From 279e9346d33d86555931397c857eebe71cb93e77 Mon Sep 17 00:00:00 2001 From: AIFSH <1509359472@qq.com> Date: Wed, 17 Apr 2024 08:38:00 +0000 Subject: [PATCH] first code --- README.md | 75 + __init__.py | 18 + inference.py | 139 + models/put model in here | 0 .../models/__pycache__/unet.cpython-310.pyc | Bin 0 -> 2017 bytes .../models/__pycache__/vae.cpython-310.pyc | Bin 0 -> 4876 bytes musetalk/models/unet.py | 47 + musetalk/models/vae.py | 148 + musetalk/utils/__init__.py | 5 + .../__pycache__/__init__.cpython-310.pyc | Bin 0 -> 340 bytes .../__pycache__/blending.cpython-310.pyc | Bin 0 -> 1737 bytes .../__pycache__/preprocessing.cpython-310.pyc | Bin 0 -> 4690 bytes .../utils/__pycache__/utils.cpython-310.pyc | Bin 0 -> 2119 bytes musetalk/utils/blending.py | 57 + musetalk/utils/dwpose/default_runtime.py | 54 + ...8xb32-270e_coco-ubody-wholebody-384x288.py | 257 + musetalk/utils/face_detection/README.md | 1 + musetalk/utils/face_detection/__init__.py | 7 + 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100644 nodes.py create mode 100644 requirements.txt create mode 100644 web/js/previewVideo.js create mode 100644 web/js/uploadVideo.js diff --git a/README.md b/README.md index a5ad81b..345b739 100644 --- a/README.md +++ b/README.md @@ -1 +1,76 @@ # ComfyUI-MuseTalk_FSH +the comfyui custom node of [MuseTalk](https://github.com/TMElyralab/MuseTalk.git) to make audio driven videos! +
+
+ webpage +
+
+ +## How to use +make sure `ffmpeg` is worked in your commandline +for Linux +``` +apt update +apt install ffmpeg +``` +for Windows,you can install `ffmpeg` by [WingetUI](https://github.com/marticliment/WingetUI) automatically + +then! +``` +git clone https://github.com/AIFSH/ComfyUI-MuseTalk_FSH.git +cd ComfyUI-MuseTalk_FSH +pip install -r requirements.txt +``` +### mmlab packages +```bash +pip install --no-cache-dir -U openmim +mim install mmengine +mim install "mmcv>=2.0.1" +mim install "mmdet>=3.1.0" +mim install "mmpose>=1.1.0" +``` + +### Download weights +You can download weights manually as follows: + +1. Download our trained [weights](https://huggingface.co/TMElyralab/MuseTalk). + +2. Download the weights of other components: + - [sd-vae-ft-mse](https://huggingface.co/stabilityai/sd-vae-ft-mse) + - [whisper](https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt) + - [dwpose](https://huggingface.co/yzd-v/DWPose/tree/main) + - [face-parse-bisent](https://github.com/zllrunning/face-parsing.PyTorch) + - [resnet18](https://download.pytorch.org/models/resnet18-5c106cde.pth) + + +Finally, these weights should be organized in `models` as follows: +``` +ComfyUI-MuseTalk_FSH/models/ +├── musetalk +│ └── musetalk.json +│ └── pytorch_model.bin +├── dwpose +│ └── dw-ll_ucoco_384.pth +├── face-parse-bisent +│ ├── 79999_iter.pth +│ └── resnet18-5c106cde.pth +├── sd-vae-ft-mse +│ ├── config.json +│ └── diffusion_pytorch_model.bin +└── whisper + └── tiny.pt +``` + +## Tutorial +todo + +## WeChat Group && Donate +
+
+ Wechat + donate +
+
+ +## Thanks +- [MuseTalk](https://github.com/TMElyralab/MuseTalk.git) diff --git a/__init__.py b/__init__.py new file mode 100644 index 0000000..460f42b --- /dev/null +++ b/__init__.py @@ -0,0 +1,18 @@ +from .nodes import MuseTalk,LoadVideo,PreViewVideo,CombineAudioVideo +WEB_DIRECTORY = "./web" +# A dictionary that contains all nodes you want to export with their names +# NOTE: names should be globally unique +NODE_CLASS_MAPPINGS = { + "MuseTalk": MuseTalk, + "LoadVideo": LoadVideo, + "PreViewVideo": PreViewVideo, + "CombineAudioVideo": CombineAudioVideo +} + +# A dictionary that contains the friendly/humanly readable titles for the nodes +NODE_DISPLAY_NAME_MAPPINGS = { + "MuseTalk": "MuseTalk Node", + "LoadVideo": "Video Loader", + "PreViewVideo": "PreView Video", + "CombineAudioVideo": "Combine Audio Video" +} diff --git a/inference.py b/inference.py new file mode 100644 index 0000000..8ec1a1f --- /dev/null +++ b/inference.py @@ -0,0 +1,139 @@ + +import os +import cv2 +import glob,copy +import shutil +from tqdm import tqdm +import torch +import pickle +import numpy as np +from cuda_malloc import cuda_malloc_supported +from typing import Any +import folder_paths +from .musetalk.utils.face_parsing import FaceParsing +from mmpose.apis import init_model +from .musetalk.utils.blending import get_image +from .musetalk.utils.utils import load_all_model,get_file_type,get_video_fps,datagen +from .musetalk.utils.preprocessing import get_landmark_and_bbox,read_imgs,coord_placeholder + +parent_directory = os.path.dirname(os.path.abspath(__file__)) +input_path = folder_paths.get_input_directory() +out_path = folder_paths.get_output_directory() + +class MuseTalk_INFER: + def __init__(self,bbox_shift=0,fps=25, + batch_size=8,batch_size_fa=2, + use_saved_coord=False) -> None: + self.fps = fps + self.bbox_shift = bbox_shift + self.batch_size = batch_size + self.batch_size_fa = batch_size_fa + self.use_saved_coord = use_saved_coord + self.device = torch.device("cuda" if cuda_malloc_supported() else "cpu") + config_file = os.path.join(parent_directory,"musetalk/utils/dwpose/rtmpose-l_8xb32-270e_coco-ubody-wholebody-384x288.py") + checkpoint_file = os.path.join(parent_directory,'models/dwpose/dw-ll_ucoco_384.pth') + resnet_path = os.path.join(parent_directory,'models/face-parse-bisent/resnet18-5c106cde.pth') + face_model_pth = os.path.join(parent_directory,"models/face-parse-bisent/79999_iter.pth") + self.fp_model = FaceParsing(resnet_path,face_model_pth) + self.dwpose_model = init_model(config_file, checkpoint_file, device=self.device) + self.audio_processor,self.vae,self.unet,self.pe = load_all_model(os.path.join(parent_directory,"models")) + self.timesteps = torch.tensor([0], device=self.device) + + def __call__(self, video_path,audio_path,*args: Any, **kwds: Any) -> Any: + input_basename = os.path.basename(video_path).split('.')[0] + audio_basename = os.path.basename(audio_path).split('.')[0] + output_basename = f"{input_basename}_{audio_basename}" + result_img_save_path = os.path.join(out_path,"musetalk_result", output_basename) # related to video & audio inputs + os.makedirs(result_img_save_path, exist_ok=True) + crop_coord_save_path = os.path.join(result_img_save_path, input_basename+".pkl") # only related to video input + output_vid_name = os.path.join(out_path, output_basename+".mp4") + + ############################################## extract frames from source video ############################################## + if get_file_type(video_path)=="video": + save_dir_full = os.path.join(out_path,"musetalk_result",input_basename) + os.makedirs(save_dir_full,exist_ok = True) + cmd = f"ffmpeg -v fatal -i {video_path} -start_number 0 {save_dir_full}/%08d.png" + os.system(cmd) + input_img_list = sorted(glob.glob(os.path.join(save_dir_full, '*.[jpJP][pnPN]*[gG]'))) + fps = get_video_fps(video_path) + else: # input img folder + input_img_list = glob.glob(os.path.join(video_path, '*.[jpJP][pnPN]*[gG]')) + input_img_list = sorted(input_img_list, key=lambda x: int(os.path.splitext(os.path.basename(x))[0])) + fps = self.fps + + #print(input_img_list) + ############################################## extract audio feature ############################################## + whisper_feature = self.audio_processor.audio2feat(audio_path) + whisper_chunks = self.audio_processor.feature2chunks(feature_array=whisper_feature,fps=fps) + ############################################## preprocess input image ############################################## + if os.path.exists(crop_coord_save_path) and self.use_saved_coord: + print("using extracted coordinates") + with open(crop_coord_save_path,'rb') as f: + coord_list = pickle.load(f) + frame_list = read_imgs(input_img_list) + else: + print("extracting landmarks...time consuming") + coord_list, frame_list = get_landmark_and_bbox(self.dwpose_model,input_img_list, self.batch_size_fa,self.bbox_shift) + with open(crop_coord_save_path, 'wb') as f: + pickle.dump(coord_list, f) + + i = 0 + input_latent_list = [] + for bbox, frame in zip(coord_list, frame_list): + if bbox == coord_placeholder: + continue + x1, y1, x2, y2 = bbox + crop_frame = frame[y1:y2, x1:x2] + crop_frame = cv2.resize(crop_frame,(256,256),interpolation = cv2.INTER_LANCZOS4) + latents = self.vae.get_latents_for_unet(crop_frame) + input_latent_list.append(latents) + + # to smooth the first and the last frame + frame_list_cycle = frame_list + frame_list[::-1] + coord_list_cycle = coord_list + coord_list[::-1] + input_latent_list_cycle = input_latent_list + input_latent_list[::-1] + ############################################## inference batch by batch ############################################## + print("start inference") + video_num = len(whisper_chunks) + batch_size = self.batch_size + gen = datagen(whisper_chunks,input_latent_list_cycle,batch_size) + res_frame_list = [] + for i, (whisper_batch,latent_batch) in enumerate(tqdm(gen,total=int(np.ceil(float(video_num)/batch_size)))): + tensor_list = [torch.FloatTensor(arr) for arr in whisper_batch] + audio_feature_batch = torch.stack(tensor_list).to(self.unet.device) # torch, B, 5*N,384 + audio_feature_batch = self.pe(audio_feature_batch) + + pred_latents = self.unet.model(latent_batch, self.timesteps, encoder_hidden_states=audio_feature_batch).sample + recon = self.vae.decode_latents(pred_latents) + for res_frame in recon: + res_frame_list.append(res_frame) + + ############################################## pad to full image ############################################## + print("pad talking image to original video") + for i, res_frame in enumerate(tqdm(res_frame_list)): + bbox = coord_list_cycle[i%(len(coord_list_cycle))] + ori_frame = copy.deepcopy(frame_list_cycle[i%(len(frame_list_cycle))]) + x1, y1, x2, y2 = bbox + try: + res_frame = cv2.resize(res_frame.astype(np.uint8),(x2-x1,y2-y1)) + except: +# print(bbox) + continue + + combine_frame = get_image(self.fp_model, ori_frame,res_frame,bbox) + cv2.imwrite(f"{result_img_save_path}/{str(i).zfill(8)}.png",combine_frame) + + cmd_img2video = f"ffmpeg -y -v fatal -r {fps} -f image2 -i {result_img_save_path}/%08d.png -vcodec libx264 -vf format=rgb24,scale=out_color_matrix=bt709,format=yuv420p -crf 18 {output_vid_name}" + print(cmd_img2video) + os.system(cmd_img2video) + ''' + cmd_combine_audio = f"ffmpeg -y -v fatal -i {audio_path} -i temp.mp4 {output_vid_name}" + print(cmd_combine_audio) + os.system(cmd_combine_audio) + ''' + del self.fp_model,self.dwpose_model,self.audio_processor,self.vae,self.unet,self.pe + torch.cuda.empty_cache() + # os.remove("temp.mp4") + shutil.rmtree(result_img_save_path) + print(f"result is save to {output_vid_name}") + return output_vid_name diff --git a/models/put model in here b/models/put model in here new file mode 100644 index 0000000..e69de29 diff --git a/musetalk/models/__pycache__/unet.cpython-310.pyc b/musetalk/models/__pycache__/unet.cpython-310.pyc new file mode 100644 index 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x = torch.squeeze(torch.FloatTensor(x)) + if half_mask: + x = x * (self._mask_tensor>0.5) + x = self.transform(x) + + x = x.unsqueeze(0) # [1, 3, 256, 256] torch tensor + x = x.to(self.vae.device) + + return x + + def encode_latents(self,image): + """ + Encode an image into latent variables. + + :param image: The image tensor to encode. + :return: The encoded latent variables. + """ + with torch.no_grad(): + init_latent_dist = self.vae.encode(image.to(self.vae.dtype)).latent_dist + init_latents = self.scaling_factor * init_latent_dist.sample() + return init_latents + + def decode_latents(self, latents): + """ + Decode latent variables back into an image. + :param latents: The latent variables to decode. + :return: A NumPy array representing the decoded image. + """ + latents = (1/ self.scaling_factor) * latents + image = self.vae.decode(latents.to(self.vae.dtype)).sample + image = (image / 2 + 0.5).clamp(0, 1) + image = image.detach().cpu().permute(0, 2, 3, 1).float().numpy() + image = (image * 255).round().astype("uint8") + image = image[...,::-1] # RGB to BGR + return image + + def get_latents_for_unet(self,img): + """ + Prepare latent variables for a U-Net model. + :param img: The image to process. + :return: A concatenated tensor of latents for U-Net input. + """ + + ref_image = self.preprocess_img(img,half_mask=True) # [1, 3, 256, 256] RGB, torch tensor + masked_latents = self.encode_latents(ref_image) # [1, 4, 32, 32], torch tensor + ref_image = self.preprocess_img(img,half_mask=False) # [1, 3, 256, 256] RGB, torch tensor + ref_latents = self.encode_latents(ref_image) # [1, 4, 32, 32], torch tensor + latent_model_input = torch.cat([masked_latents, ref_latents], dim=1) + return latent_model_input + +if __name__ == "__main__": + vae_mode_path = "./models/sd-vae-ft-mse/" + vae = VAE(model_path = vae_mode_path,use_float16=False) + img_path = "./results/sun001_crop/00000.png" + + crop_imgs_path = "./results/sun001_crop/" + latents_out_path = "./results/latents/" + if not os.path.exists(latents_out_path): + os.mkdir(latents_out_path) + + files = os.listdir(crop_imgs_path) + files.sort() + files = [file for file in files if file.split(".")[-1] == "png"] + + for file in files: + index = file.split(".")[0] + img_path = crop_imgs_path + file + latents = vae.get_latents_for_unet(img_path) + print(img_path,"latents",latents.size()) + #torch.save(latents,os.path.join(latents_out_path,index+".pt")) + #reload_tensor = torch.load('tensor.pt') + #print(reload_tensor.size()) + + + \ No newline at end of file diff --git a/musetalk/utils/__init__.py b/musetalk/utils/__init__.py new file mode 100644 index 0000000..b58c41d --- /dev/null +++ b/musetalk/utils/__init__.py @@ -0,0 +1,5 @@ +import sys +from os.path import abspath, dirname +current_dir = dirname(abspath(__file__)) +parent_dir = dirname(current_dir) +sys.path.append(parent_dir+'/utils') diff --git a/musetalk/utils/__pycache__/__init__.cpython-310.pyc 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b/musetalk/utils/blending.py @@ -0,0 +1,57 @@ +from PIL import Image +import numpy as np +import cv2 + + +def get_crop_box(box, expand): + x, y, x1, y1 = box + x_c, y_c = (x+x1)//2, (y+y1)//2 + w, h = x1-x, y1-y + s = int(max(w, h)//2*expand) + crop_box = [x_c-s, y_c-s, x_c+s, y_c+s] + return crop_box, s + +def face_seg(fp, image): + seg_image = fp(image) + if seg_image is None: + print("error, no person_segment") + return None + + seg_image = seg_image.resize(image.size) + return seg_image + +def get_image(fp_model,image,face,face_box,upper_boundary_ratio = 0.5,expand=1.2): + #print(image.shape) + #print(face.shape) + + body = Image.fromarray(image[:,:,::-1]) + face = Image.fromarray(face[:,:,::-1]) + + x, y, x1, y1 = face_box + #print(x1-x,y1-y) + crop_box, s = get_crop_box(face_box, expand) + x_s, y_s, x_e, y_e = crop_box + face_position = (x, y) + + face_large = body.crop(crop_box) + ori_shape = face_large.size + + mask_image = face_seg(fp_model,face_large) + mask_small = mask_image.crop((x-x_s, y-y_s, x1-x_s, y1-y_s)) + mask_image = Image.new('L', ori_shape, 0) + mask_image.paste(mask_small, (x-x_s, y-y_s, x1-x_s, y1-y_s)) + + # keep upper_boundary_ratio of talking area + width, height = mask_image.size + top_boundary = int(height * upper_boundary_ratio) + modified_mask_image = Image.new('L', ori_shape, 0) + modified_mask_image.paste(mask_image.crop((0, top_boundary, width, height)), (0, top_boundary)) + + blur_kernel_size = int(0.1 * ori_shape[0] // 2 * 2) + 1 + mask_array = cv2.GaussianBlur(np.array(modified_mask_image), (blur_kernel_size, blur_kernel_size), 0) + mask_image = Image.fromarray(mask_array) + + face_large.paste(face, (x-x_s, y-y_s, x1-x_s, y1-y_s)) + body.paste(face_large, crop_box[:2], mask_image) + body = np.array(body) + return body[:,:,::-1] diff --git a/musetalk/utils/dwpose/default_runtime.py b/musetalk/utils/dwpose/default_runtime.py new file mode 100644 index 0000000..6f27c03 --- /dev/null +++ b/musetalk/utils/dwpose/default_runtime.py @@ -0,0 +1,54 @@ +default_scope = 'mmpose' + +# hooks +default_hooks = dict( + timer=dict(type='IterTimerHook'), + logger=dict(type='LoggerHook', interval=50), + param_scheduler=dict(type='ParamSchedulerHook'), + checkpoint=dict(type='CheckpointHook', interval=10), + sampler_seed=dict(type='DistSamplerSeedHook'), + visualization=dict(type='PoseVisualizationHook', enable=False), + badcase=dict( + type='BadCaseAnalysisHook', + enable=False, + out_dir='badcase', + metric_type='loss', + badcase_thr=5)) + +# custom hooks +custom_hooks = [ + # Synchronize model buffers such as running_mean and running_var in BN + # at the end of each epoch + dict(type='SyncBuffersHook') +] + +# multi-processing backend +env_cfg = dict( + cudnn_benchmark=False, + mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), + dist_cfg=dict(backend='nccl'), +) + +# visualizer +vis_backends = [ + dict(type='LocalVisBackend'), + # dict(type='TensorboardVisBackend'), + # dict(type='WandbVisBackend'), +] +visualizer = dict( + type='PoseLocalVisualizer', vis_backends=vis_backends, name='visualizer') + +# logger +log_processor = dict( + type='LogProcessor', window_size=50, by_epoch=True, num_digits=6) +log_level = 'INFO' +load_from = None +resume = False + +# file I/O backend +backend_args = dict(backend='local') + +# training/validation/testing progress +train_cfg = dict(by_epoch=True) +val_cfg = dict() +test_cfg = dict() diff --git a/musetalk/utils/dwpose/rtmpose-l_8xb32-270e_coco-ubody-wholebody-384x288.py b/musetalk/utils/dwpose/rtmpose-l_8xb32-270e_coco-ubody-wholebody-384x288.py new file mode 100644 index 0000000..293efad --- /dev/null +++ b/musetalk/utils/dwpose/rtmpose-l_8xb32-270e_coco-ubody-wholebody-384x288.py @@ -0,0 +1,257 @@ +#_base_ = ['../../../_base_/default_runtime.py'] +_base_ = ['default_runtime.py'] + +# runtime +max_epochs = 270 +stage2_num_epochs = 30 +base_lr = 4e-3 +train_batch_size = 32 +val_batch_size = 32 + +train_cfg = dict(max_epochs=max_epochs, val_interval=10) +randomness = dict(seed=21) + +# optimizer +optim_wrapper = dict( + type='OptimWrapper', + optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05), + paramwise_cfg=dict( + norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) + +# learning rate +param_scheduler = [ + dict( + type='LinearLR', + start_factor=1.0e-5, + by_epoch=False, + begin=0, + end=1000), + dict( + # use cosine lr from 150 to 300 epoch + type='CosineAnnealingLR', + eta_min=base_lr * 0.05, + begin=max_epochs // 2, + end=max_epochs, + T_max=max_epochs // 2, + by_epoch=True, + convert_to_iter_based=True), +] + +# automatically scaling LR based on the actual training batch size +auto_scale_lr = dict(base_batch_size=512) + +# codec settings +codec = dict( + type='SimCCLabel', + input_size=(288, 384), + sigma=(6., 6.93), + simcc_split_ratio=2.0, + normalize=False, + use_dark=False) + +# model settings +model = dict( + type='TopdownPoseEstimator', + data_preprocessor=dict( + type='PoseDataPreprocessor', + mean=[123.675, 116.28, 103.53], + std=[58.395, 57.12, 57.375], + bgr_to_rgb=True), + backbone=dict( + _scope_='mmdet', + type='CSPNeXt', + arch='P5', + expand_ratio=0.5, + deepen_factor=1., + widen_factor=1., + out_indices=(4, ), + channel_attention=True, + norm_cfg=dict(type='SyncBN'), + act_cfg=dict(type='SiLU'), + init_cfg=dict( + type='Pretrained', + prefix='backbone.', + checkpoint='https://download.openmmlab.com/mmpose/v1/projects/' + 'rtmpose/cspnext-l_udp-aic-coco_210e-256x192-273b7631_20230130.pth' # noqa: E501 + )), + head=dict( + type='RTMCCHead', + in_channels=1024, + out_channels=133, + input_size=codec['input_size'], + in_featuremap_size=(9, 12), + simcc_split_ratio=codec['simcc_split_ratio'], + final_layer_kernel_size=7, + gau_cfg=dict( + hidden_dims=256, + s=128, + expansion_factor=2, + dropout_rate=0., + drop_path=0., + act_fn='SiLU', + use_rel_bias=False, + pos_enc=False), + loss=dict( + type='KLDiscretLoss', + use_target_weight=True, + beta=10., + label_softmax=True), + decoder=codec), + test_cfg=dict(flip_test=True, )) + +# base dataset settings +dataset_type = 'UBody2dDataset' +data_mode = 'topdown' +data_root = 'data/UBody/' + +backend_args = dict(backend='local') + +scenes = [ + 'Magic_show', 'Entertainment', 'ConductMusic', 'Online_class', 'TalkShow', + 'Speech', 'Fitness', 'Interview', 'Olympic', 'TVShow', 'Singing', + 'SignLanguage', 'Movie', 'LiveVlog', 'VideoConference' +] + +train_datasets = [ + dict( + type='CocoWholeBodyDataset', + data_root='data/coco/', + data_mode=data_mode, + ann_file='annotations/coco_wholebody_train_v1.0.json', + data_prefix=dict(img='train2017/'), + pipeline=[]) +] + +for scene in scenes: + train_dataset = dict( + type=dataset_type, + data_root=data_root, + data_mode=data_mode, + ann_file=f'annotations/{scene}/train_annotations.json', + data_prefix=dict(img='images/'), + pipeline=[], + sample_interval=10) + train_datasets.append(train_dataset) + +# pipelines +train_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', scale_factor=[0.5, 1.5], rotate_factor=90), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=1.0), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] +val_pipeline = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='PackPoseInputs') +] + +train_pipeline_stage2 = [ + dict(type='LoadImage', backend_args=backend_args), + dict(type='GetBBoxCenterScale'), + dict(type='RandomFlip', direction='horizontal'), + dict(type='RandomHalfBody'), + dict( + type='RandomBBoxTransform', + shift_factor=0., + scale_factor=[0.5, 1.5], + rotate_factor=90), + dict(type='TopdownAffine', input_size=codec['input_size']), + dict(type='mmdet.YOLOXHSVRandomAug'), + dict( + type='Albumentation', + transforms=[ + dict(type='Blur', p=0.1), + dict(type='MedianBlur', p=0.1), + dict( + type='CoarseDropout', + max_holes=1, + max_height=0.4, + max_width=0.4, + min_holes=1, + min_height=0.2, + min_width=0.2, + p=0.5), + ]), + dict(type='GenerateTarget', encoder=codec), + dict(type='PackPoseInputs') +] + +# data loaders +train_dataloader = dict( + batch_size=train_batch_size, + num_workers=10, + persistent_workers=True, + sampler=dict(type='DefaultSampler', shuffle=True), + dataset=dict( + type='CombinedDataset', + metainfo=dict(from_file='configs/_base_/datasets/coco_wholebody.py'), + datasets=train_datasets, + pipeline=train_pipeline, + test_mode=False, + )) + +val_dataloader = dict( + batch_size=val_batch_size, + num_workers=10, + persistent_workers=True, + drop_last=False, + sampler=dict(type='DefaultSampler', shuffle=False, round_up=False), + dataset=dict( + type='CocoWholeBodyDataset', + data_root=data_root, + data_mode=data_mode, + ann_file='data/coco/annotations/coco_wholebody_val_v1.0.json', + bbox_file='data/coco/person_detection_results/' + 'COCO_val2017_detections_AP_H_56_person.json', + data_prefix=dict(img='coco/val2017/'), + test_mode=True, + pipeline=val_pipeline, + )) +test_dataloader = val_dataloader + +# hooks +default_hooks = dict( + checkpoint=dict( + save_best='coco-wholebody/AP', rule='greater', max_keep_ckpts=1)) + +custom_hooks = [ + dict( + type='EMAHook', + ema_type='ExpMomentumEMA', + momentum=0.0002, + update_buffers=True, + priority=49), + dict( + type='mmdet.PipelineSwitchHook', + switch_epoch=max_epochs - stage2_num_epochs, + switch_pipeline=train_pipeline_stage2) +] + +# evaluators +val_evaluator = dict( + type='CocoWholeBodyMetric', + ann_file='data/coco/annotations/coco_wholebody_val_v1.0.json') +test_evaluator = val_evaluator diff --git a/musetalk/utils/face_detection/README.md b/musetalk/utils/face_detection/README.md new file mode 100644 index 0000000..c073376 --- /dev/null +++ b/musetalk/utils/face_detection/README.md @@ -0,0 +1 @@ +The code for Face Detection in this folder has been taken from the wonderful [face_alignment](https://github.com/1adrianb/face-alignment) repository. This has been modified to take batches of faces at a time. \ No newline at end of file diff --git a/musetalk/utils/face_detection/__init__.py b/musetalk/utils/face_detection/__init__.py new file mode 100644 index 0000000..31c3e22 --- /dev/null +++ b/musetalk/utils/face_detection/__init__.py @@ -0,0 +1,7 @@ +# -*- coding: utf-8 -*- + +__author__ = """Adrian Bulat""" +__email__ = 'adrian.bulat@nottingham.ac.uk' +__version__ = '1.0.1' + +from .api import FaceAlignment, LandmarksType, NetworkSize, YOLOv8_face diff --git a/musetalk/utils/face_detection/__pycache__/__init__.cpython-310.pyc b/musetalk/utils/face_detection/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7c84960df696ce3ca9c0ff6c193f97bc62b92ad4 GIT binary patch literal 399 zcmYk2y-ve05P%^h?6+PO-l8{aw?b;o#Ab_mgwv?XnqVAVMB{JQ zj2NOeoZ^&Da<&ni3f+b?EC|OcD_3*vQftx7F9 z2uW!9cR6~hO1i>%2Giw3%#COxaiv*a#`Z}-sHUklb7KG|ma&E_)iTl{YT6(zi72KPfYPxyW3{ll(gf*Eox`E^9DGX+6>#x_TQ818*ZT 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zFz7i9Zoa*5q`{e?ZSSAWA!GlV)-4!XbYSqi3>-}RRJ$#<;7}OqOzX1SBG%+r@IiAN zF@GfXVTUY-W3AiAiu^EeSVrYq(iB zj&K*&^P6Nyt~xSsabwtgQ!S*-Gj{-Zod(YO3= zwRGu&Gp8?|{_w-Ir>~w)@{@omT#4)?f63pFXyd(ur+22nxzg|T0(^sEV&mWQ2;VI@ zt|N?8c(1s&wmyy6aP35EYIfkE+(_73c{$zd%657qr_>X|>+==- z)F(cQl-c<*lXP&Gd8JZjaB)}8mk%a(4IfOwU+&dvl2lXq6_LLJNsI^wdFnKwlR3$y zNKfjKJWGW15_KW393ILstegmvlwNfrD2!L_n;8?Oek(_Yb*h7#; 1: + newh, neww = self.input_height, int(self.input_width / hw_scale) + img = cv2.resize(srcimg, (neww, newh), interpolation=cv2.INTER_AREA) + left = int((self.input_width - neww) * 0.5) + img = cv2.copyMakeBorder(img, 0, 0, left, self.input_width - neww - left, cv2.BORDER_CONSTANT, + value=(0, 0, 0)) # add border + else: + newh, neww = int(self.input_height * hw_scale), self.input_width + img = cv2.resize(srcimg, (neww, newh), interpolation=cv2.INTER_AREA) + top = int((self.input_height - newh) * 0.5) + img = cv2.copyMakeBorder(img, top, self.input_height - newh - top, 0, 0, cv2.BORDER_CONSTANT, + value=(0, 0, 0)) + else: + img = cv2.resize(srcimg, (self.input_width, self.input_height), interpolation=cv2.INTER_AREA) + return img, newh, neww, top, left + + def detect(self, srcimg): + input_img, newh, neww, padh, padw = self.resize_image(cv2.cvtColor(srcimg, cv2.COLOR_BGR2RGB)) + scale_h, scale_w = srcimg.shape[0]/newh, srcimg.shape[1]/neww + input_img = input_img.astype(np.float32) / 255.0 + + blob = cv2.dnn.blobFromImage(input_img) + self.net.setInput(blob) + outputs = self.net.forward(self.net.getUnconnectedOutLayersNames()) + # if isinstance(outputs, tuple): + # outputs = list(outputs) + # if float(cv2.