diff --git a/.gitignore b/.gitignore index 7f8c7dadfaf2c9d11cd078ddf056756451637458..61ed3355d4c7aaf82e518e41415d3ee59a3d7fd4 100644 --- a/.gitignore +++ b/.gitignore @@ -6,11 +6,11 @@ checkpoints/**/*.safetensors checkpoints/**/* # Ignore local dependencies (installed via pip/uv) -latentsync/ -tigersound/ -FastAudioSR/ -descript-audiotools/ -models/ +# latentsync/ - Keep for HuggingFace Spaces +# tigersound/ +# FastAudioSR/ +# descript-audiotools/ +# models/ # Python cache and virtual environment __pycache__/ diff --git a/ORIGINAL_README.md b/ORIGINAL_README.md new file mode 100644 index 0000000000000000000000000000000000000000..9b5041d1a15e6c4a91b7f9b14064b9affc57676d --- /dev/null +++ b/ORIGINAL_README.md @@ -0,0 +1,106 @@ +LatentSync: Audio Conditioned Latent Diffusion Models for Lip Sync + +## ๐Ÿ“– Abstract +We present LatentSync, an end-to-end lip sync framework based on audio conditioned latent diffusion models without any intermediate motion representation, diverging from previous diffusion-based lip sync methods based on pixel space diffusion or two-stage generation. Our framework can leverage powerful capabilities of Stable Diffusion to directly model complex audio-visual correlations. Additionally, we found that diffusion-based lip sync methods exhibit inferior temporal consistency due to inconsistency in diffusion process across different frames. We propose Temporal REPresentation Alignment (TREPA) to enhance temporal consistency while preserving lip-sync accuracy. TREPA uses temporal representations extracted by large-scale self-supervised video models to align generated frames with ground truth frames. + +## ๐Ÿ—๏ธ Framework +LatentSync uses Whisper to convert melspectrogram into audio embeddings, which are then integrated into U-Net via cross-attention layers. The reference and masked frames are channel-wise concatenated with noised latents as input of U-Net. In training process, we use one-step method to get estimated clean latents from predicted noises, which are then decoded to obtain the estimated clean frames. The TREPA, LPIPS and SyncNet loss are added in the pixel space. + +## ๐ŸŽฌ Demo + +| | | +| --- | --- | +| __Original video__ | __Lip-synced video__ | +| demo2_input.mp4 | demo2_output_v1.6.mp4 | +| demo3_input.mp4 | demo3_output_v1.6.mp4 | +| demo4_input.mp4 | demo4_output_v1.6.mp4 | +| demo5_input.mp4 | demo5_output_v1.6.mp4 | +| demo4_video.mp4 | demo4_output.mp4 | + +(Photorealistic videos are filmed by contracted models, and anime videos are from VASA-1 and EMO) + +## ๐Ÿ“‘ Open-source Plan + +- Inference code and checkpoints +- Data processing pipeline +- Training code + +## ๐Ÿ”ง Setting up the Environment +Install the required packages and download the checkpoints via: + +```bash +source setup_env.sh +``` + +If the download is successful, the checkpoints should appear as follows: + +``` +./checkpoints/ +|-- latentsync_unet.pt +|-- latentsync_syncnet.pt +|-- whisper +| `-- tiny.pt +|-- auxiliary +| |-- 2DFAN4-cd938726ad.zip +| |-- i3d_torchscript.pt +| |-- koniq_pretrained.pkl +| |-- s3fd-619a316812.pth +| |-- sfd_face.pth +| |-- syncnet_v2.model +| |-- vgg16-397923af.pth +| `-- vit_g_hybrid_pt_1200e_ssv2_ft.pth +``` + +These already include all the checkpoints required for latentsync training and inference. If you just want to try inference, you only need to download `latentsync_unet.pt` and `tiny.pt` from our HuggingFace repo + +## ๐Ÿš€ Inference +Run the script for inference, which requires about 6.5 GB GPU memory. + +```bash +./inference.sh +``` + +You can try adjusting the following inference parameters to achieve better results: + +- `inference_steps` [20-50]: A higher value improves visual quality but slows down the generation speed. +- `guidance_scale` [1.0-3.0]: A higher value improves lip-sync accuracy but may cause the video distortion or jitter. + +## ๐Ÿ”„ Data Processing Pipeline +The complete data processing pipeline includes the following steps: + +1. Remove the broken video files. +2. Resample the video FPS to 25, and resample the audio to 16000 Hz. +3. Scene detect via PySceneDetect. +4. Split each video into 5-10 second segments. +5. Remove videos where the face is smaller than 256 $\times$ 256, as well as videos with more than one face. +6. Affine transform the faces according to the landmarks detected by face-alignment, then resize to 256 $\times$ 256. +7. Remove videos with sync confidence score lower than 3, and adjust the audio-visual offset to 0. +8. Calculate hyperIQA score, and remove videos with scores lower than 40. + +Run the script to execute the data processing pipeline: + +```bash +./data_processing_pipeline.sh +``` + +You should change the parameter `input_dir` in the script to specify the data directory to be processed. The processed data will be saved in the same directory. Each step will generate a new directory to prevent the need to redo the entire pipeline in case the process is interrupted by an unexpected error. + +## ๐Ÿ‹๏ธโ€โ™‚๏ธ Training U-Net +Before training, you must process the data as described above and download all the checkpoints. We released a pretrained SyncNet with 94% accuracy on VoxCeleb2 dataset for the supervision of U-Net training. Note that this SyncNet is trained on affine transformed videos, so when using or evaluating this SyncNet, you need to perform affine transformation on the video first (the code of affine transformation is included in the data processing pipeline). + +If all the preparations are complete, you can train the U-Net with the following script: + +```bash +./train_unet.sh +``` + +You should change the parameters in the U-Net config file to specify the data directory, checkpoint save path, and other training hyperparameters. + +## ๐Ÿ‹๏ธโ€โ™‚๏ธ Training SyncNet +In case you want to train SyncNet on your own datasets, you can run the following script. The data processing pipeline for SyncNet is the same as for U-Net. + +```bash +./train_syncnet.sh +``` + +After `validations_steps` training, the loss charts will be saved in `train_output_dir`. They contain both the training and validation loss. diff --git a/README.md b/README.md index 4af0bbb65af43c18bc71519c74b9246b7a65bc69..47507ed80888ce8ac127b06563708a20ff866c2f 100644 --- a/README.md +++ b/README.md @@ -1,15 +1,28 @@ --- -title: LipSync -emoji: ๐Ÿ“š -colorFrom: gray -colorTo: blue +title: OutofLipSync - LatentSync 1.6 +emoji: ๐Ÿ‘„ +colorFrom: purple +colorTo: pink sdk: gradio -sdk_version: 6.4.0 +sdk_version: 5.12.0 python_version: "3.10" app_file: app.py pinned: false +short_description: Lipsync video with custom audio (English only) - LatentSync 1.6 --- +# OutofLipSync - LatentSync 1.6 + +Lipsync video with custom audio (English only) using **LatentSync 1.6** from ByteDance. + +## Features + +- **Resolution**: 512x512 (LatentSync 1.6) +- **Auto-download**: Checkpoints from `ByteDance/LatentSync-1.6` +- **Face detection**: Automatic face detection and cropping +- **Audio processing**: Audio separation, upsampling +- **Multiple outputs**: Step-by-step processing visualization + ## HuggingFace Spaces Deployment ### 1. Tแบกo Space mแป›i trรชn HuggingFace diff --git a/eval/detectors/README.md b/eval/detectors/README.md new file mode 100644 index 0000000000000000000000000000000000000000..f5a8d4feb007f86f8c60075d8538f9ee5e93b325 --- /dev/null +++ b/eval/detectors/README.md @@ -0,0 +1,3 @@ +# Face detector + +This face detector is adapted from `https://github.com/cs-giung/face-detection-pytorch`. diff --git a/eval/detectors/__init__.py b/eval/detectors/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..059d49bf0b8e8a17f641984e7d889e5b008257b9 --- /dev/null +++ b/eval/detectors/__init__.py @@ -0,0 +1 @@ +from .s3fd import S3FD \ No newline at end of file diff --git a/eval/detectors/s3fd/__init__.py b/eval/detectors/s3fd/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6767733ea66449f1332892168c457afb6bafdecc --- /dev/null +++ b/eval/detectors/s3fd/__init__.py @@ -0,0 +1,61 @@ +import time +import numpy as np +import cv2 +import torch +from torchvision import transforms +from .nets import S3FDNet +from .box_utils import nms_ + +PATH_WEIGHT = 'checkpoints/auxiliary/sfd_face.pth' +img_mean = np.array([104., 117., 123.])[:, np.newaxis, np.newaxis].astype('float32') + + +class S3FD(): + + def __init__(self, device='cuda'): + + tstamp = time.time() + self.device = device + + print('[S3FD] loading with', self.device) + self.net = S3FDNet(device=self.device).to(self.device) + state_dict = torch.load(PATH_WEIGHT, map_location=self.device) + self.net.load_state_dict(state_dict) + self.net.eval() + print('[S3FD] finished loading (%.4f sec)' % (time.time() - tstamp)) + + def detect_faces(self, image, conf_th=0.8, scales=[1]): + + w, h = image.shape[1], image.shape[0] + + bboxes = np.empty(shape=(0, 5)) + + with torch.no_grad(): + for s in scales: + scaled_img = cv2.resize(image, dsize=(0, 0), fx=s, fy=s, interpolation=cv2.INTER_LINEAR) + + scaled_img = np.swapaxes(scaled_img, 1, 2) + scaled_img = np.swapaxes(scaled_img, 1, 0) + scaled_img = scaled_img[[2, 1, 0], :, :] + scaled_img = scaled_img.astype('float32') + scaled_img -= img_mean + scaled_img = scaled_img[[2, 1, 0], :, :] + x = torch.from_numpy(scaled_img).unsqueeze(0).to(self.device) + y = self.net(x) + + detections = y.data + scale = torch.Tensor([w, h, w, h]) + + for i in range(detections.size(1)): + j = 0 + while detections[0, i, j, 0] > conf_th: + score = detections[0, i, j, 0] + pt = (detections[0, i, j, 1:] * scale).cpu().numpy() + bbox = (pt[0], pt[1], pt[2], pt[3], score) + bboxes = np.vstack((bboxes, bbox)) + j += 1 + + keep = nms_(bboxes, 0.1) + bboxes = bboxes[keep] + + return bboxes diff --git a/eval/detectors/s3fd/box_utils.py b/eval/detectors/s3fd/box_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..03c1e99c9ca9b04326ad536397cfc2a8b764975f --- /dev/null +++ b/eval/detectors/s3fd/box_utils.py @@ -0,0 +1,221 @@ +import numpy as np +from itertools import product as product +import torch +from torch.autograd import Function +import warnings + + +def nms_(dets, thresh): + """ + Courtesy of Ross Girshick + [https://github.com/rbgirshick/py-faster-rcnn/blob/master/lib/nms/py_cpu_nms.py] + """ + x1 = dets[:, 0] + y1 = dets[:, 1] + x2 = dets[:, 2] + y2 = dets[:, 3] + scores = dets[:, 4] + + areas = (x2 - x1) * (y2 - y1) + order = scores.argsort()[::-1] + + keep = [] + while order.size > 0: + i = order[0] + keep.append(int(i)) + xx1 = np.maximum(x1[i], x1[order[1:]]) + yy1 = np.maximum(y1[i], y1[order[1:]]) + xx2 = np.minimum(x2[i], x2[order[1:]]) + yy2 = np.minimum(y2[i], y2[order[1:]]) + + w = np.maximum(0.0, xx2 - xx1) + h = np.maximum(0.0, yy2 - yy1) + inter = w * h + ovr = inter / (areas[i] + areas[order[1:]] - inter) + + inds = np.where(ovr <= thresh)[0] + order = order[inds + 1] + + return np.array(keep).astype(np.int32) + + +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 nms(boxes, scores, overlap=0.5, top_k=200): + """Apply non-maximum suppression at test time to avoid detecting too many + overlapping bounding boxes for a given object. + Args: + boxes: (tensor) The location preds for the img, Shape: [num_priors,4]. + scores: (tensor) The class predscores for the img, Shape:[num_priors]. + overlap: (float) The overlap thresh for suppressing unnecessary boxes. + top_k: (int) The Maximum number of box preds to consider. + Return: + The indices of the kept boxes with respect to num_priors. + """ + + keep = scores.new(scores.size(0)).zero_().long() + if boxes.numel() == 0: + return keep, 0 + x1 = boxes[:, 0] + y1 = boxes[:, 1] + x2 = boxes[:, 2] + y2 = boxes[:, 3] + area = torch.mul(x2 - x1, y2 - y1) + v, idx = scores.sort(0) # sort in ascending order + # I = I[v >= 0.01] + idx = idx[-top_k:] # indices of the top-k largest vals + xx1 = boxes.new() + yy1 = boxes.new() + xx2 = boxes.new() + yy2 = boxes.new() + w = boxes.new() + h = boxes.new() + + # keep = torch.Tensor() + count = 0 + while idx.numel() > 0: + i = idx[-1] # index of current largest val + # keep.append(i) + keep[count] = i + count += 1 + if idx.size(0) == 1: + break + idx = idx[:-1] # remove kept element from view + # load bboxes of next highest vals + with warnings.catch_warnings(): + # Ignore UserWarning within this block + warnings.simplefilter("ignore", category=UserWarning) + torch.index_select(x1, 0, idx, out=xx1) + torch.index_select(y1, 0, idx, out=yy1) + torch.index_select(x2, 0, idx, out=xx2) + torch.index_select(y2, 0, idx, out=yy2) + # store element-wise max with next highest score + xx1 = torch.clamp(xx1, min=x1[i]) + yy1 = torch.clamp(yy1, min=y1[i]) + xx2 = torch.clamp(xx2, max=x2[i]) + yy2 = torch.clamp(yy2, max=y2[i]) + w.resize_as_(xx2) + h.resize_as_(yy2) + w = xx2 - xx1 + h = yy2 - yy1 + # check sizes of xx1 and xx2.. after each iteration + w = torch.clamp(w, min=0.0) + h = torch.clamp(h, min=0.0) + inter = w * h + # IoU = i / (area(a) + area(b) - i) + rem_areas = torch.index_select(area, 0, idx) # load remaining areas) + union = (rem_areas - inter) + area[i] + IoU = inter / union # store result in iou + # keep only elements with an IoU <= overlap + idx = idx[IoU.le(overlap)] + return keep, count + + +class Detect(object): + + def __init__(self, num_classes=2, + top_k=750, nms_thresh=0.3, conf_thresh=0.05, + variance=[0.1, 0.2], nms_top_k=5000): + + self.num_classes = num_classes + self.top_k = top_k + self.nms_thresh = nms_thresh + self.conf_thresh = conf_thresh + self.variance = variance + self.nms_top_k = nms_top_k + + def forward(self, loc_data, conf_data, prior_data): + + num = loc_data.size(0) + num_priors = prior_data.size(0) + + conf_preds = conf_data.view(num, num_priors, self.num_classes).transpose(2, 1) + batch_priors = prior_data.view(-1, num_priors, 4).expand(num, num_priors, 4) + batch_priors = batch_priors.contiguous().view(-1, 4) + + decoded_boxes = decode(loc_data.view(-1, 4), batch_priors, self.variance) + decoded_boxes = decoded_boxes.view(num, num_priors, 4) + + output = torch.zeros(num, self.num_classes, self.top_k, 5) + + for i in range(num): + boxes = decoded_boxes[i].clone() + conf_scores = conf_preds[i].clone() + + for cl in range(1, self.num_classes): + c_mask = conf_scores[cl].gt(self.conf_thresh) + scores = conf_scores[cl][c_mask] + + if scores.dim() == 0: + continue + l_mask = c_mask.unsqueeze(1).expand_as(boxes) + boxes_ = boxes[l_mask].view(-1, 4) + ids, count = nms(boxes_, scores, self.nms_thresh, self.nms_top_k) + count = count if count < self.top_k else self.top_k + + output[i, cl, :count] = torch.cat((scores[ids[:count]].unsqueeze(1), boxes_[ids[:count]]), 1) + + return output + + +class PriorBox(object): + + def __init__(self, input_size, feature_maps, + variance=[0.1, 0.2], + min_sizes=[16, 32, 64, 128, 256, 512], + steps=[4, 8, 16, 32, 64, 128], + clip=False): + + super(PriorBox, self).__init__() + + self.imh = input_size[0] + self.imw = input_size[1] + self.feature_maps = feature_maps + + self.variance = variance + self.min_sizes = min_sizes + self.steps = steps + self.clip = clip + + def forward(self): + mean = [] + for k, fmap in enumerate(self.feature_maps): + feath = fmap[0] + featw = fmap[1] + for i, j in product(range(feath), range(featw)): + f_kw = self.imw / self.steps[k] + f_kh = self.imh / self.steps[k] + + cx = (j + 0.5) / f_kw + cy = (i + 0.5) / f_kh + + s_kw = self.min_sizes[k] / self.imw + s_kh = self.min_sizes[k] / self.imh + + mean += [cx, cy, s_kw, s_kh] + + output = torch.FloatTensor(mean).view(-1, 4) + + if self.clip: + output.clamp_(max=1, min=0) + + return output diff --git a/eval/detectors/s3fd/nets.py b/eval/detectors/s3fd/nets.py new file mode 100644 index 0000000000000000000000000000000000000000..85b5c82c142f02cef75c1e03557b2a1a748c32b0 --- /dev/null +++ b/eval/detectors/s3fd/nets.py @@ -0,0 +1,174 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.nn.init as init +from .box_utils import Detect, PriorBox + + +class L2Norm(nn.Module): + + def __init__(self, n_channels, scale): + super(L2Norm, self).__init__() + self.n_channels = n_channels + self.gamma = scale or None + self.eps = 1e-10 + self.weight = nn.Parameter(torch.Tensor(self.n_channels)) + self.reset_parameters() + + def reset_parameters(self): + init.constant_(self.weight, self.gamma) + + def forward(self, x): + norm = x.pow(2).sum(dim=1, keepdim=True).sqrt() + self.eps + x = torch.div(x, norm) + out = self.weight.unsqueeze(0).unsqueeze(2).unsqueeze(3).expand_as(x) * x + return out + + +class S3FDNet(nn.Module): + + def __init__(self, device='cuda'): + super(S3FDNet, self).__init__() + self.device = device + + self.vgg = nn.ModuleList([ + nn.Conv2d(3, 64, 3, 1, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(64, 64, 3, 1, padding=1), + nn.ReLU(inplace=True), + nn.MaxPool2d(2, 2), + + nn.Conv2d(64, 128, 3, 1, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(128, 128, 3, 1, padding=1), + nn.ReLU(inplace=True), + nn.MaxPool2d(2, 2), + + nn.Conv2d(128, 256, 3, 1, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(256, 256, 3, 1, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(256, 256, 3, 1, padding=1), + nn.ReLU(inplace=True), + nn.MaxPool2d(2, 2, ceil_mode=True), + + nn.Conv2d(256, 512, 3, 1, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(512, 512, 3, 1, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(512, 512, 3, 1, padding=1), + nn.ReLU(inplace=True), + nn.MaxPool2d(2, 2), + + nn.Conv2d(512, 512, 3, 1, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(512, 512, 3, 1, padding=1), + nn.ReLU(inplace=True), + nn.Conv2d(512, 512, 3, 1, padding=1), + nn.ReLU(inplace=True), + nn.MaxPool2d(2, 2), + + nn.Conv2d(512, 1024, 3, 1, padding=6, dilation=6), + nn.ReLU(inplace=True), + nn.Conv2d(1024, 1024, 1, 1), + nn.ReLU(inplace=True), + ]) + + self.L2Norm3_3 = L2Norm(256, 10) + self.L2Norm4_3 = L2Norm(512, 8) + self.L2Norm5_3 = L2Norm(512, 5) + + self.extras = nn.ModuleList([ + nn.Conv2d(1024, 256, 1, 1), + nn.Conv2d(256, 512, 3, 2, padding=1), + nn.Conv2d(512, 128, 1, 1), + nn.Conv2d(128, 256, 3, 2, padding=1), + ]) + + self.loc = nn.ModuleList([ + nn.Conv2d(256, 4, 3, 1, padding=1), + nn.Conv2d(512, 4, 3, 1, padding=1), + nn.Conv2d(512, 4, 3, 1, padding=1), + nn.Conv2d(1024, 4, 3, 1, padding=1), + nn.Conv2d(512, 4, 3, 1, padding=1), + nn.Conv2d(256, 4, 3, 1, padding=1), + ]) + + self.conf = nn.ModuleList([ + nn.Conv2d(256, 4, 3, 1, padding=1), + nn.Conv2d(512, 2, 3, 1, padding=1), + nn.Conv2d(512, 2, 3, 1, padding=1), + nn.Conv2d(1024, 2, 3, 1, padding=1), + nn.Conv2d(512, 2, 3, 1, padding=1), + nn.Conv2d(256, 2, 3, 1, padding=1), + ]) + + self.softmax = nn.Softmax(dim=-1) + self.detect = Detect() + + def forward(self, x): + size = x.size()[2:] + sources = list() + loc = list() + conf = list() + + for k in range(16): + x = self.vgg[k](x) + s = self.L2Norm3_3(x) + sources.append(s) + + for k in range(16, 23): + x = self.vgg[k](x) + s = self.L2Norm4_3(x) + sources.append(s) + + for k in range(23, 30): + x = self.vgg[k](x) + s = self.L2Norm5_3(x) + sources.append(s) + + for k in range(30, len(self.vgg)): + x = self.vgg[k](x) + sources.append(x) + + # apply extra layers and cache source layer outputs + for k, v in enumerate(self.extras): + x = F.relu(v(x), inplace=True) + if k % 2 == 1: + sources.append(x) + + # apply multibox head to source layers + loc_x = self.loc[0](sources[0]) + conf_x = self.conf[0](sources[0]) + + max_conf, _ = torch.max(conf_x[:, 0:3, :, :], dim=1, keepdim=True) + conf_x = torch.cat((max_conf, conf_x[:, 3:, :, :]), dim=1) + + loc.append(loc_x.permute(0, 2, 3, 1).contiguous()) + conf.append(conf_x.permute(0, 2, 3, 1).contiguous()) + + for i in range(1, len(sources)): + x = sources[i] + conf.append(self.conf[i](x).permute(0, 2, 3, 1).contiguous()) + loc.append(self.loc[i](x).permute(0, 2, 3, 1).contiguous()) + + features_maps = [] + for i in range(len(loc)): + feat = [] + feat += [loc[i].size(1), loc[i].size(2)] + features_maps += [feat] + + loc = torch.cat([o.view(o.size(0), -1) for o in loc], 1) + conf = torch.cat([o.view(o.size(0), -1) for o in conf], 1) + + with torch.no_grad(): + self.priorbox = PriorBox(size, features_maps) + self.priors = self.priorbox.forward() + + output = self.detect.forward( + loc.view(loc.size(0), -1, 4), + self.softmax(conf.view(conf.size(0), -1, 