diff --git a/.gitignore b/.gitignore new file mode 100644 index 000000000..7ae8fabc6 --- /dev/null +++ b/.gitignore @@ -0,0 +1,11 @@ +__pycache__/ +*.py[cod] +*$py.class +*.egg-info +.pytest_cache +.ipynb_checkpoints + +thumbs.db +.DS_Store +.idea + diff --git a/LICENSE b/LICENSE new file mode 100644 index 000000000..d25552598 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2022 OpenAI + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 000000000..838aa2d9c --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,4 @@ +include whisper/assets/* +include whisper/assets/gpt2/* +include whisper/assets/multilingual/* +include whisper/normalizers/english.json diff --git a/README.md b/README.md new file mode 100644 index 000000000..aa7c2097b --- /dev/null +++ b/README.md @@ -0,0 +1,101 @@ +# Whisper + +[[Blog]](https://openai.com/blog/whisper) +[[Paper]](https://cdn.openai.com/papers/whisper.pdf) +[[Model card]](model-card.md) +[[Colab example]](https://colab.research.google.com/github/openai/whisper/blob/master/notebooks/LibriSpeech.ipynb) + +Whisper is a general-purpose speech recognition model. It is trained on a large dataset of diverse audio and is also a multi-task model that can perform multilingual speech recognition as well as speech translation and language identification. + + +## Approach + +![Approach](approach.png) + +A Transformer sequence-to-sequence model is trained on various speech processing tasks, including multilingual speech recognition, speech translation, spoken language identification, and voice activity detection. All of these tasks are jointly represented as a sequence of tokens to be predicted by the decoder, allowing for a single model to replace many different stages of a traditional speech processing pipeline. The multitask training format uses a set of special tokens that serve as task specifiers or classification targets. + + +## Setup + +We used Python 3.9.9 and [PyTorch](https://pytorch.org/) 1.10.1 to train and test our models, but the codebase is expected to be compatible with Python 3.7 or later and recent PyTorch versions. The codebase also depends on a few Python packages, most notably [HuggingFace Transformers](https://huggingface.co/docs/transformers/index) for their fast tokenizer implementation and [ffmpeg-python](https://github.com/kkroening/ffmpeg-python) for reading audio files. The following command will pull and install the latest commit from this repository, along with its Python dependencies + + pip install git+https://github.com/openai/whisper.git + +It also requires the command-line tool [`ffmpeg`](https://ffmpeg.org/) to be installed on your system, which is available from most package managers: + +```bash +# on Ubuntu or Debian +sudo apt update && sudo apt install ffmpeg + +# on MacOS using Homebrew (https://brew.sh/) +brew install ffmpeg + +# on Windows using Chocolatey (https://chocolatey.org/) +choco install ffmpeg +``` + + +## Command-line usage + +The following command will transcribe speech in audio files + + whisper audio.flac audio.mp3 audio.wav + +The default setting works well for transcribing English. To transcribe an audio file containing non-English speech, you can specify the language using the `--language` option: + + whisper ~/japanese.wav --language Japanese + +Adding `--task translate` will translate the speech into English: + + whisper ~/japanese.wav --language Japanese --task translate + +Run the following to view all available transcription options: + + whisper --help + +See [tokenizer.py](whisper/tokenizer.py) for the list of all available languages. + + +## Python usage + +Transcription can also be performed within Python: + +```python +import whisper + +model = whisper.load_model("base") +result = model.transcribe("audio.mp3") +print(result["text"]) +``` + +Internally, the `transcribe()` method reads the entire file and processes the audio with a sliding 30-second window, performing autoregressive sequence-to-sequence predictions on each window. + +Below is an example usage of `whisper.detect_language()` and `whisper.decode()` which provide lower-level access to the model. + +```python +import whisper + +model = whisper.load_model("base") + +# load audio and pad/trim it to fit 30 seconds +audio = whisper.load_audio("audio.mp3") +audio = whisper.pad_or_trim(audio) + +# make log-Mel spectrogram and move to the same device as the model +mel = whisper.log_mel_spectrogram(audio).to(model.device) + +# detect the spoken language +_, probs = model.detect_language(mel) +print(f"Detected language: {max(probs, key=probs.get)}") + +# decode the audio +options = whisper.DecodingOptions() +result = whisper.decode(model, mel, options) + +# print the recognized text +print(result.text) +``` + +## License + +The code and the model weights of Whisper are released under the MIT License. See `LICENSE` for further details. diff --git a/approach.png b/approach.png new file mode 100644 index 000000000..355c500b2 Binary files /dev/null and b/approach.png differ diff --git a/data/README.md b/data/README.md new file mode 100644 index 000000000..3b4aea12d --- /dev/null +++ b/data/README.md @@ -0,0 +1,118 @@ +This directory supplements the paper with more details on how we prepared the data for evaluation, to help replicate our experiments. + +## Short-form English-only datasets + +### LibriSpeech + +We used the test-clean and test-other splits from the [LibriSpeech ASR corpus](https://www.openslr.org/12). + +### TED-LIUM 3 + +We used the test split of [TED-LIUM Release 3](https://www.openslr.org/51/), using the segmented manual transcripts included in the release. + +### Common Voice 5.1 + +We downloaded the English subset of Common Voice Corpus 5.1 from [the official website](https://commonvoice.mozilla.org/en/datasets) + +### Artie + +We used the [Artie bias corpus](https://github.com/artie-inc/artie-bias-corpus). This is a subset of the Common Voice dataset. + +### CallHome & Switchboard + +We used the two corpora from [LDC2002S09](https://catalog.ldc.upenn.edu/LDC2002S09) and [LDC2002T43](https://catalog.ldc.upenn.edu/LDC2002T43) and followed the [eval2000_data_prep.sh](https://github.com/kaldi-asr/kaldi/blob/master/egs/fisher_swbd/s5/local/eval2000_data_prep.sh) script for preprocessing. The `wav.scp` files can be converted to WAV files with the following bash commands: + +```bash +mkdir -p wav +while read name cmd; do + echo $name + echo ${cmd/\|/} wav/$name.wav | bash +done < wav.scp +``` + + +### WSJ + +We used [LDC93S6B](https://catalog.ldc.upenn.edu/LDC93S6B) and [LDC94S13B](https://catalog.ldc.upenn.edu/LDC94S13B) and followed the [s5 recipe](https://github.com/kaldi-asr/kaldi/tree/master/egs/wsj/s5) to preprocess the dataset. + +### CORAAL + +We used the 231 interviews from [CORAAL (v. 2021.07)](https://oraal.uoregon.edu/coraal) and used the segmentations from [the FairSpeech project](https://github.com/stanford-policylab/asr-disparities/blob/master/input/CORAAL_transcripts.csv). + +### CHiME-6 + +We downloaded the [CHiME-5 dataset](https://spandh.dcs.shef.ac.uk//chime_challenge/CHiME5/download.html) and followed the stage 0 of the [s5_track1 recipe](https://github.com/kaldi-asr/kaldi/tree/master/egs/chime6/s5_track1) to create the CHiME-6 dataset which fixes synchronization. We then used the binaural recordings (`*_P??.wav`) and the corresponding transcripts. + +### AMI-IHM, AMI-SDM1 + +We preprocessed the [AMI Corpus](https://groups.inf.ed.ac.uk/ami/corpus/overview.shtml) by following the stage 0 ad 2 of the [s5b recipe](https://github.com/kaldi-asr/kaldi/tree/master/egs/ami/s5b). + + +## Long-form English-only datasets + +### TED-LIUM 3 + +To create a long-form transcription dataset from the [TED-LIUM3](https://www.openslr.org/51/) dataset, we sliced the audio between the beginning of the first labeled segment and the end of the last labeled segment of each talk, and we used the concatenated text as the label. Below are the timestamps used for slicing each of the 11 TED talks in the test split. + +| Filename | Begin time (s) | End time (s) | +|---------------------|----------------|--------------| +| DanBarber_2010 | 16.09 | 1116.24 | +| JaneMcGonigal_2010 | 15.476 | 1187.61 | +| BillGates_2010 | 15.861 | 1656.94 | +| TomWujec_2010U | 16.26 | 402.17 | +| GaryFlake_2010 | 16.06 | 367.14 | +| EricMead_2009P | 18.434 | 536.44 | +| MichaelSpecter_2010 | 16.11 | 979.312 | +| DanielKahneman_2010 | 15.8 | 1199.44 | +| AimeeMullins_2009P | 17.82 | 1296.59 | +| JamesCameron_2010 | 16.75 | 1010.65 | +| RobertGupta_2010U | 16.8 | 387.03 | + +### Meanwhile + +This dataset consists of 64 segments from The Late Show with Stephen Colbert. The YouTube video ID, start and end timestamps, and the labels can be found in [meanwhile.json](meanwhile.json). The labels are collected from the closed-caption data for each video and corrected with manual inspection. + +### Rev16 + +We use a subset of 16 files from the 30 podcast episodes in [Rev.AI's Podcast Transcription Benchmark](https://www.rev.ai/blog/podcast-transcription-benchmark-part-1/), after finding that there are multiple cases where a significant portion of the audio and the labels did not match, mostly on the parts introducing the sponsors. We selected 16 episodes that do not have this error, whose "file number" are: + + 3 4 9 10 11 14 17 18 20 21 23 24 26 27 29 32 + +### Kincaid46 + +This dataset consists of 46 audio files and the corresponding transcripts compiled in the blog article [Which automatic transcription service is the most accurate - 2018](https://medium.com/descript/which-automatic-transcription-service-is-the-most-accurate-2018-2e859b23ed19) by Jason Kincaid. We used the 46 audio files and reference transcripts from the Airtable widget in the article. + +For the human transcription benchmark in the paper, we use a subset of 25 examples from this data, whose "Ref ID" are: + + 2 4 5 8 9 10 12 13 14 16 19 21 23 25 26 28 29 30 33 35 36 37 42 43 45 + +### Earnings-21, Earnings-22 + +For these datasets, we used the files available in [the speech-datasets repository](https://github.com/revdotcom/speech-datasets), as of their `202206` version. + +### CORAAL + +We used the 231 interviews from [CORAAL (v. 2021.07)](https://oraal.uoregon.edu/coraal) and used the full-length interview files and transcripts. + + +## Multilingual datasets + +### Multilingual LibriSpeech + +We used the test splits from each language in [the Multilingual LibriSpeech (MLS) corpus](https://www.openslr.org/94/). + +### Fleurs + +We collected audio files and transcripts using the implementation available as [HuggingFace datasets](https://huggingface.co/datasets/google/fleurs/blob/main/fleurs.py). To use as a translation dataset, we matched the numerical utterance IDs to find the corresponding transcript in English. + +### VoxPopuli + +We used the `get_asr_data.py` script from [the official repository](https://github.com/facebookresearch/voxpopuli) to collect the ASR data in 14 languages. + +### Common Voice 9 + +We downloaded the Common Voice Corpus 9 from [the official website](https://commonvoice.mozilla.org/en/datasets) + +### CoVOST 2 + +We collected the `X into English` data collected using [the official repository](https://github.com/facebookresearch/covost). diff --git a/data/meanwhile.json b/data/meanwhile.json new file mode 100644 index 000000000..863e50d09 --- /dev/null +++ b/data/meanwhile.json @@ -0,0 +1,322 @@ +{ + "1YOmY-Vjy-o": { + "begin": "1:04.0", + "end": "2:11.0", + "text": "FOLKS, IF YOU WATCH THE SHOW,\nYOU KNOW I SPEND A LOT OF TIME\nRIGHT OVER THERE, PATIENTLY AND\nASTUTELY SCRUTINIZING THE\nBOXWOOD AND MAHOGANY CHESS SET\nOF THE DAY'S BIGGEST STORIES,\nDEVELOPING THE CENTRAL\nHEADLINE-PAWNS, DEFTLY\nMANEUVERING AN OH-SO-TOPICAL\nKNIGHT TO F-6, FEIGNING A\nCLASSIC SICILIAN-NAJDORF\nVARIATION ON THE NEWS, ALL THE\nWHILE, SEEING EIGHT MOVES DEEP\nAND PATIENTLY MARSHALING THE\nLATEST PRESS RELEASES INTO A\nFISCHER SOZIN LIPNITZKY ATTACK\nTHAT CULMINATES IN THE ELEGANT,\nLETHAL, SLOW-PLAYED EN PASSANT\nCHECKMATE THAT IS MY NIGHTLY\nMONOLOGUE.\nBUT SOMETIMES, SOMETIMES,\nFOLKS-- I,\nSOMETIMES,\nI STARTLE AWAKE UPSIDE DOWN ON\nTHE MONKEY BARS OF A CONDEMNED\nPLAYGROUND ON A SUPERFUND SITE,\nGET ALL HEPPED UP ON GOOFBALLS,\nRUMMAGE THROUGH A DISCARDED TAG\nBAG OF DEFECTIVE TOYS, YANK\nOUT A FISTFUL OF DISEMBODIED\nDOLL LIMBS, TOSS THEM ON A\nSTAINED KID'S PLACEMAT FROM A\nDEFUNCT DENNY'S, SET UP A TABLE\nINSIDE A RUSTY CARGO CONTAINER\nDOWN BY THE WHARF, AND CHALLENGE\nTOOTHLESS DRIFTERS TO THE\nGODLESS BUGHOUSE BLITZ\nOF TOURNAMENT OF NEWS THAT IS MY\nSEGMENT:\nMEANWHILE!\n" + }, + "3P_XnxdlXu0": { + "begin": "2:08.3", + "end": "3:02.3", + "text": "FOLKS, I SPEND A LOT OF TIME\nRIGHT OVER THERE, NIGHT AFTER NIGHT ACTUALLY, CAREFULLY\nSELECTING FOR YOU THE DAY'S NEWSIEST,\nMOST AERODYNAMIC HEADLINES,\nSTRESS TESTING THE MOST TOPICAL\nANTILOCK BRAKES AND POWER\nSTEERING, PAINSTAKINGLY\nSTITCHING LEATHER SEATING SO\nSOFT, IT WOULD MAKE J.D. POWER\nAND HER ASSOCIATES BLUSH, TO\nCREATE THE LUXURY SEDAN THAT IS\nMY NIGHTLY MONOLOGUE.\nBUT SOMETIMES, JUST SOMETIMES,\nFOLKS, I LURCH TO CONSCIOUSNESS\nIN THE BACK OF AN ABANDONED\nSCHOOL BUS AND SLAP MYSELF AWAKE\nWITH A CRUSTY FLOOR MAT BEFORE\nUSING A MOUSE-BITTEN TIMING BELT\nTO STRAP SOME OLD PLYWOOD TO A\nCOUPLE OF DISCARDED OIL DRUMS.\nTHEN, BY THE LIGHT OF A HEATHEN\nMOON, RENDER A GAS TANK OUT OF\nAN EMPTY BIG GULP, FILL IT WITH\nWHITE CLAW AND DENATURED\nALCOHOL, THEN LIGHT A MATCH AND\nLET HER RIP, IN THE DEMENTED\nONE-MAN SOAP BOX DERBY OF NEWS\nTHAT IS MY SEGMENT: MEANWHILE!" + }, + "3elIlQzJEQ0": { + "begin": "1:08.5", + "end": "1:58.5", + "text": "LADIES AND GENTLEMEN, YOU KNOW, I SPEND A\nLOT OF TIME RIGHT OVER THERE,\nRAISING THE FINEST HOLSTEIN NEWS\nCATTLE, FIRMLY, YET TENDERLY,\nMILKING THE LATEST HEADLINES\nFROM THEIR JOKE-SWOLLEN TEATS,\nCHURNING THE DAILY STORIES INTO\nTHE DECADENT, PROVENCAL-STYLE\nTRIPLE CREME BRIE THAT IS MY\nNIGHTLY MONOLOGUE.\nBUT SOMETIMES, SOMETIMES, FOLKS,\nI STAGGER HOME HUNGRY AFTER\nBEING RELEASED BY THE POLICE,\nAND ROOT AROUND IN THE NEIGHBOR'S\nTRASH CAN FOR AN OLD MILK\nCARTON, SCRAPE OUT THE BLOOMING\nDAIRY RESIDUE ONTO THE REMAINS\nOF A WET CHEESE RIND I WON\nFROM A RAT IN A PRE-DAWN STREET\nFIGHT, PUT IT IN A DISCARDED\nPAINT CAN, AND LEAVE IT TO\nFERMENT NEXT TO A TRASH FIRE,\nTHEN HUNKER DOWN AND HALLUCINATE\nWHILE EATING THE LISTERIA-LADEN\nDEMON CUSTARD OF NEWS THAT IS\nMY SEGMENT: MEANWHILE!" + }, + "43P4q1KGKEU": { + "begin": "0:29.3", + "end": "1:58.3", + "text": "FOLKS, IF YOU WATCH THE SHOW, YOU KNOW I SPEND MOST\nOF MY TIME, RIGHT OVER THERE.\nCAREFULLY SORTING THROUGH THE\nDAY'S BIGGEST STORIES, AND\nSELECTING ONLY THE MOST\nSUPPLE AND UNBLEMISHED OSTRICH\nAND CROCODILE NEWS LEATHER,\nWHICH I THEN ENTRUST TO ARTISAN\nGRADUATES OF THE \"ECOLE\nGREGOIRE-FERRANDI,\" WHO\nCAREFULLY DYE THEM IN A PALETTE\nOF BRIGHT, ZESTY SHADES, AND\nADORN THEM WITH THE FINEST, MOST\nTOPICAL INLAY WORK USING HAND\nTOOLS AND DOUBLE MAGNIFYING\nGLASSES, THEN ASSEMBLE THEM\nACCORDING TO NOW CLASSIC AND\nELEGANT GEOMETRY USING OUR\nSIGNATURE SADDLE STITCHING, AND\nLINE IT WITH BEESWAX-COATED\nLINEN, AND FINALLY ATTACH A\nMALLET-HAMMERED STRAP, PEARLED\nHARDWARE, AND A CLOCHETTE TO\nCREATE FOR YOU THE ONE-OF-A-KIND\nHAUTE COUTURE HERMES BIRKIN BAG\nTHAT IS MY MONOLOGUE.\nBUT SOMETIMES, SOMETIMES, FOLKS,\nSOMETIMES, SOMETIMES I WAKE UP IN THE\nLAST CAR OF AN ABANDONED ROLLER\nCOASTER AT CONEY ISLAND, WHERE\nI'M HIDING FROM THE TRIADS, I\nHUFF SOME ENGINE LUBRICANTS OUT\nOF A SAFEWAY BAG AND STAGGER\nDOWN THE SHORE TO TEAR THE SAIL\nOFF A BEACHED SCHOONER, THEN I\nRIP THE CO-AXIAL CABLE OUT OF\nTHE R.V. OF AN ELDERLY COUPLE\nFROM UTAH, HANK AND MABEL,\nLOVELY FOLKS, AND USE IT TO\nSTITCH THE SAIL INTO A LOOSE,\nPOUCH-LIKE RUCKSACK, THEN I\nSTOW AWAY IN THE BACK OF A\nGARBAGE TRUCK TO THE JUNK YARD\nWHERE I PICK THROUGH THE DEBRIS\nFOR ONLY THE BROKEN TOYS THAT\nMAKE ME THE SADDEST UNTIL I HAVE\nLOADED, FOR YOU, THE HOBO\nFUGITIVE'S BUG-OUT BINDLE OF\nNEWS THAT IS MY SEGMENT:\nMEANWHILE!" + }, + "4ktyaJkLMfo": { + "begin": "0:42.5", + "end": "1:26.5", + "text": "YOU KNOW, FOLKS, I SPEND A LOT\nOF TIME CRAFTING FOR YOU A\nBESPOKE PLAYLIST OF THE DAY'S\nBIGGEST STORIES, RIGHT OVER THERE, METICULOUSLY\nSELECTING THE MOST TOPICAL\nCHAKRA-AFFIRMING SCENTED\nCANDLES, AND USING FENG SHUI TO\nPERFECTLY ALIGN THE JOKE ENERGY\nIN THE EXCLUSIVE BOUTIQUE YOGA\nRETREAT THAT IS MY MONOLOGUE.\nBUT SOMETIMES, JUST SOMETIMES,\nI GO TO THE DUMPSTER BEHIND THE\nWAFFLE HOUSE AT 3:00 IN THE\nMORNING, TAKE OFF MY SHIRT,\nCOVER MYSELF IN USED FRY OIL,\nWRAP MY HANDS IN SOME OLD DUCT\nTAPE I STOLE FROM A BROKEN CAR\nWINDOW, THEN POUND A SIX-PACK OF\nBLUEBERRY HARD SELTZER AND A\nSACK OF PILLS I STOLE FROM A\nPARKED AMBULANCE, THEN\nARM-WRESTLE A RACCOON IN THE\nBACK ALLEY VISION QUEST OF NEWS\nTHAT IS MY SEGMENT:\n\"MEANWHILE!\"" + }, + "5Dsh9AgqRG0": { + "begin": "1:06.0", + "end": "2:34.0", + "text": "YOU KNOW, FOLKS, I SPEND MOST OF\nMY TIME RIGHT OVER THERE, MINING\nTHE DAY'S BIGGEST, MOST\nIMPORTANT STORIES, COLLECTING\nTHE FINEST, MOST TOPICAL IRON\nORE, HAND HAMMERING IT INTO JOKE\nPANELS.\nTHEN I CRAFT SHEETS OF BRONZE\nEMBLAZONED WITH PATTERNS THAT\nTELL AN EPIC TALE OF CONQUEST\nAND GLORY.\nTHEN, USING THE GERMANIC\nTRADITIONAL PRESSBLECH\nPROCESS, I PLACE THIN SHEETS OF\nFOIL AGAINST THE SCENES, AND BY\nHAMMERING OR OTHERWISE,\nAPPLYING PRESSURE FROM THE BACK,\nI PROJECT THESE SCENES INTO A\nPAIR OF CHEEK GUARDS AND A\nFACEPLATE.\nAND, FINALLY, USING FLUTED\nSTRIPS OF WHITE ALLOYED\nMOULDING, I DIVIDE THE DESIGNS\nINTO FRAMED PANELS AND HOLD IT\nALL TOGETHER USING BRONZE RIVETS\nTO CREATE THE BEAUTIFUL AND\nINTIMIDATING ANGLO-SAXON\nBATTLE HELM THAT IS MY\nNIGHTLY MONOLOGUE.\nBUT SOMETIMES, SOMETIMES, FOLKS,\nSOMETIMES, JUST SOMETIMES, I COME TO MY SENSES FULLY NAKED\nON THE DECK OF A PIRATE-BESIEGED\nMELEE CONTAINER SHIP THAT PICKED\nME UP FLOATING ON THE DETACHED\nDOOR OF A PORT-A-POTTY IN THE\nINDIAN OCEAN.\nTHEN, AFTER A SUNSTROKE-INDUCED\nREALIZATION THAT THE CREW OF\nTHIS SHIP PLANS TO SELL ME IN\nEXCHANGE FOR A BAG OF ORANGES TO\nFIGHT OFF SCURVY, I LEAD A\nMUTINY USING ONLY A P.V.C. PIPE\nAND A POOL CHAIN.\nTHEN, ACCEPTING MY NEW ROLE AS\nCAPTAIN, AND DECLARING MYSELF\nKING OF THE WINE-DARK SEAS, I\nGRAB A DIRTY MOP BUCKET COVERED\nIN BARNACLES AND ADORN IT WITH\nTHE TEETH OF THE VANQUISHED, TO\nCREATE THE SOPPING WET PIRATE\nCROWN OF NEWS THAT IS MY\nSEGMENT:\n\"MEANWHILE!\" " + }, + "748OyesQy84": { + "begin": "0:40.0", + "end": "1:41.0", + "text": "FOLKS, IF YOU WATCH THE SHOW, YOU KNOW, I SPEND MOST OF\nMY TIME, RIGHT OVER THERE,\nCAREFULLY BLENDING FOR YOU THE\nDAY'S NEWSIEST, MOST TOPICAL\nFLOUR, EGGS, MILK, AND BUTTER,\nAND STRAINING IT INTO A FINE\nBATTER TO MAKE DELICATE, YET\nINFORMATIVE COMEDY PANCAKES.\nTHEN I GLAZE THEM IN THE JUICE\nAND ZEST OF THE MOST RELEVANT\nMIDNIGHT VALENCIA ORANGES, AND\nDOUSE IT ALL IN A FINE DELAMAIN\nDE VOYAGE COGNAC, BEFORE\nFLAMBEING AND BASTING THEM TABLE\nSIDE, TO SERVE FOR YOU THE JAMES\nBEARD AWARD-WORTHY CREPES\nSUZETTE THAT IS MY NIGHTLY,\nMONOLOGUE.\nBUT SOMETIMES, JUST SOMETIMES FOLKS, I WAKE UP IN THE\nBAGGAGE HOLD OF A GREYHOUND BUS\nAS ITS BEING HOISTED BY THE\nSCRAPYARD CLAW TOWARD THE BURN\nPIT, ESCAPE TO A NEARBY\nABANDONED PRICE CHOPPER, WHERE I\nSCROUNGE FOR OLD BREAD SCRAPS,\nBUSTED OPEN BAGS OF STAR FRUIT\nCANDIES, AND EXPIRED EGGS,\nCHUCK IT ALL IN A DIRTY HUBCAP\nAND SLAP IT OVER A TIRE FIRE\nBEFORE USING THE LEGS OF A\nSTAINED PAIR OF SWEATPANTS AS\nOVEN MITTS TO EXTRACT AND SERVE\nTHE DEMENTED TRANSIENT'S POUND\nCAKE OF NEWS THAT IS MY SEGMENT:\n\"MEANWHILE.\"" + }, + "8prs9Pq5Xhk": { + "begin": "1:18.5", + "end": "2:17.5", + "text": "FOLKS, IF YOU WATCH THE SHOW,\nAND I HOPE YOU DO, I SPEND A\nLOT OF TIME RIGHT OVER THERE,\nTIRELESSLY STUDYING THE LINEAGE\nOF THE DAY'S MOST IMPORTANT\nTHOROUGHBRED STORIES AND\nHOLSTEINER HEADLINES, WORKING\nWITH THE BEST TRAINERS MONEY CAN\nBUY TO REAR THEIR COMEDY\nOFFSPRING WITH A HAND THAT IS\nSTERN, YET GENTLE, INTO THE\nTRIPLE-CROWN-WINNING EQUINE\nSPECIMEN THAT IS MY NIGHTLY\nMONOLOGUE.\nBUT SOMETIMES, SOMETIMES, FOLKS\nI BREAK INTO AN UNINCORPORATED\nVETERINARY GENETICS LAB AND GRAB\nWHATEVER TEST TUBES I CAN FIND.\nAND THEN, UNDER A GROW LIGHT I GOT\nFROM A DISCARDED CHIA PET, I MIX\nTHE PILFERED D.N.A. OF A HORSE\nAND WHATEVER WAS IN A TUBE\nLABELED \"KEITH-COLON-EXTRA,\"\nSLURRYING THE CONCOCTION WITH\nCAFFEINE PILLS AND A MICROWAVED\nRED BULL, I SCREAM-SING A PRAYER\nTO JANUS, INITIATOR OF HUMAN\nLIFE AND GOD OF TRANSFORMATION\nAS A HALF-HORSE, HALF-MAN FREAK,\nSEIZES TO LIFE BEFORE ME IN THE\nHIDEOUS COLLECTION OF LOOSE\nANIMAL PARTS AND CORRUPTED MAN\nTISSUE THAT IS MY SEGMENT:\nMEANWHILE!" + }, + "9gX4kdFajqE": { + "begin": "0:44.0", + "end": "1:08.0", + "text": "FOLKS, IF YOU WATCH THE SHOW,\nYOU KNOW SOMETIMES I'M OVER\nTHERE DOING THE MONOLOGUE.\nAND THEN THERE'S A COMMERCIAL\nBREAK, AND THEN I'M SITTING\nHERE.\nAND I DO A REALLY LONG\nDESCRIPTION OF A DIFFERENT\nSEGMENT ON THE SHOW, A SEGMENT\nWE CALL... \"MEANWHILE!\"" + }, + "9ssGpE9oem8": { + "begin": "0:00.0", + "end": "0:58.0", + "text": "WELCOME BACK, EVERYBODY.\nYOU KNOW, FOLKS, I SPEND MOST OF\nMY TIME RIGHT OVER THERE,\nCOMBING OVER THE DAY'S NEWS,\nSELECTING ONLY THE HIGHEST\nQUALITY AND MOST TOPICAL BONDED\nCALFSKIN-LEATHER STORIES,\nCAREFULLY TANNING THEM AND CUTTING\nTHEM WITH MILLIMETER PRECISION,\nTHEN WEAVING IT TOGETHER IN\nA DOUBLE-FACED INTRECCIATO\nPATTERN TO CREATE FOR YOU THE\nEXQUISITE BOTTEGA VENETA CLUTCH\nTHAT IS MY MONOLOGUE.\nBUT SOMETIMES, WHILE AT A RAVE\nIN A CONDEMNED CEMENT FACTORY, I\nGET INJECTED WITH A MYSTERY\nCOCKTAIL OF HALLUCINOGENS AND\nPAINT SOLVENTS, THEN, OBEYING\nTHE VOICES WHO WILL STEAL MY\nTEETH IF I DON'T, I STUMBLE INTO\nA SHIPYARD WHERE I RIP THE\nCANVAS TARP FROM A GRAVEL TRUCK,\nAND TIE IT OFF WITH THE ROPE FROM A\nROTTING FISHING NET, THEN WANDER\nA FOOD COURT, FILLING IT WITH\nWHAT I THINK ARE GOLD COINS BUT\nARE, IN FACT, OTHER PEOPLE'S CAR\nKEYS, TO DRAG AROUND THE\nROOTLESS TRANSIENT'S CLUSTER\nSACK OF NEWS THAT IS MY SEGMENT:\n\"MEANWHILE!