__version__[:3])>=4.7: + # outputs = [outputs[2], outputs[0], outputs[1]] ###opencv4.7需要这一步，opencv4.5不需要 + # Perform inference on the image + det_bboxes, det_conf, det_classid, landmarks = self.post_process(outputs, scale_h, scale_w, padh, padw) + return det_bboxes, det_conf, det_classid, landmarks + + def post_process(self, preds, scale_h, scale_w, padh, padw): + bboxes, scores, landmarks = [], [], [] + for i, pred in enumerate(preds): + stride = int(self.input_height/pred.shape[2]) + pred = pred.transpose((0, 2, 3, 1)) + + box = pred[..., :self.reg_max * 4] + cls = 1 / (1 + np.exp(-pred[..., self.reg_max * 4:-15])).reshape((-1,1)) + kpts = pred[..., -15:].reshape((-1,15)) ### x1,y1,score1, ..., x5,y5,score5 + + # tmp = box.reshape(self.feats_hw[i][0], self.feats_hw[i][1], 4, self.reg_max) + tmp = box.reshape(-1, 4, self.reg_max) + bbox_pred = self.softmax(tmp, axis=-1) + bbox_pred = np.dot(bbox_pred, self.project).reshape((-1,4)) + + bbox = self.distance2bbox(self.anchors[stride], bbox_pred, max_shape=(self.input_height, self.input_width)) * stride + kpts[:, 0::3] = (kpts[:, 0::3] * 2.0 + (self.anchors[stride][:, 0].reshape((-1,1)) - 0.5)) * stride + kpts[:, 1::3] = (kpts[:, 1::3] * 2.0 + (self.anchors[stride][:, 1].reshape((-1,1)) - 0.5)) * stride + kpts[:, 2::3] = 1 / (1+np.exp(-kpts[:, 2::3])) + + bbox -= np.array([[padw, padh, padw, padh]]) ###合理使用广播法则 + bbox *= np.array([[scale_w, scale_h, scale_w, scale_h]]) + kpts -= np.tile(np.array([padw, padh, 0]), 5).reshape((1,15)) + kpts *= np.tile(np.array([scale_w, scale_h, 1]), 5).reshape((1,15)) + + bboxes.append(bbox) + scores.append(cls) + landmarks.append(kpts) + + bboxes = np.concatenate(bboxes, axis=0) + scores = np.concatenate(scores, axis=0) + landmarks = np.concatenate(landmarks, axis=0) + + bboxes_wh = bboxes.copy() + bboxes_wh[:, 2:4] = bboxes[:, 2:4] - bboxes[:, 0:2] ####xywh + classIds = np.argmax(scores, axis=1) + confidences = np.max(scores, axis=1) ####max_class_confidence + + mask = confidences>self.conf_threshold + bboxes_wh = bboxes_wh[mask] ###合理使用广播法则 + confidences = confidences[mask] + classIds = classIds[mask] + landmarks = landmarks[mask] + + indices = cv2.dnn.NMSBoxes(bboxes_wh.tolist(), confidences.tolist(), self.conf_threshold, + self.iou_threshold).flatten() + if len(indices) > 0: + mlvl_bboxes = bboxes_wh[indices] + confidences = confidences[indices] + classIds = classIds[indices] + landmarks = landmarks[indices] + return mlvl_bboxes, confidences, classIds, landmarks + else: + print('nothing detect') + return np.array([]), np.array([]), np.array([]), np.array([]) + + def distance2bbox(self, points, distance, max_shape=None): + x1 = points[:, 0] - distance[:, 0] + y1 = points[:, 1] - distance[:, 1] + x2 = points[:, 0] + distance[:, 2] + y2 = points[:, 1] + distance[:, 3] + if max_shape is not None: + x1 = np.clip(x1, 0, max_shape[1]) + y1 = np.clip(y1, 0, max_shape[0]) + x2 = np.clip(x2, 0, max_shape[1]) + y2 = np.clip(y2, 0, max_shape[0]) + return np.stack([x1, y1, x2, y2], axis=-1) + + def draw_detections(self, image, boxes, scores, kpts): + for box, score, kp in zip(boxes, scores, kpts): + x, y, w, h = box.astype(int) + # Draw rectangle + cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), thickness=3) + cv2.putText(image, "face:"+str(round(score,2)), (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), thickness=2) + for i in range(5): + cv2.circle(image, (int(kp[i * 3]), int(kp[i * 3 + 1])), 4, (0, 255, 0), thickness=-1) + # cv2.putText(image, str(i), (int(kp[i * 3]), int(kp[i * 3 + 1]) - 10), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), thickness=1) + return image + +ROOT = os.path.dirname(os.path.abspath(__file__)) \ No newline at end of file diff --git a/musetalk/utils/face_detection/detection/__init__.py b/musetalk/utils/face_detection/detection/__init__.py new file mode 100644 index 0000000..1a6b040 --- /dev/null +++ b/musetalk/utils/face_detection/detection/__init__.py @@ -0,0 +1 @@ +from .core import FaceDetector \ No newline at end of file diff --git a/musetalk/utils/face_detection/detection/__pycache__/__init__.cpython-310.pyc b/musetalk/utils/face_detection/detection/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4174f71729ce5027d36d3e64372fb24fc9c69817 GIT binary patch literal 235 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cv2 + + +class FaceDetector(object): + """An abstract class representing a face detector. + + Any other face detection implementation must subclass it. All subclasses + must implement ``detect_from_image``, that return a list of detected + bounding boxes. Optionally, for speed considerations detect from path is + recommended. + """ + + def __init__(self, device, verbose): + self.device = device + self.verbose = verbose + + if verbose: + if 'cpu' in device: + logger = logging.getLogger(__name__) + logger.warning("Detection running on CPU, this may be potentially slow.") + + if 'cpu' not in device and 'cuda' not in device: + if verbose: + logger.error("Expected values for device are: {cpu, cuda} but got: %s", device) + raise ValueError + + def detect_from_image(self, tensor_or_path): + """Detects faces in a given image. + + This function detects the faces present in a provided BGR(usually) + image. The input can be either the image itself or the path to it. + + Arguments: + tensor_or_path {numpy.ndarray, torch.tensor or string} -- the path + to an image or the image itself. + + Example:: + + >>> path_to_image = 'data/image_01.jpg' + ... detected_faces = detect_from_image(path_to_image) + [A list of bounding boxes (x1, y1, x2, y2)] + >>> image = cv2.imread(path_to_image) + ... detected_faces = detect_from_image(image) + [A list of bounding boxes (x1, y1, x2, y2)] + + """ + raise NotImplementedError + + def detect_from_directory(self, path, extensions=['.jpg', '.png'], recursive=False, show_progress_bar=True): + """Detects faces from all the images present in a given directory. + + Arguments: + path {string} -- a string containing a path that points to the folder containing the images + + Keyword Arguments: + extensions {list} -- list of string containing the extensions to be + consider in the following format: ``.extension_name`` (default: + {['.jpg', '.png']}) recursive {bool} -- option wherever to scan the + folder recursively (default: {False}) show_progress_bar {bool} -- + display a progressbar (default: {True}) + + Example: + >>> directory = 'data' + ... detected_faces = detect_from_directory(directory) + {A dictionary of [lists containing bounding boxes(x1, y1, x2, y2)]} + + """ + if self.verbose: + logger = logging.getLogger(__name__) + + if len(extensions) == 0: + if self.verbose: + logger.error("Expected at list one extension, but none was received.") + raise ValueError + + if self.verbose: + logger.info("Constructing the list of images.") + additional_pattern = '/**/*' if recursive else '/*' + files = [] + for extension in extensions: + files.extend(glob.glob(path + additional_pattern + extension, recursive=recursive)) + + if self.verbose: + logger.info("Finished searching for images. %s images found", len(files)) + logger.info("Preparing to run the detection.") + + predictions = {} + for image_path in tqdm(files, disable=not show_progress_bar): + if self.verbose: + logger.info("Running the face detector on image: %s", image_path) + predictions[image_path] = self.detect_from_image(image_path) + + if self.verbose: + logger.info("The detector was successfully run on all %s images", len(files)) + + return predictions + + @property + def reference_scale(self): + raise NotImplementedError + + @property + def reference_x_shift(self): + raise NotImplementedError + + @property + def reference_y_shift(self): + raise NotImplementedError + + @staticmethod + def tensor_or_path_to_ndarray(tensor_or_path, rgb=True): + """Convert path (represented as a string) or torch.tensor to a numpy.ndarray + + Arguments: + tensor_or_path {numpy.ndarray, torch.tensor or string} -- path to the image, or the image itself + """ + if isinstance(tensor_or_path, str): + return cv2.imread(tensor_or_path) if not rgb else cv2.imread(tensor_or_path)[..., ::-1] + elif torch.is_tensor(tensor_or_path): + # Call cpu in case its coming from cuda + return tensor_or_path.cpu().numpy()[..., ::-1].copy() if not rgb else tensor_or_path.cpu().numpy() + elif isinstance(tensor_or_path, np.ndarray): + return tensor_or_path[..., ::-1].copy() if not rgb else tensor_or_path + else: + raise TypeError diff --git a/musetalk/utils/face_detection/detection/sfd/__init__.py b/musetalk/utils/face_detection/detection/sfd/__init__.py new file mode 100644 index 0000000..5a63ecd --- /dev/null +++ b/musetalk/utils/face_detection/detection/sfd/__init__.py @@ -0,0 +1 @@ +from .sfd_detector import SFDDetector as FaceDetector \ No newline at end of file diff --git a/musetalk/utils/face_detection/detection/sfd/__pycache__/__init__.cpython-310.pyc b/musetalk/utils/face_detection/detection/sfd/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5b6ad700bd01682b6dd964a30a7684c246465a04 GIT binary patch literal 260 zcmd1j<>g`k0wX24G*ck`7{oyaOhAqU5Elyoi4=wu#vF!R#waF6h7_h?22JLdj6h*c 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b/musetalk/utils/face_detection/detection/sfd/bbox.py new file mode 100644 index 0000000..4bd7222 --- /dev/null +++ b/musetalk/utils/face_detection/detection/sfd/bbox.py @@ -0,0 +1,129 @@ +from __future__ import print_function +import os +import sys +import cv2 +import random +import datetime +import time +import math +import argparse +import numpy as np +import torch + +try: + from iou import IOU +except BaseException: + # IOU cython speedup 10x + def IOU(ax1, ay1, ax2, ay2, bx1, by1, bx2, by2): + sa = abs((ax2 - ax1) * (ay2 - ay1)) + sb = abs((bx2 - bx1) * (by2 - by1)) + x1, y1 = max(ax1, bx1), max(ay1, by1) + x2, y2 = min(ax2, bx2), min(ay2, by2) + w = x2 - x1 + h = y2 - y1 + if w < 0 or h < 0: + return 0.0 + else: + return 1.0 * w * h / (sa + sb - w * h) + + +def bboxlog(x1, y1, x2, y2, axc, ayc, aww, ahh): + xc, yc, ww, hh = (x2 + x1) / 2, (y2 + y1) / 2, x2 - x1, y2 - y1 + dx, dy = (xc - axc) / aww, (yc - ayc) / ahh + dw, dh = math.log(ww / aww), math.log(hh / ahh) + return dx, dy, dw, dh + + +def bboxloginv(dx, dy, dw, dh, axc, ayc, aww, ahh): + xc, yc = dx * aww + axc, dy * ahh + ayc + ww, hh = math.exp(dw) * aww, math.exp(dh) * ahh + x1, x2, y1, y2 = xc - ww / 2, xc + ww / 2, yc - hh / 2, yc + hh / 2 + return x1, y1, x2, y2 + + +def nms(dets, thresh): + if 0 == len(dets): + return [] + x1, y1, x2, y2, scores = dets[:, 0], dets[:, 1], dets[:, 2], dets[:, 3], dets[:, 4] + areas = (x2 - x1 + 1) * (y2 - y1 + 1) + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(i) + xx1, yy1 = np.maximum(x1[i], x1[order[1:]]), np.maximum(y1[i], y1[order[1:]]) + xx2, yy2 = np.minimum(x2[i], x2[order[1:]]), np.minimum(y2[i], y2[order[1:]]) + + w, h = np.maximum(0.0, xx2 - xx1 + 1), np.maximum(0.0, yy2 - yy1 + 1) + ovr = w * h / (areas[i] + areas[order[1:]] - w * h) + + inds = np.where(ovr <= thresh)[0] + order = order[inds + 1] + + return keep + + +def encode(matched, priors, variances): + """Encode the variances from the priorbox layers into the ground truth boxes + we have matched (based on jaccard overlap) with the prior boxes. + Args: + matched: (tensor) Coords of ground truth for each prior in point-form + Shape: [num_priors, 4]. + priors: (tensor) Prior boxes in center-offset form + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + encoded boxes (tensor), Shape: [num_priors, 4] + """ + + # dist b/t match center and prior's center + g_cxcy = (matched[:, :2] + matched[:, 2:]) / 2 - priors[:, :2] + # encode variance + g_cxcy /= (variances[0] * priors[:, 2:]) + # match wh / prior wh + g_wh = (matched[:, 2:] - matched[:, :2]) / priors[:, 2:] + g_wh = torch.log(g_wh) / variances[1] + # return target for smooth_l1_loss + return torch.cat([g_cxcy, g_wh], 1) # [num_priors,4] + + +def decode(loc, priors, variances): + """Decode locations from predictions using priors to undo + the encoding we did for offset regression at train time. + Args: + loc (tensor): location predictions for loc layers, + Shape: [num_priors,4] + priors (tensor): Prior boxes in center-offset form. + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + decoded bounding box predictions + """ + + boxes = torch.cat(( + priors[:, :2] + loc[:, :2] * variances[0] * priors[:, 2:], + priors[:, 2:] * torch.exp(loc[:, 2:] * variances[1])), 1) + boxes[:, :2] -= boxes[:, 2:] / 2 + boxes[:, 2:] += boxes[:, :2] + return boxes + +def batch_decode(loc, priors, variances): + """Decode locations from predictions using priors to undo + the encoding we did for offset regression at train time. + Args: + loc (tensor): location predictions for loc layers, + Shape: [num_priors,4] + priors (tensor): Prior boxes in center-offset form. + Shape: [num_priors,4]. + variances: (list[float]) Variances of priorboxes + Return: + decoded bounding box predictions + """ + + boxes = torch.cat(( + priors[:, :, :2] + loc[:, :, :2] * variances[0] * priors[:, :, 2:], + priors[:, :, 2:] * torch.exp(loc[:, :, 2:] * variances[1])), 2) + boxes[:, :, :2] -= boxes[:, :, 2:] / 2 + boxes[:, :, 2:] += boxes[:, :, :2] + return boxes diff --git a/musetalk/utils/face_detection/detection/sfd/detect.py b/musetalk/utils/face_detection/detection/sfd/detect.py new file mode 100644 index 0000000..15ccdc5 --- /dev/null +++ b/musetalk/utils/face_detection/detection/sfd/detect.py @@ -0,0 +1,114 @@ +import torch +import torch.nn.functional as F + +import os +import sys +import cv2 +import random +import datetime +import math +import argparse +import numpy as np + +import scipy.io as sio +import zipfile +from .net_s3fd import s3fd +from .bbox import * + + +def detect(net, img, device): + img = img - np.array([104, 117, 123]) + img = img.transpose(2, 0, 1) + img = img.reshape((1,) + img.shape) + + if 'cuda' in device: + torch.backends.cudnn.benchmark = True + + img = torch.from_numpy(img).float().to(device) + BB, CC, HH, WW = img.size() + with torch.no_grad(): + olist = net(img) + + bboxlist = [] + for i in range(len(olist) // 2): + olist[i * 2] = F.softmax(olist[i * 2], dim=1) + olist = [oelem.data.cpu() for oelem in olist] + for i in range(len(olist) // 2): + ocls, oreg = olist[i * 2], olist[i * 2 + 1] + FB, FC, FH, FW = ocls.size() # feature map size + stride = 2**(i + 2) # 4,8,16,32,64,128 + anchor = stride * 4 + poss = zip(*np.where(ocls[:, 1, :, :] > 0.05)) + for Iindex, hindex, windex in poss: + axc, ayc = stride / 2 + windex * stride, stride / 2 + hindex * stride + score = ocls[0, 1, hindex, windex] + loc = oreg[0, :, hindex, windex].contiguous().view(1, 4) + priors = torch.Tensor([[axc / 1.0, ayc / 1.0, stride * 4 / 1.0, stride * 4 / 1.0]]) + variances = [0.1, 0.2] + box = decode(loc, priors, variances) + x1, y1, x2, y2 = box[0] * 1.0 + # cv2.rectangle(imgshow,(int(x1),int(y1)),(int(x2),int(y2)),(0,0,255),1) + bboxlist.append([x1, y1, x2, y2, score]) + bboxlist = np.array(bboxlist) + if 0 == len(bboxlist): + bboxlist = np.zeros((1, 5)) + + return bboxlist + +def batch_detect(net, imgs, device): + imgs = imgs - np.array([104, 117, 123]) + imgs = imgs.transpose(0, 3, 1, 2) + + if 'cuda' in device: + torch.backends.cudnn.benchmark = True + + imgs = torch.from_numpy(imgs).float().to(device) + BB, CC, HH, WW = imgs.size() + with torch.no_grad(): + olist = net(imgs) +# print(olist) + + bboxlist = [] + for i in range(len(olist) // 2): + olist[i * 2] = F.softmax(olist[i * 2], dim=1) + + olist = [oelem.cpu() for oelem in olist] + for i in range(len(olist) // 2): + ocls, oreg = olist[i * 2], olist[i * 2 + 1] + FB, FC, FH, FW = ocls.size() # feature map size + stride = 2**(i + 2) # 4,8,16,32,64,128 + anchor = stride * 4 + poss = zip(*np.where(ocls[:, 1, :, :] > 0.05)) + for Iindex, hindex, windex in poss: + axc, ayc = stride / 2 + windex * stride, stride / 2 + hindex * stride + score = ocls[:, 1, hindex, windex] + loc = oreg[:, :, hindex, windex].contiguous().view(BB, 1, 4) + priors = torch.Tensor([[axc / 1.0, ayc / 1.0, stride * 4 / 1.0, stride * 4 / 1.0]]).view(1, 1, 4) + variances = [0.1, 0.2] + box = batch_decode(loc, priors, variances) + box = box[:, 0] * 1.0 + # cv2.rectangle(imgshow,(int(x1),int(y1)),(int(x2),int(y2)),(0,0,255),1) + bboxlist.append(torch.cat([box, score.unsqueeze(1)], 1).cpu().numpy()) + bboxlist = np.array(bboxlist) + if 0 == len(bboxlist): + bboxlist = np.zeros((1, BB, 5)) + + return bboxlist + +def flip_detect(net, img, device): + img = cv2.flip(img, 1) + b = detect(net, img, device) + + bboxlist = np.zeros(b.shape) + bboxlist[:, 0] = img.shape[1] - b[:, 2] + bboxlist[:, 1] = b[:, 1] + bboxlist[:, 2] = img.shape[1] - b[:, 0] + bboxlist[:, 3] = b[:, 3] + bboxlist[:, 4] = b[:, 4] + return bboxlist + + +def pts_to_bb(pts): + min_x, min_y = np.min(pts, axis=0) + max_x, max_y = np.max(pts, axis=0) + return np.array([min_x, min_y, max_x, max_y]) diff --git a/musetalk/utils/face_detection/detection/sfd/net_s3fd.py b/musetalk/utils/face_detection/detection/sfd/net_s3fd.py new file mode 100644 index 0000000..fc64313 --- /dev/null +++ b/musetalk/utils/face_detection/detection/sfd/net_s3fd.py @@ -0,0 +1,129 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class L2Norm(nn.Module): + def __init__(self, n_channels, scale=1.0): + super(L2Norm, self).__init__() + self.n_channels = n_channels + self.scale = scale + self.eps = 1e-10 + self.weight = nn.Parameter(torch.Tensor(self.n_channels)) + self.weight.data *= 0.0 + self.weight.data += self.scale + + def forward(self, x): + norm = x.pow(2).sum(dim=1, keepdim=True).sqrt() + self.eps + x = x / norm * self.weight.view(1, -1, 1, 1) + return x + + +class s3fd(nn.Module): + def __init__(self): + super(s3fd, self).__init__() + self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1) + self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1) + + self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1) + self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, stride=1, padding=1) + + self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1) + self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1) + self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1) + + self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1) + self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) + self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) + + self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) + self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) + self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1) + + self.fc6 = nn.Conv2d(512, 1024, kernel_size=3, stride=1, padding=3) + self.fc7 = nn.Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0) + + self.conv6_1 = nn.Conv2d(1024, 256, kernel_size=1, stride=1, padding=0) + self.conv6_2 = nn.Conv2d(256, 512, kernel_size=3, stride=2, padding=1) + + self.conv7_1 = nn.Conv2d(512, 128, kernel_size=1, stride=1, padding=0) + self.conv7_2 = nn.Conv2d(128, 256, kernel_size=3, stride=2, padding=1) + + self.conv3_3_norm = L2Norm(256, scale=10) + self.conv4_3_norm = L2Norm(512, scale=8) + self.conv5_3_norm = L2Norm(512, scale=5) + + self.conv3_3_norm_mbox_conf = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1) + self.conv3_3_norm_mbox_loc = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1) + self.conv4_3_norm_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1) + self.conv4_3_norm_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1) + self.conv5_3_norm_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1) + self.conv5_3_norm_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1) + + self.fc7_mbox_conf = nn.Conv2d(1024, 2, kernel_size=3, stride=1, padding=1) + self.fc7_mbox_loc = nn.Conv2d(1024, 4, kernel_size=3, stride=1, padding=1) + self.conv6_2_mbox_conf = nn.Conv2d(512, 2, kernel_size=3, stride=1, padding=1) + self.conv6_2_mbox_loc = nn.Conv2d(512, 4, kernel_size=3, stride=1, padding=1) + self.conv7_2_mbox_conf = nn.Conv2d(256, 2, kernel_size=3, stride=1, padding=1) + self.conv7_2_mbox_loc = nn.Conv2d(256, 4, kernel_size=3, stride=1, padding=1) + + def forward(self, x): + h = F.relu(self.conv1_1(x)) + h = F.relu(self.conv1_2(h)) + h = F.max_pool2d(h, 2, 2) + + h = F.relu(self.conv2_1(h)) + h = F.relu(self.conv2_2(h)) + h = F.max_pool2d(h, 2, 2) + + h = F.relu(self.conv3_1(h)) + h = F.relu(self.conv3_2(h)) + h = F.relu(self.conv3_3(h)) + f3_3 = h + h = F.max_pool2d(h, 2, 2) + + h = F.relu(self.conv4_1(h)) + h = F.relu(self.conv4_2(h)) + h = F.relu(self.conv4_3(h)) + f4_3 = h + h = F.max_pool2d(h, 2, 2) + + h = F.relu(self.conv5_1(h)) + h = F.relu(self.conv5_2(h)) + h = F.relu(self.conv5_3(h)) + f5_3 = h + h = F.max_pool2d(h, 2, 2) + + h = F.relu(self.fc6(h)) + h = F.relu(self.fc7(h)) + ffc7 = h + h = F.relu(self.conv6_1(h)) + h = F.relu(self.conv6_2(h)) + f6_2 = h + h = F.relu(self.conv7_1(h)) + h = F.relu(self.conv7_2(h)) + f7_2 = h + + f3_3 = self.conv3_3_norm(f3_3) + f4_3 = self.conv4_3_norm(f4_3) + f5_3 = self.conv5_3_norm(f5_3) + + cls1 = self.conv3_3_norm_mbox_conf(f3_3) + reg1 = self.conv3_3_norm_mbox_loc(f3_3) + cls2 = self.conv4_3_norm_mbox_conf(f4_3) + reg2 = self.conv4_3_norm_mbox_loc(f4_3) + cls3 = self.conv5_3_norm_mbox_conf(f5_3) + reg3 = self.conv5_3_norm_mbox_loc(f5_3) + cls4 = self.fc7_mbox_conf(ffc7) + reg4 = self.fc7_mbox_loc(ffc7) + cls5 = self.conv6_2_mbox_conf(f6_2) + reg5 = self.conv6_2_mbox_loc(f6_2) + cls6 = self.conv7_2_mbox_conf(f7_2) + reg6 = self.conv7_2_mbox_loc(f7_2) + + # max-out background label + chunk = torch.chunk(cls1, 4, 1) + bmax = torch.max(torch.max(chunk[0], chunk[1]), chunk[2]) + cls1 = torch.cat([bmax, chunk[3]], dim=1) + + return [cls1, reg1, cls2, reg2, cls3, reg3, cls4, reg4, cls5, reg5, cls6, reg6] diff --git a/musetalk/utils/face_detection/detection/sfd/sfd_detector.py b/musetalk/utils/face_detection/detection/sfd/sfd_detector.py new file mode 100644 index 0000000..8fbce15 --- /dev/null +++ b/musetalk/utils/face_detection/detection/sfd/sfd_detector.py @@ -0,0 +1,59 @@ +import os +import cv2 +from torch.utils.model_zoo import load_url + +from ..core import FaceDetector + +from .net_s3fd import s3fd +from .bbox import * +from .detect import * + +models_urls = { + 's3fd': 'https://www.adrianbulat.com/downloads/python-fan/s3fd-619a316812.pth', +} + + +class SFDDetector(FaceDetector): + def __init__(self, device, path_to_detector=os.path.join(os.path.dirname(os.path.abspath(__file__)), 's3fd.pth'), verbose=False): + super(SFDDetector, self).__init__(device, verbose) + + # Initialise the face detector + if not os.path.isfile(path_to_detector): + model_weights = load_url(models_urls['s3fd']) + else: + model_weights = torch.load(path_to_detector) + + self.face_detector = s3fd() + self.face_detector.load_state_dict(model_weights) + self.face_detector.to(device) + self.face_detector.eval() + + def detect_from_image(self, tensor_or_path): + image = self.tensor_or_path_to_ndarray(tensor_or_path) + + bboxlist = detect(self.face_detector, image, device=self.device) + keep = nms(bboxlist, 0.3) + bboxlist = bboxlist[keep, :] + bboxlist = [x for x in bboxlist if x[-1] > 0.5] + + return bboxlist + + def detect_from_batch(self, images): + bboxlists = batch_detect(self.face_detector, images, device=self.device) + keeps = [nms(bboxlists[:, i, :], 0.3) for i in range(bboxlists.shape[1])] + bboxlists = [bboxlists[keep, i, :] for i, keep in enumerate(keeps)] + bboxlists = [[x for x in bboxlist if x[-1] > 0.5] for bboxlist in bboxlists] + + return bboxlists + + @property + def reference_scale(self): + return 195 + + @property + def reference_x_shift(self): + return 0 + + @property + def reference_y_shift(self): + return 0 diff --git a/musetalk/utils/face_detection/models.py b/musetalk/utils/face_detection/models.py new file mode 100644 index 0000000..ee2dde3 --- /dev/null +++ b/musetalk/utils/face_detection/models.py @@ -0,0 +1,261 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import math + + +def conv3x3(in_planes, out_planes, strd=1, padding=1, bias=False): + "3x3 convolution with padding" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, + stride=strd, padding=padding, bias=bias) + + +class ConvBlock(nn.Module): + def __init__(self, in_planes, out_planes): + super(ConvBlock, self).__init__() + self.bn1 = nn.BatchNorm2d(in_planes) + self.conv1 = conv3x3(in_planes, int(out_planes / 2)) + self.bn2 = nn.BatchNorm2d(int(out_planes / 2)) + self.conv2 = conv3x3(int(out_planes / 2), int(out_planes / 4)) + self.bn3 = nn.BatchNorm2d(int(out_planes / 4)) + self.conv3 = conv3x3(int(out_planes / 4), int(out_planes / 4)) + + if in_planes != out_planes: + self.downsample = nn.Sequential( + nn.BatchNorm2d(in_planes), + nn.ReLU(True), + nn.Conv2d(in_planes, out_planes, + kernel_size=1, stride=1, bias=False), + ) + else: + self.downsample = None + + def forward(self, x): + residual = x + + out1 = self.bn1(x) + out1 = F.relu(out1, True) + out1 = self.conv1(out1) + + out2 = self.bn2(out1) + out2 = F.relu(out2, True) + out2 = self.conv2(out2) + + out3 = self.bn3(out2) + out3 = F.relu(out3, True) + out3 = self.conv3(out3) + + out3 = torch.cat((out1, out2, out3), 1) + + if self.downsample is not None: + residual = self.downsample(residual) + + out3 += residual + + return out3 + + +class Bottleneck(nn.Module): + + expansion = 4 + + def __init__(self, inplanes, planes, stride=1, downsample=None): + super(Bottleneck, self).