2)), + self.priors.type(type(x.data)).to(self.device) + ) + + return output diff --git a/eval/draw_syncnet_lines.py b/eval/draw_syncnet_lines.py new file mode 100644 index 0000000000000000000000000000000000000000..304ad8fc0ff1328a98daa5a1cf5d3a34089248f7 --- /dev/null +++ b/eval/draw_syncnet_lines.py @@ -0,0 +1,70 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +import matplotlib.pyplot as plt + + +class Chart: + def __init__(self): + self.loss_list = [] + + def add_ckpt(self, ckpt_path, line_name): + ckpt = torch.load(ckpt_path, map_location="cpu") + train_step_list = ckpt["train_step_list"] + train_loss_list = ckpt["train_loss_list"] + val_step_list = ckpt["val_step_list"] + val_loss_list = ckpt["val_loss_list"] + val_step_list = [val_step_list[0]] + val_step_list[4::5] + val_loss_list = [val_loss_list[0]] + val_loss_list[4::5] + self.loss_list.append((line_name, train_step_list, train_loss_list, val_step_list, val_loss_list)) + + def draw(self, save_path, plot_val=True): + # Global settings + plt.rcParams["font.size"] = 14 + plt.rcParams["font.family"] = "serif" + plt.rcParams["font.sans-serif"] = ["Arial", "DejaVu Sans", "Lucida Grande"] + plt.rcParams["font.serif"] = ["Times New Roman", "DejaVu Serif"] + + # Creating the plot + plt.figure(figsize=(7.766, 4.8)) # Golden ratio + for loss in self.loss_list: + if plot_val: + (line,) = plt.plot(loss[1], loss[2], label=loss[0], linewidth=0.5, alpha=0.5) + line_color = line.get_color() + plt.plot(loss[3], loss[4], linewidth=1.5, color=line_color) + else: + plt.plot(loss[1], loss[2], label=loss[0], linewidth=1) + plt.xlabel("Step") + plt.ylabel("Loss") + legend = plt.legend() + # legend = plt.legend(loc='upper right', bbox_to_anchor=(1, 0.82)) + + # Adjust the linewidth of legend + for line in legend.get_lines(): + line.set_linewidth(2) + + plt.savefig(save_path, transparent=True) + plt.close() + + +if __name__ == "__main__": + chart = Chart() + # chart.add_ckpt("output/syncnet/train-2024_10_25-18:14:43/checkpoints/checkpoint-10000.pt", "w/ self-attn") + # chart.add_ckpt("output/syncnet/train-2024_10_25-18:21:59/checkpoints/checkpoint-10000.pt", "w/o self-attn") + chart.add_ckpt("output/syncnet/train-2024_10_24-21:03:11/checkpoints/checkpoint-10000.pt", "Dim 512") + chart.add_ckpt("output/syncnet/train-2024_10_25-18:21:59/checkpoints/checkpoint-10000.pt", "Dim 2048") + chart.add_ckpt("output/syncnet/train-2024_10_24-22:37:04/checkpoints/checkpoint-10000.pt", "Dim 4096") + chart.add_ckpt("output/syncnet/train-2024_10_25-02:30:17/checkpoints/checkpoint-10000.pt", "Dim 6144") + chart.draw("ablation.pdf", plot_val=True) diff --git a/eval/eval_fvd.py b/eval/eval_fvd.py new file mode 100644 index 0000000000000000000000000000000000000000..5e68408edbb5345dd2a5fff4518bdbcd5d377ea5 --- /dev/null +++ b/eval/eval_fvd.py @@ -0,0 +1,96 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import mediapipe as mp +import cv2 +from decord import VideoReader +from einops import rearrange +import os +import numpy as np +import torch +import tqdm +from eval.fvd import compute_our_fvd + + +class FVD: + def __init__(self, resolution=(224, 224)): + self.face_detector = mp.solutions.face_detection.FaceDetection(model_selection=0, min_detection_confidence=0.5) + self.resolution = resolution + + def detect_face(self, image): + height, width = image.shape[:2] + # Process the image and detect faces. + results = self.face_detector.process(image) + + if not results.detections: # Face not detected + raise Exception("Face not detected") + + detection = results.detections[0] # Only use the first face in the image + bounding_box = detection.location_data.relative_bounding_box + xmin = int(bounding_box.xmin * width) + ymin = int(bounding_box.ymin * height) + face_width = int(bounding_box.width * width) + face_height = int(bounding_box.height * height) + + # Crop the image to the bounding box. + xmin = max(0, xmin) + ymin = max(0, ymin) + xmax = min(width, xmin + face_width) + ymax = min(height, ymin + face_height) + image = image[ymin:ymax, xmin:xmax] + + return image + + def detect_video(self, video_path, real: bool = True): + vr = VideoReader(video_path) + video_frames = vr[20:36].asnumpy() # Use one frame per second + vr.seek(0) # avoid memory leak + faces = [] + for frame in video_frames: + face = self.detect_face(frame) + face = cv2.resize(face, (self.resolution[1], self.resolution[0]), interpolation=cv2.INTER_AREA) + faces.append(face) + + if len(faces) != 16: + return None + faces = np.stack(faces, axis=0) # (f, h, w, c) + faces = torch.from_numpy(faces) + return faces + + +def eval_fvd(real_videos_dir, fake_videos_dir): + fvd = FVD() + real_features_list = [] + fake_features_list = [] + for file in tqdm.tqdm(os.listdir(fake_videos_dir)): + if file.endswith(".mp4"): + real_video_path = os.path.join(real_videos_dir, file.replace("_out.mp4", ".mp4")) + fake_video_path = os.path.join(fake_videos_dir, file) + real_features = fvd.detect_video(real_video_path, real=True) + fake_features = fvd.detect_video(fake_video_path, real=False) + if real_features is None or fake_features is None: + continue + real_features_list.append(real_features) + fake_features_list.append(fake_features) + + real_features = torch.stack(real_features_list) / 255.0 + fake_features = torch.stack(fake_features_list) / 255.0 + print(compute_our_fvd(real_features, fake_features, device="cpu")) + + +if __name__ == "__main__": + real_videos_dir = "/mnt/bn/maliva-gen-ai-v2/chunyu.li/VoxCeleb2/segmented/cross" + fake_videos_dir = "/mnt/bn/maliva-gen-ai-v2/chunyu.li/VoxCeleb2/segmented/latentsync_cross" + + eval_fvd(real_videos_dir, fake_videos_dir) diff --git a/eval/eval_sync_conf.py b/eval/eval_sync_conf.py new file mode 100644 index 0000000000000000000000000000000000000000..741a0c3505c0206fe4e8076c98e5260c36afbf11 --- /dev/null +++ b/eval/eval_sync_conf.py @@ -0,0 +1,77 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import os +import tqdm +from statistics import fmean +from eval.syncnet import SyncNetEval +from eval.syncnet_detect import SyncNetDetector +from latentsync.utils.util import red_text +import torch + + +def syncnet_eval(syncnet, syncnet_detector, video_path, temp_dir, detect_results_dir="detect_results"): + syncnet_detector(video_path=video_path, min_track=50) + crop_videos = os.listdir(os.path.join(detect_results_dir, "crop")) + if crop_videos == []: + raise Exception(red_text(f"Face not detected in {video_path}")) + av_offset_list = [] + conf_list = [] + for video in crop_videos: + av_offset, _, conf = syncnet.evaluate( + video_path=os.path.join(detect_results_dir, "crop", video), temp_dir=temp_dir + ) + av_offset_list.append(av_offset) + conf_list.append(conf) + av_offset = int(fmean(av_offset_list)) + conf = fmean(conf_list) + print(f"Input video: {video_path}\nSyncNet confidence: {conf:.2f}\nAV offset: {av_offset}") + return av_offset, conf + + +def main(): + parser = argparse.ArgumentParser(description="SyncNet") + parser.add_argument("--initial_model", type=str, default="checkpoints/auxiliary/syncnet_v2.model", help="") + parser.add_argument("--video_path", type=str, default=None, help="") + parser.add_argument("--videos_dir", type=str, default="/root/processed") + parser.add_argument("--temp_dir", type=str, default="temp", help="") + + args = parser.parse_args() + + device = "cuda" if torch.cuda.is_available() else "cpu" + + syncnet = SyncNetEval(device=device) + syncnet.loadParameters(args.initial_model) + + syncnet_detector = SyncNetDetector(device=device, detect_results_dir="detect_results") + + if args.video_path is not None: + syncnet_eval(syncnet, syncnet_detector, args.video_path, args.temp_dir) + else: + sync_conf_list = [] + video_names = sorted([f for f in os.listdir(args.videos_dir) if f.endswith(".mp4")]) + for video_name in tqdm.tqdm(video_names): + try: + _, conf = syncnet_eval( + syncnet, syncnet_detector, os.path.join(args.videos_dir, video_name), args.temp_dir + ) + sync_conf_list.append(conf) + except Exception as e: + print(e) + print(f"The average sync confidence is {fmean(sync_conf_list):.02f}") + + +if __name__ == "__main__": + main() diff --git a/eval/eval_sync_conf.sh b/eval/eval_sync_conf.sh new file mode 100644 index 0000000000000000000000000000000000000000..49b105dcc2af1f663c2bd9aa242841edda7cd3ec --- /dev/null +++ b/eval/eval_sync_conf.sh @@ -0,0 +1,2 @@ +#!/bin/bash +python -m eval.eval_sync_conf --video_path "RD_Radio1_000_006_out.mp4" diff --git a/eval/eval_syncnet_acc.py b/eval/eval_syncnet_acc.py new file mode 100644 index 0000000000000000000000000000000000000000..acde8d804389b70520eb4f3ccb26b860516a2c4c --- /dev/null +++ b/eval/eval_syncnet_acc.py @@ -0,0 +1,118 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +from tqdm.auto import tqdm +import torch +import torch.nn as nn +from einops import rearrange +from latentsync.models.syncnet import SyncNet +from latentsync.data.syncnet_dataset import SyncNetDataset +from diffusers import AutoencoderKL +from omegaconf import OmegaConf +from accelerate.utils import set_seed + + +def main(config): + set_seed(config.run.seed) + + device = "cuda" if torch.cuda.is_available() else "cpu" + + if config.data.latent_space: + vae = AutoencoderKL.from_pretrained( + "runwayml/stable-diffusion-inpainting", subfolder="vae", revision="fp16", torch_dtype=torch.float16 + ) + vae.requires_grad_(False) + vae.to(device) + + # Dataset and Dataloader setup + dataset = SyncNetDataset(config.data.val_data_dir, config.data.val_fileslist, config) + + test_dataloader = torch.utils.data.DataLoader( + dataset, + batch_size=config.data.batch_size, + shuffle=False, + num_workers=config.data.num_workers, + drop_last=False, + worker_init_fn=dataset.worker_init_fn, + ) + + # Model + syncnet = SyncNet(OmegaConf.to_container(config.model)).to(device) + + print(f"Load checkpoint from: {config.ckpt.inference_ckpt_path}") + checkpoint = torch.load(config.ckpt.inference_ckpt_path, map_location=device) + + syncnet.load_state_dict(checkpoint["state_dict"]) + syncnet.to(dtype=torch.float16) + syncnet.requires_grad_(False) + syncnet.eval() + + global_step = 0 + num_val_batches = config.data.num_val_samples // config.data.batch_size + progress_bar = tqdm(range(0, num_val_batches), initial=0, desc="Testing accuracy") + + num_correct_preds = 0 + num_total_preds = 0 + + while True: + for step, batch in enumerate(test_dataloader): + ### >>>> Test >>>> ### + + frames = batch["frames"].to(device, dtype=torch.float16) + audio_samples = batch["audio_samples"].to(device, dtype=torch.float16) + y = batch["y"].to(device, dtype=torch.float16).squeeze(1) + + if config.data.latent_space: + frames = rearrange(frames, "b f c h w -> (b f) c h w") + + with torch.no_grad(): + frames = vae.encode(frames).latent_dist.sample() * 0.18215 + + frames = rearrange(frames, "(b f) c h w -> b (f c) h w", f=config.data.num_frames) + else: + frames = rearrange(frames, "b f c h w -> b (f c) h w") + + if config.data.lower_half: + height = frames.shape[2] + frames = frames[:, :, height // 2 :, :] + + with torch.no_grad(): + vision_embeds, audio_embeds = syncnet(frames, audio_samples) + + sims = nn.functional.cosine_similarity(vision_embeds, audio_embeds) + + preds = (sims > 0.5).to(dtype=torch.float16) + num_correct_preds += (preds == y).sum().item() + num_total_preds += len(sims) + + progress_bar.update(1) + global_step += 1 + + if global_step >= num_val_batches: + progress_bar.close() + print(f"Accuracy score: {num_correct_preds / num_total_preds*100:.2f}%") + return + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Code to test the accuracy of expert lip-sync discriminator") + + parser.add_argument("--config_path", type=str, default="configs/syncnet/syncnet_16_latent.yaml") + args = parser.parse_args() + + # Load a configuration file + config = OmegaConf.load(args.config_path) + + main(config) diff --git a/eval/eval_syncnet_acc.sh b/eval/eval_syncnet_acc.sh new file mode 100644 index 0000000000000000000000000000000000000000..d6b5d89f5ebaf6135c5bc442836c23ed18d0dfe9 --- /dev/null +++ b/eval/eval_syncnet_acc.sh @@ -0,0 +1,3 @@ +#!/bin/bash + +python -m eval.eval_syncnet_acc --config_path "configs/syncnet/syncnet_16_pixel.yaml" diff --git a/eval/fvd.py b/eval/fvd.py new file mode 100644 index 0000000000000000000000000000000000000000..ac36f31b575f8376b4b4ec616e692df57fe95d20 --- /dev/null +++ b/eval/fvd.py @@ -0,0 +1,56 @@ +# Adapted from https://github.com/universome/fvd-comparison/blob/master/our_fvd.py + +from typing import Tuple +import scipy +import numpy as np +import torch + + +def compute_fvd(feats_fake: np.ndarray, feats_real: np.ndarray) -> float: + mu_gen, sigma_gen = compute_stats(feats_fake) + mu_real, sigma_real = compute_stats(feats_real) + + m = np.square(mu_gen - mu_real).sum() + s, _ = scipy.linalg.sqrtm(np.dot(sigma_gen, sigma_real), disp=False) # pylint: disable=no-member + fid = np.real(m + np.trace(sigma_gen + sigma_real - s * 2)) + + return float(fid) + + +def compute_stats(feats: np.ndarray) -> Tuple[np.ndarray, np.ndarray]: + mu = feats.mean(axis=0) # [d] + sigma = np.cov(feats, rowvar=False) # [d, d] + + return mu, sigma + + +@torch.no_grad() +def compute_our_fvd(videos_fake: np.ndarray, videos_real: np.ndarray, device: str = "cuda") -> float: + i3d_path = "checkpoints/auxiliary/i3d_torchscript.pt" + i3d_kwargs = dict( + rescale=False, resize=False, return_features=True + ) # Return raw features before the softmax layer. + + with open(i3d_path, "rb") as f: + i3d_model = torch.jit.load(f).eval().to(device) + + videos_fake = videos_fake.permute(0, 4, 1, 2, 3).to(device) + videos_real = videos_real.permute(0, 4, 1, 2, 3).to(device) + + feats_fake = i3d_model(videos_fake, **i3d_kwargs).cpu().numpy() + feats_real = i3d_model(videos_real, **i3d_kwargs).cpu().numpy() + + return compute_fvd(feats_fake, feats_real) + + +def main(): + # input shape: (b, f, h, w, c) + videos_fake = torch.rand(10, 16, 224, 224, 3) + videos_real = torch.rand(10, 16, 224, 224, 3) + + our_fvd_result = compute_our_fvd(videos_fake, videos_real) + print(f"[FVD scores] Ours: {our_fvd_result}") + + +if __name__ == "__main__": + main() diff --git a/eval/hyper_iqa.py b/eval/hyper_iqa.py new file mode 100644 index 0000000000000000000000000000000000000000..1ed8004c7f7951f08f38ce9708bb97cf48c9db85 --- /dev/null +++ b/eval/hyper_iqa.py @@ -0,0 +1,343 @@ +# Adapted from https://github.com/SSL92/hyperIQA/blob/master/models.py + +import torch as torch +import torch.nn as nn +from torch.nn import functional as F +from torch.nn import init +import math +import torch.utils.model_zoo as model_zoo + +model_urls = { + 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth', + 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth', + 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth', + 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth', + 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth', +} + + +class HyperNet(nn.Module): + """ + Hyper network for learning perceptual rules. + + Args: + lda_out_channels: local distortion aware module output size. + hyper_in_channels: input feature channels for hyper network. + target_in_size: input vector size for target network. + target_fc(i)_size: fully connection layer size of target network. + feature_size: input feature map width/height for hyper network. + + Note: + For size match, input args must satisfy: 'target_fc(i)_size * target_fc(i+1)_size' is divisible by 'feature_size ^ 2'. + + """ + def __init__(self, lda_out_channels, hyper_in_channels, target_in_size, target_fc1_size, target_fc2_size, target_fc3_size, target_fc4_size, feature_size): + super(HyperNet, self).__init__() + + self.hyperInChn = hyper_in_channels + self.target_in_size = target_in_size + self.f1 = target_fc1_size + self.f2 = target_fc2_size + self.f3 = target_fc3_size + self.f4 = target_fc4_size + self.feature_size = feature_size + + self.res = resnet50_backbone(lda_out_channels, target_in_size, pretrained=True) + + self.pool = nn.AdaptiveAvgPool2d((1, 1)) + + # Conv layers for resnet output features + self.conv1 = nn.Sequential( + nn.Conv2d(2048, 1024, 1, padding=(0, 0)), + nn.ReLU(inplace=True), + nn.Conv2d(1024, 512, 1, padding=(0, 0)), + nn.ReLU(inplace=True), + nn.Conv2d(512, self.hyperInChn, 1, padding=(0, 0)), + nn.ReLU(inplace=True) + ) + + # Hyper network part, conv for generating target fc weights, fc for generating target fc biases + self.fc1w_conv = nn.Conv2d(self.hyperInChn, int(self.target_in_size * self.f1 / feature_size ** 2), 3, padding=(1, 1)) + self.fc1b_fc = nn.Linear(self.hyperInChn, self.f1) + + self.fc2w_conv = nn.Conv2d(self.hyperInChn, int(self.f1 * self.f2 / feature_size ** 2), 3, padding=(1, 1)) + self.fc2b_fc = nn.Linear(self.hyperInChn, self.f2) + + self.fc3w_conv = nn.Conv2d(self.hyperInChn, int(self.f2 * self.f3 / feature_size ** 2), 3, padding=(1, 1)) + self.fc3b_fc = nn.Linear(self.hyperInChn, self.f3) + + self.fc4w_conv = nn.Conv2d(self.hyperInChn, int(self.f3 * self.f4 / feature_size ** 2), 3, padding=(1, 1)) + self.fc4b_fc = nn.Linear(self.hyperInChn, self.f4) + + self.fc5w_fc = nn.Linear(self.hyperInChn, self.f4) + self.fc5b_fc = nn.Linear(self.hyperInChn, 1) + + # initialize + for i, m_name in enumerate(self._modules): + if i > 2: + nn.init.kaiming_normal_(self._modules[m_name].weight.data) + + def forward(self, img): + feature_size = self.feature_size + + res_out = self.res(img) + + # input vector for target net + target_in_vec = res_out['target_in_vec'].reshape(-1, self.target_in_size, 1, 1) + + # input features for hyper net + hyper_in_feat = self.conv1(res_out['hyper_in_feat']).reshape(-1, self.hyperInChn, feature_size, feature_size) + + # generating target net weights & biases + target_fc1w = self.fc1w_conv(hyper_in_feat).reshape(-1, self.f1, self.target_in_size, 1, 1) + target_fc1b = self.fc1b_fc(self.pool(hyper_in_feat).squeeze()).reshape(-1, self.f1) + + target_fc2w = self.fc2w_conv(hyper_in_feat).reshape(-1, self.f2, self.f1, 1, 1) + target_fc2b = self.fc2b_fc(self.pool(hyper_in_feat).squeeze()).reshape(-1, self.f2) + + target_fc3w = self.fc3w_conv(hyper_in_feat).reshape(-1, self.f3, self.f2, 1, 1) + target_fc3b = self.fc3b_fc(self.pool(hyper_in_feat).squeeze()).reshape(-1, self.f3) + + target_fc4w = self.fc4w_conv(hyper_in_feat).reshape(-1, self.f4, self.f3, 1, 1) + target_fc4b = self.fc4b_fc(self.pool(hyper_in_feat).squeeze()).reshape(-1, self.f4) + + target_fc5w = self.fc5w_fc(self.pool(hyper_in_feat).squeeze()).reshape(-1, 1, self.f4, 1, 1) + target_fc5b = self.fc5b_fc(self.pool(hyper_in_feat).squeeze()).reshape(-1, 1) + + out = {} + out['target_in_vec'] = target_in_vec + out['target_fc1w'] = target_fc1w + out['target_fc1b'] = target_fc1b + out['target_fc2w'] = target_fc2w + out['target_fc2b'] = target_fc2b + out['target_fc3w'] = target_fc3w + out['target_fc3b'] = target_fc3b + out['target_fc4w'] = target_fc4w + out['target_fc4b'] = target_fc4b + out['target_fc5w'] = target_fc5w + out['target_fc5b'] = target_fc5b + + return out + + +class TargetNet(nn.Module): + """ + Target network for quality prediction. + """ + def __init__(self, paras): + super(TargetNet, self).__init__() + self.l1 = nn.Sequential( + TargetFC(paras['target_fc1w'], paras['target_fc1b']), + nn.Sigmoid(), + ) + self.l2 = nn.Sequential( + TargetFC(paras['target_fc2w'], paras['target_fc2b']), + nn.Sigmoid(), + ) + + self.l3 = nn.Sequential( + TargetFC(paras['target_fc3w'], paras['target_fc3b']), + nn.Sigmoid(), + ) + + self.l4 = nn.Sequential( + TargetFC(paras['target_fc4w'], paras['target_fc4b']), + nn.Sigmoid(), + TargetFC(paras['target_fc5w'], paras['target_fc5b']), + ) + + def forward(self, x): + q = self.l1(x) + # q = F.dropout(q) + q = self.l2(q) + q = self.l3(q) + q = self.l4(q).squeeze() + return q + + +class TargetFC(nn.Module): + """ + Fully connection operations for target net + + Note: + Weights & biases are different for different images in a batch, + thus here we use group convolution for calculating images in a batch with individual weights & biases. + """ + def __init__(self, weight, bias): + super(TargetFC, self).