\"" + }, + "ARw4K9BRCAE": { + "begin": "0:26.0", + "end": "1:16.0", + "text": "YOU KNOW, FOLKS, I SPEND\nMOST OF MY TIME STANDING RIGHT OVER\nTHERE,\nGOING OVER THE DAY'S NEWS\nAND SELECTING THE FINEST,\nMOST\nTOPICAL CARBON FIBER\nSTORIES, SHAPING THEM IN DRY\nDOCK INTO A\nSLEEK AND SEXY HULL, KITTING\nIT OUT WITH THE MOST TOPICAL\nFIBERGLASS AND TEAK FITTINGS\nBRASS RAILINGS, HOT TUBS,\nAND\nNEWS HELIPADS, TO CREATE THE\nCUSTOM DESIGNED, GLEAMING\nMEGA-YACHT THAT IS MY NIGHTLY\nMONOLOGUE. BUT SOMETIMES, JUST SOMETIMES FOLKS, I\nWASH ASHORE AT\nAN ABANDONED BEACH RESORT\nAFTER A NIGHT OF BATH SALTS\nAND\nSCOPOLAMINE, LASH SOME\nROTTING PICNIC TABLES\nTOGETHER, THEN\nDREDGE THE NEWS POND TO\nHAUL UP WHATEVER DISCARDED\nTRICYCLES AND BROKEN\nFRISBEES I CAN FIND, STEAL\nAN EYE PATCH\nFROM A HOBO, STAPLE A DEAD\nPIGEON TO MY SHOULDER, AND\nSAIL\nINTO INTERNATIONAL WATERS\nON THE PIRATE GARBAGE SCOW\nOF\nNEWS THAT IS MY SEGMENT:\nMEANWHILE!" + }, + "B1DRmrOlKtY": { + "begin": "1:34.0", + "end": "2:17.0", + "text": "FOLKS, I SPEND A LOT OF TIME\nSTANDING RIGHT OVER THERE, OKAY,\nHANDPICKING ONLY THE RIPEST\nMOST TOPICAL DAILY HEADLINES,\nSEEKING OUT THE SWEETEST, MOST\nREFINED OF JEST-JAMS AND\nJOKE-JELLIES, CURATING A PLATE\nOF LOCAL COMEDY-FED MEATS AND\nSATIRICAL CHEESES TO LAY OUT THE\nARTISANAL CHARCUTERIE BOARD\nA-NEWS BOUCHE THAT IS MY NIGHTLY\nMONOLOGUE.\nBUT SOMETIMES, I WAKE UP IN THE\nGREASE TRAP OF AN\nUNLICENSED SLAUGHTERHOUSE,\nSPLASH MY FACE WITH SOME BEEF TALLOW,\nRENDER A RUDIMENTARY PRESERVE\nFROM BONE MARROW AND MELTED\nGUMMY WORMS, AND THROW IT\nTOGETHER WITH SOME SWEETBREADS\nAND TRIPE TO PRESENT THE\nPLOWMAN'S PLATTER OF UNCLAIMED\nOFFAL THAT IS MY SEGMENT:\nMEANWHILE!" + }, + "BT950jqCCUY": { + "begin": "1:00.0", + "end": "2:09.0", + "text": "YOU KNOW, FOLKS,\nI SPEND SO MUCH OF MY TIME RIGHT\nOVER THERE, SIFTING THROUGH THE\nDAY'S BIGGEST STORIES, HAND\nSELECTING ONLY THE FINEST, MOST\nPERFECTLY AGED BURMESE NEWS\nTEAK.\nTHEN CAREFULLY CARVING AND\nSHAPING IT INTO A REFINED\nBUDDHA, DEPICTED IN THE \"CALLING\nTHE EARTH TO WITNESS\" GESTURE,\nWITH CHARACTERISTIC CIRCULAR\nPATTERNS ON THE ROBE, SHINS, AND\nKNEES, OF COURSE, WHICH I\nTHEN CAREFULLY GILD WITH THE\nMOST TOPICAL GOLD LEAF.\nTHEN I HARVEST THE SAP OF A\nUSITATA TREE TO APPLY THE DELICATE\nTAI O LACQUER, AND FINALLY HAND\nDECORATE IT WITH THE WHITE GLASS\nINLAYS TO CREATE FOR YOU THE\nGLORIOUS AMARA-PURA PERIOD\nSCULPTURE THAT IS MY MONOLOGUE.\nBUT SOMETIMES, SOMETIMES FOLKS,\nI WASH UP ON A GALVESTON\nBEACH, NAKED ON A RAFT OF GAS\nCANS AFTER ESCAPING FROM A FIGHT CLUB\nIN INTERNATIONAL WATERS.\nTHEN, STILL DERANGED ON A\nCOCKTAIL OF MESCALINE AND COUGH\nSYRUP, I STEAL A CINDER BLOCK\nFROM UNDER A STRIPPED '87 FORD\nTAURUS, AND CHIP AWAY AT IT\nWITH A BROKEN UMBRELLA HANDLE I\nSCAVENGED FROM A GOODWILL\nDUMPSTER UNTIL IT VAGUELY\nRESEMBLES THE HUNGERING WOLF\nTHAT SCRATCHES AT THE DOOR\nOF MY DREAMS AND PRESENT TO YOU\nTHE TORMENTED DREAD-EFFIGY OF\nNEWS THAT IS MY SEGMENT:\nMEANWHILE!" + }, + "C0e8XM30tQI": { + "begin": "0:57.0", + "end": "2:40.0", + "text": "YOU KNOW, IF YOU WATCH THE SHOW, YOU'RE AWARE THAT I SPEND MOST OF\nMY TIME RIGHT OVER THERE,\nWANDERING THE NEWS FOREST FOR\nYOU, FELLING ONLY THE BIGGEST\nAND HEARTIEST WHITE STORY OAKS,\nCUTTING AND SHAPING THEM INTO\nTHE NEWSIEST, MOST TOPICAL\nCLEATS, CLAMPS AND PLANKS,\nKEEPING THEM AT A CONSTANT\nANGLE, GRADUALLY CREATING A\nSHELL-SHAPED, SHALLOW BOWL HULL\nUSING THE FIRE-BENDING TECHNIQUE\nINSTEAD OF STEAM-BENDING\nOBVIOUSLY.\nTHEN I LAY OUT ALL THE KEEL\nBLOCKS TO CAREFULLY SET UP THE\nSTEM, STERN AND, GARBOARD,\nATTACH THE BILGE FUTTOCKS TO THE\nTIMBER AND LOVINGLY CRAFT A FLAT\nTRANSOM STERN OUT OF\nNATURALLY-CURVED QUARTER\nCIRCLES.\nTHEN SECURE ALL THE PLANKS WITH\nTRUNNELS HANDMADE FROM THE\nFINEST LOCUST WOOD AND, FINALLY,\nADORN IT WITH A PROUD BOWSPRIT,\nFOREPEAK, AND CUSTOM GILDED\nFIGUREHEAD TO PRESENT TO YOU THE\nDUTCH GOLDEN AGE \"SPIEGEL-JACHT\"\nTHAT IS MY MONOLOGUE.\nBUT SOMETIMES, SOMETIMES,\nFOLKS-- YOU GOT TO HYDRATE\nAFTER THAT.\n\"SPIEGEL-JACHT\"\nBUT SOMETIMES, I\nAWAKEN FROM A MEAT-SWEAT-\nINDUCED FEVER STRAPPED TO A\nBASKET ON THE WONDER WHEEL AT\nCONEY ISLAND, STUMBLE ACROSS THE\nGARBAGE-FLECKED BEACH TO THE\nSOUND OF A TERRIFYING RAGGED\nBELLOW I REALIZE IS COMING FROM\nMY OWN LUNGS, WHICH THEN SUMMONS AN\nARMY OF SEAGULLS WHOM I INSTRUCT\nTO GATHER THE HALF-EMPTIED CANS\nOF BUSCH LIGHT LEFT BY A MOB OF\nBELGIAN TOURISTS, ALL OF WHICH\nI GATHER INTO A SACK I FASHIONED\nOUT OF PANTS I STOLE FROM A\nSLEEPING COP. THEN I SWIPE A\nGIANT INFLATABLE BABY YODA FROM\nA CARNY GAME, STRAP IT TO THE\nMAST I MADE BY RIPPING A B-68\nBUS STOP SIGN OUT OF THE GROUND\nON THE CORNER OF STILLWELL, AND\nLAUNCH THE VESSEL AS CAPTAIN OF\nTHE UNREGULATED PIRATE BOOZE\nCRUISE OF NEWS THAT IS MY\nSEGMENT:\n\"MEANWHILE\"!" + }, + "CKsASCGr_4A": { + "begin": "1:48.3", + "end": "2:37.3", + "text": "FOLKS, YOU KNOW, I SPEND A LOT\nOF TIME RIGHT OVER THERE, CARVING THE\nFINEST, MOST-TOPICAL JAPANESE\nNEWS CYPRESS INTO AN EXPRESSIVE\nHANNYA DEMON MASK, DONNING MY\nBLACK AND GOLD SHOZOKU ROBES\nMADE OF THE SMOOTHEST STORY\nSILK, AND MASTERING THE ELABORATE\nCHOREOGRAPHY OF STILLNESS AND\nMOVEMENT, TO CREATE THE JAPANESE\nNOH THEATER PRODUCTION THAT IS\nMY NIGHTLY MONOLOGUE.\nBUT SOMETIMES, JUST SOMETIMES,\nFOLKS, I ENTER A FUGUE STATE IN\nTHE MIDDLE OF THE NIGHT, SIPHON\nA BUCKETFUL OF GASOLINE OUT OF MY\nNEIGHBOR'S MAZDA, RUN BAREFOOT\nFROM MY HOUSE TO A HOVEL UNDER\nTHE TURNPIKE WHERE I ASK A HOBO\nFOR A LIGHT AND SET A GARBAGE\nCAN ABLAZE, THEN PLACE MY\nFROSTBITTEN HANDS IN FRONT OF\nTHE DUMPSTER TO PROJECT THE\nSHADOW PUPPET WINO OPERA OF NEWS\nTHAT IS MY SEGMENT, \"MEANWHILE.\"" + }, + "DSc26qAJp_g": { + "begin": "0:28.0", + "end": "1:46.0", + "text": "FOLKS, I SPEND MOST OF MY TIME\nRIGHT OVER THERE, ASSEMBLING THE\nDAY'S BIGGEST, MOST-IMPORTANT\nSTORIES, THEN HAND-SHAPING THEM\nINTO SLEEK, ELEGANT BODYWORK,\nWHICH I LINE WITH ONLY THE\nFINEST, MOST TOPICAL POLISHED\nMACASSAR EBONY AND OPEN-PORE\nPALDAO VENEER, ADDING LIGHT\nMOCCASIN AND DARK SPICE LEATHER\nSEATS, AND MALABAR TEAK WOOD TO\nSET OFF A SCULPTED MINIMALIST\nSWITCHGEAR, ACCOMPANIED BY A\nSTERLING SILVER HUMIDOR AND\nCHAMPAGNE CELLARETTE, THEN\nHAND-SET 1,600 FIBER OPTIC\nLIGHTS ALIGNED WITH PINPOINT\nPERFORATIONS IN THE ROOF-LINING,\nTO CREATE FOR YOU THE BESPOKE\nCOACH BUILT ROLLS-ROYCE SWEPTAIL\nTHAT IS MY MONOLOGUE.\nBUT SOMETIMES, SOMETIMES, FOLKS,\nSOMETIMES, I JUST, I JUST SHRIEK AWAKE IN THE\nSCRAPYARD OF A DERELICT MACHINE\nSHOP, SCAVENGE A\n3.6-HORSEPOWER BRIGGS AND STRATTON\nLAWN MOWER ENGINE, BULLY A\nDOG INTO GIVING ME ITS FRISBEE\nTO USE AS A STEERING WHEEL, THEN\nI BRIEFLY CONSIDER-- BUT DECIDE\nAGAINST-- SWIPING THE BRAKE PADS\nOFF AN UNATTENDED HEARSE,\nBECAUSE WHERE I'M GOING, WE DON'T\nNEED BRAKES.\nI HOOK IT ALL UP TO A RUSTY\nDOLLAR TREE SHOPPING CART,\nSHOTGUN A WHITE CLAW, NO LAW ON THE CLAW, SPRAY\nPAINT MY TEETH, AND BLAST OFF IN\nTHE \"FURY ROAD\" THUG-BUGGY OF\nNEWS THAT IS MY SEGMENT:\n\"MEANWHILE\"!" + }, + "DhuCyncmFgM": { + "begin": "0:43.5", + "end": "1:30.5", + "text": "YOU KNOW, FOLKS, I\nSPEND A LOT OF TIME STANDING RIGHT\nOVER THERE, COMBING THROUGH\nTHE DAY'S\nSTORIES TO FIND AND ERECT\nFOR YOU THE FINEST GRECIAN\nNEWS\nCOLUMNS, ADORNING THEM WITH\nTHE MOST UP-TO-THE-MINUTE\nBAS-RELIEF.\nAND THEN I IMPART MY MOST\nTOPICAL\nTEACHINGS TO BE ABSORBED BY\nEAGER, SPONGE-LIKE\nMINDS IN THE\nAUGUST ATHENIAN ACADEMY THAT\nIS MY MONOLOGUE.\nBUT SOMETIMES, JUST\nSOMETIMES, FOLKS, I COME TO IN A\nDRIED-OUT BABY POOL\nON AN ABANDONED RANCH I WON\nIN A RUSSIAN ROULETTE GAME\nOFF THE COAST OF MOZAMBIQUE\nI GATHER TUMBLEWEEDS\nAND I LASH THEM TOGETHER WITH\nSOME TWINE I FOUND IN A DUMPSTER\nBY A BURNED-OUT REST STOP,\nTHEN I\nSHOTGUN A HOT MONSTER ENERGY\nDRINK AND CHEW ON MACA ROOT\nAS I\nHALLUCINATE THROUGH THE\nNIGHT IN THE VAGRANT'S HOT\n-BOX YURT OF\nNEWS THAT IS MY SEGMENT:\n\"MEANWILE!\"" + }, + "EnGHyZS4f-8": { + "begin": "1:33.0", + "end": "2:12.0", + "text": "YOU KNOW, FOLKS, I'VE SPENT\nDECADES CULTIVATING THE MOST\nRELEVANT RED OAK, FELLING THEM\nWITH TOPICAL HUSQVARNAS,\nMULTIGRADING THEM INTO THE MOST\nBUZZWORTHY THREE-QUARTER INCH\nFLOORSTRIPS, AND FINISHING THEM\nWITH UP-TO-THE-MINUTE HIGH-GLOSS\nPOLYURETHANE, TO LAY FOR YOU THE\nFLAWLESS PARQUET N.B.A.-QUALITY\nBASKETBALL COURT THAT IS MY\nNIGHTLY MONOLOGUE.\nBUT SOMETIMES, I SCARE THE GEESE\nOUT OF THE BOG BEHIND MY UNCLE'S\nSHED, FILL IT WITH SAND I STOLE\nFROM AN ABANDONED PLAYGROUND,\nTHEN BLANKET IT WITH WET TRASH\nAND DISCARDED AMAZON BOXES, TO\nCREATE FOR YOU THE\nMUSKRAT-RIDDLED BOUNCELESS\nBACKYARD WALLBALL PIT OF NEWS\nTHAT IS MY SEGMENT:\n\"MEANWHILE!\"" + }, + "G8ajua4Mb5I": { + "begin": "1:39.0", + "end": "2:24.0", + "text": "YOU KNOW, FOLKS,\nIF YOU WATCH THIS SHOW AND I\nHOPE YOU DO,\nI SPEND A LOT OF TIME RIGHT OVER\nTHERE, CAREFULLY ERECTING THE\nNEWSIEST, MOST TOPICAL\nCORINTHIAN COLUMNS, FOLLOWING\nTHE GOLDEN RATIO, POLISHING THE\nFINEST CARRARA MARBLE, AND\nENGRAVING ONTO IT MYTHIC TALES\nWITH FLOURISHES OF HUMOR AND\nPATHOS, TO CREATE THE\nGRECO-ROMAN ACROPOLIS THAT IS MY\nNIGHTLY MONOLOGUE.\nBUT SOMETIMES, JUST SOMETIMES, FOLKS,\nI GRAB A COUPLE OF OFF-BRAND\nLEGOS THAT HAVE BEEN JAMMED\nBETWEEN MY TOES SINCE I STEPPED\nON THEM IN 2003, FISH THE STICKS\nOUT OF SOME MELTED POPSICLES IN\nA BROKEN FREEZER, COLLECT THE\nPRE-SOAKED SAND FROM A\nPLAYGROUND I BROKE INTO IN THE\nDEAD OF NIGHT, AND THROW IT ALL\nTOGETHER TO CONSTRUCT FOR YOU\nTHE RAMSHACKLE PARTHENON OF NEWS\nTHAT IS MY SEGMENT:\n\"MEANWHILE!\"" + }, + "I4s-44cPYVE": { + "begin": "1:10.0", + "end": "2:59.0", + "text": "FOLKS, YOU KNOW, IF YOU WATCH THE SHOW, YOU KNOW, I SPEND\nMOST OF MY TIME\nRIGHT OVER THERE, SURVEYING THE\nNEWS MARKET FOR THE\nBIGGEST STORIES, THEN CAREFULLY\nSELECTING THE FINEST, MOST\nTOPICAL BUFFALO NEWS HIDE WHICH\nI THEN SOAK USING NATURAL SPRING\nAND LIMEWATER-- ONLY DURING\nCOLDER MONTHS-- AND SCRAPE IT\nUNTIL IT IS EVENLY TRANSLUCENT\nAND SUPPLE.\nAND THEN, USING THE TRADITIONAL\nPUSH-KNIFE METHOD, I DELICATELY\nMAKE MORE THAN 3,000 CUTS TO\nCREATE THE EMOTIVE AND POWERFUL\nFIGURINE WHICH I DECORATE WITH\nGRADATIONS AND CONTRAST,\nEMPLOYING THE SHAANXI REGION\nFIVE-COLOR SYSTEM.\nTHEN I CAREFULLY CONNECT THE\nFIGURINE'S JOINTS WITH COTTON\nTHREADS SO THEY CAN BE OPERATED\nFREELY, AND FIRE UP A PAPER\nLANTERN BEHIND A FINE HUANGZHOU\nSILK SCREEN, AND, BACKED BY A\nSUONA HORN, AND YUEQIN, AND\nBANHU FIDDLE, I OPERATE NO LESS\nTHAN FIVE OF THESE FIGURINES AT\nONCE, BECOMING THE LIVING\nEMBODIMENT OF THE 1,000-HAND\nGWAN-YIN, TO MOUNT FOR YOU THE\nEPIC AND MOVING TONG DYNASTY\nPI-YING SHADOW PLAY THAT IS MY\nNIGHTLY MONOLOGUE.\nBUT SOMETIMES, FOLKS,\nSOMETIMES,\nIT'S CRAFTSMANSHIP.\nIT GOES LIKE THAT, RIGHT\nTHERE.\nSOMETIMES, FOLKS,\nI AM PECKED\nAWAKE BY A DIRTY SEAGULL ON THE\nINTRACOASTAL WATERWAY, WHILE\nSTILL LYING ON THE BACK OF A\nMANATEE WHO RESCUED ME FROM SOME\nARMS DEALERS I DOUBLE-CROSSED\nOFF THE COAST OF CAPE FEAR, AND WHO THEN\nDUMPS ME ON AN ABANDONED WHARF\nWHERE I SLIP THEIR DIRTY SOCK OFF\nA SEVERED FOOT I FISHED OUT OF A\nSTORM DRAIN, AND SLAP GOOGLY\nEYES ON IT MADE FROM TWO MENTOS\nI PRIED OUT OF THE MOUTH OF A\nMANGY COYOTE.\nTHEN I FASHION A KAZOO OUT OF A\nPOCKET COMB I STOLE FROM A\nFISHERMAN AND THE WAX PAPER FROM\nHIS MEATBALL SUB, TO HONK OUT A\nDIRGE WHILE YAMMERING A TONE\nPOEM ABOUT THE DEMONS INFESTING\nMY BEARD IN THE UNBALANCED MANIC\nSOCK PUPPET SHOW OF NEWS THAT IS\nMY SEGMENT:\n\"MEANWHILE!\"" + }, + "JAfAApqOeFU": { + "begin": "1:49.5", + "end": "2:42.5", + "text": "FOLKS.\nI SPEND A LOT OF MY TIME GATHERING FOR YOU\nTHE FINEST, MOST TOPICAL STORIES\nABOUT NATIONAL STUDIES,\nSCIENTIFIC BREAKTHROUGHS, AND\nDRUNK MOOSE BREAKING INTO ICE\nCREAM SHOPS, ONLY TO HAVE A\nPANDEMIC HIT, DURING WHICH I\nTAKE THEM INTO A SAD, EMPTY\nLITTLE ROOM WHERE MY ONLY\nFRIENDS ARE ROBOT CAMERAS AND A\nPURELL BOTTLE, AND I LET MYSELF\nGO WHILE SLOWLY DESCENDING INTO\nMADNESS AS I SHOVE MY SWEET\nINNOCENT LITTLE JOKES INTO A\nSEGMENT THAT I AM FORCED TO\nRENAME \"QUARANTINE-WHILE.\"\nBUT SOMETIMES, I CRAWL OUT OF\nTHE BROOM CLOSET AFTER 15\nMONTHS, POUR MYSELF BACK INTO A\nSUIT, ASSEMBLE THE TOP TEAM IN\nTHE BUSINESS, THE SWINGINGEST\nBAND IN LATE NIGHT, AND THE\nBEST DAMN AUDIENCE IN THE WORLD.\nSO I CAN RETURN TO YOUR LOVING\nARMS IN THE KICK-ASS,\nPROPERLY-PRESENTED\nCELEBRATION OF MARGINAL NEWS\nTHAT IS MY SEGMENT:\n\"MEANWHILE!\"" + }, + "JfT59wBSQME": { + "begin": "0:52.0", + "end": "1:37.0", + "text": "YOU KNOW, FOLKS, I SPEND A\nLOT OF TIME HARVESTING THE DAY'S\nMOST TOPICAL MATCHA POWDER,\nCAREFULLY POLISHING THE NEWSIEST\nCHAWAN TEA BOWL WITH A HEMP\nCLOTH, AND ADDING THE PUREST\nBOILED WATER COLLECTED FROM THE\nRIVER OF STORIES, TO STAGE THE\nELEGANT JAPANESE TEA CEREMONY\nTHAT IS MY NIGHTLY MONOLOGUE.\nBUT SOMETIMES, JUST SOMETIMES, I CONVINCE A\nTRUCK DRIVER TO GIVE ME A RIDE\nIN EXCHANGE FOR AN UNREGISTERED\nHAND GUN AND A HALF-EATEN CAN OF\nBEANS, HITCHHIKE TO THE SONORA\nDESERT WITH NOTHING BUT AN OLD\nPOT THAT I FILL WITH THE\nNEWSPAPER I'VE BEEN USING FOR A\nBLANKET, AND THE SALVAGED\nTOBACCO FROM A SIDEWALK CIGARETTE\nBUTT, TO BREW FOR YOU, THE\nNIGHTMARE HALLUCINATION-INDUCING\nFERMENTED AYAHUASCA SLURRY OF\nNEWS THAT IS MY SEGMENT:\nMEANWHILE!" + }, + "KT8pCZ5Xw9I": { + "begin": "0:37.6", + "end": "1:26.6", + "text": "FOLKS, YOU KNOW,\nIF YOU WATCH THE SHOW,\nTHAT I SPEND A LOT OF TIME RIGHT OVER\nTHERE, GATHERING THE FRESHEST,\nNEWSIEST HEADLINE FLOWERS,\nSCOURING THE FIELDS AND FORESTS\nFOR THE MOST TOPICAL AND\nFRAGRANT SYLVAN NEWS BOUGHS, THE\nJOKIEST FESTIVE GOURDS, AND THEN\nCAREFULLY ASSEMBLING AND\nARRANGING THEM ALL INTO THE\nGRAND YET TASTEFUL STATE\nDINNER-WORTHY CENTERPIECE THAT\nIS MY NIGHTLY MONOLOGUE.\nBUT SOMETIMES, JUST SOMETIMES, NOW, SOMETIMES,\nI RUB SOME LEAD PAINT CHIPS ONTO\nMY GUMS, STAGGER INTO THE WOODS\nWITH NOTHING BUT A STAPLE GUN\nAND SOME EMPTY CANS OF SPRAY\nPAINT, AND THEN, BY THE LIGHT OF THE\nTIRE-FIRE, USING SMASHED LARVAE\nAND MY OWN SALIVA AS GLUE, I\nCOBBLE TOGETHER A CRUDE PILE OF\nPUNKY WOOD AND ANIMAL SKULLS TO\nPRESENT TO YOU THE UNHINGED\nLONER'S CORNUCOPIA OF NEWS THAT\nIS MY SEGMENT:\n\"MEANWHILE!\"" + }, + "L-kR7UCzhTU": { + "begin": "1:36.0", + "end": "2:19.0", + "text": "YOU KNOW, I SPEND A LOT OF TIME\nRIGHT OVER THERE HAND-RAISING\nAND SELECTING THE NEWEST,\nMOST-TOPICAL SEVILLE ORANGES,\nCAREFULLY SIMMERING THEM WITH\nTURBINADO SUGAR AND PRISTINE\nFILTERED WATER TO CREATE FOR YOU\nA DOUBLE-GOLD-MEDAL-WINNING\nBITTERSWEET ENGLISH MARMALADE TO\nSPREAD ON THE PERFECTLY TOASTED\nARTISANAL BRIOCHE THAT IS\nMY NIGHTLY MONOLOGUE.\nBUT SOMETIMES, JUST SOMETIMES, FOLKS,\nI GRAB AN EXPIRED TUB OF\nWHIPPING CREAM, TOSS IT IN A\nBLENDER WITH THE HALF OF A\nGOGURT I FOUND IN A SCHOOLYARD,\nAND LET THAT FERMENT BEHIND THE\nFURNACE WHILE I FISH A DRIED\nPITA OUT OF THE GUTTER\nUNDERNEATH THE CORN-- CORNER KEBAB\nSTAND, THEN SLATHER IT WITH THE\nUNPRESSURIZED NIGHTMARE\nDAIRY OF NEWS THAT IS MY\nSEGMENT:\n\"MEANWHILE!\"" + }, + "Lf-LkJhKVhk": { + "begin": "1:14.0", + "end": "1:53.0", + "text": "YOU KNOW, I SPEND A LOT OF TIME\nOVER THERE CAREFULLY ASSEMBLING\nTHE MOST TOPICAL VIRTUOSO WIND\nSECTION, TUNING THE VIOLAS,\nCELLOS, AND CONTRABASS TO THE\nCOUNTRY'S ZEITGEIST, AND WAVING\nMY CONDUCTOR'S BATON TO THE\nTEMPLE OF HUMOR TO PRESENT FOR\nYOU THE SPECTACULAR BRAHMS\nCONCERTO IN SATIRE MAJOR THAT IS\nMY MONOLOGUE.\nBUT SOMETIMES, I WAKE UP IN A\nFUGUE STATE BEHIND THE ABANDONED\nVIDEO STORE, STEAL A GARBAGE CAN\nLID AND A BROKEN UMBRELLA\nHANDLE, AND THEN GRAB AN EMPTY CAN\nOF P.B.R. I'VE BEEN USING AS AN\nASHTRAY TO FASHION A RUSTED\nKAZOO, ALL TO CREATE THE\n2-IN-THE-MORNING ONE-MAN-STOMP\nSHOW OF NEWS THAT IS MY SEGMENT:\n\"MEANWHILE!\"" + }, + "P72uFdrkaVA": { + "begin": "0:49.3", + "end": "1:48.3", + "text": "FOLKS, IF YOU WATCH THE SHOW, YOU KNOW I SPEND A LOT OF\nTIME RIGHT OVER THERE, TENDERLY\nCLIPPING THE NEWSIEST, MOST\nFRAGRANT TEA-LEAF BUDS OF THE\nDAY, GINGERLY LAYING THEM TO DRY\nBY THE LIGHT OF THE\nROSY-FINGERED DAWN,\nPAINSTAKINGLY STEEPING THEM TO\nPERFECTION IN THE MOST TOPICAL\nOF FRESH WATER GATHERED FROM THE\nNATION'S NEWS RESERVOIR, BEFORE\nCEREMONIOUSLY SERVING TO YOU THE\nANTIOXIDANT-RICH ELIXIR OF\nBESPOKE TEA SHAN THAT IS MY\nNIGHTLY MONOLOGUE.\nBUT SOMETIMES, SOMETIMES FOLKS, AFTER\nA BENDER ON ABSINTHE AND\nMOUNTAIN DEW CODE RED, I SWEAT\nMYSELF AWAKE HOVERED OVER A VAT\nOF BLISTERING SEWER RUNOFF.\nI ADD TO THE GURGLING POT OF\nNIGHTMARES WHATEVER THE VOICES\nDEMAND: SCRAPS OF WET TRASHCAN\nLETTUCE AND BAND-AIDS FROM THE\nCOMMUNITY POOL.\nTHEN, USING AN OLD GYM SOCK I\nFOUND AT THE HOBOKEN Y.M.C.A., I\nSTRAIN IT ALL INTO A DISUSED\nGASOLINE CONTAINER TO OFFER YOU\nTHE SCALDING-HOT DEMENTED DEMON\nTONIC OF NEWS THAT IS MY\nSEGMENT: \"MEANWHILE!\"" + }, + "PT5_00Bld_8": { + "begin": "0:57.0", + "end": "2:02.0", + "text": "FOLKS, YOU KNOW, I SPEND A LOT OF MY TIME,\nRIGHT OVER THERE, CRUISING THE\nVAST TSUKIJI FISH MARKET OF THE\nDAY'S BIGGEST STORIES, CAREFULLY\nSURVEYING THE FRESHEST, MOST\nTOPICAL CATCH OF THE DAY,\nCHOOSING ONLY THE HIGHEST GRADE\nAHI NEWS TUNA, AWABI, AND UNAGI,\nTHEN DELICATELY PICKING THROUGH\nTHE RICE GRAINS OF THE\nHEADLINES, GENTLY WRAPPING THE\nINGREDIENTS IN THE FRESHEST\nHAND-PICKED NORI, AND CAREFULLY\nLAYING IT ALL OUT TO PRESENT TO\nYOU THE YAMANAKA LACQUERED\nTHREE-TIER JUBAKO BENTO BOX THAT\nIS MY NIGHTLY MONOLOGUE.\nBUT -- YOU KNOW WHAT I'M SAYING.\nYOU KNOW WHAT I'M SAYING.\nYOU KNOW WHAT'S COMING.\nBUT SOMETIMES, JUST SOMETIMES FOLKS, I AM SLAPPED\nAWAKE BY A POSSUM ON A BED OF\nWET TIRES NEAR A LONG-ABANDONED\nWHARF, SELL MY LAST REMAINING\nADULT TEETH TO A VAGRANT\nFISHMONGER FOR AN EXPIRED SALMON\nCARCASS, AND GRIND THAT INTO A\nSOFT PASTE, THEN ROLL IT IN ENOUGH\nSTALE RICE KRISPIES TO MAKE\nSNAP, CRACKLE, AND POP TAKE A\nHARD LOOK IN THE MIRROR, AND SERVE\nYOU THE NIGHT-SCREAM INDUCING\nHOBO HAND-ROLL OF NEWS THAT IS\nMY SEGMENT:\n\"MEANWHILE!\"" + }, + "QPDZbNEhsuw": { + "begin": "1:31.0", + "end": "2:15.0", + "text": "YOU KNOW, FOLKS, I SPEND MOST\nOF MY TIME RIGHT OVER THERE,\nDIGGING DOWN INTO THE NEWS\nPIT AND MINING FOR YOU THE\nDAY'S\nCLEAREST STORY DIAMONDS,\nCLEAVING THEM INTO THE MOST\nTOPICAL CUTS, FACETING THEM,\nPOLISHING THEM TO A HIGH\nFINISH,\nTHEN SETTING THEM ALL IN A\nDELICATE 24 KARAT GOLD CHAIN\nTO\nCREATE THE BESPOKE CARTIER\nNECKLACE THAT IS MY NIGHTLY MONOLOGUE,\nBUT SOMETIMES, SOMETIMES FOLKS,\nI JUST HUFF A LITTLE TOO MUCH\nEPOXY\nAND STUMBLE DOWN TO AN\nABANDONED PIER, WHERE I FIND\nA PIECE OF\nDISUSED FISHING LINE AND\nSTRING IT WITH OLD BOTTLE\nCAPS, RUSTY\nPADLOCKS, AND BABY TEETH,\nTHEN RIP THE SEAT BELT OUT\nOF A\nBURNED-OUT POLICE CAR TO\nMAKE A CLASP, AND PARADE\nNAKED THROUGH\nA CHI-CHI'S WEARING THE\nPSYCHO CHOKER OF NEWS THAT\nIS MY\nSEGMENT:\n\"MEANWHILE.\"" + }, + "QjQbQlN9Ev8": { + "begin": "3:53.0", + "end": "4:35.0", + "text": "YOU KNOW, FOLKS, I SPEND A LOT OF\nMY TIME RIGHT OVER THERE,\nHANGING FOR YOU THE DAY'S\nHOTTEST, MOST TOPICAL NEWS\nDECORATIONS, BOOKING THE SEXIEST\nBAND, CURATING THE MOST RELEVANT\nDRINKS MENU, DISTRIBUTING\nPLAYFUL, YET TASTEFUL PARTY\nFAVORS, AND GLITTER HATS THAT\nSAY \"2022,\" AND THEN SETTING THE\nMOST AU-COURANT LIGHTING TO\nTHROW FOR YOU THE UPSCALE,\n\"PITCH PERFECT\" NEW YEAR'S EVE\nPARTY THAT IS MY MONOLOGUE.\nBUT SOMETIMES, JUST SOMETIMES,\nFOLKS, I WAKE UP IN THE RAFTERS\nAFTER LOSING A BET TO A CROW.\nAND THEN I SPIKE THE PUNCH BOWL WITH\nCHLOROFORM AND MILITARY-GRADE\nHELICOPTER LUBRICANTS, MAKE A\nBUNCH OF RESOLUTIONS TO QUIT\nHUFFING WD-40, AND PUNCH A\nPOLICE HORSE DURING THE FUGITIVE\nNEW YEAR'S HO-DOWN OF NEWS THAT\nIS MY SEGMENT:\nMEANWHILE!" + }, + "R6JV_I36It8": { + "begin": "0:38.0", + "end": "1:18.0", + "text": "YOU KNOW, FOLKS, I SPEND\nA LOT OF TIME\nSHUCKING FOR YOU THE DAY'S MOST\nTOPICAL CLAMS, BONING THE\nFINEST, MOST CURRENT NEWS\nCHICKENS, AND COLLECTING THE\nHIGHEST QUALITY STORY SHRIMP\nAND SAFFRON RICE, THEN GENTLY\nSIMMERING IT ALL IN A CAST-IRON\nCOMEDY PAI-YERA, TO CREATE\nTHE FRAGRANT SEAFOOD PAELLA THAT\nIS MY MONOLOGUE.\nBUT SOMETIMES, JUST SOMETIMES FOLKS, I\nSHAMBLE DOWN TO THE DOCKS WITH A\nRUSTY CROWBAR, MANEUVER THROUGH\nTHE POLLUTED CANAL USING A\nMCDONALD'S STRAW AS A SNORKEL,\nAND SCRAPE THE BARNACLES OFF A\nPASSING GARBAGE SCOW, TOSS THEM\nIN A POT WITH SOME HALF-USED\nRAMEN FLAVORED PACKETS AND\nMOUNTAIN DEW, TO BREW FOR YOU\nTHE CHUNKY STEW OF NEWS THAT IS\nMY SEGMENT:\n\"MEANWHILE!\"" + }, + "RFVggCw58lo": { + "begin": "1:00.0", + "end": "1:43.0", + "text": "YOU KNOW FOLKS, I SPEND A LOT OF TIME\nSTANDING RIGHT OVER THERE, PAINSTAKINGLY\nPENCILING THE DAY'S MOST TOPICAL\nAND HEROIC STORIES, STAGING THEM\nIN METICULOUSLY PLANNED PANELS,\nHAND-INKING THEM WITH THE\nPITHIEST DIALOGUE, THEN COLOR\nBALANCING THE FINISHED PAGES TO\nCREATE FOR YOU THE GENRE-BENDING\nONCE-IN-A-GENERATION GRAPHIC\nNOVEL THAT IS MY MONOLOGUE.\nBUT SOMETIMES, JUST SOMETIMES FOLKS, I JUST TEAR A PAGE OUT\nOF A WET NEWSPAPER I PEELED OFF\nA SUBWAY SEAT, GRAB A BROKEN\nCRAYON I FOUND IN MY COUCH,\nCRUDELY DRAW SOME FILTHY\nCARTOONS, SCRIBBLE IN AS\nMANY CURSE WORDS AS I CAN IN THE\nPORNOGRAPHIC DRIFTER ZINE OF\nNEWS THAT IS MY SEGMENT:\nMEANWHILE!" + }, + "RHDQpOVLKeM": { + "begin": "0:39.0", + "end": "2:11.0", + "text": "YOU KNOW, FOLKS, I\nSPEND MOST OF MY TIME, RIGHT\nOVER THERE, PORING OVER THE\nDAY'S BIGGEST STORIES,\nCOLLECTING THE FINEST,\nMOST-TOPICAL NEWS CALFSKINS AND\nPAINSTAKINGLY WASHING THEM IN A\nCALCIUM HYDROXIDE SOLUTION, THEN\nSOAKING THEM IN LIME FOR DAYS TO\nREMOVE ALL NARRATIVE IMPURITIES\nAND CREATE A PALE VELLUM THAT I\nLATER PLACE ON MY SCRIPTORIUM IN\nA MONASTERY ON THE CLIFFS OF\nDOVER.\nTHERE, USING A PEN CUT FROM THE\nWING FEATHER OF A SWAN OF THE\nRIVER AVON, I DESIGN COPTIC AND\nSYRIAC ILLUSTRATIONS, ADORNED\nWITH WHIMSICAL CELTIC SPIRALS\nAND USE GERMANIC ZOOMORPHIC\nDESIGNS TO CREATE THE MARGINALIA\nSURROUNDING THE PAGES OF ELEGANT\nHALF-UNCIAL INSULAR SCRIPT THAT\nTELL THE HOLIEST OF STORIES,\nWHICH I THEN BIND WITH GOLDEN\nTHREAD UNDER A PROTECTIVE CASE\nOF CARVED OAK TO CREATE FOR YOU\nTHE GLORIOUS LATE\nANGLO-SAXON PERIOD ILLUMINATED\nMANUSCRIPT THAT IS MY MONOLOGUE.\nBUT SOMETIMES, FOLKS, SOMETIMES,\nTHESE PEOPLE KNOW, THEY KNOW, THEY KNOW\nBUT SOMETIMES, FOLKS,\nI COME TO UNDER A RAMP IN THE\nMIDDLE OF A DEMOLITION DERBY,\nHOTWIRE THE TRUCKASAURUS\nAND LEAD THE POLICE ON A CHASE\nBEFORE CRASHING INTO A SWAMP\nGATHERING JUST AS, WHAT I ASSUME\nIS A PRIEST SAYS, \"YOU MAY KISS\nTHE BRIDE,\" RIP A LEECH OFF MY\nASS, AND USE IT TO HASTILY\nDOODLE A SKETCH OF THE SCENE IN\nMY OWN BLOOD ON AN OLD DAVE AND\nBUSTERS RECEIPT, THEN STAGGER\nTOWARD THE HAPPY COUPLE\nCLUTCHING THE NIGHTMARE\nSTALKER'S WEDDING ALBUM OF NEWS\nTHAT IS MY SEGMENT:\nMEANWHILE!" + }, + "TZSw9iRk03E": { + "begin": "0:22.5", + "end": "1:08.5", + "text": "FOLKS, YOU KNOW I\nSPEND A LOT\nOF RIGHT TIME OVER THERE, CAREFULLY\nSTUDYING THE LATEST, NEWSIEST\nCLINICAL STUDIES, PRACTICING AND\nTRAINING UNDER THE BEST, MOST\nTOPICAL DOCTORS, CAREFULLY\nSTERILIZING ALL MY EQUIPMENT,\nAND ASSEMBLING THE WORLD'S GREATEST\nSURGICAL TEAM TO PERFORM FOR YOU THE\nDAZZLINGLY COMPLEX AND\nGROUNDBREAKING THORACIC AORTIC\nDISSECTION REPAIR THAT IS MY\nMONOLOGUE.