__init__() + self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) + self.bn1 = nn.BatchNorm2d(planes) + self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, + padding=1, bias=False) + self.bn2 = nn.BatchNorm2d(planes) + self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) + self.bn3 = nn.BatchNorm2d(planes * 4) + self.relu = nn.ReLU(inplace=True) + self.downsample = downsample + self.stride = stride + + def forward(self, x): + residual = x + + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + + out = self.conv2(out) + out = self.bn2(out) + out = self.relu(out) + + out = self.conv3(out) + out = self.bn3(out) + + if self.downsample is not None: + residual = self.downsample(x) + + out += residual + out = self.relu(out) + + return out + + +class HourGlass(nn.Module): + def __init__(self, num_modules, depth, num_features): + super(HourGlass, self).__init__() + self.num_modules = num_modules + self.depth = depth + self.features = num_features + + self._generate_network(self.depth) + + def _generate_network(self, level): + self.add_module('b1_' + str(level), ConvBlock(self.features, self.features)) + + self.add_module('b2_' + str(level), ConvBlock(self.features, self.features)) + + if level > 1: + self._generate_network(level - 1) + else: + self.add_module('b2_plus_' + str(level), ConvBlock(self.features, self.features)) + + self.add_module('b3_' + str(level), ConvBlock(self.features, self.features)) + + def _forward(self, level, inp): + # Upper branch + up1 = inp + up1 = self._modules['b1_' + str(level)](up1) + + # Lower branch + low1 = F.avg_pool2d(inp, 2, stride=2) + low1 = self._modules['b2_' + str(level)](low1) + + if level > 1: + low2 = self._forward(level - 1, low1) + else: + low2 = low1 + low2 = self._modules['b2_plus_' + str(level)](low2) + + low3 = low2 + low3 = self._modules['b3_' + str(level)](low3) + + up2 = F.interpolate(low3, scale_factor=2, mode='nearest') + + return up1 + up2 + + def forward(self, x): + return self._forward(self.depth, x) + + +class FAN(nn.Module): + + def __init__(self, num_modules=1): + super(FAN, self).__init__() + self.num_modules = num_modules + + # Base part + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3) + self.bn1 = nn.BatchNorm2d(64) + self.conv2 = ConvBlock(64, 128) + self.conv3 = ConvBlock(128, 128) + self.conv4 = ConvBlock(128, 256) + + # Stacking part + for hg_module in range(self.num_modules): + self.add_module('m' + str(hg_module), HourGlass(1, 4, 256)) + self.add_module('top_m_' + str(hg_module), ConvBlock(256, 256)) + self.add_module('conv_last' + str(hg_module), + nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)) + self.add_module('bn_end' + str(hg_module), nn.BatchNorm2d(256)) + self.add_module('l' + str(hg_module), nn.Conv2d(256, + 68, kernel_size=1, stride=1, padding=0)) + + if hg_module < self.num_modules - 1: + self.add_module( + 'bl' + str(hg_module), nn.Conv2d(256, 256, kernel_size=1, stride=1, padding=0)) + self.add_module('al' + str(hg_module), nn.Conv2d(68, + 256, kernel_size=1, stride=1, padding=0)) + + def forward(self, x): + x = F.relu(self.bn1(self.conv1(x)), True) + x = F.avg_pool2d(self.conv2(x), 2, stride=2) + x = self.conv3(x) + x = self.conv4(x) + + previous = x + + outputs = [] + for i in range(self.num_modules): + hg = self._modules['m' + str(i)](previous) + + ll = hg + ll = self._modules['top_m_' + str(i)](ll) + + ll = F.relu(self._modules['bn_end' + str(i)] + (self._modules['conv_last' + str(i)](ll)), True) + + # Predict heatmaps + tmp_out = self._modules['l' + str(i)](ll) + outputs.append(tmp_out) + + if i < self.num_modules - 1: + ll = self._modules['bl' + str(i)](ll) + tmp_out_ = self._modules['al' + str(i)](tmp_out) + previous = previous + ll + tmp_out_ + + return outputs + + +class ResNetDepth(nn.Module): + + def __init__(self, block=Bottleneck, layers=[3, 8, 36, 3], num_classes=68): + self.inplanes = 64 + super(ResNetDepth, self).__init__() + self.conv1 = nn.Conv2d(3 + 68, 64, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.relu = nn.ReLU(inplace=True) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = self._make_layer(block, 64, layers[0]) + self.layer2 = self._make_layer(block, 128, layers[1], stride=2) + self.layer3 = self._make_layer(block, 256, layers[2], stride=2) + self.layer4 = self._make_layer(block, 512, layers[3], stride=2) + self.avgpool = nn.AvgPool2d(7) + self.fc = nn.Linear(512 * block.expansion, num_classes) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels + m.weight.data.normal_(0, math.sqrt(2. / n)) + elif isinstance(m, nn.BatchNorm2d): + m.weight.data.fill_(1) + m.bias.data.zero_() + + def _make_layer(self, block, planes, blocks, stride=1): + downsample = None + if stride != 1 or self.inplanes != planes * block.expansion: + downsample = nn.Sequential( + nn.Conv2d(self.inplanes, planes * block.expansion, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(planes * block.expansion), + ) + + layers = [] + layers.append(block(self.inplanes, planes, stride, downsample)) + self.inplanes = planes * block.expansion + for i in range(1, blocks): + layers.append(block(self.inplanes, planes)) + + return nn.Sequential(*layers) + + def forward(self, x): + x = self.conv1(x) + x = self.bn1(x) + x = self.relu(x) + x = self.maxpool(x) + + x = self.layer1(x) + x = self.layer2(x) + x = self.layer3(x) + x = self.layer4(x) + + x = self.avgpool(x) + x = x.view(x.size(0), -1) + x = self.fc(x) + + return x diff --git a/musetalk/utils/face_detection/utils.py b/musetalk/utils/face_detection/utils.py new file mode 100644 index 0000000..3dc4cf3 --- /dev/null +++ b/musetalk/utils/face_detection/utils.py @@ -0,0 +1,313 @@ +from __future__ import print_function +import os +import sys +import time +import torch +import math +import numpy as np +import cv2 + + +def _gaussian( + size=3, sigma=0.25, amplitude=1, normalize=False, width=None, + height=None, sigma_horz=None, sigma_vert=None, mean_horz=0.5, + mean_vert=0.5): + # handle some defaults + if width is None: + width = size + if height is None: + height = size + if sigma_horz is None: + sigma_horz = sigma + if sigma_vert is None: + sigma_vert = sigma + center_x = mean_horz * width + 0.5 + center_y = mean_vert * height + 0.5 + gauss = np.empty((height, width), dtype=np.float32) + # generate kernel + for i in range(height): + for j in range(width): + gauss[i][j] = amplitude * math.exp(-(math.pow((j + 1 - center_x) / ( + sigma_horz * width), 2) / 2.0 + math.pow((i + 1 - center_y) / (sigma_vert * height), 2) / 2.0)) + if normalize: + gauss = gauss / np.sum(gauss) + return gauss + + +def draw_gaussian(image, point, sigma): + # Check if the gaussian is inside + ul = [math.floor(point[0] - 3 * sigma), math.floor(point[1] - 3 * sigma)] + br = [math.floor(point[0] + 3 * sigma), math.floor(point[1] + 3 * sigma)] + if (ul[0] > image.shape[1] or ul[1] > image.shape[0] or br[0] < 1 or br[1] < 1): + return image + size = 6 * sigma + 1 + g = _gaussian(size) + g_x = [int(max(1, -ul[0])), int(min(br[0], image.shape[1])) - int(max(1, ul[0])) + int(max(1, -ul[0]))] + g_y = [int(max(1, -ul[1])), int(min(br[1], image.shape[0])) - int(max(1, ul[1])) + int(max(1, -ul[1]))] + img_x = [int(max(1, ul[0])), int(min(br[0], image.shape[1]))] + img_y = [int(max(1, ul[1])), int(min(br[1], image.shape[0]))] + assert (g_x[0] > 0 and g_y[1] > 0) + image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1] + ] = image[img_y[0] - 1:img_y[1], img_x[0] - 1:img_x[1]] + g[g_y[0] - 1:g_y[1], g_x[0] - 1:g_x[1]] + image[image > 1] = 1 + return image + + +def transform(point, center, scale, resolution, invert=False): + """Generate and affine transformation matrix. + + Given a set of points, a center, a scale and a targer resolution, the + function generates and affine transformation matrix. If invert is ``True`` + it will produce the inverse transformation. + + Arguments: + point {torch.tensor} -- the input 2D point + center {torch.tensor or numpy.array} -- the center around which to perform the transformations + scale {float} -- the scale of the face/object + resolution {float} -- the output resolution + + Keyword Arguments: + invert {bool} -- define wherever the function should produce the direct or the + inverse transformation matrix (default: {False}) + """ + _pt = torch.ones(3) + _pt[0] = point[0] + _pt[1] = point[1] + + h = 200.0 * scale + t = torch.eye(3) + t[0, 0] = resolution / h + t[1, 1] = resolution / h + t[0, 2] = resolution * (-center[0] / h + 0.5) + t[1, 2] = resolution * (-center[1] / h + 0.5) + + if invert: + t = torch.inverse(t) + + new_point = (torch.matmul(t, _pt))[0:2] + + return new_point.int() + + +def crop(image, center, scale, resolution=256.0): + """Center crops an image or set of heatmaps + + Arguments: + image {numpy.array} -- an rgb image + center {numpy.array} -- the center of the object, usually the same as of the bounding box + scale {float} -- scale of the face + + Keyword Arguments: + resolution {float} -- the size of the output cropped image (default: {256.0}) + + Returns: + [type] -- [description] + """ # Crop around the center point + """ Crops the image around the center. Input is expected to be an np.ndarray """ + ul = transform([1, 1], center, scale, resolution, True) + br = transform([resolution, resolution], center, scale, resolution, True) + # pad = math.ceil(torch.norm((ul - br).float()) / 2.0 - (br[0] - ul[0]) / 2.0) + if image.ndim > 2: + newDim = np.array([br[1] - ul[1], br[0] - ul[0], + image.shape[2]], dtype=np.int32) + newImg = np.zeros(newDim, dtype=np.uint8) + else: + newDim = np.array([br[1] - ul[1], br[0] - ul[0]], dtype=np.int) + newImg = np.zeros(newDim, dtype=np.uint8) + ht = image.shape[0] + wd = image.shape[1] + newX = np.array( + [max(1, -ul[0] + 1), min(br[0], wd) - ul[0]], dtype=np.int32) + newY = np.array( + [max(1, -ul[1] + 1), min(br[1], ht) - ul[1]], dtype=np.int32) + oldX = np.array([max(1, ul[0] + 1), min(br[0], wd)], dtype=np.int32) + oldY = np.array([max(1, ul[1] + 1), min(br[1], ht)], dtype=np.int32) + newImg[newY[0] - 1:newY[1], newX[0] - 1:newX[1] + ] = image[oldY[0] - 1:oldY[1], oldX[0] - 1:oldX[1], :] + newImg = cv2.resize(newImg, dsize=(int(resolution), int(resolution)), + interpolation=cv2.INTER_LINEAR) + return newImg + + +def get_preds_fromhm(hm, center=None, scale=None): + """Obtain (x,y) coordinates given a set of N heatmaps. If the center + and the scale is provided the function will return the points also in + the original coordinate frame. + + Arguments: + hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H] + + Keyword Arguments: + center {torch.tensor} -- the center of the bounding box (default: {None}) + scale {float} -- face scale (default: {None}) + """ + max, idx = torch.max( + hm.view(hm.size(0), hm.size(1), hm.size(2) * hm.size(3)), 2) + idx += 1 + preds = idx.view(idx.size(0), idx.size(1), 1).repeat(1, 1, 2).float() + preds[..., 0].apply_(lambda x: (x - 1) % hm.size(3) + 1) + preds[..., 1].add_(-1).div_(hm.size(2)).floor_().add_(1) + + for i in range(preds.size(0)): + for j in range(preds.size(1)): + hm_ = hm[i, j, :] + pX, pY = int(preds[i, j, 0]) - 1, int(preds[i, j, 1]) - 1 + if pX > 0 and pX < 63 and pY > 0 and pY < 63: + diff = torch.FloatTensor( + [hm_[pY, pX + 1] - hm_[pY, pX - 1], + hm_[pY + 1, pX] - hm_[pY - 1, pX]]) + preds[i, j].add_(diff.sign_().mul_(.25)) + + preds.add_(-.5) + + preds_orig = torch.zeros(preds.size()) + if center is not None and scale is not None: + for i in range(hm.size(0)): + for j in range(hm.size(1)): + preds_orig[i, j] = transform( + preds[i, j], center, scale, hm.size(2), True) + + return preds, preds_orig + +def get_preds_fromhm_batch(hm, centers=None, scales=None): + """Obtain (x,y) coordinates given a set of N heatmaps. If the centers + and the scales is provided the function will return the points also in + the original coordinate frame. + + Arguments: + hm {torch.tensor} -- the predicted heatmaps, of shape [B, N, W, H] + + Keyword Arguments: + centers {torch.tensor} -- the centers of the bounding box (default: {None}) + scales {float} -- face scales (default: {None}) + """ + max, idx = torch.max( + hm.view(hm.size(0), hm.size(1), hm.size(2) * hm.size(3)), 2) + idx += 1 + preds = idx.view(idx.size(0), idx.size(1), 1).repeat(1, 1, 2).float() + preds[..., 0].apply_(lambda x: (x - 1) % hm.size(3) + 1) + preds[..., 1].add_(-1).div_(hm.size(2)).floor_().add_(1) + + for i in range(preds.size(0)): + for j in range(preds.size(1)): + hm_ = hm[i, j, :] + pX, pY = int(preds[i, j, 0]) - 1, int(preds[i, j, 1]) - 1 + if pX > 0 and pX < 63 and pY > 0 and pY < 63: + diff = torch.FloatTensor( + [hm_[pY, pX + 1] - hm_[pY, pX - 1], + hm_[pY + 1, pX] - hm_[pY - 1, pX]]) + preds[i, j].add_(diff.sign_().mul_(.25)) + + preds.add_(-.5) + + preds_orig = torch.zeros(preds.size()) + if centers is not None and scales is not None: + for i in range(hm.size(0)): + for j in range(hm.size(1)): + preds_orig[i, j] = transform( + preds[i, j], centers[i], scales[i], hm.size(2), True) + + return preds, preds_orig + +def shuffle_lr(parts, pairs=None): + """Shuffle the points left-right according to the axis of symmetry + of the object. + + Arguments: + parts {torch.tensor} -- a 3D or 4D object containing the + heatmaps. + + Keyword Arguments: + pairs {list of integers} -- [order of the flipped points] (default: {None}) + """ + if pairs is None: + pairs = [16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, + 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 27, 28, 29, 30, 35, + 34, 33, 32, 31, 45, 44, 43, 42, 47, 46, 39, 38, 37, 36, 41, + 40, 54, 53, 52, 51, 50, 49, 48, 59, 58, 57, 56, 55, 64, 63, + 62, 61, 60, 67, 66, 65] + if parts.ndimension() == 3: + parts = parts[pairs, ...] + else: + parts = parts[:, pairs, ...] + + return parts + + +def flip(tensor, is_label=False): + """Flip an image or a set of heatmaps left-right + + Arguments: + tensor {numpy.array or torch.tensor} -- [the input image or heatmaps] + + Keyword Arguments: + is_label {bool} -- [denote wherever the input is an image or a set of heatmaps ] (default: {False}) + """ + if not torch.is_tensor(tensor): + tensor = torch.from_numpy(tensor) + + if is_label: + tensor = shuffle_lr(tensor).flip(tensor.ndimension() - 1) + else: + tensor = tensor.flip(tensor.ndimension() - 1) + + return tensor + +# From pyzolib/paths.py (https://bitbucket.org/pyzo/pyzolib/src/tip/paths.py) + + +def appdata_dir(appname=None, roaming=False): + """ appdata_dir(appname=None, roaming=False) + + Get the path to the application directory, where applications are allowed + to write user specific files (e.g. configurations). For non-user specific + data, consider using common_appdata_dir(). + If appname is given, a subdir is appended (and created if necessary). + If roaming is True, will prefer a roaming directory (Windows Vista/7). + """ + + # Define default user directory + userDir = os.getenv('FACEALIGNMENT_USERDIR', None) + if userDir is None: + userDir = os.path.expanduser('~') + if not os.path.isdir(userDir): # pragma: no cover + userDir = '/var/tmp' # issue #54 + + # Get system app data dir + path = None + if sys.platform.startswith('win'): + path1, path2 = os.getenv('LOCALAPPDATA'), os.getenv('APPDATA') + path = (path2 or path1) if roaming else (path1 or path2) + elif sys.platform.startswith('darwin'): + path = os.path.join(userDir, 'Library', 'Application Support') + # On Linux and as fallback + if not (path and os.path.isdir(path)): + path = userDir + + # Maybe we should store things local to the executable (in case of a + # portable distro or a frozen application that wants to be portable) + prefix = sys.prefix + if getattr(sys, 'frozen', None): + prefix = os.path.abspath(os.path.dirname(sys.executable)) + for reldir in ('settings', '../settings'): + localpath = os.path.abspath(os.path.join(prefix, reldir)) + if os.path.isdir(localpath): # pragma: no cover + try: + open(os.path.join(localpath, 'test.write'), 'wb').close() + os.remove(os.path.join(localpath, 'test.write')) + except IOError: + pass # We cannot write in this directory + else: + path = localpath + break + + # Get path specific for this app + if appname: + if path == userDir: + appname = '.' + appname.lstrip('.') # Make it a hidden directory + path = os.path.join(path, appname) + if not os.path.isdir(path): # pragma: no cover + os.mkdir(path) + + # Done + return path diff --git a/musetalk/utils/face_parsing/__init__.py b/musetalk/utils/face_parsing/__init__.py new file mode 100755 index 0000000..52b9fb3 --- /dev/null +++ b/musetalk/utils/face_parsing/__init__.py @@ -0,0 +1,56 @@ +import torch +import time +import os +import cv2 +import numpy as np +from PIL import Image +from .model import BiSeNet +import torchvision.transforms as transforms + +class FaceParsing(): + def __init__(self,resnet_path,model_pth): + self.net = self.model_init(resnet_path,model_pth) + self.preprocess = self.image_preprocess() + + def model_init(self, + resnet_path='./models/face-parse-bisent/resnet18-5c106cde.pth', + model_pth='./models/face-parse-bisent/79999_iter.pth'): + net = BiSeNet(resnet_path) + if torch.cuda.is_available(): + net.cuda() + net.load_state_dict(torch.load(model_pth)) + else: + net.load_state_dict(torch.load(model_pth, map_location=torch.device('cpu'))) + net.eval() + return net + + def image_preprocess(self): + return transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), + ]) + + def __call__(self, image, size=(512, 512)): + if isinstance(image, str): + image = Image.open(image) + + width, height = image.size + with torch.no_grad(): + image = image.resize(size, Image.BILINEAR) + img = self.preprocess(image) + if torch.cuda.is_available(): + img = torch.unsqueeze(img, 0).cuda() + else: + img = torch.unsqueeze(img, 0) + out = self.net(img)[0] + parsing = out.squeeze(0).cpu().numpy().argmax(0) + parsing[np.where(parsing>13)] = 0 + parsing[np.where(parsing>=1)] = 255 + parsing = Image.fromarray(parsing.astype(np.uint8)) + return parsing + +if __name__ == "__main__": + fp = FaceParsing() + segmap = fp('154_small.png') + segmap.save('res.png') + diff --git a/musetalk/utils/face_parsing/__pycache__/__init__.cpython-310.pyc b/musetalk/utils/face_parsing/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..09bb09b625b5ecfc5089f95f93bea81b2043eda1 GIT binary patch literal 2159 zcmaJ?-ESN<5V!aH_CAt^G(ah$yi__w?i$oW3xz-{3X$5QYKl--NUQDIxoomu_4-m0 z-Mx^$A;cT6?1`sJ{0+P#wa*Aks1it^`~y4`QJC2!O?94Z z0W|8nNz-dm@&REcv+fXP?P^|&x0$`EiDll}C0>Vjn6pd089r0avc{BxCS9$1*WH76 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as F +import torchvision + +from .resnet import Resnet18 +# from modules.bn import InPlaceABNSync as BatchNorm2d + + +class ConvBNReLU(nn.Module): + def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs): + super(ConvBNReLU, self).__init__() + self.conv = nn.Conv2d(in_chan, + out_chan, + kernel_size = ks, + stride = stride, + padding = padding, + bias = False) + self.bn = nn.BatchNorm2d(out_chan) + self.init_weight() + + def forward(self, x): + x = self.conv(x) + x = F.relu(self.bn(x)) + return x + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + +class BiSeNetOutput(nn.Module): + def __init__(self, in_chan, mid_chan, n_classes, *args, **kwargs): + super(BiSeNetOutput, self).__init__() + self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1) + self.conv_out = nn.Conv2d(mid_chan, n_classes, kernel_size=1, bias=False) + self.init_weight() + + def forward(self, x): + x = self.conv(x) + x = self.conv_out(x) + return x + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + def get_params(self): + wd_params, nowd_params = [], [] + for name, module in self.named_modules(): + if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): + wd_params.append(module.weight) + if not module.bias is None: + nowd_params.append(module.bias) + elif isinstance(module, nn.BatchNorm2d): + nowd_params += list(module.parameters()) + return wd_params, nowd_params + + +class AttentionRefinementModule(nn.Module): + def __init__(self, in_chan, out_chan, *args, **kwargs): + super(AttentionRefinementModule, self).__init__() + self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1) + self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size= 1, bias=False) + self.bn_atten = nn.BatchNorm2d(out_chan) + self.sigmoid_atten = nn.Sigmoid() + self.init_weight() + + def forward(self, x): + feat = self.conv(x) + atten = F.avg_pool2d(feat, feat.size()[2:]) + atten = self.conv_atten(atten) + atten = self.bn_atten(atten) + atten = self.sigmoid_atten(atten) + out = torch.mul(feat, atten) + return out + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + +class ContextPath(nn.Module): + def __init__(self, resnet_path, *args, **kwargs): + super(ContextPath, self).__init__() + self.resnet = Resnet18(resnet_path) + self.arm16 = AttentionRefinementModule(256, 128) + self.arm32 = AttentionRefinementModule(512, 128) + self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1) + self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1) + self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0) + + self.init_weight() + + def forward(self, x): + H0, W0 = x.size()[2:] + feat8, feat16, feat32 = self.resnet(x) + H8, W8 = feat8.size()[2:] + H16, W16 = feat16.size()[2:] + H32, W32 = feat32.size()[2:] + + avg = F.avg_pool2d(feat32, feat32.size()[2:]) + avg = self.conv_avg(avg) + avg_up = F.interpolate(avg, (H32, W32), mode='nearest') + + feat32_arm = self.arm32(feat32) + feat32_sum = feat32_arm + avg_up + feat32_up = F.interpolate(feat32_sum, (H16, W16), mode='nearest') + feat32_up = self.conv_head32(feat32_up) + + feat16_arm = self.arm16(feat16) + feat16_sum = feat16_arm + feat32_up + feat16_up = F.interpolate(feat16_sum, (H8, W8), mode='nearest') + feat16_up = self.conv_head16(feat16_up) + + return feat8, feat16_up, feat32_up # x8, x8, x16 + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + def get_params(self): + wd_params, nowd_params = [], [] + for name, module in self.named_modules(): + if isinstance(module, (nn.Linear, nn.Conv2d)): + wd_params.append(module.weight) + if not module.bias is None: + nowd_params.append(module.bias) + elif isinstance(module, nn.BatchNorm2d): + nowd_params += list(module.parameters()) + return wd_params, nowd_params + + +### This is not used, since I replace this with the resnet feature with the same size +class SpatialPath(nn.Module): + def __init__(self, *args, **kwargs): + super(SpatialPath, self).__init__() + self.conv1 = ConvBNReLU(3, 64, ks=7, stride=2, padding=3) + self.conv2 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1) + self.conv3 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1) + self.conv_out = ConvBNReLU(64, 128, ks=1, stride=1, padding=0) + self.init_weight() + + def forward(self, x): + feat = self.conv1(x) + feat = self.conv2(feat) + feat = self.conv3(feat) + feat = self.conv_out(feat) + return feat + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + def get_params(self): + wd_params, nowd_params = [], [] + for name, module in self.named_modules(): + if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): + wd_params.append(module.weight) + if not module.bias is None: + nowd_params.append(module.bias) + elif isinstance(module, nn.BatchNorm2d): + nowd_params += list(module.parameters()) + return wd_params, nowd_params + + +class FeatureFusionModule(nn.Module): + def __init__(self, in_chan, out_chan, *args, **kwargs): + super(FeatureFusionModule, self).__init__() + self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0) + self.conv1 = nn.Conv2d(out_chan, + out_chan//4, + kernel_size = 1, + stride = 1, + padding = 0, + bias = False) + self.conv2 = nn.Conv2d(out_chan//4, + out_chan, + kernel_size = 1, + stride = 1, + padding = 0, + bias = False) + self.relu = nn.ReLU(inplace=True) + self.sigmoid = nn.Sigmoid() + self.init_weight() + + def forward(self, fsp, fcp): + fcat = torch.cat([fsp, fcp], dim=1) + feat = self.convblk(fcat) + atten = F.avg_pool2d(feat, feat.size()[2:]) + atten = self.conv1(atten) + atten = self.relu(atten) + atten = self.conv2(atten) + atten = self.sigmoid(atten) + feat_atten = torch.mul(feat, atten) + feat_out = feat_atten + feat + return feat_out + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + def get_params(self): + wd_params, nowd_params = [], [] + for name, module in self.named_modules(): + if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d): + wd_params.append(module.weight) + if not module.bias is None: + nowd_params.append(module.bias) + elif isinstance(module, nn.BatchNorm2d): + nowd_params += list(module.parameters()) + return wd_params, nowd_params + + +class BiSeNet(nn.Module): + def __init__(self, resnet_path='models/resnet18-5c106cde.pth', n_classes=19, *args, **kwargs): + super(BiSeNet, self).__init__() + self.cp = ContextPath(resnet_path) + ## here self.sp is deleted + self.ffm = FeatureFusionModule(256, 256) + self.conv_out = BiSeNetOutput(256, 256, n_classes) + self.conv_out16 = BiSeNetOutput(128, 64, n_classes) + self.conv_out32 = BiSeNetOutput(128, 64, n_classes) + self.init_weight() + + def forward(self, x): + H, W = x.size()[2:] + feat_res8, feat_cp8, feat_cp16 = self.cp(x) # here return res3b1 feature + feat_sp = feat_res8 # use res3b1 feature to replace spatial path feature + feat_fuse = self.ffm(feat_sp, feat_cp8) + + feat_out = self.conv_out(feat_fuse) + feat_out16 = self.conv_out16(feat_cp8) + feat_out32 = self.conv_out32(feat_cp16) + + feat_out = F.interpolate(feat_out, (H, W), mode='bilinear', align_corners=True) + feat_out16 = F.interpolate(feat_out16, (H, W), mode='bilinear', align_corners=True) + feat_out32 = F.interpolate(feat_out32, (H, W), mode='bilinear', align_corners=True) + return feat_out, feat_out16, feat_out32 + + def init_weight(self): + for ly in self.children(): + if isinstance(ly, nn.Conv2d): + nn.init.kaiming_normal_(ly.weight, a=1) + if not ly.bias is None: nn.init.constant_(ly.bias, 0) + + def get_params(self): + wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], [] + for name, child in self.named_children(): + child_wd_params, child_nowd_params = child.get_params() + if isinstance(child, FeatureFusionModule) or isinstance(child, BiSeNetOutput): + lr_mul_wd_params += child_wd_params + lr_mul_nowd_params += child_nowd_params + else: + wd_params += child_wd_params + nowd_params += child_nowd_params + return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params + + +if __name__ == "__main__": + net = BiSeNet(19) + net.cuda() + net.eval() + in_ten = torch.randn(16, 3, 640, 480).cuda() + out, out16, out32 = net(in_ten) + print(out.shape) + + net.get_params() diff --git a/musetalk/utils/face_parsing/resnet.py b/musetalk/utils/face_parsing/resnet.py new file mode 100755 index 0000000..e2e5d87 --- /dev/null +++ b/musetalk/utils/face_parsing/resnet.py @@ -0,0 +1,109 @@ +#!