__init__() + self.weight = weight + self.bias = bias + + def forward(self, input_): + + input_re = input_.reshape(-1, input_.shape[0] * input_.shape[1], input_.shape[2], input_.shape[3]) + weight_re = self.weight.reshape(self.weight.shape[0] * self.weight.shape[1], self.weight.shape[2], self.weight.shape[3], self.weight.shape[4]) + bias_re = self.bias.reshape(self.bias.shape[0] * self.bias.shape[1]) + out = F.conv2d(input=input_re, weight=weight_re, bias=bias_re, groups=self.weight.shape[0]) + + return out.reshape(input_.shape[0], self.weight.shape[1], input_.shape[2], input_.shape[3]) + + +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 ResNetBackbone(nn.Module): + + def __init__(self, lda_out_channels, in_chn, block, layers, num_classes=1000): + super(ResNetBackbone, self).__init__() + self.inplanes = 64 + self.conv1 = nn.Conv2d(3, 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) + + # local distortion aware module + self.lda1_pool = nn.Sequential( + nn.Conv2d(256, 16, kernel_size=1, stride=1, padding=0, bias=False), + nn.AvgPool2d(7, stride=7), + ) + self.lda1_fc = nn.Linear(16 * 64, lda_out_channels) + + self.lda2_pool = nn.Sequential( + nn.Conv2d(512, 32, kernel_size=1, stride=1, padding=0, bias=False), + nn.AvgPool2d(7, stride=7), + ) + self.lda2_fc = nn.Linear(32 * 16, lda_out_channels) + + self.lda3_pool = nn.Sequential( + nn.Conv2d(1024, 64, kernel_size=1, stride=1, padding=0, bias=False), + nn.AvgPool2d(7, stride=7), + ) + self.lda3_fc = nn.Linear(64 * 4, lda_out_channels) + + self.lda4_pool = nn.AvgPool2d(7, stride=7) + self.lda4_fc = nn.Linear(2048, in_chn - lda_out_channels * 3) + + 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_() + + # initialize + nn.init.kaiming_normal_(self.lda1_pool._modules['0'].weight.data) + nn.init.kaiming_normal_(self.lda2_pool._modules['0'].weight.data) + nn.init.kaiming_normal_(self.lda3_pool._modules['0'].weight.data) + nn.init.kaiming_normal_(self.lda1_fc.weight.data) + nn.init.kaiming_normal_(self.lda2_fc.weight.data) + nn.init.kaiming_normal_(self.lda3_fc.weight.data) + nn.init.kaiming_normal_(self.lda4_fc.weight.data) + + 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) + + # the same effect as lda operation in the paper, but save much more memory + lda_1 = self.lda1_fc(self.lda1_pool(x).reshape(x.size(0), -1)) + x = self.layer2(x) + lda_2 = self.lda2_fc(self.lda2_pool(x).reshape(x.size(0), -1)) + x = self.layer3(x) + lda_3 = self.lda3_fc(self.lda3_pool(x).reshape(x.size(0), -1)) + x = self.layer4(x) + lda_4 = self.lda4_fc(self.lda4_pool(x).reshape(x.size(0), -1)) + + vec = torch.cat((lda_1, lda_2, lda_3, lda_4), 1) + + out = {} + out['hyper_in_feat'] = x + out['target_in_vec'] = vec + + return out + + +def resnet50_backbone(lda_out_channels, in_chn, pretrained=False, **kwargs): + """Constructs a ResNet-50 model_hyper. + + Args: + pretrained (bool): If True, returns a model_hyper pre-trained on ImageNet + """ + model = ResNetBackbone(lda_out_channels, in_chn, Bottleneck, [3, 4, 6, 3], **kwargs) + if pretrained: + save_model = model_zoo.load_url(model_urls['resnet50']) + model_dict = model.state_dict() + state_dict = {k: v for k, v in save_model.items() if k in model_dict.keys()} + model_dict.update(state_dict) + model.load_state_dict(model_dict) + else: + model.apply(weights_init_xavier) + return model + + +def weights_init_xavier(m): + classname = m.__class__.__name__ + # print(classname) + # if isinstance(m, nn.Conv2d): + if classname.find('Conv') != -1: + init.kaiming_normal_(m.weight.data) + elif classname.find('Linear') != -1: + init.kaiming_normal_(m.weight.data) + elif classname.find('BatchNorm2d') != -1: + init.uniform_(m.weight.data, 1.0, 0.02) + init.constant_(m.bias.data, 0.0) diff --git a/eval/inference_videos.py b/eval/inference_videos.py new file mode 100644 index 0000000000000000000000000000000000000000..c5f717aee775f6ba8e1de36d1181f67adf5ba989 --- /dev/null +++ b/eval/inference_videos.py @@ -0,0 +1,37 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import subprocess +from tqdm import tqdm + + +def inference_video_from_dir(input_dir, output_dir, unet_config_path, ckpt_path): + os.makedirs(output_dir, exist_ok=True) + video_names = sorted([f for f in os.listdir(input_dir) if f.endswith(".mp4")]) + for video_name in tqdm(video_names): + video_path = os.path.join(input_dir, video_name) + audio_path = os.path.join(input_dir, video_name.replace(".mp4", "_audio.wav")) + video_out_path = os.path.join(output_dir, video_name.replace(".mp4", "_out.mp4")) + inference_command = f"python inference.py --unet_config_path {unet_config_path} --video_path {video_path} --audio_path {audio_path} --video_out_path {video_out_path} --inference_ckpt_path {ckpt_path} --seed 1247" + subprocess.run(inference_command, shell=True) + + +if __name__ == "__main__": + input_dir = "/mnt/bn/maliva-gen-ai-v2/chunyu.li/HDTF/segmented/cross" + output_dir = "/mnt/bn/maliva-gen-ai-v2/chunyu.li/HDTF/segmented/latentsync_cross" + unet_config_path = "configs/unet/unet_latent_16_diffusion.yaml" + ckpt_path = "output/unet/train-2024_10_08-16:23:43/checkpoints/checkpoint-1920000.pt" + + inference_video_from_dir(input_dir, output_dir, unet_config_path, ckpt_path) diff --git a/eval/syncnet/__init__.py b/eval/syncnet/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5d09875b56508c4bc1ec3224e743c308b17a645c --- /dev/null +++ b/eval/syncnet/__init__.py @@ -0,0 +1 @@ +from .syncnet_eval import SyncNetEval diff --git a/eval/syncnet/syncnet.py b/eval/syncnet/syncnet.py new file mode 100644 index 0000000000000000000000000000000000000000..c19350bec7a6af3a5bd66bb6f40135c35dfe5f79 --- /dev/null +++ b/eval/syncnet/syncnet.py @@ -0,0 +1,113 @@ +# https://github.com/joonson/syncnet_python/blob/master/SyncNetModel.py + +import torch +import torch.nn as nn + + +def save(model, filename): + with open(filename, "wb") as f: + torch.save(model, f) + print("%s saved." % filename) + + +def load(filename): + net = torch.load(filename) + return net + + +class S(nn.Module): + def __init__(self, num_layers_in_fc_layers=1024): + super(S, self).__init__() + + self.__nFeatures__ = 24 + self.__nChs__ = 32 + self.__midChs__ = 32 + + self.netcnnaud = nn.Sequential( + nn.Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), + nn.BatchNorm2d(64), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=(1, 1), stride=(1, 1)), + nn.Conv2d(64, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)), + nn.BatchNorm2d(192), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=(3, 3), stride=(1, 2)), + nn.Conv2d(192, 384, kernel_size=(3, 3), padding=(1, 1)), + nn.BatchNorm2d(384), + nn.ReLU(inplace=True), + nn.Conv2d(384, 256, kernel_size=(3, 3), padding=(1, 1)), + nn.BatchNorm2d(256), + nn.ReLU(inplace=True), + nn.Conv2d(256, 256, kernel_size=(3, 3), padding=(1, 1)), + nn.BatchNorm2d(256), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=(3, 3), stride=(2, 2)), + nn.Conv2d(256, 512, kernel_size=(5, 4), padding=(0, 0)), + nn.BatchNorm2d(512), + nn.ReLU(), + ) + + self.netfcaud = nn.Sequential( + nn.Linear(512, 512), + nn.BatchNorm1d(512), + nn.ReLU(), + nn.Linear(512, num_layers_in_fc_layers), + ) + + self.netfclip = nn.Sequential( + nn.Linear(512, 512), + nn.BatchNorm1d(512), + nn.ReLU(), + nn.Linear(512, num_layers_in_fc_layers), + ) + + self.netcnnlip = nn.Sequential( + nn.Conv3d(3, 96, kernel_size=(5, 7, 7), stride=(1, 2, 2), padding=0), + nn.BatchNorm3d(96), + nn.ReLU(inplace=True), + nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2)), + nn.Conv3d(96, 256, kernel_size=(1, 5, 5), stride=(1, 2, 2), padding=(0, 1, 1)), + nn.BatchNorm3d(256), + nn.ReLU(inplace=True), + nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2), padding=(0, 1, 1)), + nn.Conv3d(256, 256, kernel_size=(1, 3, 3), padding=(0, 1, 1)), + nn.BatchNorm3d(256), + nn.ReLU(inplace=True), + nn.Conv3d(256, 256, kernel_size=(1, 3, 3), padding=(0, 1, 1)), + nn.BatchNorm3d(256), + nn.ReLU(inplace=True), + nn.Conv3d(256, 256, kernel_size=(1, 3, 3), padding=(0, 1, 1)), + nn.BatchNorm3d(256), + nn.ReLU(inplace=True), + nn.MaxPool3d(kernel_size=(1, 3, 3), stride=(1, 2, 2)), + nn.Conv3d(256, 512, kernel_size=(1, 6, 6), padding=0), + nn.BatchNorm3d(512), + nn.ReLU(inplace=True), + ) + + def forward_aud(self, x): + + mid = self.netcnnaud(x) + # N x ch x 24 x M + mid = mid.view((mid.size()[0], -1)) + # N x (ch x 24) + out = self.netfcaud(mid) + + return out + + def forward_lip(self, x): + + mid = self.netcnnlip(x) + mid = mid.view((mid.size()[0], -1)) + # N x (ch x 24) + out = self.netfclip(mid) + + return out + + def forward_lipfeat(self, x): + + mid = self.netcnnlip(x) + out = mid.view((mid.size()[0], -1)) + # N x (ch x 24) + + return out diff --git a/eval/syncnet/syncnet_eval.py b/eval/syncnet/syncnet_eval.py new file mode 100644 index 0000000000000000000000000000000000000000..bb84786684f027e0c1846a2f09058095494d33f8 --- /dev/null +++ b/eval/syncnet/syncnet_eval.py @@ -0,0 +1,220 @@ +# Adapted from https://github.com/joonson/syncnet_python/blob/master/SyncNetInstance.py + +import torch +import numpy +import time, pdb, argparse, subprocess, os, math, glob +import cv2 +import python_speech_features + +from scipy import signal +from scipy.io import wavfile +from .syncnet import S +from shutil import rmtree + + +# ==================== Get OFFSET ==================== + +# Video 25 FPS, Audio 16000HZ + + +def calc_pdist(feat1, feat2, vshift=10): + win_size = vshift * 2 + 1 + + feat2p = torch.nn.functional.pad(feat2, (0, 0, vshift, vshift)) + + dists = [] + + for i in range(0, len(feat1)): + + dists.append( + torch.nn.functional.pairwise_distance(feat1[[i], :].repeat(win_size, 1), feat2p[i : i + win_size, :]) + ) + + return dists + + +# ==================== MAIN DEF ==================== + + +class SyncNetEval(torch.nn.Module): + def __init__(self, dropout=0, num_layers_in_fc_layers=1024, device="cpu"): + super().__init__() + + self.__S__ = S(num_layers_in_fc_layers=num_layers_in_fc_layers).to(device) + self.device = device + + def evaluate(self, video_path, temp_dir="temp", batch_size=20, vshift=15): + + self.__S__.eval() + + # ========== ========== + # Convert files + # ========== ========== + + if os.path.exists(temp_dir): + rmtree(temp_dir) + + os.makedirs(temp_dir) + + # temp_video_path = os.path.join(temp_dir, "temp.mp4") + # command = f"ffmpeg -loglevel error -nostdin -y -i {video_path} -vf scale='224:224' {temp_video_path}" + # subprocess.call(command, shell=True) + + command = ( + f"ffmpeg -loglevel error -nostdin -y -i {video_path} -f image2 {os.path.join(temp_dir, '%06d.jpg')}" + ) + subprocess.call(command, shell=True, stdout=None) + + command = f"ffmpeg -loglevel error -nostdin -y -i {video_path} -async 1 -ac 1 -vn -acodec pcm_s16le -ar 16000 {os.path.join(temp_dir, 'audio.wav')}" + subprocess.call(command, shell=True, stdout=None) + + # ========== ========== + # Load video + # ========== ========== + + images = [] + + flist = glob.glob(os.path.join(temp_dir, "*.jpg")) + flist.sort() + + for fname in flist: + img_input = cv2.imread(fname) + img_input = cv2.resize(img_input, (224, 224)) # HARD CODED, CHANGE BEFORE RELEASE + images.append(img_input) + + im = numpy.stack(images, axis=3) + im = numpy.expand_dims(im, axis=0) + im = numpy.transpose(im, (0, 3, 4, 1, 2)) + + imtv = torch.autograd.Variable(torch.from_numpy(im.astype(float)).float()) + + # ========== ========== + # Load audio + # ========== ========== + + sample_rate, audio = wavfile.read(os.path.join(temp_dir, "audio.wav")) + mfcc = zip(*python_speech_features.mfcc(audio, sample_rate)) + mfcc = numpy.stack([numpy.array(i) for i in mfcc]) + + cc = numpy.expand_dims(numpy.expand_dims(mfcc, axis=0), axis=0) + cct = torch.autograd.Variable(torch.from_numpy(cc.astype(float)).float()) + + # ========== ========== + # Check audio and video input length + # ========== ========== + + # if (float(len(audio)) / 16000) != (float(len(images)) / 25): + # print( + # "WARNING: Audio (%.4fs) and video (%.4fs) lengths are different." + # % (float(len(audio)) / 16000, float(len(images)) / 25) + # ) + + min_length = min(len(images), math.floor(len(audio) / 640)) + + # ========== ========== + # Generate video and audio feats + # ========== ========== + + lastframe = min_length - 5 + im_feat = [] + cc_feat = [] + + tS = time.time() + for i in range(0, lastframe, batch_size): + + im_batch = [imtv[:, :, vframe : vframe + 5, :, :] for vframe in range(i, min(lastframe, i + batch_size))] + im_in = torch.cat(im_batch, 0) + im_out = self.__S__.forward_lip(im_in.to(self.device)) + im_feat.append(im_out.data.cpu()) + + cc_batch = [ + cct[:, :, :, vframe * 4 : vframe * 4 + 20] for vframe in range(i, min(lastframe, i + batch_size)) + ] + cc_in = torch.cat(cc_batch, 0) + cc_out = self.__S__.forward_aud(cc_in.to(self.device)) + cc_feat.append(cc_out.data.cpu()) + + im_feat = torch.cat(im_feat, 0) + cc_feat = torch.cat(cc_feat, 0) + + # ========== ========== + # Compute offset + # ========== ========== + + dists = calc_pdist(im_feat, cc_feat, vshift=vshift) + mean_dists = torch.mean(torch.stack(dists, 1), 1) + + min_dist, minidx = torch.min(mean_dists, 0) + + av_offset = vshift - minidx + conf = torch.median(mean_dists) - min_dist + + fdist = numpy.stack([dist[minidx].numpy() for dist in dists]) + # fdist = numpy.pad(fdist, (3,3), 'constant', constant_values=15) + fconf = torch.median(mean_dists).numpy() - fdist + framewise_conf = signal.medfilt(fconf, kernel_size=9) + + # numpy.set_printoptions(formatter={"float": "{: 0.3f}".format}) + rmtree(temp_dir) + return av_offset.item(), min_dist.item(), conf.item() + + def extract_feature(self, opt, videofile): + + self.__S__.eval() + + # ========== ========== + # Load video + # ========== ========== + cap = cv2.VideoCapture(videofile) + + frame_num = 1 + images = [] + while frame_num: + frame_num += 1 + ret, image = cap.read() + if ret == 0: + break + + images.append(image) + + im = numpy.stack(images, axis=3) + im = numpy.expand_dims(im, axis=0) + im = numpy.transpose(im, (0, 3, 4, 1, 2)) + + imtv = torch.autograd.Variable(torch.from_numpy(im.astype(float)).float()) + + # ========== ========== + # Generate video feats + # ========== ========== + + lastframe = len(images) - 4 + im_feat = [] + + tS = time.time() + for i in range(0, lastframe, opt.batch_size): + + im_batch = [ + imtv[:, :, vframe : vframe + 5, :, :] for vframe in range(i, min(lastframe, i + opt.batch_size)) + ] + im_in = torch.cat(im_batch, 0) + im_out = self.__S__.forward_lipfeat(im_in.to(self.device)) + im_feat.append(im_out.data.cpu()) + + im_feat = torch.cat(im_feat, 0) + + # ========== ========== + # Compute offset + # ========== ========== + + print("Compute time %.3f sec." % (time.time() - tS)) + + return im_feat + + def loadParameters(self, path): + loaded_state = torch.load(path, map_location=lambda storage, loc: storage) + + self_state = self.__S__.state_dict() + + for name, param in loaded_state.items(): + + self_state[name].copy_(param) diff --git a/eval/syncnet_detect.py b/eval/syncnet_detect.py new file mode 100644 index 0000000000000000000000000000000000000000..b3428b9b528510cf5f23cf7999dd8a2577587fe4 --- /dev/null +++ b/eval/syncnet_detect.py @@ -0,0 +1,251 @@ +# Adapted from https://github.com/joonson/syncnet_python/blob/master/run_pipeline.py + +import os, pdb, subprocess, glob, cv2 +import numpy as np +from shutil import rmtree +import torch + +from scenedetect.video_manager import VideoManager +from scenedetect.scene_manager import SceneManager +from scenedetect.stats_manager import StatsManager +from scenedetect.detectors import ContentDetector + +from scipy.interpolate import interp1d +from scipy.io import wavfile +from scipy import signal + +from eval.detectors import S3FD + + +class SyncNetDetector: + def __init__(self, device, detect_results_dir="detect_results"): + self.s3f_detector = S3FD(device=device) + self.detect_results_dir = detect_results_dir + + def __call__(self, video_path: str, min_track=50, scale=False): + crop_dir = os.path.join(self.detect_results_dir, "crop") + video_dir = os.path.join(self.detect_results_dir, "video") + frames_dir = os.path.join(self.detect_results_dir, "frames") + temp_dir = os.path.join(self.detect_results_dir, "temp") + + # ========== DELETE EXISTING DIRECTORIES ========== + if os.path.exists(crop_dir): + rmtree(crop_dir) + + if os.path.exists(video_dir): + rmtree(video_dir) + + if os.path.exists(frames_dir): + rmtree(frames_dir) + + if os.path.exists(temp_dir): + rmtree(temp_dir) + + # ========== MAKE NEW DIRECTORIES ========== + + os.makedirs(crop_dir) + os.makedirs(video_dir) + os.makedirs(frames_dir) + os.makedirs(temp_dir) + + # ========== CONVERT VIDEO AND EXTRACT FRAMES ========== + + if scale: + scaled_video_path = os.path.join(video_dir, "scaled.mp4") + command = f"ffmpeg -loglevel error -y -nostdin -i {video_path} -vf scale='224:224' {scaled_video_path}" + subprocess.run(command, shell=True) + video_path = scaled_video_path + + command = f"ffmpeg -y -nostdin -loglevel error -i {video_path} -qscale:v 2 -async 1 -r 25 {os.path.join(video_dir, 'video.mp4')}" + subprocess.run(command, shell=True, stdout=None) + + command = f"ffmpeg -y -nostdin -loglevel error -i {os.path.join(video_dir, 'video.mp4')} -qscale:v 2 -f image2 {os.path.join(frames_dir, '%06d.jpg')}" + subprocess.run(command, shell=True, stdout=None) + + command = f"ffmpeg -y -nostdin -loglevel error -i {os.path.join(video_dir, 'video.mp4')} -ac 1 -vn -acodec pcm_s16le -ar 16000 {os.path.join(video_dir, 'audio.wav')}" + subprocess.run(command, shell=True, stdout=None) + + faces = self.detect_face(frames_dir) + + scene = self.scene_detect(video_dir) + + # Face tracking + alltracks = [] + + for shot in scene: + if shot[1].frame_num - shot[0].frame_num >= min_track: + alltracks.extend(self.track_face(faces[shot[0].frame_num : shot[1].frame_num], min_track=min_track)) + + # Face crop + for ii, track in enumerate(alltracks): + self.crop_video(track, os.path.join(crop_dir, "%05d" % ii), frames_dir, 25, temp_dir, video_dir) + + rmtree(temp_dir) + + def scene_detect(self, video_dir): + video_manager = VideoManager([os.path.join(video_dir, "video.mp4")]) + stats_manager = StatsManager() + scene_manager = SceneManager(stats_manager) + # Add ContentDetector algorithm (constructor takes detector options like threshold). + scene_manager.add_detector(ContentDetector()) + base_timecode = video_manager.get_base_timecode() + + video_manager.set_downscale_factor() + + video_manager.start() + + scene_manager.detect_scenes(frame_source=video_manager) + + scene_list = scene_manager.get_scene_list(base_timecode) + + if scene_list == []: + scene_list = [(video_manager.get_base_timecode(), video_manager.get_current_timecode())] + + return scene_list + + def track_face(self, scenefaces, num_failed_det=25, min_track=50, min_face_size=100): + + iouThres = 0.5 # Minimum IOU between consecutive face detections + tracks = [] + + while True: + track = [] + for framefaces in scenefaces: + for face in framefaces: + if track == []: + track.append(face) + framefaces.remove(face) + elif face["frame"] - track[-1]["frame"] <= num_failed_det: + iou = bounding_box_iou(face["bbox"], track[-1]["bbox"]) + if iou > iouThres: + track.append(face) + framefaces.remove(face) + continue + else: + break + + if track == []: + break + elif len(track) > min_track: + + framenum = np.array([f["frame"] for f in track]) + bboxes = np.array([np.array(f["bbox"]) for f in track]) + + frame_i = np.arange(framenum[0], framenum[-1] + 1) + + bboxes_i = [] + for ij in range(0, 4): + interpfn = interp1d(framenum, bboxes[:, ij]) + bboxes_i.append(interpfn(frame_i)) + bboxes_i = np.stack(bboxes_i, axis=1) + + if ( + max(np.mean(bboxes_i[:, 2] - bboxes_i[:, 0]), np.mean(bboxes_i[:, 3] - bboxes_i[:, 1])) + > min_face_size + ): + tracks.append({"frame": frame_i, "bbox": bboxes_i}) + + return tracks + + def detect_face(self, frames_dir, facedet_scale=0.25): + flist = glob.glob(os.path.join(frames_dir, "*.jpg")) + flist.sort() + + dets = [] + + for fidx, fname in enumerate(flist): + image = cv2.imread(fname) + + image_np = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) + bboxes = self.s3f_detector.detect_faces(image_np, conf_th=0.9, scales=[facedet_scale]) + + dets.append([]) + for bbox in bboxes: + dets[-1].append({"frame": fidx, "bbox": (bbox[:-1]).tolist(), "conf": bbox[-1]}) + + return dets + + def crop_video(self, track, cropfile, frames_dir, frame_rate, temp_dir, video_dir, crop_scale=0.4): + + flist = glob.glob(os.path.join(frames_dir, "*.jpg")) + flist.sort() + + fourcc = cv2.VideoWriter_fourcc(*"mp4v") + vOut = cv2.VideoWriter(cropfile + "t.mp4", fourcc, frame_rate, (224, 224)) + + dets = {"x": [], "y": [], "s": []} + + for det in track["bbox"]: + + dets["s"].append(max((det[3] - det[1]), (det[2] - det[0])) / 2) + dets["y"].append((det[1] + det[3]) / 2) # crop center x + dets["x"].append((det[0] + det[2]) / 2) # crop center y + + # Smooth detections + dets["s"] = signal.medfilt(dets["s"], kernel_size=13) + dets["x"] = signal.medfilt(dets["x"], kernel_size=13) + dets["y"] = signal.medfilt(dets["y"], kernel_size=13) + + for fidx, frame in enumerate(track["frame"]): + + cs = crop_scale + + bs = dets["s"][fidx] # Detection box size + bsi = int(bs * (1 + 2 * cs)) # Pad videos by this amount + + image = cv2.imread(flist[frame]) + + frame = np.pad(image, ((bsi, bsi), (bsi, bsi), (0, 0)), "constant", constant_values=(110, 110)) + my = dets["y"][fidx] + bsi # BBox center Y + mx = dets["x"][fidx] + bsi # BBox center X + + face = frame[int(my - bs) : int(my + bs * (1 + 2 * cs)), int(mx - bs * (1 + cs)) : int(mx + bs * (1 + cs))] + + vOut.write(cv2.resize(face, (224, 224))) + + audiotmp = os.path.join(temp_dir, "audio.wav") + audiostart = (track["frame"][0]) / frame_rate + audioend = (track["frame"][-1] + 1) / frame_rate + + vOut.release() + + # ========== CROP AUDIO FILE ========== + + command = "ffmpeg -y -nostdin -loglevel error -i %s -ss %.3f -to %.3f %s" % ( + os.path.join(video_dir, "audio.wav"), + audiostart, + audioend, + audiotmp, + ) + output = subprocess.run(command, shell=True, stdout=None) + + sample_rate, audio = wavfile.read(audiotmp) + + # ========== COMBINE AUDIO AND VIDEO FILES ========== + + command = "ffmpeg -y -nostdin -loglevel error -i %st.mp4 -i %s -c:v copy -c:a aac %s.mp4" % ( + cropfile, + audiotmp, + cropfile, + ) + output = subprocess.run(command, shell=True, stdout=None) + + os.remove(cropfile + "t.mp4") + + return {"track": track, "proc_track": dets} + + +def bounding_box_iou(boxA, boxB): + xA = max(boxA[0], boxB[0]) + yA = max(boxA[1], boxB[1]) + xB = min(boxA[2], boxB[2]) + yB = min(boxA[3], boxB[3]) + + interArea = max(0, xB - xA) * max(0, yB - yA) + + boxAArea = (boxA[2] - boxA[0]) * (boxA[3] - boxA[1]) + boxBArea = (boxB[2] - boxB[0]) * (boxB[3] - boxB[1]) + + iou = interArea / float(boxAArea + boxBArea - interArea) + + return iou diff --git a/latentsync/data/syncnet_dataset.py b/latentsync/data/syncnet_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..04345002087cc2d22256b806a3f1b96395e42172 --- /dev/null +++ b/latentsync/data/syncnet_dataset.py @@ -0,0 +1,153 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import numpy as np +from torch.utils.data import Dataset +import torch +import random +from ..utils.util import gather_video_paths_recursively +from ..utils.image_processor import ImageProcessor +from ..utils.audio import melspectrogram +import math + +from decord import AudioReader, VideoReader, cpu + + +class SyncNetDataset(Dataset): + def __init__(self, data_dir: str, fileslist: str, config): + if fileslist != "": + with open(fileslist) as file: + self.video_paths = [line.rstrip() for line in file] + elif data_dir != "": + self.video_paths = gather_video_paths_recursively(data_dir) + else: + raise ValueError("data_dir and fileslist cannot be both empty") + + self.resolution = config.data.resolution + self.num_frames = config.data.num_frames + + self.mel_window_length = math.ceil(self.num_frames / 5 * 16) + + self.audio_sample_rate = config.data.audio_sample_rate + self.video_fps = config.data.video_fps + self.audio_samples_length = int( + config.data.audio_sample_rate // config.data.video_fps * config.data.num_frames + ) + self.image_processor = ImageProcessor(resolution=config.data.resolution, mask="half") + self.audio_mel_cache_dir = config.data.audio_mel_cache_dir + os.makedirs(self.audio_mel_cache_dir, exist_ok=True) + + def __len__(self): + return len(self.video_paths) + + def read_audio(self, video_path: str): + ar = AudioReader(video_path, ctx=cpu(self.worker_id), sample_rate=self.audio_sample_rate) + original_mel = melspectrogram(ar[:].asnumpy().squeeze(0)) + return torch.from_numpy(original_mel) + + def crop_audio_window(self, original_mel, start_index): + start_idx = int(80.0 * (start_index / float(self.video_fps))) + end_idx = start_idx + self.mel_window_length + return original_mel[:, start_idx:end_idx].unsqueeze(0) + + def get_frames(self, video_reader: VideoReader): + total_num_frames = len(video_reader) + + start_idx = random.randint(0, total_num_frames - self.num_frames) + frames_index = np.arange(start_idx, start_idx + self.num_frames, dtype=int) + + while True: + wrong_start_idx = random.randint(0, total_num_frames - self.num_frames) + # wrong_start_idx = random.randint( + # max(0, start_idx - 25), min(total_num_frames - self.num_frames, start_idx + 25) + # ) + if wrong_start_idx == start_idx: + continue + # if wrong_start_idx >= start_idx - self.num_frames and wrong_start_idx <= start_idx + self.num_frames: + # continue + wrong_frames_index = np.arange(wrong_start_idx, wrong_start_idx + self.num_frames, dtype=int) + break + + frames = video_reader.get_batch(frames_index).asnumpy() + wrong_frames = video_reader.get_batch(wrong_frames_index).asnumpy() + + return frames, wrong_frames, start_idx + + def worker_init_fn(self, worker_id): + # Initialize the face mesh object in each worker process, + # because the face mesh object cannot be called in subprocesses + self.worker_id = worker_id + # setattr(self, f"image_processor_{worker_id}", ImageProcessor(self.resolution, self.mask)) + + def __getitem__(self, idx): + # image_processor = getattr(self, f"image_processor_{self.worker_id}") + while True: + try: + idx = random.randint(0, len(self) - 1) + + # Get video file path + video_path = self.video_paths[idx] + + vr = VideoReader(video_path, ctx=cpu(self.worker_id)) + + if len(vr) < 2 * self.num_frames: + continue + + frames, wrong_frames, start_idx = self.get_frames(vr) + + mel_cache_path = os.path.join( + self.audio_mel_cache_dir, os.path.basename(video_path).replace(".mp4", "_mel.pt") + ) + + if os.path.isfile(mel_cache_path): + try: + original_mel = torch.load(mel_cache_path) + except Exception as e: + print(f"{type(e).__name__} - {e} - {mel_cache_path}") + os.remove(mel_cache_path) + original_mel = self.read_audio(video_path) + torch.save(original_mel, mel_cache_path) + else: + original_mel = self.read_audio(video_path) + torch.save(original_mel, mel_cache_path) + + mel = self.crop_audio_window(original_mel, start_idx) + + if mel.shape[-1] != self.mel_window_length: + continue + + if random.choice([True, False]): + y = torch.ones(1).float() + chosen_frames = frames + else: + y = torch.zeros(1).float() + chosen_frames = wrong_frames + + chosen_frames = self.image_processor.process_images(chosen_frames) + # chosen_frames, _, _ = image_processor.prepare_masks_and_masked_images( + # chosen_frames, affine_transform=True + # ) + + vr.seek(0) # avoid memory leak + break + + except Exception as e: # Handle the exception of face not detcted + print(f"{type(e).__name__} - {e} - {video_path}") + if "vr" in locals(): + vr.seek(0) # avoid memory leak + + sample = dict(frames=chosen_frames, audio_samples=mel, y=y) + + return sample diff --git a/latentsync/data/unet_dataset.py b/latentsync/data/unet_dataset.py new file mode 100644 index 0000000000000000000000000000000000000000..6ed3b95ac050a8f2b4fe15ac760fa63d9e4015bc --- /dev/null +++ b/latentsync/data/unet_dataset.py @@ -0,0 +1,164 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import numpy as np +from torch.utils.data import Dataset +import torch +import random +import cv2 +from ..utils.image_processor import ImageProcessor, load_fixed_mask +from ..utils.audio import melspectrogram +from decord import AudioReader, VideoReader, cpu + + +class UNetDataset(Dataset): + def __init__(self, train_data_dir: str, config): + if config.data.train_fileslist != "": + with open(config.data.train_fileslist) as file: + self.video_paths = [line.rstrip() for line in file] + elif train_data_dir != "": + self.video_paths = [] + for file in os.listdir(train_data_dir): + if file.endswith(".mp4"): + self.video_paths.append(os.path.join(train_data_dir, file)) + else: + raise ValueError("data_dir and fileslist cannot be both empty") + + self.resolution = config.data.resolution + self.num_frames = config.data.num_frames + + if self.num_frames == 16: + self.mel_window_length = 52 + elif self.num_frames == 5: + self.mel_window_length = 16 + else: + raise NotImplementedError("Only support 16 and 5 frames now") + + self.audio_sample_rate = config.data.audio_sample_rate + self.video_fps = config.data.video_fps + self.mask = config.data.mask + self.mask_image = load_fixed_mask(self.resolution) + self.load_audio_data = config.model.add_audio_layer and config.run.use_syncnet + self.audio_mel_cache_dir = config.data.audio_mel_cache_dir + os.makedirs(self.audio_mel_cache_dir, exist_ok=True) + + def __len__(self): + return len(self.video_paths) + + def read_audio(self, video_path: str): + ar = AudioReader(video_path, ctx=cpu(self.worker_id), sample_rate=self.audio_sample_rate) + original_mel = melspectrogram(ar[:].asnumpy().squeeze(0)) + return torch.from_numpy(original_mel) + + def crop_audio_window(self, original_mel, start_index): + start_idx = int(80.0 * (start_index / float(self.video_fps))) + end_idx = start_idx + self.mel_window_length + return original_mel[:, start_idx:end_idx].unsqueeze(0) + + def get_frames(self, video_reader: VideoReader): + total_num_frames = len(video_reader) + + start_idx = random.randint(self.num_frames // 2, total_num_frames - self.num_frames - self.num_frames // 2) + frames_index = np.arange(start_idx, start_idx + self.num_frames, dtype=int) + + while True: + wrong_start_idx = random.randint(0, total_num_frames - self.num_frames) + if wrong_start_idx > start_idx - self.num_frames and wrong_start_idx < start_idx + self.num_frames: + continue + wrong_frames_index = np.arange(wrong_start_idx, wrong_start_idx + self.num_frames, dtype=int) + break + + frames = video_reader.get_batch(frames_index).asnumpy() + wrong_frames = video_reader.get_batch(wrong_frames_index).asnumpy() + + return frames, wrong_frames, start_idx + + def worker_init_fn(self, worker_id): + # Initialize the face mesh object in each worker process, + # because the face mesh object cannot be called in subprocesses + self.worker_id = worker_id + setattr( + self, + f"image_processor_{worker_id}", + ImageProcessor(self.resolution, self.mask, mask_image=self.mask_image), + ) + + def __getitem__(self, idx): + image_processor = getattr(self, f"image_processor_{self.worker_id}") + while True: + try: + idx = random.randint(0, len(self) - 1) + + # Get video file path + video_path = self.video_paths[idx] + + vr = VideoReader(video_path, ctx=cpu(self.worker_id)) + + if len(vr) < 3 * self.num_frames: + continue + + continuous_frames, ref_frames, start_idx = self.get_frames(vr) + + if self.load_audio_data: + mel_cache_path = os.path.join( + self.audio_mel_cache_dir, os.path.basename(video_path).replace(".mp4", "_mel.pt") + ) + + if os.path.isfile(mel_cache_path): + try: + original_mel = torch.load(mel_cache_path) + except Exception as e: + print(f"{type(e).__name__} - {e} - {mel_cache_path}") + os.remove(mel_cache_path) + original_mel = self.read_audio(video_path) + torch.save(original_mel, mel_cache_path) + else: + original_mel = self.read_audio(video_path) + torch.save(original_mel, mel_cache_path) + + mel = self.crop_audio_window(original_mel, start_idx) + + if mel.shape[-1] != self.mel_window_length: + continue + else: + mel = [] + + gt, masked_gt, mask = image_processor.prepare_masks_and_masked_images( + continuous_frames, affine_transform=False + ) + + if self.mask == "fix_mask": + ref, _, _ = image_processor.prepare_masks_and_masked_images(ref_frames, affine_transform=False) + else: + ref = image_processor.process_images(ref_frames) + vr.seek(0) # avoid memory leak + break + + except Exception as e: # Handle the exception of face not detcted + print(f"{type(e).__name__} - {e} - {video_path}") + if "vr" in locals(): + vr.seek(0) # avoid memory leak + + sample = dict( + gt=gt, + masked_gt=masked_gt, + ref=ref, + mel=mel, + mask=mask, + video_path=video_path, + start_idx=start_idx, + ) + + return sample diff --git a/latentsync/models/attention.py b/latentsync/models/attention.py new file mode 100644 index 0000000000000000000000000000000000000000..5b933823e91b8acb82e7424398aebdd3b3e76b72 --- /dev/null +++ b/latentsync/models/attention.py @@ -0,0 +1,492 @@ +# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention.py + +from dataclasses import dataclass +from turtle import forward +from typing import Optional + +import torch +import torch.nn.functional as F +from torch import nn + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.modeling_utils import ModelMixin +from diffusers.utils import BaseOutput +from diffusers.utils.import_utils import is_xformers_available +from diffusers.models.attention import CrossAttention, FeedForward, AdaLayerNorm + +from einops import rearrange, repeat +from .utils import zero_module + + +@dataclass +class Transformer3DModelOutput(BaseOutput): + sample: torch.FloatTensor + + +if is_xformers_available(): + import xformers + import xformers.ops +else: + xformers = None + + +class Transformer3DModel(ModelMixin, ConfigMixin): + @register_to_config + def __init__( + self, + num_attention_heads: int = 16, + attention_head_dim: int = 88, + in_channels: Optional[int] = None, + num_layers: int = 1, + dropout: float = 0.0, + norm_num_groups: int = 32, + cross_attention_dim: Optional[int] = None, + attention_bias: bool = False, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + use_linear_projection: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + use_motion_module: bool = False, + unet_use_cross_frame_attention=None, + unet_use_temporal_attention=None, + add_audio_layer=False, + audio_condition_method="cross_attn", + custom_audio_layer: bool = False, + ): + super().__init__() + self.use_linear_projection = use_linear_projection + self.num_attention_heads = num_attention_heads + self.attention_head_dim = attention_head_dim + inner_dim = num_attention_heads * attention_head_dim + + # Define input layers + self.in_channels = in_channels + + self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True) + if use_linear_projection: + self.proj_in = nn.Linear(in_channels, inner_dim) + else: + self.proj_in = nn.Conv2d(in_channels, inner_dim, kernel_size=1, stride=1, padding=0) + + if not custom_audio_layer: + # Define transformers blocks + self.transformer_blocks = nn.ModuleList( + [ + BasicTransformerBlock( + inner_dim, + num_attention_heads, + attention_head_dim, + dropout=dropout, + cross_attention_dim=cross_attention_dim, + activation_fn=activation_fn, + num_embeds_ada_norm=num_embeds_ada_norm, + attention_bias=attention_bias, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + use_motion_module=use_motion_module, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + add_audio_layer=add_audio_layer, + custom_audio_layer=custom_audio_layer, + audio_condition_method=audio_condition_method, + ) + for d in range(num_layers) + ] + ) + else: + self.transformer_blocks = nn.ModuleList( + [ + AudioTransformerBlock( + inner_dim, + num_attention_heads, + attention_head_dim, + dropout=dropout, + cross_attention_dim=cross_attention_dim, + activation_fn=activation_fn, + num_embeds_ada_norm=num_embeds_ada_norm, + attention_bias=attention_bias, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + use_motion_module=use_motion_module, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + add_audio_layer=add_audio_layer, + ) + for d in range(num_layers) + ] + ) + + # 4. Define output layers + if use_linear_projection: + self.proj_out = nn.Linear(in_channels, inner_dim) + else: + self.proj_out = nn.Conv2d(inner_dim, in_channels, kernel_size=1, stride=1, padding=0) + + if custom_audio_layer: + self.proj_out = zero_module(self.proj_out) + + def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, return_dict: bool = True): + # Input + assert hidden_states.dim() == 5, f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}." + video_length = hidden_states.shape[2] + hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w") + + # No need to do this for audio input, because different audio samples are independent + # encoder_hidden_states = repeat(encoder_hidden_states, 'b n c -> (b f) n c', f=video_length) + + batch, channel, height, weight = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + if not self.use_linear_projection: + hidden_states = self.proj_in(hidden_states) + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim) + else: + inner_dim = hidden_states.shape[1] + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, inner_dim) + hidden_states = self.proj_in(hidden_states) + + # Blocks + for block in self.transformer_blocks: + hidden_states = block( + hidden_states, + encoder_hidden_states=encoder_hidden_states, + timestep=timestep, + video_length=video_length, + ) + + # Output + if not self.use_linear_projection: + hidden_states = hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous() + hidden_states = self.proj_out(hidden_states) + else: + hidden_states = self.proj_out(hidden_states) + hidden_states = hidden_states.reshape(batch, height, weight, inner_dim).permute(0, 3, 1, 2).contiguous() + + output = hidden_states + residual + + output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length) + if not return_dict: + return (output,) + + return Transformer3DModelOutput(sample=output) + + +class BasicTransformerBlock(nn.Module): + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + dropout=0.0, + cross_attention_dim: Optional[int] = None, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + attention_bias: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + use_motion_module: bool = False, + unet_use_cross_frame_attention=None, + unet_use_temporal_attention=None, + add_audio_layer=False, + custom_audio_layer=False, + audio_condition_method="cross_attn", + ): + super().