\nBUT SOMETIMES, SOMETIMES,\nI GET KICKED OUT OF MY HEALTH CARE\nPLAN FOR LISTING MY DOG BENNY AS\nA GASTROENTEROLOGIST, SO I\nSTIPPLE SOME INCISION MARKS ON\nMY ABDOMEN WITH A DRIED-OUT\nSHARPIE, SLAM A COUPLE OF RED BULLS\nIN FRONT OF A SHATTERED MIRROR,\nAND FISH A RUSTY BONING KNIFE\nOUT OF A STOLEN CRAB BOAT TO\nPERFORM THE EXPLORATORY HOBO\nAPPENDECTOMY OF NEWS THAT IS MY\nSEGMENT:\n\"MEANWHILE!\"" + }, + "VV3UJmb8kHw": { + "begin": "2:04.0", + "end": "2:54.0", + "text": "YOU KNOW, FOLKS,\nIF YOU WATCH THE SHOW, YOU KNOW\nI SPEND A LOT OF MY TIME RIGHT\nOVER THERE.\nCAREFULLY COMBING THROUGH THE\nBIGGEST STORIES OF THE DAY,\nSOURCING FOR YOU THE NEWSIEST\nMIKADO ORGANZA IN A HIGH SHEEN,\nADDING THE MOST TOPICAL IVORY\nFEATHER FRINGE AND A DIPPED\nBACK, THEN THROWING ON A DEMURE\nBUT KICKY FLORAL EMBROIDERED\nTULLE SHRUG WITH STATEMENT PEARL\nACCENTS TO PRESENT TO YOU THE\nGLORIOUS \"VOGUE\" COVER-READY\nWEDDING GOWN THAT IS MY\nMONOLOGUE.\nBUT SOMETIMES, WHILE ON A\nGLUE-HUFFING BINGE, I CRASH A\nSTOLEN HEARSE INTO AN ABANDONED\nCHILDREN'S HOSPITAL WHERE I USE\nMY TEETH TO TEAR UP SOME OLD\nCURTAINS AND STAINED CARPETING,\nAND STEAL A BUTTON OFF AN OLD\nSURGICAL APRON, AND STITCH IT\nALL TOGETHER WITH A NEEDLE MADE\nFROM A CHICKEN BONE TO THROW\nTOGETHER THE SHRIEKING CAT\nLADY'S SACK DRESS OF NEWS THAT\nIS MY SEGMENT: \"MEANWHILE.\"" + }, + "VYVbTzoggKc": { + "begin": "0:00.0", + "end": "0:49.0", + "text": "FOLKS, YOU KNOW, I\nSPEND A LOT OF\nMY TIME ON THE SHOW-- IF YOU\nWATCH THE SHOW YOU'D FIGURE\nTHIS OUT,\nRIGHT OVER THERE, STANDING RIGHT OVER THERE IN THE MONOLOGUE SPELUNKING\nTHROUGH THE DAY'S STORIES TO\nSELECT AND SOURCE THE\nNEWSIEST MARBLE, CHISELING\nIT INTO A\nPEDESTAL OF HUMOR AS WIDE AS\nTWO GREEK ISLES.\nTHEN I CAST THE MOST TOPICAL\nCURRENT-EVENTS-BRONZE INTO A\nFINELY CRAFTED MOULD TO\nERECT FOR YOU THE TOWERING\nGRECIAN\nCOLOSSUS THAT IS MY NIGHTLY\nMONOLOGUE.\nBUT SOMETIMES, JUST\nSOMETIMES, FOLKS, I JOLT\nAWAKE INSIDE\nWHAT'S LEFT OF A RUSTED\nMAZDA MIATA IN A WHITE CLAW\nAND\nOVEN-CLEANER-INDUCED FUGUE\nSTATE, SHAMBLE THROUGH THE\nJUNKYARD, RANSACKING THE\nDEBRIS FOR OLD FISHING RODS,\nMELTED\nBATTERIES AND THE SHOVEL OF\nA DERELICT BACKHOE, AND THEN\nBOOST AN\nACETYLENE TORCH TO HASTILY\nWELD TOGETHER THE BOOTLEG\nTRUCKASAURUS OF NEWS THAT\nIS MY SEGMENT:\nMEANWHILE!" + }, + "WWWeV8xVNtI": { + "begin": "2:00.0", + "end": "2:35.0", + "text": "YOU KNOW, FOLKS, I SPENT A LOT\nOF TIME CAREFULLY RESEARCHING\nTHE DAY'S MOST CULTURALLY\nPRECIOUS STORIES,\nCROSS-REFERENCING HISTORICAL\nACCOUNTS WITH TOPOGRAPHICAL\nMAPS, AND ASSEMBLING THE FINEST\nTEAM OF ARCHAEOLOGISTS TO\nUNEARTH THE UNESCO WORLD\nHERITAGE EXCAVATION SITE OF\nHUMOR THAT IS MY MONOLOGUE.\nBUT SOMETIMES, I DISTRACT AN\nORPHAN WITH A PIECE OF\nLINT-COVERED CANDY AND STEAL\nTHEIR BUCKET AND PAIL, THEN\nSNEAK INTO THE POTTER'S FIELD IN\nTHE DEAD OF NIGHT WITH TWO\nDRIFTERS I PICKED UP ON THE\nCOMMUTER TRAIN, AND FORCE THEM\nTO DIG FOR THE ABANDONED\nPAUPER'S GRAVE\nOF NEWS THAT IS MY SEGMENT:\nMEANWHILE!" + }, + "XzJAtzdrY_w": { + "begin": "2:57.0", + "end": "4:32.0", + "text": "FOLKS, IF YOU WATCH THIS SHOW, YOU KNOW I SPEND MOST\nOF MY TIME, RIGHT OVER THERE,\nCAREFULLY COMBING THE NEWS\nLANDSCAPE AND HARVESTING THE\nFINEST, MOST BEAUTIFUL STORY\nPIGMENTS LIKE MALACHITE,\nAZURITE, AND CINNABAR, WHICH I\nSLOWLY GRIND UNDER A GLASS\nOF MULLER WITH ONLY THE MOST\nTOPICAL LINSEED OIL, WORKING\nTHEM INTO SMOOTH, BUTTERY\nVERMILLIONS, VERDIGRIS, AND NEW\nGAMBOGES, WHICH I THEN APPLY TO\nA GRISAILLE PREPARED ON A CANVAS\nOF FLAX, TOW, AND JUTE, SLOWLY\nWORKING UP THE SHADOW\nSHAPES AND MAJOR MASSES, THEN\nDELICATELY RENDERING THE\nINTERPLAY OF LIGHT AND FORM,\nBEFORE APPLYING THE FINE DAMMAR\nAND MASTIC VARNISH TO UNVEIL FOR\nYOU THE GLORIOUS REMBRANDT\nPORTRAIT OF THE DAY'S EVENTS\nTHAT IS MY NIGHTLY MONOLOGUE.\nBUT SOMETIMES--\nSOMETIMES, FOLKS, SOMETIMES\nI'M SHAKEN AWAKE INSIDE THE\nDARKENED TRUNK OF A BULGARIAN\nMOBSTER'S VOLVO 940, I QUIETLY\nRELEASE THE SAFETY CATCH AND\nTUMBLE ONTO THE SIDE OF A DIRT\nROAD, BREAKING BOTH CLAVICLES,\nWHICH I DO NOT FEEL BECAUSE OF\nALL THE ANGEL DUST.\nI STAGGER INTO AN ABANDONED\nTANNERY WHERE I BEFRIEND AN OWL\nWHO TELLS ME TO I HAVE TO LET\nHIM SPEAK THROUGH ME OR HE'LL\nMURDER THE CLOUDS.\nAND IN HIS DIRECTION, I MIX THE\nFUN DIP I FOUND IN MY POCKET\nWITH THE FISTFULS OF HEXAVALENT\nCHROMIUM I SCOOP UP FROM THE\nDISUSED TANNING PITS, THEN HURL\nIT AT THE SIDE OF A NEARBY\nDEFUNCT DAIRY QUEEN IN A FUGUE\nSTATE OF LASHING OUT AT LIGHT\nAND COLOR, TO UNLEASH FOR YOU\nTHE ABSTRACT EXPRESSIONIST\nSPLATTER FRESCO OF NEWS THAT IS\nMY SEGMENT:\nMEANWHILE!" + }, + "YyV6l8HPmdQ": { + "begin": "0:48.0", + "end": "1:32.0", + "text": "FOLKS, LADIES AND\nGENTLEMEN, YOU KNOW I SPEND A\nLOT\nOF TIME DELICATELY WHITTLING A\nMELANGE OF THE DAY'S MOST\nPRESSING STORY TIMBERS,\nPRECISELY MEASURING THE NECKS,\nRIBS, AND BACKS OF THE NEWS,\nEMPLOYING ONLY THE MOST\nSOPHISTICATED AND TOPICAL\nPURFLING, THEN LAYING 15\nEXQUISITE COATS OF INSIGHT ONTO\nTHE ORNATE YET ROBUST\nSTRADIVARIUS VIOLIN THAT IS MY\nNIGHTLY MONOLOGUE.\nBUT SOMETIMES, SOMETIMES, FOLKS,\nI GATHER UP FRAYED ELECTRICAL\nWIRE FROM A BURNT-OUT BOWLING\nALLEY, TAPE IT TO A\nTERMITE-INFESTED 2-by-4, THEN SHOVE\nONE END TO A DISCARDED CHUM\nBUCKET TO MAKE FOR YOU THE APPALACHIAN\nDRIFTER'S BANJO OF NEWS THAT IS\nMY SEGMENT:\n\"MEANWHILE!\"" + }, + "a8DD__mRtPk": { + "begin": "1:13.0", + "end": "1:58.0", + "text": "FOLKS, YOU KNOW, I SPEND MOST OF\nMY TIME GATHERING FOR YOU THE LATEST,\nMOST CUTTING-EDGE NEWS STORIES,\nCAREFULLY EXAMINING THE DAY'S\nCT SCAN, THEN ASSEMBLING\nAMERICA'S CRACK MEDICAL TEAM,\nAND MAKING PRECISE INCISIONS\nWITH THE AID OF A STRYKER 1588\nLAPAROSCOPE IN THE\nGROUNDBREAKING SURGICAL ARTISTRY\nTHAT IS MY MONOLOGUE.\nBUT SOMETIMES, JUST SOMETIMES,\nFOLKS, WHEN I NEED A LITTLE EXTRA CASH TO\nPAY OFF MY COCK-FIGHTING DEBTS,\nI SET UP A RUSTY COT UNDER A\nTARP IN WASHINGTON SQUARE PARK,\nWHERE I PLY CURIOUS PASSERSBY\nWITH BATHTUB COUGH SYRUP TO HELP\nDULL THE PAIN WHILE I USE GARDEN\nSHEARS TO CUT OUT ANYTHING THAT\nLOOKS SUPERFLUOUS IN THE AMATEUR\nAPPENDECTOMY TENT OF NEWS\nTHAT IS MY SEGMENT...\nMEANWHILE!" + }, + "cHhomJMwY1I": { + "begin": "2:42.0", + "end": "3:24.0", + "text": "FOLKS, I SPEND A LOT OF TIME\nSTANDING RIGHT OVER THERE,\nCOMBING THROUGH HOURS UPON HOURS\nOF GAME TAPE ON THE MOST PROMISING\nHEADLINES, METICULOUSLY CRAFTING\nMY BIG BOARD TO RANK STORIES\nBASED ON THEIR RAW TALENT AND\nINTANGIBLES, AND CUT DEALS FOR\nTHE MOST TOPICAL TRADES TO DRAFT\nTHE ONCE-IN-A-GENERATION,\nHEISMAN-WINNING QUARTERBACK THAT\nIS MY MONOLOGUE.\nBUT SOMETIMES, JUST SOMETIMES I\nWAKE UP IN AN ICE BATH AFTER\nDOING RAILS OF GATORADE POWDER,\nREALIZE IT'S DRAFT DAY, AND I\nHAVE 15 SECONDS LEFT TO MAKE A\nCHOICE AND BLURT OUT THE FIRST\nNAME I SEE TO WASTE THE NUMBER\nONE OVERALL PICK ON THE SCRAWNY,\nUNPOLISHED THIRD-STRING PUNTER\nOF NEWS THAT IS MY SEGMENT,\n\"MEANWHILE!\"" + }, + "hhwTiwUAaf8": { + "begin": "1:06.5", + "end": "1:54.5", + "text": "YOU KNOW, FOLKS, I SPEND MOST\nOF MY TIME SOURCING FOR YOU THE DAY'S\nFINEST HANGZHOU SILK NEWS\nSTORIES, MOUNTING THEM ON THE\nMOST TOPICAL, PREMIUM,\nPOLYHEDRAL BAMBOO JOKE FRAME,\nDECORATING IT WITH ARTISANAL ASH\nINK, INSERTING A HAND-POURED\nBEESWAX CANDLE, FILLING IT WITH\nINTENTION AND THEN SENDING IT ALOFT\nON THE UPDRAFT OF AUDIENCE\nLAUGHTER IN THE SPECTACULAR\nCHINESE LANTERN FESTIVAL THAT IS\nMY NIGHTLY MONOLOGUE.\nBUT SOMETIMES, JUST SOMETIMES, FOLKS, I GO VISIT MY\nBUDDY, BARRACUDA AT THE\nABANDONED MALL, DROP A COUPLE OF\nHUNDOS ON SOME WET ROMAN\nCANDLES, BENT SPARKLERS SMUGGLED\nIN FROM THE PHILIPPINES, AND A\nFLARE GUN STOLEN FROM A CRASHED\nCOAST GUARD BOAT, SET IT\nALL OFF IN THE DERANGED,\nUNREGULATED FIREWORKS ACCIDENT\nOF NEWS THAT IS MY SEGMENT:\n\"MEANWHILE!\"" + }, + "iB6diOGE8y4": { + "begin": "0:52.8", + "end": "1:50.8", + "text": "YOU KNOW, IF YOU WATCH THE SHOW, YOU KNOW I SPEND MOST OF MY TIME\nRIGHT OVER THERE, COMBING\nTHROUGH THE DAY'S BIGGEST NEWS,\nAND SELECTING FOR YOU THE\nFINEST, MOST TOPICAL INDIAN\nROSEWOOD, SPRUCE, AND MAHOGANY\nSTORIES.\nI THEN HAND-SHAPE AND COMBINE\nTHEM WITH AN ABALONE\nMULTI-STRIPE BACK INLAY, AND\nFORWARD-SHIFTED SCALLOPED\nBRACES, ANTIQUE WHITE BINDING,\nAND A HIGH-PERFORMANCE NECK WITH\nA HEXAGON FINGERBOARD, AND\nFINALLY LAY IN A\nTORTOISE-PATTERN PEARL PICK\nGUARD, AND A COMPENSATED\nBONE SADDLE, TO CRAFT FOR YOU\nTHE EXQUISITE MARTIN D-45\nDREADNOUGHT ACOUSTIC GUITAR\nTHAT IS MY NIGHTLY MONOLOGUE.\nBUT SOMETIMES, JUST SOMETIMES, FOLKS, I SNAP\nAWAKE IN A RUSTY COFFIN FREEZER\nBEHIND AN ABANDONED DAIRY QUEEN\nOUTSIDE OF GALVESTON.\nTHEN I NAIL A 2-BY-4 TO A CEDAR URN\nI STOLE FROM A FUNERAL PARLOR, STRING\nON SOME BRAKE CABLES I RIPPED\nOUT OF A COP CAR, THEN CUT EYE\nHOLES IN A GOODWILL BAG FOR A MASK,\nHIT A WHIPPET, AND TERRORIZE\nTHE LOCALS ON THE TEXAS CHAINSAW\nBANJO OF NEWS THAT IS MY\nSEGMENT:\n\"MEANWHILE!\"" + }, + "iQFwGF0aW-o": { + "begin": "2:10.7", + "end": "3:33.7", + "text": "I SPEND A LOT OF MY TIME, RIGHT\nOVER THERE, CULTIVATING FOR YOU THE\nDAY'S BIGGEST STORIES,\nPLUCKING THE MOST BEAUTIFUL AND\nTOPICAL NEWS VIOLETS AND\nMARIGOLDS, STRIPPING THE FRENCH\nLAVENDER FROM THE STEM AND\nLOVINGLY PRESSING THEM ALL\nBETWEEN THE PAGES OF A GILDED\nFIRST EDITION OF \"PRIDE AND\nPREJUDICE.\"\nTHEN I FOLD THEM INTO A DOUGH I\nHAND ROLLED FROM PASINI BAKERY\nFLOUR, BORDIER BUTTER, AND\nCHILLED SOMERDALE DEVON DOUBLE\nCREAM, SPRINKLE THEM WITH A\nPINCH OF MUSCOVADO SUGAR, AND\nBAKE THEM IN A \"LA CORNUE GRAND\nPALAIS\" RANGE TO PERFECTLY PREP\nTHE GOURMET FLORAL SHORTBREAD\nCOOKIE THAT IS MY NIGHTLY MONOLOGUE.\nBUT SOMETIMES, FOLKS, SOMETIMES,\nSOMETIMES, I AM NIBBLED AWAKE BY AN AMOROUS\nRACCOON IN THE ABANDONED WALK-IN\nFREEZER OF A HAUNTED BAKERY IN\nWHICH I HAVE ESTABLISHED\nSQUATTER'S RIGHTS, I SLIP INTO\nTHE TWISTED KITCHENAID PADDLES I\nCALL SHOES, AND KNIFE FIGHT A\nPOSSUM FOR AN EXPIRED BAG OF\nCRUSHED BREAKFAST CEREAL DUST\nAND A BROKEN EGG, WHICH I MIX\nWITH THREE SMUSHED RESTAURANT\nBUTTER PACKETS I STOLE FROM A\nNAPPING RETIREE'S PURSE, POUR\nTHE REST OF A SHATTERED BOTTLE\nOF RUBBING ALCOHOL I FOUND IN\nTHE DUMPSTER OUT BACK INTO A\nRUSTY BARREL TO IGNITE THE HOBO\nFIRE OVER WHICH I BAKE MY\nSLUDGE, THEN DISPLAY IT IN A\nFILTHY CHEF'S HAT TO SERVE YOU\nTHE DERANGED RAT KING BISCUIT OF\nNEWS THAT IS MY SEGMENT:\n\"MEANWHILE\"!" + }, + "jIL7kvG7d10": { + "begin": "1:55.3", + "end": "2:40.3", + "text": "FOLKS, YOU KNOW, I SPEND A LOT\nOF TIME, RIGHT OVER THERE\nCAREFULLY PLANTING AND TENDING\nTO THE DAY'S BIGGEST, MOST\nIMPORTANT STORIES, TRIMMING\nTHE TOPICAL HEDGES WITH DELICATE\nEXACTITUDE, RESEARCHING AND\nSEEDING THE SOIL OF TODAY'S\nNEWS IN ORDER TO YIELD THE MOST\nBEAUTIFUL, FRAGRANT JOKE\nFLOWERS, AND PRECISELY TIMING\nTHE BLOOM IN THE EXQUISITE\nENGLISH GARDEN THAT IS MY\nNIGHTLY MONOLOGUE.\nBUT SOMETIMES, JUST SOMETIMES FOLKS, I DRIVE A 2003\nPONTIAC SUNFIRE THROUGH A HOME\nDEPOT GARDEN CENTER, DOWN A JUG\nOF MIRACLE GRO, SMEAR MY BODY IN\nMUD AND PEA GRAVEL, BUILD A FORT\nOUT OF PAVERS, PLOP A SUCCULENT\nDISH GARDEN ON MY HEAD, AND\nBARRICADE MYSELF INSIDE A\nPORTABLE TOOL SHED TO CREATE THE\nPARANOID BACKYARD STANDOFF OF\nNEWS THAT IS MY SEGMENT:\nMEANWHILE!" + }, + "jpq8eXZcvpo": { + "begin": "0:52.5", + "end": "1:37.5", + "text": "FOLKS, YOU KNOW, I SPEND A LOT OF MY TIME\nRIGHT OVER THERE, SORTING\nTHROUGH THE DAY'S TOP STORIES,\nCAREFULLY SELECTING FOR YOU THE\nFRESHEST, MOST TOPICAL\nNEWS-FRUIT, ARTFULLY CARVING IT\nINTO SATIRICAL SHAPES, DIPPING\nIT IN THE FINEST ARTISANAL\nCHOCOLATE, AND GENTLY PLACING THEM\nINTO THE FLAWLESSLY COMPOSED AND\nDELICIOUS EDIBLE ARRANGEMENT\nTHAT IS MY NIGHTLY MONOLOGUE.\nBUT SOMETIMES, JUST SOMETIMES,\nFOLKS, I WAKE UP FACE DOWN IN\nTHE RECYCLING BIN BEHIND A JAMBA\nJUICE, FIGHT A SEAGULL FOR THE\nDISCARDED CANTALOUPE RINDS\nAND PINEAPPLE STEMS, DIP THEM\nINTO A BUCKET OF DIESEL SIPHONED OFF\nFROM A SEMI FULL OF UNWASHED\nBIRD BONES, WHICH I USE TO\nSKEWER TOGETHER MY GARBAGE\nKEBABS, THEN STAB THEM ONTO A\nWATERLOGGED TEDDY BEAR TO CREATE\nTHE CRIMINALLY INSANE NIGHTMARE\nGIFT BASKET OF NEWS THAT IS MY\nSEGMENT:\n\"MEANWHILE!\"" + }, + "jq2LhJ9rMpg": { + "begin": "2:52.0", + "end": "3:34.0", + "text": "YOU KNOW, I SPEND A LOT OF TIME\nRIGHT OVER THERE, PULLING\nTOGETHER THE FINEST, NEWSIEST\nLIMESTONE, CHISELING IN THE MOST\nDELICATE AND TOPICAL OF\nBAS-RELIEF, AND THE MOST ORNATE\nARCHES, MAKING SURE THERE'S NARY\nA BAD SEAT IN THE HOUSE, THEN\nASSEMBLING THE MOST FEARSOME\nNEWS WARRIORS THE ARENA HAS EVER\nSEEN TO CONSTRUCT FOR YOU THE\nROMAN COLOSSEUM THAT IS MY\nNIGHTLY MONOLOGUE.\nBUT SOMETIMES, SOMETIMES FOLKS, I WAKE UP MY\nNEIGHBOR AT 3:00 IN THE MORNING,\nDRAG HIM INTO MY SHED, WHERE\nI'VE SET UP A KIDDIE POOL I\nBOUGHT 20 YEARS AGO AND FILLED\nWITH EXPIRED JELL-O AND LUKEWARM\nBEER, HUFF SOME ACETONE OUT OF A\nPRICE CHOPPER BAG, THEN\nCHALLENGE HIM TO JOIN ME IN THE\nOLD MAN WRESTLING LEAGUE OF\nNEWS THAT IS MY SEGMENT:\n\"MEANWHILE!\"" + }, + "lWyia3aF92o": { + "begin": "1:44.0", + "end": "2:30.0", + "text": "FRIENDS, EVERY NIGHT I STAND\nRIGHT OVER THERE, AND I CAREFULLY WORK ON THE LIGHTING\nAND STAGING OF THE DAY'S MOST\nTOPICAL NEWS STORIES, COMPOSING\nGROUNDBREAKING ORCHESTRAL\nARRANGEMENTS TO SUPPORT THEM,\nAND THEN METICULOUSLY CHOREOGRAPHING\nTHEM AND MYSELF INTO A DELICATE,\nHEARTBREAKING, AND YET UPLIFTING\nPAS DE DEUX, TO PRESENT FOR YOU\nTHE EPOCH-DEFINING BALLET THAT\nIS MY MONOLOGUE.\nBUT SOMETIMES, JUST SOMETIMES FOLKS, I FISH A STAINED\nVELOUR JUMPSUIT OUT OF A CANAL,\nHOOK A RADIO I RIPPED OUT OF A\nGARBAGE TRUCK TO AN ABANDONED\nCAR BATTERY, AND SLAP THE DIAL\nTHROUGH FUZZ TILL IT LANDS ON A\nRANDOM A.M. OLDIES STATION, AND THEN\nSHAKE MY ASS FOR NICKELS IN THE\nDEMENTED VAGRANT MACARENA OF\nNEWS OF THAT IS MY SEGMENT:\nMEANWHILE!" + }, + "ldTzn1RpsNY": { + "begin": "1:00.0", + "end": "1:48.0", + "text": "FOLKS, I SPEND A LOT OF TIME\nRIGHT OVER THERE, COMPILING THE\nMOST CURRENT GEOMETRY QUESTIONS,\nSPRINKLING IN A TOPICAL SET OF\nDATA ANALYSES, FOLDING THEM\nTOGETHER ALONG THE NEWSWORTHIEST\nWORD PROBLEMS, THEN PAIRING ALL\nOF THAT WITH THE DAY'S MOST\nPRESSING READING PASSAGES TO\nCOLLATE FOR YOU THE PERFECTLY\nCALIBRATED, BESPOKE S.A.T. TEST\nTHAT IS MY MONOLOGUE.\nBUT SOMETIMES, JUST SOMETIMES,\nI HUFF A PILE OF SALVIA AND\nSTAGGER INTO A LANDFILL WHERE I\nFORAGE FOR CRUSTY OLD SUDOKUS,\nGRAB A SACKFUL OF USED AND WET\nMADLIBS, AND CRAZY-GLUE THEM INTO\nTHE SPINE OF A DISCARDED\n\"READER'S DIGEST\" I FOUND IN A\nBURNT-OUT WALDENBOOKS, TO\nPRESENT TO YOU THE ILLEGIBLE\nHOBO BUZZFEED QUIZ OF NEWS THAT\nIS MY SEGMENT:\n\"MEANWHILE!\"" + }, + "lgH-itFA_hg": { + "begin": "1:53.2", + "end": "2:33.2", + "text": "FOLKS, I SPENT A LOT OF TIME\nSTANDING RIGHT OVER THERE, OKAY,\nSETTING\nUP MY NEWS EASEL, LAYING OUT THE\nMOST TOPICAL BRUSH STROKES,\nCHOOSING THE MOST RELEVANT\nCOLORS, ALL TO FAITHFULLY\nCAPTURE FOR YOU, THE SOUL OF THE\nSTORIES OF THE DAY IN THE\nOIL-ON-CANVAS MASTERPIECE THAT\nIS MY NIGHTLY MONOLOGUE.\nBUT SOMETIMES -- JUST SOMETIMES,\nFOLKS -- I SET A LIQUOR\nSTORE ON FIRE AND COME BACK THE\nNEXT DAY TO SCRAPE SOME CHARCOAL\nOFF THE BURNT TIMBERS, USE THE\nCARDBOARD FROM THE DISCARDED\nREFRIGERATOR BOX I'VE BEEN\nCALLING HOME FOR THE WEEKEND,\nTHEN HARASS TOURISTS TO ETCH THE\nOFFENSIVE BOARDWALK CARICATURE\nOF NEWS THAT IS MY SEGMENT:\nMEANWHILE!" + }, + "ll5DeZrejsM": { + "begin": "2:02.7", + "end": "2:52.7", + "text": "YOU KNOW, FOLKS,\nIF YOU WATCH THE SHOW, YOU KNOW\nI SPEND A LOT OF TIME RIGHT OVER\nTHERE, COMBING THROUGH THE\nDAY'S BIG STORIES, SELECTING THE\nFINEST NEWS TENORS AND THE\nSILKIEST SOPRANOS.\nTHEN, I BRUSH UP ON THE WORKS OF\nCERVANTES AND FIND THE PERFECT\nSWEET SPOT BETWEEN DRAMA, HUMOR,\nAND OPERA TO COMPOSE FOR YOU THE\nTIMELESS AND SEDUCTIVE SPANISH\nZARZUELA THAT IS MY\nMONOLOGUE.\nBUT SOMETIMES, SOMETIMES, FOLKS,\nI WAKE UP IN THE FREEZER OF A\nCOMBINATION TACO BELL PIZZA\nHUT ON THE EXPRESSWAY, AND I CUT A\nPAIR OF LEG HOLES INTO A POTATO\nSACK AND RACE BAREFOOT INTO THE\nCITY TO BREAK INTO AN ABANDONED\nDOLLAR STORE, WHERE I FASHION A\nPAIR OF CASTANETS FROM DEFECTIVE\nCHATTERING TEETH TOYS.\nTHEN I DOWN A JERRY CAN FULL OF\nRED BULL AND COUGH MEDICINE\nBEFORE STAGGERING INTO A PUBLIC\nPARK TO DISTURB TOURISTS WITH\nTHE DRIFTER'S FLAMENCO SHOWCASE\nOF NEWS THAT IS MY SEGMENT:\n\"MEANWHILE!\"" + }, + "lzviJMlii7A": { + "begin": "1:36.5", + "end": "3:21.5", + "text": "LADIES AND GENTLEMEN, YOU KNOW, IF YOU\nWATCH THIS SHOW, YOU KNOW I\nSPEND A LOT OF MY TIME RIGHT OVER\nTHERE, METICULOUSLY SIFTING\nTHROUGH THE DAILY NEWS\nDESERT, HARVESTING THE FINEST,\nMOST TOPICAL MINERAL SANDS--\nABOUT 65% SILICA, 10% FLUX\nOF SODIUM OXIDE, AND A\nSTABILIZER OF CALCIUM OXIDE--\nWHICH I THEN SMELT IN A\nHIGH-TEMPERATURE CALCERA FURNACE\nAT 1,200 TO 1,400 DEGREES\nCELSIUS, FUSING THEM INTO LIQUID\nGLASS, THEN CAREFULLY DROPPING\nMY FURNACE TEMPERATURE SO I CAN\nFOLD IN HAND-SELECTED CULLET AND\nCOBALT TO OBTAIN MY INTENDED\nCOLOR AND CREATE THE MOST\nPRISMATIC NEWS CRYSTALS, WHICH\nI THEN DELICATELY HANG ON A\nHAND-CRAFTED BALUSTER OF\nHEADLINES, ARRANGING THEM TO\nCATCH AND REFRACT, IN A GENTLE\nDANCE OF LIGHTS AND SHADOWS, THE\nMOST TOPICAL REFLECTIONS OF THE\nDAY, ADORNING THE ARRANGEMENT\nWITH ONE FINAL FINIAL OF QUIPS\nTO PRESENT TO YOU THE VENETIAN\nMURANO GLASS CHANDELIER THAT IS\nMY MONOLOGUE.\nBUT SOMETIMES FOLKS, SOMETIMES,\nFOLKS, SOMETIMES, I JOLT AWAKE\nNAKED IN THE BACK BOOTH OF A\nLONG-ABANDONED COUNTY FAIR, I\nPULL ON SOME OVERALLS I STOLE\nOFF A SCARECROW, AND CLAW\nTHROUGH THE GROUNDS SCRAPING THE\nLEAVINGS OFF SOME DISUSED\nSPARKLERS, PICK THROUGH BROKEN\nCOKE BOTTLES AND BIRTHDAY\nCANDLES, BOIL OFF THE REMNANTS\nIN A DISCARDED TUB OF LAUNDRY\nDETERGENT TO EXTRACT THE\nBENZENE.\nTHEN, USING THE SHOELACES I TOOK\nOFF A HOBO SLEEPING UNDER\nTHE FERRIS WHEEL AND DENTAL\nFLOSS CURRENTLY IN USE BY SAID\nHOBO, I BIND THE CONGLOMERATE\nOF SHARDS AND ACCELERANT\nTOGETHER AND HOLD IT NEAR THE\nSPUTTERING SPARK PLUG OF AN\nOLD ICE CREAM TRUCK TO IGNITE\nTHE CHAOTIC CANDELABRA OF\nFLAMING NEWS THAT IS MY SEGMENT:\n\"MEANWHILE\"!" + }, + "okJDGV6Jjmc": { + "begin": "2:03.0", + "end": "2:55.0", + "text": "YOU KNOW, FOLKS, I SPEND A LOT\nOF TIME RIGHT OVER THERE, PORING\nOVER THE DAY'S NEWSIEST, MOST\nTOPICAL NAUTICAL RECORDS TO\nDETERMINE THE ROUGH POSITIONS OF\nTHE DAY'S TRENDING SHIPWRECKS.\nTHEN I USE THE LATEST SONAR TECH\nTO LOCATE AND FIND THE\nORIENTATION OF THE FINEST\nSALVAGE SITE.\nTHEN MY TEAM OF CERTIFIED AND\nLICENSED COMEDY DIVERS DESCEND\nTO THE OCEAN FLOOR AND USE\nCUTTING EDGE UNDERWATER CAMERAS\nTO STITCH TOGETHER THE DETAILED\n3D-MODELED HISTORIC ATOSHA\nSHIPWRECK SITE OF SATIRICAL\nOBSERVATIONS THAT IS MY\nNIGHTLY MONOLOGUE.\nBUT SOMETIMES, JUST SOMETIMES, FOLKS,\nI WAKE UP NAKED BY THE DOCKS\nCOVERED IN PIRATE TATTOOS, BLAST\nA STRING OF WHIPPETS, THEN\nSTAGGER INTO THE FESTERING\nHUDSON RIVER, WHERE I SLOWLY\nSINK THROUGH THE MURK UNTIL I\nIMPALE MYSELF ON THE RUSTY AXLE\nOF A SUNKEN TAXI IN THE\nTETANUS-LACED CRIME SCENE OF\nNEWS THAT IS MY SEGMENT...\n\"MEANWHILE!\"" + }, + "pbR-kF0PjlA": { + "begin": "1:09.0", + "end": "2:43.0", + "text": "WELL FOLKS, I SPEND A LOT\nOF MY TIME, ON THE SHOW, RIGHT OVER THERE,\nWANDERING THROUGH THE\nFARMERS' MARKET THAT IS TODAY'S\nBIGGEST STORIES, SQUEEZING THE\nFINEST NEWS RADISHES, THE RIPEST\nSTORY PEPPERS, SNIFFING THE MOST\nTOPICAL DATES, WHICH I THEN PAIR\nWITH FRA'MANI SOPPRESSATA, AND\nTHE MOST SUCCULENT HANDRAISED\nPATA NEGRA JAMON IBERICO, BACKED\nUP BY GENEROUS HELPINGS OF\nBEEMSTER GOUDA, AND A WEDGE OF\nBRILLAT-SAVARIN TRIPLE CREAM\nBRIE, THEN I ADD FORTNUM AND MASON\nAPRICOT AND FIG SPREADS WITH\nGRISSINI BREADSTICKS AND LA\nPANZANELLA CROCCANTINI, AND\nFINALLY LIBERAL SPRINKLINGS OF\nSAN SABA ELLIOT PECANS AND\nSICILIAN CASTEL-VETRANO OLIVES\nON A RAW CARRARA MARBLE SLAB TO\nLAY OUT FOR YOU THE SPECTACULAR\nGOURMET CHARCUTERIE BOARD THAT\nIS MY MONOLOGUE.\nBUT SOMETIMES, SOMETIMES FOLKS, SOMETIMES, I AM HOSED AWAKE\nINSIDE AN EMPTY 6,000 GALLON\nDIESEL TANKER OFF OF I-24, WHERE I\nAM HIDING FROM A CULT THAT I\nSTARTED, THEN DASH, NAKED BEHIND\nA RECENTLY DEFUNCT QUIZNOS,\nWHERE I MUST WRESTLE A POSSUM\nFOR THE REMAINS OF A BAJA\nCHICKEN FOOTLONG, STAGGER INTO A\nMIDDLE SCHOOL REC. YARD AFTER\nFIGHTING A SEAGULL FOR THE LAST\nHAM CUBE IN A LUNCHABLES TRAY,\nPUNCH A RACCOON TO STEAL HIS\nPEANUT, THEN DUMP IT ALL INTO A\nHUBCAP I STRIPPED OFF AN\nABANDONED '76 CHEVY VEGA TO\nOFFER FOR YOU THE RAIL YARD BUFFET\nOF NEWS THAT IS MY SEGMENT:\nMEANWHILE!" + }, + "pyhaU-_1Szk": { + "begin": "2:48.0", + "end": "4:31.0", + "text": "YOU KNOW FOLKS, I SPEND A LOT OF\nTIME RIGHT OVER THERE, ISOLATING\nTHE BIGGEST, NEWSIEST STORIES\nOF THE DAY AND CONTAINING THEM\nIN THE MOST TOPICAL CIRCULAR\nTUNNEL, WITH A CIRCUMFERENCE OF\n26.7 KILOMETERS, AND A DEPTH\nRANGING FROM 50 TO 175 METERS.\nTHEN, I ADD TWO ADJACENT\nPARALLEL BEAM-LINES, WHICH\nTRAVEL IN OPPOSITE DIRECTIONS\nAROUND THE RING, INTERSECTING AT\nFOUR POINTS.\nI ADD 1,232 DIPOLE MAGNETS TO\nKEEP THE BEAMS IN THEIR CIRCULAR\nPATH, WHILE AN ADDITIONAL 392\nQUADRUPOLE MAGNETS ARE USED TO\nKEEP THE BEAMS FOCUSED, THEN\nI ADD STRONGER QUADRUPOLE\nMAGNETS CLOSE TO THE\nINTERSECTION POINTS IN ORDER TO\nMAXIMIZE THE CHANCES OF\nINTERACTION BETWEEN THE TWO BEAMS\nCROSS, ALL SO I CAN SMASH JOKE\nPROTONS AGAINST EACH OTHER AT\nNEAR THE SPEED OF LIGHT TO\nGENERATE THE HIGGS BOSON HEAVY\nCOMEDY PARTICLES THAT MAKE UP MY\nNIGHTLY MONOLOGUE.\nBUT SOMETIMES, FOLKS, SOMETIMES \nSOMETIMES, SOMETIMES I WAKE UP IN AN\nABANDONED JUNKYARD, STRAPPED TO\nTHE CHASSIS OF WHAT USED TO BE A\nSCHOOL BUS.\nI GNAW MYSELF FREE OF MY\nRESTRAINTS AND CLIMB ATOP A HILL\nOF CRUSHED MAZDA MIATAS TO UTTER\nA CALL THAT CAN BE HEARD ONLY BY\nTHOSE IN THE MIDST OF A\nLIFE-CHANGING PEYOTE TRIP.\nWITH MY FREAKS GATHERED AROUND ME,\nHOTWIRE AS MANY BURNT-OUT\n'91 BUICK LESABRES AS WE CAN\nFIND TO ANIMATE A FLEET OF\nFURY-ROAD-WORTHY LEMONS,\nTHEN ROLL THEM TO THE ABANDONED\nSUBWAY STATION BELOW CITY HALL,\nWHERE I LAUNCH THEM HEAD ON AT\nTOP SPEED IN THE UNREGULATED\nHOTWHEELS COLOSSAL CRASH TRACK\nOF NEWS THAT IS MY SEGMENT:\nMEANWHILE!" + }, + "q8zlh8XKfLc": { + "begin": "1:00.0", + "end": "1:54.0", + "text": "FOLKS, YOU KNOW, I\nSPEND MOST OF MY TIME, RIGHT\nOVER THERE, WITH MY EARS, MY MIND,\nAND MY HEART OPEN TO THE DAY'S\nBIGGEST STORIES, AUDITIONING AND\nSELECTING ONLY THE MOST TOPICAL\nNEWS-OBOES, THE MOST RELEVANT\nAND LILTING VIOLAS, ROUNDING IT\nOUT WITH SOME NOBLE FRENCH\nHORNS, AND INSOUCIANT\nBASSOONS, THEN COMPOSING AND\nARRANGING THE NEWSIEST, MOST\nUPLIFTING YET BITTERSWEET\nRONDOS, ALLEGROS, SCHERZOS, AND\nSONATAS TO PRESENT TO YOU THE\nTIMELESSLY MOVING YET\nINFORMATIVE POST-MODERN OPUS\nNUMBER ONE SYMPHONY THAT IS MY\nMONOLOGUE.