/usr/bin/python +# -*- encoding: utf-8 -*- + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.model_zoo as modelzoo + +# from modules.bn import InPlaceABNSync as BatchNorm2d + +resnet18_url = 'https://download.pytorch.org/models/resnet18-5c106cde.pth' + + +def conv3x3(in_planes, out_planes, stride=1): + """3x3 convolution with padding""" + return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, + padding=1, bias=False) + + +class BasicBlock(nn.Module): + def __init__(self, in_chan, out_chan, stride=1): + super(BasicBlock, self).__init__() + self.conv1 = conv3x3(in_chan, out_chan, stride) + self.bn1 = nn.BatchNorm2d(out_chan) + self.conv2 = conv3x3(out_chan, out_chan) + self.bn2 = nn.BatchNorm2d(out_chan) + self.relu = nn.ReLU(inplace=True) + self.downsample = None + if in_chan != out_chan or stride != 1: + self.downsample = nn.Sequential( + nn.Conv2d(in_chan, out_chan, + kernel_size=1, stride=stride, bias=False), + nn.BatchNorm2d(out_chan), + ) + + def forward(self, x): + residual = self.conv1(x) + residual = F.relu(self.bn1(residual)) + residual = self.conv2(residual) + residual = self.bn2(residual) + + shortcut = x + if self.downsample is not None: + shortcut = self.downsample(x) + + out = shortcut + residual + out = self.relu(out) + return out + + +def create_layer_basic(in_chan, out_chan, bnum, stride=1): + layers = [BasicBlock(in_chan, out_chan, stride=stride)] + for i in range(bnum-1): + layers.append(BasicBlock(out_chan, out_chan, stride=1)) + return nn.Sequential(*layers) + + +class Resnet18(nn.Module): + def __init__(self, model_path): + super(Resnet18, self).__init__() + self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, + bias=False) + self.bn1 = nn.BatchNorm2d(64) + self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1) + self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1) + self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2) + self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2) + self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2) + self.init_weight(model_path) + + def forward(self, x): + x = self.conv1(x) + x = F.relu(self.bn1(x)) + x = self.maxpool(x) + + x = self.layer1(x) + feat8 = self.layer2(x) # 1/8 + feat16 = self.layer3(feat8) # 1/16 + feat32 = self.layer4(feat16) # 1/32 + return feat8, feat16, feat32 + + def init_weight(self, model_path): + state_dict = torch.load(model_path) #modelzoo.load_url(resnet18_url) + self_state_dict = self.state_dict() + for k, v in state_dict.items(): + if 'fc' in k: continue + self_state_dict.update({k: v}) + self.load_state_dict(self_state_dict) + + def get_params(self): + wd_params, nowd_params = [], [] + for name, module in self.named_modules(): + if isinstance(module, (nn.Linear, nn.Conv2d)): + wd_params.append(module.weight) + if not module.bias is None: + nowd_params.append(module.bias) + elif isinstance(module, nn.BatchNorm2d): + nowd_params += list(module.parameters()) + return wd_params, nowd_params + + +if __name__ == "__main__": + net = Resnet18() + x = torch.randn(16, 3, 224, 224) + out = net(x) + print(out[0].size()) + print(out[1].size()) + print(out[2].size()) + net.get_params() diff --git a/musetalk/utils/preprocessing.py b/musetalk/utils/preprocessing.py new file mode 100644 index 0000000..5c92acc --- /dev/null +++ b/musetalk/utils/preprocessing.py @@ -0,0 +1,157 @@ +import sys +from face_detection import FaceAlignment,LandmarksType +from os import listdir, path +import subprocess +import numpy as np +import cv2 +import pickle +import os +import json +from mmpose.apis import inference_topdown, init_model +from mmpose.structures import merge_data_samples +import torch +from tqdm import tqdm +parent_directory = os.path.dirname(os.path.abspath(__file__)) +# initialize the mmpose model +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") +''' +config_file = os.path.join(parent_directory,"dwpose/rtmpose-l_8xb32-270e_coco-ubody-wholebody-384x288.py") +checkpoint_file = './models/dwpose/dw-ll_ucoco_384.pth' +model = init_model(config_file, checkpoint_file, device=device) +''' + + +# initialize the face detection model +device = "cuda" if torch.cuda.is_available() else "cpu" +fa = FaceAlignment(LandmarksType._2D, flip_input=False,device=device) + +# maker if the bbox is not sufficient +coord_placeholder = (0.0,0.0,0.0,0.0) + +def resize_landmark(landmark, w, h, new_w, new_h): + w_ratio = new_w / w + h_ratio = new_h / h + landmark_norm = landmark / [w, h] + landmark_resized = landmark_norm * [new_w, new_h] + return landmark_resized + +def read_imgs(img_list): + frames = [] + print('reading images...') + for img_path in tqdm(img_list): + frame = cv2.imread(img_path) + frames.append(frame) + return frames + +def get_bbox_range(model,img_list,batch_size_fa,upperbondrange =0): + frames = read_imgs(img_list) + # batch_size_fa = 1 + batches = [frames[i:i + batch_size_fa] for i in range(0, len(frames), batch_size_fa)] + coords_list = [] + landmarks = [] + if upperbondrange != 0: + print('get key_landmark and face bounding boxes with the bbox_shift:',upperbondrange) + else: + print('get key_landmark and face bounding boxes with the default value') + average_range_minus = [] + average_range_plus = [] + for fb in tqdm(batches): + results = inference_topdown(model, np.asarray(fb)[0]) + results = merge_data_samples(results) + keypoints = results.pred_instances.keypoints + face_land_mark= keypoints[0][23:91] + face_land_mark = face_land_mark.astype(np.int32) + + # get bounding boxes by face detetion + bbox = fa.get_detections_for_batch(np.asarray(fb)) + + # adjust the bounding box refer to landmark + # Add the bounding box to a tuple and append it to the coordinates list + for j, f in enumerate(bbox): + if f is None: # no face in the image + coords_list += [coord_placeholder] + continue + + half_face_coord = face_land_mark[29]#np.mean([face_land_mark[28], face_land_mark[29]], axis=0) + range_minus = (face_land_mark[30]- face_land_mark[29])[1] + range_plus = (face_land_mark[29]- face_land_mark[28])[1] + average_range_minus.append(range_minus) + average_range_plus.append(range_plus) + if upperbondrange != 0: + half_face_coord[1] = upperbondrange+half_face_coord[1] #手动调整 + 向下（偏29） - 向上（偏28） + + text_range=f"Total frame:「{len(frames)}」 Manually adjust range : [ -{int(sum(average_range_minus) / len(average_range_minus))}~{int(sum(average_range_plus) / len(average_range_plus))} ] , the current value: {upperbondrange}" + return text_range + + +def get_landmark_and_bbox(model,img_list,batch_size_fa,upperbondrange =0): + frames = read_imgs(img_list) + # batch_size_fa = 1 + batches = [frames[i:i + batch_size_fa] for i in range(0, len(frames), batch_size_fa)] + coords_list = [] + landmarks = [] + if upperbondrange != 0: + print('get key_landmark and face bounding boxes with the bbox_shift:',upperbondrange) + else: + print('get key_landmark and face bounding boxes with the default value') + average_range_minus = [] + average_range_plus = [] + for fb in tqdm(batches): + results = inference_topdown(model, np.asarray(fb)[0]) + results = merge_data_samples(results) + keypoints = results.pred_instances.keypoints + face_land_mark= keypoints[0][23:91] + face_land_mark = face_land_mark.astype(np.int32) + + # get bounding boxes by face detetion + bbox = fa.get_detections_for_batch(np.asarray(fb)) + + # adjust the bounding box refer to landmark + # Add the bounding box to a tuple and append it to the coordinates list + for j, f in enumerate(bbox): + if f is None: # no face in the image + coords_list += [coord_placeholder] + continue + + half_face_coord = face_land_mark[29]#np.mean([face_land_mark[28], face_land_mark[29]], axis=0) + range_minus = (face_land_mark[30]- face_land_mark[29])[1] + range_plus = (face_land_mark[29]- face_land_mark[28])[1] + average_range_minus.append(range_minus) + average_range_plus.append(range_plus) + if upperbondrange != 0: + half_face_coord[1] = upperbondrange+half_face_coord[1] #手动调整 + 向下（偏29） - 向上（偏28） + half_face_dist = np.max(face_land_mark[:,1]) - half_face_coord[1] + upper_bond = half_face_coord[1]-half_face_dist + + f_landmark = (np.min(face_land_mark[:, 0]),int(upper_bond),np.max(face_land_mark[:, 0]),np.max(face_land_mark[:,1])) + x1, y1, x2, y2 = f_landmark + + if y2-y1<=0 or x2-x1<=0 or x1<0: # if the landmark bbox is not suitable, reuse the bbox + coords_list += [f] + w,h = f[2]-f[0], f[3]-f[1] + print("error bbox:",f) + else: + coords_list += [f_landmark] + + print("********************************************bbox_shift parameter adjustment**********************************************************") + print(f"Total frame:「{len(frames)}」 Manually adjust range : [ -{int(sum(average_range_minus) / len(average_range_minus))}~{int(sum(average_range_plus) / len(average_range_plus))} ] , the current value: {upperbondrange}") + print("*************************************************************************************************************************************") + return coords_list,frames + + +if __name__ == "__main__": + img_list = ["./results/lyria/00000.png","./results/lyria/00001.png","./results/lyria/00002.png","./results/lyria/00003.png"] + crop_coord_path = "./coord_face.pkl" + coords_list,full_frames = get_landmark_and_bbox(img_list) + with open(crop_coord_path, 'wb') as f: + pickle.dump(coords_list, f) + + for bbox, frame in zip(coords_list,full_frames): + if bbox == coord_placeholder: + continue + x1, y1, x2, y2 = bbox + crop_frame = frame[y1:y2, x1:x2] + print('Cropped shape', crop_frame.shape) + + #cv2.imwrite(path.join(save_dir, '{}.png'.format(i)),full_frames[i][0][y1:y2, x1:x2]) + print(coords_list) diff --git a/musetalk/utils/utils.py b/musetalk/utils/utils.py new file mode 100644 index 0000000..1cc3706 --- /dev/null +++ b/musetalk/utils/utils.py @@ -0,0 +1,61 @@ +import os +import cv2 +import numpy as np +import torch + +ffmpeg_path = os.getenv('FFMPEG_PATH') +if ffmpeg_path is None: + print("please download ffmpeg-static and export to FFMPEG_PATH. \nFor example: export FFMPEG_PATH=/musetalk/ffmpeg-4.4-amd64-static") +elif ffmpeg_path not in os.getenv('PATH'): + print("add ffmpeg to path") + os.environ["PATH"] = f"{ffmpeg_path}:{os.environ['PATH']}" + + +from ..whisper.audio2feature import Audio2Feature +from ..models.vae import VAE +from ..models.unet import UNet,PositionalEncoding + +def load_all_model(base_dir): + audio_processor = Audio2Feature(model_path=os.path.join(base_dir,"whisper/tiny.pt")) + vae = VAE(model_path =os.path.join(base_dir,"sd-vae-ft-mse/")) + unet = UNet(unet_config=os.path.join(base_dir,"musetalk/musetalk.json"), + model_path =os.path.join(base_dir,"musetalk/pytorch_model.bin")) + pe = PositionalEncoding(d_model=384) + return audio_processor,vae,unet,pe + +def get_file_type(video_path): + _, ext = os.path.splitext(video_path) + + if ext.lower() in ['.jpg', '.jpeg', '.png', '.bmp', '.tif', '.tiff']: + return 'image' + elif ext.lower() in ['.avi', '.mp4', '.mov', '.flv', '.mkv']: + return 'video' + else: + return 'unsupported' + +def get_video_fps(video_path): + video = cv2.VideoCapture(video_path) + fps = video.get(cv2.CAP_PROP_FPS) + video.release() + return fps + +def datagen(whisper_chunks,vae_encode_latents,batch_size=8,delay_frame = 0): + whisper_batch, latent_batch = [], [] + for i, w in enumerate(whisper_chunks): + idx = (i+delay_frame)%len(vae_encode_latents) + latent = vae_encode_latents[idx] + whisper_batch.append(w) + latent_batch.append(latent) + + if len(latent_batch) >= batch_size: + whisper_batch = np.asarray(whisper_batch) + latent_batch = torch.cat(latent_batch, dim=0) + yield whisper_batch, 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[2,2],fps = 25): + """ + Get sliced features based on a given index + :param feature_array: + :param start_idx: the start index of the feature + :param audio_feat_length: + :return: + """ + length = len(feature_array) + selected_feature = [] + selected_idx = [] + + center_idx = int(vid_idx*50/fps) + left_idx = center_idx-audio_feat_length[0]*2 + right_idx = center_idx + (audio_feat_length[1]+1)*2 + + for idx in range(left_idx,right_idx): + idx = max(0, idx) + idx = min(length-1, idx) + x = feature_array[idx] + selected_feature.append(x) + selected_idx.append(idx) + + selected_feature = np.concatenate(selected_feature, axis=0) + selected_feature = selected_feature.reshape(-1, 384)# 50*384 + return selected_feature,selected_idx + + def get_sliced_feature_sparse(self,feature_array, vid_idx, audio_feat_length= [2,2],fps = 25): + """ + Get sliced features based on a given index + :param feature_array: + :param start_idx: the start index of the feature + :param audio_feat_length: + :return: + """ + length = len(feature_array) + selected_feature = [] + selected_idx = [] + + for dt in range(-audio_feat_length[0],audio_feat_length[1]+1): + left_idx = int((vid_idx+dt)*50/fps) + if left_idx<1 or left_idx>length-1: + left_idx = max(0, left_idx) + left_idx = min(length-1, left_idx) + + x = feature_array[left_idx] + x = x[np.newaxis,:,:] + x = np.repeat(x, 2, axis=0) + selected_feature.append(x) + selected_idx.append(left_idx) + selected_idx.append(left_idx) + else: + x = feature_array[left_idx-1:left_idx+1] + selected_feature.append(x) + selected_idx.append(left_idx-1) + selected_idx.append(left_idx) + selected_feature = np.concatenate(selected_feature, axis=0) + selected_feature = selected_feature.reshape(-1, 384)# 50*384 + return selected_feature,selected_idx + + + def feature2chunks(self,feature_array,fps,audio_feat_length = [2,2]): + whisper_chunks = [] + whisper_idx_multiplier = 50./fps + i = 0 + print(f"video in {fps} FPS, audio idx in 50FPS") + while 1: + start_idx = int(i * whisper_idx_multiplier) + selected_feature,selected_idx = self.get_sliced_feature(feature_array= feature_array,vid_idx = i,audio_feat_length=audio_feat_length,fps=fps) + #print(f"i:{i},selected_idx {selected_idx}") + whisper_chunks.append(selected_feature) + i += 1 + if start_idx>len(feature_array): + break + + return whisper_chunks + + def audio2feat(self,audio_path): + # get the sample rate of the audio + result = self.model.transcribe(audio_path) + embed_list = [] + for emb in result['segments']: + encoder_embeddings = emb['encoder_embeddings'] + encoder_embeddings = encoder_embeddings.transpose(0,2,1,3) + encoder_embeddings = encoder_embeddings.squeeze(0) + start_idx = int(emb['start']) + end_idx = int(emb['end']) + emb_end_idx = int((end_idx - start_idx)/2) + embed_list.append(encoder_embeddings[:emb_end_idx]) + concatenated_array = np.concatenate(embed_list, axis=0) + return concatenated_array + +if __name__ == "__main__": + audio_processor = Audio2Feature(model_path="../../models/whisper/whisper_tiny.pt") + audio_path = "./test.mp3" + array = audio_processor.audio2feat(audio_path) + print(array.shape) + fps = 25 + whisper_idx_multiplier = 50./fps + + i = 0 + print(f"video in {fps} FPS, audio idx in 50FPS") + while 1: + start_idx = int(i * whisper_idx_multiplier) + selected_feature,selected_idx = audio_processor.get_sliced_feature(feature_array= array,vid_idx = i,audio_feat_length=[2,2],fps=fps) + print(f"video idx {i},\t audio idx {selected_idx},\t shape {selected_feature.shape}") + i += 1 + if start_idx>len(array): + break diff --git a/musetalk/whisper/whisper/__init__.py b/musetalk/whisper/whisper/__init__.py new file mode 100644 index 0000000..b925553 --- /dev/null +++ b/musetalk/whisper/whisper/__init__.py @@ -0,0 +1,116 @@ +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", + "large-v1": "https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large-v1.pt", + "large-v2": "https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt", + "large-v3": "https://openaipublic.azureedge.net/main/whisper/models/e5b1a55b89c1367dacf97e3e19bfd829a01529dbfdeefa8caeb59b3f1b81dadb/large-v3.pt", +} + + +def _download(url: str, root: str, in_memory: bool) -> Union[bytes, str]: + os.makedirs(root, exist_ok=True) + + expected_sha256 = url.split("/")[-2] + download_target = os.path.join(root, os.path.basename(url)) + + if os.path.exists(download_target) and not os.path.isfile(download_target): + raise RuntimeError(f"{download_target} exists and is not a regular file") + + if os.path.isfile(download_target): + model_bytes = open(download_target, "rb").read() + if hashlib.sha256(model_bytes).hexdigest() == expected_sha256: + return model_bytes if in_memory else download_target + else: + warnings.warn(f"{download_target} exists, but the SHA256 checksum does not match; re-downloading the file") + + with urllib.request.urlopen(url) as source, open(download_target, "wb") as output: + with tqdm(total=int(source.info().get("Content-Length")), ncols=80, unit='iB', unit_scale=True, unit_divisor=1024) as loop: + while True: + buffer = source.read(8192) + if not buffer: + break + + output.write(buffer) + loop.update(len(buffer)) + + model_bytes = open(download_target, "rb").read() + if hashlib.sha256(model_bytes).hexdigest() != expected_sha256: + raise RuntimeError("Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.") + + return model_bytes if in_memory else download_target + + +def available_models() -> List[str]: + """Returns the names of available models""" + return list(_MODELS.keys()) + + +def load_model(name: str, device: Optional[Union[str, torch.device]] = None, download_root: str = None, in_memory: bool = False) -> Whisper: + """ + Load a Whisper ASR model + + Parameters + ---------- + name : str + one of the official model names listed by `whisper.available_models()`, or + path to a model checkpoint containing the model dimensions and the model state_dict. + device : Union[str, torch.device] + the PyTorch device to put the model into + download_root: str + path to download the model files; by default, it uses "~/.cache/whisper" + in_memory: bool + whether to preload the model weights into host memory + + Returns + ------- + model : Whisper + The Whisper ASR model instance + """ + + if device is None: + device = "cuda" if torch.cuda.is_available() else "cpu" + if download_root is None: + download_root = os.getenv( + "XDG_CACHE_HOME", + os.path.join(os.path.expanduser("~"), ".cache", "whisper") + ) + + if name in _MODELS: + checkpoint_file = _download(_MODELS[name], download_root, in_memory) + elif os.path.isfile(name): + checkpoint_file = open(name, "rb").read() if in_memory else name + else: + raise RuntimeError(f"Model {name} not found; available models = {available_models()}") + + with (io.BytesIO(checkpoint_file) if in_memory else open(checkpoint_file, "rb")) as fp: + checkpoint = torch.load(fp, map_location=device) + del checkpoint_file + + dims = ModelDimensions(**checkpoint["dims"]) + model = Whisper(dims) + model.load_state_dict(checkpoint["model_state_dict"]) + + return model.to(device) diff --git a/musetalk/whisper/whisper/__main__.py b/musetalk/whisper/whisper/__main__.py new file mode 100644 index 0000000..bc9b04a --- /dev/null +++ b/musetalk/whisper/whisper/__main__.py @@ -0,0 +1,4 @@ +from .transcribe import cli + + +cli() diff --git a/musetalk/whisper/whisper/__pycache__/__init__.cpython-310.pyc b/musetalk/whisper/whisper/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..f15e9ea13520eb118b0400000d8cecd20a561674 GIT binary patch literal 5295 zcmb_gTW{RP6(+gdt5z#nwq?t)6WVQnEMRGQ=hA6{#J4!DZ?K%E*`nACIYTXNUN*y7 zD_X45G|Fr0x4yInB%m(^`ZxN}zrfc%`Mpm;5%&x!DM}2afLtzkIM*|0&N=g)Gn>xO zw*+|fORoo4PYc4oc;)Cb50&@e&CZL0fCVf@!l3+$1F@(}1F85{29@F~4`ldCQ8lg& zYH@u~j~j!ASk7q}Xvgz|`M5La6m7L=AzmCT#wP|R;-$e- zd~$HIXs<`7;?slE@$z7~s5hcB@tMIHC^vBn&*3(n#~r+Y7x4+agiqpA`1D`egR^*< ztl%^7o+an-3Tcqn$Vwos2tfm%!>@fU56(ktjjTY6gwNv(&~gF)9KQiG6jF!rE2R18 zbv|Q7z!wksoDN#}68_oO)xjHBTo<};KIeM7+pXmC?J#3``cVRfu9(a0 zGaNq$MG7#g6{QFd(FBKSelkiohB1kT*_il@rW+K+`P>-#Q#xccjJtBaaGm%m4wH?N zZgpFzIcU{CY8Gm-VoLvE^Vx zN4`&x>glfKyEf5n+tm=#Ox*A5dVqWfyI6Oyg?&}^Odsie-NslodMr$~)=088X4Bsv zQwH18Ovg4&WNCe@>49nzL&tVtc)ATs`$o{m*tCgdxT)D>>s{{vq$3@unG|kgcU?G**$g{BL>Z%p=T`jQfe$e-z*|!bdCWc8$89)g; 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"Ġgaan": 14118, "Ġupside": 14119, "Ġparece": 14120, "ãĥ³ãĥ": 14121, "Ġshipping": 14122, "ĠCR": 14123, "Ġdisrupt": 14124, "acter": 14125, "UND": 14126, "fu": 14127, "å®Į": 14128, "ĠPick": 14129, "ĠCharl": 14130, "ĠBull": 14131, "Ġenterprise": 14132, "Ġpunishment": 14133, "acking": 14134, "Ġfraction": 14135, "Ġtablet": 14136, "Ġchord": 14137, "Ġsimilarly": 14138, "åħ¶å¯¦": 14139, "ĠToronto": 14140, "Ġcourts": 14141, "ÄŁl": 14142, "eszcze": 14143, "Ġpronoun": 14144, "ĠSister": 14145, "ĠMP": 14146, "Ġgreatly": 14147, "ĠDank": 14148, "icop": 14149, "Ġgarbage": 14150, "Ġresolve": 14151, "ĠSaf": 14152, "ĠGun": 14153, "Ġcompound": 14154, "Ġë°°": 14155, "ĠMusik": 14156, "âĻ«": 14157, "Ġchaos": 14158, "ĠWhenever": 14159, "Ġeuros": 14160, "Ġorchest": 14161, "Ġrefriger": 14162, "alan": 14163, "ื": 14164, "ĠAmazing": 14165, "Ġpud": 14166, "agan": 14167, "Ġjeszcze": 14168, "isy": 14169, "Ġaccuracy": 14170, "ĠAma": 14171, "isode": 14172, "ëĮĢ": 14173, "Ġinterpretation": 14174, "ĠLiber": 14175, "æ·": 14176, "cam": 14177, "Ġevolved": 14178, "ĠKay": 14179, "ÑĨÑĭ": 14180, "Ġcreator": 14181, "itas": 14182, "Ġalarm": 14183, "Ġcelebration": 14184, "zent": 14185, "Ġfuncion": 14186, "Ġov": 14187, "umbling": 14188, "Ġ%": 14189, "à¸Ī": 14190, "Ġrestrictions": 14191, "Ġнав": 14192, "ĠKinder": 14193, "Ġbanana": 14194, "ÑĮÑı": 14195, "Ġdiameter": 14196, "Ġnorthern": 14197, "urers": 14198, "ĠPas": 14199, "æĪijçļĦ": 14200, "Ġworkforce": 14201, "Ġjung": 14202, "Ġguarante": 14203, "Ġequilib": 14204, "Ġsuite": 14205, "Ġeuro": 14206, "Ġdeliber": 14207, "Ste": 14208, "Ġdowntown": 14209, "Ġchin": 14210, "Ġcodes": 14211, "edia": 14212, "Ġsheep": 14213, "reshold": 14214, "wnie": 14215, "ób": 14216, "Ġunderlying": 14217, "lia": 14218, "jer": 14219, "ÏĢÏĮ": 14220, "çĿ": 14221, "throp": 14222, "Ġzap": 14223, "Ġvacuum": 14224, "ĠHab": 14225, "Ġwrapped": 14226, "ì¢": 14227, "Ġinventory": 14228, "ма": 14229, "Ġcoord": 14230, "Ġplates": 14231, "Ġsymm": 14232, "Te": 14233, "ĠwÅĤaÅĽnie": 14234, "Ġreaches": 14235, 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"Ġaument": 17128, "ÑĢоб": 17129, "Ġairplane": 17130, "Ġtransparency": 17131, "Ġadjustment": 17132, "ĠCDC": 17133, "çon": 17134, "Ġuploaded": 17135, "ĠдейÑģÑĤв": 17136, "ĠгоÑĤов": 17137, "Ġiter": 17138, "Ġcurse": 17139, "ôn": 17140, "merce": 17141, "aran": 17142, "Ġleak": 17143, "çµIJ": 17144, "Ġabsence": 17145, "Ñģкий": 17146, "Ġreaders": 17147, "aler": 17148, "Ġbeneath": 17149, "ango": 17150, "hetic": 17151, "Ġfinns": 17152, "Ġpoop": 17153, "Ġduplic": 17154, "Hi": 17155, "igs": 17156, "ologically": 17157, "opp": 17158, "Ġdizer": 17159, "ĠAllen": 17160, "Ġgli": 17161, "Ġacceleration": 17162, "Ġvitamin": 17163, "ãĥŃ": 17164, "vä": 17165, "ĠAccess": 17166, "à®Ļ": 17167, "rás": 17168, "Ġappreciated": 17169, "Ġnah": 17170, "Ġposter": 17171, "Ġtale": 17172, "Ġhighlighted": 17173, "æĸĩ": 17174, "żeli": 17175, "Ġblockchain": 17176, "Ġmicrow": 17177, "Ġcinema": 17178, "ĠChang": 17179, "ĠSearch": 17180, "usters": 17181, "ĠZero": 17182, "ĠDivision": 17183, "ÑĢаÑģ": 17184, "Ġscare": 17185, 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18008, "Ġlover": 18009, "'T": 18010, "Ġdense": 18011, "Ġtão": 18012, "Ġnotified": 18013, "Ġidi": 18014, "ìĹŃ": 18015, "íĨ": 18016, "Ġinteracting": 18017, "Ġrapport": 18018, "еÑĢи": 18019, "ski": 18020, "Ġbesser": 18021, "Ġmanufacturer": 18022, "ĠKyle": 18023, "Ġaccountable": 18024, "ĠSak": 18025, "ĠPil": 18026, "ĠDomin": 18027, "Ġpresum": 18028, "ĠÐĴÑģе": 18029, "Ġvinegar": 18030, "Ġguaranteed": 18031, "çľĭåĪ°": 18032, "Ġhandled": 18033, "éŁ³": 18034, "cat": 18035, "Ġcivilization": 18036, "Ġaccomp": 18037, "ĠVM": 18038, "émon": 18039, "Ġdeze": 18040, "Ġgrades": 18041, "Ġsollte": 18042, "Ġstaring": 18043, "×IJת": 18044, "arnt": 18045, "Ġhorizon": 18046, "Ġtravail": 18047, "hour": 18048, "第ä¸Ģ": 18049, "ĠED": 18050, "ĠDak": 18051, "Ġny": 18052, "Ġconve": 18053, "ĠCham": 18054, "Ġfirms": 18055, "ĠLiu": 18056, "ĠÑģÑĤÑĢан": 18057, "Ġlibert": 18058, "Ġlenses": 18059, "Ġintake": 18060, "ĠвÑĭб": 18061, "Ġmensen": 18062, "hel": 18063, "Ġpractition": 18064, "Ġ350": 18065, "ãĤ³": 18066, "FO": 18067, "Ġbeds": 18068, "Ġancestors": 18069, "ĠìĹĦì²Ń": 18070, "Ġdisturb": 18071, "ĠLastly": 18072, "ĠSupport": 18073, "ีà¹ī": 18074, "ĠCorona": 18075, "Ġenthusi": 18076, "Ġвозм": 18077, "ĠìĤ¬ëŀĮë": 18078, "Ġ52": 18079, "bird": 18080, "Ġreduces": 18081, "ĠìŀĪìĿĦ": 18082, "ĠGene": 18083, "êµIJ": 18084, "ÄĻp": 18085, "ĠÃľber": 18086, "Ġconcerning": 18087, "user": 18088, "Ġconcentrate": 18089, "ĠWHAT": 18090, "ishop": 18091, "onymous": 18092, "nold": 18093, "Ġsuggesting": 18094, "©°": 18095, "ĠFish": 18096, "........": 18097, "Ġvessel": 18098, "Ġtrabajo": 18099, "ãģµ": 18100, "ĠOcean": 18101, "å§IJ": 18102, "yg": 18103, "Ġtowns": 18104, "del": 18105, "Ġterrifying": 18106, "ĠçalÄ±ÅŁ": 18107, "Ġsino": 18108, "Ġeats": 18109, "Ġgez": 18110, "Ġgeme": 18111, "ĠìĻĦ": 18112, "Ġcompart": 18113, "Ġimplementing": 18114, "ĠPotter": 18115, "ĠGermans": 18116, "ĠgÅĤ": 18117, "Ġtennis": 18118, "Ġcarpet": 18119, "auer": 18120, "ĠSaudi": 