__init__() + self.only_cross_attention = only_cross_attention + self.use_ada_layer_norm = num_embeds_ada_norm is not None + self.unet_use_cross_frame_attention = unet_use_cross_frame_attention + self.unet_use_temporal_attention = unet_use_temporal_attention + self.use_motion_module = use_motion_module + self.add_audio_layer = add_audio_layer + + # SC-Attn + assert unet_use_cross_frame_attention is not None + if unet_use_cross_frame_attention: + raise NotImplementedError("SparseCausalAttention2D not implemented yet.") + else: + self.attn1 = CrossAttention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + ) + self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim) + + # Cross-Attn + if add_audio_layer and audio_condition_method == "cross_attn" and not custom_audio_layer: + self.audio_cross_attn = AudioCrossAttn( + dim=dim, + cross_attention_dim=cross_attention_dim, + num_attention_heads=num_attention_heads, + attention_head_dim=attention_head_dim, + dropout=dropout, + attention_bias=attention_bias, + upcast_attention=upcast_attention, + num_embeds_ada_norm=num_embeds_ada_norm, + use_ada_layer_norm=self.use_ada_layer_norm, + zero_proj_out=False, + ) + else: + self.audio_cross_attn = None + + # Feed-forward + self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn) + self.norm3 = nn.LayerNorm(dim) + + # Temp-Attn + assert unet_use_temporal_attention is not None + if unet_use_temporal_attention: + self.attn_temp = CrossAttention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + ) + nn.init.zeros_(self.attn_temp.to_out[0].weight.data) + self.norm_temp = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim) + + def set_use_memory_efficient_attention_xformers(self, use_memory_efficient_attention_xformers: bool): + if not is_xformers_available(): + print("Here is how to install it") + raise ModuleNotFoundError( + "Refer to https://github.com/facebookresearch/xformers for more information on how to install" + " xformers", + name="xformers", + ) + elif not torch.cuda.is_available(): + raise ValueError( + "torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is only" + " available for GPU " + ) + else: + try: + # Make sure we can run the memory efficient attention + _ = xformers.ops.memory_efficient_attention( + torch.randn((1, 2, 40), device="cuda"), + torch.randn((1, 2, 40), device="cuda"), + torch.randn((1, 2, 40), device="cuda"), + ) + except Exception as e: + raise e + self.attn1._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers + if self.audio_cross_attn is not None: + self.audio_cross_attn.attn._use_memory_efficient_attention_xformers = ( + use_memory_efficient_attention_xformers + ) + # self.attn_temp._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers + + def forward( + self, hidden_states, encoder_hidden_states=None, timestep=None, attention_mask=None, video_length=None + ): + # SparseCausal-Attention + norm_hidden_states = ( + self.norm1(hidden_states, timestep) if self.use_ada_layer_norm else self.norm1(hidden_states) + ) + + # if self.only_cross_attention: + # hidden_states = ( + # self.attn1(norm_hidden_states, encoder_hidden_states, attention_mask=attention_mask) + hidden_states + # ) + # else: + # hidden_states = self.attn1(norm_hidden_states, attention_mask=attention_mask, video_length=video_length) + hidden_states + + # pdb.set_trace() + if self.unet_use_cross_frame_attention: + hidden_states = ( + self.attn1(norm_hidden_states, attention_mask=attention_mask, video_length=video_length) + + hidden_states + ) + else: + hidden_states = self.attn1(norm_hidden_states, attention_mask=attention_mask) + hidden_states + + if self.audio_cross_attn is not None and encoder_hidden_states is not None: + hidden_states = self.audio_cross_attn( + hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask + ) + + # Feed-forward + hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states + + # Temporal-Attention + if self.unet_use_temporal_attention: + d = hidden_states.shape[1] + hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=video_length) + norm_hidden_states = ( + self.norm_temp(hidden_states, timestep) if self.use_ada_layer_norm else self.norm_temp(hidden_states) + ) + hidden_states = self.attn_temp(norm_hidden_states) + hidden_states + hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d) + + return hidden_states + + +class AudioTransformerBlock(nn.Module): + def __init__( + self, + dim: int, + num_attention_heads: int, + attention_head_dim: int, + dropout=0.0, + cross_attention_dim: Optional[int] = None, + activation_fn: str = "geglu", + num_embeds_ada_norm: Optional[int] = None, + attention_bias: bool = False, + only_cross_attention: bool = False, + upcast_attention: bool = False, + use_motion_module: bool = False, + unet_use_cross_frame_attention=None, + unet_use_temporal_attention=None, + add_audio_layer=False, + ): + super().__init__() + self.only_cross_attention = only_cross_attention + self.use_ada_layer_norm = num_embeds_ada_norm is not None + self.unet_use_cross_frame_attention = unet_use_cross_frame_attention + self.unet_use_temporal_attention = unet_use_temporal_attention + self.use_motion_module = use_motion_module + self.add_audio_layer = add_audio_layer + + # SC-Attn + assert unet_use_cross_frame_attention is not None + if unet_use_cross_frame_attention: + raise NotImplementedError("SparseCausalAttention2D not implemented yet.") + else: + self.attn1 = CrossAttention( + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + ) + self.norm1 = AdaLayerNorm(dim, num_embeds_ada_norm) if self.use_ada_layer_norm else nn.LayerNorm(dim) + + self.audio_cross_attn = AudioCrossAttn( + dim=dim, + cross_attention_dim=cross_attention_dim, + num_attention_heads=num_attention_heads, + attention_head_dim=attention_head_dim, + dropout=dropout, + attention_bias=attention_bias, + upcast_attention=upcast_attention, + num_embeds_ada_norm=num_embeds_ada_norm, + use_ada_layer_norm=self.use_ada_layer_norm, + zero_proj_out=False, + ) + + # Feed-forward + self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn) + self.norm3 = nn.LayerNorm(dim) + + def set_use_memory_efficient_attention_xformers(self, use_memory_efficient_attention_xformers: bool): + if not is_xformers_available(): + print("Here is how to install it") + raise ModuleNotFoundError( + "Refer to https://github.com/facebookresearch/xformers for more information on how to install" + " xformers", + name="xformers", + ) + elif not torch.cuda.is_available(): + raise ValueError( + "torch.cuda.is_available() should be True but is False. xformers' memory efficient attention is only" + " available for GPU " + ) + else: + try: + # Make sure we can run the memory efficient attention + _ = xformers.ops.memory_efficient_attention( + torch.randn((1, 2, 40), device="cuda"), + torch.randn((1, 2, 40), device="cuda"), + torch.randn((1, 2, 40), device="cuda"), + ) + except Exception as e: + raise e + self.attn1._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers + if self.audio_cross_attn is not None: + self.audio_cross_attn.attn._use_memory_efficient_attention_xformers = ( + use_memory_efficient_attention_xformers + ) + # self.attn_temp._use_memory_efficient_attention_xformers = use_memory_efficient_attention_xformers + + def forward( + self, hidden_states, encoder_hidden_states=None, timestep=None, attention_mask=None, video_length=None + ): + # SparseCausal-Attention + norm_hidden_states = ( + self.norm1(hidden_states, timestep) if self.use_ada_layer_norm else self.norm1(hidden_states) + ) + + # pdb.set_trace() + if self.unet_use_cross_frame_attention: + hidden_states = ( + self.attn1(norm_hidden_states, attention_mask=attention_mask, video_length=video_length) + + hidden_states + ) + else: + hidden_states = self.attn1(norm_hidden_states, attention_mask=attention_mask) + hidden_states + + if self.audio_cross_attn is not None and encoder_hidden_states is not None: + hidden_states = self.audio_cross_attn( + hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask + ) + + # Feed-forward + hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states + + return hidden_states + + +class AudioCrossAttn(nn.Module): + def __init__( + self, + dim, + cross_attention_dim, + num_attention_heads, + attention_head_dim, + dropout, + attention_bias, + upcast_attention, + num_embeds_ada_norm, + use_ada_layer_norm, + zero_proj_out=False, + ): + super().__init__() + + self.norm = AdaLayerNorm(dim, num_embeds_ada_norm) if use_ada_layer_norm else nn.LayerNorm(dim) + self.attn = CrossAttention( + query_dim=dim, + cross_attention_dim=cross_attention_dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + ) + + if zero_proj_out: + self.proj_out = zero_module(nn.Linear(dim, dim)) + + self.zero_proj_out = zero_proj_out + self.use_ada_layer_norm = use_ada_layer_norm + + def forward(self, hidden_states, encoder_hidden_states=None, timestep=None, attention_mask=None): + previous_hidden_states = hidden_states + hidden_states = self.norm(hidden_states, timestep) if self.use_ada_layer_norm else self.norm(hidden_states) + + if encoder_hidden_states.dim() == 4: + encoder_hidden_states = rearrange(encoder_hidden_states, "b f n d -> (b f) n d") + + hidden_states = self.attn( + hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask + ) + + if self.zero_proj_out: + hidden_states = self.proj_out(hidden_states) + return hidden_states + previous_hidden_states diff --git a/latentsync/models/motion_module.py b/latentsync/models/motion_module.py new file mode 100644 index 0000000000000000000000000000000000000000..0622c86463a7033be170d01096ee47127188ad9a --- /dev/null +++ b/latentsync/models/motion_module.py @@ -0,0 +1,332 @@ +# Adapted from https://github.com/guoyww/AnimateDiff/blob/main/animatediff/models/motion_module.py + +# Actually we don't use the motion module in the final version of LatentSync +# When we started the project, we used the codebase of AnimateDiff and tried motion module +# But the results are poor, and we decied to leave the code here for possible future usage + +from dataclasses import dataclass + +import torch +import torch.nn.functional as F +from torch import nn + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.modeling_utils import ModelMixin +from diffusers.utils import BaseOutput +from diffusers.utils.import_utils import is_xformers_available +from diffusers.models.attention import CrossAttention, FeedForward + +from einops import rearrange, repeat +import math +from .utils import zero_module + + +@dataclass +class TemporalTransformer3DModelOutput(BaseOutput): + sample: torch.FloatTensor + + +if is_xformers_available(): + import xformers + import xformers.ops +else: + xformers = None + + +def get_motion_module(in_channels, motion_module_type: str, motion_module_kwargs: dict): + if motion_module_type == "Vanilla": + return VanillaTemporalModule( + in_channels=in_channels, + **motion_module_kwargs, + ) + else: + raise ValueError + + +class VanillaTemporalModule(nn.Module): + def __init__( + self, + in_channels, + num_attention_heads=8, + num_transformer_block=2, + attention_block_types=("Temporal_Self", "Temporal_Self"), + cross_frame_attention_mode=None, + temporal_position_encoding=False, + temporal_position_encoding_max_len=24, + temporal_attention_dim_div=1, + zero_initialize=True, + ): + super().__init__() + + self.temporal_transformer = TemporalTransformer3DModel( + in_channels=in_channels, + num_attention_heads=num_attention_heads, + attention_head_dim=in_channels // num_attention_heads // temporal_attention_dim_div, + num_layers=num_transformer_block, + attention_block_types=attention_block_types, + cross_frame_attention_mode=cross_frame_attention_mode, + temporal_position_encoding=temporal_position_encoding, + temporal_position_encoding_max_len=temporal_position_encoding_max_len, + ) + + if zero_initialize: + self.temporal_transformer.proj_out = zero_module(self.temporal_transformer.proj_out) + + def forward(self, input_tensor, temb, encoder_hidden_states, attention_mask=None, anchor_frame_idx=None): + hidden_states = input_tensor + hidden_states = self.temporal_transformer(hidden_states, encoder_hidden_states, attention_mask) + + output = hidden_states + return output + + +class TemporalTransformer3DModel(nn.Module): + def __init__( + self, + in_channels, + num_attention_heads, + attention_head_dim, + num_layers, + attention_block_types=( + "Temporal_Self", + "Temporal_Self", + ), + dropout=0.0, + norm_num_groups=32, + cross_attention_dim=768, + activation_fn="geglu", + attention_bias=False, + upcast_attention=False, + cross_frame_attention_mode=None, + temporal_position_encoding=False, + temporal_position_encoding_max_len=24, + ): + super().__init__() + + inner_dim = num_attention_heads * attention_head_dim + + self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True) + self.proj_in = nn.Linear(in_channels, inner_dim) + + self.transformer_blocks = nn.ModuleList( + [ + TemporalTransformerBlock( + dim=inner_dim, + num_attention_heads=num_attention_heads, + attention_head_dim=attention_head_dim, + attention_block_types=attention_block_types, + dropout=dropout, + norm_num_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim, + activation_fn=activation_fn, + attention_bias=attention_bias, + upcast_attention=upcast_attention, + cross_frame_attention_mode=cross_frame_attention_mode, + temporal_position_encoding=temporal_position_encoding, + temporal_position_encoding_max_len=temporal_position_encoding_max_len, + ) + for d in range(num_layers) + ] + ) + self.proj_out = nn.Linear(inner_dim, in_channels) + + def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None): + assert hidden_states.dim() == 5, f"Expected hidden_states to have ndim=5, but got ndim={hidden_states.dim()}." + video_length = hidden_states.shape[2] + hidden_states = rearrange(hidden_states, "b c f h w -> (b f) c h w") + + batch, channel, height, weight = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm(hidden_states) + hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * weight, channel) + hidden_states = self.proj_in(hidden_states) + + # Transformer Blocks + for block in self.transformer_blocks: + hidden_states = block( + hidden_states, encoder_hidden_states=encoder_hidden_states, video_length=video_length + ) + + # output + hidden_states = self.proj_out(hidden_states) + hidden_states = hidden_states.reshape(batch, height, weight, channel).permute(0, 3, 1, 2).contiguous() + + output = hidden_states + residual + output = rearrange(output, "(b f) c h w -> b c f h w", f=video_length) + + return output + + +class TemporalTransformerBlock(nn.Module): + def __init__( + self, + dim, + num_attention_heads, + attention_head_dim, + attention_block_types=( + "Temporal_Self", + "Temporal_Self", + ), + dropout=0.0, + norm_num_groups=32, + cross_attention_dim=768, + activation_fn="geglu", + attention_bias=False, + upcast_attention=False, + cross_frame_attention_mode=None, + temporal_position_encoding=False, + temporal_position_encoding_max_len=24, + ): + super().__init__() + + attention_blocks = [] + norms = [] + + for block_name in attention_block_types: + attention_blocks.append( + VersatileAttention( + attention_mode=block_name.split("_")[0], + cross_attention_dim=cross_attention_dim if block_name.endswith("_Cross") else None, + query_dim=dim, + heads=num_attention_heads, + dim_head=attention_head_dim, + dropout=dropout, + bias=attention_bias, + upcast_attention=upcast_attention, + cross_frame_attention_mode=cross_frame_attention_mode, + temporal_position_encoding=temporal_position_encoding, + temporal_position_encoding_max_len=temporal_position_encoding_max_len, + ) + ) + norms.append(nn.LayerNorm(dim)) + + self.attention_blocks = nn.ModuleList(attention_blocks) + self.norms = nn.ModuleList(norms) + + self.ff = FeedForward(dim, dropout=dropout, activation_fn=activation_fn) + self.ff_norm = nn.LayerNorm(dim) + + def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, video_length=None): + for attention_block, norm in zip(self.attention_blocks, self.norms): + norm_hidden_states = norm(hidden_states) + hidden_states = ( + attention_block( + norm_hidden_states, + encoder_hidden_states=encoder_hidden_states if attention_block.is_cross_attention else None, + video_length=video_length, + ) + + hidden_states + ) + + hidden_states = self.ff(self.ff_norm(hidden_states)) + hidden_states + + output = hidden_states + return output + + +class PositionalEncoding(nn.Module): + def __init__(self, d_model, dropout=0.0, max_len=24): + super().__init__() + self.dropout = nn.Dropout(p=dropout) + position = torch.arange(max_len).unsqueeze(1) + div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model)) + pe = torch.zeros(1, max_len, d_model) + pe[0, :, 0::2] = torch.sin(position * div_term) + pe[0, :, 1::2] = torch.cos(position * div_term) + self.register_buffer("pe", pe) + + def forward(self, x): + x = x + self.pe[:, : x.size(1)] + return self.dropout(x) + + +class VersatileAttention(CrossAttention): + def __init__( + self, + attention_mode=None, + cross_frame_attention_mode=None, + temporal_position_encoding=False, + temporal_position_encoding_max_len=24, + *args, + **kwargs, + ): + super().__init__(*args, **kwargs) + assert attention_mode == "Temporal" + + self.attention_mode = attention_mode + self.is_cross_attention = kwargs["cross_attention_dim"] is not None + + self.pos_encoder = ( + PositionalEncoding(kwargs["query_dim"], dropout=0.0, max_len=temporal_position_encoding_max_len) + if (temporal_position_encoding and attention_mode == "Temporal") + else None + ) + + def extra_repr(self): + return f"(Module Info) Attention_Mode: {self.attention_mode}, Is_Cross_Attention: {self.is_cross_attention}" + + def forward(self, hidden_states, encoder_hidden_states=None, attention_mask=None, video_length=None): + batch_size, sequence_length, _ = hidden_states.shape + + if self.attention_mode == "Temporal": + d = hidden_states.shape[1] + hidden_states = rearrange(hidden_states, "(b f) d c -> (b d) f c", f=video_length) + + if self.pos_encoder is not None: + hidden_states = self.pos_encoder(hidden_states) + + encoder_hidden_states = ( + repeat(encoder_hidden_states, "b n c -> (b d) n c", d=d) + if encoder_hidden_states is not None + else encoder_hidden_states + ) + else: + raise NotImplementedError + + # encoder_hidden_states = encoder_hidden_states + + if self.group_norm is not None: + hidden_states = self.group_norm(hidden_states.transpose(1, 2)).transpose(1, 2) + + query = self.to_q(hidden_states) + dim = query.shape[-1] + query = self.reshape_heads_to_batch_dim(query) + + if self.added_kv_proj_dim is not None: + raise NotImplementedError + + encoder_hidden_states = encoder_hidden_states if encoder_hidden_states is not None else hidden_states + key = self.to_k(encoder_hidden_states) + value = self.to_v(encoder_hidden_states) + + key = self.reshape_heads_to_batch_dim(key) + value = self.reshape_heads_to_batch_dim(value) + + if attention_mask is not None: + if attention_mask.shape[-1] != query.shape[1]: + target_length = query.shape[1] + attention_mask = F.pad(attention_mask, (0, target_length), value=0.0) + attention_mask = attention_mask.repeat_interleave(self.heads, dim=0) + + # attention, what we cannot get enough of + if self._use_memory_efficient_attention_xformers: + hidden_states = self._memory_efficient_attention_xformers(query, key, value, attention_mask) + # Some versions of xformers return output in fp32, cast it back to the dtype of the input + hidden_states = hidden_states.to(query.dtype) + else: + if self._slice_size is None or query.shape[0] // self._slice_size == 1: + hidden_states = self._attention(query, key, value, attention_mask) + else: + hidden_states = self._sliced_attention(query, key, value, sequence_length, dim, attention_mask) + + # linear proj + hidden_states = self.to_out[0](hidden_states) + + # dropout + hidden_states = self.to_out[1](hidden_states) + + if self.attention_mode == "Temporal": + hidden_states = rearrange(hidden_states, "(b d) f c -> (b f) d c", d=d) + + return hidden_states diff --git a/latentsync/models/resnet.py b/latentsync/models/resnet.py new file mode 100644 index 0000000000000000000000000000000000000000..3654ae3510a69bfb06516162a574916ac5c17fe1 --- /dev/null +++ b/latentsync/models/resnet.py @@ -0,0 +1,234 @@ +# Adapted from https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/resnet.py + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from einops import rearrange + + +class InflatedConv3d(nn.Conv2d): + def forward(self, x): + video_length = x.shape[2] + + x = rearrange(x, "b c f h w -> (b f) c h w") + x = super().forward(x) + x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length) + + return x + + +class InflatedGroupNorm(nn.GroupNorm): + def forward(self, x): + video_length = x.shape[2] + + x = rearrange(x, "b c f h w -> (b f) c h w") + x = super().forward(x) + x = rearrange(x, "(b f) c h w -> b c f h w", f=video_length) + + return x + + +class Upsample3D(nn.Module): + def __init__(self, channels, use_conv=False, use_conv_transpose=False, out_channels=None, name="conv"): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.use_conv_transpose = use_conv_transpose + self.name = name + + conv = None + if use_conv_transpose: + raise NotImplementedError + elif use_conv: + self.conv = InflatedConv3d(self.channels, self.out_channels, 3, padding=1) + + def forward(self, hidden_states, output_size=None): + assert hidden_states.shape[1] == self.channels + + if self.use_conv_transpose: + raise NotImplementedError + + # Cast to float32 to as 'upsample_nearest2d_out_frame' op does not support bfloat16 + dtype = hidden_states.dtype + if dtype == torch.bfloat16: + hidden_states = hidden_states.to(torch.float32) + + # upsample_nearest_nhwc fails with large batch sizes. see https://github.com/huggingface/diffusers/issues/984 + if hidden_states.shape[0] >= 64: + hidden_states = hidden_states.contiguous() + + # if `output_size` is passed we force the interpolation output + # size and do not make use of `scale_factor=2` + if output_size is None: + hidden_states = F.interpolate(hidden_states, scale_factor=[1.0, 2.0, 2.0], mode="nearest") + else: + hidden_states = F.interpolate(hidden_states, size=output_size, mode="nearest") + + # If the input is bfloat16, we cast back to bfloat16 + if dtype == torch.bfloat16: + hidden_states = hidden_states.to(dtype) + + # if self.use_conv: + # if self.name == "conv": + # hidden_states = self.conv(hidden_states) + # else: + # hidden_states = self.Conv2d_0(hidden_states) + hidden_states = self.conv(hidden_states) + + return hidden_states + + +class Downsample3D(nn.Module): + def __init__(self, channels, use_conv=False, out_channels=None, padding=1, name="conv"): + super().