\nBUT SOMETIMES, SOMETIMES FOLKS, I WAKE UP AT THE\nWHEEL OF A STOLEN CEMENT TRUCK,\nSNORT ANOTHER RAIL OF KETAMINE\nAND BATH SALTS, THEN I STRIP\nDOWN AND SCAMPER THROUGH A\nCEMETERY TRAPPING RATS UNDER\nRUSTY COFFEE CANS.\nAFTER AN IMPASSIONED SPEECH TO THEM\nABOUT THEIR NEED\nTO HELP ME SAVE AN OLD THEATER, THEY\nACCOMPANY ME ON A RAID TO A\nPRESCHOOL MUSIC ROOM TO STEAL\nITS FLUTES, RECORDERS, AND\nKAZOOS, WHERE I CONDUCT THE\nFUGITIVE VERMIN PHILHARMONIC OF\nNEWS THAT IS MY SEGMENT:\nMEANWHILE!\n" + }, + "qEY5SUevhgU": { + "begin": "1:59.0", + "end": "3:07.0", + "text": "FOLKS, IF YOU\nWATCH THIS SHOW, YOU KNOW I\nSPEND MUCH OF MY TIME,\nRIGHT OVER THERE, PLANTING AND\nGROWING THE DAY'S BIGGEST NEWS\nIN A PARCELED TERROIR AT\nPRECISELY 80 METERS, ON A\nNORTH-FACING SLOPE, WITH JUST\nTHE RIGHT MICROCLIMATE, THEN\nHAND-PICKING ONLY THE RIPEST,\nMOST TOPICAL BOTRYTIS-PRUNED\nSTORY GRAPES.\nAFTER THREE PRESSINGS, I THEN\nCAREFULLY BARREL-AGE THEIR\nNOBLE-ROTTED NECTAR FOR 30\nMONTHS EXCLUSIVELY IN NEW OAK BARRELS TO\nBRING OUT THE AROMAS OF TROPICAL\nFRUITS, HONEYED PEARS, AND\nROASTED NUTS IN THE CHATEAU\nD'YQUEM SAUTERNES THAT IS MY\nNIGHTLY MONOLOGUE.\nBUT SOMETIMES, SOMETIMES, FOLKS,\nI WAKE UP IN A BULGARIAN PRISON,\nCONVICTED OF WHAT MY NON-ENGLISH\nSPEAKING, COURT-APPOINTED LAWYER\nONLY CALLS \"ANIMAL WRONGS.\"\nI TRADE THE CIGARETTES I WON IN\nA BARE-KNUCKLE MATCH WITH A\nGUARD FOR SOME FIG MARMALADE,\nAPPLE CORES, AND DISCARDED\nKETCHUP PACKETS, TOSS IT ALL IN\nTHE PLASTIC BAG I STOLE OFF A\nCELLMATE DRAGOMIR'S FOOT WHILE\nHE SLEPT, LEAVE IT UNDER A\nFERMENTING PIPE\nOVERNIGHT, TO SERVE UP THE\nSOUR-MASHED GOON PLONK OF NEWS\nTHAT IS MY SEGMENT:\n\"MEANWHILE!\"" + }, + "r7NnpAGIkEY": { + "begin": "1:46.5", + "end": "2:32.5", + "text": "AND, YOU KNOW,\nFOLKS, I SPEND A LOT OF TIME ON THIS SHOW, RIGHT\nOVER THERE, CAREFULLY HARVESTING\nTHE HIGHEST-QUALITY ORGANIC ACAI\nNEWS BERRIES, PUTTING THEM INTO\nMY CURRENT EVENTS BLENDER, THEN\nPULSING ON HIGH UNTIL THEY'VE\nBECOME THE SMOOTH PURPLE PUREE OF\nSTORIES TO BE PILED WITH\nGRANOLA, CHIA SEEDS, AND SLICED\nJOKE BANANA, TO MAKE THE\nHIGH-PRICED, ARTISANAL SMOOTHIE\nBOWL OF NEWS THAT IS MY\nMONOLOGUE.\nBUT SOMETIMES, JUST SOMETIMES\nFOLKS, I LIKE TO SCROUNGE\nTOGETHER SOME EXPIRED KALE FROM\nTHE BACK OF THE FRIDGE, MIX IT\nWITH THE FERMENTING ORANGE\nSLICES LEFT IN THE BACK SEAT\nAFTER LAST WEEK'S LITTLE LEAGUE\nGAME, AND AN APPLE CORE I FOUND\nINSIDE A COFFEE CUP, THEN\nPULVERIZE IT ALL IN A LEAKY\nNUTRI-BULLET TO MAKE THE PRISON\nTOILET GREEN JUICE OF NEWS THAT\nIS MY SEGMENT:\n\"MEANWHILE!\"" + }, + "sKCeqiWA-gQ": { + "begin": "0:43.5", + "end": "1:36.5", + "text": "FOLKS, I SPEND A LOT OF TIME\nRIGHT OVER THERE, NIGHT AFTER NIGHT, COMBING\nTHROUGH THE DAY'S NEWS,\nCAREFULLY SELECTING THE MOST\nTOPICAL, FRAGRANT HERBS AND\nJOKE-RICH ALLIUM, DELICATELY\nSTIRRING THEM INTO A SATIRICAL\nSTOCK, BRINGING THE CONCOCTION\nTO A BREAKING NEWS BOIL BEFORE\nPAINSTAKINGLY EDITING AWAY THE\nSCRAPS, LEAVING ONLY THE PUREST,\nNUTRIENT-RICH CONSOMME OF COMEDY\nTHAT IS MY NIGHTLY MONOLOGUE.\nBUT SOMETIMES, I SWEAT MYSELF\nAWAKE INSIDE A DEFLATED BOUNCY\nCASTLE AT A DEFUNCT AMUSEMENT\nPARK, BREAK INTO A COMBINATION\nGAS STATION PIZZA HUT WHERE I\nTHROW TOGETHER WHATEVER OLD HOT\nDOGS AND REPURPOSED CHEESE\nPRODUCT I CAN GET MY CHAPPED AND\nCRACKING HANDS ON.\nAND THERE, BY THE CRUEL LIGHT OF\nA PIZZA WARMING DRAWER, I DROWN\nTHE MIXTURE IN A CAN OF\nDISCONTINUED SURGE FROM 2002\nBEFORE STRAINING IT THROUGH THE\nGREASE-SOILED BEARD NET TO\nCREATE THE FESTERING MOP BUCKET\nSOUR MASH OF NEWS THAT IS MY\nSEGMENT:\nMEANWHILE!" + }, + "tSdWz6CvpIc": { + "begin": "1:50.0", + "end": "2:39.0", + "text": "FOLKS, YOU KNOW, IF YOU WATCH\nTHE SHOW, YOU KNOW I SPEND A\nLOT OF MY TIME, RIGHT OVER THERE,\nCAREFULLY WELDING TOGETHER THE\nDAY'S TOP STORIES, FORGED FROM\nTHE FINEST NEWS METALS, WIRING\nIN THE MOST EFFICIENT, HIGH-\nSPEED ENGINE.\nTHEN I COMBINE THE MOST TOPICAL\nTITANIUM ACCENTS WITH ITALIAN\nCURRENT-EVENTS CRAFTSMANSHIP TO\nCREATE THE BESPOKE TRIUMPH\nMOTORCYCLE THAT IS MY MONOLOGUE.\nBUT SOMETIMES, JUST SOMETIMES FOLKS,\nAFTER STANDING TOO LONG OVER AN\nEPHEDRINE BARREL FIRE, I STUMBLE\nINTO A DEFUNCT BODY SHOP, SLAP A\nHALF-EMPTY CANISTER OF PROPANE\nONTO A STOLEN HUFFY, WRAP IT IN\nNEWSPAPER AND BITS OF CAUTION\nTAPE THAT I SWIPED FROM A\nSTILL-ACTIVE CRIME SCENE, AND\nHOOK IT UP TO AN OLD MILK JUG\nFULL OF NITROUS I STOLE FROM A\nBLACK MARKET ORTHODONTIST, IN\nORDER TO MAKE THE FLAMING DEATH\nROCKET OF NEWS THAT IS MY\nSEGMENT:\n\"MEANWHILE!\"" + }, + "thFDua8MF_w": { + "begin": "1:21.5", + "end": "2:18.5", + "text": "FOLKS, I SPEND A\nLOT OF TIME\nRIGHT OVER THERE, COMBING\nTHROUGH THE DAY'S NEWS AND\nCAREFULLY SELECTING THE MOST\nPRISTINE OPALESCENT GLASS\nSTORIES, ORNATELY FUSING THE\nPIECES USING ONLY THE MOST\nTOPICAL COPPER WIRE AND LEAD\nCASING BEFORE COLORING THEM WITH\nTHE MOST PIGMENT-RICH JOKES\nAVAILABLE TO CONSTRUCT FOR YOU AND YOU ALONE\nTHE ELEGANT STAINED-GLASS\nTIFFANY DOME THAT IS MY NIGHTLY\nMONOLOGUE.\nBUT SOMETIMES, JUST SOMETIMES FOLKS, I JOLT AWAKE\nBEHIND THE WHEEL OF A '79 BUICK\nREGAL LOWRIDER WHILE DOING\nDONUTS IN THE PARKING LOT OF A\nBOARDED UP JOANNE FABRICS, WHEN\nI CLIP A BARREL FIRE AND I'M\nTHROWN FROM THE CAR INTO THE\nDUMPSTERS.\nTHERE, I RUMMAGE THROUGH THE\nBITS OF BROKEN FANTA BOTTLES,\nAND GLUE THEM TOGETHER WITH\nSTILL-WARM CHEWING GUM, AND\nSTAIN THEM WITH WHATEVER\nREMNANTS I CAN SCRAPE FROM OLD\nKETCHUP AND FUN-DIP PACKETS.\nTHEN I DOUSE MY PANTS IN\nKEROSENE AND LET HER BLAZE TO\nPROJECT THE DEMENTED NIGHTMARE\nKALEIDOSCOPE OF NEWS THAT IS MY\nSEGMENT:\nMEANWHILE" + }, + "u9oMwS3I12s": { + "begin": "0:53.0", + "end": "1:47.0", + "text": "FOLKS, YOU KNOW, IF YOU WATCH THE SHOW, YOU KNOW I SPEND A LOT OF\nMY TIME RIGHT OVER THERE,\nWORKING THE OLD MIRTH KILN,\nMELTING DOWN THE DAY'S MOST\nIMPORTANT GOLDEN STORY INGOTS\nTO MAKE AN ORNATE SET OF CUSTOM\nNEWS VAMBRACES.\nTHEN CARVE A MOULDED CUIRASS\nINTO THE MOST TOPICAL\nANIMAL-THEMED CHEST PLATE THAT I\nDECORATE WITH FINE FILIGREE AND\nORNATE PRE-COLUMBIAN PATTERNS TO\nCREATE THE BESPOKE SET OF GOLD\nMUISCA ARMOR THAT IS MY\nMONOLOGUE.\nBUT SOMETIMES, JUST SOMETIMES,\nFOLKS, I WAKE UP IN THE BASEMENT\nOF A DERELICT ROW HOUSE DURING A\nFULL MOON, RIFLE THROUGH A\nDISCARDED BOX OF ELBOW PASTA AND\nSTRING SOME NOODLES TOGETHER\nINTO CRUDE SHIN GUARDS WITH\nCOPPER WIRE I STRIPPED OUT OF\nTHE FUNERAL HOME I ROBBED\nEARLIER.\nTHEN, I FASHION A HAT BY\nSTAPLING OLD NEWSPAPER CLIPPINGS\nTO A BIKE HELMET AND WRAP MYSELF\nIN A TARP I SWIPED FROM THE\nRETIREMENT HOME'S HOT TUB TO\nFROLIC BEFORE YOU IN THE\nMADMAN'S HAZMAT SUIT OF NEWS\nTHAT IS MY SEGMENT:\n\"MEANWHILE!\"" + }, + "z2dPp5yM-NA": { + "begin": "1:34.3", + "end": "2:34.3", + "text": "YOU KNOW, FOLKS, IF YOU WATCH\nTHIS SHOW, AND I HOPE YOU DO...\nTHEN YOU KNOW I SPEND MOST OF MY TIME\nRIGHT OVER THERE, CAREFULLY\nUNWRAPPING THE DAY'S NEWS,\nPLACING THE FINEST, MOST TOPICAL\nORNAMENTS UPON THE HAND-CUT\nDOUGLAS FIR OF THE DAY'S TOP\nSTORIES, SPRINKLING\nBIODEGRADABLE TINSEL ON THE\nBOUGHS WITH PRECISION AND\nDECADES OF TRAINING THAT COMES\nACROSS AS EFFORTLESS, CHECKING\nEACH JOKE BULB FOR THE OPTIMAL\nTWINKLE, AND FINALLY TOPPING IT\nOFF WITH A FAMILY HEIRLOOM STAR\nTO CREATE THE MAGICAL CHRISTMAS\nMEMORY THAT IS MY MONOLOGUE.\nBUT SOMETIMES, SOMETIMES,\nI BREAK INTO A HOME DEPOT, HUFF\nA BOTTLE OF GOO GONE, STEAL A\nPALLET OF TWO BY FOURS, A PILE\nOF RUSTY NAILS, A BUCKET OF\nDISCONTINUED FAUCET PARTS, SLAP\nTHEM TOGETHER WITH A RECEIPT PAPER\nAND INDUSTRIAL ADHESIVE, PUT IT\nOUTSIDE THE LIVING ROOM WINDOW\nOF THE RETIREMENT HOME I WAS\nKICKED OUT OF FOR A VERY GOOD\nREASON, AND THROW IN A COUPLE OF\nMANNEQUINS STOLEN FROM A BURNED\nOUT FOREVER 21 TO CREATE THE\nHELLSCAPE CRECHE OF NEWS THAT IS\nMY SEGMENT:\n\"MEANWHILE\"!" + }, + "zFRXCwdPD-M": { + "begin": "1:31.6", + "end": "2:10.6", + "text": "YOU KNOW, FOLKS, I SPEND A LOT\nOF MY TIME ON THE SHOW RIGHT OVER THERE PRECISELY\nMEASURING THE NEWS' INSEAM,\nSELECTING THE FINEST, MOST\nTOPICAL IMPORTED MERINO STORY\nWOOL, THEN HAND-STITCHING IT\nWITH JOKES TO CREATE FOR YOU THE\nBESPOKE, DOUBLE-BREASTED SAVILE\nROW CURRENT EVENT SUIT THAT IS\nMY MONOLOGUE.\nBUT SOMETIMES, SOMETIMES,\nI LIKE TO GATHER UP SOME USED\nBURLAP FROM BEHIND THE\nMEAT-PACKING PLANT, DRAPE IT\nOVER AN ABANDONED MANNEQUIN AT\nOLD MAN JENKINS' BURNED-DOWN\nDRESS FACTORY, AND SEW IT\nTOGETHER WITH SHOESTRINGS AND A\nSTAPLE GUN, TO CREATE FOR YOU\nTHE HAUNTED POTATO-SACK\nSCARECROW OF NEWS THAT IS MY\nSEGMENT:\n\"MEANWHILE!\"" + }, + "zIS1lp9CS-E": { + "begin": "1:16.5", + "end": "2:04.5", + "text": "FOLKS, I SPEND A\nLOT OF TIME RIGHT OVER THERE,\nSELECTING THE FINEST GRAINS OF\nNEWS, HAND SIFTING THROUGH\nBARRELS OF STEELCUT JOKE OATS,\nSELECTING THE RIPEST SEASONAL\nSTORY BERRIES, AND\nHOME-FERMENTING MACROBIOTIC\nALMOND MILK INTO THE UPSCALE ORGANIC\nYOGURT TO LOVINGLY FOLD TOGETHER\nTHE BUZZWORTHY BREAKFAST PARFAIT\nTHAT IS MY NIGHTLY MONOLOGUE.\nBUT SOMETIMES, SOMETIMES I SWEAT MYSELF\nAWAKE IN THE MIDDLE OF THE\nNIGHT, CREEP OUT TO AN ABANDONED\nSCHOOLYARD, SCRAPE A BUNCH OF\nSPILLED CHEERIOS AND DISCARDED\nGOGURT WRAPPERS INTO A BIG GULP\nTRAVEL MUG I WON IN THE GREAT\nTRUCKER-DRIFTER WARS OF 2019,\nADD SOME FERMENTED CRABAPPLES\nFROM BEHIND THE SWING SET, AND\nCHUG BACK THE FETID PRISON\nPORRIDGE OF NEWS THAT IS MY\nSEGMENT:\nMEANWHILE!" + } +} \ No newline at end of file diff --git a/model-card.md b/model-card.md new file mode 100644 index 000000000..8a2461c2c --- /dev/null +++ b/model-card.md @@ -0,0 +1,67 @@ +# Model Card: Whisper + +This is the official codebase for running the automatic speech recognition (ASR) models (Whisper models) trained and released by OpenAI. + +Following [Model Cards for Model Reporting (Mitchell et al.)](https://arxiv.org/abs/1810.03993), we're providing some information about the automatic speech recognition model. More information on how these models were trained and evaluated can be found [in the paper](TBA). + + +## Model Details + +The Whisper models are trained for speech recognition and translation tasks, capable of transcribing speech audio into the text in the language it is spoken (ASR) as well as translated into English (speech translation). Researchers at OpenAI developed the models to study the robustness of speech processing systems trained under large-scale weak supervision. There are 9 models of different sizes and capabilities, summarized in the following table. + +| Size | Parameters | English-only model | Multilingual model | +|:------:|:----------:|:------------------:|:------------------:| +| tiny | 39 M | ✓ | ✓ | +| base | 74 M | ✓ | ✓ | +| small | 244 M | ✓ | ✓ | +| medium | 769 M | ✓ | ✓ | +| large | 1550 M | | ✓ | + + +### Release date + +September 2022 + +### Model type + +Sequence-to-sequence ASR (automatic speech recognition) and speech translation model + +### Paper & samples + +[Paper](https://cdn.openai.com/papers/whisper.pdf) / [Blog](https://openai.com/blog/whisper) + + +## Model Use + +### Evaluated Use + +The primary intended users of these models are AI researchers studying robustness, generalization, capabilities, biases, and constraints of the current model. However, Whisper is also potentially quite useful as an ASR solution for developers, especially for English speech recognition. We recognize that once models are released, it is impossible to restrict access to only “intended” uses or to draw reasonable guidelines around what is or is not research. + +The models are primarily trained and evaluated on ASR and speech translation to English tasks. They show strong ASR results in ~10 languages. They may exhibit additional capabilities, particularly if fine-tuned on certain tasks like voice activity detection, speaker classification, or speaker diarization but have not been robustly evaluated in these areas. We strongly recommend that users perform robust evaluations of the models in a particular context and domain before deploying them. + +In particular, we caution against using Whisper models to transcribe recordings of individuals taken without their consent or purporting to use these models for any kind of subjective classification. We recommend against use in high-risk domains like decision-making contexts, where flaws in accuracy can lead to pronounced flaws in outcomes. The models are intended to transcribe and translate speech, use of the model for classification is not only not evaluated but also not appropriate, particularly to infer human attributes. + + +## Training Data + +The models are trained on 680,000 hours of audio and the corresponding transcripts collected from the internet. 65% of this data (or 438,000 hours) represents English-language audio and matched English transcripts, roughly 18% (or 126,000 hours) represents non-English audio and English transcripts, while the final 17% (or 117,000 hours) represents non-English audio and the corresponding transcript. This non-English data represents 98 different languages. + +As discussed in [the accompanying paper](https://cdn.openai.com/papers/whisper.pdf), we see that performance on transcription in a given language is directly correlated with the amount of training data we employ in that language. + + +## Performance and Limitations + +Our studies show that, over many existing ASR systems, the models exhibit improved robustness to accents, background noise, technical language, as well as zero shot translation from multiple languages into English; and that accuracy on speech recognition and translation is near the state-of-the-art level. + +However, because the models are trained in a weakly supervised manner using large-scale noisy data, the predictions may include texts that are not actually spoken in the audio input (i.e. hallucination). We hypothesize that this happens because, given their general knowledge of language, the models combine trying to predict the next word in audio with trying to transcribe the audio itself. + +Our models perform unevenly across languages, and we observe lower accuracy on low-resource and/or low-discoverability languages or languages where we have less training data. The models also exhibit disparate performance on different accents and dialects of particular languages, which may include higher word error rate across speakers of different genders, races, ages, or other demographic criteria. Our full evaluation results are presented in [the paper accompanying this release](https://cdn.openai.com/papers/whisper.pdf). + +In addition, the sequence-to-sequence architecture of the model makes it prone to generating repetitive texts, which can be mitigated to some degree by beam search and temperature scheduling but not perfectly. Further analysis on these limitations are provided in [the paper](https://cdn.openai.com/papers/whisper.pdf). It is likely that this behavior and hallucinations may be worse on lower-resource and/or lower-discoverability languages. + + +## Broader Implications + +We anticipate that Whisper models’ transcription capabilities may be used for improving accessibility tools. While Whisper models cannot be used for real-time transcription out of the box – their speed and size suggest that others may be able to build applications on top of them that allow for near-real-time speech recognition and translation. The real value of beneficial applications built on top of Whisper models suggests that the disparate performance of these models may have real economic implications. + +There are also potential dual use concerns that come with releasing Whisper. While we hope the technology will be used primarily for beneficial purposes, making ASR technology more accessible could enable more actors to build capable surveillance technologies or scale up existing surveillance efforts, as the speed and accuracy allow for affordable automatic transcription and translation of large volumes of audio communication. Moreover, these models may have some capabilities to recognize specific individuals out of the box, which in turn presents safety concerns related both to dual use and disparate performance. In practice, we expect that the cost of transcription is not the limiting factor of scaling up surveillance projects. diff --git a/notebooks/LibriSpeech.ipynb b/notebooks/LibriSpeech.ipynb new file mode 100644 index 000000000..3d90e652c --- /dev/null +++ b/notebooks/LibriSpeech.ipynb @@ -0,0 +1,958 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "v5hvo8QWN-a9" + }, + "source": [ + "# Installing Whisper\n", + "\n", + "The commands below will install the Python packages needed to use Whisper models and evaluate the transcription results." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "ZsJUxc0aRsAf" + }, + "outputs": [], + "source": [ + "! pip install git+https://github.com/openai/whisper.git\n", + "! pip install jiwer" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1IMEkgyagYto" + }, + "source": [ + "# Loading the LibriSpeech dataset\n", + "\n", + "The following will load the test-clean split of the LibriSpeech corpus using torchaudio." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "3CqtR2Fi5-vP" + }, + "outputs": [], + "source": [ + "import os\n", + "import numpy as np\n", + "\n", + "try:\n", + " import tensorflow # required in Colab to avoid protobuf compatibility issues\n", + "except ImportError:\n", + " pass\n", + "\n", + "import torch\n", + "import pandas as pd\n", + "import whisper\n", + "import torchaudio\n", + "\n", + "from tqdm.notebook import tqdm\n", + "\n", + "\n", + "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "GuCCB2KYOJCE" + }, + "outputs": [], + "source": [ + "class LibriSpeech(torch.utils.data.Dataset):\n", + " \"\"\"\n", + " A simple class to wrap LibriSpeech and trim/pad the audio to 30 seconds.\n", + " It will drop the last few seconds of a very small portion of the utterances.\n", + " \"\"\"\n", + " def __init__(self, split=\"test-clean\", device=DEVICE):\n", + " self.dataset = torchaudio.datasets.LIBRISPEECH(\n", + " root=os.path.expanduser(\"~/.cache\"),\n", + " url=split,\n", + " download=True,\n", + " )\n", + " self.device = device\n", + "\n", + " def __len__(self):\n", + " return len(self.dataset)\n", + "\n", + " def __getitem__(self, item):\n", + " audio, sample_rate, text, _, _, _ = self.dataset[item]\n", + " assert sample_rate == 16000\n", + " audio = whisper.pad_or_trim(audio.flatten()).to(self.device)\n", + " mel = whisper.log_mel_spectrogram(audio)\n", + " \n", + " return (mel, text)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "-YcRU5jqNqo2" + }, + "outputs": [], + "source": [ + "dataset = LibriSpeech(\"test-clean\")\n", + "loader = torch.utils.data.DataLoader(dataset, batch_size=16)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0ljocCNuUAde" + }, + "source": [ + "# Running inference on the dataset using a base Whisper model\n", + "\n", + "The following will take a few minutes to transcribe all utterances in the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_PokfNJtOYNu", + "outputId": "2c53ec44-bc93-4107-b4fa-214e3f71fe8e" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model is English-only and has 71,825,408 parameters.\n" + ] + } + ], + "source": [ + "model = whisper.load_model(\"base.en\")\n", + "print(\n", + " f\"Model is {'multilingual' if model.is_multilingual else 'English-only'} \"\n", + " f\"and has {sum(np.prod(p.shape) for p in model.parameters()):,} parameters.\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# predict without timestamps for short-form transcription\n", + "options = whisper.DecodingOptions(language=\"en\", without_timestamps=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "09a29a91f58d4462942505a3cc415801", + "83391f98a240490987c397048fc1a0d4", + "06b9aa5f49fa44ba8c93b647dc7db224", + "da9c231ee67047fb89073c95326b72a5", + "48da931ebe7f4fd299f8c98c7d2460ff", + "7a901f447c1d477bb49f954e0feacedd", + "39f5a6ae8ba74c8598f9c6d5b8ad2d65", + "a0d10a42c753453283e5219c22239337", + "09f4cb79ff86465aaf48b0de24869af9", + "1b9cecf5b3584fba8258a81d4279a25b", + "039b53f2702c4179af7e0548018d0588" + ] + }, + "id": "7OWTn_KvNk59", + "outputId": "a813a792-3c91-4144-f11f-054fd6778023" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9df048b46f764cf68cbe0045b8ff73a8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/164 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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0He hoped there would be stew for dinner, turni...HE HOPED THERE WOULD BE STEW FOR DINNER TURNIP...
1Stuffered into you, his belly counseled him.STUFF IT INTO YOU HIS BELLY COUNSELLED HIM
2After early nightfall the yellow lamps would l...AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD L...
3Hello Bertie, any good in your mind?HELLO BERTIE ANY GOOD IN YOUR MIND
4Number 10. Fresh Nelly is waiting on you. Good...NUMBER TEN FRESH NELLY IS WAITING ON YOU GOOD ...
.........
2615Oh, to shoot my soul's full meaning into futur...OH TO SHOOT MY SOUL'S FULL MEANING INTO FUTURE...
2616Then I, long tried by natural ills, received t...THEN I LONG TRIED BY NATURAL ILLS RECEIVED THE...
2617I love thee freely as men strive for right. I ...I LOVE THEE FREELY AS MEN STRIVE FOR RIGHT I L...
2618I love thee with the passion put to use, in my...I LOVE THEE WITH THE PASSION PUT TO USE IN MY ...
2619I love thee with the love I seemed to lose wit...I LOVE THEE WITH A LOVE I SEEMED TO LOSE WITH ...
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hypothesisreferencehypothesis_cleanreference_clean
0He hoped there would be stew for dinner, turni...HE HOPED THERE WOULD BE STEW FOR DINNER TURNIP...he hoped there would be stew for dinner turnip...he hoped there would be stew for dinner turnip...
1Stuffered into you, his belly counseled him.STUFF IT INTO YOU HIS BELLY COUNSELLED HIMstuffered into you his belly counseled himstuff it into you his belly counseled him
2After early nightfall the yellow lamps would l...AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD L...after early nightfall the yellow lamps would l...after early nightfall the yellow lamps would l...
3Hello Bertie, any good in your mind?HELLO BERTIE ANY GOOD IN YOUR MINDhello bertie any good in your mindhello bertie any good in your mind
4Number 10. Fresh Nelly is waiting on you. Good...NUMBER TEN FRESH NELLY IS WAITING ON YOU GOOD ...number 10 fresh nelly is waiting on you good n...number 10 fresh nelly is waiting on you good n...
...............
2615Oh, to shoot my soul's full meaning into futur...OH TO SHOOT MY SOUL'S FULL MEANING INTO FUTURE...0 to shoot my soul is full meaning into future...0 to shoot my soul is full meaning into future...
2616Then I, long tried by natural ills, received t...THEN I LONG TRIED BY NATURAL ILLS RECEIVED THE...then i long tried by natural ills received the...then i long tried by natural ills received the...
2617I love thee freely as men strive for right. I ...I LOVE THEE FREELY AS MEN STRIVE FOR RIGHT I L...i love thee freely as men strive for right i l...i love thee freely as men strive for right i l...
2618I love thee with the passion put to use, in my...I LOVE THEE WITH THE PASSION PUT TO USE IN MY ...i love thee with the passion put to use in my ...i love thee with the passion put to use in my ...
2619I love thee with the love I seemed to lose wit...I LOVE THEE WITH A LOVE I SEEMED TO LOSE WITH ...i love thee with the love i seemed to lose wit...i love thee with a love i seemed to lose with ...
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" + ], + "text/plain": [ + " hypothesis \\\n", + "0 He hoped there would be stew for dinner, turni... \n", + "1 Stuffered into you, his belly counseled him. \n", + "2 After early nightfall the yellow lamps would l... \n", + "3 Hello Bertie, any good in your mind? \n", + "4 Number 10. Fresh Nelly is waiting on you. Good... \n", + "... ... \n", + "2615 Oh, to shoot my soul's full meaning into futur... \n", + "2616 Then I, long tried by natural ills, received t... \n", + "2617 I love thee freely as men strive for right. I ... \n", + "2618 I love thee with the passion put to use, in my... \n", + "2619 I love thee with the love I seemed to lose wit... \n", + "\n", + " reference \\\n", + "0 HE HOPED THERE WOULD BE STEW FOR DINNER TURNIP... \n", + "1 STUFF IT INTO YOU HIS BELLY COUNSELLED HIM \n", + "2 AFTER EARLY NIGHTFALL THE YELLOW LAMPS WOULD L... \n", + "3 HELLO BERTIE ANY GOOD IN YOUR MIND \n", + "4 NUMBER TEN FRESH NELLY IS WAITING ON YOU GOOD ... \n", + "... ... \n", + "2615 OH TO SHOOT MY SOUL'S FULL MEANING INTO FUTURE... \n", + "2616 THEN I LONG TRIED BY NATURAL ILLS RECEIVED THE... \n", + "2617 I LOVE THEE FREELY AS MEN STRIVE FOR RIGHT I L... \n", + "2618 I LOVE THEE WITH THE PASSION PUT TO USE IN MY ... \n", + "2619 I LOVE THEE WITH A LOVE I SEEMED TO LOSE WITH ... \n", + "\n", + " hypothesis_clean \\\n", + "0 he hoped there would be stew for dinner turnip... \n", + "1 stuffered into you his belly counseled him \n", + "2 after early nightfall the yellow lamps would l... \n", + "3 hello bertie any good in your mind \n", + "4 number 10 fresh nelly is waiting on you good n... \n", + "... ... \n", + "2615 0 to shoot my soul is full meaning into future... \n", + "2616 then i long tried by natural ills received the... \n", + "2617 i love thee freely as men strive for right i l... \n", + "2618 i love thee with the passion put to use in my ... \n", + "2619 i love thee with the love i seemed to lose wit... \n", + "\n", + " reference_clean \n", + "0 he hoped there would be stew for dinner turnip... \n", + "1 stuff it into you his belly counseled him \n", + "2 after early nightfall the yellow lamps would l... \n", + "3 hello bertie any good in your mind \n", + "4 number 10 fresh nelly is waiting on you good n... \n", + "... ... \n", + "2615 0 to shoot my soul is full meaning into future... \n", + "2616 then i long tried by natural ills received the... \n", + "2617 i love thee freely as men strive for right i l... \n", + "2618 i love thee with the passion put to use in my ... \n", + "2619 i love thee with a love i seemed to lose with ... \n", + "\n", + "[2620 rows x 4 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[\"hypothesis_clean\"] = [normalizer(text) for text in data[\"hypothesis\"]]\n", + "data[\"reference_clean\"] = [normalizer(text) for text in data[\"reference\"]]\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EBGSITeBYPTT", + "outputId": "7b3dbe7c-a37e-4a07-a50a-b27d5f88b68f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "WER: 4.26 %\n" + ] + } + ], + "source": [ + "wer = jiwer.wer(list(data[\"reference_clean\"]), list(data[\"hypothesis_clean\"]))\n", + "\n", + "print(f\"WER: {wer * 100:.2f} %\")" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "provenance": [] + }, + 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"v5hvo8QWN-a9" + }, + "source": [ + "# Installing Whisper\n", + "\n", + "The commands below will install the Python packages needed to use Whisper models and evaluate the transcription results." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "ZsJUxc0aRsAf" + }, + "outputs": [], + "source": [ + "! pip install git+https://github.com/openai/whisper.git" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "3CqtR2Fi5-vP" + }, + "outputs": [], + "source": [ + "import io\n", + "import os\n", + "import numpy as np\n", + "\n", + "try:\n", + " import tensorflow # required in Colab to avoid protobuf compatibility issues\n", + "except ImportError:\n", + " pass\n", + "\n", + "import torch\n", + "import pandas as pd\n", + "import urllib\n", + "import tarfile\n", + "import whisper\n", + "import torchaudio\n", + "\n", + "from scipy.io import wavfile\n", + "from tqdm.notebook import tqdm\n", + "\n", + "\n", + "pd.options.display.max_rows = 100\n", + "pd.options.display.max_colwidth = 1000\n", + "DEVICE = \"cuda\" if torch.cuda.is_available() else \"cpu\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1IMEkgyagYto" + }, + "source": [ + "# Loading the Fleurs dataset\n", + "\n", + "Select the language of the Fleur dataset to download. Please note that the transcription and translation performance varies widely depending on the language. Appendix D.2 in the paper contains the performance breakdown by language." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "d36195a3d8184ec98132aa9e857bcc64", + "10736c04e5b94344a9e948a89b859d8a", + "d458d02a2dfe45ae918cbb1d53f4abfe" + ] + }, + "id": "L4lPK5106Of2", + "outputId": "313c1993-fb43-4315-800a-fc7cf34d7be5" + }, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Dropdown(description='Language:', index=39, options=(('Select language', None), ('----------', None), ('Afrika…" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "d36195a3d8184ec98132aa9e857bcc64" + } + }, + "metadata": {} + } + ], + "source": [ + "import ipywidgets as widgets\n", + "\n", + "languages = {\"af_za\": \"Afrikaans\", \"am_et\": \"Amharic\", \"ar_eg\": \"Arabic\", \"as_in\": \"Assamese\", \"az_az\": \"Azerbaijani\", \"be_by\": \"Belarusian\", \"bg_bg\": \"Bulgarian\", \"bn_in\": \"Bengali\", \"bs_ba\": \"Bosnian\", \"ca_es\": \"Catalan\", \"cmn_hans_cn\": \"Chinese\", \"cs_cz\": \"Czech\", \"cy_gb\": \"Welsh\", \"da_dk\": \"Danish\", \"de_de\": \"German\", \"el_gr\": \"Greek\", \"en_us\": \"English\", \"es_419\": \"Spanish\", \"et_ee\": \"Estonian\", \"fa_ir\": \"Persian\", \"fi_fi\": \"Finnish\", \"fil_ph\": \"Tagalog\", \"fr_fr\": \"French\", \"gl_es\": \"Galician\", \"gu_in\": \"Gujarati\", \"ha_ng\": \"Hausa\", \"he_il\": \"Hebrew\", \"hi_in\": \"Hindi\", \"hr_hr\": \"Croatian\", \"hu_hu\": \"Hungarian\", \"hy_am\": \"Armenian\", \"id_id\": \"Indonesian\", \"is_is\": \"Icelandic\", \"it_it\": \"Italian\", \"ja_jp\": \"Japanese\", \"jv_id\": \"Javanese\", \"ka_ge\": \"Georgian\", \"kk_kz\": \"Kazakh\", \"km_kh\": \"Khmer\", \"kn_in\": \"Kannada\", \"ko_kr\": \"Korean\", \"lb_lu\": \"Luxembourgish\", \"ln_cd\": \"Lingala\", \"lo_la\": \"Lao\", \"lt_lt\": \"Lithuanian\", \"lv_lv\": \"Latvian\", \"mi_nz\": \"Maori\", \"mk_mk\": \"Macedonian\", \"ml_in\": \"Malayalam\", \"mn_mn\": \"Mongolian\", \"mr_in\": \"Marathi\", \"ms_my\": \"Malay\", \"mt_mt\": \"Maltese\", \"my_mm\": \"Myanmar\", \"nb_no\": \"Norwegian\", \"ne_np\": \"Nepali\", \"nl_nl\": \"Dutch\", \"oc_fr\": \"Occitan\", \"pa_in\": \"Punjabi\", \"pl_pl\": \"Polish\", \"ps_af\": \"Pashto\", \"pt_br\": \"Portuguese\", \"ro_ro\": \"Romanian\", \"ru_ru\": \"Russian\", \"sd_in\": \"Sindhi\", \"sk_sk\": \"Slovak\", \"sl_si\": \"Slovenian\", \"sn_zw\": \"Shona\", \"so_so\": \"Somali\", \"sr_rs\": \"Serbian\", \"sv_se\": \"Swedish\", \"sw_ke\": \"Swahili\", \"ta_in\": \"Tamil\", \"te_in\": \"Telugu\", \"tg_tj\": \"Tajik\", \"th_th\": \"Thai\", \"tr_tr\": \"Turkish\", \"uk_ua\": \"Ukrainian\", \"ur_pk\": \"Urdu\", \"uz_uz\": \"Uzbek\", \"vi_vn\": \"Vietnamese\", \"yo_ng\": \"Yoruba\"}\n", + "selection = widgets.Dropdown(\n", + " options=[(\"Select language\", None), (\"----------\", None)] + sorted([(f\"{v} ({k})\", k) for k, v in languages.items()]),\n", + " value=\"ko_kr\",\n", + " description='Language:',\n", + " disabled=False,\n", + ")\n", + "\n", + "selection" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4eihI6oK6Of2", + "outputId": "42874540-06ec-442a-aa14-71f52f964de0" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Selected language: Korean (ko_kr)\n" + ] + } + ], + "source": [ + "lang = selection.value\n", + "language = languages[lang]\n", + "\n", + "assert lang is not None, \"Please select a language\"\n", + "print(f\"Selected language: {language} ({lang})\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "GuCCB2KYOJCE" + }, + "outputs": [], + "source": [ + "class Fleurs(torch.utils.data.Dataset):\n", + " \"\"\"\n", + " A simple class to wrap Fleurs and subsample a portion of the dataset as needed.\n", + " \"\"\"\n", + " def __init__(self, lang, split=\"test\", subsample_rate=1, device=DEVICE):\n", + " url = f\"https://storage.googleapis.com/xtreme_translations/FLEURS102/{lang}.tar.gz\"\n", + " tar_path = os.path.expanduser(f\"~/.cache/fleurs/{lang}.tgz\")\n", + " os.makedirs(os.path.dirname(tar_path), exist_ok=True)\n", + "\n", + " if not os.path.exists(tar_path):\n", + " with urllib.request.urlopen(url) as source, open(tar_path, \"wb\") as output:\n", + " with tqdm(total=int(source.info().get(\"Content-Length\")), ncols=80, unit='iB', unit_scale=True, unit_divisor=1024) as loop:\n", + " while True:\n", + " buffer = source.read(8192)\n", + " if not buffer:\n", + " break\n", + "\n", + " output.write(buffer)\n", + " loop.update(len(buffer))\n", + "\n", + " labels = {}\n", + " all_audio = {}\n", + " with tarfile.open(tar_path, \"r:gz\") as tar:\n", + " for member in tar.getmembers():\n", + " name = member.name\n", + " if name.endswith(f\"{split}.tsv\"):\n", + " labels = pd.read_table(tar.extractfile(member), names=(\"id\", \"file_name\", \"raw_transcription\", \"transcription\", \"_\", \"num_samples\", \"gender\"))\n", + "\n", + " if f\"/{split}/\" in name and name.endswith(\".wav\"):\n", + " audio_bytes = tar.extractfile(member).read()\n", + " all_audio[os.path.basename(name)] = wavfile.read(io.BytesIO(audio_bytes))[1]\n", + " \n", + "\n", + " self.labels = labels.to_dict(\"records\")[::subsample_rate]\n", + " self.all_audio = all_audio\n", + " self.device = device\n", + "\n", + " def __len__(self):\n", + " return len(self.labels)\n", + "\n", + " def __getitem__(self, item):\n", + " record = self.labels[item]\n", + " audio = torch.from_numpy(self.all_audio[record[\"file_name\"]].copy())\n", + " text = record[\"transcription\"]\n", + " \n", + " return (audio, text)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "-YcRU5jqNqo2" + }, + "outputs": [], + "source": [ + "dataset = Fleurs(lang, subsample_rate=10) # subsample 10% of the dataset for a quick demo" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0ljocCNuUAde" + }, + "source": [ + "# Running inference on the dataset using a medium Whisper model\n", + "\n", + "The following will take a few minutes to transcribe and translate utterances in the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_PokfNJtOYNu", + "outputId": "7d848480-a589-42c5-d456-611522091f51" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Model is multilingual and has 762,321,920 parameters.\n" + ] + } + ], + "source": [ + "model = whisper.load_model(\"medium\")\n", + "print(\n", + " f\"Model is {'multilingual' if model.is_multilingual else 'English-only'} \"\n", + " f\"and has {sum(np.prod(p.shape) for p in model.parameters()):,} parameters.\"\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "F74Yfr696Of5" + }, + "outputs": [], + "source": [ + "options = dict(language=language, beam_size=5, best_of=5)\n", + "transcribe_options = dict(task=\"transcribe\", **options)\n", + "translate_options = dict(task=\"translate\", **options)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "97bfe56acfa04fd4899ece60a26bee17", + "4ca421b430b54f5ba66bf981e4d4e003", + "ad6e4f8221c843f9b406d5bf18206aab", + "e7ca545307994521996af790a22497bf", + "822ae53e841b42008c5aefb9cc7da64a", + "37a30b9b7bd34421bd3d0679b81d8bad", + "61dee3eff0b14127b06de151cabb5bf8", + "0ed86ac53e8146eca0ab4aa7e9e8da19", + "8b1c829b6c2b47a280069b758a6dec2b", + "3f2f720394cd406eac1487e37994a9b8", + "ddefd1c4b2174b538a1df784050899d7" + ] + }, + "id": "7OWTn_KvNk59", + "outputId": "31cfe3bd-73dd-489b-b972-1920d727afce", + "scrolled": false + }, + "outputs": [ + { + 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referencetranscriptiontranslation
0재입국 충격은 신혼 단계가 없기 때문에 문화 충격보다 빠르게 발생하며 더 오래 지속하고 더 극심할 수 있습니다재입국 충격은 신혼 단계가 없기 때문에 문화 충격보다 빠르게 발생하며 더 오래 지속하고 더 극심할 수 있습니다.The reentry shock does not have a honeymoon stage, so it occurs faster than the cultural shock and can last longer and be more severe.
1불행히도 운전자의 행동이 100% 확실하다고 예측할 수 없기 때문에 교통 흐름을 연구하기란 어렵다불행히도 운전자의 행동이 100% 확실하다고 예측할 수 없기 때문에 교통 흐름을 연구하기란 어렵다.Unfortunately, it is difficult to study the flow of traffic because the driver's behavior is not 100% certain.
2합금은 기본적으로 금속 두 개 이상의 혼합물이다 주기율표 상에 원소가 많이 있다는 것을 잊지 마라합금은 기본적으로 금속 2개 이상의 혼합물이다. 주기율표상의 원소가 많이 있다는 것을 잊지 마라.Don't forget that there are more than two metals in the alloy. Don't forget that there are more than two metals in the alloy.
3홍콩 섬은 홍콩 영토에 홍콩이라는 이름을 부여한 곳이자 많은 관광객들이 주안점으로 여기는 곳이다홍콩섬은 홍콩 영토에 홍콩이라는 이름을 부여한 곳이자 많은 관광객들이 주한점으로 여기는 곳이다.Hong Kong Island is a place named Hong Kong on Hong Kong territory, and it is the main destination of many tourists.
4남자들을 단호히 거절하고 자신의 입장을 고수하는 것을 두려워하지 말라문화적 차이든 아니든 희롱은 간과될 수 없다!남자들을 단호히 거절하고 자신의 입장을 고수하는 것을 두려워하지 말라. 문화적 차이든 아니든 희롱은 간과 될 수 없다.Do not be afraid to reject men and defend your position. Whether it is a cultural difference or not, it cannot be ignored.
5좀 더 작은 섬의 대부분은 독립 국가이거나 프랑스와 관련이 있고 호화로운 비치 리조트로 알려져 있다좀 더 작은 섬의 대부분은 독립국가이거나 프랑스와 관련이 있고, 호화로운 비치 리조트로 알려져 있다.Most of the smaller islands are independent or related to France, and are known as luxurious beach resorts.
6사실 이게 존재한다는 사실을 아는 사람이 있다고 해도 찾는 것조차 쉽지 않습니다 일단 동굴 안에 들어가면 완전한 고립입니다사실 이게 존재한다는 사실을 아는 사람이 있다고 해도 찾는 것조차 쉽지 않습니다. 일단 동그란에 들어가면 완전한 고립입니다.In fact, even if there are people who know that this exists, it is not easy to find it. Once you enter the cave, it is a complete isolation.
7원단이 너무 뜨거워지지 않도록 주의하세요원단이 수축하거나 그을릴 수 있습니다원단이 너무 뜨거워지지 않도록 주의하세요. 원단이 수축하거나 그을릴 수 있습니다.Be careful not to heat the fabric too much. The fabric may shrink or get dirty.
8도널드 트럼프 미국 대통령은 늦은 일요일 시간대에 대변인을 통해 발표한 성명에서 미군이 시리아에서 철수할 것이라고 발표했다도널드 트럼프 미국 대통령은 늦은 일요일 시간대에 대변인을 통해 발표한 성명에서 미군이 시리아에서 철수할 것이라고 발표했다.U.S. President Donald Trump announced on Sunday that the U.S. military would withdraw from Syria in his speech.
9미국 체조 협회는 미국 올림픽 위원회의 서한을 지지하며 미국의 전 운동선수가 안전한 환경을 가질 수 있도록 돕는 올림픽 관계자의 절대적 필요성을 인정합니다미국 체조협회는 미국 올림픽위원회의 서한을 지지하며 미국의 전 운동선수가 안전한 환경을 가질 수 있도록 돕는 올림픽 관계자의 절대적 필요성을 인정합니다.The U.S. Sports Association supports the call of the U.S. Olympic Committee to help all athletes in the U.S. have a safe environment.
10\"이건 작별 인사가 아닙니다. 이것은 한 장의 끝이며 새로운 장의 시작입니다.\"\\t \" \" 이 건 | 작 별 | 인 사 가 | 아 닙 니 다 . | 이 것 은 | 한 | 장 의 | 끝 이 며 | 새 로 운 | 장 의 | 시 작 입 니 다 . \" \" |이건 작별인사가 아닙니다. 이것은 한작의 끝이며 새로운 작의 시작입니다.This is not a farewell. This is the end of one work and the beginning of a new work.
11남아프리카 공화국의 특정 공원 또는 남아프리카 공화국 국립공원 전체에 입장할 수 있는 와일드 카드를 구매하는 것이 이득일 수 있습니다남아프리카 공화국의 특정 공원 또는 남아프리카 공화국 국립공원 전체에 입장할 수 있는 와일드카드를 구매하는 것이 이득일 수 있습니다.It may be beneficial to purchase a wild card that allows you to enter a specific park or a national park of South Africa.
12높은 고도나 산길을 넘어 운전하려고 하면 눈 빙판 또는 영하의 기온 등이 생길 가능성에 대해 고려해야 한다높은 고도나 산길을 넘어 운전하려고 하면 눈, 빙판 또는 영하의 기온 등이 생길 가능성에 대해 고려해야 한다.If you want to drive over high altitude or mountain roads, you have to consider the possibility of snow, ice, or the temperature of a movie.
13이 이야기에 관계된 두 명의 성인 자녀를 둔 기혼자인 듀발은 밀러에게 깊은 인상을 남기지 않았습니다이 이야기에 관계된 두 명의 성인 자녀를 둔 기혼자인 듀발은 밀러에게 깊은 임상을 남기지 않았습니다.Duval, who had two adult children related to this story, did not leave a deep impression on Miller.
14찬드라 셰카르 솔랑키 경찰서장은 피고인이 얼굴을 가린 채 법정에 나왔다고 말했다찬드라 시에카르 솔랑키 경찰서장은 피고인이 얼굴을 가린 채 법적에 나왔다고 말했다.The police chief of Chandra Shekhar Solanki said that the defendant covered his face and came out legally.
15지하철의 정규 안내 방송은 카탈로니아어로만 제공되나 계획에 없던 운행 중단 시에는 자동 시스템을 통해 스페인어 영어 프랑스어 아라비아어 일본어를 포함한 다양한 언어로 방송됩니다지하철의 정규 안내 방송은 카탈로니아어로만 제공되나 계획에 없던 운행 중단 시에는 자동 시스템을 통해 스페인어, 영어, 프랑스어, 아라비아어, 일본어를 포함한 다양한 언어로 방송됩니다.Subway's regular announcement broadcast is only available in Catalan, but it will be broadcast in Spanish, English, French, Arabic, and Japanese.
16그는 그 소문을 정치적 수다와 어리석음\"\"이라고 언급했습니다.그는 그 소문을 정치적 수다와 어리석음이라고 언급했습니다.He mentioned the rumor as political talk and foolishness.
17지구가 마치 움직이지 않는 것처럼 느껴지기에 언뜻 보면 맞는 말 같기도 하죠지구가 마치 움직이지 않은 것처럼 느껴지기에 언특보면 맞는 말 같기도 하죠?It feels as if the earth is not moving, and it seems to be right.
18어린이와 노약자를 포함한 6명의 인질들은 필리핀 사진작가들과 마찬가지로 일찍 풀려났다어린이와 노약자를 포함한 6명의 인질들은 필리핀 사진작가들과 마찬가지로 일찍 풀려났다.Children and old people, including 6 people, were released early, just like the Philippine photographers.
19같은 줄을 따라 남자는 무릎을 덮는 바지를 입을 것을 요구받는다같은 줄을 따라 남자는 무릎을 덮는 바지를 입을 것을 요구받는다.The man is asked to wear pants that cover his knees along the same line.
20그것은 기차와 자동차 및 여러 교통수단을 가져다주었습니다그것은 기차와 자동차 및 여러 교통수단을 가져다 주었습니다.It was given by trains, cars, and other means of transport.
21모로코 술탄은 다루 이 바드야daru l-badya라는 도시로 재건축하고 여기에 무역 거점을 세운 스페인 상인들은 이곳을 카사블랑카라고 불렀다모로코 슬타는 다루이 바드야라는 도시로 재건축하고 여기에 무역 거점을 세운 스페인 상인들은 이곳을 카사블랑카라고 불렀다.Morocco Sultan was rebuilt as a city called Darui Badia The Spanish merchants who built a trade point here called this place Casablanca.
22비록 대부분 이론에 의존하긴 해도 프로그램은 궁수자리 은하 관측을 시뮬레이션하도록 짜여 있습니다비록 대부분 이론에 의존하긴 해도, 프로그램은 궁수자리 은하관측을 시뮬레이션하도록 짜여 있습니다.Although most of them rely on theory, the program is designed to simulate the Milky Way of the palace.
23아동이 위탁 보호를 받게 되는 이유는 방치에서 학대 심지어 강탈에 이르기까지 광범위하게 다양합니다아동이 위탁보호를 받게 되는 이유는 방치에서 학대, 심지어 강탈에 이르기까지 광범위하게 다양합니다.The reason why children are protected is that they can be abused, abused, and even robbed.
24뇌 병리와 행동 사이의 상관관계가 과학자들의 연구를 돕습니다뇌병리와 행동사이의 상관관계가 과학자들의 연구를 돕습니다.The relationship between brain
25사람들은 귀국길에 오르는 여행객들에게도 인내심과 이해심이 필요하다고 생각하지 않을 수 있다사람들은 귀국길에 오르는 여행객들에게도 인내심과 이해심이 필요하다고 생각하지 않을 수 있다.People may not think that patience and understanding are necessary for travelers on their way back home.
26교전이 발발한 직후 영국은 독일에 대한 해상 봉쇄를 시작한다교전이 발발한 직후 영국은 독일에 대한 해상붕쇄를 시작한다.As there is a fierce conflict,
27이렇게 되면 플레이어들이 장치를 허공에서 움직여 동작과 움직임을 제어할 수 있게 된다이렇게 되면 플레이어들이 장치를 허공해서 움직여 동작과 움직임을 제어할 수 있게 된다.In this way, players can control the movement by moving the device in the air.
28스펙트럼의 또 다른 끝에서는 팀이 해오던 모든 것을 바꾸고 자신만의 방식으로 만들어야 한다고 생각하는 인지하지 못한 개인으로 바뀝니다스펙트럼의 또 다른 끝에서는 팀이 해오던 모든 것을 바꾸고 자신만의 방식으로 만들어야 한다고 생각하는 인지하지 못한 개인으로 바뀝니다.At the other end of the spectrum, you have to change everything your team has been doing and make it your own way. It turns into an unrecognizable individual.
29피라미드 사운드와 광선 쇼는 이 지역에서 어린이들의 흥미를 가장 많이 끌어들이는 것들 중 하나입니다피라미드 사운드와 광선쇼는 이 지역에서 어린이들의 흥미를 가장 많이 끌어들이는 것들 중 하나입니다.Pyramid Sound and Glow Show is one of the things that attracts children's interest the most in this area.
30장면들이 피라미드들 위에 비쳤고 다른 피라미드들에는 불이 밝혀졌다장면들이 피라미드들 위에 비쳤고, 다른 피라미드들에는 불이 밝혀졌다.The scenes were reflected on the pyramids, and the fire was lit on the other pyramids.
31이곳은 남아프리카의 명소 중 하나로 남아프리카 국립 공원sanparks의 플래그십입니다이곳은 남아프리카의 명소 중 하나로 남아프리카 국립공원의 플래그십입니다.This is one of the famous places in South Africa and is a flagship of South African National Park.
32아직도 존재하는 것으로 알려진 25개의 던랩 브로드 사이드는 이 문서의 남아있는 가장 오래된 사본이다 손으로 쓴 원본은 더 이상 남아 있지 않다아직도 존재하는 것으로 알려진 25개의 덜랩 브로드 사이드는 이 문서에 남아있는 가장 오래된 사번이다. 손으로 쓴 원보는 더 이상 남아있지 않다.The 25th Dunlap Broadside, which is known to still exist, is the oldest document in this document. The original document written by hand no longer exists.
33상트페테르부르크 크루즈는 시내에서의 시간이 포함됩니다. 크루즈 승객은 비자 요건에서 제외됩니다약관을 확인하세요상트 페테르브르크 크루즈는 시내에서의 시간이 포함됩니다. 크루즈 승객은 비자 요건에서 제외됩니다. 약관을 확인하세요.St.Petersburg Cruise includes time in the city. Cruise passengers are excluded from visa conditions. Please check your passport.
34블로그는 학생들의 글쓰기를 개선하는 데에도 도움이 됩니다 학생들이 종종 부족한 문법과 철자법으로 블로그를 시작하지만 독자들의 존재가 일반적으로 그러한 점들을 변화시킵니다블로그는 학생들의 글쓰기를 개선하는 데에도 도움이 됩니다. 학생들이 종종 부족한 문법과 철자법으로 블로그를 시작하지만 독자들의 존재가 일반적으로 그러한 점들을 변화시킵니다.Blogs help students to improve their writing skills. Students often start blogs with poor grammar and spelling, as readers tend to change in a mysterious way.
35델 포트로가 2세트에서 먼저 어드밴티지를 얻었지만 6 대 6이 된 후 다시 타이 브레이크가 필요했다델포트로가 2세트에서 먼저 어드밴티지를 얻었지만 6대6이 된 후 다시 타이브레이크가 필요했다.Del Potro got an advantage in the second set first, but needed a tiebreak again after 6 to 6.
36이것은 몇몇 동사와 사물을 구별하는 중요한 방법이다이것은 몇몇 동사와 사물을 구별하는 중요한 방법이다.This is an important method of distinguishing some verbs and things.
37교회의 핵심 권력은 천 년 넘게 로마에 머물렀다 이런 힘과 돈의 편중은 이 교리가 합당한지에 대한 의문을 낳았다교회의 핵심 권력은 1000년 넘게 로마에 머물렀다. 이러한 힘과 돈의 편중은 이 교리가 합당한지에 대한 의문을 낳았다.The main power of the church has remained in Rome for over 1,000 years. The power of the church has remained in Rome for over 1,000 years.
38보고서는 행정부의 현재 이라크 정책에 대해 거의 모든 측면에서 매우 비판적이며 즉각적인 방향 변경을 촉구합니다보고서는 행정부의 현재 이라크 정책에 대해 거의 모든 측면에서 매우 비판적이며 즉각적인 방향변경을 촉구합니다.The report is very critical of the current Iraqi policy of the administration, and calls for an immediate change of direction.
