJë": 38919, "rire": 38920, "fle": 38921, "ĠMcL": 38922, "ĠÑĤеп": 38923, "ĠпеÑĢек": 38924, "нолог": 38925, "Ġíģ¬ê²Į": 38926, "çĻ¼çı¾": 38927, "Ġprosperous": 38928, "ĠSpiritual": 38929, "ĠChick": 38930, "DIA": 38931, "ĠÐŁÑĢивеÑĤ": 38932, "ĠperÃŃ": 38933, "ÑĮÑİÑĤ": 38934, "Ġconsultants": 38935, "ĠEarl": 38936, "ä»Ĭå¹´": 38937, "Ġruining": 38938, "оÑĢе": 38939, "Ġpenser": 38940, "Ġtakiej": 38941, "Ġstrengthened": 38942, "ĠLiquid": 38943, "онеÑĨ": 38944, "аваÑĤÑĮ": 38945, "Ġcamer": 38946, "Ġdisagreement": 38947, "Ġbathing": 38948, "ĠYosh": 38949, "aal": 38950, "prechen": 38951, "RISADAS": 38952, "Ġsuperstar": 38953, "æģŃ": 38954, "лÑıÑĤÑĮ": 38955, "Ġnib": 38956, "ĠTherm": 38957, "ĠDANIEL": 38958, "Ġpaw": 38959, "Ġliquids": 38960, "Ġcapacit": 38961, "arken": 38962, "Ġvagina": 38963, "Ġmashed": 38964, "Ġemerges": 38965, "yscy": 38966, "Ġunrelated": 38967, "ĠGuild": 38968, "Ġinverted": 38969, "itives": 38970, "Tra": 38971, "Ġbegr": 38972, "Ġalte": 38973, "ì§ķ": 38974, "ãĤģãģ¦": 38975, "ĠÑĢазÑĢабоÑĤ": 38976, "finder": 38977, "Ġдалее": 38978, "ĠблагодаÑĢ": 38979, "walker": 38980, "Ġcrater": 38981, "assadors": 38982, "rences": 38983, "inski": 38984, "ĠKIM": 38985, "ĠElliot": 38986, "2017": 38987, "ĠSr": 38988, "inka": 38989, "anov": 38990, "Ġìŀĺ못": 38991, "Ġproprietary": 38992, "displaystyle": 38993, "ĠÑģим": 38994, "Ġизб": 38995, "ĠPanel": 38996, "Ġinstincts": 38997, "ĠCommunications": 38998, "麻": 38999, "midt": 39000, "Ġë§Įëĵ¤ìĸ´": 39001, "ĠÑģлова": 39002, "ĠGilbert": 39003, "缮åīį": 39004, "Так": 39005, "voorbeeld": 39006, "еÑİÑģÑĮ": 39007, "aryn": 39008, "quez": 39009, "Ġdart": 39010, "ÑĸÑĪ": 39011, "ĠHut": 39012, "Sal": 39013, "Ġsoutheast": 39014, "Ġpesticides": 39015, "Ġhelicopters": 39016, "Ġendured": 39017, "iada": 39018, "Ġbrewing": 39019, "ìŬë": 39020, "ĠÑģвобод": 39021, "ĠSaints": 39022, "ĠFrançais": 39023, "ĠEconomics": 39024, "Ġdisloc": 39025, "ophobia": 39026, "Camer": 39027, "Ġnegotiated": 39028, "ĠÑģÑĤали": 39029, "ìĬ¤íģ": 39030, "ogie": 39031, "Ġtsunami": 39032, "Ġpeeled": 39033, "Ġmotivations": 39034, "è¨Ń": 39035, "ostat": 39036, "flan": 39037, "ĠDAC": 39038, "Ġkav": 39039, "'RE": 39040, "ĠPearson": 39041, "bbe": 39042, "czenie": 39043, "Ġatenção": 39044, "íĨµëł¹": 39045, "ãģ£ãģ¡": 39046, "ĠÑĥдаÑĢ": 39047, "Ġintroductory": 39048, "ĠIci": 39049, "ëĮĢë": 39050, "akat": 39051, "Ġtrench": 39052, "Ġproceeded": 39053, "ĠCoin": 39054, "Ġderecho": 39055, "ĠRede": 39056, "æ¯Ľ": 39057, "аннÑĭй": 39058, "Ġincarcerated": 39059, "ĠRichmond": 39060, "Rock": 39061, "ĠPav": 39062, "ĠKarma": 39063, "uges": 39064, "Ġconteú": 39065, "ë¹Ħ": 39066, "Ġê·¸ë§Į": 39067, "ĠGone": 39068, "ĠwspóÅĤ": 39069, 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39127, "akis": 39128, "Ġfragen": 39129, "Ġfiner": 39130, "ĠSug": 39131, "Ġuphill": 39132, "Ġéén": 39133, "âĢ¦)": 39134, "ĠÑģоп": 39135, "ĠCorey": 39136, "Ġsiebie": 39137, "Ġmuse": 39138, "Ġcloves": 39139, "Ġpous": 39140, "ĠFinanz": 39141, "ĠRoute": 39142, "amat": 39143, "Ġmutually": 39144, "ĠвнÑĥÑĤÑĢи": 39145, "ĠSelena": 39146, "ëĶ": 39147, "ĠGaussian": 39148, "ë¶ĢíĦ°": 39149, "Ġ×ij׼": 39150, "Ġejerc": 39151, "å¾®": 39152, "kea": 39153, "ĠGerry": 39154, "ĠSic": 39155, "大çļĦ": 39156, "Ġ1966": 39157, "iese": 39158, "Ġfossils": 39159, "Ġestad": 39160, "ĠKane": 39161, "ciÄĩ": 39162, "ĠìľłíĬľë": 39163, "Ġпам": 39164, "ĠCruise": 39165, "intérieur": 39166, "Ġbekannt": 39167, "ĠPode": 39168, "Ġdemander": 39169, "Rem": 39170, "Ġinvade": 39171, "Ġdecorating": 39172, "ropic": 39173, "Ġcowboy": 39174, "ĠPhoto": 39175, "opolit": 39176, "Ġì»¬ëŁ¬ë": 39177, "Ġreap": 39178, "Ġhandwriting": 39179, "à¹Ħร": 39180, "Ġëļ": 39181, "Ġبعد": 39182, "ĠMt": 39183, "ÙĢ": 39184, "Ġspaceship": 39185, "Ġnationalism": 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47730, "üd": 47731, "Ġpsychedel": 47732, "Ġenvisioned": 47733, "åĿĹ": 47734, "Ġï·»": 47735, "Ġwunder": 47736, "ĠBulgaria": 47737, "Brid": 47738, "Ġmarrow": 47739, "Ġdepiction": 47740, "ĠTin": 47741, "ĠPharise": 47742, "Ġeinzige": 47743, "Ġblindly": 47744, "ãģĽãģ¦": 47745, "Ġdefens": 47746, "Dire": 47747, "Ġvibrating": 47748, "Ġtrolls": 47749, "Ġdisrespectful": 47750, "Ġwod": 47751, "Ġstimuli": 47752, "Ġcreeping": 47753, "Ġclairement": 47754, "Ġscariest": 47755, "Ġdécouvrir": 47756, "Ġ104": 47757, "ĠвеÑĢÑħ": 47758, "ĠÅĤat": 47759, "Ġróżne": 47760, "Ġbarley": 47761, "ĠRepl": 47762, "ĠTwe": 47763, "kke": 47764, "ĠãģĿãĤĮ": 47765, "ĠRedmi": 47766, "ĠMetroid": 47767, "ĠήÏĦαν": 47768, "Check": 47769, "ĠSEN": 47770, "Ġido": 47771, "ÑĤоÑĢии": 47772, "óp": 47773, "UNKNOWN": 47774, "Ġändern": 47775, "ĠJuice": 47776, "ĠGesicht": 47777, "å°±æľĥ": 47778, "ĠнаÑģÑĤолÑĮко": 47779, "íĥķ": 47780, "ÂŃ": 47781, "exhales": 47782, "Ġì´ī": 47783, "Ġjsem": 47784, "ÏĢÏīÏĤ": 47785, "Ġitt": 47786, "ëªħìĿ´": 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"ĠScore": 47901, "ĠSavannah": 47902, "Ġgarde": 47903, "ĠNOR": 47904, "å°įåIJ§": 47905, "Ġscheint": 47906, "ĠpóÅĤ": 47907, "Ġcorri": 47908, "Ġbrute": 47909, "ĠÅĤad": 47910, "ä»ĸ们": 47911, "Ġsucceeding": 47912, "Ġbicycles": 47913, "Non": 47914, "Ġseekers": 47915, "Ġunconditional": 47916, "Ġrhymes": 47917, "ĠGarage": 47918, "Ġinvoice": 47919, "Ġcanvi": 47920, "neck": 47921, "Ġcustomizable": 47922, "iritual": 47923, "Queen": 47924, "íķĺìĭľëĬĶ": 47925, "Ġpowerless": 47926, "Ġcsak": 47927, "ä¸įä¼ļ": 47928, "isoft": 47929, "ĠìłķíĻķ": 47930, "Ġnhân": 47931, "ĠMAND": 47932, "ĠHaf": 47933, "Ġrevolves": 47934, "ä¹Łåı¯ä»¥": 47935, "ovan": 47936, "aroo": 47937, "ĠGrind": 47938, "éĽª": 47939, "Ġindispensable": 47940, "Ġconsulted": 47941, "ĠClinical": 47942, "Acc": 47943, "Ġolhos": 47944, "Ġmonter": 47945, "ĠHana": 47946, "etah": 47947, "Ġvaan": 47948, "Ġtigers": 47949, "Ġcaucus": 47950, "ðŁĺĤ": 47951, "³´ìŀIJ": 47952, "powers": 47953, "iums": 47954, "ĠíĨłë": 47955, "Ġtradicional": 47956, 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48070, "apons": 48071, "ĠUFC": 48072, "ĠSole": 48073, "Ġjoystick": 48074, "ĠOlivier": 48075, "warming": 48076, "Ġsyllabus": 48077, "ĠобÑīе": 48078, "Ġhiá»ĩn": 48079, "Ġfesta": 48080, "Ġcradle": 48081, "ĠZac": 48082, "Ġremembrance": 48083, "Ġê°ĻìķĦìĦľ": 48084, "ĠpiÄĻk": 48085, "Ġcoexist": 48086, "ĠVII": 48087, "Ġáreas": 48088, "Ġuważ": 48089, "Ġobservers": 48090, "Ġmänniskor": 48091, "coon": 48092, "ĠDAM": 48093, "Ġnaszym": 48094, "Ġalligator": 48095, "ĠFreeze": 48096, "ĠEstate": 48097, "ĠÑĤÑĢади": 48098, "Ġundercover": 48099, "Ġnies": 48100, "ĠFehler": 48101, "plin": 48102, "ĠKabul": 48103, "ilate": 48104, "Ġê³łìĸij": 48105, "Ġmop": 48106, "ìĦ¼": 48107, "Ġanderer": 48108, "ĠKELL": 48109, "оки": 48110, "ĠжеÑģÑĤ": 48111, "Ġgrazing": 48112, "ĠdaÃŃ": 48113, "Ġcapitalize": 48114, "Ġapex": 48115, "Ġnurturing": 48116, "Ġcortar": 48117, "Ġcontrac": 48118, "ımızı": 48119, "Ġtandem": 48120, "éĥ½æľī": 48121, "gement": 48122, "ĠÑģиÑģÑĤема": 48123, "Ġmanque": 48124, "iajÄħ": 48125, "WOR": 48126, 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"Ġneurological": 48185, "Ġmotivational": 48186, "ĠMcMahon": 48187, "ĠFinished": 48188, "Ġë³´ìĿ´": 48189, "ĠFields": 48190, "Ġadolescents": 48191, "ĠTisch": 48192, "ĠNeben": 48193, "ĠFlowers": 48194, "ĠEnerg": 48195, "Ġdiret": 48196, "ĠThi": 48197, "ĠPicas": 48198, "æĥľ": 48199, "æĢİä¹Īæł·": 48200, "Ġavete": 48201, "ĠFors": 48202, "ĠChapel": 48203, "Não": 48204, "Et": 48205, "ĠÑģодеÑĢж": 48206, "reno": 48207, "Ġsven": 48208, "ĠdostÄĻp": 48209, "nee": 48210, "ĠSnapdragon": 48211, "ĠIDs": 48212, "ìķĺëĬĶëį°": 48213, "ר×ļ": 48214, "Ġsunflower": 48215, "Ġperpetual": 48216, "ç³ĸ": 48217, "Ġknights": 48218, "Ġgird": 48219, "ĠTold": 48220, "Ġvolcanoes": 48221, "Ġadversary": 48222, "ĠEconomy": 48223, "Ġextrapol": 48224, "Ġbluetooth": 48225, "Ġzooming": 48226, "Ġskys": 48227, "Ġgenial": 48228, "ÃŃculos": 48229, "ambre": 48230, "ĠмеÑĢ": 48231, "Ġteeny": 48232, "Ġstressing": 48233, "ìķĮ": 48234, "ONY": 48235, "Ġtranslucent": 48236, "Ġrounding": 48237, "Ġgrues": 48238, "×Ļ׳×Ķ": 48239, "après": 48240, "Ġprueba": 48241, "Ġpolygon": 48242, "Ġblueberry": 48243, "ĠProgramm": 48244, "Ġtrenches": 48245, "Ġsebagai": 48246, "Ġpalate": 48247, "Ġlaude": 48248, "Ġbehaved": 48249, "Ġlongitudinal": 48250, "ĠModule": 48251, "Ġadmir": 48252, "λι": 48253, "Greg": 48254, "Ġwyst": 48255, "Ġpropagate": 48256, "Ġmolds": 48257, "ĠTub": 48258, "ĠLoud": 48259, "usto": 48260, "Ġunstoppable": 48261, "Ġreinforcing": 48262, "éĿŀ常çļĦ": 48263, "ĠпÑĢоблема": 48264, "Ġpotencial": 48265, "Ġhemp": 48266, "ìŀĶ": 48267, "य": 48268, "Ġoptic": 48269, "Ġerfolgreich": 48270, "ÑģÑĭ": 48271, "олÑĮÑĪе": 48272, "urst": 48273, "ĠPois": 48274, "Ġrespondents": 48275, "Ġnehme": 48276, "ĠExternal": 48277, "olate": 48278, "Hyun": 48279, "Ġquartz": 48280, "Ġmathematician": 48281, "Ġbásicamente": 48282, "Ġail": 48283, "ìłľë¥¼": 48284, "attutto": 48285, "Ġnooit": 48286, "Ġafflict": 48287, "ĠOlga": 48288, "èŃ·": 48289, "ĠнаÑĤ": 48290, "Ġdites": 48291, "Ġrealidade": 48292, "Ġkän": 48293, "Ġuniqueness": 48294, "Ġpadres": 48295, "Ġsubsidi": 48296, "Ġpigeons": 48297, "βα": 48298, "stad": 48299, "Ġderen": 48300, "ĠСлед": 48301, "doo": 48302, "ĠопиÑģании": 48303, "Ġamber": 48304, "Ġgoosebumps": 48305, "ĠfrÃ¥gor": 48306, "ĠVital": 48307, "ĠIsraelites": 48308, "wasser": 48309, "Isn": 48310, "Ġcommits": 48311, "ĠSTEVEN": 48312, "ĠBevölker": 48313, "uitive": 48314, "Ġlegen": 48315, "Ġbruk": 48316, "иÑĢован": 48317, "ynen": 48318, "helm": 48319, "Ġgenerational": 48320, "ĠLändern": 48321, "οιÏĢÏĮν": 48322, "uzu": 48323, "Ġcaller": 48324, "онÑĮ": 48325, "ümü": 48326, "Ġbesar": 48327, "Ġplats": 48328, "Ġmigrated": 48329, "Ġjap": 48330, "ĠWAR": 48331, "Ġdissect": 48332, "ĠZusch": 48333, "ĠZeiten": 48334, "ĠLions": 48335, "ĠDF": 48336, "âĶ": 48337, "кив": 48338, "Ġpedestrians": 48339, "ĠMarilyn": 48340, "dock": 48341, "Ġyht": 48342, "Ġreincarn": 48343, "ĠSono": 48344, "ĠGrowth": 48345, "ÑĥÑģов": 48346, "Ġdungeons": 48347, "Ġbagus": 48348, "kich": 48349, "ĠÑĥкÑĢаÑĹ": 48350, "éĨ«": 48351, "ĠKeller": 48352, "chemistry": 48353, "Japanese": 48354, "Ġwillst": 48355, "Ġdecomposition": 48356, "ĠÑģÑĤен": 48357, "Ġrevived": 48358, "íķĻêµIJ": 48359, "ĠÅĵ": 48360, "ä½IJ": 48361, "ìĭ¸": 48362, "ippy": 48363, "Ġhourly": 48364, "jän": 48365, "ĠWorkshop": 48366, "Ŀ¼ìĦľ": 48367, "Ġcuarto": 48368, "Ġpatrim": 48369, "ĠBurch": 48370, "ĠìŀĪ기": 48371, "Ġhepat": 48372, "ĠhÃłng": 48373, "ĠëĮĢíķ´": 48374, "ĠваÑĪи": 48375, "Ġrework": 48376, "Ġparse": 48377, "Ġçıktı": 48378, "ĠSax": 48379, "ĠMongo": 48380, "ĠAaah": 48381, "ramble": 48382, "DJ": 48383, "Ġstabilized": 48384, "ĠSpeech": 48385, "Books": 48386, "Ġhurdles": 48387, "ĠWO": 48388, "ĠLamborg": 48389, "Ġ1933": 48390, "Ġvorbere": 48391, "Ġclinically": 48392, "Ġbreathtaking": 48393, "ĠGateway": 48394, "пеÑĢвÑĭÑħ": 48395, "uters": 48396, "Ġë¹µ": 48397, "Ġyeter": 48398, "Ġpulley": 48399, "Ġmuffin": 48400, "ĠPrefer": 48401, "ĠPence": 48402, "Ġinformação": 48403, "ìĬ¤íĬ¸ë": 48404, "ãĤ¸ãĥ£": 48405, "ĠTurtle": 48406, "ĠRegina": 48407, "ĠLoad": 48408, "does": 48409, "panze": 48410, "¸Ķ": 48411, "Ġmina": 48412, "ĠLatinos": 48413, "ammers": 48414, "ĠTort": 48415, "ĠBeyonce": 48416, "имоÑģÑĤи": 48417, "ĠвопÑĢоÑģÑĭ": 48418, "Ġbulun": 48419, "èĢĮå·²": 48420, "inek": 48421, "bereich": 48422, "Ġpasture": 48423, "ĠOA": 48424, "ĠMelt": 48425, "ĠEtt": 48426, "ĠDY": 48427, "Ġobwohl": 48428, "Ġleagues": 48429, "ÑĤеÑģÑĮ": 48430, "ĠкÑĥÑģ": 48431, "Ġvors": 48432, "Ġtopp": 48433, "ographical": 48434, "asst": 48435, "Ġlindo": 48436, "Ġë°ĿíĺĶ": 48437, "Ġréfl": 48438, "Ġclimbs": 48439, "Ġvarsa": 48440, "Ġmethyl": 48441, "ĠKarere": 48442, "Æ°á»Ł": 48443, "Rad": 48444, "Ġpreparedness": 48445, "онÑĩ": 48446, "ĠOD": 48447, "ĠCGI": 48448, "Ġम": 48449, "Ġspeechless": 48450, "Ġlasci": 48451, "Ġbolag": 48452, "ĠÑħоÑĩеÑĤÑģÑı": 48453, "Ġgrieving": 48454, "ĠJohannes": 48455, "ĠCarroll": 48456, "adaki": 48457, "Ī¬ë": 48458, "ĠsÅĤu": 48459, "Ġinnerhalb": 48460, "Ġgymnastics": 48461, "пÑĢи": 48462, "ifiques": 48463, "Ġkarate": 48464, "Ġdomu": 48465, "ãģĿãĤĮãģ§": 48466, "OTHER": 48467, "Ġdemandé": 48468, "Ġbooklet": 48469, "ĠKyoto": 48470, "Ġwoh": 48471, "ĠMarÃŃa": 48472, "violent": 48473, "JE": 48474, "Ġlóg": 48475, "Ġbrutally": 48476, "cot": 48477, "ĠÙħÛĮ": 48478, "ĠWarsz": 48479, "å®Ī": 48480, "wol": 48481, "Ġmikä": 48482, "ĠPronounce": 48483, "ĠBrendan": 48484, "Ġroup": 48485, "Ġitaliano": 48486, "å¦ĤæѤ": 48487, "ĠкомпÑĮÑİÑĤ": 48488, "Ġurging": 48489, "edes": 48490, "Ġcarbono": 48491, "ĠRichardson": 48492, "ĠÐĿаÑĩ": 48493, "ĠTrainer": 48494, "ĠCrimea": 48495, "Ġdiapers": 48496, "Ġcovet": 48497, "ĠMahar": 48498, "ĠHutch": 48499, "ĠAusw": 48500, "berty": 48501, "Ġindifferent": 48502, "кÑĢеÑĤ": 48503, "uldade": 48504, "Ġharms": 48505, "¢ÙĨ": 48506, "lesia": 48507, "Ġgio": 48508, "ĠMistress": 48509, "ĠKnox": 48510, "ĠFREE": 48511, "Ġ루ë": 48512, "ĠнаÑĪа": 48513, "Ġinvincible": 48514, "Ġmaiden": 48515, "ĠJeez": 48516, "Ġbreve": 48517, "pole": 48518, "Ġcriticisms": 48519, "ĠRusia": 48520, "म": 48521, "phin": 48522, "ĠCompare": 48523, "ĠBON": 48524, "Ġsneaking": 48525, "ĠRails": 48526, "ĠGeral": 48527, "Ġ1953": 48528, "Hola": 48529, "ĠопÑĭÑĤ": 48530, "Ġrainforest": 48531, "Ġbelum": 48532, "ĠObi": 48533, "ĠISS": 48534, "ãĤĮãģªãģĦ": 48535, "ĠСв": 48536, "Ġblond": 48537, "Ġwzgl": 48538, "ĠpowiedziaÅĤ": 48539, "Ġchoking": 48540, "ĠSongs": 48541, "ĠBiraz": 48542, "Ġyells": 48543, "Ġstylist": 48544, "ÏĮÏĦε": 48545, "Ġschreiben": 48546, "ĠJaw": 48547, "ĠEleven": 48548, "ĠRif": 48549, "/.": 48550, "Ġìĺ¤ëŀľë§Į": 48551, "Ġtreaties": 48552, "uffed": 48553, "ĠâĪĴ": 48554, "Ġroofs": 48555, "à¹Ģส": 48556, "Ġë»": 48557, "Ġsparkle": 48558, "ĠKiev": 48559, "ĠArgu": 48560, "erecht": 48561, "ĠÐĿадо": 48562, "ĠFIL": 48563, "Ġmolta": 48564, "ĠDevi": 48565, "Ġcampe": 48566, "Ġbenevol": 48567, "ĠTough": 48568, "Ġmoim": 48569, "Ġevacuate": 48570, "Ġerrado": 48571, "å©Ĩ": 48572, "ÑĢÑĥго": 48573, "Ġíİĺ": 48574, "ĠÎĵια": 48575, "Ġweaken": 48576, "Ġilluminated": 48577, "Ġsiglo": 48578, "ĠVacc": 48579, "ией": 48580, "alis": 48581, "ĠÑĥÑģÑĤÑĢой": 48582, "Ġdona": 48583, "ÅĤos": 48584, "üman": 48585, "Ġproducción": 48586, "Ġclot": 48587, "ĠMango": 48588, "Ġuneasy": 48589, "Ġshuts": 48590, "ĠExamples": 48591, "vell": 48592, "ebe": 48593, "Ġpromptly": 48594, "ĠTeles": 48595, "ĠпÑĢоÑĪл": 48596, "Ġpuerta": 48597, "Ġüberzeug": 48598, "Ġcoch": 48599, "social": 48600, "ĠBenson": 48601, "ĠMeth": 48602, "ĠExped": 48603, "Ġsupplemental": 48604, "Ġconceive": 48605, "Ġ×ĺ×ķ×ij": 48606, "Ġcaptivity": 48607, "ıĻìķĪ": 48608, "ĠÑħÑĥд": 48609, "forming": 48610, "Ġuploads": 48611, "Ġturbulence": 48612, "joint": 48613, "Ġsatisfactory": 48614, "ĠAnime": 48615, "Ġwashes": 48616, "Ġliberals": 48617, "ĠSunshine": 48618, "ĠREAL": 48619, "ublik": 48620, "binary": 48621, "Tony": 48622, "Ġpolarized": 48623, "Ġenriched": 48624, "taking": 48625, "ĠëģĿëĤĺ": 48626, "Ġpleasures": 48627, "Ġextermin": 48628, "inese": 48629, "atl": 48630, "vär": 48631, "аÑĢÑĭ": 48632, "ĠmyÅĽ": 48633, "narrator": 48634, "Ġодном": 48635, "ĠnajwiÄĻ": 48636, "Ġmobilize": 48637, "Ġmillor": 48638, "Ġata": 48639, "æ··": 48640, "ĠpolÃŃtico": 48641, "Ġplead": 48642, "Ġpainters": 48643, "ĠSow": 48644, "оÑĦ": 48645, "ĠìĺĽëĤł": 48646, "ĠÑĩÑĤоб": 48647, "Ġsabor": 48648, "ĠUndert": 48649, "ĠJERRY": 48650, "Å¡ÃŃ": 48651, "Ġë°ĸìĹIJ": 48652, "Ġprécéd": 48653, "Ġannotation": 48654, "ĠInaudible": 48655, "Ġtextured": 48656, "Ġfisherman": 48657, "vordan": 48658, "icherung": 48659, "ĠìłģìĿ´": 48660, "Ġgezeigt": 48661, "Ġmandates": 48662, "Ġbeak": 48663, "ĠTWO": 48664, "ĠAkbar": 48665, "ilian": 48666, "Ġtiếp": 48667, "Ġsuperiority": 48668, "inku": 48669, "Ġlys": 48670, "ĠFCC": 48671, "ĠCPA": 48672, "ustering": 48673, "nicos": 48674, "anja": 48675, "Ġchills": 48676, "ĠCage": 48677, "Ġsealing": 48678, "Ġsaç": 48679, "Ġdedans": 48680, "ĠAlger": 48681, "Ġspezie": 48682, "Ġcoloss": 48683, "ıyı": 48684, "clockwise": 48685, "Ġexactamente": 48686, "Ġiemand": 48687, "amı": 48688, "Ġmandar": 48689, "raj": 48690, "faced": 48691, "agua": 48692, "Ġê¹Ķë": 48693, "Ġinsbesondere": 48694, "Ġdrizzle": 48695, "Ġdiminish": 48696, "ĠYoda": 48697, "AI": 48698, "Ġbilmiyorum": 48699, "ĠMMA": 48700, "ategory": 48701, "ĠпеÑĢеп": 48702, "Ġparticipar": 48703, "Ġnormalized": 48704, "Ġcomplexities": 48705, "æ´²": 48706, "æݧ": 48707, "аÑĢов": 48708, "mist": 48709, "icha": 48710, "Group": 48711, "Ġresiliency": 48712, "Ġnogle": 48713, "ĠCNC": 48714, "prü": 48715, "Ġphysicists": 48716, "нок": 48717, "LI": 48718, "Ġstuffs": 48719, "Ġsistemas": 48720, "Ġinterfering": 48721, "ĠMarvin": 48722, "ército": 48723, "ĠìĹĨê³ł": 48724, "Ġsonic": 48725, "Ġequiv": 48726, "Ġabord": 48727, "ĠRamen": 48728, "Ġ09": 48729, "medim": 48730, "atiques": 48731, "ĠделаÑİÑĤ": 48732, "Ġunanimously": 48733, "Ġskirts": 48734, "ĠíĬ¹ë³Ħ": 48735, "ĠPrix": 48736, "kami": 48737, "Ġfruition": 48738, "Ġbirthdays": 48739, "иком": 48740, "Ġinaugural": 48741, "Ġcorrelate": 48742, "ĠTory": 48743, "ĠëĤĺìģ": 48744, "Ġdew": 48745, "ĠPrecis": 48746, "ihi": 48747, "Ġë¬¸ìłľê°Ģ": 48748, "Ġciting": 48749, "ĠLana": 48750, "ĠKag": 48751, "Ġplaythrough": 48752, "ĠProtocol": 48753, "frist": 48754, "hovah": 48755, "Ġmerciful": 48756, "Ġbilingual": 48757, "ĠGuitar": 48758, "rh": 48759, "Ġglamorous": 48760, "ĠVikings": 48761, "ĠOoooh": 48762, "íķĺëĬĶëį°": 48763, "ĠUganda": 48764, "Ġcollapses": 48765, "entry": 48766, "Ġantioxidants": 48767, "ëĤĺë": 48768, "ÑĪаÑı": 48769, "Ġtrivia": 48770, "Ġgäller": 48771, "Ġfungi": 48772, "Ġmilks": 48773, "Ġdicht": 48774, "μη": 48775, "poke": 48776, "ĠвÑĭпÑĥÑģк": 48777, "Ġfeeder": 48778, "ĠAlcohol": 48779, "hower": 48780, "Ġdeserving": 48781, "ĠRebel": 48782, "iosis": 48783, "Ġ103": 48784, "Ġhandout": 48785, "Ġenm": 48786, "Ġlandlords": 48787, "Ġgeology": 48788, "rils": 48789, "Ġcobra": 48790, "ĠVold": 48791, "ĠPanch": 48792, "ĠGREG": 48793, "Ġpross": 48794, "Ġbracelets": 48795, "ĠVega": 48796, "Ġrozum": 48797, "款": 48798, "азд": 48799, "ĠLynd": 48800, "ĠHonors": 48801, "Ġsurrendered": 48802, "Ġlibrarians": 48803, "125": 48804, "ĠÑģиг": 48805, "Ġuniformly": 48806, "ĠEagles": 48807, "ìķĻ": 48808, "иÑĤан": 48809, "andid": 48810, "ĠìłĪëĮĢ": 48811, "Ġض": 48812, "Ġarrests": 48813, "ĠCSV": 48814, "ĠAzerbaijan": 48815, "ortic": 48816, "ĠDX": 48817, "ĠAdventures": 48818, "Ġabus": 48819, "ĠFau": 48820, "Ġschlimm": 48821, "Ġrattling": 48822, "Ġconsumes": 48823, "ĠTolkien": 48824, "Ġresurrected": 48825, "ĠXY": 48826, "íĬ¸ê°Ģ": 48827, "ĠвÑĭÑģÑĤÑĥп": 48828, "ĠAngie": 48829, "żenia": 48830, "Mic": 48831, "ĠSheila": 48832, "achtet": 48833, "Ġoverst": 48834, "Ġlâ": 48835, "Ġineffective": 48836, "æĿ¡": 48837, "æĢİä¹ĪäºĨ": 48838, "å¿Ļ": 48839, "Ġwichtiger": 48840, "Ġvino": 48841, "Ġpum": 48842, "Ġangled": 48843, "ĠPione": 48844, "ĠMỹ": 48845, "ãģĿãĤĮãģ¯": 48846, "woÅĽÄĩ": 48847, "draw": 48848, "ัà¹Ī": 48849, "markets": 48850, "Ġcafes": 48851, "ĠCem": 48852, "âĿ¤": 48853, "ĠSuit": 48854, "MK": 48855, "Ġemphasizes": 48856, "Ġtortilla": 48857, "Ġmejorar": 48858, "ĠSurviv": 48859, "casting": 48860, "Ġeducación": 48861, "ĠGum": 48862, "uely": 48863, "ĠìĹ¬ê¸°ëĬĶ": 48864, "Ġstretchy": 48865, "ença": 48866, "Ġwithhold": 48867, "Ġexiting": 48868, "Ġenthalpy": 48869, "ĠTransit": 48870, "ılmÄ±ÅŁ": 48871, "alies": 48872, "Ġsalvar": 48873, "Ġleaned": 48874, "ĠgroÃŁes": 48875, "Ġfitt": 48876, "аки": 48877, "Sarah": 48878, "Ġhostel": 48879, "Ġfingerna": 48880, "ĠnadziejÄĻ": 48881, "wives": 48882, "Rec": 48883, "Ġspool": 48884, "аÑĤов": 48885, "ĠEnemy": 48886, "Ġfury": 48887, "Ġdetta": 48888, "ĠFay": 48889, "éļ¨": 48890, "ÑıÑİÑĤ": 48891, "Ġaproximadamente": 48892, "Ġsilos": 48893, "Ġmagist": 48894, "Ġcree": 48895, "ĠKrank": 48896, "ĠDOWN": 48897, "Ġstartled": 48898, "Ġreborn": 48899, "ĠUmwelt": 48900, "ĠSuzanne": 48901, "ниÑĨÑĭ": 48902, "outez": 48903, "ĠJAC": 48904, "yards": 48905, "radas": 48906, "rau": 48907, "ipts": 48908, "hail": 48909, "Ġparagraphs": 48910, "Ġmeglio": 48911, "Ġisolating": 48912, "Ġaceite": 48913, "ĠHarsh": 48914, "Ġcyst": 48915, "ĠBlockchain": 48916, "ĠÑħоÑĢоÑĪий": 48917, "Ġvirtuous": 48918, "Ġinvestigación": 48919, "Ġdevoir": 48920, "Ġmasturb": 48921, "ĠSale": 48922, "ÙĬرة": 48923, "ĠΧ": 48924, "ĠStraÃŁen": 48925, "Ġdikk": 48926, "Ġafore": 48927, "ĠJungkook": 48928, "Ġchociaż": 48929, "ĠDebatte": 48930, "Ġweirdly": 48931, "Ġviaje": 48932, "regist": 48933, "Help": 48934, "Ġkinderen": 48935, "Ġformulated": 48936, "Ġenfim": 48937, "ĠTowards": 48938, "коÑĹ": 48939, "ivering": 48940, "ĠдеÑĤи": 48941, "charger": 48942, "Ġpurl": 48943, "Ġacademically": 48944, "ĠNurse": 48945, "Ġdeleting": 48946, "ayo": 48947, "Ġrefusal": 48948, "Ġdepicts": 48949, "ĠDracula": 48950, "Ġtoasted": 48951, "ĠZombie": 48952, "ĠSuperior": 48953, "ĠBold": 48954, "Ġquizzes": 48955, "Ġgle": 48956, "450": 48957, "Ġcomeço": 48958, "ynn": 48959, "Ġverst": 48960, "ĠOlaf": 48961, "Ġpomoc": 48962, "ĠSask": 48963, "ëĺ": 48964, "ĠTCP": 48965, "ĠProperty": 48966, "íķĺì£ł": 48967, "à¸ľà¸¡": 48968, "boom": 48969, "aros": 48970, "ĠÑĢоÑģÑģий": 48971, "ĠбÑĭваеÑĤ": 48972, "åĩºåİ»": 48973, "ĠìĿ´ìķ¼ê¸°ë¥¼": 48974, "Ġcombien": 48975, "vacc": 48976, "Ġebenfalls": 48977, "para": 48978, "Ġзм": 48979, "Ġdesperation": 48980, "ordre": 48981, "Ġש׾×Ļ": 48982, "Ġgenerously": 48983, "ĠÐŀк": 48984, "Ġorbiting": 48985, "> length: + array = array.index_select( + dim=axis, index=torch.arange(length, device=array.device) + ) + + if array.shape[axis] < length: + pad_widths = [(0, 0)] * array.ndim + pad_widths[axis] = (0, length - array.shape[axis]) + array = F.pad(array, [pad for sizes in pad_widths[::-1] for pad in sizes]) + else: + if array.shape[axis] > length: + array = array.take(indices=range(length), axis=axis) + + if array.shape[axis] < length: + pad_widths = [(0, 0)] * array.ndim + pad_widths[axis] = (0, length - array.shape[axis]) + array = np.pad(array, pad_widths) + + return array + + +@lru_cache(maxsize=None) +def mel_filters(device, n_mels: int = N_MELS) -> torch.Tensor: + """ + load the mel filterbank matrix for projecting STFT into a Mel spectrogram. + Allows decoupling librosa dependency; saved using: + + np.savez_compressed( + "mel_filters.npz", + mel_80=librosa.filters.mel(sr=16000, n_fft=400, n_mels=80), + ) + """ + assert n_mels == 80, f"Unsupported n_mels: {n_mels}" + with np.load( + os.path.join(os.path.dirname(__file__), "assets", "mel_filters.npz") + ) as f: + return torch.from_numpy(f[f"mel_{n_mels}"]).to(device) + + +def log_mel_spectrogram( + audio: Union[str, np.ndarray, torch.Tensor], + n_mels: int = N_MELS, + padding: int = 0, + device: Optional[Union[str, torch.device]] = None, +): + """ + Compute the log-Mel spectrogram of + + Parameters + ---------- + audio: Union[str, np.ndarray, torch.Tensor], shape = (*) + The path to audio or either a NumPy array or Tensor containing the audio waveform in 16 kHz + + n_mels: int + The number of Mel-frequency filters, only 80 is supported + + padding: int + Number of zero samples to pad to the right + + device: Optional[Union[str, torch.device]] + If given, the audio tensor is moved to this device before STFT + + Returns + ------- + torch.Tensor, shape = (80, n_frames) + A Tensor that contains the Mel spectrogram + """ + if not torch.is_tensor(audio): + if isinstance(audio, str): + audio = load_audio(audio) + audio = torch.from_numpy(audio) + + if device is not None: + audio = audio.to(device) + if padding > 0: + audio = F.pad(audio, (0, padding)) + window = torch.hann_window(N_FFT).to(audio.device) + stft = torch.stft(audio, N_FFT, HOP_LENGTH, window=window, return_complex=True) + magnitudes = stft[..., :-1].abs() ** 2 + + filters = mel_filters(audio.device, n_mels) + mel_spec = filters @ magnitudes + + log_spec = torch.clamp(mel_spec, min=1e-10).log10() + log_spec = torch.maximum(log_spec, log_spec.max() - 8.0) + log_spec = (log_spec + 4.0) / 4.0 + return log_spec diff --git a/src/subtitle/whisper/decoding.py b/src/subtitle/whisper/decoding.py new file mode 100644 index 0000000..81cd845 --- /dev/null +++ b/src/subtitle/whisper/decoding.py @@ -0,0 +1,813 @@ +from dataclasses import dataclass, field, replace +from typing import TYPE_CHECKING, Dict, Iterable, List, Optional, Sequence, Tuple, Union + +import numpy as np +import torch +import torch.nn.functional as F +from torch import Tensor +from torch.distributions import Categorical + +from .audio import CHUNK_LENGTH +from .tokenizer import Tokenizer, get_tokenizer +from .utils import compression_ratio + +if TYPE_CHECKING: + from .model import Whisper + + +@torch.no_grad() +def detect_language( + model: "Whisper", mel: Tensor, tokenizer: Tokenizer = None +) -> Tuple[Tensor, List[dict]]: + """ + Detect the spoken language in the audio, and return them as list of strings, along with the ids + of the most probable language tokens and the probability distribution over all language tokens. + This is performed outside the main decode loop in order to not interfere with kv-caching. + + Returns + ------- + language_tokens : Tensor, shape = (n_audio,) + ids of the most probable language tokens, which appears after the startoftranscript token. + language_probs : List[Dict[str, float]], length = n_audio + list of dictionaries containing the probability distribution over all languages. + """ + if tokenizer is None: + tokenizer = get_tokenizer(model.is_multilingual) + if ( + tokenizer.language is None + or tokenizer.language_token not in tokenizer.sot_sequence + ): + raise ValueError( + "This model doesn't have language tokens so it can't perform lang id" + ) + + single = mel.ndim == 2 + if single: + mel = mel.unsqueeze(0) + + # skip encoder forward pass if already-encoded audio features were given + if mel.shape[-2:] != (model.dims.n_audio_ctx, model.dims.n_audio_state): + mel = model.encoder(mel) + + # forward pass using a single token, startoftranscript + n_audio = mel.shape[0] + x = torch.tensor([[tokenizer.sot]] * n_audio).to(mel.device) # [n_audio, 1] + logits = model.logits(x, mel)[:, 0] + + # collect detected languages; suppress all non-language tokens + mask = torch.ones(logits.shape[-1], dtype=torch.bool) + mask[list(tokenizer.all_language_tokens)] = False + logits[:, mask] = -np.inf + language_tokens = logits.argmax(dim=-1) + language_token_probs = logits.softmax(dim=-1).cpu() + language_probs = [ + { + c: language_token_probs[i, j].item() + for j, c in zip(tokenizer.all_language_tokens, tokenizer.all_language_codes) + } + for i in range(n_audio) + ] + + if single: + language_tokens = language_tokens[0] + language_probs = language_probs[0] + + return language_tokens, language_probs + + +@dataclass(frozen=True) +class DecodingOptions: + # whether to perform X->X "transcribe" or X->English "translate" + task: str = "transcribe" + + # language that the audio is in; uses detected language if None + language: Optional[str] = None + + # sampling-related options + temperature: float = 0.0 + sample_len: Optional[int] = None # maximum number of tokens to sample + best_of: Optional[int] = None # number of independent sample trajectories, if t > 0 + beam_size: Optional[int] = None # number of beams in beam search, if t == 0 + patience: Optional[float] = None # patience in beam search (arxiv:2204.05424) + + # "alpha" in Google NMT, or None for length norm, when ranking generations + # to select which to return among the beams or best-of-N samples + length_penalty: Optional[float] = None + + # text or tokens to feed as the prompt or the prefix; for more info: + # https://github.com/openai/whisper/discussions/117#discussioncomment-3727051 + prompt: Optional[Union[str, List[int]]] = None # for the previous context + prefix: Optional[Union[str, List[int]]] = None # to prefix the current context + + # list of tokens ids (or comma-separated token ids) to suppress + # "-1" will suppress a set of symbols as defined in `tokenizer.non_speech_tokens()` + suppress_tokens: Optional[Union[str, Iterable[int]]] = "-1" + suppress_blank: bool = True # this will suppress blank outputs + + # timestamp sampling options + without_timestamps: bool = False # use <|notimestamps|> to sample text tokens only + max_initial_timestamp: Optional[float] = 1.0 + + # implementation details + fp16: bool = True # use fp16 for most of the calculation + + +@dataclass(frozen=True) +class DecodingResult: + audio_features: Tensor + language: str + language_probs: Optional[Dict[str, float]] = None + tokens: List[int] = field(default_factory=list) + text: str = "" + avg_logprob: float = np.nan + no_speech_prob: float = np.nan + temperature: float = np.nan + compression_ratio: float = np.nan + + +class Inference: + def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor: + """Perform a forward pass on the decoder and return per-token logits""" + raise NotImplementedError + + def rearrange_kv_cache(self, source_indices) -> None: + """Update the key-value cache according to the updated beams""" + raise NotImplementedError + + def cleanup_caching(self) -> None: + """Clean up any resources or hooks after decoding is finished""" + pass + + +class PyTorchInference(Inference): + def __init__(self, model: "Whisper", initial_token_length: int): + self.model: "Whisper" = model + self.initial_token_length = initial_token_length + self.kv_cache = {} + self.hooks = [] + + def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor: + if not self.kv_cache: + self.kv_cache, self.hooks = self.model.install_kv_cache_hooks() + + if tokens.shape[-1] > self.initial_token_length: + # only need to use the last token except in the first forward pass + tokens = tokens[:, -1:] + + return self.model.decoder(tokens, audio_features, kv_cache=self.kv_cache) + + def cleanup_caching(self): + for hook in self.hooks: + hook.remove() + + self.kv_cache = {} + self.hooks = [] + + def rearrange_kv_cache(self, source_indices): + for module, tensor in self.kv_cache.items(): + # update the key/value cache to contain the selected sequences + self.kv_cache[module] = tensor[source_indices].detach() + + +class SequenceRanker: + def rank( + self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]] + ) -> List[int]: + """ + Given a list of groups of samples and their cumulative log probabilities, + return the indices of the samples in each group to select as the final result + """ + raise NotImplementedError + + +class MaximumLikelihoodRanker(SequenceRanker): + """ + Select the sample with the highest log probabilities, penalized using either + a simple length normalization or Google NMT paper's length penalty + """ + + def __init__(self, length_penalty: Optional[float]): + self.length_penalty = length_penalty + + def rank(self, tokens: List[List[Tensor]], sum_logprobs: List[List[float]]): + def scores(logprobs, lengths): + result = [] + for logprob, length in zip(logprobs, lengths): + if self.length_penalty is None: + penalty = length + else: + # from the Google NMT paper + penalty = ((5 + length) / 6) ** self.length_penalty + result.append(logprob / penalty) + return result + + # get the sequence with the highest score + lengths = [[len(t) for t in s] for s in tokens] + return [np.argmax(scores(p, l)) for p, l in zip(sum_logprobs, lengths)] + + +class TokenDecoder: + def reset(self): + """Initialize any stateful variables for decoding a new sequence""" + + def update( + self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor + ) -> Tuple[Tensor, bool]: + """Specify how to select the next token, based on the current trace and logits + + Parameters + ---------- + tokens : Tensor, shape = (n_batch, current_sequence_length) + all tokens in the context so far, including the prefix and sot_sequence tokens + + logits : Tensor, shape = (n_batch, vocab_size) + per-token logits of the probability distribution at the current step + + sum_logprobs : Tensor, shape = (n_batch) + cumulative log probabilities for each sequence + + Returns + ------- + tokens : Tensor, shape = (n_batch, current_sequence_length + 1) + the tokens, appended with the selected next token + + completed : bool + True if all sequences has reached the end of text + + """ + raise NotImplementedError + + def finalize( + self, tokens: Tensor, sum_logprobs: Tensor + ) -> Tuple[Sequence[Sequence[Tensor]], List[List[float]]]: + """Finalize search and return the final candidate sequences + + Parameters + ---------- + tokens : Tensor, shape = (n_audio, n_group, current_sequence_length) + all tokens in the context so far, including the prefix and sot_sequence + + sum_logprobs : Tensor, shape = (n_audio, n_group) + cumulative log probabilities for each sequence + + Returns + ------- + tokens : Sequence[Sequence[Tensor]], length = n_audio + sequence of Tensors containing candidate token sequences, for each audio input + + sum_logprobs : List[List[float]], length = n_audio + sequence of cumulative log probabilities corresponding to the above + + """ + raise NotImplementedError + + +class GreedyDecoder(TokenDecoder): + def __init__(self, temperature: float, eot: int): + self.temperature = temperature + self.eot = eot + + def update( + self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor + ) -> Tuple[Tensor, bool]: + if self.temperature == 0: + next_tokens = logits.argmax(dim=-1) + else: + next_tokens = Categorical(logits=logits / self.temperature).sample() + + logprobs = F.log_softmax(logits.float(), dim=-1) + current_logprobs = logprobs[torch.arange(logprobs.shape[0]), next_tokens] + sum_logprobs += current_logprobs * (tokens[:, -1] != self.eot) + + next_tokens[tokens[:, -1] == self.eot] = self.eot + tokens = torch.cat([tokens, next_tokens[:, None]], dim=-1) + + completed = (tokens[:, -1] == self.eot).all() + return tokens, completed + + def finalize(self, tokens: Tensor, sum_logprobs: Tensor): + # make sure each sequence has at least one EOT token at the end + tokens = F.pad(tokens, (0, 1), value=self.eot) + return tokens, sum_logprobs.tolist() + + +class BeamSearchDecoder(TokenDecoder): + def __init__( + self, + beam_size: int, + eot: int, + inference: Inference, + patience: Optional[float] = None, + ): + self.beam_size = beam_size + self.eot = eot + self.inference = inference + self.patience = patience or 1.0 + self.max_candidates: int = round(beam_size * self.patience) + self.finished_sequences = None + + assert ( + self.max_candidates > 0 + ), f"Invalid beam size ({beam_size}) or patience ({patience})" + + def reset(self): + self.finished_sequences = None + + def update( + self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor + ) -> Tuple[Tensor, bool]: + if tokens.shape[0] % self.beam_size != 0: + raise ValueError(f"{tokens.shape}[0] % {self.beam_size} != 0") + + n_audio = tokens.shape[0] // self.beam_size + if self.finished_sequences is None: # for the first update + self.finished_sequences = [{} for _ in range(n_audio)] + + logprobs = F.log_softmax(logits.float(), dim=-1) + next_tokens, source_indices, finished_sequences = [], [], [] + for i in range(n_audio): + scores, sources, finished = {}, {}, {} + + # STEP 1: calculate the cumulative log probabilities for possible candidates + for j in range(self.beam_size): + idx = i * self.beam_size + j + prefix = tokens[idx].tolist() + for logprob, token in zip(*logprobs[idx].topk(self.beam_size + 1)): + new_logprob = (sum_logprobs[idx] + logprob).item() + sequence = tuple(prefix + [token.item()]) + scores[sequence] = new_logprob + sources[sequence] = idx + + # STEP 2: rank the candidates and keep the top beam_size sequences for each audio + saved = 0 + for sequence in sorted(scores, key=scores.get, reverse=True): + if sequence[-1] == self.eot: + finished[sequence] = scores[sequence] + else: + sum_logprobs[len(next_tokens)] = scores[sequence] + next_tokens.append(sequence) + source_indices.append(sources[sequence]) + + saved += 1 + if saved == self.beam_size: + break + + finished_sequences.append(finished) + + tokens = torch.tensor(next_tokens, device=tokens.device) + self.inference.rearrange_kv_cache(source_indices) + + # add newly finished sequences to self.finished_sequences + assert len(self.finished_sequences) == len(finished_sequences) + for previously_finished, newly_finished in zip( + self.finished_sequences, finished_sequences + ): + for seq in sorted(newly_finished, key=newly_finished.get, reverse=True): + if len(previously_finished) >= self.max_candidates: + break # the candidate list is full + previously_finished[seq] = newly_finished[seq] + + # mark as completed if all audio has enough number of samples + completed = all( + len(sequences) >= self.max_candidates + for sequences in self.finished_sequences + ) + return tokens, completed + + def finalize(self, preceding_tokens: Tensor, sum_logprobs: Tensor): + # collect all finished sequences, including patience, and add unfinished ones if not enough + sum_logprobs = sum_logprobs.cpu() + for i, sequences in enumerate(self.finished_sequences): + if ( + len(sequences) < self.beam_size + ): # when not enough sequences are finished + for j in list(np.argsort(sum_logprobs[i]))[::-1]: + sequence = preceding_tokens[i, j].tolist() + [self.eot] + sequences[tuple(sequence)] = sum_logprobs[i][j].item() + if len(sequences) >= self.beam_size: + break + + tokens: List[List[Tensor]] = [ + [torch.tensor(seq) for seq in sequences.keys()] + for sequences in self.finished_sequences + ] + sum_logprobs: List[List[float]] = [ + list(sequences.values()) for sequences in self.finished_sequences + ] + return tokens, sum_logprobs + + +class LogitFilter: + def apply(self, logits: Tensor, tokens: Tensor) -> None: + """Apply any filtering or masking to logits in-place + + Parameters + ---------- + logits : Tensor, shape = (n_batch, vocab_size) + per-token logits of the probability distribution at the current step + + tokens : Tensor, shape = (n_batch, current_sequence_length) + all tokens in the context so far, including the prefix and sot_sequence tokens + + """ + raise NotImplementedError + + +class SuppressBlank(LogitFilter): + def __init__(self, tokenizer: Tokenizer, sample_begin: int): + self.tokenizer = tokenizer + self.sample_begin = sample_begin + + def apply(self, logits: Tensor, tokens: Tensor): + if tokens.shape[1] == self.sample_begin: + logits[:, self.tokenizer.encode(" ") + [self.tokenizer.eot]] = -np.inf + + +class SuppressTokens(LogitFilter): + def __init__(self, suppress_tokens: Sequence[int]): + self.suppress_tokens = list(suppress_tokens) + + def apply(self, logits: Tensor, tokens: Tensor): + logits[:, self.suppress_tokens] = -np.inf + + +class ApplyTimestampRules(LogitFilter): + def __init__( + self, + tokenizer: Tokenizer, + sample_begin: int, + max_initial_timestamp_index: Optional[int], + ): + self.tokenizer = tokenizer + self.sample_begin = sample_begin + self.max_initial_timestamp_index = max_initial_timestamp_index + + def apply(self, logits: Tensor, tokens: Tensor): + # suppress <|notimestamps|> which is handled by without_timestamps + if self.tokenizer.no_timestamps is not None: + logits[:, self.tokenizer.no_timestamps] = -np.inf + + # timestamps have to appear in pairs, except directly before EOT; mask logits accordingly + for k in range(tokens.shape[0]): + sampled_tokens = tokens[k, self.sample_begin :] + seq = [t for t in sampled_tokens.tolist()] + last_was_timestamp = ( + len(seq) >= 1 and seq[-1] >= self.tokenizer.timestamp_begin + ) + penultimate_was_timestamp = ( + len(seq) < 2 or seq[-2] >= self.tokenizer.timestamp_begin + ) + + if last_was_timestamp: + if penultimate_was_timestamp: # has to be non-timestamp + logits[k, self.tokenizer.timestamp_begin :] = -np.inf + else: # cannot be normal text tokens + logits[k, : self.tokenizer.eot] = -np.inf + + timestamps = sampled_tokens[ + sampled_tokens.ge(self.tokenizer.timestamp_begin) + ] + if timestamps.numel() > 0: + # timestamps shouldn't decrease; forbid timestamp tokens smaller than the last + logits[k, self.tokenizer.timestamp_begin : timestamps[-1]] = -np.inf + + if tokens.shape[1] == self.sample_begin: + # suppress generating non-timestamp tokens at the beginning + logits[:, : self.tokenizer.timestamp_begin] = -np.inf + + # apply the `max_initial_timestamp` option + if self.max_initial_timestamp_index is not None: + last_allowed = ( + self.tokenizer.timestamp_begin + self.max_initial_timestamp_index + ) + logits[:, last_allowed + 1 :] = -np.inf + + # if sum of probability over timestamps is above any other token, sample timestamp + logprobs = F.log_softmax(logits.float(), dim=-1) + for k in range(tokens.shape[0]): + timestamp_logprob = logprobs[k, self.tokenizer.timestamp_begin :].logsumexp( + dim=-1 + ) + max_text_token_logprob = logprobs[k, : self.tokenizer.timestamp_begin].max() + if timestamp_logprob > max_text_token_logprob: + logits[k, : self.tokenizer.timestamp_begin] = -np.inf + + +class DecodingTask: + inference: Inference + sequence_ranker: SequenceRanker + decoder: TokenDecoder + logit_filters: List[LogitFilter] + + def __init__(self, model: "Whisper", options: DecodingOptions): + self.model = model + + language = options.language or "en" + tokenizer = get_tokenizer( + model.is_multilingual, language=language, task=options.task + ) + self.tokenizer: Tokenizer = tokenizer + self.options: DecodingOptions = self._verify_options(options) + + self.n_group: int = options.beam_size or options.best_of or 1 + self.n_ctx: int = model.dims.n_text_ctx + self.sample_len: int = options.sample_len or model.dims.n_text_ctx // 2 + + self.sot_sequence: Tuple[int] = tokenizer.sot_sequence + if self.options.without_timestamps: + self.sot_sequence = tokenizer.sot_sequence_including_notimestamps + + self.initial_tokens: Tuple[int] = self._get_initial_tokens() + self.sample_begin: int = len(self.initial_tokens) + self.sot_index: int = self.initial_tokens.index(tokenizer.sot) + + # inference: implements the forward pass through the decoder, including kv caching + self.inference = PyTorchInference(model, len(self.initial_tokens)) + + # sequence ranker: implements how to rank a group of sampled sequences + self.sequence_ranker = MaximumLikelihoodRanker(options.length_penalty) + + # decoder: implements how to select the next tokens, given the autoregressive distribution + if options.beam_size is not None: + self.decoder = BeamSearchDecoder( + options.beam_size, tokenizer.eot, self.inference, options.patience + ) + else: + self.decoder = GreedyDecoder(options.temperature, tokenizer.eot) + + # logit filters: applies various rules to suppress or penalize certain tokens + self.logit_filters = [] + if self.options.suppress_blank: + self.logit_filters.append(SuppressBlank(self.tokenizer, self.sample_begin)) + if self.options.suppress_tokens: + self.logit_filters.append(SuppressTokens(self._get_suppress_tokens())) + if not options.without_timestamps: + precision = CHUNK_LENGTH / model.dims.n_audio_ctx # usually 0.02 seconds + max_initial_timestamp_index = None + if options.max_initial_timestamp: + max_initial_timestamp_index = round( + self.options.max_initial_timestamp / precision + ) + self.logit_filters.append( + ApplyTimestampRules( + tokenizer, self.sample_begin, max_initial_timestamp_index + ) + ) + + def _verify_options(self, options: DecodingOptions) -> DecodingOptions: + if options.beam_size is not None and options.best_of is not None: + raise ValueError("beam_size and best_of can't be given together") + if options.temperature == 0: + if options.best_of is not None: + raise ValueError("best_of with greedy sampling (T=0) is not compatible") + if options.patience is not None and options.beam_size is None: + raise ValueError("patience requires beam_size to be given") + if options.length_penalty is not None and not ( + 0 <= options.length_penalty <= 1 + ): + raise ValueError("length_penalty (alpha) should be a value between 0 and 1") + + return options + + def _get_initial_tokens(self) -> Tuple[int]: + tokens = list(self.sot_sequence) + + if prefix := self.options.prefix: + prefix_tokens = ( + self.tokenizer.encode(" " + prefix.strip()) + if isinstance(prefix, str) + else prefix + ) + if self.sample_len is not None: + max_prefix_len = self.n_ctx // 2 - self.sample_len + prefix_tokens = prefix_tokens[-max_prefix_len:] + tokens = tokens + prefix_tokens + + if prompt := self.options.prompt: + prompt_tokens = ( + self.tokenizer.encode(" " + prompt.strip()) + if isinstance(prompt, str) + else prompt + ) + tokens = ( + [self.tokenizer.sot_prev] + + prompt_tokens[-(self.n_ctx // 2 - 1) :] + + tokens + ) + + return tuple(tokens) + + def _get_suppress_tokens(self) -> Tuple[int]: + suppress_tokens = self.options.suppress_tokens + + if isinstance(suppress_tokens, str): + suppress_tokens = [int(t) for t in suppress_tokens.split(",")] + + if -1 in suppress_tokens: + suppress_tokens = [t for t