__init__() + self.channels = channels + self.out_channels = out_channels or channels + self.use_conv = use_conv + self.padding = padding + stride = 2 + self.name = name + + if use_conv: + self.conv = InflatedConv3d(self.channels, self.out_channels, 3, stride=stride, padding=padding) + else: + raise NotImplementedError + + def forward(self, hidden_states): + assert hidden_states.shape[1] == self.channels + if self.use_conv and self.padding == 0: + raise NotImplementedError + + assert hidden_states.shape[1] == self.channels + hidden_states = self.conv(hidden_states) + + return hidden_states + + +class ResnetBlock3D(nn.Module): + def __init__( + self, + *, + in_channels, + out_channels=None, + conv_shortcut=False, + dropout=0.0, + temb_channels=512, + groups=32, + groups_out=None, + pre_norm=True, + eps=1e-6, + non_linearity="swish", + time_embedding_norm="default", + output_scale_factor=1.0, + use_in_shortcut=None, + use_inflated_groupnorm=False, + ): + super().__init__() + self.pre_norm = pre_norm + self.pre_norm = True + self.in_channels = in_channels + out_channels = in_channels if out_channels is None else out_channels + self.out_channels = out_channels + self.use_conv_shortcut = conv_shortcut + self.time_embedding_norm = time_embedding_norm + self.output_scale_factor = output_scale_factor + + if groups_out is None: + groups_out = groups + + assert use_inflated_groupnorm != None + if use_inflated_groupnorm: + self.norm1 = InflatedGroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True) + else: + self.norm1 = torch.nn.GroupNorm(num_groups=groups, num_channels=in_channels, eps=eps, affine=True) + + self.conv1 = InflatedConv3d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + + if temb_channels is not None: + time_emb_proj_out_channels = out_channels + # if self.time_embedding_norm == "default": + # time_emb_proj_out_channels = out_channels + # elif self.time_embedding_norm == "scale_shift": + # time_emb_proj_out_channels = out_channels * 2 + # else: + # raise ValueError(f"unknown time_embedding_norm : {self.time_embedding_norm} ") + + self.time_emb_proj = torch.nn.Linear(temb_channels, time_emb_proj_out_channels) + else: + self.time_emb_proj = None + + if self.time_embedding_norm == "scale_shift": + self.double_len_linear = torch.nn.Linear(time_emb_proj_out_channels, 2 * time_emb_proj_out_channels) + else: + self.double_len_linear = None + + if use_inflated_groupnorm: + self.norm2 = InflatedGroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True) + else: + self.norm2 = torch.nn.GroupNorm(num_groups=groups_out, num_channels=out_channels, eps=eps, affine=True) + + self.dropout = torch.nn.Dropout(dropout) + self.conv2 = InflatedConv3d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) + + if non_linearity == "swish": + self.nonlinearity = lambda x: F.silu(x) + elif non_linearity == "mish": + self.nonlinearity = Mish() + elif non_linearity == "silu": + self.nonlinearity = nn.SiLU() + + self.use_in_shortcut = self.in_channels != self.out_channels if use_in_shortcut is None else use_in_shortcut + + self.conv_shortcut = None + if self.use_in_shortcut: + self.conv_shortcut = InflatedConv3d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) + + def forward(self, input_tensor, temb): + hidden_states = input_tensor + + hidden_states = self.norm1(hidden_states) + hidden_states = self.nonlinearity(hidden_states) + + hidden_states = self.conv1(hidden_states) + + if temb is not None: + if temb.dim() == 2: + # input (1, 1280) + temb = self.time_emb_proj(self.nonlinearity(temb)) + temb = temb[:, :, None, None, None] # unsqueeze + else: + # input (1, 1280, 16) + temb = temb.permute(0, 2, 1) + temb = self.time_emb_proj(self.nonlinearity(temb)) + if self.double_len_linear is not None: + temb = self.double_len_linear(self.nonlinearity(temb)) + temb = temb.permute(0, 2, 1) + temb = temb[:, :, :, None, None] + + if temb is not None and self.time_embedding_norm == "default": + hidden_states = hidden_states + temb + + hidden_states = self.norm2(hidden_states) + + if temb is not None and self.time_embedding_norm == "scale_shift": + scale, shift = torch.chunk(temb, 2, dim=1) + hidden_states = hidden_states * (1 + scale) + shift + + hidden_states = self.nonlinearity(hidden_states) + + hidden_states = self.dropout(hidden_states) + hidden_states = self.conv2(hidden_states) + + if self.conv_shortcut is not None: + input_tensor = self.conv_shortcut(input_tensor) + + output_tensor = (input_tensor + hidden_states) / self.output_scale_factor + + return output_tensor + + +class Mish(torch.nn.Module): + def forward(self, hidden_states): + return hidden_states * torch.tanh(torch.nn.functional.softplus(hidden_states)) diff --git a/latentsync/models/syncnet.py b/latentsync/models/syncnet.py new file mode 100644 index 0000000000000000000000000000000000000000..ed130de60b44a24009a02340bc1be385d7b88ed5 --- /dev/null +++ b/latentsync/models/syncnet.py @@ -0,0 +1,233 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +from torch import nn +from einops import rearrange +from torch.nn import functional as F +from ..utils.util import cosine_loss + +import torch.nn as nn +import torch.nn.functional as F + +from diffusers.models.attention import CrossAttention, FeedForward +from diffusers.utils.import_utils import is_xformers_available +from einops import rearrange + + +class SyncNet(nn.Module): + def __init__(self, config): + super().__init__() + self.audio_encoder = DownEncoder2D( + in_channels=config["audio_encoder"]["in_channels"], + block_out_channels=config["audio_encoder"]["block_out_channels"], + downsample_factors=config["audio_encoder"]["downsample_factors"], + dropout=config["audio_encoder"]["dropout"], + attn_blocks=config["audio_encoder"]["attn_blocks"], + ) + + self.visual_encoder = DownEncoder2D( + in_channels=config["visual_encoder"]["in_channels"], + block_out_channels=config["visual_encoder"]["block_out_channels"], + downsample_factors=config["visual_encoder"]["downsample_factors"], + dropout=config["visual_encoder"]["dropout"], + attn_blocks=config["visual_encoder"]["attn_blocks"], + ) + + self.eval() + + def forward(self, image_sequences, audio_sequences): + vision_embeds = self.visual_encoder(image_sequences) # (b, c, 1, 1) + audio_embeds = self.audio_encoder(audio_sequences) # (b, c, 1, 1) + + vision_embeds = vision_embeds.reshape(vision_embeds.shape[0], -1) # (b, c) + audio_embeds = audio_embeds.reshape(audio_embeds.shape[0], -1) # (b, c) + + # Make them unit vectors + vision_embeds = F.normalize(vision_embeds, p=2, dim=1) + audio_embeds = F.normalize(audio_embeds, p=2, dim=1) + + return vision_embeds, audio_embeds + + +class ResnetBlock2D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + dropout: float = 0.0, + norm_num_groups: int = 32, + eps: float = 1e-6, + act_fn: str = "silu", + downsample_factor=2, + ): + super().__init__() + + self.norm1 = nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=eps, affine=True) + self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1) + + self.norm2 = nn.GroupNorm(num_groups=norm_num_groups, num_channels=out_channels, eps=eps, affine=True) + self.dropout = nn.Dropout(dropout) + self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1) + + if act_fn == "relu": + self.act_fn = nn.ReLU() + elif act_fn == "silu": + self.act_fn = nn.SiLU() + + if in_channels != out_channels: + self.conv_shortcut = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0) + else: + self.conv_shortcut = None + + if isinstance(downsample_factor, list): + downsample_factor = tuple(downsample_factor) + + if downsample_factor == 1: + self.downsample_conv = None + else: + self.downsample_conv = nn.Conv2d( + out_channels, out_channels, kernel_size=3, stride=downsample_factor, padding=0 + ) + self.pad = (0, 1, 0, 1) + if isinstance(downsample_factor, tuple): + if downsample_factor[0] == 1: + self.pad = (0, 1, 1, 1) # The padding order is from back to front + elif downsample_factor[1] == 1: + self.pad = (1, 1, 0, 1) + + def forward(self, input_tensor): + hidden_states = input_tensor + + hidden_states = self.norm1(hidden_states) + hidden_states = self.act_fn(hidden_states) + + hidden_states = self.conv1(hidden_states) + hidden_states = self.norm2(hidden_states) + hidden_states = self.act_fn(hidden_states) + + hidden_states = self.dropout(hidden_states) + hidden_states = self.conv2(hidden_states) + + if self.conv_shortcut is not None: + input_tensor = self.conv_shortcut(input_tensor) + + hidden_states += input_tensor + + if self.downsample_conv is not None: + hidden_states = F.pad(hidden_states, self.pad, mode="constant", value=0) + hidden_states = self.downsample_conv(hidden_states) + + return hidden_states + + +class AttentionBlock2D(nn.Module): + def __init__(self, query_dim, norm_num_groups=32, dropout=0.0): + super().__init__() + if not is_xformers_available(): + raise ModuleNotFoundError( + "You have to install xformers to enable memory efficient attetion", name="xformers" + ) + # inner_dim = dim_head * heads + self.norm1 = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=query_dim, eps=1e-6, affine=True) + self.norm2 = nn.LayerNorm(query_dim) + self.norm3 = nn.LayerNorm(query_dim) + + self.ff = FeedForward(query_dim, dropout=dropout, activation_fn="geglu") + + self.conv_in = nn.Conv2d(query_dim, query_dim, kernel_size=1, stride=1, padding=0) + self.conv_out = nn.Conv2d(query_dim, query_dim, kernel_size=1, stride=1, padding=0) + + self.attn = CrossAttention(query_dim=query_dim, heads=8, dim_head=query_dim // 8, dropout=dropout, bias=True) + self.attn._use_memory_efficient_attention_xformers = True + + def forward(self, hidden_states): + assert hidden_states.dim() == 4, f"Expected hidden_states to have ndim=4, but got ndim={hidden_states.dim()}." + + batch, channel, height, width = hidden_states.shape + residual = hidden_states + + hidden_states = self.norm1(hidden_states) + hidden_states = self.conv_in(hidden_states) + hidden_states = rearrange(hidden_states, "b c h w -> b (h w) c") + + norm_hidden_states = self.norm2(hidden_states) + hidden_states = self.attn(norm_hidden_states, attention_mask=None) + hidden_states + hidden_states = self.ff(self.norm3(hidden_states)) + hidden_states + + hidden_states = rearrange(hidden_states, "b (h w) c -> b c h w", h=height, w=width) + hidden_states = self.conv_out(hidden_states) + + hidden_states = hidden_states + residual + return hidden_states + + +class DownEncoder2D(nn.Module): + def __init__( + self, + in_channels=4 * 16, + block_out_channels=[64, 128, 256, 256], + downsample_factors=[2, 2, 2, 2], + layers_per_block=2, + norm_num_groups=32, + attn_blocks=[1, 1, 1, 1], + dropout: float = 0.0, + act_fn="silu", + ): + super().__init__() + self.layers_per_block = layers_per_block + + # in + self.conv_in = nn.Conv2d(in_channels, block_out_channels[0], kernel_size=3, stride=1, padding=1) + + # down + self.down_blocks = nn.ModuleList([]) + + output_channels = block_out_channels[0] + for i, block_out_channel in enumerate(block_out_channels): + input_channels = output_channels + output_channels = block_out_channel + # is_final_block = i == len(block_out_channels) - 1 + + down_block = ResnetBlock2D( + in_channels=input_channels, + out_channels=output_channels, + downsample_factor=downsample_factors[i], + norm_num_groups=norm_num_groups, + dropout=dropout, + act_fn=act_fn, + ) + + self.down_blocks.append(down_block) + + if attn_blocks[i] == 1: + attention_block = AttentionBlock2D(query_dim=output_channels, dropout=dropout) + self.down_blocks.append(attention_block) + + # out + self.norm_out = nn.GroupNorm(num_channels=block_out_channels[-1], num_groups=norm_num_groups, eps=1e-6) + self.act_fn_out = nn.ReLU() + + def forward(self, hidden_states): + hidden_states = self.conv_in(hidden_states) + + # down + for down_block in self.down_blocks: + hidden_states = down_block(hidden_states) + + # post-process + hidden_states = self.norm_out(hidden_states) + hidden_states = self.act_fn_out(hidden_states) + + return hidden_states diff --git a/latentsync/models/syncnet_wav2lip.py b/latentsync/models/syncnet_wav2lip.py new file mode 100644 index 0000000000000000000000000000000000000000..faddaba7b894c7f5e7c34abb316b2f99b6923a8f --- /dev/null +++ b/latentsync/models/syncnet_wav2lip.py @@ -0,0 +1,90 @@ +# Adapted from https://github.com/primepake/wav2lip_288x288/blob/master/models/syncnetv2.py +# The code here is for ablation study. + +from torch import nn +from torch.nn import functional as F + + +class SyncNetWav2Lip(nn.Module): + def __init__(self, act_fn="leaky"): + super().__init__() + + # input image sequences: (15, 128, 256) + self.visual_encoder = nn.Sequential( + Conv2d(15, 32, kernel_size=(7, 7), stride=1, padding=3, act_fn=act_fn), # (128, 256) + Conv2d(32, 64, kernel_size=5, stride=(1, 2), padding=1, act_fn=act_fn), # (126, 127) + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(64, 128, kernel_size=3, stride=2, padding=1, act_fn=act_fn), # (63, 64) + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(128, 256, kernel_size=3, stride=3, padding=1, act_fn=act_fn), # (21, 22) + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(256, 512, kernel_size=3, stride=2, padding=1, act_fn=act_fn), # (11, 11) + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(512, 1024, kernel_size=3, stride=2, padding=1, act_fn=act_fn), # (6, 6) + Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(1024, 1024, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(1024, 1024, kernel_size=3, stride=2, padding=1, act_fn="relu"), # (3, 3) + Conv2d(1024, 1024, kernel_size=3, stride=1, padding=0, act_fn="relu"), # (1, 1) + Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0, act_fn="relu"), + ) + + # input audio sequences: (1, 80, 16) + self.audio_encoder = nn.Sequential( + Conv2d(1, 32, kernel_size=3, stride=1, padding=1, act_fn=act_fn), + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(32, 32, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(32, 64, kernel_size=3, stride=(3, 1), padding=1, act_fn=act_fn), # (27, 16) + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(64, 64, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(64, 128, kernel_size=3, stride=3, padding=1, act_fn=act_fn), # (9, 6) + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(128, 128, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(128, 256, kernel_size=3, stride=(3, 2), padding=1, act_fn=act_fn), # (3, 3) + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(256, 256, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(256, 512, kernel_size=3, stride=1, padding=1, act_fn=act_fn), + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(512, 512, kernel_size=3, stride=1, padding=1, residual=True, act_fn=act_fn), + Conv2d(512, 1024, kernel_size=3, stride=1, padding=0, act_fn="relu"), # (1, 1) + Conv2d(1024, 1024, kernel_size=1, stride=1, padding=0, act_fn="relu"), + ) + + def forward(self, image_sequences, audio_sequences): + vision_embeds = self.visual_encoder(image_sequences) # (b, c, 1, 1) + audio_embeds = self.audio_encoder(audio_sequences) # (b, c, 1, 1) + + vision_embeds = vision_embeds.reshape(vision_embeds.shape[0], -1) # (b, c) + audio_embeds = audio_embeds.reshape(audio_embeds.shape[0], -1) # (b, c) + + # Make them unit vectors + vision_embeds = F.normalize(vision_embeds, p=2, dim=1) + audio_embeds = F.normalize(audio_embeds, p=2, dim=1) + + return vision_embeds, audio_embeds + + +class Conv2d(nn.Module): + def __init__(self, cin, cout, kernel_size, stride, padding, residual=False, act_fn="relu", *args, **kwargs): + super().__init__(*args, **kwargs) + self.conv_block = nn.Sequential(nn.Conv2d(cin, cout, kernel_size, stride, padding), nn.BatchNorm2d(cout)) + if act_fn == "relu": + self.act_fn = nn.ReLU() + elif act_fn == "tanh": + self.act_fn = nn.Tanh() + elif act_fn == "silu": + self.act_fn = nn.SiLU() + elif act_fn == "leaky": + self.act_fn = nn.LeakyReLU(0.2, inplace=True) + + self.residual = residual + + def forward(self, x): + out = self.conv_block(x) + if self.residual: + out += x + return self.act_fn(out) diff --git a/latentsync/models/unet.py b/latentsync/models/unet.py new file mode 100644 index 0000000000000000000000000000000000000000..1429cf37401988106c7e669d92594cbf9b479a1b --- /dev/null +++ b/latentsync/models/unet.py @@ -0,0 +1,528 @@ +# Adapted from https://github.com/guoyww/AnimateDiff/blob/main/animatediff/models/unet.py + +from dataclasses import dataclass +from typing import List, Optional, Tuple, Union +import copy + +import torch +import torch.nn as nn +import torch.utils.checkpoint + +from diffusers.configuration_utils import ConfigMixin, register_to_config +from diffusers.modeling_utils import ModelMixin +from diffusers import UNet2DConditionModel +from diffusers.utils import BaseOutput, logging +from diffusers.models.embeddings import TimestepEmbedding, Timesteps +from .unet_blocks import ( + CrossAttnDownBlock3D, + CrossAttnUpBlock3D, + DownBlock3D, + UNetMidBlock3DCrossAttn, + UpBlock3D, + get_down_block, + get_up_block, +) +from .resnet import InflatedConv3d, InflatedGroupNorm + +from ..utils.util import zero_rank_log +from einops import rearrange +from .utils import zero_module + + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +@dataclass +class UNet3DConditionOutput(BaseOutput): + sample: torch.FloatTensor + + +class UNet3DConditionModel(ModelMixin, ConfigMixin): + _supports_gradient_checkpointing = True + + @register_to_config + def __init__( + self, + sample_size: Optional[int] = None, + in_channels: int = 4, + out_channels: int = 4, + center_input_sample: bool = False, + flip_sin_to_cos: bool = True, + freq_shift: int = 0, + down_block_types: Tuple[str] = ( + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "CrossAttnDownBlock3D", + "DownBlock3D", + ), + mid_block_type: str = "UNetMidBlock3DCrossAttn", + up_block_types: Tuple[str] = ("UpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D", "CrossAttnUpBlock3D"), + only_cross_attention: Union[bool, Tuple[bool]] = False, + block_out_channels: Tuple[int] = (320, 640, 1280, 1280), + layers_per_block: int = 2, + downsample_padding: int = 1, + mid_block_scale_factor: float = 1, + act_fn: str = "silu", + norm_num_groups: int = 32, + norm_eps: float = 1e-5, + cross_attention_dim: int = 1280, + attention_head_dim: Union[int, Tuple[int]] = 8, + dual_cross_attention: bool = False, + use_linear_projection: bool = False, + class_embed_type: Optional[str] = None, + num_class_embeds: Optional[int] = None, + upcast_attention: bool = False, + resnet_time_scale_shift: str = "default", + use_inflated_groupnorm=False, + # Additional + use_motion_module=False, + motion_module_resolutions=(1, 2, 4, 8), + motion_module_mid_block=False, + motion_module_decoder_only=False, + motion_module_type=None, + motion_module_kwargs={}, + unet_use_cross_frame_attention=False, + unet_use_temporal_attention=False, + add_audio_layer=False, + audio_condition_method: str = "cross_attn", + custom_audio_layer=False, + ): + super().