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], + entry_points = { + 'console_scripts': ['whisper=whisper.transcribe:cli'], + }, + include_package_data=True, + extras_require={'dev': ['pytest']}, +) diff --git a/tests/jfk.flac b/tests/jfk.flac new file mode 100644 index 000000000..e44b7c138 Binary files /dev/null and b/tests/jfk.flac differ diff --git a/tests/test_audio.py b/tests/test_audio.py new file mode 100644 index 000000000..ad6a6dacb --- /dev/null +++ b/tests/test_audio.py @@ -0,0 +1,19 @@ +import os.path + +import numpy as np + +from whisper.audio import load_audio, log_mel_spectrogram, SAMPLE_RATE + + +def test_audio(): + audio_path = os.path.join(os.path.dirname(__file__), "jfk.flac") + audio = load_audio(audio_path) + assert audio.ndim == 1 + assert SAMPLE_RATE * 10 < audio.shape[0] < SAMPLE_RATE * 12 + assert 0 < audio.std() < 1 + + mel_from_audio = log_mel_spectrogram(audio) + mel_from_file = log_mel_spectrogram(audio_path) + + assert np.allclose(mel_from_audio, mel_from_file) + assert mel_from_audio.max() - mel_from_audio.min() <= 2.0 diff --git a/tests/test_normalizer.py b/tests/test_normalizer.py new file mode 100644 index 000000000..a6c16cb0f --- /dev/null +++ b/tests/test_normalizer.py @@ -0,0 +1,92 @@ +import pytest + +from whisper.normalizers import EnglishTextNormalizer +from whisper.normalizers.english import EnglishNumberNormalizer, EnglishSpellingNormalizer + + +@pytest.mark.parametrize("std", [EnglishNumberNormalizer(), EnglishTextNormalizer()]) +def test_number_normalizer(std): + assert std("two") == "2" + assert std("thirty one") == "31" + assert std("five twenty four") == "524" + assert std("nineteen ninety nine") == "1999" + assert std("twenty nineteen") == "2019" + + assert std("two point five million") == "2500000" + assert std("four point two billions") == "4200000000s" + assert std("200 thousand") == "200000" + assert std("200 thousand dollars") == "$200000" + assert std("$20 million") == "$20000000" + assert std("€52.4 million") == "€52400000" + assert std("£77 thousands") == "£77000s" + + assert std("two double o eight") == "2008" + + assert std("three thousand twenty nine") == "3029" + assert std("forty three thousand two hundred sixty") == "43260" + assert std("forty three thousand two hundred and sixty") == "43260" + + assert std("nineteen fifties") == "1950s" + assert std("thirty first") == "31st" + assert std("thirty three thousand and three hundred and thirty third") == "33333rd" + + assert std("three billion") == "3000000000" + assert std("millions") == "1000000s" + + assert std("july third twenty twenty") == "july 3rd 2020" + assert std("august twenty sixth twenty twenty one") == "august 26th 2021" + assert std("3 14") == "3 14" + assert std("3.14") == "3.14" + assert std("3 point 2") == "3.2" + assert std("3 point 14") == "3.14" + assert std("fourteen point 4") == "14.4" + assert std("two point two five dollars") == "$2.25" + assert std("two hundred million dollars") == "$200000000" + assert std("$20.1 million") == "$20100000" + + assert std("ninety percent") == "90%" + assert std("seventy six per cent") == "76%" + + assert std("double oh seven") == "007" + assert std("double zero seven") == "007" + assert std("nine one one") == "911" + assert std("nine double one") == "911" + assert std("one triple oh one") == "10001" + + assert std("two thousandth") == "2000th" + assert std("thirty two thousandth") == "32000th" + + assert std("minus 500") == "-500" + assert std("positive twenty thousand") == "+20000" + + assert std("two dollars and seventy cents") == "$2.70" + assert std("3 cents") == "¢3" + assert std("$0.36") == "¢36" + assert std("three euros and sixty five cents") == "€3.65" + + assert std("three and a half million") == "3500000" + assert std("forty eight and a half dollars") == "$48.5" + assert std("b747") == "b 747" + assert std("10 th") == "10th" + assert std("10th") == "10th" + + +def test_spelling_normalizer(): + std = EnglishSpellingNormalizer() + + assert std("mobilisation") == "mobilization" + assert std("cancelation") == "cancellation" + + +def test_text_normalizer(): + std = EnglishTextNormalizer() + assert std("Let's") == "let us" + assert std("he's like") == "he is like" + assert std("she's been like") == "she has been like" + assert std("10km") == "10 km" + assert std("RC232") == "rc 232" + + assert ( + std("Mr. Park visited Assoc. Prof. Kim Jr.") + == "mister park visited associate professor kim junior" + ) diff --git a/tests/test_tokenizer.py b/tests/test_tokenizer.py new file mode 100644 index 000000000..f4cd80b9f --- /dev/null +++ b/tests/test_tokenizer.py @@ -0,0 +1,14 @@ +from whisper.tokenizer import get_tokenizer + + +def test_tokenizer(): + gpt2_tokenizer = get_tokenizer(multilingual=False) + multilingual_tokenizer = get_tokenizer(multilingual=True) + + text = "다람쥐 헌 쳇바퀴에 타고파" + gpt2_tokens = gpt2_tokenizer.encode(text) + multilingual_tokens = multilingual_tokenizer.encode(text) + + assert gpt2_tokenizer.decode(gpt2_tokens) == text + assert multilingual_tokenizer.decode(multilingual_tokens) == text + assert len(gpt2_tokens) > len(multilingual_tokens) diff --git a/tests/test_transcribe.py b/tests/test_transcribe.py new file mode 100644 index 000000000..836cf4000 --- /dev/null +++ b/tests/test_transcribe.py @@ -0,0 +1,20 @@ +import os + +import pytest + +import whisper + + +@pytest.mark.parametrize('model_name', whisper.available_models()) +def test_transcribe(model_name: str): + model = whisper.load_model(model_name).cuda() + audio_path = os.path.join(os.path.dirname(__file__), "jfk.flac") + + language = "en" if model_name.endswith(".en") else None + result = model.transcribe(audio_path, language=language, temperature=0.0) + assert result["language"] == "en" + + transcription = result["text"].lower() + assert "my fellow americans" in transcription + assert "your country" in transcription + assert "do for you" in transcription diff --git a/whisper/__init__.py b/whisper/__init__.py new file mode 100644 index 000000000..e596887b6 --- /dev/null +++ b/whisper/__init__.py @@ -0,0 +1,106 @@ +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 Whisper, ModelDimensions +from .transcribe import transcribe + + +_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": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt", +} + + +def _download(url: str, root: str) -> bytes: + os.makedirs(root, exist_ok=True) + filename = os.path.basename(url) + + expected_sha256 = url.split("/")[-2] + download_target = os.path.join(root, filename) + + 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): + model_bytes = open(download_target, "rb").read() + if hashlib.sha256(model_bytes).hexdigest() == expected_sha256: + return model_bytes + 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 + + +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) -> 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" + + Returns + ------- + model : Whisper + The Whisper ASR model instance + """ + if name in _MODELS: + model_bytes = _download(_MODELS[name], download_root or os.path.expanduser("~/.cache/whisper")) + elif os.path.isfile(name): + model_bytes = open(name, "rb").read() + else: + raise RuntimeError(f"Model {name} not found; available models = {available_models()}") + + with io.BytesIO(model_bytes) as fp: + checkpoint = torch.load(fp, map_location="cpu") + + dims = ModelDimensions(**checkpoint["dims"]) + state_dict = checkpoint["model_state_dict"] + model = Whisper(dims) + model.load_state_dict(state_dict) + + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + + return model.to(device) diff --git a/whisper/__main__.py b/whisper/__main__.py new file mode 100644 index 000000000..bc9b04a39 --- /dev/null +++ b/whisper/__main__.py @@ -0,0 +1,4 @@ +from .transcribe import cli + + +cli() diff --git a/whisper/assets/gpt2/merges.txt b/whisper/assets/gpt2/merges.txt new file mode 100644 index 000000000..6636bda4a --- /dev/null +++ b/whisper/assets/gpt2/merges.txt @@ -0,0 +1,50001 @@ +#version: 0.2 - Trained by `huggingface/tokenizers` +Ġ t +Ġ a +h e +i n +r e +o n +Ġt he +e r +Ġ s +a t +Ġ w +Ġ o +e n +Ġ c +i t +i s +a n +o r +e s +Ġ b +e d +Ġ f +in g +Ġ p +o u +Ġa n +a l +a r +Ġt o +Ġ m +Ġo f +Ġ in +Ġ d +Ġ h +Ġan d +i c +a s +l e +Ġt h +i on +o m +l l +en t +Ġ n +Ġ l +s t +Ġ re +v e +Ġ e +r o +l y +Ġb e +Ġ g +Ġ T +c t +Ġ S +i d +o t +Ġ I +u t +e t +Ġ A +Ġ is +Ġ on +i m +a m +o w +a y +a d +s e +Ġth at +Ġ C +i g +Ġf or +a c +Ġ y +v er +u r +Ġ u +l d +Ġs t +Ġ M +' s +Ġ he +Ġ it +at ion +it h +i r +c e +Ġy ou +i l +Ġ B +Ġw h +o l +Ġ P +Ġw ith +Ġ 1 +t er +c h +Ġa s +Ġw e +Ġ ( +n d +i ll +Ġ D +i f +Ġ 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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"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, 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"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": 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18373, "Ġpopping": 18374, "Ġchim": 18375, "ayı": 18376, "Ġkunnen": 18377, "Ġgallery": 18378, "Ġsunlight": 18379, "esehen": 18380, "Ġyelling": 18381, "ĠMein": 18382, "ĠPhoenix": 18383, "Ġmano": 18384, "Ġhistoria": 18385, "Ġoccurring": 18386, "欸": 18387, "ì¸": 18388, "ади": 18389, "å¾ħ": 18390, "Ġinstitutional": 18391, "ĠTut": 18392, "ç²": 18393, "Ġslaves": 18394, "ãģ©ãģĨ": 18395, "Ġforgiveness": 18396, "Ġtwin": 18397, "ĠHyun": 18398, "нÑĮ": 18399, "ĠKomm": 18400, "andra": 18401, "shot": 18402, "ssä": 18403, "ĠÑĨе": 18404, "atta": 18405, "Ġexpense": 18406, "ĠGPU": 18407, "ĠPast": 18408, "ribly": 18409, "ĠëŃIJìķ¼": 18410, "Ġгода": 18411, "Ġrespir": 18412, "æĿ±": 18413, "ĠQueens": 18414, "hops": 18415, "Ġsérie": 18416, "Ġpref": 18417, "Ġcomed": 18418, "Ġplut": 18419, "ĠOverall": 18420, "ĠãģĿ": 18421, "Ġcush": 18422, "Ġringing": 18423, "Ġincorrect": 18424, "ĠÑģÑĤÑĢ": 18425, "Ġgeometry": 18426, "Ġadvertis": 18427, "ĠШ": 18428, "Ġreviewed": 18429, "ãģĤãģĤ": 18430, "Ġdozens": 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, 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"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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22215, "äsident": 22216, "é¤": 22217, "ĠìĦłë": 22218, "Ġvolumes": 22219, "æIJ": 22220, "Ġoutras": 22221, "ĠTwitch": 22222, "Ġfounding": 22223, "Ġawhile": 22224, "Ġcoil": 22225, "ê°Ļ": 22226, "Ġcả": 22227, "ĠThrow": 22228, "ĠHence": 22229, "ommt": 22230, "ĠBenjamin": 22231, "глÑıд": 22232, "Time": 22233, "obic": 22234, "Ġmour": 22235, "Ġdread": 22236, "ĠLÃł": 22237, "ĠChile": 22238, "Ġpreval": 22239, "Ġvain": 22240, "Ġartık": 22241, "Ġpreserved": 22242, "ĠоÑĤд": 22243, "Ġwarehouse": 22244, "Ġbeste": 22245, "ĠSeveral": 22246, "ĠSituation": 22247, "Ġcardboard": 22248, "Tod": 22249, "erna": 22250, "Ġgarant": 22251, "Ġgesture": 22252, "Ġhen": 22253, "Ġspelling": 22254, "osexual": 22255, "Ġanne": 22256, "Ġmice": 22257, "ĠMeine": 22258, "card": 22259, "Ġrebell": 22260, "Ġcerto": 22261, "Ġìľłë": 22262, "Ġverschied": 22263, "ĠBos": 22264, "Ġinvention": 22265, "Ġtrze": 22266, "Ġmanière": 22267, "ĠChad": 22268, "Ġspre": 22269, "Ġorganisations": 22270, "Ġpoorly": 22271, "Ġanterior": 22272, 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22450, "ĠOUT": 22451, "67": 22452, "Ġhormones": 22453, "ĠAvenue": 22454, "ĠSuperman": 22455, "Ġprescription": 22456, "ubernetes": 22457, "CL": 22458, "otive": 22459, "NIS": 22460, "ienen": 22461, "Ġsadness": 22462, "ĠVit": 22463, "Ty": 22464, "Ġstarter": 22465, "Ġbede": 22466, "Ġfoundations": 22467, "Ġsore": 22468, "åºĹ": 22469, "ÑīеÑģÑĤв": 22470, "ìļ°ë": 22471, "ĠÑĩÑĥв": 22472, "link": 22473, "Ġmaneu": 22474, "working": 22475, "Ãłn": 22476, "ĠAttack": 22477, "ĠCart": 22478, "veis": 22479, "ĠResp": 22480, "ensing": 22481, "Ġì¢ĭìķĦìļĶ": 22482, "Ġescuch": 22483, "ĠRNA": 22484, "Ĥ´": 22485, "Ġadop": 22486, "Ġbending": 22487, "عد": 22488, "Ġmanages": 22489, "usp": 22490, "Ġtart": 22491, "Ġrouter": 22492, "Bo": 22493, "Ġestablishing": 22494, "Ġbalancing": 22495, "Ġathletic": 22496, "ĠSlo": 22497, "Ġfills": 22498, "Ġнаб": 22499, "Ġдал": 22500, "Ġposso": 22501, "ĠVielen": 22502, "Ġcritics": 22503, "Ġlawsuit": 22504, "ĠIsaac": 22505, "ĠÑĦилÑĮм": 22506, "Ġtras": 22507, "Ġpraw": 22508, "ĠCrazy": 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26077, "QU": 26078, "é¡ĺãģĦ": 26079, "ĠIF": 26080, "anın": 26081, "ìĤ´": 26082, "Ġbiblical": 26083, "ĠVed": 26084, "Ġstoring": 26085, "ÑĢавлÑı": 26086, "æĩī該": 26087, "Ġnast": 26088, "Ġdö": 26089, "ÑĢоп": 26090, "elia": 26091, "Ġsideways": 26092, "ĠUnderstand": 26093, "ĠQur": 26094, "Ġperpend": 26095, "ĠMillionen": 26096, "Ġwatermelon": 26097, "ĠDivine": 26098, "ultur": 26099, "abord": 26100, "Ġsuccesses": 26101, "Ġhombre": 26102, "Ġcarp": 26103, "Ġsuscept": 26104, "ungkin": 26105, "Ġkij": 26106, "ulus": 26107, "اج": 26108, "Ġnotch": 26109, "Ġpolynomial": 26110, "å¹²": 26111, "å©": 26112, "Ġúnico": 26113, "Ġtelescope": 26114, "Ġpolitique": 26115, "kiem": 26116, "ĠÎŃνα": 26117, "Ġaggregate": 26118, "ĠGeoff": 26119, "Ġtril": 26120, "ĠGRA": 26121, "Ġsubscriber": 26122, "imet": 26123, "ĠдоллаÑĢ": 26124, "oping": 26125, "Ġtherapeut": 26126, "ĠCancer": 26127, "Ġparade": 26128, "Ġirrig": 26129, "âĻªâĻª": 26130, "Ġclearer": 26131, "Ġbog": 26132, "ĠMaur": 26133, "าà¸ĩ": 26134, "ĠShanghai": 26135, "achte": 26136, "ĠKol": 26137, "elujah": 26138, "Ġhav": 26139, "ĠCrime": 26140, "sek": 26141, "Ġë¡ľ": 26142, "ienna": 26143, "ĠGor": 26144, "èĽ": 26145, "ĠпоÑĤÑĢ": 26146, "ĠкажеÑĤÑģÑı": 26147, "ĠLift": 26148, "ĠSort": 26149, "ĠPsal": 26150, "Ġping": 26151, "ĵĿ": 26152, "phis": 26153, "ĠFUCK": 26154, "ĠSyn": 26155, "Ġbamboo": 26156, "¬ìĺģ": 26157, "cuts": 26158, "Ġmmm": 26159, "Ġfunktioniert": 26160, "Ġ_": 26161, "ÃŃcio": 26162, "Stop": 26163, "Ġimaginary": 26164, "Ġnotamment": 26165, "ĠInitiative": 26166, "ãĥ¥": 26167, "ĠKurt": 26168, "Ġloosen": 26169, "Ġbuscar": 26170, "çģ«": 26171, "Ġzelf": 26172, "Ġprops": 26173, "åĽī": 26174, "Ġmoeten": 26175, "Ġmilli": 26176, "Ġhalls": 26177, "ĠMatch": 26178, "Ġbrackets": 26179, "ĠCou": 26180, "æ¦Ĥ": 26181, "ĠÐľÐ°ÑĢ": 26182, "ISA": 26183, "Ġcigarette": 26184, "Ġcompetitions": 26185, "ĠMIN": 26186, "Ġbehö": 26187, "voor": 26188, "Ġust": 26189, "ĠZi": 26190, "ĠOcc": 26191, "ulates": 26192, "Ġballoons": 26193, "Ġpronto": 26194, "ĠMiy": 26195, "ĠFile": 26196, "ĠклаÑģÑģ": 26197, "нÑĥл": 26198, "Ġcereal": 26199, "Ġincrement": 26200, "Ġrefined": 26201, "åı¦å¤ĸ": 26202, "prising": 26203, "ĠRF": 26204, "Ġrespectful": 26205, "Ġloot": 26206, "asket": 26207, "Ġdeixa": 26208, "ingle": 26209, "Ġfunciona": 26210, "ĠRevel": 26211, "Ġsober": 26212, "Ġperforms": 26213, "ĠGentle": 26214, "ãĤ¨": 26215, "Ġrecipient": 26216, "ĠHause": 26217, "Ġëĥ": 26218, "From": 26219, "Ġministers": 26220, "Ġparadox": 26221, "å°±æĺ¯èªª": 26222, "Ġtasting": 26223, "Ġ×Ķ×Ĺ": 26224, "Ġreuse": 26225, "ĠLane": 26226, "ĠÑģовеÑĢÑĪ": 26227, "Ġremembers": 26228, "Ġfeminist": 26229, "Ġcommitments": 26230, "Ġprojected": 26231, "Ġgaz": 26232, "iyoruz": 26233, "Ġobligations": 26234, "Ro": 26235, "zar": 26236, "Ġchw": 26237, "ĠJAM": 26238, "ĠbÄĻdÄħ": 26239, "aspberry": 26240, "ĠмеÑģÑĤо": 26241, "ë²ķ": 26242, "Ġregulated": 26243, "Ġwicht": 26244, "ĠTrevor": 26245, "Ġsecondly": 26246, "ĠIhre": 26247, "elsh": 26248, "Ġreporters": 26249, "ÑĤоÑĢа": 26250, "oyo": 26251, "GI": 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, "Ġretrou": 26311, "Ġsharks": 26312, "Ġdor": 26313, "ĠRever": 26314, "Ġevapor": 26315, "Ġreservoir": 26316, "Ġalleged": 26317, "uler": 26318, "Ġverm": 26319, "Ġcommerce": 26320, "Ġfitted": 26321, "gem": 26322, "Ġtactical": 26323, "Ġlith": 26324, "éīĦå¡Ķ": 26325, "had": 26326, "è®Ĭ": 26327, "Ġcarbohyd": 26328, "Ġlengths": 26329, "ιο": 26330, "Ġdemographic": 26331, "Rob": 26332, "ĠSkin": 26333, "ccoli": 26334, "Ġsimplified": 26335, "Ġreadily": 26336, "ĠCum": 26337, "adesh": 26338, "ĠDÃ¥": 26339, "usst": 26340, "igne": 26341, "eton": 26342, "Ġmenor": 26343, "qi": 26344, "OOM": 26345, "à¸Ńà¸Ļ": 26346, "Ġpsychiat": 26347, "Ġeighty": 26348, "Ġмилли": 26349, "ĠTob": 26350, "edo": 26351, "網": 26352, "ĠÄijến": 26353, "Ġcircuits": 26354, "ĠLAUGH": 26355, "icism": 26356, "emor": 26357, "Ġregener": 26358, "egree": 26359, "Ġbureauc": 26360, "ĠAlber": 26361, "ä¹ĭå¾Į": 26362, "ĠWor": 26363, "夫": 26364, "Ġresin": 26365, "ĠbyÅĤy": 26366, "ĠIG": 26367, "à¯į,": 26368, "Ġ78": 26369, "Ġweeds": 26370, "ĠMyth": 26371, "93": 26372, "æ¿": 26373, "ĠëĤĺìĻĶ": 26374, "év": 26375, "á½": 26376, "ören": 26377, "çar": 26378, "ĠPAUL": 26379, "Ġdisadvant": 26380, "Ġpositioning": 26381, "Ġcocktail": 26382, "Ġagrees": 26383, "nn": 26384, "ĠSally": 26385, "Ms": 26386, "Ġinherent": 26387, "Ġmonetary": 26388, "Ġnatur": 26389, "ĠNh": 26390, "ĠImport": 26391, "Ġleben": 26392, "Ġwi": 26393, "ussy": 26394, "Ġobes": 26395, "Ġwandering": 26396, "Ġìĭłë": 26397, "Äħda": 26398, "etchup": 26399, "Ġdisposal": 26400, "ĠJA": 26401, "ĠCer": 26402, "zilla": 26403, "Ġvirgin": 26404, "ĠSlide": 26405, "andel": 26406, "Ġrighteousness": 26407, "ĠΣ": 26408, "Ġideia": 26409, "ä½łå¥½": 26410, "иÑĢоваÑĤÑĮ": 26411, "ר×IJ": 26412, "Comment": 26413, "Ġprelim": 26414, "ĠVale": 26415, "Ġì§ĢëĤľ": 26416, "ĠVanc": 26417, "OMAN": 26418, "ĠпÑĸд": 26419, "Ġyum": 26420, "stre": 26421, "cem": 26422, "Ġpocz": 26423, "Ġfragment": 26424, "ĠÑģлÑĥÑĩае": 26425, "Ġundergo": 26426, "ĠHank": 26427, "ceks": 26428, "ĠFPS": 26429, "Ġocur": 26430, "Ġdeterior": 26431, "注": 26432, "Ġempresas": 26433, "Paul": 26434, "Ġ)))": 26435, "ĠвÑĢемени": 26436, "Ġscold": 26437, "×Ļ×¢": 26438, "Ġsuspected": 26439, "Ġaccessing": 26440, "Ġsubstit": 26441, "Ġhistorians": 26442, "ä»»": 26443, "Ġдело": 26444, "Ġsocied": 26445, "rone": 26446, "Ġreden": 26447, "Ġextends": 26448, "epherd": 26449, "Ġbalcon": 26450, "ä¸įèµ·": 26451, "ĠSolo": 26452, "Ġpolitician": 26453, "олÑĮно": 26454, "Ġirgendw": 26455, "Ġtraumatic": 26456, "Ġrapper": 26457, "ĠROBERT": 26458, "Really": 26459, "æģ¯": 26460, "Ġlineup": 26461, "ASE": 26462, "Ġcontractor": 26463, "ĠCorporation": 26464, "gor": 26465, "ĠTodo": 26466, "ÑģÑĤÑĢой": 26467, "FBE": 26468, "Ġnewsletter": 26469, "ĠkoÅĦ": 26470, "alties": 26471, "ĠпÑĢиÑĩ": 26472, "ĠHeavy": 26473, "Ġswords": 26474, "Ġmanipulation": 26475, "Ġfunk": 26476, "ĠvÃ¥r": 26477, "ĠTaliban": 26478, "Ġë°¥": 26479, "Ġacne": 26480, "ürü": 26481, "Ġdeswegen": 26482, "ĠDust": 26483, "Ġsilic": 26484, "Ġhooks": 26485, "Ġblij": 26486, "Ġpetits": 26487, "Ġfilme": 26488, "ĠBereich": 26489, "ĠSaid": 26490, "Ġimposed": 26491, "Ġdiary": 26492, "ĠгоÑĢ": 26493, "ĠGates": 26494, "Ġalta": 26495, "å¸Į": 26496, "Ġchcia": 26497, "pleasant": 26498, "Ġë°Ŀ": 26499, "Ġmożemy": 26500, "ĠAustria": 26501, "Ġbroker": 26502, "Ġsucked": 26503, "èĢĥ": 26504, "Ġcompartment": 26505, "Ġclone": 26506, "Ġ×Ķ×¢": 26507, "ĠDanke": 26508, "Ġnochmal": 26509, "езд": 26510, "Ġadrenal": 26511, "Ġkleinen": 26512, "ãģ¾ãģĹãĤĩãģĨ": 26513, "Ġsubsequently": 26514, "Ġdecentral": 26515, "Ġgenetics": 26516, "Ġê´ij": 26517, "Ġmonitors": 26518, "ĠApplic": 26519, "ĠReporter": 26520, "wert": 26521, "Ġwiem": 26522, "ĠMovement": 26523, "Ġinterviewing": 26524, "Ġhairs": 26525, "Ġpuò": 26526, "ĠChelsea": 26527, "Ġcoher": 26528, "Ġcot": 26529, "Ġzas": 26530, "Ġpatches": 26531, "Ġlah": 26532, "Ñĥнк": 26533, "ĠReagan": 26534, "ĠMarco": 26535, "city": 26536, "Ġdefender": 26537, "Ġdecoration": 26538, "iji": 26539, "Ġlitter": 26540, "Ш": 26541, "Ġjego": 26542, "REW": 26543, "ĠPik": 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27008, "Ġamendments": 27009, "Ġöz": 27010, "Ġdigits": 27011, "urai": 27012, "Ġchaotic": 27013, "ĠMasters": 27014, "eon": 27015, "ĠCash": 27016, "ĠCuz": 27017, "Ġbedeutet": 27018, "Ġscanning": 27019, "Ġжд": 27020, "неÑĤ": 27021, "Ġcertainty": 27022, "jek": 27023, "Ġdijo": 27024, "ĠClimate": 27025, "Ġrinse": 27026, "Ġkrij": 27027, "veland": 27028, "Ġsoundtrack": 27029, "ĠSafe": 27030, "ĠNova": 27031, "94": 27032, "Ġathe": 27033, "ĠVerb": 27034, "oler": 27035, "ìĿ´ì£ł": 27036, "Ġvin": 27037, "Ġrespiratory": 27038, "ĠStudy": 27039, "ĠCAM": 27040, "Ġavocado": 27041, "ĠZhen": 27042, "Ġlatency": 27043, "Ġfeathers": 27044, "Ġcontar": 27045, "ĠвеÑī": 27046, "Ġfark": 27047, "Ġblended": 27048, "Ġexploded": 27049, "ĠXX": 27050, "ĠBenim": 27051, "Ġalguém": 27052, "istoire": 27053, "Ġconfidential": 27054, "Ġmast": 27055, "Ġì¿": 27056, "geh": 27057, "Ġdisrespect": 27058, "ĠSystems": 27059, "Æ°a": 27060, "Ed": 27061, "Ġwys": 27062, "Ġexotic": 27063, "Ġglowing": 27064, "ùng": 27065, "ounge": 27066, 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"Ġhalten": 27184, "ĠAhhh": 27185, "insula": 27186, "erting": 27187, "ĠاÙĦÙģ": 27188, "Ġrelacion": 27189, "Ġkomen": 27190, "Ġdome": 27191, "Ġpriests": 27192, "ĠIntrodu": 27193, "rophe": 27194, "shore": 27195, "velt": 27196, "clipse": 27197, "ĠÑĢÑĥÑģ": 27198, "×Ļס": 27199, "Ġsabemos": 27200, "ĠHolland": 27201, "ogi": 27202, "anki": 27203, "ĠMats": 27204, "Ġsmoked": 27205, "ullie": 27206, "Ġeurope": 27207, "ĠдейÑģÑĤвиÑĤелÑĮно": 27208, "Ġbardziej": 27209, "Ġtransforming": 27210, "ĠEz": 27211, "opath": 27212, "Ġìĸ¸ëĭĪ": 27213, "ĠÑģÑĤан": 27214, "ằng": 27215, "ัà¹ī": 27216, "ĠOuch": 27217, "Ġclearance": 27218, "ustain": 27219, "Ġsolidarity": 27220, "Ġproving": 27221, "ĠÐĺн": 27222, "ĠÑģÑĬ": 27223, "Ġprolong": 27224, "адно": 27225, "Ġsos": 27226, "ĠDeal": 27227, "Ġ170": 27228, "mons": 27229, "Ġзем": 27230, "Ġlogged": 27231, "Ġlifelong": 27232, "Ġsensory": 27233, "Ġbehold": 27234, "ĠFAR": 27235, "ètement": 27236, "ĠFederation": 27237, "Ġdodge": 27238, "ĠShir": 27239, "Ġdragons": 27240, "ĠArctic": 27241, "Äħż": 27242, "Åį": 27243, "º": 27244, "Ġdenke": 27245, "ĠpodrÃŃa": 27246, "cole": 27247, "ÑĥлÑĮÑĤаÑĤ": 27248, "Ġsystematic": 27249, "ама": 27250, "chos": 27251, "Ġclinics": 27252, "ĠBS": 27253, "Ġtales": 27254, "usions": 27255, "ĠíĪ¬": 27256, "Ġpreservation": 27257, "Ġlore": 27258, "ĠProtest": 27259, "Ỽ": 27260, "å¸Ĥ": 27261, "Ġacknowledged": 27262, "ĠIsaiah": 27263, "ĠëķĮëĬĶ": 27264, "Ġ×ĺ": 27265, "Ġcompetitor": 27266, "Ġadvancing": 27267, "zip": 27268, "Ġtenth": 27269, "ĠLaure": 27270, "Ġhints": 27271, "Ġexercising": 27272, "ŀľë": 27273, "ĠIntelligence": 27274, "uated": 27275, "OUT": 27276, "oped": 27277, "Ġautonomy": 27278, "Ġbranding": 27279, "ĠMediterranean": 27280, "Ñĸк": 27281, "Ġscrewdriver": 27282, "Ġsupre": 27283, "Ġstap": 27284, "Ġjurisdiction": 27285, "ĠSettings": 27286, "Ġforefront": 27287, "ĠFemale": 27288, "comfort": 27289, "Ġmultiplication": 27290, "ĠMurray": 27291, "Ġbob": 27292, "ĠTas": 27293, "Ġtahu": 27294, "Ġonun": 27295, "etter": 27296, 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30132, "Ġgrit": 30133, "Ġcited": 30134, "gado": 30135, "Ġmelts": 30136, "Ġforcé": 30137, "âĸĪâĸĪ": 30138, "Ġbajo": 30139, "Ġdiscretion": 30140, "°°": 30141, "ativity": 30142, "Ġsituated": 30143, "ãĥ«ãĤ¯": 30144, "Ñīее": 30145, "åľ°æĸ¹": 30146, "ĠпÑĢинÑĨип": 30147, "amaz": 30148, "Ġaquarium": 30149, "Ġdissolve": 30150, "ĠGods": 30151, "Super": 30152, "Ġamid": 30153, "zk": 30154, "ĠãģĦ": 30155, "éłIJ": 30156, "ampf": 30157, "Ġhela": 30158, "'!": 30159, "Ġdevelopmental": 30160, "ĠDise": 30161, "ĠÑĢабоÑĤаеÑĤ": 30162, "Ġsnapshot": 30163, "好好": 30164, "Õ¸": 30165, "ĠYue": 30166, "ĠHulk": 30167, "ĠDoom": 30168, "ĠFelix": 30169, "Ġréf": 30170, "Male": 30171, "ç·Ĭ": 30172, "phants": 30173, "ENS": 30174, "ĠMechan": 30175, "ĠGolf": 30176, "åĨįè¦ĭ": 30177, "Ġgenerosity": 30178, "ätze": 30179, "Ġunlocked": 30180, "ĠãĤĴ": 30181, "íĥģ": 30182, "ocalypse": 30183, "Alright": 30184, "Ġê°ľë": 30185, "Ġ×IJ×ij׾": 30186, "ĠKeeping": 30187, "Ġcollaborating": 30188, "chief": 30189, "ĠFernando": 30190, "Ġchefs": 30191, "ĠíĶ¼ë¶Ģ": 30192, "Ġskipped": 30193, "Ġpersonn": 30194, "Ġaxe": 30195, "chez": 30196, "Ġextraction": 30197, "ĠAV": 30198, "ĠGibbs": 30199, "Ġíľ": 30200, "Ġsı": 30201, "IAM": 30202, "View": 30203, "ĠGRANT": 30204, "Ġ몸": 30205, "Ġverification": 30206, "Ġdepicted": 30207, "ĠMoz": 30208, "oux": 30209, "Ġtul": 30210, "Ġscanner": 30211, "Ġcomedian": 30212, "ĠVolks": 30213, "ĠJEFF": 30214, "è¨Ĥéĸ±": 30215, "§Ħ": 30216, "Ġdistraction": 30217, "rá": 30218, "ĠINTER": 30219, "Ġsincer": 30220, "Ġ×ŀת": 30221, "Ġש׳": 30222, "Ġconstructive": 30223, "arf": 30224, "ĠëĪĦë": 30225, "Ġeco": 30226, "ramos": 30227, "Ġrenewed": 30228, "inement": 30229, "ĠUb": 30230, "ĠPepper": 30231, "ì§Ģê°Ģ": 30232, "ĠDarwin": 30233, "Ġmerchand": 30234, "Ġvárias": 30235, "èce": 30236, "NG": 30237, "ĠìľĦíķ´ìĦľ": 30238, "ĠакÑĤив": 30239, "ĠUnters": 30240, "عÙĦ": 30241, "Ġintric": 30242, "omma": 30243, "ieving": 30244, "ĠCaroline": 30245, "åĵģ": 30246, "ĠPRES": 30247, "Ġperformer": 30248, "Ġautour": 30249, "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, "ĠSpread": 30308, "ĠHAVE": 30309, "Ġswirl": 30310, "Ġsponsoring": 30311, "ä»ĭ": 30312, "iovascular": 30313, "mesi": 30314, "Ġrelaxation": 30315, "ĠÑģвоиÑħ": 30316, "Ġmargins": 30317, "ĠsaÄŁ": 30318, "ĠPride": 30319, "ĠÏĦοÏħÏĤ": 30320, "иÑĨи": 30321, "enci": 30322, "Does": 30323, "Ġcorpse": 30324, "Ġendurance": 30325, "Ġíŀĺ": 30326, "ì¹´": 30327, "Ġhaircut": 30328, "Ġinterrupted": 30329, "Ġwindy": 30330, "ĠCaleb": 30331, "ÏģÏĩ": 30332, "ĠPourquoi": 30333, "Ġholistic": 30334, "uclear": 30335, "ĠWhole": 30336, "士": 30337, "Act": 30338, "Ġgallon": 30339, "cade": 30340, "ĠRegional": 30341, "roads": 30342, "ĠSchne": 30343, "áng": 30344, "Ġизмен": 30345, "ãĤĪãģŃ": 30346, "Ġmenus": 30347, "Ġsplitting": 30348, "Ġpriced": 30349, "ĠÎĵ": 30350, "Ġusername": 30351, "ĠÐŀÑĩ": 30352, "Ġcompressed": 30353, "yin": 30354, "Ġguardian": 30355, "Ġgoof": 30356, "Ġchecklist": 30357, "Ġinterchange": 30358, "Ġexpedition": 30359, "Ġextern": 30360, "Ġinfrared": 30361, "engo": 30362, "Ġdenying": 30363, "Ġpackets": 30364, "onent": 30365, "BB": 30366, "ĠIncre": 30367, "Ġsini": 30368, "ÃŁer": 30369, "èg": 30370, "maal": 30371, "generation": 30372, "Ġminorities": 30373, "Ġllevar": 30374, "Ġnomination": 30375, "Ġconsid": 30376, "Ġ×ľ×¢": 30377, "muÅŁ": 30378, "ĠEsc": 30379, "Ġnumerator": 30380, "Ġkaik": 30381, "Ġktórych": 30382, "iesen": 30383, "Ġvê": 30384, "ĠUSS": 30385, "ĠPrivate": 30386, "Ġодно": 30387, "Ġalém": 30388, "ÃŃtulo": 30389, "Ġlimb": 30390, "Ġforgiven": 30391, "Ġdisclosure": 30392, "ÏĦί": 30393, "Ġningún": 30394, "Ġtherapeutic": 30395, "Ġnegotiating": 30396, "ĠNike": 30397, "enseful": 30398, "Ġincap": 30399, "Ġflagship": 30400, "town": 30401, "âĪ": 30402, "ĠÏĢολ": 30403, "Ġwolves": 30404, "Ġviolations": 30405, "ĠArnold": 30406, "Ġintervene": 30407, "Ġheater": 30408, "Ġrecursos": 30409, "Ġmaid": 30410, "ê²¼": 30411, "ĠдавайÑĤе": 30412, "ĠCelebr": 30413, "Ġcape": 30414, "ĠSty": 30415, "ainen": 30416, "site": 30417, "bij": 30418, "ĠполÑĮз": 30419, "Ġframed": 30420, "Ġpublishers": 30421, "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": 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"ĠPalestine": 33030, "idir": 33031, "Ġmaze": 33032, "Ġfluids": 33033, "ĠProducer": 33034, "ĠKrsna": 33035, "好åķ¦": 33036, "laf": 33037, "Ġ×IJ×ķ": 33038, "Ġmiesz": 33039, "ĠXing": 33040, "ointed": 33041, "sein": 33042, "ĠFuk": 33043, "ĠDepression": 33044, "ĠDuty": 33045, "ĠPanther": 33046, "Ġsund": 33047, "Ġrefere": 