18121, "yeong": 18122, "Ġcurry": 18123, "ĠForest": 18124, "Ñĭл": 18125, "Ġfifteen": 18126, "Ġbolts": 18127, "Ġ{\\": 18128, "¬´": 18129, "Ġsettlement": 18130, "Ġlange": 18131, "Ġbam": 18132, "Get": 18133, "íķĻ": 18134, "Ġswap": 18135, "ĠKhan": 18136, "Ġcommence": 18137, "Ġquarantine": 18138, "Ġscored": 18139, "çĸ": 18140, "Ġ1950": 18141, "Ġthicker": 18142, "Ġsûr": 18143, "åı£": 18144, "ĠLarry": 18145, "Ġallez": 18146, "ìĭľëĬĶ": 18147, "Ġgü": 18148, "Ġspectacular": 18149, "//": 18150, "both": 18151, "Ġstats": 18152, "妳": 18153, "ĠNancy": 18154, "Ġbunu": 18155, "Ġcrust": 18156, "Ġactivated": 18157, "Ġê·¸ëŀ": 18158, "outhe": 18159, "Ġports": 18160, "Ġneural": 18161, "Ġjaw": 18162, "Ġobservations": 18163, "Ġvoit": 18164, "aban": 18165, "ải": 18166, "¦¬ë¥¼": 18167, "omes": 18168, "à¯ĭ": 18169, "qui": 18170, "Ġkindness": 18171, "Ðij": 18172, "Ġ41": 18173, "Ġmoderate": 18174, "Ġangels": 18175, "ĠTamb": 18176, "èt": 18177, "Ġchlor": 18178, "ĠBilly": 18179, "ì²ĺë": 18180, "acon": 18181, "Ġselecting": 18182, "ĠDelta": 18183, "Ġnull": 18184, "denly": 18185, "Ġciud": 18186, "Ġtendency": 18187, "Ġbreakdown": 18188, "Ġmint": 18189, "ÑĦоÑĢм": 18190, "orph": 18191, "Ġdawn": 18192, "spr": 18193, "ĠWILL": 18194, "ächlich": 18195, "Ġpuppy": 18196, "700": 18197, "Ġத": 18198, "Ġfails": 18199, "ĠConc": 18200, "Ġrelatives": 18201, "Ġinviting": 18202, "Ġautonom": 18203, "Ġcomposed": 18204, "Ġunity": 18205, "Ġdecis": 18206, "Ġaccessories": 18207, "ĠCass": 18208, "Ġbist": 18209, "ĠTip": 18210, "째": 18211, "Ġpunt": 18212, "Ġráp": 18213, "éĢ²": 18214, "ANK": 18215, "ãģļ": 18216, "exist": 18217, "Ġcompatible": 18218, "Ġner": 18219, "ĠемÑĥ": 18220, "Ġaplic": 18221, "Ġbapt": 18222, "Ġfailing": 18223, "ĠTamam": 18224, "Ġoscill": 18225, "Ġletzten": 18226, "Ġrepeatedly": 18227, "Ġjungle": 18228, "ĠPush": 18229, "hai": 18230, "Ġη": 18231, "Ġdeadly": 18232, "Ñıж": 18233, "wiÄħ": 18234, "ĠCommon": 18235, "ĠÎķ": 18236, "Ġskate": 18237, "TC": 18238, "ĠMini": 18239, "Ġhobby": 18240, "ần": 18241, "Ġroutes": 18242, "Ġamigos": 18243, "Ġconjun": 18244, "Ġpartnerships": 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18306, "ĠUniverse": 18307, "ĠDirect": 18308, "Ġcheating": 18309, "ĠCin": 18310, "Ġpatri": 18311, "Ġadvise": 18312, "ĠNether": 18313, "Ġprimeiro": 18314, "Ġmentioning": 18315, "nut": 18316, "56": 18317, "arı": 18318, "Ġpetite": 18319, "bled": 18320, "Ġpensar": 18321, "icio": 18322, "IND": 18323, "Ġveteran": 18324, "Ġladder": 18325, "Ġconsequence": 18326, "ожал": 18327, "ĠBurn": 18328, "Ġrug": 18329, "ĠMade": 18330, "Ġgit": 18331, "\"...": 18332, "Ġcompetitors": 18333, "Ġprzed": 18334, "Ġapparent": 18335, "ĠArgentina": 18336, "ĠWorking": 18337, "Ġcollaborate": 18338, "woman": 18339, "Ġretain": 18340, "Ġleurs": 18341, "Ġdashboard": 18342, "×Ļ×ĵ": 18343, "ĠEarly": 18344, "BM": 18345, "ĠеÑij": 18346, "олог": 18347, "Ġsatisfying": 18348, "Ġoftentimes": 18349, "Ġmapping": 18350, "ünkü": 18351, "arth": 18352, "fold": 18353, "Ġlaunching": 18354, "Ġaura": 18355, "Ġprecision": 18356, "works": 18357, "God": 18358, "Ġstrap": 18359, "ĠImper": 18360, "Ġrivers": 18361, "Ġ|": 18362, "Ġcuer": 18363, 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"ĠPath": 21914, "dles": 21915, "à¸Ħร": 21916, "imore": 21917, "Ġë³´ìŬ": 21918, "Ġdisciplines": 21919, "á»ı": 21920, "Ġmesma": 21921, "ĠìĥĪë": 21922, "Ġìĭ¬": 21923, "Ġging": 21924, "Ġumbrella": 21925, "IGHT": 21926, "Ġpension": 21927, "Ġcombining": 21928, "SS": 21929, "Ġrectangle": 21930, "á»ĩt": 21931, "Ġproxim": 21932, "ĠCow": 21933, "¸Į": 21934, "Ġintentional": 21935, "æķĻ": 21936, "Ġdecid": 21937, "ĠÑģкаж": 21938, "ĠUma": 21939, "iasm": 21940, "buz": 21941, "Ġdebris": 21942, "Ġcass": 21943, "ĠProp": 21944, "iska": 21945, "ëł¥": 21946, "esterol": 21947, "ussian": 21948, "ìĿ´ëŀij": 21949, "Ġunlimited": 21950, "Ġadmire": 21951, "Ġtightly": 21952, "Ġgenome": 21953, "ĠJunior": 21954, "venir": 21955, "gus": 21956, "ĠcÄĥ": 21957, "ĠVlad": 21958, "ĠíĤ": 21959, "Ġrelativ": 21960, "inci": 21961, "Ġaunque": 21962, "ĠBoys": 21963, "ÑĨион": 21964, "ĠSwiss": 21965, "Ġphysicians": 21966, "Ġíıī": 21967, "ĠPET": 21968, "Ġwounds": 21969, "about": 21970, "Ãłi": 21971, "onz": 21972, "urities": 21973, 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"Ġpolynom": 22560, "avam": 22561, "Ġtambé": 22562, "оном": 22563, "Ġplasma": 22564, "Ġefect": 22565, "Ġläng": 22566, "Ġcasi": 22567, "Ñģа": 22568, "ımı": 22569, "ãģĻãĤĭ": 22570, "ĵ¤ìĿĢ": 22571, "Ġlabour": 22572, "ossen": 22573, "ĠPun": 22574, "rif": 22575, "Ġdoses": 22576, "Ġoperates": 22577, "илли": 22578, "Ġjaar": 22579, "staw": 22580, "ĠìĤ¬ëŀij": 22581, "Ġatm": 22582, "Ġprotects": 22583, "Ġimped": 22584, "HO": 22585, "Ġcima": 22586, "Ġtoch": 22587, "abis": 22588, "Ġsendo": 22589, "laus": 22590, "Ġcurl": 22591, "ĠNum": 22592, "Ġsponsors": 22593, "Ġdébut": 22594, "ĠAlexa": 22595, "ĠBür": 22596, "ĠAmer": 22597, "Ġcope": 22598, "Ġизв": 22599, "jal": 22600, "Ġ1995": 22601, "apat": 22602, "resse": 22603, "ĠPrize": 22604, "ĠClaire": 22605, "ĠBrandon": 22606, "Ġwszystko": 22607, "Ġvalued": 22608, "à¸Ļะ": 22609, "Ġsect": 22610, "Ġsecretly": 22611, "Ġdiamonds": 22612, "ĠEvan": 22613, "ĠRPG": 22614, "ãģ«ãģª": 22615, "ĪëıĦ": 22616, "ĠUniversal": 22617, "Ġdoubts": 22618, "ĠPin": 22619, "wiÄħz": 22620, "ļ©": 22621, "Ġalbo": 22622, "Ġbraucht": 22623, "AUL": 22624, "ĠMobile": 22625, "grades": 22626, "Ġschem": 22627, "why": 22628, "ĠNicht": 22629, "pi": 22630, "gle": 22631, "Ġchorus": 22632, "Ġgly": 22633, "Ġreinforce": 22634, "Ġmuff": 22635, "ĠShen": 22636, "ĠHola": 22637, "Ñĥг": 22638, "videmment": 22639, "vial": 22640, "acious": 22641, "laimed": 22642, "ĠRico": 22643, "Ġvegg": 22644, "Ġillustration": 22645, "ĠButter": 22646, "owad": 22647, "Ġeux": 22648, "Ġenfants": 22649, "ĠLeader": 22650, "ĠVillage": 22651, "etically": 22652, "ÙĨÙĬ": 22653, "Ġstew": 22654, "Ġsurprises": 22655, "Ġcue": 22656, "ĠGrandma": 22657, "ĠCelsius": 22658, "ĠRicht": 22659, "enc": 22660, "Ġpetition": 22661, "Ġherb": 22662, "Ġwicked": 22663, "Ġschle": 22664, "ocaly": 22665, "Ġtransf": 22666, "Ġtokens": 22667, "ĠGray": 22668, "ĠBBC": 22669, "IK": 22670, "Ġ1500": 22671, "zn": 22672, "ĠNev": 22673, "Ġkoy": 22674, "Ġzar": 22675, "Ġbullshit": 22676, "ĠColombia": 22677, "ulative": 22678, "Ġwidespread": 22679, "yect": 22680, "kit": 22681, "Ġempresa": 22682, "Ġnour": 22683, "Ġburns": 22684, "atin": 22685, "aired": 22686, "Ġrevolutionary": 22687, "ĠгодÑĥ": 22688, "ĠLogan": 22689, "Ġ1996": 22690, "ĠGraham": 22691, "reb": 22692, "ĠNHS": 22693, "æľĽ": 22694, "Ġcostumes": 22695, "Ġnawet": 22696, "Ġlovers": 22697, "ĠLucy": 22698, "ĠIndigenous": 22699, "íķĺ기": 22700, "Ġimmunity": 22701, "¥´ë": 22702, "uito": 22703, "Ġexcessive": 22704, "Ġdonations": 22705, "Ġ×Ķר": 22706, "Ġ첫": 22707, "éīĦ": 22708, "Ġdrying": 22709, "melon": 22710, "Ġsurveys": 22711, "Ġ무ìĬ¨": 22712, "風": 22713, "aaa": 22714, "Ġprobe": 22715, "ancial": 22716, "Ġlouder": 22717, "Ġhotels": 22718, "Ã¼ÄŁ": 22719, "agner": 22720, "Ġorigins": 22721, "Ġë§Īì§Ģë§ī": 22722, "Ġ**": 22723, "Ġstrangers": 22724, "ĠHaus": 22725, "comed": 22726, "Ġanthrop": 22727, "Ġuso": 22728, "ĠìķĦì§ģ": 22729, "ĠYuan": 22730, "ĠíķĦìļĶ": 22731, "pler": 22732, "ressive": 22733, "Ġspraw": 22734, "ĠStew": 22735, "Ġ1994": 22736, "Ġelders": 22737, "Ġmeinen": 22738, 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"Ġobra": 22798, "owitz": 22799, "ĠZone": 22800, "ĠÑĢек": 22801, "Ġsug": 22802, "arded": 22803, "Ġgì": 22804, "ffentlich": 22805, "anche": 22806, "ØŁ": 22807, "astically": 22808, "ìĿ¼ë": 22809, "лав": 22810, "Ġsimplest": 22811, "ĠFriend": 22812, "Ġquello": 22813, "Ġambition": 22814, "Ġabbiamo": 22815, "åºķ": 22816, "ĠÑĦоÑĢм": 22817, "ĠEssa": 22818, "Ġeducators": 22819, "Ġstatistical": 22820, "éĢĻéĤĬ": 22821, "Ġchanger": 22822, "Ġatau": 22823, "étais": 22824, "ĠShakespeare": 22825, "ëIJĺ": 22826, "Ġtriggers": 22827, "Ġrealiz": 22828, "Ġcelui": 22829, "wheel": 22830, "Ġloyalty": 22831, "Ġscreams": 22832, "kehr": 22833, "ĠMega": 22834, "east": 22835, "Ġtops": 22836, "ĠTotally": 22837, "ountain": 22838, "lord": 22839, "Ġviolation": 22840, "ĠGA": 22841, "Ġnicer": 22842, "ĠFresh": 22843, "ĠMelissa": 22844, "function": 22845, "Ġrape": 22846, "Ġexceptions": 22847, "Ġsilicon": 22848, "Ġliberty": 22849, "Ġhouseholds": 22850, "ãģįãģ¾ãģĻ": 22851, "ĠCA": 22852, "ĠÐŀб": 22853, "Ġlib": 22854, "ŀĮ": 22855, "cific": 22856, "Ġtropical": 22857, "Ġinvestigating": 22858, "HD": 22859, "Ġadapter": 22860, "ĠPitt": 22861, "ancia": 22862, "ĠShell": 22863, "friendly": 22864, "Ġconclusions": 22865, "Ġturtle": 22866, "Ġdecomp": 22867, "Ġanimations": 22868, "ĠÑģек": 22869, "insi": 22870, "Ġretention": 22871, "kie": 22872, "Ġinjection": 22873, "ĠMadison": 22874, "ì°°": 22875, "Ġvient": 22876, "Ġvaried": 22877, "Ġviolin": 22878, "ĠBil": 22879, "Ġluckily": 22880, "Ġhtt": 22881, "lä": 22882, "Ġranch": 22883, "çľĭçľĭ": 22884, "Ġsólo": 22885, "ìķħ": 22886, "ĠDerek": 22887, "ĠScripture": 22888, "оÑĢа": 22889, "Ġclassrooms": 22890, "avil": 22891, "formed": 22892, "Ġbeforehand": 22893, "ĠGem": 22894, "prech": 22895, "Ġlin": 22896, "Ġgreens": 22897, "ÑĨев": 22898, "ĠMercedes": 22899, "Ġdrought": 22900, "gasps": 22901, "Ġabortion": 22902, "Ġterribly": 22903, "Ġsposób": 22904, "Ġsecured": 22905, "Ġatrás": 22906, "Ġwavelength": 22907, "Ġgrains": 22908, "ective": 22909, "Ġspacecraft": 22910, "Ġtours": 22911, "Ġprofes": 22912, "Ġsurgeon": 22913, "ĠPie": 22914, "Ġideally": 22915, "arner": 22916, "UP": 22917, "opard": 22918, "sce": 22919, "Ġimmense": 22920, "ĠOrt": 22921, "roller": 22922, "ĠDallas": 22923, "ĠNicholas": 22924, "Ġsulf": 22925, "ĠToyota": 22926, "Ġquantities": 22927, "ceans": 22928, "Ġcui": 22929, "ança": 22930, "ĠCAN": 22931, "itzerland": 22932, "åĦ¿": 22933, "Ġzou": 22934, "ĠCyber": 22935, "legen": 22936, "ĠInit": 22937, "edu": 22938, "Ġapert": 22939, "Ġadjac": 22940, "ouv": 22941, "èĢĮä¸Ķ": 22942, "rs": 22943, "Ġcabbage": 22944, "Ġwheelchair": 22945, "inyl": 22946, "ĠDynam": 22947, "ĠìķĦëĭĪëĿ¼": 22948, "Ġling": 22949, "hl": 22950, "ĠмогÑĥ": 22951, "Ġcrisp": 22952, "Ġmij": 22953, "Ġdug": 22954, "nin": 22955, "Ġbloss": 22956, "Ġbelonging": 22957, "Ġloudly": 22958, "Ġminerals": 22959, "Ġconcluded": 22960, "Ġsearched": 22961, "96": 22962, "ĠMeet": 22963, "ĠSEO": 22964, "ĠСк": 22965, "ĠHob": 22966, "otta": 22967, "Ġpropaganda": 22968, "Ġcinnamon": 22969, "Ġhunter": 22970, 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25134, "Ġcasos": 25135, "ĠKlim": 25136, "Ġ1993": 25137, "Ġrealization": 25138, "Ġhistorian": 25139, "Ġcracked": 25140, "ëĤ´": 25141, "Ġsystème": 25142, "ĠCIA": 25143, "ĠÑĤво": 25144, "ospheric": 25145, "Ġflee": 25146, "Ġrất": 25147, "ĠRegardless": 25148, "Ġreluct": 25149, "Ġtimely": 25150, "ĠJulian": 25151, "GM": 25152, "éĴ": 25153, "adura": 25154, "é£Ł": 25155, "Ġdresses": 25156, "çģ£": 25157, "ĠëĶĶ": 25158, "Ġnominated": 25159, "Ġadvocates": 25160, "ymph": 25161, "Ġrecordings": 25162, "Ġdeviation": 25163, "Ġprioritize": 25164, "Ġspiral": 25165, "ĠYOUR": 25166, "Ġtranspose": 25167, "ampoo": 25168, "ĠìĽIJëŀĺ": 25169, "ĠVision": 25170, "Ġpolite": 25171, "Ġhamb": 25172, "ĠPatient": 25173, "æ¯Ķè¼ĥ": 25174, "íģ¬ë": 25175, "Ġsia": 25176, "Ġê³³": 25177, "Ġže": 25178, "è§Ģ": 25179, "Ġsupermarket": 25180, "ë¹": 25181, "ĠSierra": 25182, "Ġgrilled": 25183, "ĠUpon": 25184, "Ġabsent": 25185, "Ġmec": 25186, "ĠApollo": 25187, "Ġpunk": 25188, "ĠPaÅĦst": 25189, "ĠÑģвой": 25190, "Ġ거기": 25191, "Girl": 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"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": 32276, "اس": 32277, "Tim": 32278, "Ġrecurring": 32279, "Ġproceeds": 32280, "ĠParticularly": 32281, "ĠDownload": 32282, "etrical": 32283, "Ġmatrices": 32284, "Ġproyecto": 32285, "ancies": 32286, "ĠUhm": 32287, "Ġcaves": 32288, "Ġìĸ´ëł¤": 32289, "ĠLeaf": 32290, "ĠобÑĭÑĩ": 32291, "ĠìĿ´ìľł": 32292, "Europe": 32293, "ĠtÄħ": 32294, "Ġpuls": 32295, "Ġtakiego": 32296, "ÐĿе": 32297, "GU": 32298, "Ġfors": 32299, "Ïģγ": 32300, "Ġfotos": 32301, "Ġ))": 32302, "Ġ멤ë": 32303, "Ġaquilo": 32304, "ĠKurd": 32305, "ï¸ı": 32306, "ptic": 32307, "ĠDort": 32308, "Ġmisery": 32309, "auso": 32310, "åĬŁ": 32311, "chuckling": 32312, "ĠRidge": 32313, "ĠíĸĪìĬµëĭĪëĭ¤": 32314, "Ġ***": 32315, "客": 32316, "ĠHmmm": 32317, "Ġgeographic": 32318, "Ġanys": 32319, "Ġtalvez": 32320, "Ġskelet": 32321, "Ġsignatures": 32322, "Ġliters": 32323, "IJë©´": 32324, "ĠÑģвоего": 32325, "Ġskiing": 32326, "ĠÐľÐ¾Ñģ": 32327, "Ġadopting": 32328, "Ġhaft": 32329, "Ġsymmetric": 32330, "ĠLiqu": 32331, "Ġthyroid": 32332, "Ġmisin": 32333, "lude": 32334, "Ġhull": 32335, "ĠXD": 32336, "ĠGust": 32337, "zeich": 32338, "Ġvibrations": 32339, "Ġesemp": 32340, "ĠвÑģÑİ": 32341, "ĠQuem": 32342, "Ġübrig": 32343, "ĠSke": 32344, "ĠLynch": 32345, "rooms": 32346, "artet": 32347, "fest": 32348, "Ġfrüher": 32349, "Ġlure": 32350, "ä¸į好æĦıæĢĿ": 32351, "ĠìķĮìķĦ": 32352, "ĠWIN": 32353, "ĠRYAN": 32354, "ĠкоÑĤоÑĢÑĥÑİ": 32355, "ĠKash": 32356, "Ġ×Ķ×ŀ": 32357, "Ġsafeg": 32358, "ĠHallelujah": 32359, "ĠдвÑĥÑħ": 32360, "Ġstaple": 32361, "Ġsediment": 32362, "ĠActs": 32363, "Ġblaming": 32364, "Ġmainland": 32365, "Ġsporting": 32366, "Ġdecorations": 32367, "Ġexecuting": 32368, "Ġparan": 32369, "ĠDollar": 32370, "Ġprojections": 32371, "Ġcommissioned": 32372, "Ġbour": 32373, "öm": 32374, "Ġsteamed": 32375, "ĠëŃĺ": 32376, "Ġpetrol": 32377, "Ġcelular": 32378, "帶": 32379, "ĠHungary": 32380, 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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": 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"ĠTages": 33601, "Ġsá»±": 33602, "Ġbullied": 33603, "ؤ": 33604, "Ġbenefited": 33605, "ĠPreviously": 33606, "ĠÑįÑĦÑĦ": 33607, "Ùį": 33608, "Ġsenate": 33609, "ĠMorm": 33610, "ijke": 33611, "ĠFlu": 33612, "Ġincorporating": 33613, "jack": 33614, "ĠпиÑĤ": 33615, "Ġimply": 33616, "Ġhacks": 33617, "ĠRICH": 33618, "ĠкваÑĢ": 33619, "ĠпÑĢекÑĢаÑģ": 33620, "Ġdependency": 33621, "Ġìļ©": 33622, "Ġì±ħ": 33623, "Ġwährend": 33624, "Ġsulla": 33625, "ĠPittsburgh": 33626, "Ġesempio": 33627, "¼ë¡ľ": 33628, "prot": 33629, "ĠRosen": 33630, "ĠIndependence": 33631, "Ġparsley": 33632, "iegen": 33633, "Ġhaw": 33634, "Ġaquell": 33635, "ĠCAP": 33636, "ĠÑĢабоÑĤаÑĤÑĮ": 33637, "ĠCliff": 33638, "ionar": 33639, "Ġsecuring": 33640, "æĪijåĢijçļĦ": 33641, "νε": 33642, "Ġutilis": 33643, "Ġcoule": 33644, "ĠPing": 33645, "Ġtrek": 33646, "Ġfak": 33647, "Ġenorme": 33648, "Ġìĭ«": 33649, "让": 33650, "Ġdoubling": 33651, "ĠнÑĢавиÑĤÑģÑı": 33652, "Ġhed": 33653, "hoven": 33654, "ĠStanding": 33655, "ĠmÃŃn": 33656, "ĠJimin": 33657, "Ġmonarch": 33658, "Ġcoke": 33659, "Ġmr": 33660, "Ġclic": 33661, "Ãį": 33662, "Ġimpeachment": 33663, "Ġdurability": 33664, "Ġvarios": 33665, "Ġcommercials": 33666, "Ġgreetings": 33667, "ĠRi": 33668, "ĠAppreci": 33669, "ìŀĪëĬĶ": 33670, "Ġrésult": 33671, "ért": 33672, "Ġsalute": 33673, "Ġpoderia": 33674, "Ġsunrise": 33675, "veck": 33676, "Ġreluctant": 33677, "Ġcommissioner": 33678, "念": 33679, "âte": 33680, "ĠKenny": 33681, "ĠSiri": 33682, "ãĥĥãĥĹ": 33683, "ĠëĬĺ": 33684, "ĠEE": 33685, "Ġunch": 33686, "кон": 33687, "ĠاÙĦØ¥": 33688, "Ġbelts": 33689, "Ġhass": 33690, "ĠмоÑı": 33691, "Ġdisplaced": 33692, "Ġabra": 33693, "ÎŃλ": 33694, "Ġscratches": 33695, "Ġcomet": 33696, "Ġauthorization": 33697, "ĠLLC": 33698, "Ġproduk": 33699, "Ġrehabilitation": 33700, "åŀ": 33701, "ÑĸÑĩ": 33702, "uding": 33703, "olit": 33704, "Ġ105": 33705, "Ġexpands": 33706, "Ġaltri": 33707, "ĠKomment": 33708, "Ġanf": 33709, "Pl": 33710, "ĠMana": 33711, "fed": 33712, "Ġbri": 33713, "Ġora": 33714, "Gs": 33715, "ĠGur": 33716, "uckland": 33717, "Ġjunction": 33718, "Ġironic": 33719, "ĠFeed": 33720, "Ġprakt": 33721, "ĠHammer": 33722, "ĮëıĦ": 33723, "ĠTracy": 33724, "çµ±": 33725, "ĠAside": 33726, "него": 33727, "ĠиÑģполÑĮзоваÑĤÑĮ": 33728, "Ġzaj": 33729, "Ġequitable": 33730, "Ġcurb": 33731, "ĠãģĵãĤĮ": 33732, "Ġderivatives": 33733, "Ġpuppies": 33734, "ĠKenneth": 33735, "ĠCompl": 33736, "igram": 33737, "ĠGarcia": 33738, ")\"": 33739, "ĠHarbor": 33740, "estial": 33741, "Ġä¾Ĩ": 33742, "Ġers": 33743, "æ¹": 33744, "Ġunwanted": 33745, "Ġbelang": 33746, "аго": 33747, "emb": 33748, "dos": 33749, "ĠìĻľë": 33750, "ĠBudget": 33751, "Ġbattling": 33752, "ØŃت": 33753, "kok": 33754, "наÑĩала": 33755, "Ġplag": 33756, "Ġcantidad": 33757, "Ġgrupos": 33758, "Ġplugins": 33759, "lerini": 33760, "ĠимееÑĤ": 33761, "Ġsozusagen": 33762, "olics": 33763, "Ġpueblo": 33764, "Ġreminis": 33765, "rän": 33766, "ĠMorrison": 33767, "Ġlinha": 33768, "Ġbreaths": 33769, "ĠTaste": 33770, "Ġenfrent": 33771, "ĠDocker": 33772, "Ġден": 33773, "Ġethnicity": 33774, "Ġwob": 33775, "Ġsuffers": 33776, "Ġtransitioning": 33777, "ĠRange": 33778, "ÄĻdzy": 33779, "ĠкаÑĤ": 33780, "Ġsyner": 33781, "Ġdonut": 33782, "Ġprobabilities": 33783, "ĠOmar": 33784, "Which": 33785, "uish": 33786, "isin": 33787, "Ġdemos": 33788, "ĠìłĢ기": 33789, "Ġëĺijê°Ļ": 33790, "Ġедин": 33791, "Ġcerve": 33792, "Ġjoka": 33793, "IAN": 33794, "Ġkilometer": 33795, "Ġhorizontally": 33796, "ĠBhag": 33797, "Ġ->": 33798, "ĠMonitor": 33799, "Ġknowledgeable": 33800, "Ġfav": 33801, "Ġpinned": 33802, "ĠeBay": 33803, "icker": 33804, "Ġìŀłê¹IJë§Į": 33805, "ĠXiaomi": 33806, "Ġcapit": 33807, "Ġnp": 33808, "Ġ1965": 33809, "hoe": 33810, "Ġnok": 33811, "ĠSage": 33812, "ĠнелÑĮзÑı": 33813, "ĠTow": 33814, "gam": 33815, "Ġdicen": 33816, "ĠSUBSCRIBE": 33817, "Ġreboot": 33818, "Ġpaj": 33819, "Ġë³´ìŬë": 33820, "Ġthicken": 33821, "ĠReality": 33822, "idän": 33823, "Na": 33824, "Ġê²ĥìĿĢ": 33825, "!!)": 33826, "Ġroutines": 33827, "Ġодного": 33828, "Ġexting": 33829, "Ġì¦Ŀ": 33830, "Ġsulfur": 33831, "Ġcarve": 33832, "Ġasteroid": 33833, "ĠWarrior": 33834, "Ġphotographers": 33835, "Ġpell": 33836, "Ġcrossover": 33837, "æĪijçŁ¥éģĵ": 33838, "Ġhacemos": 33839, "ĠNej": 33840, "Ġsettling": 33841, "Ġirm": 33842, "ĠBooks": 33843, "ientôt": 33844, "Ġespacio": 33845, "ĠScholars": 33846, "Ġdoomed": 33847, "ĠIRS": 33848, "wohl": 33849, "Ġsegue": 33850, "ĠëĪĦê°Ģ": 33851, "Ġpratic": 33852, "BT": 33853, "ĠConsidering": 33854, "ĠBuffalo": 33855, "Ġtrainings": 33856, "Ġgebru": 33857, "ĠGleich": 33858, "Ġpirates": 33859, "Ġenvelop": 33860, "Ġreopen": 33861, "imat": 33862, "Ġtee": 33863, "Ġsued": 33864, "feh": 33865, "Ġ×Ķק": 33866, "Ġdiets": 33867, "Ġjuntos": 33868, "asto": 33869, "Ġmisunderstood": 33870, "Ġruim": 33871, "Ġclassify": 33872, "ĠпÑĢодÑĥк": 33873, "Ġinse": 33874, "Ġillustrated": 33875, "Ġcorrosion": 33876, "Ġaccred": 33877, "ĠAuntie": 33878, "ĠпÑĢивеÑĤ": 33879, "ĠLIVE": 33880, "Ġrek": 33881, "Ġreceipt": 33882, "åĪ°åºķ": 33883, "ĠBarbie": 33884, "ĠSnake": 33885, "turn": 33886, 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"standen": 33946, "Ġpedestrian": 33947, "Ġdifférents": 33948, "Ġìĭ¸": 33949, "èī¯": 33950, "Ġcomplained": 33951, "ç¦ı": 33952, "ĠÐļÑĤо": 33953, "Ġ׾פ": 33954, "aliÅĽmy": 33955, "Ġmortar": 33956, "Ġverdict": 33957, "Ġsuficiente": 33958, "ĠMillion": 33959, "mittel": 33960, "inals": 33961, "ĠاÙĦØ®": 33962, "аÑİÑģÑĮ": 33963, "ĠmiÄĻdzy": 33964, "ĠOle": 33965, "Ġinvert": 33966, "czyÄĩ": 33967, "озможно": 33968, "starter": 33969, "Ġauditor": 33970, "ĠScout": 33971, "chien": 33972, "ĠSverige": 33973, "uffled": 33974, "Ġzehn": 33975, "ĠAuckland": 33976, "Ġargent": 33977, "Ġ1976": 33978, "ĠHoe": 33979, "Ġbothers": 33980, "Ġsocialist": 33981, "Ġpliers": 33982, "Ġemergen": 33983, "ĠXP": 33984, "еÑĢов": 33985, "More": 33986, "ĠLevi": 33987, "ĠAnders": 33988, "ibilidad": 33989, "ĠParents": 33990, "Ġinduced": 33991, "ìĸ´ì¤": 33992, "Ġbalances": 33993, "ĠвÑĭÑĪ": 33994, "Ġsubmarine": 33995, "Start": 33996, "Ġdries": 33997, "Ġvolver": 33998, "Ġticking": 33999, "cott": 34000, "Ġfaj": 34001, "prés": 34002, "ĠSabb": 34003, "ĠзаÑĩ": 34004, "ĠпокÑĥп": 34005, "Ġbaptized": 34006, "ĠBrilliant": 34007, "ĠÐijог": 34008, "Ġmots": 34009, "bits": 34010, "Ġlattice": 34011, "æĪijè·Łä½ł": 34012, "Ġcoriander": 34013, "Ġresidency": 34014, "ync": 34015, "Ġpierwszy": 34016, "ĠKnock": 34017, "ĠZap": 34018, "ĠÐķв": 34019, "견": 34020, "å°ıå¿ĥ": 34021, "Ġuneven": 34022, "ĠJas": 34023, "odor": 34024, "ç¿Ĵ": 34025, "74": 34026, "ĠSite": 34027, "Ġaconteceu": 34028, "ympt": 34029, "Ġtrilogy": 34030, "Ġlantern": 34031, "ĠZucker": 34032, "vari": 34033, "welling": 34034, "ĠPotato": 34035, "gomery": 34036, "Ġreacted": 34037, "ĠChron": 34038, "Ġjede": 34039, "beeld": 34040, "Ġtwent": 34041, "Ġlact": 34042, "æ¨Ĥ": 34043, "Ġrése": 34044, "Ġrelent": 34045, "Ġfurnace": 34046, "Ġwidget": 34047, "Ġearthquakes": 34048, "ĠAdjust": 34049, "ilit": 34050, "ĠØ£ÙĪ": 34051, "Ġhearings": 34052, "Ġdefendant": 34053, "irsiniz": 34054, "Ġbask": 34055, "cja": 34056, "ľ¨": 34057, "Ġrifles": 34058, "Ġinstal": 34059, "ĠForgive": 34060, "pical": 34061, "ĠÐŀÑĩенÑĮ": 34062, "Ġpetites": 34063, "Ġhp": 34064, "Ġrenowned": 34065, "ĠInn": 34066, "Ġ주ìĦ¸ìļĶ": 34067, "Ġemphasized": 34068, "éĹ®é¢ĺ": 34069, "ĠìŀĪì£ł": 34070, "Ġê²ĥìľ¼ë¡ľ": 34071, "ãĤĨ": 34072, "Åĵ": 34073, "gili": 34074, "Dave": 34075, "Ġexhausting": 34076, "ÅĤug": 34077, "Ġschema": 34078, "μά": 34079, "cycl": 34080, "Ġautant": 34081, "Ġparcel": 34082, "Ġmateria": 34083, "ĠBerry": 34084, "ĠÑģами": 34085, "Ġextracted": 34086, "ĠSaying": 34087, "ismatic": 34088, "ĠпопÑĢоб": 34089, "Ġneuron": 34090, "graph": 34091, "ľë©´": 34092, "Ġenclosure": 34093, "ĠJohann": 34094, "Ġaftermath": 34095, "ÑĤоб": 34096, "Ġuży": 34097, "Ġsamp": 34098, "360": 34099, "ĠMei": 34100, "Ġtaco": 34101, "Ġreceptors": 34102, "Ġpunches": 34103, "ĠHoje": 34104, "ĠÙĩÙĨا": 34105, "=\"#": 34106, "ĠAngular": 34107, "Ġmusique": 34108, "Ġrol": 34109, "Ġñ": 34110, "sterreich": 34111, "Ġclam": 34112, "ĠTreasury": 34113, "chemical": 34114, "Ġapar": 34115, "Ġappend": 34116, "Ġforbid": 34117, "ĠHamburg": 34118, "аков": 34119, "Ġê¸Ī": 34120, "ilda": 34121, "Ġpreparations": 34122, "ĠmogÄħ": 34123, "Ġcamino": 34124, "Eric": 34125, "ĠBlind": 34126, "èĪĩ": 34127, "å¹´çļĦ": 34128, "ĠDiscovery": 34129, "ì¸ł": 34130, "çĪ¶": 34131, "Ġinterpreter": 34132, "Ġbred": 34133, "ĠPsalm": 34134, "Ġdefended": 34135, "ìī¬": 34136, "ĠErfahr": 34137, "ĠPeach": 34138, "Ġmoons": 34139, "ĠOst": 34140, "Ġspécial": 34141, "Ġarriver": 34142, "ĠWis": 34143, "uci": 34144, "Ġrobotics": 34145, "IVE": 34146, "Ġsiege": 34147, "arla": 34148, "Ġseparates": 34149, "ĠTC": 34150, "íı°": 34151, "quisite": 34152, "Ġparentheses": 34153, "ике": 34154, "ç«Ļ": 34155, "Ġtrous": 34156, "建": 34157, "ĠÑģилÑĮ": 34158, "Ġbeers": 34159, "ĠплаÑĤ": 34160, "ãģĻãģĶãģĦ": 34161, "Ġsola": 34162, "Ġdès": 34163, "mingham": 34164, "ikte": 34165, "Ġoops": 34166, "Ġtwitch": 34167, "å°ĩ": 34168, "ÏĪ": 34169, "ĠShouldn": 34170, "uvre": 34171, "Ġleer": 34172, "criptions": 34173, "Ġeyeshadow": 34174, "ĠGuo": 34175, "ĠPowell": 34176, "Ġsupuesto": 34177, "Ġana": 34178, "rals": 34179, "ĠMontreal": 34180, "Ġsurfing": 34181, "ĠÐŁÐµÑĢв": 34182, "×ŀ×ķ": 34183, "Ġmilliseconds": 34184, "Ġsuburbs": 34185, "Ġplaneta": 34186, "ÑĥÑĪка": 34187, "hrlich": 34188, "ĠHY": 34189, "ĠسÛĴ": 34190, "ĠMM": 34191, "ĠEff": 34192, "åı¯æĦĽ": 34193, "ĠHS": 34194, "anson": 34195, "Ġì§ģìłij": 34196, "Ġsuo": 34197, "Ġdeploying": 34198, "Ġkunt": 34199, "tering": 34200, "Ġerect": 34201, "ìŀ¥ìĿ´": 34202, "ĠìĿĮìĭĿ": 34203, "Ġspecimen": 34204, "!...": 34205, "æĪij說": 34206, "Ġligne": 34207, "Ġkonst": 34208, "adequ": 34209, "Ġìĥģíĥľ": 34210, "Ġaccessed": 34211, "ĠPole": 34212, "kill": 34213, "Ġë²Ħë": 34214, "Ġauthenticity": 34215, "Ġappelle": 34216, "ulle": 34217, "Ġrevision": 34218, "Ġgoats": 34219, "гли": 34220, "Ġpau": 34221, "ĠRanger": 34222, "ĠImag": 34223, "author": 34224, "Ġeve": 34225, "ĠMessenger": 34226, "Ġnay": 34227, "Ġwholes": 34228, "ätte": 34229, "Ġonwards": 34230, "ĠDepois": 34231, "ĠíijľíĺĦ": 34232, "ĠSARS": 34233, "Ġwszystkich": 34234, "Ġdestru": 34235, "umbing": 34236, "Ġcompatibility": 34237, "Ġmisinformation": 34238, "odore": 34239, "ĠFavor": 34240, "eko": 34241, "ıĮ": 34242, "waukee": 34243, "ĠTeaching": 34244, "ĠKO": 34245, "Ġbetting": 34246, "Ġquests": 34247, "Ġvivre": 34248, "ĠмÑĥзÑĭ": 34249, "Ġsaga": 34250, "Ġswell": 34251, "Ġgehe": 34252, "æĢİ麼樣": 34253, "ĠоÑĢганиз": 34254, "Ġgide": 34255, "ĠGross": 34256, "Ġdalej": 34257, "Ġclaws": 34258, "á»Ļc": 34259, "Ġprejudice": 34260, "Ġinsign": 34261, "ihood": 34262, "Ġpled": 34263, "Ġdónde": 34264, "ĠPolitical": 34265, "Ġpremises": 34266, "undert": 34267, "عت": 34268, "onnen": 34269, "Ġespaço": 34270, "Ġfé": 34271, "ĠHarrison": 34272, "ĠCensus": 34273, "Ġcardio": 34274, "Ġdiy": 34275, "Ġmilieu": 34276, "Ġjournée": 34277, "ĠRelease": 34278, "NIE": 34279, "ĠMuk": 34280, "idée": 34281, "á»įi": 34282, "Ġiçinde": 34283, "ŀĻ": 34284, "Ġresonate": 34285, "Ġmoles": 34286, "ĠFlying": 34287, "ĠGloria": 34288, "ĠPastor": 34289, "ĠArena": 34290, "好ä¸į好": 34291, "NON": 34292, "олов": 34293, "ĠallÃŃ": 34294, "omat": 34295, "ìĸ´ëıĦ": 34296, "ĠcaracterÃŃst": 34297, "Ġdeclining": 34298, "ÑĸÑı": 34299, "anco": 34300, "ĠInform": 34301, "Ġbargain": 34302, "Ġbushes": 34303, "ĠNaturally": 34304, "Ġrechts": 34305, "ĠTensor": 34306, "ĠPatricia": 34307, "Ġprincipio": 34308, "ĠMumbai": 34309, "Ġwomb": 34310, "Ġnostra": 34311, "Ġdilemma": 34312, "Ġirgendwann": 34313, "Ġ1964": 34314, "ĠenergÃŃa": 34315, "ĠнаÑĢ": 34316, "Ġsegregation": 34317, "ĠAthlet": 34318, "Ġ»,": 34319, "Ġyeni": 34320, "ĠSeit": 34321, "Ġvenom": 34322, "Ġdakika": 34323, "ĠëıĮë": 34324, "ĠÃīl": 34325, "Ġfus": 34326, "ĠMog": 34327, "¦½ëĭĪëĭ¤": 34328, "Ġremar": 34329, "ĠTeddy": 34330, "Ġbreasts": 34331, "icans": 34332, "æĶ¶çľĭ": 34333, "kap": 34334, "ĠhÆ¡n": 34335, "ĠJP": 34336, "ãĥ³ãĤ¿": 34337, "Ġresurrect": 34338, "ĠìĿ¸ë": 34339, "herical": 34340, "Ġfotograf": 34341, "ĠJosé": 34342, "Ġlivelihood": 34343, "Ġbibli": 34344, "teri": 34345, "Ġvorstellen": 34346, "ĠAAA": 34347, "Ġassessing": 34348, "YA": 34349, "Ġsplend": 34350, "Ġexcav": 34351, "Ġbaptism": 34352, "yll": 34353, "wow": 34354, "Mac": 34355, "Ġplastics": 34356, "teokbokki": 34357, "Ġintéressant": 34358, "Ġcommanded": 34359, "Ġfamously": 34360, "ĠÐĺли": 34361, "ĠManuel": 34362, "Ġsouthwest": 34363, "Ġdeformation": 34364, "ÃŃculo": 34365, "ĠнаÑħодиÑĤÑģÑı": 34366, "ĠPatter": 34367, "degree": 34368, "ĠczÄĻsto": 34369, "\"-": 34370, "Ġìħĭ": 34371, "Ġmanger": 34372, "ĠTrustee": 34373, "Ģ리": 34374, "Ġpuntos": 34375, "ivable": 