__init__() + + self.sample_size = sample_size + time_embed_dim = block_out_channels[0] * 4 + self.use_motion_module = use_motion_module + self.add_audio_layer = add_audio_layer + + self.conv_in = zero_module(InflatedConv3d(in_channels, block_out_channels[0], kernel_size=3, padding=(1, 1))) + + # time + self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) + timestep_input_dim = block_out_channels[0] + + self.time_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) + + # class embedding + if class_embed_type is None and num_class_embeds is not None: + self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) + elif class_embed_type == "timestep": + self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) + elif class_embed_type == "identity": + self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) + else: + self.class_embedding = None + + self.down_blocks = nn.ModuleList([]) + self.mid_block = None + self.up_blocks = nn.ModuleList([]) + + if isinstance(only_cross_attention, bool): + only_cross_attention = [only_cross_attention] * len(down_block_types) + + if isinstance(attention_head_dim, int): + attention_head_dim = (attention_head_dim,) * len(down_block_types) + + # down + output_channel = block_out_channels[0] + for i, down_block_type in enumerate(down_block_types): + res = 2**i + input_channel = output_channel + output_channel = block_out_channels[i] + is_final_block = i == len(block_out_channels) - 1 + + down_block = get_down_block( + down_block_type, + num_layers=layers_per_block, + in_channels=input_channel, + out_channels=output_channel, + temb_channels=time_embed_dim, + add_downsample=not is_final_block, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=attention_head_dim[i], + downsample_padding=downsample_padding, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module + and (res in motion_module_resolutions) + and (not motion_module_decoder_only), + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + add_audio_layer=add_audio_layer, + audio_condition_method=audio_condition_method, + custom_audio_layer=custom_audio_layer, + ) + self.down_blocks.append(down_block) + + # mid + if mid_block_type == "UNetMidBlock3DCrossAttn": + self.mid_block = UNetMidBlock3DCrossAttn( + in_channels=block_out_channels[-1], + temb_channels=time_embed_dim, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + output_scale_factor=mid_block_scale_factor, + resnet_time_scale_shift=resnet_time_scale_shift, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=attention_head_dim[-1], + resnet_groups=norm_num_groups, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module and motion_module_mid_block, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + add_audio_layer=add_audio_layer, + audio_condition_method=audio_condition_method, + custom_audio_layer=custom_audio_layer, + ) + else: + raise ValueError(f"unknown mid_block_type : {mid_block_type}") + + # count how many layers upsample the videos + self.num_upsamplers = 0 + + # up + reversed_block_out_channels = list(reversed(block_out_channels)) + reversed_attention_head_dim = list(reversed(attention_head_dim)) + only_cross_attention = list(reversed(only_cross_attention)) + output_channel = reversed_block_out_channels[0] + for i, up_block_type in enumerate(up_block_types): + res = 2 ** (3 - i) + is_final_block = i == len(block_out_channels) - 1 + + prev_output_channel = output_channel + output_channel = reversed_block_out_channels[i] + input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] + + # add upsample block for all BUT final layer + if not is_final_block: + add_upsample = True + self.num_upsamplers += 1 + else: + add_upsample = False + + up_block = get_up_block( + up_block_type, + num_layers=layers_per_block + 1, + in_channels=input_channel, + out_channels=output_channel, + prev_output_channel=prev_output_channel, + temb_channels=time_embed_dim, + add_upsample=add_upsample, + resnet_eps=norm_eps, + resnet_act_fn=act_fn, + resnet_groups=norm_num_groups, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=reversed_attention_head_dim[i], + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention[i], + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module and (res in motion_module_resolutions), + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + add_audio_layer=add_audio_layer, + audio_condition_method=audio_condition_method, + custom_audio_layer=custom_audio_layer, + ) + self.up_blocks.append(up_block) + prev_output_channel = output_channel + + # out + if use_inflated_groupnorm: + self.conv_norm_out = InflatedGroupNorm( + num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps + ) + else: + self.conv_norm_out = nn.GroupNorm( + num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps + ) + self.conv_act = nn.SiLU() + + self.conv_out = zero_module(InflatedConv3d(block_out_channels[0], out_channels, kernel_size=3, padding=1)) + + def set_attention_slice(self, slice_size): + r""" + Enable sliced attention computation. + + When this option is enabled, the attention module will split the input tensor in slices, to compute attention + in several steps. This is useful to save some memory in exchange for a small speed decrease. + + Args: + slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): + When `"auto"`, halves the input to the attention heads, so attention will be computed in two steps. If + `"max"`, maxium amount of memory will be saved by running only one slice at a time. If a number is + provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` + must be a multiple of `slice_size`. + """ + sliceable_head_dims = [] + + def fn_recursive_retrieve_slicable_dims(module: torch.nn.Module): + if hasattr(module, "set_attention_slice"): + sliceable_head_dims.append(module.sliceable_head_dim) + + for child in module.children(): + fn_recursive_retrieve_slicable_dims(child) + + # retrieve number of attention layers + for module in self.children(): + fn_recursive_retrieve_slicable_dims(module) + + num_slicable_layers = len(sliceable_head_dims) + + if slice_size == "auto": + # half the attention head size is usually a good trade-off between + # speed and memory + slice_size = [dim // 2 for dim in sliceable_head_dims] + elif slice_size == "max": + # make smallest slice possible + slice_size = num_slicable_layers * [1] + + slice_size = num_slicable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size + + if len(slice_size) != len(sliceable_head_dims): + raise ValueError( + f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" + f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." + ) + + for i in range(len(slice_size)): + size = slice_size[i] + dim = sliceable_head_dims[i] + if size is not None and size > dim: + raise ValueError(f"size {size} has to be smaller or equal to {dim}.") + + # Recursively walk through all the children. + # Any children which exposes the set_attention_slice method + # gets the message + def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]): + if hasattr(module, "set_attention_slice"): + module.set_attention_slice(slice_size.pop()) + + for child in module.children(): + fn_recursive_set_attention_slice(child, slice_size) + + reversed_slice_size = list(reversed(slice_size)) + for module in self.children(): + fn_recursive_set_attention_slice(module, reversed_slice_size) + + def _set_gradient_checkpointing(self, module, value=False): + if isinstance(module, (CrossAttnDownBlock3D, DownBlock3D, CrossAttnUpBlock3D, UpBlock3D)): + module.gradient_checkpointing = value + + def forward( + self, + sample: torch.FloatTensor, + timestep: Union[torch.Tensor, float, int], + encoder_hidden_states: torch.Tensor, + class_labels: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + # support controlnet + down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, + mid_block_additional_residual: Optional[torch.Tensor] = None, + return_dict: bool = True, + ) -> Union[UNet3DConditionOutput, Tuple]: + r""" + Args: + sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor + timestep (`torch.FloatTensor` or `float` or `int`): (batch) timesteps + encoder_hidden_states (`torch.FloatTensor`): (batch, sequence_length, feature_dim) encoder hidden states + return_dict (`bool`, *optional*, defaults to `True`): + Whether or not to return a [`models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple. + + Returns: + [`~models.unet_2d_condition.UNet2DConditionOutput`] or `tuple`: + [`~models.unet_2d_condition.UNet2DConditionOutput`] if `return_dict` is True, otherwise a `tuple`. When + returning a tuple, the first element is the sample tensor. + """ + # By default samples have to be AT least a multiple of the overall upsampling factor. + # The overall upsampling factor is equal to 2 ** (# num of upsampling layears). + # However, the upsampling interpolation output size can be forced to fit any upsampling size + # on the fly if necessary. + default_overall_up_factor = 2**self.num_upsamplers + + # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` + forward_upsample_size = False + upsample_size = None + + if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]): + logger.info("Forward upsample size to force interpolation output size.") + forward_upsample_size = True + + # prepare attention_mask + if attention_mask is not None: + attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 + attention_mask = attention_mask.unsqueeze(1) + + # center input if necessary + if self.config.center_input_sample: + sample = 2 * sample - 1.0 + + # time + timesteps = timestep + if not torch.is_tensor(timesteps): + # This would be a good case for the `match` statement (Python 3.10+) + is_mps = sample.device.type == "mps" + if isinstance(timestep, float): + dtype = torch.float32 if is_mps else torch.float64 + else: + dtype = torch.int32 if is_mps else torch.int64 + timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) + elif len(timesteps.shape) == 0: + timesteps = timesteps[None].to(sample.device) + + # broadcast to batch dimension in a way that's compatible with ONNX/Core ML + timesteps = timesteps.expand(sample.shape[0]) + + t_emb = self.time_proj(timesteps) + + # timesteps does not contain any weights and will always return f32 tensors + # but time_embedding might actually be running in fp16. so we need to cast here. + # there might be better ways to encapsulate this. + t_emb = t_emb.to(dtype=self.dtype) + emb = self.time_embedding(t_emb) + + if self.class_embedding is not None: + if class_labels is None: + raise ValueError("class_labels should be provided when num_class_embeds > 0") + + if self.config.class_embed_type == "timestep": + class_labels = self.time_proj(class_labels) + + class_emb = self.class_embedding(class_labels).to(dtype=self.dtype) + emb = emb + class_emb + + # pre-process + sample = self.conv_in(sample) + + # down + down_block_res_samples = (sample,) + for downsample_block in self.down_blocks: + if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention: + sample, res_samples = downsample_block( + hidden_states=sample, + temb=emb, + encoder_hidden_states=encoder_hidden_states, + attention_mask=attention_mask, + ) + else: + sample, res_samples = downsample_block( + hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states + ) + + down_block_res_samples += res_samples + + # support controlnet + down_block_res_samples = list(down_block_res_samples) + if down_block_additional_residuals is not None: + for i, down_block_additional_residual in enumerate(down_block_additional_residuals): + if down_block_additional_residual.dim() == 4: # boardcast + down_block_additional_residual = down_block_additional_residual.unsqueeze(2) + down_block_res_samples[i] = down_block_res_samples[i] + down_block_additional_residual + + # mid + sample = self.mid_block( + sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask + ) + + # support controlnet + if mid_block_additional_residual is not None: + if mid_block_additional_residual.dim() == 4: # boardcast + mid_block_additional_residual = mid_block_additional_residual.unsqueeze(2) + sample = sample + mid_block_additional_residual + + # up + for i, upsample_block in enumerate(self.up_blocks): + is_final_block = i == len(self.up_blocks) - 1 + + res_samples = down_block_res_samples[-len(upsample_block.resnets) :] + down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] + + # if we have not reached the final block and need to forward the + # upsample size, we do it here + if not is_final_block and forward_upsample_size: + upsample_size = down_block_res_samples[-1].shape[2:] + + if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention: + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + encoder_hidden_states=encoder_hidden_states, + upsample_size=upsample_size, + attention_mask=attention_mask, + ) + else: + sample = upsample_block( + hidden_states=sample, + temb=emb, + res_hidden_states_tuple=res_samples, + upsample_size=upsample_size, + encoder_hidden_states=encoder_hidden_states, + ) + + # post-process + sample = self.conv_norm_out(sample) + sample = self.conv_act(sample) + sample = self.conv_out(sample) + + if not return_dict: + return (sample,) + + return UNet3DConditionOutput(sample=sample) + + def load_state_dict(self, state_dict, strict=True): + # If the loaded checkpoint's in_channels or out_channels are different from config + temp_state_dict = copy.deepcopy(state_dict) + if temp_state_dict["conv_in.weight"].shape[1] != self.config.in_channels: + del temp_state_dict["conv_in.weight"] + del temp_state_dict["conv_in.bias"] + if temp_state_dict["conv_out.weight"].shape[0] != self.config.out_channels: + del temp_state_dict["conv_out.weight"] + del temp_state_dict["conv_out.bias"] + + # If the loaded checkpoint's cross_attention_dim is different from config + keys_to_remove = [] + for key in temp_state_dict: + if "audio_cross_attn.attn.to_k." in key or "audio_cross_attn.attn.to_v." in key: + if temp_state_dict[key].shape[1] != self.config.cross_attention_dim: + keys_to_remove.append(key) + + for key in keys_to_remove: + del temp_state_dict[key] + + return super().load_state_dict(state_dict=temp_state_dict, strict=strict) + + @classmethod + def from_pretrained(cls, model_config: dict, ckpt_path: str, device="cpu"): + unet = cls.from_config(model_config).to(device) + if ckpt_path != "": + zero_rank_log(logger, f"Load from checkpoint: {ckpt_path}") + ckpt = torch.load(ckpt_path, map_location=device) + if "global_step" in ckpt: + zero_rank_log(logger, f"resume from global_step: {ckpt['global_step']}") + resume_global_step = ckpt["global_step"] + else: + resume_global_step = 0 + state_dict = ckpt["state_dict"] if "state_dict" in ckpt else ckpt + unet.load_state_dict(state_dict, strict=False) + else: + resume_global_step = 0 + + return unet, resume_global_step diff --git a/latentsync/models/unet_blocks.py b/latentsync/models/unet_blocks.py new file mode 100644 index 0000000000000000000000000000000000000000..cc913a0cd7a7ed335d5e65d06af554dc018f216e --- /dev/null +++ b/latentsync/models/unet_blocks.py @@ -0,0 +1,903 @@ +# Adapted from https://github.com/guoyww/AnimateDiff/blob/main/animatediff/models/unet_blocks.py + +import torch +from torch import nn + +from .attention import Transformer3DModel +from .resnet import Downsample3D, ResnetBlock3D, Upsample3D +from .motion_module import get_motion_module + + +def get_down_block( + down_block_type, + num_layers, + in_channels, + out_channels, + temb_channels, + add_downsample, + resnet_eps, + resnet_act_fn, + attn_num_head_channels, + resnet_groups=None, + cross_attention_dim=None, + downsample_padding=None, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + resnet_time_scale_shift="default", + unet_use_cross_frame_attention=False, + unet_use_temporal_attention=False, + use_inflated_groupnorm=False, + use_motion_module=None, + motion_module_type=None, + motion_module_kwargs=None, + add_audio_layer=False, + audio_condition_method="cross_attn", + custom_audio_layer=False, +): + down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type + if down_block_type == "DownBlock3D": + return DownBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + resnet_time_scale_shift=resnet_time_scale_shift, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + elif down_block_type == "CrossAttnDownBlock3D": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock3D") + return CrossAttnDownBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + add_downsample=add_downsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + downsample_padding=downsample_padding, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=attn_num_head_channels, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + add_audio_layer=add_audio_layer, + audio_condition_method=audio_condition_method, + custom_audio_layer=custom_audio_layer, + ) + raise ValueError(f"{down_block_type} does not exist.") + + +def get_up_block( + up_block_type, + num_layers, + in_channels, + out_channels, + prev_output_channel, + temb_channels, + add_upsample, + resnet_eps, + resnet_act_fn, + attn_num_head_channels, + resnet_groups=None, + cross_attention_dim=None, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + resnet_time_scale_shift="default", + unet_use_cross_frame_attention=False, + unet_use_temporal_attention=False, + use_inflated_groupnorm=False, + use_motion_module=None, + motion_module_type=None, + motion_module_kwargs=None, + add_audio_layer=False, + audio_condition_method="cross_attn", + custom_audio_layer=False, +): + up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type + if up_block_type == "UpBlock3D": + return UpBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + resnet_time_scale_shift=resnet_time_scale_shift, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + elif up_block_type == "CrossAttnUpBlock3D": + if cross_attention_dim is None: + raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock3D") + return CrossAttnUpBlock3D( + num_layers=num_layers, + in_channels=in_channels, + out_channels=out_channels, + prev_output_channel=prev_output_channel, + temb_channels=temb_channels, + add_upsample=add_upsample, + resnet_eps=resnet_eps, + resnet_act_fn=resnet_act_fn, + resnet_groups=resnet_groups, + cross_attention_dim=cross_attention_dim, + attn_num_head_channels=attn_num_head_channels, + dual_cross_attention=dual_cross_attention, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + resnet_time_scale_shift=resnet_time_scale_shift, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + use_inflated_groupnorm=use_inflated_groupnorm, + use_motion_module=use_motion_module, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + add_audio_layer=add_audio_layer, + audio_condition_method=audio_condition_method, + custom_audio_layer=custom_audio_layer, + ) + raise ValueError(f"{up_block_type} does not exist.") + + +class UNetMidBlock3DCrossAttn(nn.Module): + def __init__( + self, + in_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + attn_num_head_channels=1, + output_scale_factor=1.0, + cross_attention_dim=1280, + dual_cross_attention=False, + use_linear_projection=False, + upcast_attention=False, + unet_use_cross_frame_attention=False, + unet_use_temporal_attention=False, + use_inflated_groupnorm=False, + use_motion_module=None, + motion_module_type=None, + motion_module_kwargs=None, + add_audio_layer=False, + audio_condition_method="cross_attn", + custom_audio_layer: bool = False, + ): + super().__init__() + + self.has_cross_attention = True + self.attn_num_head_channels = attn_num_head_channels + resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) + + # there is always at least one resnet + resnets = [ + ResnetBlock3D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ] + attentions = [] + audio_attentions = [] + motion_modules = [] + + for _ in range(num_layers): + if dual_cross_attention: + raise NotImplementedError + attentions.append( + Transformer3DModel( + attn_num_head_channels, + in_channels // attn_num_head_channels, + in_channels=in_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + use_motion_module=use_motion_module, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + add_audio_layer=add_audio_layer, + audio_condition_method=audio_condition_method, + ) + ) + audio_attentions.append( + Transformer3DModel( + attn_num_head_channels, + in_channels // attn_num_head_channels, + in_channels=in_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + upcast_attention=upcast_attention, + use_motion_module=use_motion_module, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + add_audio_layer=add_audio_layer, + audio_condition_method=audio_condition_method, + custom_audio_layer=True, + ) + if custom_audio_layer + else None + ) + motion_modules.append( + get_motion_module( + in_channels=in_channels, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + if use_motion_module + else None + ) + resnets.append( + ResnetBlock3D( + in_channels=in_channels, + out_channels=in_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ) + + self.attentions = nn.ModuleList(attentions) + self.audio_attentions = nn.ModuleList(audio_attentions) + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + + def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None): + hidden_states = self.resnets[0](hidden_states, temb) + for attn, audio_attn, resnet, motion_module in zip( + self.attentions, self.audio_attentions, self.resnets[1:], self.motion_modules + ): + hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample + hidden_states = ( + audio_attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample + if audio_attn is not None + else hidden_states + ) + hidden_states = ( + motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) + if motion_module is not None + else hidden_states + ) + hidden_states = resnet(hidden_states, temb) + + return hidden_states + + +class CrossAttnDownBlock3D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + attn_num_head_channels=1, + cross_attention_dim=1280, + output_scale_factor=1.0, + downsample_padding=1, + add_downsample=True, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + unet_use_cross_frame_attention=False, + unet_use_temporal_attention=False, + use_inflated_groupnorm=False, + use_motion_module=None, + motion_module_type=None, + motion_module_kwargs=None, + add_audio_layer=False, + audio_condition_method="cross_attn", + custom_audio_layer: bool = False, + ): + super().