33048, "Ġexclusion": 33049, "Ġnaval": 33050, "ĠWinston": 33051, "Ġslogan": 33052, "Ġhypothetical": 33053, "Ġelevate": 33054, "ëł¹": 33055, "Ġcabeça": 33056, "ĠGesund": 33057, "meter": 33058, "ĠìķĦëĭĪë©´": 33059, "Ġcloudy": 33060, "âĢ¦?": 33061, "ĠSchritt": 33062, "ĠJS": 33063, "ìį": 33064, "ĠSprings": 33065, "ĠBatter": 33066, "·°": 33067, "Ġtailor": 33068, "ĠPTSD": 33069, "ĠGent": 33070, "ĠbaÄŁ": 33071, "Ġspatula": 33072, "Ġcray": 33073, "ĠLegisl": 33074, "Ġsú": 33075, "Ġleve": 33076, "าม": 33077, "Ġerad": 33078, "Ġdong": 33079, "Ġderm": 33080, "ĠBanks": 33081, "icho": 33082, "åħĪçĶŁ": 33083, "ĠFranz": 33084, "ravel": 33085, "éģĶ": 33086, "оло": 33087, "Ġflute": 33088, "ĠEk": 33089, "Ġjoyful": 33090, "Ġchased": 33091, "ĠLarge": 33092, "Over": 33093, "Ġentrepreneurial": 33094, "Ġconsiders": 33095, "Ñĥем": 33096, "opa": 33097, "Ġdormir": 33098, "ĠElementary": 33099, "Ġprzypad": 33100, "ÑĥÑģка": 33101, "ĠоÑĩеÑĢ": 33102, "ugene": 33103, "Ġtenido": 33104, "Ġlugares": 33105, "ë¥": 33106, "ĠÑĩаÑģÑĤ": 33107, "Ġsao": 33108, "Ġbraid": 33109, "ĠVere": 33110, "ĠReich": 33111, "ĠPoss": 33112, "Ġinan": 33113, "wand": 33114, "ref": 33115, "Ġmontrer": 33116, "Ġ1981": 33117, "çķª": 33118, "asında": 33119, "Ġchrome": 33120, "ĠTrinity": 33121, "Ġexploitation": 33122, "ĠSense": 33123, "ĠCMS": 33124, "ĠNoble": 33125, "ĠìĦłíĥĿ": 33126, "Ġswelling": 33127, "electronic": 33128, "]?": 33129, "Ġbrushing": 33130, "Ġliquidity": 33131, "ĠHook": 33132, "ĠConnor": 33133, "ĠAlum": 33134, "Ġgucken": 33135, "suite": 33136, "Ġwiele": 33137, "Ġbarrels": 33138, "ĠRegel": 33139, "ĠMent": 33140, "ĠTrip": 33141, "ĠBrush": 33142, "ĠErik": 33143, "urate": 33144, "ÉĻr": 33145, "ĠCyr": 33146, 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"Ġbonuses": 33205, "Ġami": 33206, "Ġrevival": 33207, "ĠHorse": 33208, "Ġsack": 33209, "Talk": 33210, "Ġmulher": 33211, "ĠпоÑģÑĤоÑıн": 33212, "ĠHood": 33213, "Huh": 33214, "Ġë¶ģ": 33215, "Ġhyung": 33216, "ĠMeeting": 33217, "Ġimporta": 33218, "Ġì°¾ìķĦ": 33219, "ĠVern": 33220, "Ġstripped": 33221, "Ġrefuses": 33222, "Ġqualifications": 33223, "opl": 33224, "ĢëıĦ": 33225, "ixÃŃ": 33226, "Ġdiab": 33227, "itime": 33228, "flows": 33229, "Ġinac": 33230, "ĠGong": 33231, "Ġmeaningless": 33232, "Ġcourageous": 33233, "Ġmicrobi": 33234, "azy": 33235, "hist": 33236, "Ġvolunteering": 33237, "VIE": 33238, "Ġviolated": 33239, "Ġsympathy": 33240, "ĠEdit": 33241, "好åĥı": 33242, "electric": 33243, "product": 33244, "Ġpandemia": 33245, "Ġgeometric": 33246, "ĠConvers": 33247, "gre": 33248, "Ġglut": 33249, "isted": 33250, "ĠاÙĦÙĥ": 33251, "ĠChain": 33252, "ĠPresent": 33253, "ĠYin": 33254, "ĠÑģог": 33255, "ĠVlog": 33256, "Ġìĸ´ë¨¸": 33257, "Ġdonn": 33258, "Ġhitch": 33259, "ucking": 33260, "ãģĬãģĦ": 33261, 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33321, "алÑĮное": 33322, "ividual": 33323, "adım": 33324, "Ġatrav": 33325, "ĠFlug": 33326, "ĠKlein": 33327, "ê±°ìķ¼": 33328, "หà¸Ļ": 33329, "Ġappli": 33330, "ா?": 33331, "üyorum": 33332, "ĠинÑĤеÑĢеÑģно": 33333, "Ġdisinfect": 33334, ">-": 33335, "Ġchampagne": 33336, "Ġkla": 33337, "opers": 33338, "Trans": 33339, "ĠDesert": 33340, "Ġcultivate": 33341, "ĠFucking": 33342, "idelity": 33343, "ĠÑĤан": 33344, "Ġincub": 33345, "Ġtemu": 33346, "Ġlearner": 33347, "founder": 33348, "ĠSyl": 33349, "ãĤĢ": 33350, "Ġfato": 33351, "zier": 33352, "ĠìĹĨìĿ´": 33353, "ĠìĪ¨": 33354, "Ġpsycho": 33355, "ĠÑĤелеÑĦ": 33356, "Ġregarde": 33357, "Ġrepresentations": 33358, "Ġlitigation": 33359, "Ġspann": 33360, "ults": 33361, "bior": 33362, "è¦ĭãģ¦": 33363, "ä¸įå¤ļ": 33364, "ĠSurvey": 33365, "ĠLEDs": 33366, "Ġträ": 33367, "Ġlên": 33368, "Ġantioxid": 33369, "еÑĢом": 33370, "Ġinduction": 33371, "Ġfooled": 33372, "ätzlich": 33373, "ĠговоÑĢÑıÑĤ": 33374, "ĠFact": 33375, "umbai": 33376, "Ġwiggle": 33377, "NOUN": 33378, "Ġdévelopp": 33379, "ĠClaro": 33380, "Ġì¸": 33381, "ë¬": 33382, "ãģªãĤĵãģł": 33383, "Ġaccumulate": 33384, "Ġmaintains": 33385, "ëĦ": 33386, "ĠFighter": 33387, "íĨł": 33388, "Ġmatin": 33389, "Ġcoupon": 33390, "Ġstunt": 33391, "Ġdebuted": 33392, "å¾ħãģ£ãģ¦": 33393, "Ġprag": 33394, "иваем": 33395, "73": 33396, "Ġexpres": 33397, "Ġìĺ¤ë¹ł": 33398, "ĠпеÑĢÑģон": 33399, "Ġcalculus": 33400, "Ġabrupt": 33401, "ĠInspector": 33402, "ourt": 33403, "æĸĻ": 33404, "źniej": 33405, "intense": 33406, "Ba": 33407, "Ġlounge": 33408, "Ġasthma": 33409, "ĠHiç": 33410, "ª»": 33411, "Ġeditorial": 33412, "Ġseize": 33413, "Ġkır": 33414, "Ġmouve": 33415, "Ġtierra": 33416, "Ġtestosterone": 33417, "Ġrh": 33418, "ĠKingston": 33419, "ELLE": 33420, "ĠRepresentative": 33421, "Ġ1974": 33422, "Ġiba": 33423, "Ts": 33424, "Ġsorta": 33425, "Ġ(?)": 33426, "ĠتÙĪ": 33427, "ĠëĤ´ëł¤": 33428, "Ġbekommt": 33429, "Ġspiritually": 33430, "Ġdistorted": 33431, "Mad": 33432, "Ġreim": 33433, "ánh": 33434, "ĠOttoman": 33435, "ĠRelig": 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": 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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": 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"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": 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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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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, 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"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, 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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, 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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, "Ġrzecz": 36833, "Ġdisplaying": 36834, "æĪ²": 36835, "Ġsarc": 36836, "rão": 36837, "Ġtampoco": 36838, "Ġphilosophers": 36839, "ĠRecht": 36840, "æĵļ": 36841, "Ġcomentarios": 36842, "yse": 36843, "Ġìľ¤": 36844, "Ġmise": 36845, "ĠGin": 36846, "Ġном": 36847, "ĠFROM": 36848, "liner": 36849, "atif": 36850, "ĠspoÅĤec": 36851, "xa": 36852, "ĠÑĤÑĢÑĥд": 36853, "Ġwag": 36854, "기ìĹIJ": 36855, "ĠMG": 36856, "Ġoffspring": 36857, "ĠUnderstanding": 36858, "åıªæĺ¯": 36859, "ORA": 36860, "Ġwhirring": 36861, "Ġsurrend": 36862, "Ġpoker": 36863, "Ġmonuments": 36864, "ĠâĻ©": 36865, "Ġorganised": 36866, "ĠSozial": 36867, "ĠFactory": 36868, "Ñħа": 36869, "Ġresemble": 36870, "зд": 36871, "Ġexplosions": 36872, "Ġpayroll": 36873, "Ġomn": 36874, "ĠJorge": 36875, "ιÏĥ": 36876, "Ġfracture": 36877, "Ġpersecution": 36878, "Ġdemais": 36879, "ECH": 36880, ",)": 36881, "Ġcriar": 36882, "ĠJOSH": 36883, "Ġdemographics": 36884, "Ġ1600": 36885, "Ġcurrencies": 36886, "ĠTips": 36887, "ĠéĢĻåĢĭ": 36888, "ĠRefer": 36889, "ĠDancing": 36890, "Ġinconsistent": 36891, "Ġdeh": 36892, "Ġimmens": 36893, "Ġmeist": 36894, "Ġimpatient": 36895, "Ġbehaves": 36896, "æĿ¾": 36897, "ĠëĤ´ìļ©": 36898, "Ġbackstory": 36899, "Ġagreeing": 36900, "ĠÅģ": 36901, "ihin": 36902, "Ġtemperatura": 36903, "ĠBackground": 36904, "Ġnutzen": 36905, "Ġëħ¹": 36906, "ĠMänner": 36907, "Ġcollaborations": 36908, "ĠKos": 36909, "éģİåİ»": 36910, "Ġnightmares": 36911, "ëĵ±": 36912, "ĠQueensland": 36913, "Ġassociates": 36914, "ĠKok": 36915, "Ġfactorial": 36916, "ĠHyung": 36917, "Ġê·¸ëĭ¤ìĿĮ": 36918, "Ġfilho": 36919, "Ġelét": 36920, "Ġíĸīë³µ": 36921, "°±": 36922, "Ġgefunden": 36923, "Ġsemicondu": 36924, "Ġcounselors": 36925, "ĠUpper": 36926, "ĠAub": 36927, "ickers": 36928, "Ver": 36929, "Ġnorthwest": 36930, "ĠMaintenant": 36931, "ĠLakes": 36932, "аÑıв": 36933, "inté": 36934, "ì°½": 36935, "Ġгаз": 36936, "Ġgiorn": 36937, "Ġdigitally": 36938, "ĠCircuit": 36939, "ì¼Ģ": 36940, "ãĤĬãģ¾ãģĹãģŁ": 36941, "Ġcheerful": 36942, "ĠPeterson": 36943, "ĠDanish": 36944, "ativos": 36945, "Ġliken": 36946, "Ġharbor": 36947, "алиÑģÑĤ": 36948, "xe": 36949, "Ġcurls": 36950, "ĠRhod": 36951, "End": 36952, "ĠET": 36953, "Ġacquaint": 36954, "ĠKelvin": 36955, "Ġtrif": 36956, "ĠAway": 36957, "ìŀIJëĬĶ": 36958, "vs": 36959, "Ġpágina": 36960, "Ġinlet": 36961, "ĠSantos": 36962, "Ġìļ°ìĻĢ": 36963, "Ġyapıyorsun": 36964, "theme": 36965, "Ġsouff": 36966, "Ġinjected": 36967, "Ġpóźniej": 36968, "iverso": 36969, "amped": 36970, "Ġdaher": 36971, "Ġdagger": 36972, "ĠлÑİбим": 36973, "Ġtummy": 36974, "Ġenlightened": 36975, "cents": 36976, "ĠDah": 36977, "Ġcuest": 36978, "ä¾Ĩ說": 36979, "ILY": 36980, "Ġ×ijר": 36981, "Ġbanging": 36982, "ĠEmil": 36983, "ĠCler": 36984, "ĠBorder": 36985, "ижÑĥ": 36986, "Ġpresenters": 36987, "ĠSTUD": 36988, "coins": 36989, "ĠíĻį": 36990, "Ġperks": 36991, "Ġparap": 36992, "Ġcertaines": 36993, "ĠLore": 36994, "öst": 36995, "ĠMARTIN": 36996, "Ġbios": 36997, "Ġwhereby": 36998, "verts": 36999, "ĠMiranda": 37000, "Ġstip": 37001, "澤": 37002, "andez": 37003, "׼׾": 37004, "ujin": 37005, "Ġê¾": 37006, "Ġallergies": 37007, "plate": 37008, "Ġyapıl": 37009, "Ġundertake": 37010, "ĠëĤĺê°Ģ": 37011, "Part": 37012, "Ġkızım": 37013, "hguru": 37014, "ãģĤãģ¨": 37015, "ĠJohns": 37016, "Ġeyelashes": 37017, "Ġdrained": 37018, "ĠstÃ¥r": 37019, "ãģĤãĤĬãģ¾ãģĻ": 37020, "ĠJade": 37021, "Ġcalend": 37022, "film": 37023, "Ġmesa": 37024, "Ġludzie": 37025, "Ġattracts": 37026, "Ġjuices": 37027, "Ġкил": 37028, "Ġnieuwe": 37029, "Ġmencion": 37030, "Ġignition": 37031, "Ġbladder": 37032, "andaag": 37033, "ĠExtension": 37034, "íĤ¨": 37035, "feed": 37036, "ĠÙĪÙĩ": 37037, "Ġspun": 37038, "Ġtät": 37039, "оÑĢоÑĤ": 37040, "tyard": 37041, "ronics": 37042, "ĠHuge": 37043, "Ñĥжд": 37044, "string": 37045, "Ġunjust": 37046, "Ġprawn": 37047, "Ġfrosting": 37048, "Ġdisappearance": 37049, "iosa": 37050, "Ġcardi": 37051, "ĠPriest": 37052, "ĠcientÃŃfic": 37053, "åĵªè£¡": 37054, "ĠÐĴаÑģ": 37055, "Ġë¶Ģíĥģ": 37056, "Ġthieves": 37057, "Ġphysique": 37058, "ĠEugene": 37059, "Ġблиз": 37060, "Ġmonopoly": 37061, "Ġbiography": 37062, "ĠhoÅŁ": 37063, "Ġtö": 37064, "mac": 37065, "Ġshocks": 37066, "ìĦ¸ë": 37067, "hit": 37068, "Ġsnug": 37069, "Ġincl": 37070, "Ġdedic": 37071, "Ġultras": 37072, "ĠизвеÑģÑĤ": 37073, "Ġutilization": 37074, "ĠÑģовеÑĢÑĪенно": 37075, "Ġservi": 37076, "stag": 37077, "180": 37078, "Ġsewer": 37079, "ĠChoice": 37080, "Ġdischarged": 37081, "ĠJD": 37082, "олеÑĤ": 37083, "ĠкваÑĢÑĤи": 37084, "Ġtelescop": 37085, "ĠJeÅĽli": 37086, "ĠNana": 37087, "cale": 37088, "ĠÑĤон": 37089, "mmm": 37090, "äºĨåIJ§": 37091, "Ġgehabt": 37092, "ëĤł": 37093, "æĬķ": 37094, "à¸Ļà¸Ļ": 37095, "Ġether": 37096, "Ġzen": 37097, "Ġresearched": 37098, "ĠCzyli": 37099, "å®Įåħ¨": 37100, "workers": 37101, "Ġ경찰": 37102, "Ġsheriff": 37103, "allo": 37104, "Ġtipos": 37105, "Ġprosecution": 37106, "Ġfrogs": 37107, "Ġfalt": 37108, "jd": 37109, "ĠíĮĶ": 37110, "Ġfiltered": 37111, "ĠOft": 37112, "Ġìį": 37113, "Ġdisfr": 37114, "ĠMustang": 37115, "Ġwoah": 37116, "ĠREALLY": 37117, "Ġмогли": 37118, "Ġentrada": 37119, "ĠигÑĢа": 37120, "Ġmixes": 37121, "ĠавÑĤомоб": 37122, "ÐĻ": 37123, "Ġshin": 37124, "Ġparanormal": 37125, "Ġsomeplace": 37126, "Ġdishon": 37127, "etaan": 37128, "Ġfuerte": 37129, "Ù¹": 37130, "Ġdoom": 37131, "ìĪľ": 37132, "Ġexistential": 37133, "Ġbuld": 37134, "ĠSDK": 37135, "ĠпÑĢавда": 37136, "Ġturnover": 37137, "ĠìĹ¬ê¸°ìĹIJ": 37138, "Ġह": 37139, "Ġmodeled": 37140, "Ġbugün": 37141, "Ġexperimentation": 37142, "Ġmornings": 37143, "Ġmedo": 37144, "Stevie": 37145, "Ġplayable": 37146, "Ġairlines": 37147, "gments": 37148, "Ġ기ë¶Ħ": 37149, "ĠTomb": 37150, "ĠMVP": 37151, "AUDIENCE": 37152, "Ġcheckout": 37153, "Ġpasst": 37154, "Ġbeispiel": 37155, "ĠLinks": 37156, "heavy": 37157, "Ġquestionable": 37158, "Ġìĵ°ë": 37159, "Ġsill": 37160, "Ġmanipulated": 37161, "ĠLoren": 37162, "Ġìľ¼": 37163, "Ġverge": 37164, "ák": 37165, "IES": 37166, "Ġsabot": 37167, "ĠCustomer": 37168, "ależy": 37169, "Ġnominee": 37170, "ĠGad": 37171, "Ġnouvelles": 37172, "ĠSPE": 37173, "istling": 37174, "Ġoval": 37175, "обÑĢаж": 37176, "ifty": 37177, "éĩİ": 37178, "Ġbezel": 37179, "yet": 37180, "Ġfreight": 37181, "ĠHanım": 37182, "rÃŃa": 37183, "Ġzoning": 37184, "Ġindem": 37185, "ĠBü": 37186, "Ġfeminism": 37187, "Ġvoix": 37188, "Ġoficial": 37189, "Ġdiyorum": 37190, "»IJ": 37191, "Ġarose": 37192, "Ġparar": 37193, "ìĿ¸ì§Ģ": 37194, "ĠMartine": 37195, "ĠLect": 37196, "Ġrester": 37197, "Ġdrowning": 37198, "uya": 37199, "cida": 37200, "ĠAriel": 37201, "Ġ02": 37202, "Ġ×Ķ×Ķ": 37203, "ç´ł": 37204, "ĠWert": 37205, "ТÑĭ": 37206, "Ġwidow": 37207, "Ġparchment": 37208, "Ġcottage": 37209, "ĠXL": 37210, "ĠSlack": 37211, "ĠNES": 37212, "Ġrobe": 37213, "Ġgimm": 37214, "Ġcaminho": 37215, "ĠHarper": 37216, "Ġcitrus": 37217, "Ġfirefighters": 37218, "Ġdopamine": 37219, "elets": 37220, "Ġdemocrat": 37221, "ìłľë¡ľ": 37222, "Ġplayback": 37223, "oj": 37224, "ĠпÑĢок": 37225, "ĠSullivan": 37226, "semble": 37227, "ĠWorth": 37228, "ĠMustafa": 37229, "าร": 37230, "Ġmets": 37231, "éĸĢ": 37232, "лоÑģÑĮ": 37233, "Ġinertia": 37234, "Ġuniforms": 37235, "足": 37236, "ério": 37237, "×ķר×Ķ": 37238, "ént": 37239, "Ġà®Ĵ": 37240, "ĠÑģамÑĭÑħ": 37241, "Ġvoulais": 37242, "ĠZimmer": 37243, "ê²łë": 37244, "ĠноÑģ": 37245, "encias": 37246, "Ġrelación": 37247, "Ġ걸ë": 37248, "Ġfaction": 37249, "Ġgosp": 37250, "полож": 37251, "nap": 37252, "hak": 37253, "Ġproceedings": 37254, "ĠìĨĶ": 37255, "ìķĦëĭĪ": 37256, "ĠìŀIJ기": 37257, "Ġwerd": 37258, "Ġsof": 37259, "Ġschlim": 37260, "Ġflavored": 37261, "Ġquadratic": 37262, "ĠBoot": 37263, "Ġpublicity": 37264, "ĠCaro": 37265, "Ġ?\"": 37266, "ниÑĨа": 37267, "mania": 37268, "ĠSUR": 37269, "ĠBUR": 37270, "lance": 37271, "ética": 37272, "Ġzobaczy": 37273, "Ġtrio": 37274, "sama": 37275, "ĠtaÅŁ": 37276, "Ġasymm": 37277, "resser": 37278, "Ġتع": 37279, "ĠпеÑģ": 37280, "Ġbeginnings": 37281, "ladım": 37282, "ĠбÑĭÑģÑĤÑĢ": 37283, "Ġmoo": 37284, "ĠGeneva": 37285, "Ġåľ¨": 37286, "erus": 37287, "borah": 37288, "Ġrefusing": 37289, "bull": 37290, "ĠWaiting": 37291, "ĠIndividual": 37292, "Ġanonym": 37293, "imens": 37294, "Ġmedidas": 37295, "Ġfragrant": 37296, "Ġdirectement": 37297, "ĠìķĦë§Ī": 37298, "uria": 37299, "Ġspherical": 37300, "Ġabge": 37301, "ĠVictorian": 37302, "Ġspectacle": 37303, "ĠRodriguez": 37304, "Ġocup": 37305, "ĠNär": 37306, "marks": 37307, "ngulo": 37308, "ĠLuci": 37309, "Ġshouted": 37310, "Ġregulators": 37311, "ÄŁini": 37312, "Ġdisent": 37313, "ĠÑĢÑĭн": 37314, "ëĤ¨": 37315, "ĠìĤ´ë": 37316, "Ġproblèmes": 37317, "ĠFinger": 37318, "assemble": 37319, "Ġpear": 37320, "Ġdroite": 37321, "ĠEverywhere": 37322, "tam": 37323, "оÑĤив": 37324, "вой": 37325, "ordinate": 37326, "ĠLak": 37327, "ĠmỼi": 37328, "ĠTelevision": 37329, "Ġexponentially": 37330, "avas": 37331, "Ġblev": 37332, "ĠMT": 37333, "俺": 37334, "Connell": 37335, "ĠêµŃ민": 37336, "ĠÑģвоим": 37337, "Ġacha": 37338, "ĠDynasty": 37339, "Jin": 37340, "Ġtore": 37341, "Ġflor": 37342, "Ġмногие": 37343, "æ²Ĵäºĭ": 37344, "owan": 37345, "bah": 37346, "Ġì£Ħ": 37347, "ĠCela": 37348, "Ġìµľê·¼": 37349, "Ġpermettre": 37350, "Ġabras": 37351, "Ġverstehen": 37352, "Ġescort": 37353, "ĠThem": 37354, "ärke": 37355, "porter": 37356, "Ġkahkaha": 37357, "Ġhect": 37358, "Ġdau": 37359, "wah": 37360, "olve": 37361, "ĠAges": 37362, "schaft": 37363, "ĠStell": 37364, "nelle": 37365, "ĠEnsuite": 37366, "ĠÐĴÑģем": 37367, "Ġcréd": 37368, "ĠPP": 37369, "lords": 37370, "grunting": 37371, "Ġcontraction": 37372, "Got": 37373, "Ġacquiring": 37374, "Ġsopr": 37375, "Ġpoisonous": 37376, "RNA": 37377, "Ġanar": 37378, "ĠHof": 37379, "')": 37380, "Ġremarkably": 37381, "Ġinternacional": 37382, "ücke": 37383, "inqu": 37384, "Ġduy": 37385, "Ġbeasts": 37386, "ĠLAN": 37387, "Ġprecedent": 37388, "ĠRPM": 37389, "åij¨": 37390, "Ġselon": 37391, "Ġmorte": 37392, "Ġcomeçou": 37393, "Ñıла": 37394, "Ġinterpreting": 37395, "ĠBurke": 37396, "ÑĤÑĢа": 37397, "ĠìĿ´ëŁ¬": 37398, "Ġpessim": 37399, "ĠNok": 37400, "íĮĿ": 37401, "Female": 37402, "Ġìĭ¤í": 37403, "ĻĢ": 37404, "Ġstimulation": 37405, "Ġslick": 37406, "Ġê°ĢëĬĶ": 37407, "Ġказ": 37408, "ĠHBO": 37409, "Ġpapier": 37410, "Ġkönnten": 37411, "Ñĥбли": 37412, "ĠConstant": 37413, "SPEAKING": 37414, "ĠktórÄħ": 37415, "Ġcosmetics": 37416, "ĠTrend": 37417, "Ġrobbery": 37418, "Ġtitt": 37419, "Ġgjort": 37420, "Ġdietary": 37421, "łĮ": 37422, "ĠKirby": 37423, "ĠпÑĢимеÑĢно": 37424, "Ġqualification": 37425, "Ġìķī": 37426, "Ġcabinets": 37427, "Ġhttp": 37428, "ĠErica": 37429, "義": 37430, "Ġdisadvantages": 37431, "Ġchattering": 37432, "yz": 37433, "feit": 37434, "Ġguild": 37435, "ĠETF": 37436, "ĠDragons": 37437, "ĠHERE": 37438, "venth": 37439, "ÙĦاÙħ": 37440, "Ġmarché": 37441, "Dam": 37442, "Ġphoton": 37443, "Ġestable": 37444, "Mag": 37445, "Ġolhar": 37446, "Ġcoupling": 37447, "ĠHilfe": 37448, "ĠWizard": 37449, "Ġмало": 37450, "help": 37451, "ĠlÃŃnea": 37452, "Ġì«": 37453, "Ġstandalone": 37454, "Ġmorale": 37455, "Ġzweite": 37456, "ãĤĪãĤįãģĹãģı": 37457, "ährt": 37458, "Ġdotted": 37459, "Ġdripping": 37460, "ĠFlag": 37461, "éĿĴ": 37462, "rocket": 37463, "rategy": 37464, "irim": 37465, "Ġíķĺë©´ìĦľ": 37466, "Ġsogenan": 37467, "ĠUno": 37468, "ĠSchutz": 37469, "Ġestilo": 37470, "ĠSubs": 37471, "ĠDaisy": 37472, "ÐĿеÑĤ": 37473, "'...": 37474, "Ġplatinum": 37475, "Ġbirl": 37476, "ĠSovi": 37477, "Ġviolate": 37478, "ÑĥеÑĤÑģÑı": 37479, "rill": 37480, "Ġtraz": 37481, "Ġsnip": 37482, "Ġcumpl": 37483, "à¸Ńà¸ģ": 37484, "Ġcuk": 37485, "éħĴ": 37486, "ĠParlament": 37487, "Ġhypert": 37488, "Ġpulp": 37489, "Ġtongues": 37490, "atto": 37491, "Ġbusca": 37492, "ihn": 37493, "ERO": 37494, "ĠÙĬع": 37495, "Ġvarias": 37496, "ĠMarian": 37497, "Ġbounded": 37498, "Ġpitching": 37499, "Ġdeficiency": 37500, "ĠBlessed": 37501, "ĠExerc": 37502, "uchs": 37503, "ĠnhÆ°ng": 37504, "æľ¬å½ĵ": 37505, "Ġraped": 37506, "hales": 37507, "Ġmala": 37508, "pic": 37509, "Ġ401": 37510, "ÅĽniej": 37511, "arina": 37512, "ëĵ¤ìĿĦ": 37513, "otti": 37514, "Ġдолго": 37515, "Ġtracker": 37516, "ĠShelby": 37517, "Ġvanished": 37518, "Ġbakery": 37519, "Kapı": 37520, "Jesus": 37521, "ĠKR": 37522, "JO": 37523, "ħ¸": 37524, 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"groans": 37580, "ĠпÑĥÑĤ": 37581, "Ġgenetically": 37582, "éĢł": 37583, "Ġhappiest": 37584, "Ġwerk": 37585, "atoon": 37586, "Ġmusi": 37587, "Ġfunção": 37588, "ĠìŀħëĭĪëĭ¤": 37589, "ĠÑĢай": 37590, "Ġbevor": 37591, "BLANK": 37592, "Ġrepentance": 37593, "Put": 37594, "Ġpotrzeb": 37595, "Ġsala": 37596, "Ġcampa": 37597, "WER": 37598, "ĠdecÃŃa": 37599, "Ġsécurité": 37600, "ĠAppreciate": 37601, "Ñĩи": 37602, "ĠRandom": 37603, "ë³Ħ": 37604, "kah": 37605, "Ġmöj": 37606, "Ġsäger": 37607, "Ġ×Ļ׼×ķ׾": 37608, "Ġ190": 37609, "xtures": 37610, "Eu": 37611, "Ġgä": 37612, "Ġ×ijת": 37613, "ĠCroat": 37614, "apo": 37615, "PLE": 37616, "Ġpersistence": 37617, "åĬ©": 37618, "Ġblends": 37619, "Ġtreffen": 37620, "ĠSantiago": 37621, "ydia": 37622, "aldo": 37623, "ĠTensorFlow": 37624, "ĠDual": 37625, "ãĥľ": 37626, "Ġchiff": 37627, "ìĹ´": 37628, "Ġcontracted": 37629, "Ġsegreg": 37630, "ĠFairy": 37631, "Ġwisely": 37632, "Ġvulnerabilities": 37633, "Ġhandheld": 37634, "Ġgadgets": 37635, "ĠboÅŁ": 37636, "ĠPopular": 37637, "Ġcurvature": 37638, "문": 37639, "ĠMARY": 37640, "ìĿ´ìĬ": 37641, "Ġformulation": 37642, "Ġcelery": 37643, "Ġblurry": 37644, "ĠTS": 37645, "alez": 37646, "Ġws": 37647, "Ġprogramm": 37648, "ĠStack": 37649, "ĠJIM": 37650, "овали": 37651, "ıll": 37652, "Ġpère": 37653, "ĠKanye": 37654, "ĠDelaware": 37655, "Ġãģł": 37656, "Ġdaunting": 37657, "ĠбеÑģ": 37658, "ĠStupid": 37659, "big": 37660, "fficial": 37661, "Ġprecipitation": 37662, "Ġplung": 37663, "ục": 37664, "burse": 37665, "Ġdarle": 37666, "Ġcripp": 37667, "Ġpioneer": 37668, "Ġdisput": 37669, "Ġsean": 37670, "ãģĵãĤĵãģª": 37671, "Ġresistor": 37672, "Ġallein": 37673, "ipples": 37674, "arel": 37675, "Ġendors": 37676, "zust": 37677, "ĠÑĢебÑıÑĤа": 37678, "eded": 37679, "Ġì¹´ë©Ķë": 37680, "Ġlleva": 37681, "Ġkennt": 37682, "Ġбал": 37683, "ĠDocument": 37684, "ĠKnights": 37685, "Ġbuckle": 37686, "Ġìī¬": 37687, "Ġalk": 37688, "ĠEveryday": 37689, "atters": 37690, "Ġtoilets": 37691, "Ġjugar": 37692, "ĠìŀĪì§Ģ": 37693, "Ġgenauso": 37694, "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, 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39186, "Ġcouncils": 39187, "ĠGriffin": 39188, "ĠAhmed": 39189, "Ġclich": 39190, "ĠOL": 39191, "wl": 39192, "ĠPilot": 39193, "å®®": 39194, "Ġacronym": 39195, "Ġgels": 39196, "Ġelectroly": 39197, "èĵ": 39198, "Ġмной": 39199, "Ġepisod": 39200, "ĠDieses": 39201, "ĠATP": 39202, "Ġediyorum": 39203, "Ġexpresses": 39204, "Ġexhibits": 39205, "Comm": 39206, "ĠкÑĢÑĥп": 39207, "Ġmatar": 39208, "Ġ2025": 39209, "ĠArtem": 39210, "vasive": 39211, "rÃł": 39212, "ĠbeÅŁ": 39213, "é»ĥ": 39214, "Ġlizard": 39215, "Ġfille": 39216, "Ġì§Ī문": 39217, "ĠмоÑī": 39218, "Ġtür": 39219, "Ġculprit": 39220, "Ġwoven": 39221, "ĠANY": 39222, "nim": 39223, "Ġtay": 39224, "Ġpromin": 39225, "Ġacompa": 39226, "Ġidé": 39227, "Ġboiler": 39228, "ĠThemen": 39229, "Ġavenue": 39230, "ĠMud": 39231, "ĠновÑĭе": 39232, "Ġwitnessing": 39233, "Ġlance": 39234, "ĠCHAN": 39235, "ĠBever": 39236, "تÙħ": 39237, "Ġchemotherapy": 39238, "King": 39239, "ĠbÄĻdÄĻ": 39240, "Ġatual": 39241, "Ġtive": 39242, "Ġtalkin": 39243, "Ġquedar": 39244, "ieÃŁ": 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39361, "iemand": 39362, "ĠпоÑĤеÑĢ": 39363, "ê³³": 39364, "Ġê±±": 39365, "Ġformatting": 39366, "ĠLed": 39367, "è§Ģçľ¾": 39368, "Ġkillers": 39369, "ĠÄijấy": 39370, "Ġhaar": 39371, "again": 39372, "!>[": 45687, "minster": 45688, "Ġвли": 45689, "Ġidentifier": 45690, "ĠLambda": 45691, "Ġtros": 45692, "Ġflawless": 45693, "Ġdetrimental": 45694, "Ġbunları": 45695, "War": 45696, "Ġregião": 45697, "羣çļĦæĺ¯": 45698, "ĠBike": 45699, "cessors": 45700, "Ġcùng": 45701, "ĠRN": 45702, "Ġê½ĥ": 45703, "Ġküçük": 45704, "ĠBeginning": 45705, "íĺ¸ë": 45706, "Ġgewe": 45707, "Ġdenote": 45708, "ĠAlberto": 45709, "Ġprobiot": 45710, "Ġode": 45711, "Ġmolar": 45712, "Ġbursting": 45713, "assumed": 45714, "Ġfootprints": 45715, "veda": 45716, "Ġsteroids": 45717, "Ġflaming": 45718, "ĠEller": 45719, "Ġerkennen": 45720, "ätzen": 45721, "Ġlifecycle": 45722, "ĠDOU": 45723, "ĠKarena": 45724, "ĠGuerra": 45725, "è¿ĺæĺ¯": 45726, "Ġsinister": 45727, "Ġpodéis": 45728, "Ġparab": 45729, "Ġoko": 45730, "Ġmatéri": 45731, "Ġcaric": 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45847, "Ġì£¼ê³ł": 45848, "ĠgelmiÅŁ": 45849, "Ġbedtime": 45850, "ĠArgh": 45851, "ADA": 45852, "ĠгоÑĢода": 45853, "ĠÄĩ": 45854, "Ġalliances": 45855, "giggling": 45856, "Ġyerde": 45857, "Ġspies": 45858, "Ġgutes": 45859, "çi": 45860, "Ġalltid": 45861, "ĠLah": 45862, "ŀIJë": 45863, "ĠdokÅĤad": 45864, "ÙĪÙĬ": 45865, "Ġtoxicity": 45866, "Ġcancellation": 45867, "Ġ1958": 45868, "dro": 45869, "ĠìŀijìĿĢ": 45870, "ĠMotorola": 45871, "Ġmultin": 45872, "Ġenthusiasts": 45873, "ĠMighty": 45874, "ĠCoconut": 45875, ":ãĢĮ": 45876, "ĠPictures": 45877, "Ġsangre": 45878, "Ġblinking": 45879, "olesome": 45880, "ĠìĬ¤íĥĢìĿ¼": 45881, "FP": 45882, "Ġbooming": 45883, "ĠдеÑģÑıÑĤ": 45884, "Ġratchet": 45885, "Ġtimelines": 45886, "leness": 45887, "Ġcages": 45888, "ĠGoodnight": 45889, "ometimes": 45890, "Ġcunning": 45891, "ĠRisk": 45892, "uled": 45893, "dade": 45894, "Ġprata": 45895, "ĠgustarÃŃa": 45896, "amus": 45897, "ĠJinping": 45898, "Ġestrut": 45899, "Ġdescobrir": 45900, "ĠMÄģ": 45901, "ĠAllan": 45902, "ĠåĪĨ": 45903, "Ġ׾ק": 45904, "Ġpreserv": 45905, "ĠStrawberry": 45906, "Äı": 45907, "Lu": 45908, "Ġkro": 45909, "ĠReports": 45910, "ìħĶìķ¼": 45911, "Ġvalt": 45912, "Ġpouvait": 45913, "Ġappar": 45914, "ĠBone": 45915, "Ġpreferably": 45916, "ĠRepública": 45917, "å°±åĪ°": 45918, "Ġherzlich": 45919, "Ġchimney": 45920, "Ġçev": 45921, "Ġvisas": 45922, "Ġverr": 45923, "Ġcultivation": 45924, "ĠArmenia": 45925, "ĠвдÑĢÑĥг": 45926, "Ġcockro": 45927, "retched": 45928, "artz": 45929, "ĠлÑİдÑıм": 45930, "ĠpolÃŃticas": 45931, "ĠPanz": 45932, "ĠAKA": 45933, "ĠëĪĮ룬": 45934, "Ġerro": 45935, "Ġcamper": 45936, "Ġ102": 45937, "स": 45938, "done": 45939, "Ġhoard": 45940, "ĠÐŁÐ¾ÑĤом": 45941, "jeong": 45942, "Ġdesta": 45943, "pak": 45944, "Ġinim": 45945, "Ġgrowers": 45946, "ĠMessage": 45947, "Ġelector": 45948, "engage": 45949, "ĠForbes": 45950, "ĠCincinnati": 45951, "Ġdifférence": 45952, "df": 45953, "Ġspar": 45954, "Ġawaits": 45955, "ĠUSSR": 45956, "ĠRising": 45957, "ĠHoÅŁ": 45958, "Ġfooting": 45959, "Ġcondiciones": 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"ĠCred": 47560, "Ġ127": 47561, "Ġnothin": 47562, "Ġmandated": 47563, "å¯Į": 47564, "ÑĥÑĤÑģÑĤв": 47565, "Frank": 47566, "Ġwors": 47567, "ĠdzieÅĦ": 47568, "ĠUnderground": 47569, "Ġznajdu": 47570, "ĠBä": 47571, "ĠPrinzip": 47572, "аÑĤелей": 47573, "Ġveterinar": 47574, "Ġsplendid": 47575, "Ġrozp": 47576, "Ġpsychopath": 47577, "igon": 47578, "Ġhops": 47579, "Ġcần": 47580, "ĠXian": 47581, "Ġtroisième": 47582, "Ġproducto": 47583, "ĠdeÄŁer": 47584, "ĠContinuing": 47585, "ивал": 47586, "cık": 47587, "Ġmoisturizer": 47588, "White": 47589, "Ġsiis": 47590, "ĠEverest": 47591, "ienced": 47592, "Ġcảm": 47593, "ĠJapon": 47594, "´ìłĦ": 47595, "ĠtenÃŃan": 47596, "Ġencanta": 47597, "Mm": 47598, "Ġdropdown": 47599, "ĠIya": 47600, "³´ë©´": 47601, "Ġwording": 47602, "ĠSqueeze": 47603, "ĠMaple": 47604, "Ġclarified": 47605, "ĠMunicip": 47606, "ĠRouge": 47607, "ĠNicki": 47608, "ĠGoo": 47609, "volt": 47610, "tek": 47611, "fecture": 47612, "fred": 47613, "arrive": 47614, "ãĥ¼ãģĦ": 47615, "tez": 47616, "Ep": 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47672, "ĠÄ°n": 47673, "breat": 47674, "ĠEquity": 47675, "ardı": 47676, "Ġканале": 47677, "Ġдней": 47678, "ĠtỼi": 47679, "Ġfixture": 47680, "Ġabuses": 47681, "Ġvaya": 47682, "Ġouvert": 47683, "Ġmulticultural": 47684, "Ġcontexto": 47685, "ĠSesame": 47686, "Ġdépl": 47687, "Ġconsomm": 47688, "ĠParte": 47689, "Ġpem": 47690, "ĠConan": 47691, "ĠбÑĸлÑĮ": 47692, "Ġpersuaded": 47693, "Ġdrains": 47694, "Moo": 47695, "FORE": 47696, "ĠбаÑĤ": 47697, "Ġfod": 47698, "ĠProducts": 47699, "ì§Ħì§ľ": 47700, "Ġ\"[": 47701, "ĠWick": 47702, "ĠNaruto": 47703, "нали": 47704, "ryw": 47705, "Ġlodge": 47706, "Ġinh": 47707, "Ġvontade": 47708, "Ġdij": 47709, "ĠJesús": 47710, "Looking": 47711, "Ġforearm": 47712, "ĠIntegration": 47713, "ĠHARRIS": 47714, "Ġtoolbar": 47715, "leader": 47716, "Ġseldom": 47717, "ĠбÑĢоÑģ": 47718, "ĠKook": 47719, "онд": 47720, "Ġmonopol": 47721, "Ġmillet": 47722, "Ġlira": 47723, "ĠAsians": 47724, "Ġ1890": 47725, "ciÄŁim": 47726, "Ġeden": 47727, "ĠIKEA": 47728, "ĠNeighbor": 47729, "ĠKazuya": 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´": 47787, "Ġremix": 47788, "Ġblossoms": 47789, "ĠRenee": 47790, "isations": 47791, "ìĬ¤íĦ°": 47792, "Ġë³´ìĿ´ëĬĶ": 47793, "uestas": 47794, "opedia": 47795, "ĠAim": 47796, "ìĿ´ì¦Ī": 47797, "scene": 47798, "Ġleakage": 47799, "uckt": 47800, "Sad": 47801, "Ask": 47802, "Ġsuspense": 47803, "Ġimpost": 47804, "ĠStrategic": 47805, "ĠItÃŃs": 47806, "âĢĮ": 47807, "Ġkeyboards": 47808, "Ġamusing": 47809, "ogr": 47810, "iderman": 47811, "ŀĸ": 47812, "ĠвижÑĥ": 47813, "Ġdips": 47814, "Ġapologized": 47815, "ĠSTAR": 47816, "Ġescuela": 47817, "ĠChing": 47818, "нениÑı": 47819, "Ġë¶Ģë¶ĦìĿ´": 47820, "ĠFleet": 47821, "Ġsamb": 47822, "Ġentsprechend": 47823, "Ġelectrodes": 47824, "ĠFreiheit": 47825, "æĪijä¸įçŁ¥éģĵ": 47826, "ĠShrim": 47827, "iÃŁe": 47828, "Ġselections": 47829, "Ġfordi": 47830, "Ġdoss": 47831, "ÑıÑĩ": 47832, "Ġdiscriminate": 47833, "ĠAuÃŁerdem": 47834, "Ġdesenvolv": 47835, "ĠInternal": 47836, "ĠBenedict": 47837, "å¯Ĩ": 47838, "ĠShiv": 47839, "Missy": 