in suppress_tokens if t >= 0] + suppress_tokens.extend(self.tokenizer.non_speech_tokens) + elif suppress_tokens is None or len(suppress_tokens) == 0: + suppress_tokens = [] # interpret empty string as an empty list + else: + assert isinstance(suppress_tokens, list), "suppress_tokens must be a list" + + suppress_tokens.extend( + [ + self.tokenizer.transcribe, + self.tokenizer.translate, + self.tokenizer.sot, + self.tokenizer.sot_prev, + self.tokenizer.sot_lm, + ] + ) + if self.tokenizer.no_speech is not None: + # no-speech probability is collected separately + suppress_tokens.append(self.tokenizer.no_speech) + + return tuple(sorted(set(suppress_tokens))) + + def _get_audio_features(self, mel: Tensor): + if self.options.fp16: + mel = mel.half() + + if mel.shape[-2:] == ( + self.model.dims.n_audio_ctx, + self.model.dims.n_audio_state, + ): + # encoded audio features are given; skip audio encoding + audio_features = mel + else: + audio_features = self.model.encoder(mel) + + if audio_features.dtype != ( + torch.float16 if self.options.fp16 else torch.float32 + ): + return TypeError( + f"audio_features has an incorrect dtype: {audio_features.dtype}" + ) + + return audio_features + + def _detect_language(self, audio_features: Tensor, tokens: Tensor): + languages = [self.options.language] * audio_features.shape[0] + lang_probs = None + + if self.options.language is None or self.options.task == "lang_id": + lang_tokens, lang_probs = self.model.detect_language( + audio_features, self.tokenizer + ) + languages = [max(probs, key=probs.get) for probs in lang_probs] + if self.options.language is None: + tokens[:, self.sot_index + 1] = lang_tokens # write language tokens + + return languages, lang_probs + + def _main_loop(self, audio_features: Tensor, tokens: Tensor): + assert audio_features.shape[0] == tokens.shape[0] + n_batch = tokens.shape[0] + sum_logprobs: Tensor = torch.zeros(n_batch, device=audio_features.device) + no_speech_probs = [np.nan] * n_batch + + try: + for i in range(self.sample_len): + logits = self.inference.logits(tokens, audio_features) + + if ( + i == 0 and self.tokenizer.no_speech is not None + ): # save no_speech_probs + probs_at_sot = logits[:, self.sot_index].float().softmax(dim=-1) + no_speech_probs = probs_at_sot[:, self.tokenizer.no_speech].tolist() + + # now we need to consider the logits at the last token only + logits = logits[:, -1] + + # apply the logit filters, e.g. for suppressing or applying penalty to + for logit_filter in self.logit_filters: + logit_filter.apply(logits, tokens) + + # expand the tokens tensor with the selected next tokens + tokens, completed = self.decoder.update(tokens, logits, sum_logprobs) + + if completed or tokens.shape[-1] > self.n_ctx: + break + finally: + self.inference.cleanup_caching() + + return tokens, sum_logprobs, no_speech_probs + + @torch.no_grad() + def run(self, mel: Tensor) -> List[DecodingResult]: + self.decoder.reset() + tokenizer: Tokenizer = self.tokenizer + n_audio: int = mel.shape[0] + + audio_features: Tensor = self._get_audio_features(mel) # encoder forward pass + tokens: Tensor = torch.tensor([self.initial_tokens]).repeat(n_audio, 1) + + # detect language if requested, overwriting the language token + languages, language_probs = self._detect_language(audio_features, tokens) + if self.options.task == "lang_id": + return [ + DecodingResult( + audio_features=features, language=language, language_probs=probs + ) + for features, language, probs in zip( + audio_features, languages, language_probs + ) + ] + + # repeat the audio & text tensors by the group size, for beam search or best-of-n sampling + audio_features = audio_features.repeat_interleave(self.n_group, dim=0) + tokens = tokens.repeat_interleave(self.n_group, dim=0).to(audio_features.device) + + # call the main sampling loop + tokens, sum_logprobs, no_speech_probs = self._main_loop(audio_features, tokens) + + # reshape the tensors to have (n_audio, n_group) as the first two dimensions + audio_features = audio_features[:: self.n_group] + no_speech_probs = no_speech_probs[:: self.n_group] + assert audio_features.shape[0] == len(no_speech_probs) == n_audio + + tokens = tokens.reshape(n_audio, self.n_group, -1) + sum_logprobs = sum_logprobs.reshape(n_audio, self.n_group) + + # get the final candidates for each group, and slice between the first sampled token and EOT + tokens, sum_logprobs = self.decoder.finalize(tokens, sum_logprobs) + tokens: List[List[Tensor]] = [ + [t[self.sample_begin : (t == tokenizer.eot).nonzero()[0, 0]] for t in s] + for s in tokens + ] + + # select the top-ranked sample in each group + selected = self.sequence_ranker.rank(tokens, sum_logprobs) + tokens: List[List[int]] = [t[i].tolist() for i, t in zip(selected, tokens)] + texts: List[str] = [tokenizer.decode(t).strip() for t in tokens] + + sum_logprobs: List[float] = [lp[i] for i, lp in zip(selected, sum_logprobs)] + avg_logprobs: List[float] = [ + lp / (len(t) + 1) for t, lp in zip(tokens, sum_logprobs) + ] + + fields = ( + texts, + languages, + tokens, + audio_features, + avg_logprobs, + no_speech_probs, + ) + if len(set(map(len, fields))) != 1: + raise RuntimeError(f"inconsistent result lengths: {list(map(len, fields))}") + + return [ + DecodingResult( + audio_features=features, + language=language, + tokens=tokens, + text=text, + avg_logprob=avg_logprob, + no_speech_prob=no_speech_prob, + temperature=self.options.temperature, + compression_ratio=compression_ratio(text), + ) + for text, language, tokens, features, avg_logprob, no_speech_prob in zip( + *fields + ) + ] + + +@torch.no_grad() +def decode( + model: "Whisper", + mel: Tensor, + options: DecodingOptions = DecodingOptions(), + **kwargs, +) -> Union[DecodingResult, List[DecodingResult]]: + """ + Performs decoding of 30-second audio segment(s), provided as Mel spectrogram(s). + + Parameters + ---------- + model: Whisper + the Whisper model instance + + mel: torch.Tensor, shape = (80, 3000) or (*, 80, 3000) + A tensor containing the Mel spectrogram(s) + + options: DecodingOptions + A dataclass that contains all necessary options for decoding 30-second segments + + Returns + ------- + result: Union[DecodingResult, List[DecodingResult]] + The result(s) of decoding contained in `DecodingResult` dataclass instance(s) + """ + if single := mel.ndim == 2: + mel = mel.unsqueeze(0) + + if kwargs: + options = replace(options, **kwargs) + + result = DecodingTask(model, options).run(mel) + + return result[0] if single else result diff --git a/src/subtitle/whisper/model.py b/src/subtitle/whisper/model.py new file mode 100644 index 0000000..3457fcf --- /dev/null +++ b/src/subtitle/whisper/model.py @@ -0,0 +1,309 @@ +import base64 +import gzip +from dataclasses import dataclass +from typing import Dict, Iterable, Optional + +import numpy as np +import torch +import torch.nn.functional as F +from torch import Tensor, nn + +from .decoding import decode as decode_function +from .decoding import detect_language as detect_language_function +from .transcribe import transcribe as transcribe_function + + +@dataclass +class ModelDimensions: + n_mels: int + n_audio_ctx: int + n_audio_state: int + n_audio_head: int + n_audio_layer: int + n_vocab: int + n_text_ctx: int + n_text_state: int + n_text_head: int + n_text_layer: int + + +class LayerNorm(nn.LayerNorm): + def forward(self, x: Tensor) -> Tensor: + return super().forward(x.float()).type(x.dtype) + + +class Linear(nn.Linear): + def forward(self, x: Tensor) -> Tensor: + return F.linear( + x, + self.weight.to(x.dtype), + None if self.bias is None else self.bias.to(x.dtype), + ) + + +class Conv1d(nn.Conv1d): + def _conv_forward( + self, x: Tensor, weight: Tensor, bias: Optional[Tensor] + ) -> Tensor: + return super()._conv_forward( + x, weight.to(x.dtype), None if bias is None else bias.to(x.dtype) + ) + + +def sinusoids(length, channels, max_timescale=10000): + """Returns sinusoids for positional embedding""" + assert channels % 2 == 0 + log_timescale_increment = np.log(max_timescale) / (channels // 2 - 1) + inv_timescales = torch.exp(-log_timescale_increment * torch.arange(channels // 2)) + scaled_time = torch.arange(length)[:, np.newaxis] * inv_timescales[np.newaxis, :] + return torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], dim=1) + + +class MultiHeadAttention(nn.Module): + def __init__(self, n_state: int, n_head: int): + super().__init__() + self.n_head = n_head + self.query = Linear(n_state, n_state) + self.key = Linear(n_state, n_state, bias=False) + self.value = Linear(n_state, n_state) + self.out = Linear(n_state, n_state) + + def forward( + self, + x: Tensor, + xa: Optional[Tensor] = None, + mask: Optional[Tensor] = None, + kv_cache: Optional[dict] = None, + ): + q = self.query(x) + + if kv_cache is None or xa is None or self.key not in kv_cache: + # hooks, if installed (i.e. kv_cache is not None), will prepend the cached kv tensors; + # otherwise, perform key/value projections for self- or cross-attention as usual. + k = self.key(x if xa is None else xa) + v = self.value(x if xa is None else xa) + else: + # for cross-attention, calculate keys and values once and reuse in subsequent calls. + k = kv_cache[self.key] + v = kv_cache[self.value] + + wv, qk = self.qkv_attention(q, k, v, mask) + return self.out(wv), qk + + def qkv_attention( + self, q: Tensor, k: Tensor, v: Tensor, mask: Optional[Tensor] = None + ): + n_batch, n_ctx, n_state = q.shape + scale = (n_state // self.n_head) ** -0.25 + q = q.view(*q.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) * scale + k = k.view(*k.shape[:2], self.n_head, -1).permute(0, 2, 3, 1) * scale + v = v.view(*v.shape[:2], self.n_head, -1).permute(0, 2, 1, 3) + + qk = q @ k + if mask is not None: + qk = qk + mask[:n_ctx, :n_ctx] + qk = qk.float() + + w = F.softmax(qk, dim=-1).to(q.dtype) + return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2), qk.detach() + + +class ResidualAttentionBlock(nn.Module): + def __init__(self, n_state: int, n_head: int, cross_attention: bool = False): + super().__init__() + + self.attn = MultiHeadAttention(n_state, n_head) + self.attn_ln = LayerNorm(n_state) + + self.cross_attn = ( + MultiHeadAttention(n_state, n_head) if cross_attention else None + ) + self.cross_attn_ln = LayerNorm(n_state) if cross_attention else None + + n_mlp = n_state * 4 + self.mlp = nn.Sequential( + Linear(n_state, n_mlp), nn.GELU(), Linear(n_mlp, n_state) + ) + self.mlp_ln = LayerNorm(n_state) + + def forward( + self, + x: Tensor, + xa: Optional[Tensor] = None, + mask: Optional[Tensor] = None, + kv_cache: Optional[dict] = None, + ): + x = x + self.attn(self.attn_ln(x), mask=mask, kv_cache=kv_cache)[0] + if self.cross_attn: + x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache)[0] + x = x + self.mlp(self.mlp_ln(x)) + return x + + +class AudioEncoder(nn.Module): + def __init__( + self, n_mels: int, n_ctx: int, n_state: int, n_head: int, n_layer: int + ): + super().__init__() + self.conv1 = Conv1d(n_mels, n_state, kernel_size=3, padding=1) + self.conv2 = Conv1d(n_state, n_state, kernel_size=3, stride=2, padding=1) + self.register_buffer("positional_embedding", sinusoids(n_ctx, n_state)) + + self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList( + [ResidualAttentionBlock(n_state, n_head) for _ in range(n_layer)] + ) + self.ln_post = LayerNorm(n_state) + + def forward(self, x: Tensor): + """ + x : torch.Tensor, shape = (batch_size, n_mels, n_ctx) + the mel spectrogram of the audio + """ + x = F.gelu(self.conv1(x)) + x = F.gelu(self.conv2(x)) + x = x.permute(0, 2, 1) + + assert x.shape[1:] == self.positional_embedding.shape, "incorrect audio shape" + x = (x + self.positional_embedding).to(x.dtype) + + for block in self.blocks: + x = block(x) + + x = self.ln_post(x) + return x + + +class TextDecoder(nn.Module): + def __init__( + self, n_vocab: int, n_ctx: int, n_state: int, n_head: int, n_layer: int + ): + super().__init__() + + self.token_embedding = nn.Embedding(n_vocab, n_state) + self.positional_embedding = nn.Parameter(torch.empty(n_ctx, n_state)) + + self.blocks: Iterable[ResidualAttentionBlock] = nn.ModuleList( + [ + ResidualAttentionBlock(n_state, n_head, cross_attention=True) + for _ in range(n_layer) + ] + ) + self.ln = LayerNorm(n_state) + + mask = torch.empty(n_ctx, n_ctx).fill_(-np.inf).triu_(1) + self.register_buffer("mask", mask, persistent=False) + + def forward(self, x: Tensor, xa: Tensor, kv_cache: Optional[dict] = None): + """ + x : torch.LongTensor, shape = (batch_size, <= n_ctx) + the text tokens + xa : torch.Tensor, shape = (batch_size, n_mels, n_audio_ctx) + the encoded audio features to be attended on + """ + offset = next(iter(kv_cache.values())).shape[1] if kv_cache else 0 + x = ( + self.token_embedding(x) + + self.positional_embedding[offset : offset + x.shape[-1]] + ) + x = x.to(xa.dtype) + + for block in self.blocks: + x = block(x, xa, mask=self.mask, kv_cache=kv_cache) + + x = self.ln(x) + logits = ( + x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1) + ).float() + + return logits + + +class Whisper(nn.Module): + def __init__(self, dims: ModelDimensions): + super().__init__() + self.dims = dims + self.encoder = AudioEncoder( + self.dims.n_mels, + self.dims.n_audio_ctx, + self.dims.n_audio_state, + self.dims.n_audio_head, + self.dims.n_audio_layer, + ) + self.decoder = TextDecoder( + self.dims.n_vocab, + self.dims.n_text_ctx, + self.dims.n_text_state, + self.dims.n_text_head, + self.dims.n_text_layer, + ) + # use the last half layers for alignment by default; see `set_alignment_heads()` below + all_heads = torch.zeros( + self.dims.n_text_layer, self.dims.n_text_head, dtype=torch.bool + ) + all_heads[self.dims.n_text_layer // 2 :] = True + self.register_buffer("alignment_heads", all_heads.to_sparse(), persistent=False) + + def set_alignment_heads(self, dump: bytes): + array = np.frombuffer( + gzip.decompress(base64.b85decode(dump)), dtype=bool + ).copy() + mask = torch.from_numpy(array).reshape( + self.dims.n_text_layer, self.dims.n_text_head + ) + self.register_buffer("alignment_heads", mask.to_sparse(), persistent=False) + + def embed_audio(self, mel: torch.Tensor): + return self.encoder(mel) + + def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor): + return self.decoder(tokens, audio_features) + + def forward( + self, mel: torch.Tensor, tokens: torch.Tensor + ) -> Dict[str, torch.Tensor]: + return self.decoder(tokens, self.encoder(mel)) + + @property + def device(self): + return next(self.parameters()).device + + @property + def is_multilingual(self): + return self.dims.n_vocab == 51865 + + def install_kv_cache_hooks(self, cache: Optional[dict] = None): + """ + The `MultiHeadAttention` module optionally accepts `kv_cache` which stores the key and value + tensors calculated for the previous positions. This method returns a dictionary that stores + all caches, and the necessary hooks for the key and value projection modules that save the + intermediate tensors to be reused during later calculations. + + Returns + ------- + cache : Dict[nn.Module, torch.Tensor] + A dictionary object mapping the key/value projection modules to its cache + hooks : List[RemovableHandle] + List of PyTorch RemovableHandle objects to stop the hooks to be called + """ + cache = {**cache} if cache is not None else {} + hooks = [] + + def save_to_cache(module, _, output): + if module not in cache or output.shape[1] > self.dims.n_text_ctx: + # save as-is, for the first token or cross attention + cache[module] = output + else: + cache[module] = torch.cat([cache[module], output], dim=1).detach() + return cache[module] + + def install_hooks(layer: nn.Module): + if isinstance(layer, MultiHeadAttention): + hooks.append(layer.key.register_forward_hook(save_to_cache)) + hooks.append(layer.value.register_forward_hook(save_to_cache)) + + self.decoder.apply(install_hooks) + return cache, hooks + + detect_language = detect_language_function + transcribe = transcribe_function + decode = decode_function diff --git a/src/subtitle/whisper/normalizers/__init__.py b/src/subtitle/whisper/normalizers/__init__.py new file mode 100644 index 0000000..896d5e3 --- /dev/null +++ b/src/subtitle/whisper/normalizers/__init__.py @@ -0,0 +1,2 @@ +from .basic import BasicTextNormalizer as BasicTextNormalizer +from .english import EnglishTextNormalizer as EnglishTextNormalizer diff --git a/src/subtitle/whisper/normalizers/basic.py b/src/subtitle/whisper/normalizers/basic.py new file mode 100644 index 0000000..a824032 --- /dev/null +++ b/src/subtitle/whisper/normalizers/basic.py @@ -0,0 +1,76 @@ +import re +import unicodedata + +import regex + +# non-ASCII letters that are not separated by "NFKD" normalization +ADDITIONAL_DIACRITICS = { + "œ": "oe", + "Œ": "OE", + "ø": "o", + "Ø": "O", + "æ": "ae", + "Æ": "AE", + "ß": "ss", + "ẞ": "SS", + "đ": "d", + "Đ": "D", + "ð": "d", + "Ð": "D", + "þ": "th", + "Þ": "th", + "ł": "l", + "Ł": "L", +} + + +def remove_symbols_and_diacritics(s: str, keep=""): + """ + Replace any other markers, symbols, and punctuations with a space, + and drop any diacritics (category 'Mn' and some manual mappings) + """ + return "".join( + c + if c in keep + else ADDITIONAL_DIACRITICS[c] + if c in ADDITIONAL_DIACRITICS + else "" + if unicodedata.category(c) == "Mn" + else " " + if unicodedata.category(c)[0] in "MSP" + else c + for c in unicodedata.normalize("NFKD", s) + ) + + +def remove_symbols(s: str): + """ + Replace any other markers, symbols, punctuations with a space, keeping diacritics + """ + return "".join( + " " if unicodedata.category(c)[0] in "MSP" else c + for c in unicodedata.normalize("NFKC", s) + ) + + +class BasicTextNormalizer: + def __init__(self, remove_diacritics: bool = False, split_letters: bool = False): + self.clean = ( + remove_symbols_and_diacritics if remove_diacritics else remove_symbols + ) + self.split_letters = split_letters + + def __call__(self, s: str): + s = s.lower() + s = re.sub(r"[<\[][^>\]]*[>\]]", "", s) # remove words between brackets + s = re.sub(r"\(([^)]+?)\)", "", s) # remove words between parenthesis + s = self.clean(s).lower() + + if self.split_letters: + s = " ".join(regex.findall(r"\X", s, regex.U)) + + s = re.sub( + r"\s+", " ", s + ) # replace any successive whitespace characters with a space + + return s diff --git a/src/subtitle/whisper/normalizers/english.json b/src/subtitle/whisper/normalizers/english.json new file mode 100644 index 0000000..74a1c35 --- /dev/null +++ b/src/subtitle/whisper/normalizers/english.json @@ -0,0 +1,1741 @@ +{ + "accessorise": "accessorize", + "accessorised": "accessorized", + "accessorises": "accessorizes", + "accessorising": "accessorizing", + "acclimatisation": "acclimatization", + "acclimatise": "acclimatize", + "acclimatised": "acclimatized", + "acclimatises": "acclimatizes", + "acclimatising": "acclimatizing", + "accoutrements": "accouterments", + "aeon": "eon", + "aeons": "eons", + "aerogramme": "aerogram", + "aerogrammes": "aerograms", + "aeroplane": "airplane", + "aeroplanes": "airplanes", + "aesthete": "esthete", + "aesthetes": "esthetes", + "aesthetic": "esthetic", + "aesthetically": "esthetically", + "aesthetics": "esthetics", + "aetiology": "etiology", + "ageing": "aging", + "aggrandisement": "aggrandizement", + "agonise": "agonize", + "agonised": "agonized", + "agonises": "agonizes", + "agonising": "agonizing", + "agonisingly": "agonizingly", + "almanack": "almanac", + "almanacks": "almanacs", + "aluminium": "aluminum", + "amortisable": "amortizable", + "amortisation": "amortization", + "amortisations": "amortizations", + "amortise": "amortize", + "amortised": "amortized", + "amortises": "amortizes", + "amortising": "amortizing", + "amphitheatre": "amphitheater", + "amphitheatres": "amphitheaters", + "anaemia": "anemia", + "anaemic": "anemic", + "anaesthesia": "anesthesia", + "anaesthetic": "anesthetic", + "anaesthetics": "anesthetics", + "anaesthetise": "anesthetize", + "anaesthetised": "anesthetized", + "anaesthetises": "anesthetizes", + "anaesthetising": "anesthetizing", + "anaesthetist": "anesthetist", + "anaesthetists": "anesthetists", + "anaesthetize": "anesthetize", + "anaesthetized": "anesthetized", + "anaesthetizes": "anesthetizes", + "anaesthetizing": "anesthetizing", + "analogue": "analog", + "analogues": "analogs", + "analyse": "analyze", + "analysed": "analyzed", + "analyses": "analyzes", + "analysing": "analyzing", + "anglicise": "anglicize", + "anglicised": "anglicized", + "anglicises": "anglicizes", + "anglicising": "anglicizing", + "annualised": "annualized", + "antagonise": "antagonize", + "antagonised": "antagonized", + "antagonises": "antagonizes", + "antagonising": "antagonizing", + "apologise": "apologize", + "apologised": "apologized", + "apologises": "apologizes", + "apologising": "apologizing", + "appal": "appall", + "appals": "appalls", + "appetiser": "appetizer", + "appetisers": "appetizers", + "appetising": "appetizing", + "appetisingly": "appetizingly", + "arbour": "arbor", + "arbours": "arbors", + "archeological": "archaeological", + "archaeologically": "archeologically", + "archaeologist": "archeologist", + "archaeologists": "archeologists", + "archaeology": "archeology", + "ardour": "ardor", + "armour": "armor", + "armoured": "armored", + "armourer": "armorer", + "armourers": "armorers", + "armouries": "armories", + "armoury": "armory", + "artefact": "artifact", + "artefacts": "artifacts", + "authorise": "authorize", + "authorised": "authorized", + "authorises": "authorizes", + "authorising": "authorizing", + "axe": "ax", + "backpedalled": "backpedaled", + "backpedalling": "backpedaling", + "bannister": "banister", + "bannisters": "banisters", + "baptise": "baptize", + "baptised": "baptized", + "baptises": "baptizes", + "baptising": "baptizing", + "bastardise": "bastardize", + "bastardised": "bastardized", + "bastardises": "bastardizes", + "bastardising": "bastardizing", + "battleax": "battleaxe", + "baulk": "balk", + "baulked": "balked", + "baulking": "balking", + "baulks": "balks", + "bedevilled": "bedeviled", + "bedevilling": "bedeviling", + "behaviour": "behavior", + "behavioural": "behavioral", + "behaviourism": "behaviorism", + "behaviourist": "behaviorist", + "behaviourists": "behaviorists", + "behaviours": "behaviors", + "behove": "behoove", + "behoved": "behooved", + "behoves": "behooves", + "bejewelled": "bejeweled", + "belabour": "belabor", + "belaboured": "belabored", + "belabouring": "belaboring", + "belabours": "belabors", + "bevelled": "beveled", + "bevvies": "bevies", + "bevvy": "bevy", + "biassed": "biased", + "biassing": "biasing", + "bingeing": "binging", + "bougainvillaea": "bougainvillea", + "bougainvillaeas": "bougainvilleas", + "bowdlerise": "bowdlerize", + "bowdlerised": "bowdlerized", + "bowdlerises": "bowdlerizes", + "bowdlerising": "bowdlerizing", + "breathalyse": "breathalyze", + "breathalysed": "breathalyzed", + "breathalyser": "breathalyzer", + "breathalysers": "breathalyzers", + "breathalyses": "breathalyzes", + "breathalysing": "breathalyzing", + "brutalise": "brutalize", + "brutalised": "brutalized", + "brutalises": "brutalizes", + "brutalising": "brutalizing", + "busses": "buses", + "bussing": "busing", + "caesarean": "cesarean", + "caesareans": "cesareans", + "calibre": "caliber", + "calibres": "calibers", + "calliper": "caliper", + "callipers": "calipers", + "callisthenics": "calisthenics", + "canalise": "canalize", + "canalised": "canalized", + "canalises": "canalizes", + "canalising": "canalizing", + "cancelation": "cancellation", + "cancelations": "cancellations", + "cancelled": "canceled", + "cancelling": "canceling", + "candour": "candor", + "cannibalise": "cannibalize", + "cannibalised": "cannibalized", + "cannibalises": "cannibalizes", + "cannibalising": "cannibalizing", + "canonise": "canonize", + "canonised": "canonized", + "canonises": "canonizes", + "canonising": "canonizing", + "capitalise": "capitalize", + "capitalised": "capitalized", + "capitalises": "capitalizes", + "capitalising": "capitalizing", + "caramelise": "caramelize", + "caramelised": "caramelized", + "caramelises": "caramelizes", + "caramelising": "caramelizing", + "carbonise": "carbonize", + "carbonised": "carbonized", + "carbonises": "carbonizes", + "carbonising": "carbonizing", + "carolled": "caroled", + "carolling": "caroling", + "catalogue": "catalog", + "catalogued": "cataloged", + "catalogues": "catalogs", + "cataloguing": "cataloging", + "catalyse": "catalyze", + "catalysed": "catalyzed", + "catalyses": "catalyzes", + "catalysing": "catalyzing", + "categorise": "categorize", + "categorised": "categorized", + "categorises": "categorizes", + "categorising": "categorizing", + "cauterise": "cauterize", + "cauterised": "cauterized", + "cauterises": "cauterizes", + "cauterising": "cauterizing", + "cavilled": "caviled", + "cavilling": "caviling", + "centigramme": "centigram", + "centigrammes": "centigrams", + "centilitre": "centiliter", + "centilitres": "centiliters", + "centimetre": "centimeter", + "centimetres": "centimeters", + "centralise": "centralize", + "centralised": "centralized", + "centralises": "centralizes", + "centralising": "centralizing", + "centre": "center", + "centred": "centered", + "centrefold": "centerfold", + "centrefolds": "centerfolds", + "centrepiece": "centerpiece", + "centrepieces": "centerpieces", + "centres": "centers", + "channelled": "channeled", + "channelling": "channeling", + "characterise": "characterize", + "characterised": "characterized", + "characterises": "characterizes", + "characterising": "characterizing", + "cheque": "check", + "chequebook": "checkbook", + "chequebooks": "checkbooks", + "chequered": "checkered", + "cheques": "checks", + "chilli": "chili", + "chimaera": "chimera", + "chimaeras": "chimeras", + "chiselled": "chiseled", + "chiselling": "chiseling", + "circularise": "circularize", + "circularised": "circularized", + "circularises": "circularizes", + "circularising": "circularizing", + "civilise": "civilize", + "civilised": "civilized", + "civilises": "civilizes", + "civilising": "civilizing", + "clamour": "clamor", + "clamoured": "clamored", + "clamouring": "clamoring", + "clamours": "clamors", + "clangour": "clangor", + "clarinettist": "clarinetist", + "clarinettists": "clarinetists", + "collectivise": "collectivize", + "collectivised": "collectivized", + "collectivises": "collectivizes", + "collectivising": "collectivizing", + "colonisation": "colonization", + "colonise": "colonize", + "colonised": "colonized", + "coloniser": "colonizer", + "colonisers": "colonizers", + "colonises": "colonizes", + "colonising": "colonizing", + "colour": "color", + "colourant": "colorant", + "colourants": "colorants", + "coloured": "colored", + "coloureds": "coloreds", + "colourful": "colorful", + "colourfully": "colorfully", + "colouring": "coloring", + "colourize": "colorize", + "colourized": "colorized", + "colourizes": "colorizes", + "colourizing": "colorizing", + "colourless": "colorless", + "colours": "colors", + "commercialise": "commercialize", + "commercialised": "commercialized", + "commercialises": "commercializes", + "commercialising": "commercializing", + "compartmentalise": "compartmentalize", + "compartmentalised": "compartmentalized", + "compartmentalises": "compartmentalizes", + "compartmentalising": "compartmentalizing", + "computerise": "computerize", + "computerised": "computerized", + "computerises": "computerizes", + "computerising": "computerizing", + "conceptualise": "conceptualize", + "conceptualised": "conceptualized", + "conceptualises": "conceptualizes", + "conceptualising": "conceptualizing", + "connexion": "connection", + "connexions": "connections", + "contextualise": "contextualize", + "contextualised": "contextualized", + "contextualises": "contextualizes", + "contextualising": "contextualizing", + "cosier": "cozier", + "cosies": "cozies", + "cosiest": "coziest", + "cosily": "cozily", + "cosiness": "coziness", + "cosy": "cozy", + "councillor": "councilor", + "councillors": "councilors", + "counselled": "counseled", + "counselling": "counseling", + "counsellor": "counselor", + "counsellors": "counselors", + "crenelated": "crenellated", + "criminalise": "criminalize", + "criminalised": "criminalized", + "criminalises": "criminalizes", + "criminalising": "criminalizing", + "criticise": "criticize", + "criticised": "criticized", + "criticises": "criticizes", + "criticising": "criticizing", + "crueller": "crueler", + "cruellest": "cruelest", + "crystallisation": "crystallization", + "crystallise": "crystallize", + "crystallised": "crystallized", + "crystallises": "crystallizes", + "crystallising": "crystallizing", + "cudgelled": "cudgeled", + "cudgelling": "cudgeling", + "customise": "customize", + "customised": "customized", + "customises": "customizes", + "customising": "customizing", + "cypher": "cipher", + "cyphers": "ciphers", + "decentralisation": "decentralization", + "decentralise": "decentralize", + "decentralised": "decentralized", + "decentralises": "decentralizes", + "decentralising": "decentralizing", + "decriminalisation": "decriminalization", + "decriminalise": "decriminalize", + "decriminalised": "decriminalized", + "decriminalises": "decriminalizes", + "decriminalising": "decriminalizing", + "defence": "defense", + "defenceless": "defenseless", + "defences": "defenses", + "dehumanisation": "dehumanization", + "dehumanise": "dehumanize", + "dehumanised": "dehumanized", + "dehumanises": "dehumanizes", + "dehumanising": "dehumanizing", + "demeanour": "demeanor", + "demilitarisation": "demilitarization", + "demilitarise": "demilitarize", + "demilitarised": "demilitarized", + "demilitarises": "demilitarizes", + "demilitarising": "demilitarizing", + "demobilisation": "demobilization", + "demobilise": "demobilize", + "demobilised": "demobilized", + "demobilises": "demobilizes", + "demobilising": "demobilizing", + "democratisation": "democratization", + "democratise": "democratize", + "democratised": "democratized", + "democratises": "democratizes", + "democratising": "democratizing", + "demonise": "demonize", + "demonised": "demonized", + "demonises": "demonizes", + "demonising": "demonizing", + "demoralisation": "demoralization", + "demoralise": "demoralize", + "demoralised": "demoralized", + "demoralises": "demoralizes", + "demoralising": "demoralizing", + "denationalisation": "denationalization", + "denationalise": "denationalize", + "denationalised": "denationalized", + "denationalises": "denationalizes", + "denationalising": "denationalizing", + "deodorise": "deodorize", + "deodorised": "deodorized", + "deodorises": "deodorizes", + "deodorising": "deodorizing", + "depersonalise": "depersonalize", + "depersonalised": "depersonalized", + "depersonalises": "depersonalizes", + "depersonalising": "depersonalizing", + "deputise": "deputize", + "deputised": "deputized", + "deputises": "deputizes", + "deputising": "deputizing", + "desensitisation": "desensitization", + "desensitise": "desensitize", + "desensitised": "desensitized", + "desensitises": "desensitizes", + "desensitising": "desensitizing", + "destabilisation": "destabilization", + "destabilise": "destabilize", + "destabilised": "destabilized", + "destabilises": "destabilizes", + "destabilising": "destabilizing", + "dialled": "dialed", + "dialling": "dialing", + "dialogue": "dialog", + "dialogues": "dialogs", + "diarrhoea": "diarrhea", + "digitise": "digitize", + "digitised": "digitized", + "digitises": "digitizes", + "digitising": "digitizing", + "disc": "disk", + "discolour": "discolor", + "discoloured": "discolored", + "discolouring": "discoloring", + "discolours": "discolors", + "discs": "disks", + "disembowelled": "disemboweled", + "disembowelling": "disemboweling", + "disfavour": "disfavor", + "dishevelled": "disheveled", + "dishonour": "dishonor", + "dishonourable": "dishonorable", + "dishonourably": "dishonorably", + "dishonoured": "dishonored", + "dishonouring": "dishonoring", + "dishonours": "dishonors", + "disorganisation": "disorganization", + "disorganised": "disorganized", + "distil": "distill", + "distils": "distills", + "dramatisation": "dramatization", + "dramatisations": "dramatizations", + "dramatise": "dramatize", + "dramatised": "dramatized", + "dramatises": "dramatizes", + "dramatising": "dramatizing", + "draught": "draft", + "draughtboard": "draftboard", + "draughtboards": "draftboards", + "draughtier": "draftier", + "draughtiest": "draftiest", + "draughts": "drafts", + "draughtsman": "draftsman", + "draughtsmanship": "draftsmanship", + "draughtsmen": "draftsmen", + "draughtswoman": "draftswoman", + "draughtswomen": "draftswomen", + "draughty": "drafty", + "drivelled": "driveled", + "drivelling": "driveling", + "duelled": "dueled", + "duelling": "dueling", + "economise": "economize", + "economised": "economized", + "economises": "economizes", + "economising": "economizing", + "edoema": "edema", + "editorialise": "editorialize", + "editorialised": "editorialized", + "editorialises": "editorializes", + "editorialising": "editorializing", + "empathise": "empathize", + "empathised": "empathized", + "empathises": "empathizes", + "empathising": "empathizing", + "emphasise": "emphasize", + "emphasised": "emphasized", + "emphasises": "emphasizes", + "emphasising": "emphasizing", + "enamelled": "enameled", + "enamelling": "enameling", + "enamoured": "enamored", + "encyclopaedia": "encyclopedia", + "encyclopaedias": "encyclopedias", + "encyclopaedic": "encyclopedic", + "endeavour": "endeavor", + "endeavoured": "endeavored", + "endeavouring": "endeavoring", + "endeavours": "endeavors", + "energise": "energize", + "energised": "energized", + "energises": "energizes", + "energising": "energizing", + "enrol": "enroll", + "enrols": "enrolls", + "enthral": "enthrall", + "enthrals": "enthralls", + "epaulette": "epaulet", + "epaulettes": "epaulets", + "epicentre": "epicenter", + "epicentres": "epicenters", + "epilogue": "epilog", + "epilogues": "epilogs", + "epitomise": "epitomize", + "epitomised": "epitomized", + "epitomises": "epitomizes", + "epitomising": "epitomizing", + "equalisation": "equalization", + "equalise": "equalize", + "equalised": "equalized", + "equaliser": "equalizer", + "equalisers": "equalizers", + "equalises": "equalizes", + "equalising": "equalizing", + "eulogise": "eulogize", + "eulogised": "eulogized", + "eulogises": "eulogizes", + "eulogising": "eulogizing", + "evangelise": "evangelize", + "evangelised": "evangelized", + "evangelises": "evangelizes", + "evangelising": "evangelizing", + "exorcise": "exorcize", + "exorcised": "exorcized", + "exorcises": "exorcizes", + "exorcising": "exorcizing", + "extemporisation": "extemporization", + "extemporise": "extemporize", + "extemporised": "extemporized", + "extemporises": "extemporizes", + "extemporising": "extemporizing", + "externalisation": "externalization", + "externalisations": "externalizations", + "externalise": "externalize", + "externalised": "externalized", + "externalises": "externalizes", + "externalising": "externalizing", + "factorise": "factorize", + "factorised": "factorized", + "factorises": "factorizes", + "factorising": "factorizing", + "faecal": "fecal", + "faeces": "feces", + "familiarisation": "familiarization", + "familiarise": "familiarize", + "familiarised": "familiarized", + "familiarises": "familiarizes", + "familiarising": "familiarizing", + "fantasise": "fantasize", + "fantasised": "fantasized", + "fantasises": "fantasizes", + "fantasising": "fantasizing", + "favour": "favor", + "favourable": "favorable", + "favourably": "favorably", + "favoured": "favored", + "favouring": "favoring", + "favourite": "favorite", + "favourites": "favorites", + "favouritism": "favoritism", + "favours": "favors", + "feminise": "feminize", + "feminised": "feminized", + "feminises": "feminizes", + "feminising": "feminizing", + "fertilisation": "fertilization", + "fertilise": "fertilize", + "fertilised": "fertilized", + "fertiliser": "fertilizer", + "fertilisers": "fertilizers", + "fertilises": "fertilizes", + "fertilising": "fertilizing", + "fervour": "fervor", + "fibre": "fiber", + "fibreglass": "fiberglass", + "fibres": "fibers", + "fictionalisation": "fictionalization", + "fictionalisations": "fictionalizations", + "fictionalise": "fictionalize", + "fictionalised": "fictionalized", + "fictionalises": "fictionalizes", + "fictionalising": "fictionalizing", + "fillet": "filet", + "filleted": "fileted", + "filleting": "fileting", + "fillets": "filets", + "finalisation": "finalization", + "finalise": "finalize", + "finalised": "finalized", + "finalises": "finalizes", + "finalising": "finalizing", + "flautist": "flutist", + "flautists": "flutists", + "flavour": "flavor", + "flavoured": "flavored", + "flavouring": "flavoring", + "flavourings": "flavorings", + "flavourless": "flavorless", + "flavours": "flavors", + "flavoursome": "flavorsome", + "flyer / flier": "flier / flyer", + "foetal": "fetal", + "foetid": "fetid", + "foetus": "fetus", + "foetuses": "fetuses", + "formalisation": "formalization", + "formalise": "formalize", + "formalised": "formalized", + "formalises": "formalizes", + "formalising": "formalizing", + "fossilisation": "fossilization", + "fossilise": "fossilize", + "fossilised": "fossilized", + "fossilises": "fossilizes", + "fossilising": "fossilizing", + "fraternisation": "fraternization", + "fraternise": "fraternize", + "fraternised": "fraternized", + "fraternises": "fraternizes", + "fraternising": "fraternizing", + "fulfil": "fulfill", + "fulfilment": "fulfillment", + "fulfils": "fulfills", + "funnelled": "funneled", + "funnelling": "funneling", + "galvanise": "galvanize", + "galvanised": "galvanized", + "galvanises": "galvanizes", + "galvanising": "galvanizing", + "gambolled": "gamboled", + "gambolling": "gamboling", + "gaol": "jail", + "gaolbird": "jailbird", + "gaolbirds": "jailbirds", + "gaolbreak": "jailbreak", + "gaolbreaks": "jailbreaks", + "gaoled": "jailed", + "gaoler": "jailer", + "gaolers": "jailers", + "gaoling": "jailing", + "gaols": "jails", + "gasses": "gases", + "gage": "gauge", + "gaged": "gauged", + "gages": "gauges", + "gaging": "gauging", + "generalisation": "generalization", + "generalisations": "generalizations", + "generalise": "generalize", + "generalised": "generalized", + "generalises": "generalizes", + "generalising": "generalizing", + "ghettoise": "ghettoize", + "ghettoised": "ghettoized", + "ghettoises": "ghettoizes", + "ghettoising": "ghettoizing", + "gipsies": "gypsies", + "glamorise": "glamorize", + "glamorised": "glamorized", + "glamorises": "glamorizes", + "glamorising": "glamorizing", + "glamor": "glamour", + "globalisation": "globalization", + "globalise": "globalize", + "globalised": "globalized", + "globalises": "globalizes", + "globalising": "globalizing", + "glueing": "gluing", + "goitre": "goiter", + "goitres": "goiters", + "gonorrhoea": "gonorrhea", + "gramme": "gram", + "grammes": "grams", + "gravelled": "graveled", + "grey": "gray", + "greyed": "grayed", + "greying": "graying", + "greyish": "grayish", + "greyness": "grayness", + "greys": "grays", + "grovelled": "groveled", + "grovelling": "groveling", + "groyne": "groin", + "groynes": "groins", + "gruelling": "grueling", + "gruellingly": "gruelingly", + "gryphon": "griffin", + "gryphons": "griffins", + "gynaecological": "gynecological", + "gynaecologist": "gynecologist", + "gynaecologists": "gynecologists", + "gynaecology": "gynecology", + "haematological": "hematological", + "haematologist": "hematologist", + "haematologists": "hematologists", + "haematology": "hematology", + "haemoglobin": "hemoglobin", + "haemophilia": "hemophilia", + "haemophiliac": "hemophiliac", + "haemophiliacs": "hemophiliacs", + "haemorrhage": "hemorrhage", + "haemorrhaged": "hemorrhaged", + "haemorrhages": "hemorrhages", + "haemorrhaging": "hemorrhaging", + "haemorrhoids": "hemorrhoids", + "harbour": "harbor", + "harboured": "harbored", + "harbouring": "harboring", + "harbours": "harbors", + "harmonisation": "harmonization", + "harmonise": "harmonize", + "harmonised": "harmonized", + "harmonises": "harmonizes", + "harmonising": "harmonizing", + "homoeopath": "homeopath", + "homoeopathic": "homeopathic", + "homoeopaths": "homeopaths", + "homoeopathy": "homeopathy", + "homogenise": "homogenize", + "homogenised": "homogenized", + "homogenises": "homogenizes", + "homogenising": "homogenizing", + "honour": "honor", + "honourable": "honorable", + "honourably": "honorably", + "honoured": "honored", + "honouring": "honoring", + "honours": "honors", + "hospitalisation": "hospitalization", + "hospitalise": "hospitalize", + "hospitalised": "hospitalized", + "hospitalises": "hospitalizes", + "hospitalising": "hospitalizing", + "humanise": "humanize", + "humanised": "humanized", + "humanises": "humanizes", + "humanising": "humanizing", + "humour": "humor", + "humoured": "humored", + "humouring": "humoring", + "humourless": "humorless", + "humours": "humors", + "hybridise": "hybridize", + "hybridised": "hybridized", + "hybridises": "hybridizes", + "hybridising": "hybridizing", + "hypnotise": "hypnotize", + "hypnotised": "hypnotized", + "hypnotises": "hypnotizes", + "hypnotising": "hypnotizing", + "hypothesise": "hypothesize", + "hypothesised": "hypothesized", + "hypothesises": "hypothesizes", + "hypothesising": "hypothesizing", + "idealisation": "idealization", + "idealise": "idealize", + "idealised": "idealized", + "idealises": "idealizes", + "idealising": "idealizing", + "idolise": "idolize", + "idolised": "idolized", + "idolises": "idolizes", + "idolising": "idolizing", + "immobilisation": "immobilization", + "immobilise": "immobilize", + "immobilised": "immobilized", + "immobiliser": "immobilizer", + "immobilisers": "immobilizers", + "immobilises": "immobilizes", + "immobilising": "immobilizing", + "immortalise": "immortalize", + "immortalised": "immortalized", + "immortalises": "immortalizes", + "immortalising": "immortalizing", + "immunisation": "immunization", + "immunise": "immunize", + "immunised": "immunized", + "immunises": "immunizes", + "immunising": "immunizing", + "impanelled": "impaneled", + "impanelling": "impaneling", + "imperilled": "imperiled", + "imperilling": "imperiling", + "individualise": "individualize", + "individualised": "individualized", + "individualises": "individualizes", + "individualising": "individualizing", + "industrialise": "industrialize", + "industrialised": "industrialized", + "industrialises": "industrializes", + "industrialising": "industrializing", + "inflexion": "inflection", + "inflexions": "inflections", + "initialise": "initialize", + "initialised": "initialized", + "initialises": "initializes", + "initialising": "initializing", + "initialled": "initialed", + "initialling": "initialing", + "instal": "install", + "instalment": "installment", + "instalments": "installments", + "instals": "installs", + "instil": "instill", + "instils": "instills", + "institutionalisation": "institutionalization", + "institutionalise": "institutionalize", + "institutionalised": "institutionalized", + "institutionalises": "institutionalizes", + "institutionalising": "institutionalizing", + "intellectualise": "intellectualize", + "intellectualised": "intellectualized", + "intellectualises": "intellectualizes", + "intellectualising": "intellectualizing", + "internalisation": "internalization", + "internalise": "internalize", + "internalised": "internalized", + "internalises": "internalizes", + "internalising": "internalizing", + "internationalisation": "internationalization", + "internationalise": "internationalize", + "internationalised": "internationalized", + "internationalises": "internationalizes", + "internationalising": "internationalizing", + "ionisation": "ionization", + "ionise": "ionize", + "ionised": "ionized", + "ioniser": "ionizer", + "ionisers": "ionizers", + "ionises": "ionizes", + "ionising": "ionizing", + "italicise": "italicize", + "italicised": "italicized", + "italicises": "italicizes", + "italicising": "italicizing", + "itemise": "itemize", + "itemised": "itemized", + "itemises": "itemizes", + "itemising": "itemizing", + "jeopardise": "jeopardize", + "jeopardised": "jeopardized", + "jeopardises": "jeopardizes", + "jeopardising": "jeopardizing", + "jewelled": "jeweled", + "jeweller": "jeweler", + "jewellers": "jewelers", + "jewellery": "jewelry", + "judgement": "judgment", + "kilogramme": "kilogram", + "kilogrammes": "kilograms", + "kilometre": "kilometer", + "kilometres": "kilometers", + "labelled": "labeled", + "labelling": "labeling", + "labour": "labor", + "laboured": "labored", + "labourer": "laborer", + "labourers": "laborers", + "labouring": "laboring", + "labours": "labors", + "lacklustre": "lackluster", + "legalisation": "legalization", + "legalise": "legalize", + "legalised": "legalized", + "legalises": "legalizes", + "legalising": "legalizing", + "legitimise": "legitimize", + "legitimised": "legitimized", + "legitimises": "legitimizes", + "legitimising": "legitimizing", + "leukaemia": "leukemia", + "levelled": "leveled", + "leveller": "leveler", + "levellers": "levelers", + "levelling": "leveling", + "libelled": "libeled", + "libelling": "libeling", + "libellous": "libelous", + "liberalisation": "liberalization", + "liberalise": "liberalize", + "liberalised": "liberalized", + "liberalises": "liberalizes", + "liberalising": "liberalizing", + "licence": "license", + "licenced": "licensed", + "licences": "licenses", + "licencing": "licensing", + "likeable": "likable", + "lionisation": "lionization", + "lionise": "lionize", + "lionised": "lionized", + "lionises": "lionizes", + "lionising": "lionizing", + "liquidise": "liquidize", + "liquidised": "liquidized", + "liquidiser": "liquidizer", + "liquidisers": "liquidizers", + "liquidises": "liquidizes", + "liquidising": "liquidizing", + "litre": "liter", + "litres": "liters", + "localise": "localize", + "localised": "localized", + "localises": "localizes", + "localising": "localizing", + "louvre": "louver", + "louvred": "louvered", + "louvres": "louvers", + "lustre": "luster", + "magnetise": "magnetize", + "magnetised": "magnetized", + "magnetises": "magnetizes", + "magnetising": "magnetizing", + "manoeuvrability": "maneuverability", + "manoeuvrable": "maneuverable", + "manoeuvre": "maneuver", + "manoeuvred": "maneuvered", + "manoeuvres": "maneuvers", + "manoeuvring": "maneuvering", + "manoeuvrings": "maneuverings", + "marginalisation": "marginalization", + "marginalise": "marginalize", + "marginalised": "marginalized", + "marginalises": "marginalizes", + "marginalising": "marginalizing", + "marshalled": "marshaled", + "marshalling": "marshaling", + "marvelled": "marveled", + "marvelling": "marveling", + "marvellous": "marvelous", + "marvellously": "marvelously", + "materialisation": "materialization", + "materialise": "materialize", + "materialised": "materialized", + "materialises": "materializes", + "materialising": "materializing", + "maximisation": "maximization", + "maximise": "maximize", + "maximised": "maximized", + "maximises": "maximizes", + "maximising": "maximizing", + "meagre": "meager", + "mechanisation": "mechanization", + "mechanise": "mechanize", + "mechanised": "mechanized", + "mechanises": "mechanizes", + "mechanising": "mechanizing", + "mediaeval": "medieval", + "memorialise": "memorialize", + "memorialised": "memorialized", + "memorialises": "memorializes", + "memorialising": "memorializing", + "memorise": "memorize", + "memorised": "memorized", + "memorises": "memorizes", + "memorising": "memorizing", + "mesmerise": "mesmerize", + "mesmerised": "mesmerized", + "mesmerises": "mesmerizes", + "mesmerising": "mesmerizing", + "metabolise": "metabolize", + "metabolised": "metabolized", + "metabolises": "metabolizes", + "metabolising": "metabolizing", + "metre": "meter", + "metres": "meters", + "micrometre": "micrometer", + "micrometres": "micrometers", + "militarise": "militarize", + "militarised": "militarized", + "militarises": "militarizes", + "militarising": "militarizing", + "milligramme": "milligram", + "milligrammes": "milligrams", + "millilitre": "milliliter", + "millilitres": "milliliters", + "millimetre": "millimeter", + "millimetres": "millimeters", + "miniaturisation": "miniaturization", + "miniaturise": "miniaturize", + "miniaturised": "miniaturized", + "miniaturises": "miniaturizes", + "miniaturising": "miniaturizing", + "minibusses": "minibuses", + "minimise": "minimize", + "minimised": "minimized", + "minimises": "minimizes", + "minimising": "minimizing", + "misbehaviour": "misbehavior", + "misdemeanour": "misdemeanor", + "misdemeanours": "misdemeanors", + "misspelt": "misspelled", + "mitre": "miter", + "mitres": "miters", + "mobilisation": "mobilization", + "mobilise": "mobilize", + "mobilised": "mobilized", + "mobilises": "mobilizes", + "mobilising": "mobilizing", + "modelled": "modeled", + "modeller": "modeler", + "modellers": "modelers", + "modelling": "modeling", + "modernise": "modernize", + "modernised": "modernized", + "modernises": "modernizes", + "modernising": "modernizing", + "moisturise": "moisturize", + "moisturised": "moisturized", + "moisturiser": "moisturizer", + "moisturisers": "moisturizers", + "moisturises": "moisturizes", + "moisturising": "moisturizing", + "monologue": "monolog", + "monologues": "monologs", + "monopolisation": "monopolization", + "monopolise": "monopolize", + "monopolised": "monopolized", + "monopolises": "monopolizes", + "monopolising": "monopolizing", + "moralise": "moralize", + "moralised": "moralized", + "moralises": "moralizes", + "moralising": "moralizing", + "motorised": "motorized", + "mould": "mold", + "moulded": "molded", + "moulder": "molder", + "mouldered": "moldered", + "mouldering": "moldering", + "moulders": "molders", + "mouldier": "moldier", + "mouldiest": "moldiest", + "moulding": "molding", + "mouldings": "moldings", + "moulds": "molds", + "mouldy": "moldy", + "moult": "molt", + "moulted": "molted", + "moulting": "molting", + "moults": "molts", + "moustache": "mustache", + "moustached": "mustached", + "moustaches": "mustaches", + "moustachioed": "mustachioed", + "multicoloured": "multicolored", + "nationalisation": "nationalization", + "nationalisations": "nationalizations", + "nationalise": "nationalize", + "nationalised": "nationalized", + "nationalises": "nationalizes", + "nationalising": "nationalizing", + "naturalisation": "naturalization", + "naturalise": "naturalize", + "naturalised": "naturalized", + "naturalises": "naturalizes", + "naturalising": "naturalizing", + "neighbour": "neighbor", + "neighbourhood": "neighborhood", + "neighbourhoods": "neighborhoods", + "neighbouring": "neighboring", + "neighbourliness": "neighborliness", + "neighbourly": "neighborly", + "neighbours": "neighbors", + "neutralisation": "neutralization", + "neutralise": "neutralize", + "neutralised": "neutralized", + "neutralises": "neutralizes", + "neutralising": "neutralizing", + "normalisation": "normalization", + "normalise": "normalize", + "normalised": "normalized", + "normalises": "normalizes", + "normalising": "normalizing", + "odour": "odor", + "odourless": "odorless", + "odours": "odors", + "oesophagus": "esophagus", + "oesophaguses": "esophaguses", + "oestrogen": "estrogen", + "offence": "offense", + "offences": "offenses", + "omelette": "omelet", + "omelettes": "omelets", + "optimise": "optimize", + "optimised": "optimized", + "optimises": "optimizes", + "optimising": "optimizing", + "organisation": "organization", + "organisational": "organizational", + "organisations": "organizations", + "organise": "organize", + "organised": "organized", + "organiser": "organizer", + "organisers": "organizers", + "organises": "organizes", + "organising": "organizing", + "orthopaedic": "orthopedic", + "orthopaedics": "orthopedics", + "ostracise": "ostracize", + "ostracised": "ostracized", + "ostracises": "ostracizes", + "ostracising": "ostracizing", + "outmanoeuvre": "outmaneuver", + "outmanoeuvred": "outmaneuvered", + "outmanoeuvres": "outmaneuvers", + "outmanoeuvring": "outmaneuvering", + "overemphasise": "overemphasize", + "overemphasised": "overemphasized", + "overemphasises": "overemphasizes", + "overemphasising": "overemphasizing", + "oxidisation": "oxidization", + "oxidise": "oxidize", + "oxidised": "oxidized", + "oxidises": "oxidizes", + "oxidising": "oxidizing", + "paederast": "pederast", + "paederasts": "pederasts", + "paediatric": "pediatric", + "paediatrician": "pediatrician", + "paediatricians": "pediatricians", + "paediatrics": "pediatrics", + "paedophile": "pedophile", + "paedophiles": "pedophiles", + "paedophilia": "pedophilia", + "palaeolithic": "paleolithic", + "palaeontologist": "paleontologist", + "palaeontologists": "paleontologists", + "palaeontology": "paleontology", + "panelled": "paneled", + "panelling": "paneling", + "panellist": "panelist", + "panellists": "panelists", + "paralyse": "paralyze", + "paralysed": "paralyzed", + "paralyses": "paralyzes", + "paralysing": "paralyzing", + "parcelled": "parceled", + "parcelling": "parceling", + "parlour": "parlor", + "parlours": "parlors", + "particularise": "particularize", + "particularised": "particularized", + "particularises": "particularizes", + "particularising": "particularizing", + "passivisation": "passivization", + "passivise": "passivize", + "passivised": "passivized", + "passivises": "passivizes", + "passivising": "passivizing", + "pasteurisation": "pasteurization", + "pasteurise": "pasteurize", + "pasteurised": "pasteurized", + "pasteurises": "pasteurizes", + "pasteurising": "pasteurizing", + "patronise": "patronize", + "patronised": "patronized", + "patronises": "patronizes", + "patronising": "patronizing", + "patronisingly": "patronizingly", + "pedalled": "pedaled", + "pedalling": "pedaling", + "pedestrianisation": "pedestrianization", + "pedestrianise": "pedestrianize", + "pedestrianised": "pedestrianized", + "pedestrianises": "pedestrianizes", + "pedestrianising": "pedestrianizing", + "penalise": "penalize", + "penalised": "penalized", + "penalises": "penalizes", + "penalising": "penalizing", + "pencilled": "penciled", + "pencilling": "penciling", + "personalise": "personalize", + "personalised": "personalized", + "personalises": "personalizes", + "personalising": "personalizing", + "pharmacopoeia": "pharmacopeia", + "pharmacopoeias": "pharmacopeias", + "philosophise": "philosophize", + "philosophised": "philosophized", + "philosophises": "philosophizes", + "philosophising": "philosophizing", + "philtre": "filter", + "philtres": "filters", + "phoney": "phony", + "plagiarise": "plagiarize", + "plagiarised": "plagiarized", + "plagiarises": "plagiarizes", + "plagiarising": "plagiarizing", + "plough": "plow", + "ploughed": "plowed", + "ploughing": "plowing", + "ploughman": "plowman", + "ploughmen": "plowmen", + "ploughs": "plows", + "ploughshare": "plowshare", + "ploughshares": "plowshares", + "polarisation": "polarization", + "polarise": "polarize", + "polarised": "polarized", + "polarises": "polarizes", + "polarising": "polarizing", + "politicisation": "politicization", + "politicise": "politicize", + "politicised": "politicized", + "politicises": "politicizes", + "politicising": "politicizing", + "popularisation": "popularization", + "popularise": "popularize", + "popularised": "popularized", + "popularises": "popularizes", + "popularising": "popularizing", + "pouffe": "pouf", + "pouffes": "poufs", + "practise": "practice", + "practised": "practiced", + "practises": "practices", + "practising": "practicing", + "praesidium": "presidium", + "praesidiums": "presidiums", + "pressurisation": "pressurization", + "pressurise": "pressurize", + "pressurised": "pressurized", + "pressurises": "pressurizes", + "pressurising": "pressurizing", + "pretence": "pretense", + "pretences": "pretenses", + "primaeval": "primeval", + "prioritisation": "prioritization", + "prioritise": "prioritize", + "prioritised": "prioritized", + "prioritises": "prioritizes", + "prioritising": "prioritizing", + "privatisation": "privatization", + "privatisations": "privatizations", + "privatise": "privatize", + "privatised": "privatized", + "privatises": "privatizes", + "privatising": "privatizing", + "professionalisation": "professionalization", + "professionalise": "professionalize", + "professionalised": "professionalized", + "professionalises": "professionalizes", + "professionalising": "professionalizing", + "programme": "program", + "programmes": "programs", + "prologue": "prolog", + "prologues": "prologs", + "propagandise": "propagandize", + "propagandised": "propagandized", + "propagandises": "propagandizes", + "propagandising": "propagandizing", + "proselytise": "proselytize", + "proselytised": "proselytized", + "proselytiser": "proselytizer", + "proselytisers": "proselytizers", + "proselytises": "proselytizes", + "proselytising": "proselytizing", + "psychoanalyse": "psychoanalyze", + "psychoanalysed": "psychoanalyzed", + "psychoanalyses": "psychoanalyzes", + "psychoanalysing": "psychoanalyzing", + "publicise": "publicize", + "publicised": "publicized", + "publicises": "publicizes", + "publicising": "publicizing", + "pulverisation": "pulverization", + "pulverise": "pulverize", + "pulverised": "pulverized", + "pulverises": "pulverizes", + "pulverising": "pulverizing", + "pummelled": "pummel", + "pummelling": "pummeled", + "pyjama": "pajama", + "pyjamas": "pajamas", + "pzazz": "pizzazz", + "quarrelled": "quarreled", + "quarrelling": "quarreling", + "radicalise": "radicalize", + "radicalised": "radicalized", + "radicalises": "radicalizes", + "radicalising": "radicalizing", + "rancour": "rancor", + "randomise": "randomize", + "randomised": "randomized", + "randomises": "randomizes", + "randomising": "randomizing", + "rationalisation": "rationalization", + "rationalisations": "rationalizations", + "rationalise": "rationalize", + "rationalised": "rationalized", + "rationalises": "rationalizes", + "rationalising": "rationalizing", + "ravelled": "raveled", + "ravelling": "raveling", + "realisable": "realizable", + "realisation": "realization", + "realisations": "realizations", + "realise": "realize", + "realised": "realized", + "realises": "realizes", + "realising": "realizing", + "recognisable": "recognizable", + "recognisably": "recognizably", + "recognisance": "recognizance", + "recognise": "recognize", + "recognised": "recognized", + "recognises": "recognizes", + "recognising": "recognizing", + "reconnoitre": "reconnoiter", + "reconnoitred": "reconnoitered", + "reconnoitres": "reconnoiters", + "reconnoitring": "reconnoitering", + "refuelled": "refueled", + "refuelling": "refueling", + "regularisation": "regularization", + "regularise": "regularize", + "regularised": "regularized", + "regularises": "regularizes", + "regularising": "regularizing", + "remodelled": "remodeled", + "remodelling": "remodeling", + "remould": "remold", + "remoulded": "remolded", + "remoulding": "remolding", + "remoulds": "remolds", + "reorganisation": "reorganization", + "reorganisations": "reorganizations", + "reorganise": "reorganize", + "reorganised": "reorganized", + "reorganises": "reorganizes", + "reorganising": "reorganizing", + "revelled": "reveled", + "reveller": "reveler", + "revellers": "revelers", + "revelling": "reveling", + "revitalise": "revitalize", + "revitalised": "revitalized", + "revitalises": "revitalizes", + "revitalising": "revitalizing", + "revolutionise": "revolutionize", + "revolutionised": "revolutionized", + "revolutionises": "revolutionizes", + "revolutionising": "revolutionizing", + "rhapsodise": "rhapsodize", + "rhapsodised": "rhapsodized", + "rhapsodises": "rhapsodizes", + "rhapsodising": "rhapsodizing", + "rigour": "rigor", + "rigours": "rigors", + "ritualised": "ritualized", + "rivalled": "rivaled", + "rivalling": "rivaling", + "romanticise": "romanticize", + "romanticised": "romanticized", + "romanticises": "romanticizes", + "romanticising": "romanticizing", + "rumour": "rumor", + "rumoured": "rumored", + "rumours": "rumors", + "sabre": "saber", + "sabres": "sabers", + "saltpetre": "saltpeter", + "sanitise": "sanitize", + "sanitised": "sanitized", + "sanitises": "sanitizes", + "sanitising": "sanitizing", + "satirise": "satirize", + "satirised": "satirized", + "satirises": "satirizes", + "satirising": "satirizing", + "saviour": "savior", + "saviours": "saviors", + "savour": "savor", + "savoured": "savored", + "savouries": "savories", + "savouring": "savoring", + "savours": "savors", + "savoury": "savory", + "scandalise": "scandalize", + "scandalised": "scandalized", + "scandalises": "scandalizes", + "scandalising": "scandalizing", + "sceptic": "skeptic", + "sceptical": "skeptical", + "sceptically": "skeptically", + "scepticism": "skepticism", + "sceptics": "skeptics", + "sceptre": "scepter", + "sceptres": "scepters", + "scrutinise": "scrutinize", + "scrutinised": "scrutinized", + "scrutinises": "scrutinizes", + "scrutinising": "scrutinizing", + "secularisation": "secularization", + "secularise": "secularize", + "secularised": "secularized", + "secularises": "secularizes", + "secularising": "secularizing", + "sensationalise": "sensationalize", + "sensationalised": "sensationalized", + "sensationalises": "sensationalizes", + "sensationalising": "sensationalizing", + "sensitise": "sensitize", + "sensitised": "sensitized", + "sensitises": "sensitizes", + "sensitising": "sensitizing", + "sentimentalise": "sentimentalize", + "sentimentalised": "sentimentalized", + "sentimentalises": "sentimentalizes", + "sentimentalising": "sentimentalizing", + "sepulchre": "sepulcher", + "sepulchres": "sepulchers", + "serialisation": "serialization", + "serialisations": "serializations", + "serialise": "serialize", + "serialised": "serialized", + "serialises": "serializes", + "serialising": "serializing", + "sermonise": "sermonize", + "sermonised": "sermonized", + "sermonises": "sermonizes", + "sermonising": "sermonizing", + "sheikh": "sheik", + "shovelled": "shoveled", + "shovelling": "shoveling", + "shrivelled": "shriveled", + "shrivelling": "shriveling", + "signalise": "signalize", + "signalised": "signalized", + "signalises": "signalizes", + "signalising": "signalizing", + "signalled": "signaled", + "signalling": "signaling", + "smoulder": "smolder", + "smouldered": "smoldered", + "smouldering": "smoldering", + "smoulders": "smolders", + "snivelled": "sniveled", + "snivelling": "sniveling", + "snorkelled": "snorkeled", + "snorkelling": "snorkeling", + "snowplough": "snowplow", + "snowploughs": "snowplow", + "socialisation": "socialization", + "socialise": "socialize", + "socialised": "socialized", + "socialises": "socializes", + "socialising": "socializing", + "sodomise": "sodomize", + "sodomised": "sodomized", + "sodomises": "sodomizes", + "sodomising": "sodomizing", + "solemnise": "solemnize", + "solemnised": "solemnized", + "solemnises": "solemnizes", + "solemnising": "solemnizing", + "sombre": "somber", + "specialisation": "specialization", + "specialisations": "specializations", + "specialise": "specialize", + "specialised": "specialized", + "specialises": "specializes", + "specialising": "specializing", + "spectre": "specter", + "spectres": "specters", + "spiralled": "spiraled", + "spiralling": "spiraling", + "splendour": "splendor", + "splendours": "splendors", + "squirrelled": "squirreled", + "squirrelling": "squirreling", + "stabilisation": "stabilization", + "stabilise": "stabilize", + "stabilised": "stabilized", + "stabiliser": "stabilizer", + "stabilisers": "stabilizers", + "stabilises": "stabilizes", + "stabilising": "stabilizing", + "standardisation": "standardization", + "standardise": "standardize", + "standardised": "standardized", + "standardises": "standardizes", + "standardising": "standardizing", + "stencilled": "stenciled", + "stencilling": "stenciling", + "sterilisation": "sterilization", + "sterilisations": "sterilizations", + "sterilise": "sterilize", + "sterilised": "sterilized", + "steriliser": "sterilizer", + "sterilisers": "sterilizers", + "sterilises": "sterilizes", + "sterilising": "sterilizing", + "stigmatisation": "stigmatization", + "stigmatise": "stigmatize", + "stigmatised": "stigmatized", + "stigmatises": "stigmatizes", + "stigmatising": "stigmatizing", + "storey": "story", + "storeys": "stories", + "subsidisation": "subsidization", + "subsidise": "subsidize", + "subsidised": "subsidized", + "subsidiser": "subsidizer", + "subsidisers": "subsidizers", + "subsidises": "subsidizes", + "subsidising": "subsidizing", + "succour": "succor", + "succoured": "succored", + "succouring": "succoring", + "succours": "succors", + "sulphate": "sulfate", + "sulphates": "sulfates", + "sulphide": "sulfide", + "sulphides": "sulfides", + "sulphur": "sulfur", + "sulphurous": "sulfurous", + "summarise": "summarize", + "summarised": "summarized", + "summarises": "summarizes", + "summarising": "summarizing", + "swivelled": "swiveled", + "swivelling": "swiveling", + "symbolise": "symbolize", + "symbolised": "symbolized", + "symbolises": "symbolizes", + "symbolising": "symbolizing", + "sympathise": "sympathize", + "sympathised": "sympathized", + "sympathiser": "sympathizer", + "sympathisers": "sympathizers", + "sympathises": "sympathizes", + "sympathising": "sympathizing", + "synchronisation": "synchronization", + "synchronise": "synchronize", + "synchronised": "synchronized", + "synchronises": "synchronizes", + "synchronising": "synchronizing", + "synthesise": "synthesize", + "synthesised": "synthesized", + "synthesiser": "synthesizer", + "synthesisers": "synthesizers", + "synthesises": "synthesizes", + "synthesising": "synthesizing", + "syphon": "siphon", + "syphoned": "siphoned", + "syphoning": "siphoning", + "syphons": "siphons", + "systematisation": "systematization", + "systematise": "systematize", + "systematised": "systematized", + "systematises": "systematizes", + "systematising": "systematizing", + "tantalise": "tantalize", + "tantalised": "tantalized", + "tantalises": "tantalizes", + "tantalising": "tantalizing", + "tantalisingly": "tantalizingly", + "tasselled": "tasseled", + "technicolour": "technicolor", + "temporise": "temporize", + "temporised": "temporized", + "temporises": "temporizes", + "temporising": "temporizing", + "tenderise": "tenderize", + "tenderised": "tenderized", + "tenderises": "tenderizes", + "tenderising": "tenderizing", + "terrorise": "terrorize", + "terrorised": "terrorized", + "terrorises": "terrorizes", + "terrorising": "terrorizing", + "theatre": "theater", + "theatregoer": "theatergoer", + "theatregoers": "theatergoers", + "theatres": "theaters", + "theorise": "theorize", + "theorised": "theorized", + "theorises": "theorizes", + "theorising": "theorizing", + "tonne": "ton", + "tonnes": "tons", + "towelled": "toweled", + "towelling": "toweling", + "toxaemia": "toxemia", + "tranquillise": "tranquilize", + "tranquillised": "tranquilized", + "tranquilliser": "tranquilizer", + "tranquillisers": "tranquilizers", + "tranquillises": "tranquilizes", + "tranquillising": "tranquilizing", + "tranquillity": "tranquility", + "tranquillize": "tranquilize", + "tranquillized": "tranquilized", + "tranquillizer": "tranquilizer", + "tranquillizers": "tranquilizers", + "tranquillizes": "tranquilizes", + "tranquillizing": "tranquilizing", + "tranquilly": "tranquility", + "transistorised": "transistorized", + "traumatise": "traumatize", + "traumatised": "traumatized", + "traumatises": "traumatizes", + "traumatising": "traumatizing", + "travelled": "traveled", + "traveller": "traveler", + "travellers": "travelers", + "travelling": "traveling", + "travelog": "travelogue", + "travelogs": "travelogues", + "trialled": "trialed", + "trialling": "trialing", + "tricolour": "tricolor", + "tricolours": "tricolors", + "trivialise": "trivialize", + "trivialised": "trivialized", + "trivialises": "trivializes", + "trivialising": "trivializing", + "tumour": "tumor", + "tumours": "tumors", + "tunnelled": "tunneled", + "tunnelling": "tunneling", + "tyrannise": "tyrannize", + "tyrannised": "tyrannized", + "tyrannises": "tyrannizes", + "tyrannising": "tyrannizing", + "tyre": "tire", + "tyres": "tires", + "unauthorised": "unauthorized", + "uncivilised": "uncivilized", + "underutilised": "underutilized", + "unequalled": "unequaled", + "unfavourable": "unfavorable", + "unfavourably": "unfavorably", + "unionisation": "unionization", + "unionise": "unionize", + "unionised": "unionized", + "unionises": "unionizes", + "unionising": "unionizing", + "unorganised": "unorganized", + "unravelled": "unraveled", + "unravelling": "unraveling", + "unrecognisable": "unrecognizable", + "unrecognised": "unrecognized", + "unrivalled": "unrivaled", + "unsavoury": "unsavory", + "untrammelled": "untrammeled", + "urbanisation": "urbanization", + "urbanise": "urbanize", + "urbanised": "urbanized", + "urbanises": "urbanizes", + "urbanising": "urbanizing", + "utilisable": "utilizable", + "utilisation": "utilization", + "utilise": "utilize", + "utilised": "utilized", + "utilises": "utilizes", + "utilising": "utilizing", + "valour": "valor", + "vandalise": "vandalize", + "vandalised": "vandalized", + "vandalises": "vandalizes", + "vandalising": "vandalizing", + "vaporisation": "vaporization", + "vaporise": "vaporize", + "vaporised": "vaporized", + "vaporises": "vaporizes", + "vaporising": "vaporizing", + "vapour": "vapor", + "vapours": "vapors", + "verbalise": "verbalize", + "verbalised": "verbalized", + "verbalises": "verbalizes", + "verbalising": "verbalizing", + "victimisation": "victimization", + "victimise": "victimize", + "victimised": "victimized", + "victimises": "victimizes", + "victimising": "victimizing", + "videodisc": "videodisk", + "videodiscs": "videodisks", + "vigour": "vigor", + "visualisation": "visualization", + "visualisations": "visualizations", + "visualise": "visualize", + "visualised": "visualized", + "visualises": "visualizes", + "visualising": "visualizing", + "vocalisation": "vocalization", + "vocalisations": "vocalizations", + "vocalise": "vocalize", + "vocalised": "vocalized", + "vocalises": "vocalizes", + "vocalising": "vocalizing", + "vulcanised": "vulcanized", + "vulgarisation": "vulgarization", + "vulgarise": "vulgarize", + "vulgarised": "vulgarized", + "vulgarises": "vulgarizes", + "vulgarising": "vulgarizing", + "waggon": "wagon", + "waggons": "wagons", + "watercolour": "watercolor", + "watercolours": "watercolors", + "weaselled": "weaseled", + "weaselling": "weaseling", + "westernisation": "westernization", + "westernise": "westernize", + "westernised": "westernized", + "westernises": "westernizes", + "westernising": "westernizing", + "womanise": "womanize", + "womanised": "womanized", + "womaniser": "womanizer", + "womanisers": "womanizers", + "womanises": "womanizes", + "womanising": "womanizing", + "woollen": "woolen", + "woollens": "woolens", + "woollies": "woolies", + "woolly": "wooly", + "worshipped": "worshiped", + "worshipping": "worshiping", + "worshipper": "worshiper", + "yodelled": "yodeled", + "yodelling": "yodeling", + "yoghourt": "yogurt", + "yoghourts": "yogurts", + "yoghurt": "yogurt", + "yoghurts": "yogurts", + "mhm": "hmm", + "mmm": "hmm" +} \ No newline at end of file diff --git a/src/subtitle/whisper/normalizers/english.py b/src/subtitle/whisper/normalizers/english.py new file mode 100644 index 0000000..4932042 --- /dev/null +++ b/src/subtitle/whisper/normalizers/english.py @@ -0,0 +1,550 @@ +import json +import os +import re +from fractions import Fraction +from typing import Iterator, List, Match, Optional, Union + +from more_itertools import windowed + +from .basic import remove_symbols_and_diacritics + + +class EnglishNumberNormalizer: + """ + Convert any spelled-out numbers into arabic numbers, while handling: + + - remove any commas + - keep the suffixes such as: `1960s`, `274th`, `32nd`, etc. + - spell out currency symbols after the number. e.g. `$20 million` -> `20000000 dollars` + - spell out `one` and `ones` + - interpret successive single-digit numbers as nominal: `one oh one` -> `101` + """ + + def __init__(self): + super().__init__() + + self.zeros = {"o", "oh", "zero"} + self.ones = { + name: i + for i, name in enumerate( + [ + "one", + "two", + "three", + "four", + "five", + "six", + "seven", + "eight", + "nine", + "ten", + "eleven", + "twelve", + "thirteen", + "fourteen", + "fifteen", + "sixteen", + "seventeen", + "eighteen", + "nineteen", + ], + start=1, + ) + } + self.ones_plural = { + "sixes" if name == "six" else name + "s": (value, "s") + for name, value in self.ones.items() + } + self.ones_ordinal = { + "zeroth": (0, "th"), + "first": (1, "st"), + "second": (2, "nd"), + "third": (3, "rd"), + "fifth": (5, "th"), + "twelfth": (12, "th"), + **{ + name + ("h" if name.endswith("t") else "th"): (value, "th") + for name, value in self.ones.items() + if value > 3 and value != 5 and value != 12 + }, + } + self.ones_suffixed = {**self.ones_plural, **self.ones_ordinal} + + self.tens = { + "twenty": 20, + "thirty": 30, + "forty": 40, + "fifty": 50, + "sixty": 60, + "seventy": 70, + "eighty": 80, + "ninety": 90, + } + self.tens_plural = { + name.replace("y", "ies"): (value, "s") for name, value in self.tens.items() + } + self.tens_ordinal = { + name.replace("y", "ieth"): (value, "th") + for name, value in self.tens.items() + } + self.tens_suffixed = {**self.tens_plural, **self.tens_ordinal} + + self.multipliers = { + "hundred": 100, + "thousand": 1_000, + "million": 1_000_000, + "billion": 1_000_000_000, + "trillion": 1_000_000_000_000, + "quadrillion": 1_000_000_000_000_000, + "quintillion": 1_000_000_000_000_000_000, + "sextillion": 1_000_000_000_000_000_000_000, + "septillion": 1_000_000_000_000_000_000_000_000, + "octillion": 1_000_000_000_000_000_000_000_000_000, + "nonillion": 1_000_000_000_000_000_000_000_000_000_000, + "decillion": 1_000_000_000_000_000_000_000_000_000_000_000, + } + self.multipliers_plural = { + name + "s": (value, "s") for name, value in self.multipliers.items() + } + self.multipliers_ordinal = { + name + "th": (value, "th") for name, value in self.multipliers.items() + } + self.multipliers_suffixed = { + **self.multipliers_plural, + **self.multipliers_ordinal, + } + self.decimals = {*self.ones, *self.tens, *self.zeros} + + self.preceding_prefixers = { + "minus": "-", + "negative": "-", + "plus": "+", + "positive": "+", + } + self.following_prefixers = { + "pound": "£", + "pounds": "£", + "euro": "€", + "euros": "€", + "dollar": "$", + "dollars": "$", + "cent": "¢", + "cents": "¢", + } + self.prefixes = set( + list(self.preceding_prefixers.values()) + + list(self.following_prefixers.values()) + ) + self.suffixers = { + "per": {"cent": "%"}, + "percent": "%", + } + self.specials = {"and", "double", "triple", "point"} + + self.words = set( + [ + key + for mapping in [ + self.zeros, + self.ones, + self.ones_suffixed, + self.tens, + self.tens_suffixed, + self.multipliers, + self.multipliers_suffixed, + self.preceding_prefixers, + self.following_prefixers, + self.suffixers, + self.specials, + ] + for key in mapping + ] + ) + self.literal_words = {"one", "ones"} + + def process_words(self, words: List[str]) -> Iterator[str]: + prefix: Optional[str] = None + value: Optional[Union[str, int]] = None + skip = False + + def to_fraction(s: str): + try: + return Fraction(s) + except ValueError: + return None + + def output(result: Union[str, int]): + nonlocal prefix, value + result = str(result) + if prefix is not None: + result = prefix + result + value = None + prefix = None + return result + + if len(words) == 0: + return + + for prev, current, next in windowed([None] + words + [None], 3): + if skip: + skip = False + continue + + next_is_numeric = next is not None and re.match(r"^\d+(\.\d+)?$", next) + has_prefix = current[0] in self.prefixes + current_without_prefix = current[1:] if has_prefix else current + if re.match(r"^\d+(\.\d+)?$", current_without_prefix): + # arabic numbers (potentially with signs and fractions) + f = to_fraction(current_without_prefix) + assert f is not None + if value is not None: + if isinstance(value, str) and value.endswith("."): + # concatenate decimals / ip address components + value = str(value) + str(current) + continue + else: + yield output(value) + + prefix = current[0] if has_prefix else prefix + if f.denominator == 1: + value = f.numerator # store integers as int + else: + value = current_without_prefix + elif current not in self.words: + # non-numeric words + if value is not None: + yield output(value) + yield output(current) + elif current in self.zeros: + value = str(value or "") + "0" + elif current in self.ones: + ones = self.ones[current] + + if value is None: + value = ones + elif isinstance(value, str) or prev in self.ones: + if ( + prev in self.tens and ones < 10 + ): # replace the last zero with the digit + assert value[-1] == "0" + value = value[:-1] + str(ones) + else: + value = str(value) + str(ones) + elif ones < 10: + if value % 10 == 0: + value += ones + else: + value = str(value) + str(ones) + else: # eleven to nineteen + if value % 100 == 0: + value += ones + else: + value = str(value) + str(ones) + elif current in self.ones_suffixed: + # ordinal or cardinal; yield the number right away + ones, suffix = self.ones_suffixed[current] + if value is None: + yield output(str(ones) + suffix) + elif isinstance(value, str) or prev in self.ones: + if prev in self.tens and ones < 10: + assert value[-1] == "0" + yield output(value[:-1] + str(ones) + suffix) + else: + yield output(str(value) + str(ones) + suffix) + elif ones < 10: + if value % 10 == 0: + yield output(str(value + ones) + suffix) + else: + yield output(str(value) + str(ones) + suffix) + else: # eleven to nineteen + if value % 100 == 0: + yield output(str(value + ones) + suffix) + else: + yield output(str(value) + str(ones) + suffix) + value = None + elif current in self.tens: + tens = self.tens[current] + if value is None: + value = tens + elif isinstance(value, str): + value = str(value) + str(tens) + else: + if value % 100 == 0: + value += tens + else: + value = str(value) + str(tens) + elif current in self.tens_suffixed: + # ordinal or cardinal; yield the number right away + tens, suffix = self.tens_suffixed[current] + if value is None: + yield output(str(tens) + suffix) + elif isinstance(value, str): + yield output(str(value) + str(tens) + suffix) + else: + if value % 100 == 0: + yield output(str(value + tens) + suffix) + else: + yield output(str(value) + str(tens) + suffix) + elif current in self.multipliers: + multiplier = self.multipliers[current] + if value is None: + value = multiplier + elif isinstance(value, str) or value == 0: + f = to_fraction(value) + p = f * multiplier if f is not None else None + if f is not None and p.denominator == 1: + value = p.numerator + else: + yield output(value) + value = multiplier + else: + before = value // 1000 * 1000 + residual = value % 1000 + value = before + residual * multiplier + elif current in self.multipliers_suffixed: + multiplier, suffix = self.multipliers_suffixed[current] + if value is None: + yield output(str(multiplier) + suffix) + elif isinstance(value, str): + f = to_fraction(value) + p = f * multiplier if f is not None else None + if f is not None and p.denominator == 1: + yield output(str(p.numerator) + suffix) + else: + yield output(value) + yield output(str(multiplier) + suffix) + else: # int + before = value // 1000 * 1000 + residual = value % 1000 + value = before + residual * multiplier + yield output(str(value) + suffix) + value = None + elif current in self.preceding_prefixers: + # apply prefix (positive, minus, etc.) if it precedes a number + if value is not None: + yield output(value) + + if next in self.words or next_is_numeric: + prefix = self.preceding_prefixers[current] + else: + yield output(current) + elif current in self.following_prefixers: + # apply prefix (dollars, cents, etc.) only after a number + if value is not None: + prefix = self.following_prefixers[current] + yield output(value) + else: + yield output(current) + elif current in self.suffixers: + # apply suffix symbols (percent -> '%') + if value is not None: + suffix = self.suffixers[current] + if isinstance(suffix, dict): + if next in suffix: + yield output(str(value) + suffix[next]) + skip = True + else: + yield output(value) + yield output(current) + else: + yield output(str(value) + suffix) + else: + yield output(current) + elif current in self.specials: + if next not in self.words and not next_is_numeric: + # apply special handling only if the next word can be numeric + if value is not None: + yield output(value) + yield output(current) + elif current == "and": + # ignore "and" after hundreds, thousands, etc. + if prev not in self.multipliers: + if value is not None: + yield output(value) + yield output(current) + elif current == "double" or current == "triple": + if next in self.ones or next in self.zeros: + repeats = 2 if current == "double" else 3 + ones = self.ones.get(next, 0) + value = str(value or "") + str(ones) * repeats + skip = True + else: + if value is not None: + yield output(value) + yield output(current) + elif current == "point": + if next in self.decimals or next_is_numeric: + value = str(value or "") + "." + else: + # should all have been covered at this point + raise ValueError(f"Unexpected token: {current}") + else: + # all should have been covered at this point + raise ValueError(f"Unexpected token: {current}") + + if value is not None: + yield output(value) + + def preprocess(self, s: str): + # replace " and a half" with " point five" + results = [] + + segments = re.split(r"\band\s+a\s+half\b", s) + for i, segment in enumerate(segments): + if len(segment.strip()) == 0: + continue + if i == len(segments) - 1: + results.append(segment) + else: + results.append(segment) + last_word = segment.rsplit(maxsplit=2)[-1] + if last_word in self.decimals or last_word in self.multipliers: + results.append("point five") + else: + results.append("and a half") + + s = " ".join(results) + + # put a space at number/letter boundary + s = re.sub(r"([a-z])([0-9])", r"\1 \2", s) + s = re.sub(r"([0-9])([a-z])", r"\1 \2", s) + + # but remove spaces which could be a suffix + s = re.sub(r"([0-9])\s+(st|nd|rd|th|s)\b", r"\1\2", s) + + return s + + def postprocess(self, s: str): + def combine_cents(m: Match): + try: + currency = m.group(1) + integer = m.group(2) + cents = int(m.group(3)) + return f"{currency}{integer}.{cents:02d}" + except ValueError: + return m.string + + def extract_cents(m: Match): + try: + return f"¢{int(m.group(1))}" + except ValueError: + return m.string + + # apply currency postprocessing; "$2 and ¢7" -> "$2.07" + s = re.sub(r"([€£$])([0-9]+) (?:and )?¢([0-9]{1,2})\b", combine_cents, s) + s = re.sub(r"[€£$]0.([0-9]{1,2})\b", extract_cents, s) + + # write "one(s)" instead of "1(s)", just for the readability + s = re.sub(r"\b1(s?)\b", r"one\1", s) + + return s + + def __call__(self, s: str): + s = self.preprocess(s) + s = " ".join(word for word in self.process_words(s.split()) if word is not None) + s = self.postprocess(s) + + return s + + +class EnglishSpellingNormalizer: + """ + Applies British-American spelling mappings as listed in [1]. + + [1] https://www.tysto.com/uk-us-spelling-list.html + """ + + def __init__(self): + mapping_path = os.path.join(os.path.dirname(__file__), "english.json") + self.mapping = json.load(open(mapping_path)) + + def __call__(self, s: str): + return " ".join(self.mapping.get(word, word) for word in s.split()) + + +class EnglishTextNormalizer: + def __init__(self): + self.ignore_patterns = r"\b(hmm|mm|mhm|mmm|uh|um)\b" + self.replacers = { + # common contractions + r"\bwon't\b": "will not", + r"\bcan't\b": "can not", + r"\blet's\b": "let us", + r"\bain't\b": "aint", + r"\by'all\b": "you all", + r"\bwanna\b": "want to", + r"\bgotta\b": "got to", + r"\bgonna\b": "going to", + r"\bi'ma\b": "i am going to", + r"\bimma\b": "i am going to", + r"\bwoulda\b": "would have", + r"\bcoulda\b": "could have", + r"\bshoulda\b": "should have", + r"\bma'am\b": "madam", + # contractions in titles/prefixes + r"\bmr\b": "mister ", + r"\bmrs\b": "missus ", + r"\bst\b": "saint ", + r"\bdr\b": "doctor ", + r"\bprof\b": "professor ", + r"\bcapt\b": "captain ", + r"\bgov\b": "governor ", + r"\bald\b": "alderman ", + r"\bgen\b": "general ", + r"\bsen\b": "senator ", + r"\brep\b": "representative ", + r"\bpres\b": "president ", + r"\brev\b": "reverend ", + r"\bhon\b": "honorable ", + r"\basst\b": "assistant ", + r"\bassoc\b": "associate ", + r"\blt\b": "lieutenant ", + r"\bcol\b": "colonel ", + r"\bjr\b": "junior ", + r"\bsr\b": "senior ", + r"\besq\b": "esquire ", + # prefect tenses, ideally it should be any past participles, but it's harder.. + r"'d been\b": " had been", + r"'s been\b": " has been", + r"'d gone\b": " had gone", + r"'s gone\b": " has gone", + r"'d done\b": " had done", # "'s done" is ambiguous + r"'s got\b": " has got", + # general contractions + r"n't\b": " not", + r"'re\b": " are", + r"'s\b": " is", + r"'d\b": " would", + r"'ll\b": " will", + r"'t\b": " not", + r"'ve\b": " have", + r"'m\b": " am", + } + self.standardize_numbers = EnglishNumberNormalizer() + self.standardize_spellings = EnglishSpellingNormalizer() + + def __call__(self, s: str): + s = s.lower() + + s = re.sub(r"[<\[][^>\]]*[>\]]", "", s) # remove words between brackets + s = re.sub(r"\(([^)]+?)\)", "", s) # remove words between parenthesis + s = re.sub(self.ignore_patterns, "", s) + s = re.sub(r"\s+'", "'", s) # when there's a space before an apostrophe + + for pattern, replacement in self.replacers.items(): + s = re.sub(pattern, replacement, s) + + s = re.sub(r"(\d),(\d)", r"\1\2", s) # remove commas between digits + s = re.sub(r"\.([^0-9]|$)", r" \1", s) # remove periods not followed by numbers + s = remove_symbols_and_diacritics(s, keep=".%$¢€£") # keep numeric symbols + + s = self.standardize_numbers(s) + s = self.standardize_spellings(s) + + # now remove prefix/suffix symbols that are not preceded/followed by numbers + s = re.sub(r"[.$¢€£]([^0-9])", r" \1", s) + s = re.sub(r"([^0-9])%", r"\1 ", s) + + s = re.sub(r"\s+", " ", s) # replace any successive whitespaces with a space + + return s diff --git a/src/subtitle/whisper/timing.py b/src/subtitle/whisper/timing.py new file mode 100644 index 0000000..f5d07e5 --- /dev/null +++ b/src/subtitle/whisper/timing.py @@ -0,0 +1,334 @@ +import itertools +import subprocess +import warnings +from dataclasses import dataclass +from typing import TYPE_CHECKING, List + +import numba +import numpy as np +import torch +import torch.nn.functional as F + +from .audio import HOP_LENGTH, SAMPLE_RATE, TOKENS_PER_SECOND +from .tokenizer import Tokenizer + +if TYPE_CHECKING: + from .model import Whisper + + +def median_filter(x: torch.Tensor, filter_width: int): + """Apply a median filter of width `filter_width` along the last dimension of `x`""" + pad_width = filter_width // 2 + if x.shape[-1] <= pad_width: + # F.pad requires the padding width to be smaller than the input dimension + return x + + if (ndim := x.ndim) <= 2: + # `F.pad` does not support 1D or 2D inputs for reflect padding but supports 3D and 4D + x = x[None, None, :] + + assert ( + filter_width > 0 and filter_width % 2 == 1 + ), "`filter_width` should be an odd number" + + result = None + x = F.pad(x, (filter_width // 2, filter_width // 2, 0, 0), mode="reflect") + if x.is_cuda: + try: + from .triton_ops import median_filter_cuda + + result = median_filter_cuda(x, filter_width) + except (RuntimeError, subprocess.CalledProcessError): + warnings.warn( + "Failed to launch Triton kernels, likely due to missing CUDA toolkit; " + "falling back to a slower median kernel implementation..." + ) + + if result is None: + # sort() is faster than torch.median (https://github.com/pytorch/pytorch/issues/51450) + result = x.unfold(-1, filter_width, 1).sort()[0][..., filter_width // 2] + + if ndim <= 2: + result = result[0, 0] + + return result + + +@numba.jit +def backtrace(trace: np.ndarray): + i = trace.shape[0] - 1 + j = trace.shape[1] - 1 + trace[0, :] = 2 + trace[:, 0] = 1 + + result = [] + while i > 0 or j > 0: + result.append((i - 1, j - 1)) + + if trace[i, j] == 0: + i -= 1 + j -= 1 + elif trace[i, j] == 1: + i -= 1 + elif trace[i, j] == 2: + j -= 1 + else: + raise ValueError("Unexpected trace[i, j]") + + result = np.array(result) + return result[::-1, :].T + + +@numba.jit(nopython=True, parallel=True) +def dtw_cpu(x: np.ndarray): + N, M = x.shape + cost = np.ones((N + 1, M + 1), dtype=np.float32) * np.inf + trace = -np.ones((N + 1, M + 1), dtype=np.float32) + + cost[0, 0] = 0 + for j in range(1, M + 1): + for i in range(1, N + 1): + c0 = cost[i - 1, j - 1] + c1 = cost[i - 1, j] + c2 = cost[i, j - 1] + + if c0 < c1 and c0 < c2: + c, t = c0, 0 + elif c1 < c0 and c1 < c2: + c, t = c1, 1 + else: + c, t = c2, 2 + + cost[i, j] = x[i - 1, j - 1] + c + trace[i, j] = t + + return backtrace(trace) + + +def dtw_cuda(x, BLOCK_SIZE=1024): + from .triton_ops import dtw_kernel + + M, N = x.shape + assert M < BLOCK_SIZE, f"M should be smaller than {BLOCK_SIZE=}" + + x_skew = ( + F.pad(x, (0, M + 1), value=np.inf).flatten()[: M * (N + M)].reshape(M, N + M) + ) + x_skew = x_skew.T.contiguous() + cost = torch.ones(N + M + 2, M + 2) * np.inf + cost[0, 0] = 0 + cost = cost.cuda() + trace = torch.zeros_like(cost, dtype=torch.int32) + + dtw_kernel[(1,)]( + cost, + trace, + x_skew, + x_skew.stride(0), + cost.stride(0), + trace.stride(0), + N, + M, + BLOCK_SIZE=BLOCK_SIZE, + ) + + trace = trace.T.flatten()[: (M + 1) * (M + N + 3)].reshape(M + 1, M + N + 3)[ + :, : N + 1 + ] + return backtrace(trace.cpu().numpy()) + + +def dtw(x: torch.Tensor) -> np.ndarray: + if x.is_cuda: + try: + return dtw_cuda(x) + except (RuntimeError, subprocess.CalledProcessError): + warnings.warn( + "Failed to launch Triton kernels, likely due to missing CUDA toolkit; " + "falling back to a slower DTW implementation..." + ) + + return dtw_cpu(x.double().cpu().numpy()) + + +@dataclass +class WordTiming: + word: str + tokens: List[int] + start: float + end: float + probability: float + + +def find_alignment( + model: "Whisper", + tokenizer: Tokenizer, + text_tokens: List[int], + mel: torch.Tensor, + num_frames: int, + *, + medfilt_width: int = 7, + qk_scale: float = 1.0, +) -> List[WordTiming]: + if len(text_tokens) == 0: + return [] + + tokens = torch.tensor( + [ + *tokenizer.sot_sequence, + tokenizer.no_timestamps, + *text_tokens, + tokenizer.eot, + ] + ).to(model.device) + + # install hooks on the cross attention layers to retrieve the attention weights + QKs = [None] * model.dims.n_text_layer + hooks = [ + block.cross_attn.register_forward_hook( + lambda _, ins, outs, index=i: QKs.