34376, "Ġvolatile": 34377, "ĠëĬIJ": 34378, "Ġinstability": 34379, "Ġciel": 34380, "ciÄħ": 34381, "Ġpurity": 34382, "ноÑģÑĤ": 34383, "Sil": 34384, "edar": 34385, "åĻ¨": 34386, "NOUNCER": 34387, "Ġspelled": 34388, "GER": 34389, "Ġsanctuary": 34390, "Ġaccelerating": 34391, "Ġscout": 34392, "ĠпÑĢев": 34393, "fahren": 34394, "ãģĵãģ¡ãĤī": 34395, "ĠëĤĺìĺ¨": 34396, "ĠpoczÄħt": 34397, "ĠMeu": 34398, "kaar": 34399, "³´ê³ł": 34400, "akra": 34401, "Down": 34402, "ĠÃĦr": 34403, "ĠElite": 34404, "Ġallons": 34405, "Ġmayonnaise": 34406, "ĠSustain": 34407, "prisingly": 34408, "Ġsupervis": 34409, "Ġê·¸ëłĩì£ł": 34410, "Ġunemployed": 34411, "Ġfreshly": 34412, "Ġ×ŀ×¢": 34413, "ĠDh": 34414, "Ġtackling": 34415, "Ġogr": 34416, "Ġì´Īë": 34417, "ãĤĪãĤį": 34418, "Ġloft": 34419, "arah": 34420, "ĠAirl": 34421, "ĠDir": 34422, "ĠÐľÐ¾Ð¶Ð½Ð¾": 34423, "Ġbooking": 34424, "ĠCRA": 34425, "Ġhttps": 34426, "Ġchoke": 34427, "Ġgown": 34428, "Ġnoite": 34429, "Ġzac": 34430, "istol": 34431, "Ġsecre": 34432, "Ġresembles": 34433, "Ġcuad": 34434, "ìĤ¬ê°Ģ": 34435, "show": 34436, "Ġblanc": 34437, "Ġagu": 34438, "ĠPrint": 34439, "asted": 34440, "ĠWeather": 34441, "ipl": 34442, "Ġobscure": 34443, "Ġconte": 34444, "oughs": 34445, ");": 34446, "ĠDame": 34447, "ä¸Ģ缴": 34448, "Ġclarification": 34449, "Ġintimacy": 34450, "Ġuphold": 34451, "ĠMirror": 34452, "Ġwagon": 34453, "xide": 34454, "Ġclog": 34455, "apper": 34456, "ĠImmediately": 34457, "úde": 34458, "Ġtouchdown": 34459, "Ġrooft": 34460, "аÑĪа": 34461, "Ġçıkt": 34462, "Ġlaisser": 34463, "ĠUnreal": 34464, "ensitive": 34465, "Ġ123": 34466, "Ġplaster": 34467, "Ġducks": 34468, "Ġetme": 34469, "Ġbishop": 34470, "brevi": 34471, "Ġbic": 34472, "ä¸ĭåİ»": 34473, "Ġruntime": 34474, "Ġambitions": 34475, "маÑĤ": 34476, "ĠWein": 34477, "ĠMari": 34478, "ĠíĬ¸ë": 34479, "Ġresolver": 34480, "ĠngÃły": 34481, "ĠRise": 34482, "ãĤĪãģĨãģ«": 34483, "ĠCrus": 34484, "Ġmerchandise": 34485, "Ġeli": 34486, "Ġstatewide": 34487, "Ġowl": 34488, "éģł": 34489, "æĶ¹": 34490, "Ġtwisting": 34491, "Ġcontaminated": 34492, "ĠCommerce": 34493, "hythm": 34494, "ĠÃĪ": 34495, "Ġìĭ¤ë": 34496, "Ġmusste": 34497, "uir": 34498, "Ġsums": 34499, "ĠSomewhere": 34500, "ãĥİ": 34501, "Ġkami": 34502, "Ġaired": 34503, "ĠANDREW": 34504, "Ġêº": 34505, "Ġviendo": 34506, "Ġantibody": 34507, "Ġabsolument": 34508, "Ġprotesters": 34509, "ĠQuébec": 34510, "stadt": 34511, "Shaun": 34512, "Ġchambers": 34513, "ĠWear": 34514, "ĠEffects": 34515, "Ġhazards": 34516, "Ġnei": 34517, "Ġcorazón": 34518, "Ġá¼": 34519, "ĠSG": 34520, "Ķ©": 34521, "ĠìĹŃìĭľ": 34522, "Ġcomfy": 34523, "ĠCody": 34524, "Ġpensando": 34525, "Ġganska": 34526, "ĠAcross": 34527, "öllig": 34528, "abyte": 34529, "Ġwedge": 34530, "Ġkalian": 34531, "Ġsigue": 34532, "endes": 34533, "ĠGroÃŁ": 34534, "Ġutiliser": 34535, "Ġflown": 34536, "аниÑİ": 34537, "Ġlevar": 34538, "restrial": 34539, "Ġillustrations": 34540, "Ġaslında": 34541, "BLEEP": 34542, "ĠдоÑģÑĤ": 34543, "Ġturret": 34544, "Ġsuitcase": 34545, "ziÄĻki": 34546, "Ġsketches": 34547, "Ġacred": 34548, "ĠRei": 34549, "Ġtsun": 34550, "ĠSag": 34551, "Ġthirds": 34552, "ĠKIRBY": 34553, "rai": 34554, "Ġhumanos": 34555, "Ġrecommends": 34556, "Ġextraordinarily": 34557, "Ġcommencement": 34558, "KN": 34559, "opez": 34560, "Ġ×ijש": 34561, "Ġlethal": 34562, "ĠEstamos": 34563, "Ġinspector": 34564, "ĠSeok": 34565, "eun": 34566, "Ġoffshore": 34567, "Ġgettin": 34568, "years": 34569, "ĠSilence": 34570, "ĠNatur": 34571, "upun": 34572, "Ġtrzy": 34573, "Ġnoget": 34574, "Ġhamburger": 34575, "ĠPraise": 34576, "énd": 34577, "Ġ1971": 34578, "ylie": 34579, "krit": 34580, "ĠìĥĿê°ģìĿ´": 34581, "çļ®": 34582, "Ġmomentos": 34583, "Ġesté": 34584, "Ġdissemin": 34585, "Ġgigs": 34586, "Ġdesaf": 34587, "Ġavis": 34588, "ĠZoo": 34589, "ĠìķĬìĿĢ": 34590, "häng": 34591, "åı¥": 34592, "hake": 34593, "ĠBism": 34594, "Ġrethink": 34595, "ĠMalcolm": 34596, "Ġidentifies": 34597, "lower": 34598, "ixel": 34599, "ĠtvÃ¥": 34600, "ked": 34601, "ierz": 34602, "Ġöffentlich": 34603, "Ġproclaim": 34604, "soon": 34605, "lol": 34606, "Ġloi": 34607, "Ġbitten": 34608, "rollo": 34609, "Ġsermon": 34610, "Ġesqu": 34611, "Ġjackets": 34612, "Ġgráfic": 34613, "ĠпоказÑĭв": 34614, "Ġcabeza": 34615, "chodzi": 34616, "Ġpelvis": 34617, "Ġnostalgia": 34618, "Ġbrew": 34619, "Ġshortcuts": 34620, "ĠAdemás": 34621, "Ġsuperficial": 34622, "åħ©åĢĭ": 34623, "Ġboca": 34624, "ĠæĪijæĺ¯": 34625, "imentos": 34626, "åĽłä¸º": 34627, "Ġsprouts": 34628, "é£Ľ": 34629, "ĠJonas": 34630, "ĠFlorence": 34631, "static": 34632, "daughter": 34633, "*)": 34634, "ÅĤby": 34635, "fashion": 34636, "ĠGinger": 34637, "Ġ매ë": 34638, "Ġhustle": 34639, "utos": 34640, "ĠÑĤÑıж": 34641, "ĠLös": 34642, "ש×Ļ×Ŀ": 34643, "anych": 34644, "tuber": 34645, "Ġtidy": 34646, "Ġfrontal": 34647, "Ġwhiskey": 34648, "Ġhumid": 34649, "ĠÎŁ": 34650, "Ġridge": 34651, "Ġmarin": 34652, "Ġbientôt": 34653, "ĠCarrie": 34654, "chw": 34655, "Ġtahun": 34656, "ĠErgeb": 34657, "FR": 34658, "Ġìłķë¶Ģ": 34659, "ĠSoldier": 34660, "Ġenlightenment": 34661, "Ġexamining": 34662, "ĠNotre": 34663, "Ġeram": 34664, "ĠSunny": 34665, "Ġlayered": 34666, "ĠDazu": 34667, "rades": 34668, "好åIJĥ": 34669, "ĠнаÑĪей": 34670, "Ġtimber": 34671, "Ġmanners": 34672, "ĠBirmingham": 34673, "Ġminiature": 34674, "ometers": 34675, "Ġfiller": 34676, "ĠRip": 34677, "ĠKomb": 34678, "owner": 34679, "ì¿": 34680, "idian": 34681, "Ġdemás": 34682, "ĠÙĪت": 34683, "Ġprecautions": 34684, "Ġgoverno": 34685, "zelf": 34686, "ĠComplete": 34687, "å¸ĥ": 34688, "ĠPhantom": 34689, "ãģ¾ãģļ": 34690, "Ġнез": 34691, "ĠкаÑĢÑĤ": 34692, "ĠAntwort": 34693, "ĠPfizer": 34694, "ĠFranco": 34695, "ĠwÅĤ": 34696, "Ġfrig": 34697, "esper": 34698, "Ġkale": 34699, "Ġfilmmaker": 34700, "Ġkurt": 34701, "Ġinvalid": 34702, "å±Ģ": 34703, "arella": 34704, "Äĥng": 34705, "ramento": 34706, "Ġnutritional": 34707, "Ġdictators": 34708, "Ġafin": 34709, "Ġfuzzy": 34710, "ĠGina": 34711, "ót": 34712, "ĠExtremadura": 34713, "Ġdemonstrations": 34714, "ĠMontgomery": 34715, "íķ´ìĦ¤": 34716, "ĠGandhi": 34717, "ãĥĿ": 34718, "ç½®": 34719, "Ġreunion": 34720, "ĠjakiÅĽ": 34721, "ĠZug": 34722, "OUGH": 34723, "lifting": 34724, "Ġà²": 34725, "á¹Ľá¹£": 34726, "eb": 34727, "ĠWOW": 34728, "ĠShiva": 34729, "ometry": 34730, "Ġwildly": 34731, "Ġtended": 34732, "Ġmegap": 34733, "ì²ĺ": 34734, "Ġnause": 34735, "Ġgerek": 34736, "ãĥĭ": 34737, "ĠMarcel": 34738, "Ġneste": 34739, "خر": 34740, "Ġfeh": 34741, "åĨħ": 34742, "suspenseful": 34743, "ĠWrestle": 34744, "ĠPalestinians": 34745, "ĠGORD": 34746, "iyet": 34747, "ĠÑĢади": 34748, "Ġversuchen": 34749, "Ġtransistor": 34750, "ĠÐŁÑĢоÑģÑĤо": 34751, "ĠпонÑĢав": 34752, "Ġrhyme": 34753, "ĠVermont": 34754, "platz": 34755, "è®°": 34756, "ĠÄ°ÅŁte": 34757, "ĠHag": 34758, "ĠÐĺм": 34759, "ĠÑĢаÑģÑģказ": 34760, "Ġmetros": 34761, "ĠInfinity": 34762, "wolf": 34763, "ibal": 34764, "ftig": 34765, "ĠÚĨ": 34766, "Ġíĺ¹ìĭľ": 34767, "Ġoggi": 34768, "Ġdisposit": 34769, "ĠпÑĢил": 34770, "ĠвÑĭпол": 34771, "Ġthôi": 34772, "ĠKENN": 34773, "Ġhanding": 34774, "actus": 34775, "Ġtacos": 34776, "Ġformerly": 34777, "ĠCorinthians": 34778, "ãģ«ãģ¯": 34779, "ÑĨÑĸÑĹ": 34780, "Ġpadre": 34781, "Ġcongregation": 34782, "æij": 34783, "fert": 34784, "Ġsubir": 34785, "aiser": 34786, "qua": 34787, "araoh": 34788, "ĠCurry": 34789, "ĠìķĬëĬĶ": 34790, "елÑİ": 34791, "Ġfuss": 34792, "Ġbooty": 34793, "Ġlows": 34794, "Ġhommes": 34795, "ĠMH": 34796, "ĠDisneyland": 34797, "went": 34798, "Ġresidue": 34799, "Ġbeeping": 34800, "è¼ķ": 34801, "ätta": 34802, "Ġmould": 34803, "ĠProjekt": 34804, "stalk": 34805, "Ġartifact": 34806, "ĠAntrag": 34807, "ĠAMD": 34808, "ĠCrypt": 34809, "Ġë©Ķ": 34810, "ĠFelipe": 34811, "ĠCOB": 34812, "elu": 34813, "Ġselfies": 34814, "ĠSanti": 34815, "chutz": 34816, "ĠУкÑĢаÑĹ": 34817, "gesamt": 34818, "Ġflock": 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, "Ġpremature": 34877, "ĠPromise": 34878, "екÑģÑĤ": 34879, "Ġdevastated": 34880, "ĠPremium": 34881, "ĠParam": 34882, "ĠÃĸyle": 34883, "umuz": 34884, "PO": 34885, "rators": 34886, "Ġlamps": 34887, "Ġterritorial": 34888, "Ġbackbone": 34889, "listed": 34890, "DY": 34891, "ĠاÙĦر": 34892, "Ġpursued": 34893, "ĠCommons": 34894, "Ġ곡": 34895, "locks": 34896, "edor": 34897, "Ġconceived": 34898, "gere": 34899, "Ġdisappearing": 34900, "ĠSull": 34901, "ĠìĹ°ë": 34902, "Ġhoffe": 34903, "Ġdetox": 34904, "íĶĮ": 34905, "Ġretir": 34906, "ĠëģĿëĤ": 34907, "Ġpergunta": 34908, "ĠBOY": 34909, "ç²¾": 34910, "Ġpenn": 34911, "æĿ¥äºĨ": 34912, "hés": 34913, "hon": 34914, "Ġcatastrophic": 34915, "Ġaust": 34916, "Ġtorso": 34917, "Ġìĸ´ëĬIJ": 34918, "ĠìĤ¬ëŀĮëĵ¤ìĿ´": 34919, "Ġmarvelous": 34920, "ĠHarley": 34921, "achine": 34922, "Ġtiế": 34923, "itto": 34924, "ĠIÃŃm": 34925, "ylon": 34926, "Ġshutdown": 34927, ".''": 34928, "Ġapologies": 34929, "ĠCommunication": 34930, "ĠговоÑĢÑİ": 34931, "ãģĤãĥ¼": 34932, "âĦ¢": 34933, "ÃŃveis": 34934, "acun": 34935, "Ġretaining": 34936, "Ġcontradiction": 34937, "ĠADAM": 34938, "COM": 34939, "Bryan": 34940, "ĠMonsieur": 34941, "Ġadapting": 34942, "ШÐIJ": 34943, "ĠScr": 34944, "ändert": 34945, "Ġplaus": 34946, "ä»Ĭ天çļĦ": 34947, "Ġonset": 34948, "Ġassistants": 34949, "Ġvalves": 34950, "Ġscatter": 34951, "ĠRust": 34952, "awia": 34953, "Ġreadiness": 34954, "Ġpais": 34955, "Ġbible": 34956, "Ġambiente": 34957, "ĠамеÑĢик": 34958, "Ġuncond": 34959, "Ġkalk": 34960, "åĬ¨": 34961, "Ġmoc": 34962, "unn": 34963, "Ġactu": 34964, "Ġhumming": 34965, "issimo": 34966, "ĠPatrol": 34967, "gow": 34968, "ãĥ¤": 34969, "ĠTHEY": 34970, "ĠBoden": 34971, "ĠBie": 34972, "Ġreel": 34973, "ĠÑĥÑģлов": 34974, "Ġendeavor": 34975, "ĠPeriod": 34976, "ustomed": 34977, "mals": 34978, "alon": 34979, "Box": 34980, "ĠÏĥαÏĤ": 34981, "Ġomdat": 34982, "Ġaltre": 34983, "ĠHeh": 34984, "kad": 34985, "Ġprotector": 34986, "Ġdominance": 34987, "odynamic": 34988, "Ġcommunicated": 34989, "kö": 34990, "Ġpredecessor": 34991, "ĠLuk": 34992, "ĠFlower": 34993, "Ġãģ©": 34994, "poque": 34995, "ÑĤиÑĢов": 34996, "Ġretrospect": 34997, "Ġdecisive": 34998, "Ġexempel": 34999, "{\\": 35000, "ĠRück": 35001, "rite": 35002, "ĠZeus": 35003, "Ġcalorie": 35004, "Ġattractions": 35005, "ĠHinter": 35006, "Ġuhm": 35007, "ĠíĮIJ": 35008, "Ġrulers": 35009, "Ġdiscouraged": 35010, "Ġacontecer": 35011, "Ġaccents": 35012, "ĠOptim": 35013, "ĠAlg": 35014, "kids": 35015, "2021": 35016, "ĠLindsay": 35017, "Ġfilmmakers": 35018, "prowad": 35019, "Ġterug": 35020, "ëĭ´": 35021, "ĠSommer": 35022, "2018": 35023, "Ġborrowing": 35024, "ĠTransfer": 35025, "ноп": 35026, "arias": 35027, "Ġheadphone": 35028, "ì¼ľ": 35029, "Ġtranslating": 35030, "Ġaufge": 35031, "à®ªà®Ł": 35032, "weis": 35033, "avant": 35034, "paid": 35035, "baby": 35036, "Ġtoughest": 35037, "Ġrepeats": 35038, "ĠTeresa": 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"Ġrichest": 35098, "Ġtraumat": 35099, "Ġliegen": 35100, "´ë¥¼": 35101, "è¿ĻéĩĮ": 35102, "Ġunrest": 35103, "ĠStraw": 35104, "æĭľæĭľ": 35105, "Ġcoma": 35106, "ĠKristen": 35107, "ĠÐļонеÑĩно": 35108, "ĠBryce": 35109, "ĠÑıкÑĸ": 35110, "Ġpearls": 35111, "ĠпонимаÑİ": 35112, "Ġadditions": 35113, "Ġasympt": 35114, "ĠменÑĮÑĪе": 35115, "Ġscans": 35116, "Child": 35117, "ĠHide": 35118, "кÑĥÑİ": 35119, "etas": 35120, "Ġdank": 35121, "Ġpleas": 35122, "Ġessays": 35123, "Ġjets": 35124, "åħĴ": 35125, "Ġвед": 35126, "Ġpositives": 35127, "hof": 35128, "-)": 35129, "zzo": 35130, "Ġstarters": 35131, "Ġsmiled": 35132, "Ġ1944": 35133, "quiera": 35134, "Ġrok": 35135, "Ġpuesto": 35136, "Nico": 35137, "Ġsimulations": 35138, "Ġà¶": 35139, "Ġintrigued": 35140, "ĠOverwatch": 35141, "åĸĤ": 35142, "sigh": 35143, "bai": 35144, "Ġë§IJê³ł": 35145, "idé": 35146, "Ġcrabs": 35147, "áºŃp": 35148, "ĠIraqi": 35149, "ìĿ´ë¥¼": 35150, "ÑĤÑı": 35151, "ĠSophia": 35152, "ĠDNS": 35153, "Ġönemli": 35154, "ĠLuo": 35155, "Ŀ¤": 35156, "ĠCounsel": 35157, "ligen": 35158, "анÑĮÑĪе": 35159, "Ġtrumpet": 35160, "Ġdapat": 35161, "ĠJM": 35162, "ĠEVERY": 35163, "Ġå°įä¸įå°į": 35164, "夢": 35165, "ĠLayer": 35166, "Ġcô": 35167, "нал": 35168, "ĠJoo": 35169, "ĠHack": 35170, "Ġsunt": 35171, "ĠLeonard": 35172, "ĠFirebase": 35173, "änger": 35174, "Ġexploding": 35175, "voy": 35176, "Ġì¦IJ": 35177, "ĠÑģеÑĢÑĮ": 35178, "Ġseverity": 35179, "Ġbestimm": 35180, "çµIJæŀľ": 35181, "Ġtiring": 35182, "Ġprocurement": 35183, "Ġdiplomacy": 35184, "Ġdecorative": 35185, "ĠÙĬا": 35186, "Ġpenetration": 35187, "Õ«": 35188, "Ġoutright": 35189, "ENE": 35190, "ĠUni": 35191, "odles": 35192, "Ġzeros": 35193, "Ġdelightful": 35194, "jm": 35195, "Ġdopo": 35196, "没äºĭ": 35197, "Ġpositivity": 35198, "ĠVISTA": 35199, "ĠResource": 35200, "íĥĢë": 35201, "ÑĪие": 35202, "Carl": 35203, "Ġpiping": 35204, "Ġchopping": 35205, "ĠGanze": 35206, "üss": 35207, "ĠAo": 35208, "Ġshattered": 35209, "ĠDetective": 35210, "Ġundoubtedly": 35211, "Ġhalluc": 35212, "Ġench": 35213, "ÑĭÑĩно": 35214, "ÑĥлÑıÑĢ": 35215, "isesti": 35216, "Ġpedals": 35217, "Ġdurum": 35218, "¤íĶ": 35219, "laimer": 35220, "Ġpropre": 35221, "Cu": 35222, "Ġtranslator": 35223, "ĠcaÅĤ": 35224, "Ġ그걸": 35225, "ĠcaÅĤy": 35226, "UA": 35227, "Ġrevised": 35228, "Ġподоб": 35229, "ĠArticle": 35230, "ĠHaiti": 35231, "ĠÃĵ": 35232, "ĠCtrl": 35233, "Ġrozm": 35234, "lait": 35235, "Ġletzte": 35236, "ispering": 35237, "display": 35238, "Ġaluminium": 35239, "Ġpalabras": 35240, "Ġconocer": 35241, "Ġzitten": 35242, "Ġdirig": 35243, "åıªæľī": 35244, "Ġbrainstorm": 35245, "Ġwifi": 35246, "ĠParticip": 35247, "Ġviewpoint": 35248, "ĠQuan": 35249, "Ġhierarch": 35250, "Welcome": 35251, "対": 35252, "Ġoffen": 35253, "ĠRecovery": 35254, "gano": 35255, "Would": 35256, "Ġrepro": 35257, "Ġperceptions": 35258, "Ġdemasi": 35259, "ĠBangladesh": 35260, "ĠIncredible": 35261, "Ġletzt": 35262, "Ġbehaving": 35263, "Ġastonishing": 35264, "ĠâĨ": 35265, "ĠëĤ¨ìŀIJ": 35266, "èµ°äºĨ": 35267, "ãĥĶ": 35268, "ĠGORDON": 35269, "CAR": 35270, "?!\"": 35271, "ĠPrest": 35272, "Ġë§ŀìķĦìļĶ": 35273, "Ġtand": 35274, "Ġlash": 35275, "çĬ": 35276, "ificant": 35277, "Ġintoler": 35278, "ĠгеÑĢо": 35279, "Ġteu": 35280, "aso": 35281, "ĠÑģовеÑĤ": 35282, "Ġtravelers": 35283, "ĠSynd": 35284, "ĠвеÑĢÑģ": 35285, "Fonda": 35286, "adı": 35287, "Ġtranscription": 35288, "Ġtitanium": 35289, "Ġtwists": 35290, "Ġgearbox": 35291, "ensation": 35292, "fat": 35293, "Coll": 35294, "ĠCommonwealth": 35295, "zon": 35296, "ĠPolizei": 35297, "ĠAPPLAUSE": 35298, "fry": 35299, "ĠJuda": 35300, "esteem": 35301, "Ġsock": 35302, "ĠJugend": 35303, "ĠкÑģÑĤаÑĤи": 35304, "ĠDro": 35305, "Ġprochaine": 35306, "ãĥ¼ãĥ«": 35307, "Ġliksom": 35308, "ĠEnergie": 35309, "ĠMarina": 35310, "Ġ230": 35311, "Ġê°ĢìĦľ": 35312, "umping": 35313, "Ġlone": 35314, "ç´ļ": 35315, "Ġfonts": 35316, "Ġbusinessman": 35317, "Ġply": 35318, "Ġdoe": 35319, "grid": 35320, "ĠMilwaukee": 35321, "ĠEden": 35322, "!\".": 35323, "ĠÛĮÛģ": 35324, "ogens": 35325, "Ġteaser": 35326, "Ġquién": 35327, "Ġincentiv": 35328, "govern": 35329, "Ġchildcare": 35330, "Ġsneakers": 35331, "Ġimprisoned": 35332, "®": 35333, "иÑĤеÑģÑĮ": 35334, "anbul": 35335, "Ġregain": 35336, "Ġtranquil": 35337, "Redner": 35338, "鼨": 35339, "IFA": 35340, "Ġideological": 35341, "ĠmayorÃŃa": 35342, "Ġbureau": 35343, "eterm": 35344, "ĠDID": 35345, "ìĬ·": 35346, "Ġwaving": 35347, "Ġbeb": 35348, "Ġár": 35349, "Ġкв": 35350, "Ġenvoy": 35351, "anut": 35352, "икÑĥ": 35353, "ĠEnvironment": 35354, "ĠAssass": 35355, "ãĤĵãģ§": 35356, "ĠBread": 35357, "ĠТÑĥÑĤ": 35358, "Ġstaircase": 35359, "ĠDisease": 35360, "Ġaucun": 35361, "ĠëĭĪ": 35362, "Ġconfrontation": 35363, "Ġ1941": 35364, "Ġirony": 35365, "Ġworsh": 35366, "ãĤĮãĤĭ": 35367, "Ġfick": 35368, "ĠNaomi": 35369, "Ġbackside": 35370, "ieux": 35371, "Kap": 35372, "Ġvedere": 35373, "Ġlengthy": 35374, "Ġbreaker": 35375, "ĠRolle": 35376, "Ġpredator": 35377, "Ġnossos": 35378, "Ġadvertise": 35379, "è³ĩ": 35380, "ÑĢоде": 35381, "Rednerwechsel": 35382, "reten": 35383, "Ġcollectors": 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35732, "iec": 35733, "ıı": 35734, "hanol": 35735, "Ġmisunderstand": 35736, "Ray": 35737, "Ġroadmap": 35738, "ocumented": 35739, "izione": 35740, "ĠOlive": 35741, "rift": 35742, "Ġ×Ķ׳": 35743, "æ¯į": 35744, "lest": 35745, ";;": 35746, "ĠEA": 35747, "éľĢè¦ģ": 35748, "одÑĥ": 35749, "Ġhobbies": 35750, "Ġburial": 35751, "ãģ«ãģ¡ãģ¯": 35752, "Ф": 35753, "lege": 35754, "ĠHJ": 35755, "Ġobjection": 35756, "ĠãģŃ": 35757, "ctory": 35758, "Ġincremental": 35759, "Ġgymn": 35760, "Ġepidemi": 35761, "ÑģÑĭл": 35762, "Ãij": 35763, "Ġadvancement": 35764, "Ġparch": 35765, "News": 35766, "Ġayr": 35767, "лам": 35768, "Ġ׾ש": 35769, "Ġdiploma": 35770, "ãģ¡ãĤĥãĤĵ": 35771, "Ġrobbed": 35772, "Only": 35773, "Ġincur": 35774, "Ġchanting": 35775, "Ġíķ´ëıĦ": 35776, "Ġriches": 35777, "ĠCarmen": 35778, "Ġnostro": 35779, "λÎŃ": 35780, "ĠPowder": 35781, "à¹Ģห": 35782, "ĠìŀĪìľ¼ë©´": 35783, "Ġgerçekten": 35784, "ĠPikachu": 35785, "емон": 35786, "OLL": 35787, "Ġplanetary": 35788, "Ġslows": 35789, "Ġclockwise": 35790, "alion": 35791, "ĠìĮ": 35792, "Ġvern": 35793, "Ġhomme": 35794, "Ġendpoint": 35795, "Ġinnocence": 35796, "Ġelementos": 35797, "Ġsophomore": 35798, "Ġnotions": 35799, "ĠCouldn": 35800, "pur": 35801, "Ġzat": 35802, "Ġobsess": 35803, "Ġmotivo": 35804, "ĠKub": 35805, "ĠDrug": 35806, "Ant": 35807, "ĠPlayers": 35808, "ĠHumans": 35809, "Ġmelee": 35810, "ĠWildlife": 35811, "ĠVP": 35812, "Ġvolcanic": 35813, "Ġcomin": 35814, "ĠGuang": 35815, "ĠÏĦιÏĤ": 35816, "ĠоÑģобенно": 35817, "ĠSize": 35818, "Listen": 35819, "ĠAaa": 35820, "appro": 35821, "Ġbarbar": 35822, "ĠParkinson": 35823, "нÑıÑĤÑĮ": 35824, "åį°": 35825, "Ġunderestimate": 35826, "Ġsubstitution": 35827, "Ġcosmetic": 35828, "ä¸ĭ次": 35829, "Ġwillen": 35830, "Ġbeide": 35831, "anni": 35832, "Ġconditioned": 35833, "ĠDebbie": 35834, "Ġisto": 35835, "ĠEdwards": 35836, "ìĽĮìļĶ": 35837, "ĠÑĤов": 35838, "Ġabbrevi": 35839, "ĠMün": 35840, "ĠPrinc": 35841, "ĠLiang": 35842, "Ġstink": 35843, "Ġradioactive": 35844, "ãģĨãĤı": 35845, "Ġacontec": 35846, "Ġuncon": 35847, "ĠTurbo": 35848, "ãģIJ": 35849, "Ġkisses": 35850, "æĺ¯ä»Ģ麼": 35851, "еÑĤÑĢов": 35852, "Ġfrontier": 35853, "ĠSpy": 35854, "ĠBelarus": 35855, "ĠCBS": 35856, "á»Ĺ": 35857, "amoto": 35858, "íķľëį°": 35859, "ĠÑģÑĤÑĢо": 35860, "ĠEnfin": 35861, "Ġbreadth": 35862, "éĺ²": 35863, "ĠCafe": 35864, "ĠDafür": 35865, "ĠBour": 35866, "aras": 35867, "Ġblueprint": 35868, "anı": 35869, "Ġconstants": 35870, "Ġattacker": 35871, "ĠFormula": 35872, "zaÄĩ": 35873, "Ġsowie": 35874, "Ġeyebrow": 35875, "obook": 35876, "Ġsetzen": 35877, "第ä¸ī": 35878, "onsider": 35879, "awning": 35880, "Ġsöyleye": 35881, "Ġinvaded": 35882, "Ġpronouns": 35883, "Ġdobry": 35884, "Si": 35885, "ĠХоÑĤ": 35886, "Ġvolleyball": 35887, "Ġlament": 35888, "isches": 35889, "arme": 35890, "api": 35891, "ĠWiki": 35892, "лиÑĪ": 35893, "Ġkasih": 35894, "Ġpess": 35895, "ĠÑĦоÑĤ": 35896, "ĠSul": 35897, "å¾·": 35898, "Ġpseudo": 35899, "Ġmemo": 35900, "ĠìĹ°ìĬµ": 35901, "ĠдоллаÑĢов": 35902, "ĠпеÑĢем": 35903, "ĠReach": 35904, "miral": 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36077, "Ú¾ÛĮ": 36078, "anız": 36079, "atchet": 36080, "ÙĬت": 36081, "Ġlernen": 36082, "Ġê·ĢìŬ": 36083, "Ġhoused": 36084, "Ġcleanse": 36085, "ĠWAT": 36086, "laration": 36087, "Ġbytes": 36088, "Ġtucked": 36089, "Ġfaults": 36090, "до": 36091, "FX": 36092, "Ġìĸ¼ë§ĪëĤĺ": 36093, "Ġdeform": 36094, "Ġcontracting": 36095, "ĠTIME": 36096, "irse": 36097, "Ġneben": 36098, "Ġcerc": 36099, "ĠArmstrong": 36100, "Ġtester": 36101, "Ġparfait": 36102, "Ġjealousy": 36103, "Ġtoxins": 36104, "Ġdisbel": 36105, "ÑĥÑĢÑĭ": 36106, "impression": 36107, "Ġprostate": 36108, "Ġfirewall": 36109, "Ġclassics": 36110, "еÑĩÑĮ": 36111, "Ġsocialism": 36112, "Ġgracious": 36113, "ĠÑģнова": 36114, "ĠднÑı": 36115, "Ġburner": 36116, "ĠMinor": 36117, "Ġìļ°ë¦¬ë": 36118, "Ġjedes": 36119, "Ġcontinuum": 36120, "Ġhots": 36121, "Ġoccurrence": 36122, "Ġadministered": 36123, "ĠзамеÑĤ": 36124, "Ġhesitation": 36125, "Ġdrills": 36126, "erca": 36127, "ĠвÑĤоÑĢой": 36128, "Ġsteadily": 36129, "Ġinsanlar": 36130, "Ġihan": 36131, "íij": 36132, 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36193, "ĠAbend": 36194, "Ġ컨": 36195, "Ġexaggerated": 36196, "fires": 36197, "Ġredist": 36198, "ffiti": 36199, "Ġopenness": 36200, "ê°IJìĿ´": 36201, "endeu": 36202, "енной": 36203, "Watch": 36204, "Ġavatar": 36205, "ĠPey": 36206, "urun": 36207, "Ġsenza": 36208, "Ġì§ĢìĹŃ": 36209, "ĠNatomiast": 36210, "Ġemergence": 36211, "rays": 36212, "Ġcrafted": 36213, "gary": 36214, "ãģłãģij": 36215, "üng": 36216, "-\"": 36217, "Ġhacked": 36218, "Ġstray": 36219, "encie": 36220, "emo": 36221, "Ġcomen": 36222, "ĠKız": 36223, "ĠJasmine": 36224, "ĠHindi": 36225, "manas": 36226, "Ġinfinitely": 36227, "emon": 36228, "ìĿ¸ëį°ìļĶ": 36229, "jak": 36230, "Ġroaring": 36231, "érique": 36232, "sweise": 36233, "ĠRolex": 36234, "åł±å°İ": 36235, "ĠStuart": 36236, "bnb": 36237, "Ġdiagnose": 36238, "Ġcoherent": 36239, "ĠMJ": 36240, "æºĸåĤĻ": 36241, "Ġpike": 36242, "lav": 36243, "Ġorchestral": 36244, "аÑģÑĤи": 36245, "Ġterminar": 36246, "Ġgatherings": 36247, "Ġcompliant": 36248, "Ġupgrading": 36249, "Ġregulator": 36250, "Ġlanç": 36251, "éĢ£": 36252, "Ġmerchants": 36253, "tawa": 36254, "Ġmonitored": 36255, "Ġrendre": 36256, "两": 36257, "Ġunterwegs": 36258, "anguard": 36259, "gard": 36260, "ĠBelow": 36261, "duino": 36262, "ĠЦе": 36263, "Ġimpedance": 36264, "ìľ¡": 36265, "份": 36266, "Ġaktuell": 36267, "ĠVatic": 36268, "åŃ©": 36269, "Ġstewards": 36270, "Ġbrightest": 36271, "Ġkenn": 36272, "Ġkau": 36273, "ĠMatrix": 36274, "ĠBark": 36275, "ĠðŁij": 36276, "Ġtaper": 36277, "Ġcasino": 36278, "ר×Ķ": 36279, "ysical": 36280, "Ġbuilders": 36281, "ĠczÅĤowie": 36282, "ĠNepal": 36283, "Ġ!\"": 36284, "Ġterme": 36285, "Ġinnych": 36286, "Ġmaths": 36287, "Ġdrafted": 36288, "ĠBalk": 36289, "Ġhesitant": 36290, "Ġvoltar": 36291, "Ġrevive": 36292, "ĠÑĦилÑĮма": 36293, "Ġassassin": 36294, "ĠSolutions": 36295, "Ġduel": 36296, "Ġbearings": 36297, "à¸Ħะ": 36298, "Ġrookie": 36299, "ikat": 36300, "Ġbiscuits": 36301, "Ġcords": 36302, "ÑĥваÑĤи": 36303, "ARIN": 36304, "Ġprogressing": 36305, "ĠGir": 36306, "Ġpenetrate": 36307, "ĠStorage": 36308, "eight": 36309, "ĠÑĤÑĢÑĥ": 36310, "ĠdonÃŃt": 36311, "Ġsizin": 36312, "Ġoutdated": 36313, "ĠнаÑĪи": 36314, "Ġaffir": 36315, "Ġspoons": 36316, "Ġoni": 36317, "Ġflank": 36318, "ĠGol": 36319, "hã": 36320, "Ġpéri": 36321, "Ġhonorable": 36322, "ĠBreathe": 36323, "scenes": 36324, "Ġobviamente": 36325, "икÑģ": 36326, "Ġש×ŀ×": 36327, "Ġsmoothie": 36328, "ŀĪë": 36329, "Ġdime": 36330, "ĠíĸĪìĸ´ìļĶ": 36331, "Ġappel": 36332, "ĠCatholics": 36333, "Ġsingles": 36334, "Ġlaten": 36335, "Ġçünkü": 36336, "ĠVader": 36337, "æıĽ": 36338, "Ġvardı": 36339, "ĠIstanbul": 36340, "gré": 36341, "ĠElsa": 36342, "ël": 36343, "Ġinvece": 36344, "Ġcrane": 36345, "Ġobe": 36346, "ĠShark": 36347, "Ġsmack": 36348, "Ġrestoring": 36349, ".