47840, "ĠобнаÑĢÑĥж": 47841, "ĠнаÑģÑĤÑĢо": 47842, "Ġcontrolar": 47843, "ĠLia": 47844, "Ġopioids": 47845, "antu": 47846, "Ġcupboard": 47847, "æģIJ": 47848, "ге": 47849, "achts": 47850, "Ġcurated": 47851, "Ġxem": 47852, "Ġweary": 47853, "Ġbrethren": 47854, "Ġbudgeting": 47855, "Ġpourtant": 47856, "éļ»": 47857, "aisia": 47858, "ĠоÑĤвеÑĩ": 47859, "ĠGIS": 47860, "μαι": 47861, "Ġש×Ķ×ķ×IJ": 47862, "Ġsaud": 47863, "ĠlỼ": 47864, "ÐķТ": 47865, "ubine": 47866, "ĠнÑĥжен": 47867, "Ġkidnapping": 47868, "Ġbrat": 47869, "ĠTerre": 47870, "ĠMonet": 47871, "Ġë§ĪìĬ¤íģ": 47872, "Ġflashy": 47873, "ĠISBN": 47874, "Ġfreelance": 47875, "iage": 47876, "Ġjunge": 47877, "충": 47878, "ceral": 47879, "ĠÑĤоÑĩки": 47880, "Ġformulate": 47881, "ĠFER": 47882, "ĠDartmouth": 47883, "ìľ¼ë©´ìĦľ": 47884, "å¢ĥ": 47885, "owiÄħ": 47886, "ĠëĶĶìŀIJ": 47887, "Ġregiment": 47888, "Ġmetabolismo": 47889, "ĠParr": 47890, "Ġ충ë¶Ħ": 47891, "Ġsanity": 47892, "ĠLal": 47893, "ĠGö": 47894, "ĠGla": 47895, "Ġproto": 47896, "Ġmicroscopic": 47897, "Ġkang": 47898, "ĠScalia": 47899, "Ġpug": 47900, "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, "Ġresonated": 47957, "Ġìĭłê¸°": 47958, "them": 47959, "Robert": 47960, "Ġelemento": 47961, "Ġantid": 47962, "ĠобÑģ": 47963, "Ġnatives": 47964, "Ġloca": 47965, "owment": 47966, "ĠTight": 47967, "ĠæĢĿ": 47968, "Ġmelan": 47969, "ĠNue": 47970, "amis": 47971, "Ġsorgen": 47972, "asına": 47973, "Home": 47974, "ĠPUBG": 47975, "Ġawfully": 47976, "ĠShore": 47977, "ĠPerché": 47978, "ĠLau": 47979, "ĠCinderella": 47980, "ĠChest": 47981, "Ġsemantic": 47982, "Ġdeserted": 47983, "ĠMomo": 47984, "ĠHernandez": 47985, "genes": 47986, "ĠAdult": 47987, "иÑĩеÑģкого": 47988, "oshima": 47989, "ĠcaracterÃŃsticas": 47990, "ĠKL": 47991, "´ìŀ¥": 47992, "ocar": 47993, "Ġfehlt": 47994, "Ġdruk": 47995, "ĠPoppy": 47996, "ENGLISH": 47997, "ĠVergleich": 47998, "Brien": 47999, "Ġrecomp": 48000, "ĠÑģд": 48001, "Ġmerger": 48002, "Ġmarketers": 48003, "Ġhoneymoon": 48004, "Ġpenso": 48005, "Ġbelli": 48006, "еÑĤÑĥ": 48007, "Ġbanker": 48008, "Camera": 48009, "ĠStall": 48010, "ĠStamp": 48011, "ĠBite": 48012, "ежде": 48013, "Ġsür": 48014, "Ġgüç": 48015, "ĠPassover": 48016, "ĠBugün": 48017, "ĠÑģожалениÑİ": 48018, "Ġниз": 48019, "Ġmanure": 48020, "Ġglacier": 48021, "è«ĩ": 48022, "RAY": 48023, "terror": 48024, "Ġsalads": 48025, "Ġhurricanes": 48026, "ĠDesigner": 48027, "atorio": 48028, "Ġfactual": 48029, "ĠTammy": 48030, "ĠзвÑĥÑĩ": 48031, "Ġintroductions": 48032, "Ġhousekeeping": 48033, "Ġhanger": 48034, "ëĭĺë": 48035, "akte": 48036, "ĠCola": 48037, "']": 48038, "ĠGender": 48039, "оÑĢон": 48040, "ipse": 48041, "icias": 48042, "Ġsuccessive": 48043, "Ġpolitic": 48044, "Ġhöher": 48045, "ĠQiao": 48046, "ĠGimme": 48047, "Ġлож": 48048, "Ġseb": 48049, "ĠWeiter": 48050, "ĠSakura": 48051, "ĠBoulder": 48052, "ĠAmérica": 48053, "peÅĤnie": 48054, "ĠtecnologÃŃa": 48055, "ishops": 48056, "fur": 48057, "Ġmoonlight": 48058, "Ġdispersed": 48059, "Ġrez": 48060, "енное": 48061, "алÑĮнÑĥÑİ": 48062, "ĠTwelve": 48063, "ĠHOR": 48064, "ìĭ¤íŀĪ": 48065, "ilage": 48066, "Ġshaded": 48067, "Ġresumes": 48068, "ĠPeanut": 48069, "ĠMILL": 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, "Ġاب": 48127, "Ġcarts": 48128, "ANO": 48129, "Ġë°Ľê³ł": 48130, "ĠCena": 48131, "ĠBiology": 48132, "idar": 48133, "Ġaż": 48134, "erne": 48135, "anu": 48136, "Ġthanked": 48137, "Ġsubmarines": 48138, "Ġmanic": 48139, "Ġмоз": 48140, "ä¼Ĭ": 48141, "instant": 48142, "essential": 48143, "Ġsamurai": 48144, "Ġpasti": 48145, "Ġalan": 48146, "Ġbroch": 48147, "Ġbaker": 48148, "ĠGuill": 48149, "¨¼": 48150, "Ġwithdrawn": 48151, "ëĭĿ": 48152, "Perfect": 48153, "quency": 48154, "Ġstreamlined": 48155, "Ġ1300": 48156, "´ëıĦ": 48157, "Ġëĸłë": 48158, "Ġãģ¯ãģĦ": 48159, "Ġhvad": 48160, "ä¸Ģå®ļè¦ģ": 48161, "Ġverbally": 48162, "ĠKons": 48163, "Ġì¡°ìĭ¬": 48164, "Ġdiez": 48165, "æİ°æİ°": 48166, "Ġchuckling": 48167, "ĠMih": 48168, "Ġrallies": 48169, "Ġmanter": 48170, "Ġearnest": 48171, "super": 48172, "Ġgece": 48173, "ĠRend": 48174, "ĠGerade": 48175, "jenigen": 48176, "ĠVall": 48177, "ĠìŀĪëĤĺ": 48178, "ĠÑģказала": 48179, "Ġtrabalh": 48180, "ĠнаÑĪем": 48181, "ĠмеÑħ": 48182, "ikit": 48183, "Ġnouns": 48184, "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)) + + 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): + """ + 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 + + 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) + + 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/whisper/decoding.py b/whisper/decoding.py new file mode 100644 index 000000000..fa8cf2dcb --- /dev/null +++ b/whisper/decoding.py @@ -0,0 +1,705 @@ +from dataclasses import dataclass, field +from typing import Dict, List, Tuple, Iterable, Optional, Sequence, Union, TYPE_CHECKING + +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(f"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: + task: str = "transcribe" # whether to perform X->X "transcribe" or X->English "translate" + language: Optional[str] = None # language that the audio is in; uses detected language if 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 samples to collect, when t > 0 + beam_size: Optional[int] = None # number of beams in beam search, when t == 0 + patience: float = 0.0 # patience in beam search (https://arxiv.org/abs/2204.05424) + + # options for ranking generations (either beams or best-of-N samples) + length_penalty: Optional[float] = None # "alpha" in Google NMT, None defaults to length norm + + # prompt, prefix, and token suppression + prompt: Optional[Union[str, List[int]]] = None # text or tokens for the previous context + prefix: Optional[Union[str, List[int]]] = None # text or tokens to prefix the current context + suppress_blank: bool = True # this will suppress blank outputs + + # 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" + + # timestamp sampling options + without_timestamps: bool = False # use <|notimestamps|> to sample text tokens only + max_initial_timestamp: Optional[float] = 0.0 # the initial timestamp cannot be later than this + + # 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_caption_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]: + temperature = self.temperature + if temperature == 0: + next_tokens = logits.argmax(dim=-1) + else: + next_tokens = Categorical(logits=logits / 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: float = 0.0): + self.beam_size = beam_size + self.eot = eot + self.inference = inference + self.patience = patience + self.max_candidates: int = round(beam_size * (1.0 + patience)) + self.finished_sequences = None + + 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]): + seq = [t for t in tokens[k, self.sample_begin :].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 + + # apply the `max_initial_timestamp` option + if tokens.shape[1] == self.sample_begin and 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 != 0.0 and options.beam_size is None: + raise ValueError("nonzero 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) + prefix = self.options.prefix + prompt = self.options.prompt + + if 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: + 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(self.options.suppress_tokens, list), "suppress_tokens must be a list" + suppress_tokens = self.options.suppress_tokens + + suppress_tokens.extend( + [self.tokenizer.sot, self.tokenizer.sot_prev, self.tokenizer.sot_lm] + ) + if self.tokenizer.no_captions is not None: + # no-captions probability is collected separately + suppress_tokens.append(self.tokenizer.no_captions) + + 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_caption_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_captions is not None: # save no_caption_probs + probs_at_sot = logits[:, self.sot_index].float().softmax(dim=-1) + no_caption_probs = probs_at_sot[:, self.tokenizer.no_captions].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_caption_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]).expand(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_caption_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_caption_probs = no_caption_probs[:: self.n_group] + assert audio_features.shape[0] == len(no_caption_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_caption_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_caption_prob=no_caption_prob, + temperature=self.options.temperature, + compression_ratio=compression_ratio(text), + ) + for text, language, tokens, features, avg_logprob, no_caption_prob in zip(*fields) + ] + + +@torch.no_grad() +def decode(model: "Whisper", mel: Tensor, options: DecodingOptions = DecodingOptions()) -> 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) + """ + single = mel.ndim == 2 + if single: + mel = mel.unsqueeze(0) + + result = DecodingTask(model, options).run(mel) + + if single: + result = result[0] + + return result diff --git a/whisper/model.py b/whisper/model.py new file mode 100644 index 000000000..2221570de --- /dev/null +++ b/whisper/model.py @@ -0,0 +1,267 @@ +from dataclasses import dataclass +from typing import Dict +from typing import Iterable, Optional + +import numpy as np +import torch +import torch.nn.functional as F +from torch import Tensor +from torch import nn + +from .transcribe import transcribe as transcribe_function +from .decoding import detect_language as detect_language_function, decode as decode_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: + # 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.get(self.key, self.key(xa)) + v = kv_cache.get(self.value, self.value(xa)) + + wv = self.qkv_attention(q, k, v, mask) + return self.out(wv) + + 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] + + w = F.softmax(qk.float(), dim=-1).to(q.dtype) + return (w @ v).permute(0, 2, 1, 3).flatten(start_dim=2) + + +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) + if self.cross_attn: + x = x + self.cross_attn(self.cross_attn_ln(x), xa, kv_cache=kv_cache) + 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 @ self.token_embedding.weight.to(x.dtype).T).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, + ) + + def embed_audio(self, mel: torch.Tensor): + return self.encoder.forward(mel) + + def logits(self, tokens: torch.Tensor, audio_features: torch.Tensor): + return self.decoder.forward(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.decoder.positional_embedding.shape[0]: + cache[module] = output # save as-is, for the first token or cross attention + 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/whisper/normalizers/__init__.py b/whisper/normalizers/__init__.py new file mode 100644 index 000000000..0b10d5a9d --- /dev/null +++ b/whisper/normalizers/__init__.py @@ -0,0 +1,2 @@ +from .basic import BasicTextNormalizer +from .english import EnglishTextNormalizer diff --git a/whisper/normalizers/basic.py b/whisper/normalizers/basic.py new file mode 100644 index 000000000..ef8d249bf --- /dev/null +++ b/whisper/normalizers/basic.py @@ -0,0 +1,71 @@ +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/whisper/normalizers/english.json b/whisper/normalizers/english.json new file mode 100644 index 000000000..bd84ae73f --- /dev/null +++ b/whisper/normalizers/english.json @@ -0,0 +1,1742 @@ +{ + "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", + "mm": "hmm", + "mmm": "hmm" +} \ No newline at end of file diff --git a/whisper/normalizers/english.py b/whisper/normalizers/english.py new file mode 100644 index 000000000..d5c2bb4eb --- /dev/null +++ b/whisper/normalizers/english.py @@ -0,0 +1,543 @@ +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) # standardize 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 some symbols for numerics + + 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 whitespace characters with a space + + return s diff --git a/whisper/tokenizer.py b/whisper/tokenizer.py new file mode 100644 index 000000000..c115b15ea --- /dev/null +++ b/whisper/tokenizer.py @@ -0,0 +1,331 @@ +import os +from dataclasses import dataclass +from functools import lru_cache +from typing import List, Optional, Tuple, Union + +import numpy as np +import torch +from transformers import GPT2TokenizerFast + +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", + "iw": "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(frozen=True) +class Tokenizer: + """A thin wrapper around `GPT2TokenizerFast` providing quick access to special tokens""" + + tokenizer: "GPT2TokenizerFast" + language: Optional[str] + sot_sequence: Tuple[int] + + def encode(self, text, **kwargs): + return self.tokenizer.encode(text, **kwargs) + + def decode(self, token_ids: Union[int, List[int], np.ndarray, torch.Tensor], **kwargs): + return self.tokenizer.decode(token_ids, **kwargs) + + def decode_with_timestamps(self, tokens) -> str: + """ + Timestamp tokens are above the special tokens' id range and are ignored by `decode()`. + This method decodes given tokens with timestamps tokens annotated, e.g. "<|1.08|>". + """ + outputs = [[]] + for token in tokens: + if token >= self.timestamp_begin: + timestamp = f"<|{(token - self.timestamp_begin) * 0.02:.2f}|>" + outputs.append(timestamp) + outputs.append([]) + else: + outputs[-1].append(token) + outputs = [s if isinstance(s, str) else self.tokenizer.decode(s) for s in outputs] + return "".join(outputs) + + @property + @lru_cache() + def eot(self) -> int: + return self.tokenizer.eos_token_id + + @property + @lru_cache() + def sot(self) -> int: + return self._get_single_token_id("<|startoftranscript|>") + + @property + @lru_cache() + def sot_lm(self) -> int: + return self._get_single_token_id("<|startoflm|>") + + @property + @lru_cache() + def sot_prev(self) -> int: + return self._get_single_token_id("<|startofprev|>") + + @property + @lru_cache() + def no_captions(self) -> int: + return self._get_single_token_id("<|nocaptions|>") + + @property + @lru_cache() + def no_timestamps(self) -> int: + return self._get_single_token_id("<|notimestamps|>") + + @property + @lru_cache() + def timestamp_begin(self) -> int: + return self.tokenizer.all_special_ids[-1] + 1 + + @property + @lru_cache() + def language_token(self) -> int: + """Returns the token id corresponding to the value of the `language` field""" + if self.language is None: + raise ValueError(f"This tokenizer does not have language token configured") + + additional_tokens = dict( + zip( + self.tokenizer.additional_special_tokens, + self.tokenizer.additional_special_tokens_ids, + ) + ) + candidate = f"<|{self.language}|>" + if candidate in additional_tokens: + return additional_tokens[candidate] + + raise KeyError(f"Language {self.language} not found in tokenizer.") + + @property + @lru_cache() + def all_language_tokens(self) -> Tuple[int]: + result = [] + for token, token_id in zip( + self.tokenizer.additional_special_tokens, + self.tokenizer.additional_special_tokens_ids, + ): + if token.strip("<|>") in LANGUAGES: + result.append(token_id) + return tuple(result) + + @property + @lru_cache() + def all_language_codes(self) -> Tuple[str]: + return tuple(self.decode([l]).strip("<|>") for l in self.all_language_tokens) + + @property + @lru_cache() + def sot_sequence_including_notimestamps(self) -> Tuple[int]: + return tuple(list(self.sot_sequence) + [self.no_timestamps]) + + @property + @lru_cache() + 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. + """ + + result = set() + 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) + + for symbol in symbols + list(miscellaneous): + for tokens in [self.tokenizer.encode(symbol), self.tokenizer.encode(" " + symbol)]: + if len(tokens) == 1 or symbol in miscellaneous: + result.add(tokens[0]) + + return tuple(sorted(result)) + + def _get_single_token_id(self, text) -> int: + tokens = self.tokenizer.encode(text) + assert len(tokens) == 1, f"{text} is not encoded as a single token" + return tokens[0] + + +@lru_cache(maxsize=None) +def build_tokenizer(name: str = "gpt2"): + os.environ["TOKENIZERS_PARALLELISM"] = "false" + path = os.path.join(os.path.dirname(__file__), "assets", name) + tokenizer = GPT2TokenizerFast.from_pretrained(path) + + specials = [ + "<|startoftranscript|>", + *[f"<|{lang}|>" for lang in LANGUAGES.keys()], + "<|translate|>", + "<|transcribe|>", + "<|startoflm|>", + "<|startofprev|>", + "<|nocaptions|>", + "<|notimestamps|>", + ] + + tokenizer.add_special_tokens(dict(additional_special_tokens=specials)) + return tokenizer + + +@lru_cache(maxsize=None) +def get_tokenizer( + multilingual: bool, + *, + task: Optional[str] = None, # Literal["transcribe", "translate", None] + language: Optional[str] = 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: + tokenizer_name = "multilingual" + task = task or "transcribe" + language = language or "en" + else: + tokenizer_name = "gpt2" + task = None + language = None + + tokenizer = build_tokenizer(name=tokenizer_name) + all_special_ids: List[int] = tokenizer.all_special_ids + sot: int = all_special_ids[1] + translate: int = all_special_ids[-6] + transcribe: int = all_special_ids[-5] + + langs = tuple(LANGUAGES.keys()) + sot_sequence = [sot] + if language is not None: + sot_sequence.append(sot + 1 + langs.index(language)) + if task is not None: + sot_sequence.append(transcribe if task == "transcribe" else translate) + + return Tokenizer(tokenizer=tokenizer, language=language, sot_sequence=tuple(sot_sequence)) diff --git a/whisper/transcribe.py b/whisper/transcribe.py new file mode 100644 index 000000000..d995248ee --- /dev/null +++ b/whisper/transcribe.py @@ -0,0 +1,299 @@ +import argparse +import os +import warnings +from typing import List, Optional, Tuple, Union, TYPE_CHECKING + +import numpy as np +import torch + +from .audio import SAMPLE_RATE, N_FRAMES, HOP_LENGTH, pad_or_trim, log_mel_spectrogram +from .decoding import DecodingOptions, DecodingResult +from .tokenizer import LANGUAGES, TO_LANGUAGE_CODE, get_tokenizer +from .utils import exact_div, format_timestamp, optional_int, optional_float, str2bool, write_vtt + +if TYPE_CHECKING: + from .model import Whisper + + +def transcribe( + model: "Whisper", + audio: Union[str, np.ndarray, torch.Tensor], + *, + verbose: bool = False, + 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_captions_threshold: Optional[float] = 0.6, + **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 + + temperature: Union[float, Tuple[float, ...]] + Temperature for sampling. It can be a tuple of temperatures, which will be successfully 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_captions_threshold: float + If the no_captions probability is higher than this value AND the average log probability + over sampled tokens is below `logprob_threshold`, consider the segment as silent + + 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 + + mel = log_mel_spectrogram(audio) + + if decode_options.get("language", None) is None: + if verbose: + print("Detecting language using up to the first 30 seconds. Use `--language` to specify the language") + segment = pad_or_trim(mel, N_FRAMES).to(model.device).to(dtype) + _, probs = model.detect_language(segment) + decode_options["language"] = max(probs, key=probs.get) + print(f"Detected language: {LANGUAGES[decode_options['language']]}") + + mel = mel.unsqueeze(0) + language = decode_options["language"] + task = decode_options.get("task", "transcribe") + tokenizer = get_tokenizer(model.is_multilingual, language=language, task=task) + + def decode_with_fallback(segment: torch.Tensor) -> List[DecodingResult]: + temperatures = [temperature] if isinstance(temperature, (int, float)) else temperature + kwargs = {**decode_options} + t = temperatures[0] + if t == 0: + best_of = kwargs.pop("best_of", None) + else: + best_of = kwargs.get("best_of", None) + + options = DecodingOptions(**kwargs, temperature=t) + results = model.decode(segment, options) + + kwargs.pop("beam_size", None) # no beam search for t > 0 + kwargs.pop("patience", None) # no patience for t > 0 + kwargs["best_of"] = best_of # enable best_of for t > 0 + for t in temperatures[1:]: + needs_fallback = [ + compression_ratio_threshold is not None + and result.compression_ratio > compression_ratio_threshold + or logprob_threshold is not None + and result.avg_logprob < logprob_threshold + for result in results + ] + if any(needs_fallback): + options = DecodingOptions(**kwargs, temperature=t) + retries = model.decode(segment[needs_fallback], options) + for retry_index, original_index in enumerate(np.nonzero(needs_fallback)[0]): + results[original_index] = retries[retry_index] + + return results + + 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 + + def add_segment( + *, start: float, end: float, text_tokens: torch.Tensor, result: DecodingResult + ): + text = tokenizer.decode([token for token in text_tokens if token < tokenizer.eot]) + if len(text.strip()) == 0: # skip empty text output + return + + all_segments.append( + { + "id": len(all_segments), + "seek": seek, + "start": start, + "end": end, + "text": text, + "tokens": result.tokens, + "temperature": result.temperature, + "avg_logprob": result.avg_logprob, + "compression_ratio": result.compression_ratio, + "no_caption_prob": result.no_caption_prob, + } + ) + if verbose: + print(f"[{format_timestamp(start)} --> {format_timestamp(end)}] {text}") + + while seek < mel.shape[-1]: + timestamp_offset = float(seek * HOP_LENGTH / SAMPLE_RATE) + segment = pad_or_trim(mel[:, :, seek:], N_FRAMES).to(model.device).to(dtype) + segment_duration = segment.shape[-1] * HOP_LENGTH / SAMPLE_RATE + + decode_options["prompt"] = all_tokens[prompt_reset_since:] + result = decode_with_fallback(segment)[0] + tokens = torch.tensor(result.tokens) + + if no_captions_threshold is not None: + # no voice activity check + should_skip = result.no_caption_prob > no_captions_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_captions_prob + should_skip = False + + if should_skip: + seek += segment.shape[-1] # fast-forward to the next segment boundary + continue + + timestamp_tokens: torch.Tensor = tokens.ge(tokenizer.timestamp_begin) + consecutive = torch.where(timestamp_tokens[:-1] & timestamp_tokens[1:])[0].add_(1) + if len(consecutive) > 0: # if the output contains two consecutive timestamp tokens + last_slice = 0 + for current_slice in consecutive: + sliced_tokens = tokens[last_slice:current_slice] + start_timestamp_position = ( + sliced_tokens[0].item() - tokenizer.timestamp_begin + ) + end_timestamp_position = ( + sliced_tokens[-1].item() - tokenizer.timestamp_begin + ) + add_segment( + start=timestamp_offset + start_timestamp_position * time_precision, + end=timestamp_offset + end_timestamp_position * time_precision, + text_tokens=sliced_tokens[1:-1], + result=result, + ) + last_slice = current_slice + last_timestamp_position = ( + tokens[last_slice - 1].item() - tokenizer.timestamp_begin + ) + seek += last_timestamp_position * input_stride + all_tokens.extend(tokens[: last_slice + 1].tolist()) + else: + duration = segment_duration + timestamps = tokens[timestamp_tokens.nonzero().flatten()] + if len(timestamps) > 0: + # no consecutive timestamps but it has a timestamp; use the last one. + # single timestamp at the end means no speech after the last timestamp. + last_timestamp_position = timestamps[-1].item() - tokenizer.timestamp_begin + duration = last_timestamp_position * time_precision + + add_segment( + start=timestamp_offset, + end=timestamp_offset + duration, + text_tokens=tokens, + result=result, + ) + + seek += segment.shape[-1] + all_tokens.extend(tokens.tolist()) + + if result.temperature > 0.5: + # do not feed the prompt tokens if a high temperature was used + prompt_reset_since = len(all_tokens) + + return dict(text=tokenizer.decode(all_tokens), segments=all_segments, language=language) + + +def cli(): + from . import available_models + + 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="base", choices=available_models(), help="name of the Whisper model to use") + 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("--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(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=0.0, help="optional patience value to use in beam decoding, as in https://arxiv.org/abs/2204.05424, the default (0.0) is equivalent to not using patience") + 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 lengt 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("--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_caption_threshold", type=optional_float, default=0.6, help="if the probability of the <|nocaptions|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence") + + args = parser.parse_args().__dict__ + model_name: str = args.pop("model") + output_dir: str = args.pop("output_dir") + device: str = args.pop("device") + os.makedirs(output_dir, exist_ok=True) + + if model_name.endswith(".en") and args["language"] != "en": + warnings.warn(f"{model_name} is an English-only model but receipted '{args['language']}'; using English instead.") + args["language"] = "en" + + temperature_increment_on_fallback = args.pop("temperature_increment_on_fallback") + compression_ratio_threshold = args.pop("compression_ratio_threshold") + logprob_threshold = args.pop("logprob_threshold") + no_caption_threshold = args.pop("no_caption_threshold") + + temperature = args.pop("temperature") + if temperature_increment_on_fallback is not None: + temperature = tuple(np.arange(temperature, 1.0 + 1e-6, temperature_increment_on_fallback)) + else: + temperature = [temperature] + + from . import load_model + model = load_model(model_name).to(device) + + for audio_path in args.pop("audio"): + result = transcribe( + model, + audio_path, + temperature=temperature, + compression_ratio_threshold=compression_ratio_threshold, + logprob_threshold=logprob_threshold, + no_captions_threshold=no_caption_threshold, + **args, + ) + + # save TXT + with open(os.path.join(output_dir, audio_path + ".txt"), "w") as txt: + print(result["text"], file=txt) + + # save VTT + with open(os.path.join(output_dir, audio_path + ".vtt"), "w") as vtt: + write_vtt(result["segments"], file=vtt) + + +if __name__ == '__main__': + cli() diff --git a/whisper/utils.py b/whisper/utils.py new file mode 100644 index 000000000..af58834ee --- /dev/null +++ b/whisper/utils.py @@ -0,0 +1,54 @@ +import zlib +from typing import Iterator, TextIO + + +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: + return len(text) / len(zlib.compress(text.encode("utf-8"))) + + +def format_timestamp(seconds: float): + 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 + + return (f"{hours}:" if hours > 0 else "") + f"{minutes:02d}:{seconds:02d}.{milliseconds:03d}" + + +def write_vtt(transcript: Iterator[dict], file: TextIO): + print("WEBVTT\n", file=file) + for segment in transcript: + print( + f"{format_timestamp(segment['start'])} --> {format_timestamp(segment['end'])}\n" + f"{segment['text'].replace('-->', '->')}\n", + file=file, + flush=True, + )