__setitem__(index, outs[-1][0]) + ) + for i, block in enumerate(model.decoder.blocks) + ] + + with torch.no_grad(): + logits = model(mel.unsqueeze(0), tokens.unsqueeze(0))[0] + sampled_logits = logits[len(tokenizer.sot_sequence) :, : tokenizer.eot] + token_probs = sampled_logits.softmax(dim=-1) + text_token_probs = token_probs[np.arange(len(text_tokens)), text_tokens] + text_token_probs = text_token_probs.tolist() + + for hook in hooks: + hook.remove() + + # heads * tokens * frames + weights = torch.stack([QKs[l][h] for l, h in model.alignment_heads.indices().T]) + weights = weights[:, :, : num_frames // 2] + weights = (weights * qk_scale).softmax(dim=-1) + std, mean = torch.std_mean(weights, dim=-2, keepdim=True, unbiased=False) + weights = (weights - mean) / std + weights = median_filter(weights, medfilt_width) + + matrix = weights.mean(axis=0) + matrix = matrix[len(tokenizer.sot_sequence) : -1] + text_indices, time_indices = dtw(-matrix) + + words, word_tokens = tokenizer.split_to_word_tokens(text_tokens + [tokenizer.eot]) + word_boundaries = np.pad(np.cumsum([len(t) for t in word_tokens[:-1]]), (1, 0)) + + jumps = np.pad(np.diff(text_indices), (1, 0), constant_values=1).astype(bool) + jump_times = time_indices[jumps] / TOKENS_PER_SECOND + start_times = jump_times[word_boundaries[:-1]] + end_times = jump_times[word_boundaries[1:]] + word_probabilities = [ + np.mean(text_token_probs[i:j]) + for i, j in zip(word_boundaries[:-1], word_boundaries[1:]) + ] + + # hack: ensure the first and second word is not longer than twice the median word duration. + # a better segmentation algorithm based on VAD should be able to replace this. + word_durations = end_times - start_times + word_durations = word_durations[word_durations.nonzero()] + if len(word_durations) > 0: + median_duration = np.median(word_durations) + max_duration = median_duration * 2 + if len(word_durations) >= 2 and word_durations[1] > max_duration: + boundary = max(end_times[2] / 2, end_times[2] - max_duration) + end_times[0] = start_times[1] = boundary + if len(word_durations) >= 1 and end_times[0] - start_times[0] > max_duration: + start_times[0] = max(0, end_times[0] - max_duration) + + return [ + WordTiming(word, tokens, start, end, probability) + for word, tokens, start, end, probability in zip( + words, word_tokens, start_times, end_times, word_probabilities + ) + ] + + +def merge_punctuations(alignment: List[WordTiming], prepended: str, appended: str): + # merge prepended punctuations + i = len(alignment) - 2 + j = len(alignment) - 1 + while i >= 0: + previous = alignment[i] + following = alignment[j] + if previous.word.startswith(" ") and previous.word.strip() in prepended: + # prepend it to the following word + following.word = previous.word + following.word + following.tokens = previous.tokens + following.tokens + previous.word = "" + previous.tokens = [] + else: + j = i + i -= 1 + + # merge appended punctuations + i = 0 + j = 1 + while j < len(alignment): + previous = alignment[i] + following = alignment[j] + if not previous.word.endswith(" ") and following.word in appended: + # append it to the previous word + previous.word = previous.word + following.word + previous.tokens = previous.tokens + following.tokens + following.word = "" + following.tokens = [] + else: + i = j + j += 1 + + +def add_word_timestamps( + *, + segments: List[dict], + model: "Whisper", + tokenizer: Tokenizer, + mel: torch.Tensor, + num_frames: int, + prepend_punctuations: str = "\"'“¿([{-", + append_punctuations: str = "\"'.。,，!！?？:：”)]}、", + **kwargs, +): + if len(segments) == 0: + return + + text_tokens_per_segment = [ + [token for token in segment["tokens"] if token < tokenizer.eot] + for segment in segments + ] + + text_tokens = list(itertools.chain.from_iterable(text_tokens_per_segment)) + alignment = find_alignment(model, tokenizer, text_tokens, mel, num_frames, **kwargs) + merge_punctuations(alignment, prepend_punctuations, append_punctuations) + + time_offset = segments[0]["seek"] * HOP_LENGTH / SAMPLE_RATE + word_index = 0 + + for segment, text_tokens in zip(segments, text_tokens_per_segment): + saved_tokens = 0 + words = [] + + while word_index < len(alignment) and saved_tokens < len(text_tokens): + timing = alignment[word_index] + + if timing.word: + words.append( + dict( + word=timing.word, + start=round(time_offset + timing.start, 2), + end=round(time_offset + timing.end, 2), + probability=timing.probability, + ) + ) + + saved_tokens += len(timing.tokens) + word_index += 1 + + if len(words) > 0: + # adjust the segment-level timestamps based on the word-level timestamps + segment["start"] = words[0]["start"] + segment["end"] = words[-1]["end"] + + segment["words"] = words diff --git a/src/subtitle/whisper/tokenizer.py b/src/subtitle/whisper/tokenizer.py new file mode 100644 index 0000000..236f65e --- /dev/null +++ b/src/subtitle/whisper/tokenizer.py @@ -0,0 +1,387 @@ +import base64 +import os +import string +from dataclasses import dataclass, field +from functools import cached_property, lru_cache +from typing import Dict, List, Optional, Tuple + +import tiktoken +from tiktoken_ext.openai_public import gpt2 + +LANGUAGES = { + "en": "english", + "zh": "chinese", + "de": "german", + "es": "spanish", + "ru": "russian", + "ko": "korean", + "fr": "french", + "ja": "japanese", + "pt": "portuguese", + "tr": "turkish", + "pl": "polish", + "ca": "catalan", + "nl": "dutch", + "ar": "arabic", + "sv": "swedish", + "it": "italian", + "id": "indonesian", + "hi": "hindi", + "fi": "finnish", + "vi": "vietnamese", + "he": "hebrew", + "uk": "ukrainian", + "el": "greek", + "ms": "malay", + "cs": "czech", + "ro": "romanian", + "da": "danish", + "hu": "hungarian", + "ta": "tamil", + "no": "norwegian", + "th": "thai", + "ur": "urdu", + "hr": "croatian", + "bg": "bulgarian", + "lt": "lithuanian", + "la": "latin", + "mi": "maori", + "ml": "malayalam", + "cy": "welsh", + "sk": "slovak", + "te": "telugu", + "fa": "persian", + "lv": "latvian", + "bn": "bengali", + "sr": "serbian", + "az": "azerbaijani", + "sl": "slovenian", + "kn": "kannada", + "et": "estonian", + "mk": "macedonian", + "br": "breton", + "eu": "basque", + "is": "icelandic", + "hy": "armenian", + "ne": "nepali", + "mn": "mongolian", + "bs": "bosnian", + "kk": "kazakh", + "sq": "albanian", + "sw": "swahili", + "gl": "galician", + "mr": "marathi", + "pa": "punjabi", + "si": "sinhala", + "km": "khmer", + "sn": "shona", + "yo": "yoruba", + "so": "somali", + "af": "afrikaans", + "oc": "occitan", + "ka": "georgian", + "be": "belarusian", + "tg": "tajik", + "sd": "sindhi", + "gu": "gujarati", + "am": "amharic", + "yi": "yiddish", + "lo": "lao", + "uz": "uzbek", + "fo": "faroese", + "ht": "haitian creole", + "ps": "pashto", + "tk": "turkmen", + "nn": "nynorsk", + "mt": "maltese", + "sa": "sanskrit", + "lb": "luxembourgish", + "my": "myanmar", + "bo": "tibetan", + "tl": "tagalog", + "mg": "malagasy", + "as": "assamese", + "tt": "tatar", + "haw": "hawaiian", + "ln": "lingala", + "ha": "hausa", + "ba": "bashkir", + "jw": "javanese", + "su": "sundanese", +} + +# language code lookup by name, with a few language aliases +TO_LANGUAGE_CODE = { + **{language: code for code, language in LANGUAGES.items()}, + "burmese": "my", + "valencian": "ca", + "flemish": "nl", + "haitian": "ht", + "letzeburgesch": "lb", + "pushto": "ps", + "panjabi": "pa", + "moldavian": "ro", + "moldovan": "ro", + "sinhalese": "si", + "castilian": "es", +} + + +@dataclass +class Tokenizer: + """A thin wrapper around `tiktoken` providing quick access to special tokens""" + + encoding: tiktoken.Encoding + language: Optional[str] = None + task: Optional[str] = None + sot_sequence: Tuple[int] = () + special_tokens: Dict[str, int] = field(default_factory=dict) + + def __post_init__(self): + for special in self.encoding.special_tokens_set: + special_token = self.encoding.encode_single_token(special) + self.special_tokens[special] = special_token + + sot: int = self.special_tokens["<|startoftranscript|>"] + translate: int = self.special_tokens["<|translate|>"] + transcribe: int = self.special_tokens["<|transcribe|>"] + + langs = tuple(LANGUAGES.keys()) + sot_sequence = [sot] + if self.language is not None: + sot_sequence.append(sot + 1 + langs.index(self.language)) + if self.task is not None: + task_token: int = transcribe if self.task == "transcribe" else translate + sot_sequence.append(task_token) + + self.sot_sequence = tuple(sot_sequence) + + def encode(self, text, **kwargs): + return self.encoding.encode(text, **kwargs) + + def decode(self, token_ids: List[int], **kwargs) -> str: + token_ids = [t for t in token_ids if t < self.timestamp_begin] + return self.encoding.decode(token_ids, **kwargs) + + def decode_with_timestamps(self, token_ids: List[int], **kwargs) -> str: + """ + Timestamp tokens are above other special tokens' id range and are ignored by `decode()`. + This method decodes given tokens with timestamps tokens annotated, e.g. "<|1.08|>". + """ + return self.encoding.decode(token_ids, **kwargs) + + @cached_property + def eot(self) -> int: + return self.encoding.eot_token + + @cached_property + def transcribe(self) -> int: + return self.special_tokens["<|transcribe|>"] + + @cached_property + def translate(self) -> int: + return self.special_tokens["<|translate|>"] + + @cached_property + def sot(self) -> int: + return self.special_tokens["<|startoftranscript|>"] + + @cached_property + def sot_lm(self) -> int: + return self.special_tokens["<|startoflm|>"] + + @cached_property + def sot_prev(self) -> int: + return self.special_tokens["<|startofprev|>"] + + @cached_property + def no_speech(self) -> int: + return self.special_tokens["<|nospeech|>"] + + @cached_property + def no_timestamps(self) -> int: + return self.special_tokens["<|notimestamps|>"] + + @cached_property + def timestamp_begin(self) -> int: + return self.special_tokens["<|0.00|>"] + + @cached_property + def language_token(self) -> int: + """Returns the token id corresponding to the value of the `language` field""" + if self.language is None: + raise ValueError("This tokenizer does not have language token configured") + + if token := self.special_tokens.get(f"<|{self.language}|>", None): + return token + + raise KeyError(f"Language {self.language} not found in tokenizer.") + + @cached_property + def all_language_tokens(self) -> Tuple[int]: + result = [] + for token, token_id in self.special_tokens.items(): + if token.strip("<|>") in LANGUAGES: + result.append(token_id) + return tuple(result) + + @cached_property + def all_language_codes(self) -> Tuple[str]: + return tuple(self.decode([l]).strip("<|>") for l in self.all_language_tokens) + + @cached_property + def sot_sequence_including_notimestamps(self) -> Tuple[int]: + return tuple(list(self.sot_sequence) + [self.no_timestamps]) + + @cached_property + def non_speech_tokens(self) -> Tuple[int]: + """ + Returns the list of tokens to suppress in order to avoid any speaker tags or non-speech + annotations, to prevent sampling texts that are not actually spoken in the audio, e.g. + + - ♪♪♪ + - ( SPEAKING FOREIGN LANGUAGE ) + - [DAVID] Hey there, + + keeping basic punctuations like commas, periods, question marks, exclamation points, etc. + """ + symbols = list('"#()*+/:;<=>@[\\]^_`{|}~「」『』') + symbols += ( + "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split() + ) + + # symbols that may be a single token or multiple tokens depending on the tokenizer. + # In case they're multiple tokens, suppress the first token, which is safe because: + # These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress + # in generations, and in the 3-byte UTF-8 representation they share the first two bytes. + miscellaneous = set("♩♪♫♬♭♮♯") + assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous) + + # allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word + result = {self.encoding.encode(" -")[0], self.encoding.encode(" '")[0]} + for symbol in symbols + list(miscellaneous): + for tokens in [ + self.encoding.encode(symbol), + self.encoding.encode(" " + symbol), + ]: + if len(tokens) == 1 or symbol in miscellaneous: + result.add(tokens[0]) + + return tuple(sorted(result)) + + def split_to_word_tokens(self, tokens: List[int]): + if self.language in {"zh", "ja", "th", "lo", "my"}: + # These languages don't typically use spaces, so it is difficult to split words + # without morpheme analysis. Here, we instead split words at any + # position where the tokens are decoded as valid unicode points + return self.split_tokens_on_unicode(tokens) + + return self.split_tokens_on_spaces(tokens) + + def split_tokens_on_unicode(self, tokens: List[int]): + decoded_full = self.decode_with_timestamps(tokens) + replacement_char = "\ufffd" + + words = [] + word_tokens = [] + current_tokens = [] + unicode_offset = 0 + + for token in tokens: + current_tokens.append(token) + decoded = self.decode_with_timestamps(current_tokens) + + if ( + replacement_char not in decoded + or decoded_full[unicode_offset + decoded.index(replacement_char)] + == replacement_char + ): + words.append(decoded) + word_tokens.append(current_tokens) + current_tokens = [] + unicode_offset += len(decoded) + + return words, word_tokens + + def split_tokens_on_spaces(self, tokens: List[int]): + subwords, subword_tokens_list = self.split_tokens_on_unicode(tokens) + words = [] + word_tokens = [] + + for subword, subword_tokens in zip(subwords, subword_tokens_list): + special = subword_tokens[0] >= self.eot + with_space = subword.startswith(" ") + punctuation = subword.strip() in string.punctuation + if special or with_space or punctuation or len(words) == 0: + words.append(subword) + word_tokens.append(subword_tokens) + else: + words[-1] = words[-1] + subword + word_tokens[-1].extend(subword_tokens) + + return words, word_tokens + + +@lru_cache(maxsize=None) +def get_encoding(name: str = "gpt2"): + vocab_path = os.path.join(os.path.dirname(__file__), "assets", f"{name}.tiktoken") + ranks = { + base64.b64decode(token): int(rank) + for token, rank in (line.split() for line in open(vocab_path) if line) + } + n_vocab = len(ranks) + special_tokens = {} + + specials = [ + "<|endoftext|>", + "<|startoftranscript|>", + *[f"<|{lang}|>" for lang in LANGUAGES.keys()], + "<|translate|>", + "<|transcribe|>", + "<|startoflm|>", + "<|startofprev|>", + "<|nospeech|>", + "<|notimestamps|>", + *[f"<|{i * 0.02:.2f}|>" for i in range(1501)], + ] + + for token in specials: + special_tokens[token] = n_vocab + n_vocab += 1 + + return tiktoken.Encoding( + name=os.path.basename(vocab_path), + explicit_n_vocab=n_vocab, + pat_str=gpt2()["pat_str"], + mergeable_ranks=ranks, + special_tokens=special_tokens, + ) + + +@lru_cache(maxsize=None) +def get_tokenizer( + multilingual: bool, + *, + language: Optional[str] = None, + task: Optional[str] = None, # Literal["transcribe", "translate", None] +) -> Tokenizer: + if language is not None: + language = language.lower() + if language not in LANGUAGES: + if language in TO_LANGUAGE_CODE: + language = TO_LANGUAGE_CODE[language] + else: + raise ValueError(f"Unsupported language: {language}") + + if multilingual: + encoding_name = "multilingual" + language = language or "en" + task = task or "transcribe" + else: + encoding_name = "gpt2" + language = None + task = None + + encoding = get_encoding(name=encoding_name) + + return Tokenizer(encoding=encoding, language=language, task=task) diff --git a/src/subtitle/whisper/transcribe.py b/src/subtitle/whisper/transcribe.py new file mode 100644 index 0000000..ed6d820 --- /dev/null +++ b/src/subtitle/whisper/transcribe.py @@ -0,0 +1,442 @@ +import argparse +import os +import warnings +from typing import TYPE_CHECKING, Optional, Tuple, Union + +import numpy as np +import torch +import tqdm + +from .audio import ( + FRAMES_PER_SECOND, + HOP_LENGTH, + N_FRAMES, + N_SAMPLES, + SAMPLE_RATE, + log_mel_spectrogram, + pad_or_trim, +) +from .decoding import DecodingOptions, DecodingResult +from .timing import add_word_timestamps +from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer +from .utils import ( + exact_div, + format_timestamp, + get_writer, + make_safe, + optional_float, + optional_int, + str2bool, +) + +if TYPE_CHECKING: + from .model import Whisper + + +def transcribe( + model: "Whisper", + audio: Union[str, np.ndarray, torch.Tensor], + *, + verbose: Optional[bool] = None, + temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0), + compression_ratio_threshold: Optional[float] = 2.4, + logprob_threshold: Optional[float] = -1.0, + no_speech_threshold: Optional[float] = 0.6, + condition_on_previous_text: bool = True, + initial_prompt: Optional[str] = None, + word_timestamps: bool = False, + prepend_punctuations: str = "\"'“¿([{-", + append_punctuations: str = "\"'.。,，!！?？