\\": 36350, "Ġë¹łë": 36351, "Ġfaded": 36352, "umbers": 36353, "Singing": 36354, "Ġdepressing": 36355, "thest": 36356, "ĠWahr": 36357, "Ġmultitude": 36358, "ÑĢавÑģÑĤвÑĥйÑĤе": 36359, "rijk": 36360, "eka": 36361, "Ġcompletes": 36362, "ĠWells": 36363, "Ġroy": 36364, "ĠPray": 36365, "ĠKalau": 36366, "izin": 36367, "iaÅĤem": 36368, "Ġlocom": 36369, "ĠNashville": 36370, "ĠPentagon": 36371, "미": 36372, "ĠNEW": 36373, "ÄħÄĩ": 36374, "ÃŃss": 36375, "Ġmarrying": 36376, "Ġfeud": 36377, "íĻķ": 36378, "æĢ¥": 36379, ")!": 36380, "ĠOperations": 36381, "ÑĥÑĶ": 36382, "Ġmoje": 36383, "Ġinstructed": 36384, "ĠëĪĦ구": 36385, "Ġ×Ķ×Ĵ": 36386, "ĠпомоÑīÑĮÑİ": 36387, "Ġsabia": 36388, "ìķĺìĸ´ìļĶ": 36389, "plane": 36390, "pri": 36391, "ĠполноÑģÑĤÑĮÑİ": 36392, "ĠKitty": 36393, "Ġpróprio": 36394, "edere": 36395, "Ġinteresante": 36396, "Ġде": 36397, "Ġcondensed": 36398, "Ġavent": 36399, "TOR": 36400, "Ġgreasy": 36401, "ARK": 36402, "orta": 36403, "AJ": 36404, "Ġdisreg": 36405, "Ġcorrections": 36406, "Ġstero": 36407, "Ġinfluenza": 36408, "Ġdesses": 36409, "Ġballots": 36410, "Ġmeget": 36411, "Ġmafia": 36412, "Ġböl": 36413, "nost": 36414, "ĠÑģÑĤаÑĤÑĮ": 36415, "Ġresponder": 36416, "Ġhinten": 36417, "grav": 36418, "à¸Ńะ": 36419, "ynchron": 36420, "Ġviens": 36421, "Ġsamo": 36422, "Ġdt": 36423, "pannt": 36424, "ĠÅĽwiat": 36425, "ĠзапиÑģ": 36426, "Ġmerged": 36427, "Ġkep": 36428, "Ġmisleading": 36429, "Ġdigamos": 36430, "Ġammon": 36431, "è¾Ľ": 36432, "chet": 36433, "Ġê°Ģìł¸": 36434, "Ġuni": 36435, "ĠëIJĺëĬĶëį°": 36436, "ĠнапÑĢав": 36437, "ĠкоÑĤоÑĢого": 36438, "Ġanimate": 36439, "×ķ×IJ×": 36440, "еÑĢв": 36441, "Ġminced": 36442, "Ġkaum": 36443, "ãģĤãģģ": 36444, "ÏĢε": 36445, "лег": 36446, "existing": 36447, "Ġplataform": 36448, "ĠKRIS": 36449, "ìĽł": 36450, "ĠFamilien": 36451, "ĠLibya": 36452, "Ġbiodiversity": 36453, "Ġidiots": 36454, "irdi": 36455, "Ġszyb": 36456, "ĠRolling": 36457, "ücht": 36458, "ĠÑĥдив": 36459, "ÑģÑĥд": 36460, "Ġrealizar": 36461, "Ġcanned": 36462, "ĠÑĢан": 36463, "Ġmetabolic": 36464, "ĠBeef": 36465, "Ġkilka": 36466, "лÑİÑģ": 36467, "Ġregistry": 36468, "моÑĤÑĢиÑĤе": 36469, "Ġvielä": 36470, "Ġodc": 36471, "Ġcondemned": 36472, "æ©ĭ": 36473, "fal": 36474, "ĠDil": 36475, "woÅĽci": 36476, "Aw": 36477, "Ġstatistically": 36478, "Ġsogen": 36479, "ĠBETH": 36480, "Ġshaving": 36481, "幸": 36482, "ocal": 36483, "ĠFunny": 36484, "Ġpeacefully": 36485, "Ġaddictive": 36486, "ĠInsert": 36487, "lauf": 36488, "Ġexperiencia": 36489, "é¦ĸåħĪ": 36490, "иÑĤелÑı": 36491, "ÃŃgen": 36492, "ágina": 36493, "Ġabdomen": 36494, "íķľëĭ¤": 36495, "icus": 36496, "imana": 36497, "ìį¨": 36498, "arching": 36499, "Ġkonkret": 36500, "ìķĺë": 36501, "ека": 36502, "oufl": 36503, "ivel": 36504, "Ġnude": 36505, "ètres": 36506, "Ġmonsieur": 36507, "Ġclash": 36508, "Ġtherapists": 36509, "Ġcubed": 36510, "Ġretrouver": 36511, "Ġwaveform": 36512, "Ġpotem": 36513, "ĠFormer": 36514, "isión": 36515, "åºľ": 36516, "Ġ×IJ×Ŀ": 36517, "undos": 36518, "ĠMeinung": 36519, "صÙĦ": 36520, "ĠJude": 36521, "ĠnÃ¥r": 36522, "ĠLeonardo": 36523, "ĠCristo": 36524, "ĠGOT": 36525, "ÑģÑĤÑĢÑĥк": 36526, "LAN": 36527, "ĠgÃ¥ng": 36528, "Ġdéb": 36529, "ĠFrankfurt": 36530, "Ġcrappy": 36531, "Ġlil": 36532, "année": 36533, "ĠмеÑģÑĤе": 36534, "RET": 36535, "ĠNer": 36536, "ĠCOSTA": 36537, "Ġjedem": 36538, "Ġcurtains": 36539, "Ġiterations": 36540, "Ġunav": 36541, "Ġplaque": 36542, "orum": 36543, "Ġζ": 36544, "Ġnúmeros": 36545, "Ġdesap": 36546, "²½": 36547, "Ġcompiled": 36548, "Ġrefle": 36549, "Ġrankings": 36550, "Ġrepaired": 36551, "ĠÐĿапÑĢ": 36552, "Ġdownloads": 36553, "Ġarmour": 36554, "Ġ×Ļ×ķתר": 36555, "Ġlongevity": 36556, "ĠTONER": 36557, "ĠкомменÑĤаÑĢ": 36558, "Ġczego": 36559, "Ġnotify": 36560, "Ġairports": 36561, "Ġenduring": 36562, "lette": 36563, "Ġapparat": 36564, "Ġhabil": 36565, "á»ĩc": 36566, "nad": 36567, "ICO": 36568, "ĠBrah": 36569, "Ġsegún": 36570, "Ġgovernors": 36571, "kaha": 36572, "ĠSchluss": 36573, "Ġodpowied": 36574, "irting": 36575, "Ġrempl": 36576, "ĠAboriginal": 36577, "identally": 36578, "Ġenhancing": 36579, "licting": 36580, "ĠHawaiian": 36581, "Ġstriving": 36582, "ĠNiet": 36583, "Ġznaczy": 36584, "Ġobedience": 36585, "ĠnÃ¥got": 36586, "Ġexpired": 36587, "Ġ1918": 36588, "presented": 36589, "Ġprowad": 36590, "ĠTerr": 36591, "ĠPrinceton": 36592, "Ġmorgen": 36593, "Ġattracting": 36594, "ĠSigma": 36595, "igner": 36596, "ĠRechts": 36597, "ĠPeki": 36598, "Ġmethy": 36599, "Ġhamm": 36600, "Ġdireito": 36601, "Ġdelegation": 36602, "иваÑİÑĤ": 36603, "Ġgin": 36604, "Young": 36605, "Ġdependencies": 36606, "ĠBradley": 36607, "buds": 36608, "Ġfis": 36609, "Ġpytanie": 36610, "Ġinterconnected": 36611, "Ġembaixo": 36612, "ĠSas": 36613, "Ġruh": 36614, "ĠSicht": 36615, "Sur": 36616, "Ġsuperb": 36617, "ĠSabbath": 36618, "ĠDanger": 36619, "kol": 36620, "Ġhou": 36621, "supp": 36622, "ĠNacional": 36623, "Ġsuccession": 36624, "Ġvá": 36625, "ĠMaÃŁnahmen": 36626, "ĠJessie": 36627, "ĠIdaho": 36628, "forest": 36629, "ħĺ": 36630, "Ġ×ŀ×ĵ": 36631, "ĠØ£ÙĬ": 36632, "Ġsweetheart": 36633, "Ġneatly": 36634, "ĠEvangel": 36635, "곡": 36636, "ĠSuite": 36637, "ública": 36638, "ĠÑĥли": 36639, "ĠAnnouncer": 36640, "ligh": 36641, "Ġsensations": 36642, "Ġshelters": 36643, "Ġhart": 36644, "Ġsqueezing": 36645, "ĠRivers": 36646, "ĠCooking": 36647, "ì±ħ": 36648, "personal": 36649, "Ġmanos": 36650, "ÑijÑĤÑģÑı": 36651, "wij": 36652, "Ġgogg": 36653, "ĠMilli": 36654, "ĠFP": 36655, "ünst": 36656, "ĠLS": 36657, "Ġspraying": 36658, "Ġfaux": 36659, "Ġautograph": 36660, "ologic": 36661, "Ġtorment": 36662, "Ġencrypted": 36663, "á»ħ": 36664, "Ġestre": 36665, "ç¹¼": 36666, "à±": 36667, "Ġstumbled": 36668, "Ġaider": 36669, "Ġsaben": 36670, "xter": 36671, "ĠCities": 36672, "ĠTürk": 36673, "ëĭ¥": 36674, "chine": 36675, "Ġtopping": 36676, "Ġpoisoned": 36677, "ĠRomania": 36678, "×ĵ×Ļ": 36679, "Ģë¡ľ": 36680, "ĠпоÑĢÑıд": 36681, "Ġchirping": 36682, "ĠìĻĦë": 36683, "×ij×¢": 36684, "Ġcuanto": 36685, "Ġdonating": 36686, "ĠRegent": 36687, "ĠBeruf": 36688, "Ġdistracting": 36689, "Ġstamina": 36690, "ĠDarren": 36691, "Ġì¶ķ": 36692, "lists": 36693, "dal": 36694, "chuss": 36695, "Ġeconomist": 36696, "ãģĪãĥ¼": 36697, "orgt": 36698, "Ġistiyorum": 36699, "è¿Ľ": 36700, "ĠSurprise": 36701, "ĠHao": 36702, "Ġìµľê³ł": 36703, "ĠGW": 36704, "ĠInner": 36705, "Ġquieren": 36706, "Ġminded": 36707, "Ġsupercomputer": 36708, "Ġdiagrams": 36709, "íĬľë": 36710, "ê²łìĸ´": 36711, "ĠобÑĬÑıÑģ": 36712, "Ġestaban": 36713, "Ġdestroys": 36714, "ĠBreaking": 36715, "ĠkarÄ±ÅŁ": 36716, "Ġrebuilding": 36717, "ľëĮĢ": 36718, "ливо": 36719, "ĠSauce": 36720, "ĠFusion": 36721, "×ķ×ŀ×": 36722, "ĠQuinn": 36723, "Ġgauche": 36724, "ĠÙĪØ£": 36725, "ĠÈ": 36726, "çĵľ": 36727, "Ġtechno": 36728, "Ġdispatch": 36729, "ĠaÅŁk": 36730, "Ġeinzel": 36731, "ĠGmail": 36732, "çŀ": 36733, "Ġê°ľìĿ¸": 36734, "ĠÑģемÑĮ": 36735, "Ġjourneys": 36736, "Ġiht": 36737, "Ġfibre": 36738, "Ġdramas": 36739, "ouched": 36740, "Ġrename": 36741, "ĠопеÑĢ": 36742, "Ġpoo": 36743, "ĠDru": 36744, "ĠиÑĤог": 36745, "Ġzast": 36746, "Ġcoz": 36747, "Ġzucch": 36748, "Ġobtaining": 36749, "Ġcommute": 36750, "Ġsubmer": 36751, "ĠVish": 36752, "ĠRabb": 36753, "ogg": 36754, "Ġhut": 36755, "íĸĪìĸ´": 36756, "æ¯Ķå¦Ĥ": 36757, "eremi": 36758, "Ġμα": 36759, "Ġdiskut": 36760, "ĠбÑĥк": 36761, "Ġimpaired": 36762, "depend": 36763, "ĠÙĪا": 36764, "ĠÑĢÑĥк": 36765, "ĠбаÑĢ": 36766, "Ġoxidation": 36767, "Ġsituação": 36768, "ÉĻn": 36769, "ução": 36770, "Ġsagte": 36771, "ĠSER": 36772, "ĠCake": 36773, "Ġturmeric": 36774, "ĠKak": 36775, "bung": 36776, "ĠKá¹Ľá¹£á¹ĩa": 36777, "Ġpoisoning": 36778, "Ġslipping": 36779, "ĠSays": 36780, "å°±åı¯ä»¥": 36781, "òng": 36782, "çŁ³": 36783, "«": 36784, "ĠClaudia": 36785, "ĠCharacter": 36786, "ниÑĨ": 36787, "coat": 36788, "Ġprogressed": 36789, "ĠFergus": 36790, "Ġìĺ¤ëĬ": 36791, "Ġoat": 36792, "ordable": 36793, "ĠLey": 36794, "ĠHeraus": 36795, "Ġresultados": 36796, "ĠKayla": 36797, "Ġriff": 36798, "Ġchegou": 36799, "Ġxi": 36800, "Ġspacious": 36801, "Ġrecognised": 36802, "Ġech": 36803, "ĠTie": 36804, "Ġlauncher": 36805, "Jim": 36806, "Ġsuppression": 36807, "ĠImpossible": 36808, "Ġguitars": 36809, "ĠFourier": 36810, "иÑĩеÑģкий": 36811, "ĠTherap": 36812, "ĠKaf": 36813, "centered": 36814, "ĠÑģооÑĤвеÑĤ": 36815, "Ġklim": 36816, "Ġcarbohydrates": 36817, "ignant": 36818, "ĠAstron": 36819, "Ġemple": 36820, "Ġdrastic": 36821, "ĠмиÑĢе": 36822, "вин": 36823, "uw": 36824, "Ġprettier": 36825, "Ġdonuts": 36826, "ĠAthena": 36827, "Ġdissert": 36828, "Ġplante": 36829, "Ġuranium": 36830, "ìĿĮë": 36831, "aré": 36832, "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": 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"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, 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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, 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"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, 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"Ġsuperintendent": 38834, "idores": 38835, "idy": 38836, "brahim": 38837, "Ġunlucky": 38838, "Ġperk": 38839, "anci": 38840, "Ġë§ĮëĤĺ": 38841, "ĠÐľÐ¾Ñģкв": 38842, "Ġfinans": 38843, "Ġdiferencia": 38844, "łĪìĿ´": 38845, "éħį": 38846, "ORY": 38847, "ĠTac": 38848, "ÛĮا": 38849, "Ġdesem": 38850, "Ġважно": 38851, "ĠJU": 38852, "ĠìŀĪìŀĸìķĦìļĶ": 38853, "ĠÎĿ": 38854, "Ġinformations": 38855, "ĠHEL": 38856, "hst": 38857, "ĠпоговоÑĢ": 38858, "Ġvoiture": 38859, "Ġreus": 38860, "ändig": 38861, "ĠпоÑħож": 38862, "jing": 38863, "Ġdru": 38864, "altra": 38865, "Ġproduits": 38866, "Ġkite": 38867, "Ġeyeball": 38868, "ĠBelt": 38869, "ĠRestaurant": 38870, "Ġgamb": 38871, "Ġporridge": 38872, "itters": 38873, "Ġconverts": 38874, "Ġyardım": 38875, "Ġmáximo": 38876, "wirtschaft": 38877, "ĠíķĺëĤĺë": 38878, "Ġì¤Ģ": 38879, "Ġiceberg": 38880, "Ġvorbei": 38881, "Ġ256": 38882, "ocratic": 38883, "Ġreckless": 38884, "onner": 38885, "Ġmús": 38886, "Ġlogically": 38887, "ĠPrison": 38888, "ĠNetz": 38889, "Ġvacant": 38890, "Ġnimmt": 38891, "ĠHARR": 38892, "Ġзов": 38893, "ĠDee": 38894, "ringe": 38895, "niest": 38896, "ĠRules": 38897, "ìĬ¤ëŁ½": 38898, "cussions": 38899, "Ġfloral": 38900, "Ġconstrained": 38901, "Ġdifferentiation": 38902, "ĠQuebec": 38903, "ĠÛģÛĮÚº": 38904, "Ġpública": 38905, "itel": 38906, "Ġaccommodations": 38907, "ĠGrü": 38908, "íľ": 38909, "Ġpickles": 38910, "иÑĩеÑģкиÑħ": 38911, "Ġcommissions": 38912, "ĠBaek": 38913, "ĠçocuÄŁ": 38914, "ĠMedium": 38915, "Ġperiodically": 38916, "Ġwonderfully": 38917, "Ġstaffing": 38918, "ìĽIJë": 38919, "rire": 38920, "fle": 38921, "ĠMcL": 38922, "ĠÑĤеп": 38923, "ĠпеÑĢек": 38924, "нолог": 38925, "Ġíģ¬ê²Į": 38926, "çĻ¼çı¾": 38927, "Ġprosperous": 38928, "ĠSpiritual": 38929, "ĠChick": 38930, "DIA": 38931, "ĠÐŁÑĢивеÑĤ": 38932, "ĠperÃŃ": 38933, "ÑĮÑİÑĤ": 38934, "Ġconsultants": 38935, "ĠEarl": 38936, "ä»Ĭå¹´": 38937, "Ġruining": 38938, "оÑĢе": 38939, "Ġpenser": 38940, "Ġtakiej": 38941, "Ġstrengthened": 38942, "ĠLiquid": 38943, "онеÑĨ": 38944, "аваÑĤÑĮ": 38945, "Ġcamer": 38946, "Ġdisagreement": 38947, "Ġbathing": 38948, "ĠYosh": 38949, "aal": 38950, "prechen": 38951, "RISADAS": 38952, "Ġsuperstar": 38953, "æģŃ": 38954, "лÑıÑĤÑĮ": 38955, "Ġnib": 38956, "ĠTherm": 38957, "ĠDANIEL": 38958, "Ġpaw": 38959, "Ġliquids": 38960, "Ġcapacit": 38961, "arken": 38962, "Ġvagina": 38963, "Ġmashed": 38964, "Ġemerges": 38965, "yscy": 38966, "Ġunrelated": 38967, "ĠGuild": 38968, "Ġinverted": 38969, "itives": 38970, "Tra": 38971, "Ġbegr": 38972, "Ġalte": 38973, "ì§ķ": 38974, "ãĤģãģ¦": 38975, "ĠÑĢазÑĢабоÑĤ": 38976, "finder": 38977, "Ġдалее": 38978, "ĠблагодаÑĢ": 38979, "walker": 38980, "Ġcrater": 38981, "assadors": 38982, "rences": 38983, "inski": 38984, "ĠKIM": 38985, "ĠElliot": 38986, "2017": 38987, "ĠSr": 38988, "inka": 38989, "anov": 38990, "Ġìŀĺ못": 38991, "Ġproprietary": 38992, "displaystyle": 38993, "ĠÑģим": 38994, "Ġизб": 38995, "ĠPanel": 38996, "Ġinstincts": 38997, "ĠCommunications": 38998, "麻": 38999, "midt": 39000, "Ġë§Įëĵ¤ìĸ´": 39001, "ĠÑģлова": 39002, "ĠGilbert": 39003, "缮åīį": 39004, "Так": 39005, "voorbeeld": 39006, "еÑİÑģÑĮ": 39007, "aryn": 39008, "quez": 39009, "Ġdart": 39010, "ÑĸÑĪ": 39011, "ĠHut": 39012, "Sal": 39013, "Ġsoutheast": 39014, "Ġpesticides": 39015, "Ġhelicopters": 39016, "Ġendured": 39017, "iada": 39018, "Ġbrewing": 39019, "ìŬë": 39020, "ĠÑģвобод": 39021, "ĠSaints": 39022, "ĠFrançais": 39023, "ĠEconomics": 39024, "Ġdisloc": 39025, "ophobia": 39026, "Camer": 39027, "Ġnegotiated": 39028, "ĠÑģÑĤали": 39029, "ìĬ¤íģ": 39030, "ogie": 39031, "Ġtsunami": 39032, "Ġpeeled": 39033, "Ġmotivations": 39034, "è¨Ń": 39035, "ostat": 39036, "flan": 39037, "ĠDAC": 39038, "Ġkav": 39039, "'RE": 39040, "ĠPearson": 39041, "bbe": 39042, "czenie": 39043, "Ġatenção": 39044, "íĨµëł¹": 39045, "ãģ£ãģ¡": 39046, "ĠÑĥдаÑĢ": 39047, "Ġintroductory": 39048, "ĠIci": 39049, "ëĮĢë": 39050, "akat": 39051, "Ġtrench": 39052, "Ġproceeded": 39053, "ĠCoin": 39054, "Ġderecho": 39055, "ĠRede": 39056, "æ¯Ľ": 39057, "аннÑĭй": 39058, "Ġincarcerated": 39059, "ĠRichmond": 39060, "Rock": 39061, "ĠPav": 39062, "ĠKarma": 39063, "uges": 39064, "Ġconteú": 39065, "ë¹Ħ": 39066, "Ġê·¸ë§Į": 39067, "ĠGone": 39068, "ĠwspóÅĤ": 39069, "ĠRahmen": 39070, "unken": 39071, "Ġì¤ijìļĶíķľ": 39072, "Ġib": 39073, "Ġattaching": 39074, "Hay": 39075, "Ġsuka": 39076, "ìį¹": 39077, "Ġpivotal": 39078, "ĠRespect": 39079, "ÃŃda": 39080, "IB": 39081, "ĠVerantwort": 39082, "wiet": 39083, "Ġforensic": 39084, "ÑĢиÑģÑĤ": 39085, "ĠпÑĢинÑĨипе": 39086, "Ġmarkings": 39087, "Ġkettle": 39088, "ĠOpera": 39089, "ĠDoctors": 39090, "Ġshredded": 39091, "Ġrecuer": 39092, "Ġvigil": 39093, "ĠFail": 39094, "Ġentrev": 39095, "ĠдÑĥÑĪ": 39096, "Ġoutbreaks": 39097, "èµ°åIJ§": 39098, "ĠÏĢο": 39099, "Ġrogue": 39100, "angled": 39101, "Ġyearly": 39102, "ĠCreed": 39103, "Ġwam": 39104, "Ġlotus": 39105, "ê³¼ë": 39106, "ãĢģãĢģ": 39107, "ĠSpit": 39108, "ĠItu": 39109, "Ġstrains": 39110, "Ġstamped": 39111, "Ġplaint": 39112, "Ġpotion": 39113, "Ġconsolidation": 39114, "è©ķ": 39115, "оÑĩкÑĥ": 39116, 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"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/musetalk/whisper/whisper/decoding.py b/musetalk/whisper/whisper/decoding.py new file mode 100644 index 0000000..1835054 --- /dev/null +++ b/musetalk/whisper/whisper/decoding.py @@ -0,0 +1,729 @@ +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: Optional[float] = None # 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] = 1.0 # 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 + encoder_embeddings: np.ndarray + decoder_embeddings: np.ndarray + language_probs: Optional[Dict[str, float]] = None + tokens: List[int] = field(default_factory=list) + text: str = "" + avg_logprob: float = np.nan + no_speech_prob: float = np.nan + temperature: float = np.nan + compression_ratio: float = np.nan + + +class Inference: + def logits(self, tokens: Tensor, audio_features: Tensor) -> Tensor: + """Perform a forward pass on the decoder and return per-token logits""" + raise NotImplementedError + + def rearrange_kv_cache(self, source_indices) -> None: + """Update the key-value cache according to the updated beams""" + raise NotImplementedError + + def cleanup_caching(self) -> None: + """Clean up any resources or hooks after decoding is finished""" + pass + + +class PyTorchInference(Inference): + def __init__(self, model: "Whisper", initial_token_length: int): + self.model: "Whisper" = model + self.initial_token_length = initial_token_length + self.kv_cache = {} + self.hooks = [] + + def logits(self, tokens: Tensor, audio_features: Tensor, include_embeddings=False) -> 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_val = self.model.decoder(tokens, audio_features, + kv_cache=self.kv_cache, include_embeddings=include_embeddings) + return return_val + + 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: Optional[float] = None): + self.beam_size = beam_size + self.eot = eot + self.inference = inference + self.patience = patience or 1.0 + self.max_candidates: int = round(beam_size * self.patience) + self.finished_sequences = None + + assert self.max_candidates > 0, f"Invalid beam size ({beam_size}) or patience ({patience})" + + def reset(self): + self.finished_sequences = None + + def update(self, tokens: Tensor, logits: Tensor, sum_logprobs: Tensor) -> Tuple[Tensor, bool]: + if tokens.shape[0] % self.beam_size != 0: + raise ValueError(f"{tokens.shape}[0] % {self.beam_size} != 0") + + n_audio = tokens.shape[0] // self.beam_size + if self.finished_sequences is None: # for the first update + self.finished_sequences = [{} for _ in range(n_audio)] + + logprobs = F.log_softmax(logits.float(), dim=-1) + next_tokens, source_indices, finished_sequences = [], [], [] + for i in range(n_audio): + scores, sources, finished = {}, {}, {} + + # STEP 1: calculate the cumulative log probabilities for possible candidates + for j in range(self.beam_size): + idx = i * self.beam_size + j + prefix = tokens[idx].tolist() + for logprob, token in zip(*logprobs[idx].topk(self.beam_size + 1)): + new_logprob = (sum_logprobs[idx] + logprob).item() + sequence = tuple(prefix + [token.item()]) + scores[sequence] = new_logprob + sources[sequence] = idx + + # STEP 2: rank the candidates and keep the top beam_size sequences for each audio + saved = 0 + for sequence in sorted(scores, key=scores.get, reverse=True): + if sequence[-1] == self.eot: + finished[sequence] = scores[sequence] + else: + sum_logprobs[len(next_tokens)] = scores[sequence] + next_tokens.append(sequence) + source_indices.append(sources[sequence]) + + saved += 1 + if saved == self.beam_size: + break + + finished_sequences.append(finished) + + tokens = torch.tensor(next_tokens, device=tokens.device) + self.inference.rearrange_kv_cache(source_indices) + + # add newly finished sequences to self.finished_sequences + assert len(self.finished_sequences) == len(finished_sequences) + for previously_finished, newly_finished in zip(self.finished_sequences, finished_sequences): + for seq in sorted(newly_finished, key=newly_finished.get, reverse=True): + if len(previously_finished) >= self.max_candidates: + break # the candidate list is full + previously_finished[seq] = newly_finished[seq] + + # mark as completed if all audio has enough number of samples + completed = all( + len(sequences) >= self.max_candidates for sequences in self.finished_sequences + ) + return tokens, completed + + def finalize(self, preceding_tokens: Tensor, sum_logprobs: Tensor): + # collect all finished sequences, including patience, and add unfinished ones if not enough + sum_logprobs = sum_logprobs.cpu() + for i, sequences in enumerate(self.finished_sequences): + if len(sequences) < self.beam_size: # when not enough sequences are finished + for j in list(np.argsort(sum_logprobs[i]))[::-1]: + sequence = preceding_tokens[i, j].tolist() + [self.eot] + sequences[tuple(sequence)] = sum_logprobs[i][j].item() + if len(sequences) >= self.beam_size: + break + + tokens: List[List[Tensor]] = [ + [torch.tensor(seq) for seq in sequences.keys()] for sequences in self.finished_sequences + ] + sum_logprobs: List[List[float]] = [ + list(sequences.values()) for sequences in self.finished_sequences + ] + return tokens, sum_logprobs + + +class LogitFilter: + def apply(self, logits: Tensor, tokens: Tensor) -> None: + """Apply any filtering or masking to logits in-place + + Parameters + ---------- + logits : Tensor, shape = (n_batch, vocab_size) + per-token logits of the probability distribution at the current step + + tokens : Tensor, shape = (n_batch, current_sequence_length) + all tokens in the context so far, including the prefix and sot_sequence tokens + + """ + raise NotImplementedError + + +class SuppressBlank(LogitFilter): + def __init__(self, tokenizer: Tokenizer, sample_begin: int): + self.tokenizer = tokenizer + self.sample_begin = sample_begin + + def apply(self, logits: Tensor, tokens: Tensor): + if tokens.shape[1] == self.sample_begin: + logits[:, self.tokenizer.encode(" ") + [self.tokenizer.eot]] = -np.inf + + +class SuppressTokens(LogitFilter): + def __init__(self, suppress_tokens: Sequence[int]): + self.suppress_tokens = list(suppress_tokens) + + def apply(self, logits: Tensor, tokens: Tensor): + logits[:, self.suppress_tokens] = -np.inf + + +class ApplyTimestampRules(LogitFilter): + def __init__( + self, tokenizer: Tokenizer, sample_begin: int, max_initial_timestamp_index: Optional[int] + ): + self.tokenizer = tokenizer + self.sample_begin = sample_begin + self.max_initial_timestamp_index = max_initial_timestamp_index + + def apply(self, logits: Tensor, tokens: Tensor): + # suppress <|notimestamps|> which is handled by without_timestamps + if self.tokenizer.no_timestamps is not None: + logits[:, self.tokenizer.no_timestamps] = -np.inf + + # timestamps have to appear in pairs, except directly before EOT; mask logits accordingly + for k in range(tokens.shape[0]): + 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 is not None and options.beam_size is None: + raise ValueError("patience requires beam_size to be given") + if options.length_penalty is not None and not (0 <= options.length_penalty <= 1): + raise ValueError("length_penalty (alpha) should be a value between 0 and 1") + + return options + + def _get_initial_tokens(self) -> Tuple[int]: + tokens = list(self.sot_sequence) + 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(suppress_tokens, list), "suppress_tokens must be a list" + + suppress_tokens.extend( + [self.tokenizer.sot, self.tokenizer.sot_prev, self.tokenizer.sot_lm] + ) + if self.tokenizer.no_speech is not None: + # no-speech probability is collected separately + suppress_tokens.append(self.tokenizer.no_speech) + + return tuple(sorted(set(suppress_tokens))) + + def _get_audio_features(self, mel: Tensor, include_embeddings: bool = False): + 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: + result = self.model.encoder(mel, include_embeddings) + if include_embeddings: + audio_features, embeddings = result + else: + audio_features = result + + 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}") + + if include_embeddings: + return audio_features, embeddings + else: + return audio_features + + def _detect_language(self, audio_features: Tensor, tokens: Tensor): + languages = [self.options.language] * audio_features.shape[0] + lang_probs = None + + if self.options.language is None or self.options.task == "lang_id": + lang_tokens, lang_probs = self.model.detect_language(audio_features, self.tokenizer) + languages = [max(probs, key=probs.get) for probs in lang_probs] + if self.options.language is None: + tokens[:, self.sot_index + 1] = lang_tokens # write language tokens + + return languages, lang_probs + + def _main_loop(self, audio_features: Tensor, tokens: Tensor): + assert audio_features.shape[0] == tokens.shape[0] + n_batch = tokens.shape[0] + sum_logprobs: Tensor = torch.zeros(n_batch, device=audio_features.device) + no_speech_probs = [np.nan] * n_batch + + try: + embeddings = [] + for i in range(self.sample_len): + logits, token_embeddings = self.inference.logits(tokens, audio_features, include_embeddings=True) + + if i == 0 and self.tokenizer.no_speech is not None: # save no_speech_probs + probs_at_sot = logits[:, self.sot_index].float().softmax(dim=-1) + no_speech_probs = probs_at_sot[:, self.tokenizer.no_speech].tolist() + + # now we need to consider the logits at the last token only + logits = logits[:, -1] + token_embeddings = token_embeddings[:, :, -1] + + # Append embeddings together + embeddings.append(token_embeddings) + + # 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: + if completed: + embeddings = embeddings[:-1] + embeddings = np.stack(embeddings, 2) + self.inference.cleanup_caching() + + return tokens, sum_logprobs, no_speech_probs, embeddings + + @torch.no_grad() + def run(self, mel: Tensor) -> List[DecodingResult]: + self.decoder.reset() + tokenizer: Tokenizer = self.tokenizer + n_audio: int = mel.shape[0] + + # encoder forward pass + forward_pass: Tuple[Tensor, np.ndarray] = self._get_audio_features(mel, include_embeddings=True) + audio_features, encoder_embeddings = forward_pass + tokens: Tensor = torch.tensor([self.initial_tokens]).repeat(n_audio, 1) + + # detect language if requested, overwriting the language token + languages, language_probs = self._detect_language(audio_features, tokens) + if self.options.task == "lang_id": + return [ + DecodingResult(audio_features=features, language=language, language_probs=probs) + for features, language, probs in zip(audio_features, languages, language_probs) + ] + + # repeat the audio & text tensors by the group size, for beam search or best-of-n sampling + audio_features = audio_features.repeat_interleave(self.n_group, dim=0) + tokens = tokens.repeat_interleave(self.n_group, dim=0).to(audio_features.device) + + # call the main sampling loop + tokens, sum_logprobs, no_speech_probs, decoder_embeddings = self._main_loop(audio_features, tokens) + + # reshape the tensors to have (n_audio, n_group) as the first two dimensions + audio_features = audio_features[:: self.n_group] + no_speech_probs = no_speech_probs[:: self.n_group] + assert audio_features.shape[0] == len(no_speech_probs) == n_audio + + tokens = tokens.reshape(n_audio, self.n_group, -1) + sum_logprobs = sum_logprobs.reshape(n_audio, self.n_group) + + # get the final candidates for each group, and slice between the first sampled token and EOT + tokens, sum_logprobs = self.decoder.finalize(tokens, sum_logprobs) + tokens: List[List[Tensor]] = [ + [t[self.sample_begin : (t == tokenizer.eot).nonzero()[0, 0]] for t in s] for s in tokens + ] + + # select the top-ranked sample in each group + selected = self.sequence_ranker.rank(tokens, sum_logprobs) + tokens: List[List[int]] = [t[i].tolist() for i, t in zip(selected, tokens)] + texts: List[str] = [tokenizer.decode(t).strip() for t in tokens] + + sum_logprobs: List[float] = [lp[i] for i, lp in zip(selected, sum_logprobs)] + avg_logprobs: List[float] = [lp / (len(t) + 1) for t, lp in zip(tokens, sum_logprobs)] + + fields = (texts, languages, tokens, audio_features, avg_logprobs, no_speech_probs) + if len(set(map(len, fields))) != 1: + raise RuntimeError(f"inconsistent result lengths: {list(map(len, fields))}") + + return [ + DecodingResult( + audio_features=features, + language=language, + tokens=tokens, + text=text, + avg_logprob=avg_logprob, + no_speech_prob=no_speech_prob, + temperature=self.options.temperature, + compression_ratio=compression_ratio(text), + encoder_embeddings=encoder_embeddings, + decoder_embeddings=decoder_embeddings + ) + for text, language, tokens, features, avg_logprob, no_speech_prob in zip(*fields) + ] + + +@torch.no_grad() +def decode(model: "Whisper", mel: Tensor, options: DecodingOptions = DecodingOptions()) -> 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/musetalk/whisper/whisper/model.py b/musetalk/whisper/whisper/model.py new file mode 100644 index 0000000..acb6a0b --- /dev/null +++ b/musetalk/whisper/whisper/model.py @@ -0,0 +1,290 @@ +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, include_embeddings: bool = False): + """ + x : torch.Tensor, shape = (batch_size, n_mels, n_ctx) + the mel spectrogram of the audio + include_embeddings: bool + whether to include intermediate steps in the output + """ + 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) + + if include_embeddings: + embeddings = [x.cpu().detach().numpy()] + + for block in self.blocks: + x = block(x) + if include_embeddings: + embeddings.append(x.cpu().detach().numpy()) + + x = self.ln_post(x) + + if include_embeddings: + embeddings = np.stack(embeddings, axis=1) + return x, embeddings + else: + 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, include_embeddings: bool = False): + """ + 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 + include_embeddings : bool + Whether to include intermediate values in the output to this function + """ + 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) + + if include_embeddings: + embeddings = [x.cpu().detach().numpy()] + + for block in self.blocks: + x = block(x, xa, mask=self.mask, kv_cache=kv_cache) + if include_embeddings: + embeddings.append(x.cpu().detach().numpy()) + + x = self.ln(x) + logits = (x @ torch.transpose(self.token_embedding.weight.to(x.dtype), 0, 1)).float() + + if include_embeddings: + embeddings = np.stack(embeddings, axis=1) + return logits, embeddings + else: + 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/musetalk/whisper/whisper/normalizers/__init__.py b/musetalk/whisper/whisper/normalizers/__init__.py new file mode 100644 index 0000000..0b10d5a --- /dev/null +++ b/musetalk/whisper/whisper/normalizers/__init__.py @@ -0,0 +1,2 @@ +from .basic import BasicTextNormalizer +from .english import EnglishTextNormalizer diff --git a/musetalk/whisper/whisper/normalizers/basic.py b/musetalk/whisper/whisper/normalizers/basic.py new file mode 100644 index 0000000..ef8d249 --- /dev/null +++ b/musetalk/whisper/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/musetalk/whisper/whisper/normalizers/english.json b/musetalk/whisper/whisper/normalizers/english.json new file mode 100644 index 0000000..bd84ae7 --- /dev/null +++ b/musetalk/whisper/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/musetalk/whisper/whisper/normalizers/english.py b/musetalk/whisper/whisper/normalizers/english.py new file mode 100644 index 0000000..d5c2bb4 --- /dev/null +++ b/musetalk/whisper/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/musetalk/whisper/whisper/tokenizer.py b/musetalk/whisper/whisper/tokenizer.py new file mode 100644 index 0000000..88142c4 --- /dev/null +++ b/musetalk/whisper/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_speech(self) -> int: + return self._get_single_token_id("<|nospeech|>") + + @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. + """ + symbols = list("\"#()*+/:;<=>@[\\]^_`{|}~「」『』") + symbols += "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} ♪♪ ♪♪♪".split() + + # symbols that may be a single token or multiple tokens depending on the tokenizer. + # In case they're multiple tokens, suppress the first token, which is safe because: + # These are between U+2640 and U+267F miscellaneous symbols that are okay to suppress + # in generations, and in the 3-byte UTF-8 representation they share the first two bytes. + miscellaneous = set("♩♪♫♬♭♮♯") + assert all(0x2640 <= ord(c) <= 0x267F for c in miscellaneous) + + # allow hyphens "-" and single quotes "'" between words, but not at the beginning of a word + result = {self.tokenizer.encode(" -")[0], self.tokenizer.encode(" '")[0]} + 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|>", + "<|nospeech|>", + "<|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/musetalk/whisper/whisper/transcribe.py b/musetalk/whisper/whisper/transcribe.py new file mode 100644 index 0000000..745e775 --- /dev/null +++ b/musetalk/whisper/whisper/transcribe.py @@ -0,0 +1,207 @@ +import argparse +import os +import warnings +from typing import List, Optional, Tuple, Union, TYPE_CHECKING + +import numpy as np +import torch +import tqdm + +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_txt, write_vtt, write_srt + +if TYPE_CHECKING: + from .model import Whisper + + +def transcribe( + model: "Whisper", + audio: Union[str, np.ndarray, torch.Tensor], + *, + verbose: Optional[bool] = None, + temperature: Union[float, Tuple[float, ...]] = (0.0, 0.2, 0.4, 0.6, 0.8, 1.0), + compression_ratio_threshold: Optional[float] = 2.4, + logprob_threshold: Optional[float] = -1.0, + no_speech_threshold: Optional[float] = 0.6, + condition_on_previous_text: bool = True, + force_extraction: bool = False, + **decode_options, +): + """ + Transcribe an audio file using Whisper + + Parameters + ---------- + model: Whisper + The Whisper model instance + + audio: Union[str, np.ndarray, torch.Tensor] + The path to the audio file to open, or the audio waveform + + verbose: bool + Whether to display the text being decoded to the console. If True, displays all the details, + If False, displays minimal details. If None, does not display anything + + temperature: Union[float, Tuple[float, ...]] + Temperature for sampling. It can be a tuple of temperatures, which will be 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_speech_threshold: float + If the no_speech probability is higher than this value AND the average log probability + over sampled tokens is below `logprob_threshold`, consider the segment as silent + + condition_on_previous_text: bool + if True, the previous output of the model is provided as a prompt for the next window; + disabling may make the text inconsistent across windows, but the model becomes less prone to + getting stuck in a failure loop, such as repetition looping or timestamps going out of sync. + + 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) + + all_segments = [] + def add_segment( + *, start: float, end: float, encoder_embeddings + ): + + all_segments.append( + { + "start": start, + "end": end, + "encoder_embeddings":encoder_embeddings, + } + ) + # show the progress bar when verbose is False (otherwise the transcribed text will be printed) + num_frames = mel.shape[-1] + seek = 0 + previous_seek_value = seek + sample_skip = 3000 # + with tqdm.tqdm(total=num_frames, unit='frames', disable=verbose is not False) as pbar: + while seek < num_frames: + # seek是开始的帧数 + end_seek = min(seek + sample_skip, num_frames) + segment = pad_or_trim(mel[:,seek:seek+sample_skip], N_FRAMES).to(model.device).to(dtype) + + single = segment.ndim == 2 + if single: + segment = segment.unsqueeze(0) + if dtype == torch.float16: + segment = segment.half() + audio_features, embeddings = model.encoder(segment, include_embeddings = True) + + encoder_embeddings = embeddings + #print(f"encoder_embeddings shape {encoder_embeddings.shape}") + add_segment( + start=seek, + end=end_seek, + #text_tokens=tokens, + #result=result, + encoder_embeddings=encoder_embeddings, + ) + seek+=sample_skip + + return dict(segments=all_segments) + + +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="small", choices=available_models(), help="name of the Whisper model to use") + parser.add_argument("--model_dir", type=str, default=None, help="the path to save model files; uses ~/.cache/whisper by default") + parser.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu", help="device to use for PyTorch inference") + parser.add_argument("--output_dir", "-o", type=str, default=".", help="directory to save the outputs") + parser.add_argument("--verbose", type=str2bool, default=True, help="whether to print out the progress and debug messages") + + parser.add_argument("--task", type=str, default="transcribe", choices=["transcribe", "translate"], help="whether to perform X->X speech recognition ('transcribe') or X->English translation ('translate')") + parser.add_argument("--language", type=str, default=None, choices=sorted(LANGUAGES.keys()) + sorted([k.title() for k in TO_LANGUAGE_CODE.keys()]), help="language spoken in the audio, specify None to perform language detection") + + parser.add_argument("--temperature", type=float, default=0, help="temperature to use for sampling") + parser.add_argument("--best_of", type=optional_int, default=5, help="number of candidates when sampling with non-zero temperature") + parser.add_argument("--beam_size", type=optional_int, default=5, help="number of beams in beam search, only applicable when temperature is zero") + parser.add_argument("--patience", type=float, default=None, help="optional patience value to use in beam decoding, as in https://arxiv.org/abs/2204.05424, the default (1.0) is equivalent to conventional beam search") + parser.add_argument("--length_penalty", type=float, default=None, help="optional token length penalty coefficient (alpha) as in https://arxiv.org/abs/1609.08144, uses simple length normalization by default") + + parser.add_argument("--suppress_tokens", type=str, default="-1", help="comma-separated list of token ids to suppress during sampling; '-1' will suppress most special characters except common punctuations") + parser.add_argument("--initial_prompt", type=str, default=None, help="optional text to provide as a prompt for the first window.") + parser.add_argument("--condition_on_previous_text", type=str2bool, default=True, help="if True, provide the previous output of the model as a prompt for the next window; disabling may make the text inconsistent across windows, but the model becomes less prone to getting stuck in a failure loop") + parser.add_argument("--fp16", type=str2bool, default=True, help="whether to perform inference in fp16; True by default") + + parser.add_argument("--temperature_increment_on_fallback", type=optional_float, default=0.2, help="temperature to increase when falling back when the decoding fails to meet either of the thresholds below") + parser.add_argument("--compression_ratio_threshold", type=optional_float, default=2.4, help="if the gzip compression ratio is higher than this value, treat the decoding as failed") + parser.add_argument("--logprob_threshold", type=optional_float, default=-1.0, help="if the average log probability is lower than this value, treat the decoding as failed") + parser.add_argument("--no_speech_threshold", type=optional_float, default=0.6, help="if the probability of the <|nospeech|> token is higher than this value AND the decoding has failed due to `logprob_threshold`, consider the segment as silence") + parser.add_argument("--threads", type=optional_int, default=0, help="number of threads used by torch for CPU inference; supercedes MKL_NUM_THREADS/OMP_NUM_THREADS") + + args = parser.parse_args().__dict__ + model_name: str = args.pop("model") + model_dir: str = args.pop("model_dir") + output_dir: str = args.pop("output_dir") + device: str = args.pop("device") + os.makedirs(output_dir, exist_ok=True) + + if model_name.endswith(".en") and args["language"] not in {"en", "English"}: + if args["language"] is not None: + warnings.warn(f"{model_name} is an English-only model but receipted '{args['language']}'; using English instead.") + args["language"] = "en" + + temperature = args.pop("temperature") + temperature_increment_on_fallback = args.pop("temperature_increment_on_fallback") + if temperature_increment_on_fallback is not None: + temperature = tuple(np.arange(temperature, 1.0 + 1e-6, temperature_increment_on_fallback)) + else: + temperature = [temperature] + + threads = args.pop("threads") + if threads > 0: + torch.set_num_threads(threads) + + from . import load_model + model = load_model(model_name, device=device, download_root=model_dir) + + for audio_path in args.pop("audio"): + result = transcribe(model, audio_path, temperature=temperature, **args) + + audio_basename = os.path.basename(audio_path) + + # save TXT + with open(os.path.join(output_dir, audio_basename + ".txt"), "w", encoding="utf-8") as txt: + write_txt(result["segments"], file=txt) + + # save VTT + with open(os.path.join(output_dir, audio_basename + ".vtt"), "w", encoding="utf-8") as vtt: + write_vtt(result["segments"], file=vtt) + + # save SRT + with open(os.path.join(output_dir, audio_basename + ".srt"), "w", encoding="utf-8") as srt: + write_srt(result["segments"], file=srt) + + +if __name__ == '__main__': + cli() diff --git a/musetalk/whisper/whisper/utils.py b/musetalk/whisper/whisper/utils.py new file mode 100644 index 0000000..87c91f2 --- /dev/null +++ b/musetalk/whisper/whisper/utils.py @@ -0,0 +1,87 @@ +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, always_include_hours: bool = False, decimal_marker: str = '.'): + assert seconds >= 0, "non-negative timestamp expected" + milliseconds = round(seconds * 1000.0) + + hours = milliseconds // 3_600_000 + milliseconds -= hours * 3_600_000 + + minutes = milliseconds // 60_000 + milliseconds -= minutes * 60_000 + + seconds = milliseconds // 1_000 + milliseconds -= seconds * 1_000 + + hours_marker = f"{hours:02d}:" if always_include_hours or hours > 0 else "" + return f"{hours_marker}{minutes:02d}:{seconds:02d}{decimal_marker}{milliseconds:03d}" + + +def write_txt(transcript: Iterator[dict], file: TextIO): + for segment in transcript: + print(segment['text'].strip(), file=file, flush=True) + + +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'].strip().replace('-->', '->')}\n", + file=file, + flush=True, + ) + + +def write_srt(transcript: Iterator[dict], file: TextIO): + """ + Write a transcript to a file in SRT format. + + Example usage: + from pathlib import Path + from whisper.utils import write_srt + + result = transcribe(model, audio_path, temperature=temperature, **args) + + # save SRT + audio_basename = Path(audio_path).stem + with open(Path(output_dir) / (audio_basename + ".srt"), "w", encoding="utf-8") as srt: + write_srt(result["segments"], file=srt) + """ + for i, segment in enumerate(transcript, start=1): + # write srt lines + print( + f"{i}\n" + f"{format_timestamp(segment['start'], always_include_hours=True, decimal_marker=',')} --> " + f"{format_timestamp(segment['end'], always_include_hours=True, decimal_marker=',')}\n" + f"{segment['text'].strip().replace('-->', '->')}\n", + file=file, + flush=True, + ) diff --git a/nodes.py b/nodes.py new file mode 100644 index 0000000..acf41e4 --- /dev/null +++ b/nodes.py @@ -0,0 +1,126 @@ +import os +import folder_paths +from .inference import MuseTalk_INFER +from pydub import AudioSegment +from moviepy.editor import VideoFileClip,AudioFileClip + +parent_directory = os.path.dirname(os.path.abspath(__file__)) +input_path = folder_paths.get_input_directory() +out_path = folder_paths.get_output_directory() + +class MuseTalk: + @classmethod + def INPUT_TYPES(s): + return { + "required":{ + "audio":("AUDIO",), + "video":("VIDEO",), + "bbox_shift":("INT",{ + "default":0 + }), + "fps":("INT",{ + "default":25 + }), + "batch_size":("INT",{ + "default":8 + }), + "batch_size_fa":("INT",{ + "default":2 + }), + "use_saved_coord":("BOOLEAN",{ + "default":False + }) + } + } + CATEGORY = "AIFSH_MuseTalk" + DESCRIPTION = "hello world!" + + RETURN_TYPES = ("VIDEO",) + + OUTPUT_NODE = False + + FUNCTION = "process" + + def process(self,audio,video,bbox_shift,fps,batch_size,batch_size_fa,use_saved_coord): + muse_talk = MuseTalk_INFER(bbox_shift,fps,batch_size,batch_size_fa,use_saved_coord) + output_vid_name = muse_talk(video, audio) + return (output_vid_name,) + + +class CombineAudioVideo: + @classmethod + def INPUT_TYPES(s): + return {"required": + {"vocal_AUDIO": ("AUDIO",), + "bgm_AUDIO": ("AUDIO",), + "video": ("VIDEO",) + } + } + + CATEGORY = "AIFSH_MuseTalk" + DESCRIPTION = "hello world!" + + RETURN_TYPES = ("VIDEO",) + + OUTPUT_NODE = False + + FUNCTION = "combine" + + def combine(self, vocal_AUDIO,bgm_AUDIO,video): + vocal = AudioSegment.from_file(vocal_AUDIO) + bgm = AudioSegment.from_file(bgm_AUDIO) + audio = vocal.overlay(bgm) + audio_file = os.path.join(out_path,"ip_lap_voice.wav") + audio.export(audio_file, format="wav") + cm_video_file = os.path.join(out_path,"voice_"+os.path.basename(video)) + video_clip = VideoFileClip(video) + audio_clip = AudioFileClip(audio_file) + new_video_clip = video_clip.set_audio(audio_clip) + new_video_clip.write_videofile(cm_video_file) + return (cm_video_file,) + + +class PreViewVideo: + @classmethod + def INPUT_TYPES(s): + return {"required":{ + "video":("VIDEO",), + }} + + CATEGORY = "AIFSH_MuseTalk" + DESCRIPTION = "hello world!" + + RETURN_TYPES = () + + OUTPUT_NODE = True + + FUNCTION = "load_video" + + def load_video(self, video): + video_name = os.path.basename(video) + video_path_name = os.path.basename(os.path.dirname(video)) + return {"ui":{"video":[video_name,video_path_name]}} + +class LoadVideo: + @classmethod + def INPUT_TYPES(s): + files = [f for f in os.listdir(input_path) if os.path.isfile(os.path.join(input_path, f)) and f.split('.')[-1] in ["mp4", "webm","mkv","avi"]] + return {"required":{ + "video":(files,), + }} + + CATEGORY = "AIFSH_MuseTalk" + DESCRIPTION = "hello world!" + + RETURN_TYPES = ("VIDEO","AUDIO") + + OUTPUT_NODE = False + + FUNCTION = "load_video" + + def load_video(self, video): + video_path = os.path.join(input_path,video) + video_clip = VideoFileClip(video_path) + audio_path = os.path.join(input_path,video+".wav") + video_clip.audio.write_audiofile(audio_path) + return (video_path,audio_path,) \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..b7f08ea --- /dev/null +++ b/requirements.txt @@ -0,0 +1,7 @@ +opencv-python +ffmpeg-python +soundfile +diffusers +pydub +moviepy +accelerate \ No newline at end of file diff --git a/web/js/previewVideo.js b/web/js/previewVideo.js new file mode 100644 index 0000000..5c8f1ca --- /dev/null +++ b/web/js/previewVideo.js @@ -0,0 +1,155 @@ +import { app } from "../../../scripts/app.js"; +import { api } from '../../../scripts/api.js' + +function fitHeight(node) { + node.setSize([node.size[0], node.computeSize([node.size[0], node.size[1]])[1]]) + node?.graph?.setDirtyCanvas(true); +} +function chainCallback(object, property, callback) { + if (object == undefined) { + //This should not happen. + console.error("Tried to add callback to non-existant object") + return; + } + if (property in object) { + const callback_orig = object[property] + object[property] = function () { + const r = callback_orig.apply(this, arguments); + callback.apply(this, arguments); + return r + }; + } else { + object[property] = callback; + } +} + +function addPreviewOptions(nodeType) { + chainCallback(nodeType.prototype, "getExtraMenuOptions", function(_, options) { + // The intended way of appending options is returning a list of extra options, + // but this isn't used in widgetInputs.js and would require + // less generalization of chainCallback + let optNew = [] + try { + const previewWidget = this.widgets.find((w) => w.name === "videopreview"); + + let url = null + if (previewWidget.videoEl?.hidden == false && previewWidget.videoEl.src) { + //Use full quality video + //url = api.apiURL('/view?' + new URLSearchParams(previewWidget.value.params)); + url = previewWidget.videoEl.src + } + if (url) { + optNew.push( + { + content: "Open preview", + callback: () => { + window.open(url, "_blank") + }, + }, + { + content: "Save preview", + callback: () => { + const a = document.createElement("a"); + a.href = url; + a.setAttribute("download", new URLSearchParams(previewWidget.value.params).get("filename")); + document.body.append(a); + a.click(); + requestAnimationFrame(() => a.remove()); + }, + } + ); + } + if(options.length > 0 && options[0] != null && optNew.length > 0) { + optNew.push(null); + } + options.unshift(...optNew); + + } catch (error) { + console.log(error); + } + + }); +} +function previewVideo(node,file,type){ + var element = document.createElement("div"); + const previewNode = node; + var previewWidget = node.addDOMWidget("videopreview", "preview", element, { + serialize: false, + hideOnZoom: false, + getValue() { + return element.value; + }, + setValue(v) { + element.value = v; + }, + }); + previewWidget.computeSize = function(width) { + if (this.aspectRatio && !this.parentEl.hidden) { + let height = (previewNode.size[0]-20)/ this.aspectRatio + 10; + if (!(height > 0)) { + height = 0; + } + this.computedHeight = height + 10; + return [width, height]; + } + return [width, -4];//no loaded src, widget should not display + } + // element.style['pointer-events'] = "none" + previewWidget.value = {hidden: false, paused: false, params: {}} + previewWidget.parentEl = document.createElement("div"); + previewWidget.parentEl.className = "video_preview"; + previewWidget.parentEl.style['width'] = "100%" + element.appendChild(previewWidget.parentEl); + previewWidget.videoEl = document.createElement("video"); + previewWidget.videoEl.controls = true; + previewWidget.videoEl.loop = false; + previewWidget.videoEl.muted = false; + previewWidget.videoEl.style['width'] = "100%" + previewWidget.videoEl.addEventListener("loadedmetadata", () => { + + previewWidget.aspectRatio = previewWidget.videoEl.videoWidth / previewWidget.videoEl.videoHeight; + fitHeight(this); + }); + previewWidget.videoEl.addEventListener("error", () => { + //TODO: consider a way to properly notify the user why a preview isn't shown. + previewWidget.parentEl.hidden = true; + fitHeight(this); + }); + + let params = { + "filename": file, + "type": type, + } + + previewWidget.parentEl.hidden = previewWidget.value.hidden; + previewWidget.videoEl.autoplay = !previewWidget.value.paused && !previewWidget.value.hidden; + let target_width = 256 + if (element.style?.width) { + //overscale to allow scrolling. Endpoint won't return higher than native + target_width = element.style.width.slice(0,-2)*2; + } + if (!params.force_size || params.force_size.includes("?") || params.force_size == "Disabled") { + params.force_size = target_width+"x?" + } else { + let size = params.force_size.split("x") + let ar = parseInt(size[0])/parseInt(size[1]) + params.force_size = target_width+"x"+(target_width/ar) + } + + previewWidget.videoEl.src = api.apiURL('/view?' + new URLSearchParams(params)); + + previewWidget.videoEl.hidden = false; + previewWidget.parentEl.appendChild(previewWidget.videoEl) +} + +app.registerExtension({ + name: "IP_LAP.VideoPreviewer", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData?.name == "PreViewVideo") { + nodeType.prototype.onExecuted = function (data) { + previewVideo(this, data.video[0], data.video[1]); + } + addPreviewOptions(nodeType) + } + } +}); \ No newline at end of file diff --git a/web/js/uploadVideo.js b/web/js/uploadVideo.js new file mode 100644 index 0000000..1c92ce4 --- /dev/null +++ b/web/js/uploadVideo.js @@ -0,0 +1,203 @@ +import { app } from "../../../scripts/app.js"; +import { api } from '../../../scripts/api.js' +import { ComfyWidgets } from "../../../scripts/widgets.js" + +function fitHeight(node) { + node.setSize([node.size[0], node.computeSize([node.size[0], node.size[1]])[1]]) + node?.graph?.setDirtyCanvas(true); +} + +function previewVideo(node,file){ + while (node.widgets.length > 2){ + node.widgets.pop() + } + try { + var el = document.getElementById("uploadVideo"); + el.remove(); + } catch (error) { + console.log(error); + } + var element = document.createElement("div"); + element.id = "uploadVideo"; + const previewNode = node; + var previewWidget = node.addDOMWidget("videopreview", "preview", element, { + serialize: false, + hideOnZoom: false, + getValue() { + return element.value; + }, + setValue(v) { + element.value = v; + }, + }); + previewWidget.computeSize = function(width) { + if (this.aspectRatio && !this.parentEl.hidden) { + let height = (previewNode.size[0]-20)/ this.aspectRatio + 10; + if (!(height > 0)) { + height = 0; + } + this.computedHeight = height + 10; + return [width, height]; + } + return [width, -4];//no loaded src, widget should not display + } + // element.style['pointer-events'] = "none" + previewWidget.value = {hidden: false, paused: false, params: {}} + previewWidget.parentEl = document.createElement("div"); + previewWidget.parentEl.className = "video_preview"; + previewWidget.parentEl.style['width'] = "100%" + element.appendChild(previewWidget.parentEl); + previewWidget.videoEl = document.createElement("video"); + previewWidget.videoEl.controls = true; + previewWidget.videoEl.loop = false; + previewWidget.videoEl.muted = false; + previewWidget.videoEl.style['width'] = "100%" + previewWidget.videoEl.addEventListener("loadedmetadata", () => { + + previewWidget.aspectRatio = previewWidget.videoEl.videoWidth / previewWidget.videoEl.videoHeight; + fitHeight(this); + }); + previewWidget.videoEl.addEventListener("error", () => { + //TODO: consider a way to properly notify the user why a preview isn't shown. + previewWidget.parentEl.hidden = true; + fitHeight(this); + }); + + let params = { + "filename": file, + "type": "input", + } + + previewWidget.parentEl.hidden = previewWidget.value.hidden; + previewWidget.videoEl.autoplay = !previewWidget.value.paused && !previewWidget.value.hidden; + let target_width = 256 + if (element.style?.width) { + //overscale to allow scrolling. Endpoint won't return higher than native + target_width = element.style.width.slice(0,-2)*2; + } + if (!params.force_size || params.force_size.includes("?") || params.force_size == "Disabled") { + params.force_size = target_width+"x?" + } else { + let size = params.force_size.split("x") + let ar = parseInt(size[0])/parseInt(size[1]) + params.force_size = target_width+"x"+(target_width/ar) + } + + previewWidget.videoEl.src = api.apiURL('/view?' + new URLSearchParams(params)); + + previewWidget.videoEl.hidden = false; + previewWidget.parentEl.appendChild(previewWidget.videoEl) +} + +function videoUpload(node, inputName, inputData, app) { + const videoWidget = node.widgets.find((w) => w.name === "video"); + let uploadWidget; + /* + A method that returns the required style for the html + */ + var default_value = videoWidget.value; + Object.defineProperty(videoWidget, "value", { + set : function(value) { + this._real_value = value; + }, + + get : function() { + let value = ""; + if (this._real_value) { + value = this._real_value; + } else { + return default_value; + } + + if (value.filename) { + let real_value = value; + value = ""; + if (real_value.subfolder) { + value = real_value.subfolder + "/"; + } + + value += real_value.filename; + + if(real_value.type && real_value.type !== "input") + value += ` [${real_value.type}]`; + } + return value; + } + }); + async function uploadFile(file, updateNode, pasted = false) { + try { + // Wrap file in formdata so it includes filename + const body = new FormData(); + body.append("image", file); + if (pasted) body.append("subfolder", "pasted"); + const resp = await api.fetchApi("/upload/image", { + method: "POST", + body, + }); + + if (resp.status === 200) { + const data = await resp.json(); + // Add the file to the dropdown list and update the widget value + let path = data.name; + if (data.subfolder) path = data.subfolder + "/" + path; + + if (!videoWidget.options.values.includes(path)) { + videoWidget.options.values.push(path); + } + + if (updateNode) { + videoWidget.value = path; + previewVideo(node,path) + + } + } else { + alert(resp.status + " - " + resp.statusText); + } + } catch (error) { + alert(error); + } + } + + const fileInput = document.createElement("input"); + Object.assign(fileInput, { + type: "file", + accept: "video/webm,video/mp4,video/mkv,video/avi", + style: "display: none", + onchange: async () => { + if (fileInput.files.length) { + await uploadFile(fileInput.files[0], true); + } + }, + }); + document.body.append(fileInput); + + // Create the button widget for selecting the files + uploadWidget = node.addWidget("button", "choose video file to upload", "Video", () => { + fileInput.click(); + }); + + uploadWidget.serialize = false; + + previewVideo(node, videoWidget.value); + const cb = node.callback; + videoWidget.callback = function () { + previewVideo(node,videoWidget.value); + if (cb) { + return cb.apply(this, arguments); + } + }; + + return { widget: uploadWidget }; +} + +ComfyWidgets.VIDEOPLOAD = videoUpload; + +app.registerExtension({ + name: "IP_LAP.UploadVideo", + async beforeRegisterNodeDef(nodeType, nodeData, app) { + if (nodeData?.name == "LoadVideo") { + nodeData.input.required.upload = ["VIDEOPLOAD"]; + } + }, +}); +