:：”)]}、", + **decode_options, +): + """ + Transcribe an audio file using Whisper + + Parameters + ---------- + model: Whisper + The Whisper model instance + + audio: Union[str, np.ndarray, torch.Tensor] + The path to the audio file to open, or the audio waveform + + verbose: bool + Whether to display the text being decoded to the console. If True, displays all the details, + If False, displays minimal details. If None, does not display anything + + temperature: Union[float, Tuple[float, ...]] + Temperature for sampling. It can be a tuple of temperatures, which will be successively used + upon failures according to either `compression_ratio_threshold` or `logprob_threshold`. + + compression_ratio_threshold: float + If the gzip compression ratio is above this value, treat as failed + + logprob_threshold: float + If the average log probability over sampled tokens is below this value, treat as failed + + no_speech_threshold: float + If the no_speech probability is higher than this value AND the average log probability + over sampled tokens is below `logprob_threshold`, consider the segment as silent + + condition_on_previous_text: bool + if True, the previous output of the model is provided as a prompt for the next window; + disabling may make the text inconsistent across windows, but the model becomes less prone to + getting stuck in a failure loop, such as repetition looping or timestamps going out of sync. + + word_timestamps: bool + Extract word-level timestamps using the cross-attention pattern and dynamic time warping, + and include the timestamps for each word in each segment. + + prepend_punctuations: str + If word_timestamps is True, merge these punctuation symbols with the next word + + append_punctuations: str + If word_timestamps is True, merge these punctuation symbols with the previous word + + initial_prompt: Optional[str] + Optional text to provide as a prompt for the first window. This can be used to provide, or + "prompt-engineer" a context for transcription, e.g. custom vocabularies or proper nouns + to make it more likely to predict those word correctly. + + decode_options: dict + Keyword arguments to construct `DecodingOptions` instances + + Returns + ------- + A dictionary containing the resulting text ("text") and segment-level details ("segments"), and + the spoken language ("language"), which is detected when `decode_options["language"]` is None. + """ + dtype = torch.float16 if decode_options.get("fp16", True) else torch.float32 + if model.device == torch.device("cpu"): + if torch.cuda.is_available(): + warnings.warn("Performing inference on CPU when CUDA is available") + if dtype == torch.float16: + warnings.warn("FP16 is not supported on CPU; using FP32 instead") + dtype = torch.float32 + + if dtype == torch.float32: + decode_options["fp16"] = False + + # Pad 30-seconds of silence to the input audio, for slicing + mel = log_mel_spectrogram(audio, padding=N_SAMPLES) + content_frames = mel.shape[-1] - N_FRAMES + + if decode_options.get("language", None) is None: + if not model.is_multilingual: + decode_options["language"] = "en" + else: + if verbose: + print( + "Detecting language using up to the first 30 seconds. Use `--language` to specify the language" + ) + mel_segment = pad_or_trim(mel, N_FRAMES).to(model.device).to(dtype) + _, probs = model.detect_language(mel_segment) + decode_options["language"] = max(probs, key=probs.get) + if verbose is not None: + print( + f"Detected language: {LANGUAGES[decode_options['language']].title()}" + ) + + language: str = decode_options["language"] + task: str = decode_options.get("task", "transcribe") + tokenizer = get_tokenizer(model.is_multilingual, language=language, task=task) + + if word_timestamps and task == "translate": + warnings.warn("Word-level timestamps on translations may not be reliable.") + + def decode_with_fallback(segment: torch.Tensor) -> DecodingResult: + temperatures = ( + [temperature] if isinstance(temperature, (int, float)) else temperature + ) + decode_result = None + + for t in temperatures: + kwargs = {**decode_options} + if t > 0: + # disable beam_size and patience when t > 0 + kwargs.pop("beam_size", None) + kwargs.pop("patience", None) + else: + # disable best_of when t == 0 + kwargs.pop("best_of", None) + + options = DecodingOptions(**kwargs, temperature=t) + decode_result = model.decode(segment, options) + + needs_fallback = False + if ( + compression_ratio_threshold is not None + and decode_result.compression_ratio > compression_ratio_threshold + ): + needs_fallback = True # too repetitive + if ( + logprob_threshold is not None + and decode_result.avg_logprob < logprob_threshold + ): + needs_fallback = True # average log probability is too low + + if not needs_fallback: + break + + return decode_result + + seek = 0 + input_stride = exact_div( + N_FRAMES, model.dims.n_audio_ctx + ) # mel frames per output token: 2 + time_precision = ( + input_stride * HOP_LENGTH / SAMPLE_RATE + ) # time per output token: 0.02 (seconds) + all_tokens = [] + all_segments = [] + prompt_reset_since = 0 + + if initial_prompt is not None: + initial_prompt_tokens = tokenizer.encode(" " + initial_prompt.strip()) + all_tokens.extend(initial_prompt_tokens) + else: + initial_prompt_tokens = [] + + def new_segment( + *, start: float, end: float, tokens: torch.Tensor, result: DecodingResult + ): + tokens = tokens.tolist() + text_tokens = [token for token in tokens if token < tokenizer.eot] + return { + "seek": seek, + "start": start, + "end": end, + "text": tokenizer.decode(text_tokens), + "tokens": tokens, + "temperature": result.temperature, + "avg_logprob": result.avg_logprob, + "compression_ratio": result.compression_ratio, + "no_speech_prob": result.no_speech_prob, + } + + # show the progress bar when verbose is False (if True, transcribed text will be printed) + with tqdm.tqdm( + total=content_frames, unit="frames", disable=verbose is not False + ) as pbar: + while seek < content_frames: + time_offset = float(seek * HOP_LENGTH / SAMPLE_RATE) + mel_segment = mel[:, seek : seek + N_FRAMES] + segment_size = min(N_FRAMES, content_frames - seek) + segment_duration = segment_size * HOP_LENGTH / SAMPLE_RATE + mel_segment = pad_or_trim(mel_segment, N_FRAMES).to(model.device).to(dtype) + + decode_options["prompt"] = all_tokens[prompt_reset_since:] + result: DecodingResult = decode_with_fallback(mel_segment) + tokens = torch.tensor(result.tokens) + + if no_speech_threshold is not None: + # no voice activity check + should_skip = result.no_speech_prob > no_speech_threshold + if ( + logprob_threshold is not None + and result.avg_logprob > logprob_threshold + ): + # don't skip if the logprob is high enough, despite the no_speech_prob + should_skip = False + + if should_skip: + seek += segment_size # fast-forward to the next segment boundary + continue + + previous_seek = seek + current_segments = [] + + timestamp_tokens: torch.Tensor = tokens.ge(tokenizer.timestamp_begin) + single_timestamp_ending = timestamp_tokens[-2:].tolist() == [False, True] + + consecutive = torch.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0] + consecutive.add_(1) + if len(consecutive) > 0: + # if the output contains two consecutive timestamp tokens + slices = consecutive.tolist() + if single_timestamp_ending: + slices.append(len(tokens)) + + last_slice = 0 + for current_slice in slices: + sliced_tokens = tokens[last_slice:current_slice] + start_timestamp_pos = ( + sliced_tokens[0].item() - tokenizer.timestamp_begin + ) + end_timestamp_pos = ( + sliced_tokens[-1].item() - tokenizer.timestamp_begin + ) + current_segments.append( + new_segment( + start=time_offset + start_timestamp_pos * time_precision, + end=time_offset + end_timestamp_pos * time_precision, + tokens=sliced_tokens, + result=result, + ) + ) + last_slice = current_slice + + if single_timestamp_ending: + # single timestamp at the end means no speech after the last timestamp. + seek += segment_size + else: + # otherwise, ignore the unfinished segment and seek to the last timestamp + last_timestamp_pos = ( + tokens[last_slice - 1].item() - tokenizer.timestamp_begin + ) + seek += last_timestamp_pos * input_stride + else: + duration = segment_duration + timestamps = tokens[timestamp_tokens.nonzero().flatten()] + if ( + len(timestamps) > 0 + and timestamps[-1].item() != tokenizer.timestamp_begin + ): + # no consecutive timestamps but it has a timestamp; use the last one. + last_timestamp_pos = ( + timestamps[-1].item() - tokenizer.timestamp_begin + ) + duration = last_timestamp_pos * time_precision + + current_segments.append( + new_segment( + start=time_offset, + end=time_offset + duration, + tokens=tokens, + result=result, + ) + ) + seek += segment_size + + if not condition_on_previous_text or result.temperature > 0.5: + # do not feed the prompt tokens if a high temperature was used + prompt_reset_since = len(all_tokens) + + if word_timestamps: + add_word_timestamps( + segments=current_segments, + model=model, + tokenizer=tokenizer, + mel=mel_segment, + num_frames=segment_size, + prepend_punctuations=prepend_punctuations, + append_punctuations=append_punctuations, + ) + word_end_timestamps = [ + w["end"] for s in current_segments for w in s["words"] + ] + if not single_timestamp_ending and len(word_end_timestamps) > 0: + seek_shift = round( + (word_end_timestamps[-1] - time_offset) * FRAMES_PER_SECOND + ) + if seek_shift > 0: + seek = previous_seek + seek_shift + + if verbose: + for segment in current_segments: + start, end, text = segment["start"], segment["end"], segment["text"] + line = f"[{format_timestamp(start)} --> {format_timestamp(end)}] {text}" + print(make_safe(line)) + + # if a segment is instantaneous or does not contain text, clear it + for i, segment in enumerate(current_segments): + if segment["start"] == segment["end"] or segment["text"].strip() == "": + segment["text"] = "" + segment["tokens"] = [] + segment["words"] = [] + + all_segments.extend( + [ + {"id": i, **segment} + for i, segment in enumerate( + current_segments, start=len(all_segments) + ) + ] + ) + all_tokens.extend( + [token for segment in current_segments for token in segment["tokens"]] + ) + + # update progress bar + pbar.update(min(content_frames, seek) - previous_seek) + + return dict( + text=tokenizer.decode(all_tokens[len(initial_prompt_tokens) :]), + segments=all_segments, + language=language, + ) + + +def cli(): + from . import available_models + + # fmt: off + parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) + parser.add_argument("audio", nargs="+", type=str, help="audio file(s) to transcribe") + parser.add_argument("--model", default="small", choices=available_models(), help="name of the Whisper model to use") + parser.add_argument("--model_dir", type=str, default=None, help="the path to save model files; uses ~/.cache/whisper by default") + parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="device to use for PyTorch inference") + parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs") + parser.add_argument("--output_format", "-f", type=str, default="all", choices=["txt", "vtt", "srt", "tsv", "json", "all"], help="format of the output file; if not specified, all available formats will be produced") + parser.add_argument("--verbose", type=str2bool, default=True, help="whether to print out the progress and debug messages") + + parser.add_argument("--task", type=str, default="transcribe", choices=["transcribe", "translate"], help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')") + parser.add_argument("--language", type=str, default=None, choices=sorted(LANGUAGES.keys()) + sorted([k.title() for k in TO_LANGUAGE_CODE.keys()]), help="language spoken in the audio, specify None to perform language detection") + + parser.add_argument("--temperature", type=float, default=0, help="temperature to use for sampling") + parser.add_argument("--best_of", type=optional_int, default=5, help="number of candidates when sampling with non-zero temperature") + parser.add_argument("--beam_size", type=optional_int, default=5, help="number of beams in beam search, only applicable when temperature is zero") + parser.add_argument("--patience", type=float, default=None, help="optional patience value to use in beam decoding, as in https://arxiv.org/abs/2204.05424, the default (1.0) is equivalent to conventional beam search") + parser.add_argument("--length_penalty", type=float, default=None, help="optional token length penalty coefficient (alpha) as in https://arxiv.org/abs/1609.08144, uses simple length normalization by default") + + parser.add_argument("--suppress_tokens", type=str, default="-1", help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations") + parser.add_argument("--initial_prompt", type=str, default=None, help="optional text to provide as a prompt for the first window.") + parser.add_argument("--condition_on_previous_text", type=str2bool, default=True, help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop") + parser.add_argument("--fp16", type=str2bool, default=True, help="whether to perform inference in fp16; True by default") + + parser.add_argument("--temperature_increment_on_fallback", type=optional_float, default=0.2, help="temperature to increase when falling back when the decoding fails to meet either of the thresholds below") + parser.add_argument("--compression_ratio_threshold", type=optional_float, default=2.4, help="if the gzip compression ratio is higher than this value, treat the decoding as failed") + parser.add_argument("--logprob_threshold", type=optional_float, default=-1.0, help="if the average log probability is lower than this value, treat the decoding as failed") + parser.add_argument("--no_speech_threshold", type=optional_float, default=0.6, help="if the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence") + parser.add_argument("--word_timestamps", type=str2bool, default=False, help="(experimental) extract word-level timestamps and refine the results based on them") + parser.add_argument("--prepend_punctuations", type=str, default="\"\'“¿([{-", help="if word_timestamps is True, merge these punctuation symbols with the next word") + parser.add_argument("--append_punctuations", type=str, default="\"\'.。,，!！?？:：”)]}、", help="if word_timestamps is True, merge these punctuation symbols with the previous word") + parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS") + # fmt: on + + args = parser.parse_args().__dict__ + model_name: str = args.pop("model") + model_dir: str = args.pop("model_dir") + output_dir: str = args.pop("output_dir") + output_format: str = args.pop("output_format") + device: str = args.pop("device") + os.makedirs(output_dir, exist_ok=True) + + if model_name.endswith(".en") and args["language"] not in {"en", "English"}: + if args["language"] is not None: + warnings.warn( + f"{model_name} is an English-only model but receipted '{args['language']}'; using English instead." + ) + args["language"] = "en" + + temperature = args.pop("temperature") + if (increment := args.pop("temperature_increment_on_fallback")) is not None: + temperature = tuple(np.arange(temperature, 1.0 + 1e-6, increment)) + else: + temperature = [temperature] + + if (threads := args.pop("threads")) > 0: + torch.set_num_threads(threads) + + from . import load_model + + model = load_model(model_name, device=device, download_root=model_dir) + + writer = get_writer(output_format, output_dir) + for audio_path in args.pop("audio"): + result = transcribe(model, audio_path, temperature=temperature, **args) + writer(result, audio_path) + + +if __name__ == "__main__": + cli() diff --git a/src/subtitle/whisper/triton_ops.py b/src/subtitle/whisper/triton_ops.py new file mode 100644 index 0000000..edd4564 --- /dev/null +++ b/src/subtitle/whisper/triton_ops.py @@ -0,0 +1,109 @@ +from functools import lru_cache + +import numpy as np +import torch + +try: + import triton + import triton.language as tl +except ImportError: + raise RuntimeError("triton import failed; try `pip install --pre triton`") + + +@triton.jit +def dtw_kernel( + cost, trace, x, x_stride, cost_stride, trace_stride, N, M, BLOCK_SIZE: tl.constexpr +): + offsets = tl.arange(0, BLOCK_SIZE) + mask = offsets < M + + for k in range(1, N + M + 1): # k = i + j + tl.debug_barrier() + + p0 = cost + (k - 1) * cost_stride + p1 = cost + k * cost_stride + p2 = cost + k * cost_stride + 1 + + c0 = tl.load(p0 + offsets, mask=mask) + c1 = tl.load(p1 + offsets, mask=mask) + c2 = tl.load(p2 + offsets, mask=mask) + + x_row = tl.load(x + (k - 1) * x_stride + offsets, mask=mask, other=0) + cost_row = x_row + tl.minimum(tl.minimum(c0, c1), c2) + + cost_ptr = cost + (k + 1) * cost_stride + 1 + tl.store(cost_ptr + offsets, cost_row, mask=mask) + + trace_ptr = trace + (k + 1) * trace_stride + 1 + tl.store(trace_ptr + offsets, 2, mask=mask & (c2 <= c0) & (c2 <= c1)) + tl.store(trace_ptr + offsets, 1, mask=mask & (c1 <= c0) & (c1 <= c2)) + tl.store(trace_ptr + offsets, 0, mask=mask & (c0 <= c1) & (c0 <= c2)) + + +@lru_cache(maxsize=None) +def median_kernel(filter_width: int): + @triton.jit + def kernel( + y, x, x_stride, y_stride, BLOCK_SIZE: tl.constexpr + ): # x.shape[-1] == filter_width + row_idx = tl.program_id(0) + offsets = tl.arange(0, BLOCK_SIZE) + mask = offsets < y_stride + + x_ptr = x + row_idx * x_stride # noqa: F841 + y_ptr = y + row_idx * y_stride + + LOAD_ALL_ROWS_HERE # noqa: F821 + + BUBBLESORT_HERE # noqa: F821 + + tl.store(y_ptr + offsets, MIDDLE_ROW_HERE, mask=mask) # noqa: F821 + + kernel = triton.JITFunction(kernel.fn) + kernel.src = kernel.src.replace( + " LOAD_ALL_ROWS_HERE", + "\n".join( + [ + f" row{i} = tl.load(x_ptr + offsets + {i}, mask=mask)" + for i in range(filter_width) + ] + ), + ) + kernel.src = kernel.src.replace( + " BUBBLESORT_HERE", + "\n\n".join( + [ + "\n\n".join( + [ + "\n".join( + [ + f" smaller = tl.where(row{j} < row{j + 1}, row{j}, row{j + 1})", + f" larger = tl.where(row{j} > row{j + 1}, row{j}, row{j + 1})", + f" row{j} = smaller", + f" row{j + 1} = larger", + ] + ) + for j in range(filter_width - i - 1) + ] + ) + for i in range(filter_width // 2 + 1) + ] + ), + ) + kernel.src = kernel.src.replace("MIDDLE_ROW_HERE", f"row{filter_width // 2}") + + return kernel + + +def median_filter_cuda(x: torch.Tensor, filter_width: int): + """Apply a median filter of given width along the last dimension of x""" + slices = x.contiguous().unfold(-1, filter_width, 1) + grid = np.prod(slices.shape[:-2]) + + kernel = median_kernel(filter_width) + y = torch.empty_like(slices[..., 0]) + + BLOCK_SIZE = 1 << (y.stride(-2) - 1).bit_length() + kernel[(grid,)](y, x, x.stride(-2), y.stride(-2), BLOCK_SIZE=BLOCK_SIZE) + + return y diff --git a/src/subtitle/whisper/utils.py b/src/subtitle/whisper/utils.py new file mode 100644 index 0000000..490bdd1 --- /dev/null +++ b/src/subtitle/whisper/utils.py @@ -0,0 +1,205 @@ +import json +import os +import sys +import zlib +from typing import Callable, TextIO + +system_encoding = sys.getdefaultencoding() + +if system_encoding != "utf-8": + + def make_safe(string): + # replaces any character not representable using the system default encoding with an '?', + # avoiding UnicodeEncodeError (https://github.com/openai/whisper/discussions/729). + return string.encode(system_encoding, errors="replace").decode(system_encoding) + +else: + + def make_safe(string): + # utf-8 can encode any Unicode code point, so no need to do the round-trip encoding + return string + + +def exact_div(x, y): + assert x % y == 0 + return x // y + + +def str2bool(string): + str2val = {"True": True, "False": False} + if string in str2val: + return str2val[string] + else: + raise ValueError(f"Expected one of {set(str2val.keys())}, got {string}") + + +def optional_int(string): + return None if string == "None" else int(string) + + +def optional_float(string): + return None if string == "None" else float(string) + + +def compression_ratio(text) -> float: + text_bytes = text.encode("utf-8") + return len(text_bytes) / len(zlib.compress(text_bytes)) + + +def format_timestamp( + seconds: float, always_include_hours: bool = False, decimal_marker: str = "." +): + assert seconds >= 0, "non-negative timestamp expected" + milliseconds = round(seconds * 1000.0) + + hours = milliseconds // 3_600_000 + milliseconds -= hours * 3_600_000 + + minutes = milliseconds // 60_000 + milliseconds -= minutes * 60_000 + + seconds = milliseconds // 1_000 + milliseconds -= seconds * 1_000 + + hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else "" + return ( + f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}" + ) + + +class ResultWriter: + extension: str + + def __init__(self, output_dir: str): + self.output_dir = output_dir + + def __call__(self, result: dict, audio_path: str): + audio_basename = os.path.basename(audio_path) + audio_basename = os.path.splitext(audio_basename)[0] + output_path = os.path.join( + self.output_dir, audio_basename + "." + self.extension + ) + + with open(output_path, "w", encoding="utf-8") as f: + self.write_result(result, file=f) + + def write_result(self, result: dict, file: TextIO): + raise NotImplementedError + + +class WriteTXT(ResultWriter): + extension: str = "txt" + + def write_result(self, result: dict, file: TextIO): + for segment in result["segments"]: + print(segment["text"].strip(), file=file, flush=True) + + +class SubtitlesWriter(ResultWriter): + always_include_hours: bool + decimal_marker: str + + def iterate_result(self, result: dict): + for segment in result["segments"]: + segment_start = self.format_timestamp(segment["start"]) + segment_end = self.format_timestamp(segment["end"]) + segment_text = segment["text"].strip().replace("-->", "->") + + if word_timings := segment.get("words", None): + all_words = [timing["word"] for timing in word_timings] + all_words[0] = all_words[0].strip() # remove the leading space, if any + last = segment_start + for i, this_word in enumerate(word_timings): + start = self.format_timestamp(this_word["start"]) + end = self.format_timestamp(this_word["end"]) + if last != start: + yield last, start, segment_text + + yield start, end, "".join( + [ + f"{word}" if j == i else word + for j, word in enumerate(all_words) + ] + ) + last = end + + if last != segment_end: + yield last, segment_end, segment_text + else: + yield segment_start, segment_end, segment_text + + def format_timestamp(self, seconds: float): + return format_timestamp( + seconds=seconds, + always_include_hours=self.always_include_hours, + decimal_marker=self.decimal_marker, + ) + + +class WriteVTT(SubtitlesWriter): + extension: str = "vtt" + always_include_hours: bool = False + decimal_marker: str = "." + + def write_result(self, result: dict, file: TextIO): + print("WEBVTT\n", file=file) + for start, end, text in self.iterate_result(result): + print(f"{start} --> {end}\n{text}\n", file=file, flush=True) + + +class WriteSRT(SubtitlesWriter): + extension: str = "srt" + always_include_hours: bool = True + decimal_marker: str = "," + + def write_result(self, result: dict, file: TextIO): + for i, (start, end, text) in enumerate(self.iterate_result(result), start=1): + print(f"{i}\n{start} --> {end}\n{text}\n", file=file, flush=True) + + +class WriteTSV(ResultWriter): + """ + Write a transcript to a file in TSV (tab-separated values) format containing lines like: + \t\t + + Using integer milliseconds as start and end times means there's no chance of interference from + an environment setting a language encoding that causes the decimal in a floating point number + to appear as a comma; also is faster and more efficient to parse & store, e.g., in C++. + """ + + extension: str = "tsv" + + def write_result(self, result: dict, file: TextIO): + print("start", "end", "text", sep="\t", file=file) + for segment in result["segments"]: + print(round(1000 * segment["start"]), file=file, end="\t") + print(round(1000 * segment["end"]), file=file, end="\t") + print(segment["text"].strip().replace("\t", " "), file=file, flush=True) + + +class WriteJSON(ResultWriter): + extension: str = "json" + + def write_result(self, result: dict, file: TextIO): + json.dump(result, file) + + +def get_writer(output_format: str, output_dir: str) -> Callable[[dict, TextIO], None]: + writers = { + "txt": WriteTXT, + "vtt": WriteVTT, + "srt": WriteSRT, + "tsv": WriteTSV, + "json": WriteJSON, + } + + if output_format == "all": + all_writers = [writer(output_dir) for writer in writers.values()] + + def write_all(result: dict, file: TextIO): + for writer in all_writers: + writer(result, file) + + return write_all + + return writers[output_format](output_dir) diff --git a/src/subtitle/whisper/version.py b/src/subtitle/whisper/version.py new file mode 100644 index 0000000..572259a --- /dev/null +++ b/src/subtitle/whisper/version.py @@ -0,0 +1 @@ +__version__ = "20230314" diff --git a/src/uploadProcess/uploadVideo.sh b/src/uploadProcess/uploadVideo.sh index 3468733..c211fe5 100755 --- a/src/uploadProcess/uploadVideo.sh +++ b/src/uploadProcess/uploadVideo.sh @@ -21,7 +21,7 @@ uploadPath=$1 # extract the roomid and date information fileName=$(basename "$uploadPath") # use regular expression to match the parameter -if [[ $fileName =~ ([0-9]+)_([0-9]{4})-([0-9]{2})-([0-9]{2})-([0-9]{2})\.mp4 ]]; then +if [[ $fileName =~ ([0-9]+)_([0-9]{4})-([0-9]{2})-([0-9]{2})-([0-9]{2})-s\.mp4 ]]; then roomID="${BASH_REMATCH[1]}" year="${BASH_REMATCH[2]}" month="${BASH_REMATCH[3]}"