__init__() + resnets = [] + attentions = [] + audio_attentions = [] + motion_modules = [] + + self.has_cross_attention = True + self.attn_num_head_channels = attn_num_head_channels + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock3D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ) + if dual_cross_attention: + raise NotImplementedError + attentions.append( + Transformer3DModel( + attn_num_head_channels, + out_channels // attn_num_head_channels, + in_channels=out_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + use_motion_module=use_motion_module, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + add_audio_layer=add_audio_layer, + audio_condition_method=audio_condition_method, + ) + ) + audio_attentions.append( + Transformer3DModel( + attn_num_head_channels, + out_channels // attn_num_head_channels, + in_channels=out_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + use_motion_module=use_motion_module, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + add_audio_layer=add_audio_layer, + audio_condition_method=audio_condition_method, + custom_audio_layer=True, + ) + if custom_audio_layer + else None + ) + motion_modules.append( + get_motion_module( + in_channels=out_channels, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + if use_motion_module + else None + ) + + self.attentions = nn.ModuleList(attentions) + self.audio_attentions = nn.ModuleList(audio_attentions) + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample3D( + out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward(self, hidden_states, temb=None, encoder_hidden_states=None, attention_mask=None): + output_states = () + + for resnet, attn, audio_attn, motion_module in zip( + self.resnets, self.attentions, self.audio_attentions, self.motion_modules + ): + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(attn, return_dict=False), + hidden_states, + encoder_hidden_states, + )[0] + if motion_module is not None: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(motion_module), + hidden_states.requires_grad_(), + temb, + encoder_hidden_states, + ) + + else: + hidden_states = resnet(hidden_states, temb) + hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample + + hidden_states = ( + audio_attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample + if audio_attn is not None + else hidden_states + ) + + # add motion module + hidden_states = ( + motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) + if motion_module is not None + else hidden_states + ) + + output_states += (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + output_states += (hidden_states,) + + return hidden_states, output_states + + +class DownBlock3D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor=1.0, + add_downsample=True, + downsample_padding=1, + use_inflated_groupnorm=False, + use_motion_module=None, + motion_module_type=None, + motion_module_kwargs=None, + ): + super().__init__() + resnets = [] + motion_modules = [] + + for i in range(num_layers): + in_channels = in_channels if i == 0 else out_channels + resnets.append( + ResnetBlock3D( + in_channels=in_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ) + motion_modules.append( + get_motion_module( + in_channels=out_channels, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + if use_motion_module + else None + ) + + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + + if add_downsample: + self.downsamplers = nn.ModuleList( + [ + Downsample3D( + out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" + ) + ] + ) + else: + self.downsamplers = None + + self.gradient_checkpointing = False + + def forward(self, hidden_states, temb=None, encoder_hidden_states=None): + output_states = () + + for resnet, motion_module in zip(self.resnets, self.motion_modules): + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) + if motion_module is not None: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(motion_module), + hidden_states.requires_grad_(), + temb, + encoder_hidden_states, + ) + else: + hidden_states = resnet(hidden_states, temb) + + # add motion module + hidden_states = ( + motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) + if motion_module is not None + else hidden_states + ) + + output_states += (hidden_states,) + + if self.downsamplers is not None: + for downsampler in self.downsamplers: + hidden_states = downsampler(hidden_states) + + output_states += (hidden_states,) + + return hidden_states, output_states + + +class CrossAttnUpBlock3D(nn.Module): + def __init__( + self, + in_channels: int, + out_channels: int, + prev_output_channel: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + attn_num_head_channels=1, + cross_attention_dim=1280, + output_scale_factor=1.0, + add_upsample=True, + dual_cross_attention=False, + use_linear_projection=False, + only_cross_attention=False, + upcast_attention=False, + unet_use_cross_frame_attention=False, + unet_use_temporal_attention=False, + use_inflated_groupnorm=False, + use_motion_module=None, + motion_module_type=None, + motion_module_kwargs=None, + add_audio_layer=False, + audio_condition_method="cross_attn", + custom_audio_layer=False, + ): + super().__init__() + resnets = [] + attentions = [] + audio_attentions = [] + motion_modules = [] + + self.has_cross_attention = True + self.attn_num_head_channels = attn_num_head_channels + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock3D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ) + if dual_cross_attention: + raise NotImplementedError + attentions.append( + Transformer3DModel( + attn_num_head_channels, + out_channels // attn_num_head_channels, + in_channels=out_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + use_motion_module=use_motion_module, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + add_audio_layer=add_audio_layer, + audio_condition_method=audio_condition_method, + ) + ) + audio_attentions.append( + Transformer3DModel( + attn_num_head_channels, + out_channels // attn_num_head_channels, + in_channels=out_channels, + num_layers=1, + cross_attention_dim=cross_attention_dim, + norm_num_groups=resnet_groups, + use_linear_projection=use_linear_projection, + only_cross_attention=only_cross_attention, + upcast_attention=upcast_attention, + use_motion_module=use_motion_module, + unet_use_cross_frame_attention=unet_use_cross_frame_attention, + unet_use_temporal_attention=unet_use_temporal_attention, + add_audio_layer=add_audio_layer, + audio_condition_method=audio_condition_method, + custom_audio_layer=True, + ) + if custom_audio_layer + else None + ) + motion_modules.append( + get_motion_module( + in_channels=out_channels, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + if use_motion_module + else None + ) + + self.attentions = nn.ModuleList(attentions) + self.audio_attentions = nn.ModuleList(audio_attentions) + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + + if add_upsample: + self.upsamplers = nn.ModuleList([Upsample3D(out_channels, use_conv=True, out_channels=out_channels)]) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + res_hidden_states_tuple, + temb=None, + encoder_hidden_states=None, + upsample_size=None, + attention_mask=None, + ): + for resnet, attn, audio_attn, motion_module in zip( + self.resnets, self.attentions, self.audio_attentions, self.motion_modules + ): + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module, return_dict=None): + def custom_forward(*inputs): + if return_dict is not None: + return module(*inputs, return_dict=return_dict) + else: + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(attn, return_dict=False), + hidden_states, + encoder_hidden_states, + )[0] + if motion_module is not None: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(motion_module), + hidden_states.requires_grad_(), + temb, + encoder_hidden_states, + ) + + else: + hidden_states = resnet(hidden_states, temb) + hidden_states = attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample + hidden_states = ( + audio_attn(hidden_states, encoder_hidden_states=encoder_hidden_states).sample + if audio_attn is not None + else hidden_states + ) + + # add motion module + hidden_states = ( + motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) + if motion_module is not None + else hidden_states + ) + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states, upsample_size) + + return hidden_states + + +class UpBlock3D(nn.Module): + def __init__( + self, + in_channels: int, + prev_output_channel: int, + out_channels: int, + temb_channels: int, + dropout: float = 0.0, + num_layers: int = 1, + resnet_eps: float = 1e-6, + resnet_time_scale_shift: str = "default", + resnet_act_fn: str = "swish", + resnet_groups: int = 32, + resnet_pre_norm: bool = True, + output_scale_factor=1.0, + add_upsample=True, + use_inflated_groupnorm=False, + use_motion_module=None, + motion_module_type=None, + motion_module_kwargs=None, + ): + super().__init__() + resnets = [] + motion_modules = [] + + for i in range(num_layers): + res_skip_channels = in_channels if (i == num_layers - 1) else out_channels + resnet_in_channels = prev_output_channel if i == 0 else out_channels + + resnets.append( + ResnetBlock3D( + in_channels=resnet_in_channels + res_skip_channels, + out_channels=out_channels, + temb_channels=temb_channels, + eps=resnet_eps, + groups=resnet_groups, + dropout=dropout, + time_embedding_norm=resnet_time_scale_shift, + non_linearity=resnet_act_fn, + output_scale_factor=output_scale_factor, + pre_norm=resnet_pre_norm, + use_inflated_groupnorm=use_inflated_groupnorm, + ) + ) + motion_modules.append( + get_motion_module( + in_channels=out_channels, + motion_module_type=motion_module_type, + motion_module_kwargs=motion_module_kwargs, + ) + if use_motion_module + else None + ) + + self.resnets = nn.ModuleList(resnets) + self.motion_modules = nn.ModuleList(motion_modules) + + if add_upsample: + self.upsamplers = nn.ModuleList([Upsample3D(out_channels, use_conv=True, out_channels=out_channels)]) + else: + self.upsamplers = None + + self.gradient_checkpointing = False + + def forward( + self, + hidden_states, + res_hidden_states_tuple, + temb=None, + upsample_size=None, + encoder_hidden_states=None, + ): + for resnet, motion_module in zip(self.resnets, self.motion_modules): + # pop res hidden states + res_hidden_states = res_hidden_states_tuple[-1] + res_hidden_states_tuple = res_hidden_states_tuple[:-1] + hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) + + if self.training and self.gradient_checkpointing: + + def create_custom_forward(module): + def custom_forward(*inputs): + return module(*inputs) + + return custom_forward + + hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) + if motion_module is not None: + hidden_states = torch.utils.checkpoint.checkpoint( + create_custom_forward(motion_module), + hidden_states.requires_grad_(), + temb, + encoder_hidden_states, + ) + else: + hidden_states = resnet(hidden_states, temb) + hidden_states = ( + motion_module(hidden_states, temb, encoder_hidden_states=encoder_hidden_states) + if motion_module is not None + else hidden_states + ) + + if self.upsamplers is not None: + for upsampler in self.upsamplers: + hidden_states = upsampler(hidden_states, upsample_size) + + return hidden_states diff --git a/latentsync/models/utils.py b/latentsync/models/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..b96595ca3831950bb7dee07c234cca47e629f317 --- /dev/null +++ b/latentsync/models/utils.py @@ -0,0 +1,19 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +def zero_module(module): + # Zero out the parameters of a module and return it. + for p in module.parameters(): + p.detach().zero_() + return module diff --git a/latentsync/pipelines/lipsync_pipeline.py b/latentsync/pipelines/lipsync_pipeline.py new file mode 100644 index 0000000000000000000000000000000000000000..f3a153b8540f2a2704083bc88aea7a7c6645c81e --- /dev/null +++ b/latentsync/pipelines/lipsync_pipeline.py @@ -0,0 +1,470 @@ +# Adapted from https://github.com/guoyww/AnimateDiff/blob/main/animatediff/pipelines/pipeline_animation.py + +import inspect +import os +import shutil +from typing import Callable, List, Optional, Union +import subprocess + +import numpy as np +import torch +import torchvision + +from diffusers.utils import is_accelerate_available +from packaging import version + +from diffusers.configuration_utils import FrozenDict +from diffusers.models import AutoencoderKL +from diffusers.pipeline_utils import DiffusionPipeline +from diffusers.schedulers import ( + DDIMScheduler, + DPMSolverMultistepScheduler, + EulerAncestralDiscreteScheduler, + EulerDiscreteScheduler, + LMSDiscreteScheduler, + PNDMScheduler, +) +from diffusers.utils import deprecate, logging + +from einops import rearrange + +from ..models.unet import UNet3DConditionModel +from ..utils.image_processor import ImageProcessor +from ..utils.util import read_video, read_audio, write_video +from ..whisper.audio2feature import Audio2Feature +import tqdm +import soundfile as sf + +logger = logging.get_logger(__name__) # pylint: disable=invalid-name + + +class LipsyncPipeline(DiffusionPipeline): + _optional_components = [] + + def __init__( + self, + vae: AutoencoderKL, + audio_encoder: Audio2Feature, + unet: UNet3DConditionModel, + scheduler: Union[ + DDIMScheduler, + PNDMScheduler, + LMSDiscreteScheduler, + EulerDiscreteScheduler, + EulerAncestralDiscreteScheduler, + DPMSolverMultistepScheduler, + ], + ): + super().__init__() + + if hasattr(scheduler.config, "steps_offset") and scheduler.config.steps_offset != 1: + deprecation_message = ( + f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`" + f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure " + "to update the config accordingly as leaving `steps_offset` might led to incorrect results" + " in future versions. If you have downloaded this checkpoint from the Hugging Face Hub," + " it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`" + " file" + ) + deprecate("steps_offset!=1", "1.0.0", deprecation_message, standard_warn=False) + new_config = dict(scheduler.config) + new_config["steps_offset"] = 1 + scheduler._internal_dict = FrozenDict(new_config) + + if hasattr(scheduler.config, "clip_sample") and scheduler.config.clip_sample is True: + deprecation_message = ( + f"The configuration file of this scheduler: {scheduler} has not set the configuration `clip_sample`." + " `clip_sample` should be set to False in the configuration file. Please make sure to update the" + " config accordingly as not setting `clip_sample` in the config might lead to incorrect results in" + " future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it would be very" + " nice if you could open a Pull request for the `scheduler/scheduler_config.json` file" + ) + deprecate("clip_sample not set", "1.0.0", deprecation_message, standard_warn=False) + new_config = dict(scheduler.config) + new_config["clip_sample"] = False + scheduler._internal_dict = FrozenDict(new_config) + + is_unet_version_less_0_9_0 = hasattr(unet.config, "_diffusers_version") and version.parse( + version.parse(unet.config._diffusers_version).base_version + ) < version.parse("0.9.0.dev0") + is_unet_sample_size_less_64 = hasattr(unet.config, "sample_size") and unet.config.sample_size < 64 + if is_unet_version_less_0_9_0 and is_unet_sample_size_less_64: + deprecation_message = ( + "The configuration file of the unet has set the default `sample_size` to smaller than" + " 64 which seems highly unlikely. If your checkpoint is a fine-tuned version of any of the" + " following: \n- CompVis/stable-diffusion-v1-4 \n- CompVis/stable-diffusion-v1-3 \n-" + " CompVis/stable-diffusion-v1-2 \n- CompVis/stable-diffusion-v1-1 \n- runwayml/stable-diffusion-v1-5" + " \n- runwayml/stable-diffusion-inpainting \n you should change 'sample_size' to 64 in the" + " configuration file. Please make sure to update the config accordingly as leaving `sample_size=32`" + " in the config might lead to incorrect results in future versions. If you have downloaded this" + " checkpoint from the Hugging Face Hub, it would be very nice if you could open a Pull request for" + " the `unet/config.json` file" + ) + deprecate("sample_size<64", "1.0.0", deprecation_message, standard_warn=False) + new_config = dict(unet.config) + new_config["sample_size"] = 64 + unet._internal_dict = FrozenDict(new_config) + + self.register_modules( + vae=vae, + audio_encoder=audio_encoder, + unet=unet, + scheduler=scheduler, + ) + + self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) + + self.set_progress_bar_config(desc="Steps") + + def enable_vae_slicing(self): + self.vae.enable_slicing() + + def disable_vae_slicing(self): + self.vae.disable_slicing() + + def enable_sequential_cpu_offload(self, gpu_id=0): + if is_accelerate_available(): + from accelerate import cpu_offload + else: + raise ImportError("Please install accelerate via `pip install accelerate`") + + device = torch.device(f"cuda:{gpu_id}") + + for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae]: + if cpu_offloaded_model is not None: + cpu_offload(cpu_offloaded_model, device) + + @property + def _execution_device(self): + if self.device != torch.device("meta") or not hasattr(self.unet, "_hf_hook"): + return self.device + for module in self.unet.modules(): + if ( + hasattr(module, "_hf_hook") + and hasattr(module._hf_hook, "execution_device") + and module._hf_hook.execution_device is not None + ): + return torch.device(module._hf_hook.execution_device) + return self.device + + def decode_latents(self, latents): + latents = latents / self.vae.config.scaling_factor + self.vae.config.shift_factor + latents = rearrange(latents, "b c f h w -> (b f) c h w") + decoded_latents = self.vae.decode(latents).sample + return decoded_latents + + def prepare_extra_step_kwargs(self, generator, eta): + # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature + # eta (ฮท) is only used with the DDIMScheduler, it will be ignored for other schedulers. + # eta corresponds to ฮท in DDIM paper: https://arxiv.org/abs/2010.02502 + # and should be between [0, 1] + + accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) + extra_step_kwargs = {} + if accepts_eta: + extra_step_kwargs["eta"] = eta + + # check if the scheduler accepts generator + accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) + if accepts_generator: + extra_step_kwargs["generator"] = generator + return extra_step_kwargs + + def check_inputs(self, height, width, callback_steps): + assert height == width, "Height and width must be equal" + + if height % 8 != 0 or width % 8 != 0: + raise ValueError(f"`height` and `width` have to be divisible by 8 but are {height} and {width}.") + + if (callback_steps is None) or ( + callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0) + ): + raise ValueError( + f"`callback_steps` has to be a positive integer but is {callback_steps} of type" + f" {type(callback_steps)}." + ) + + def prepare_latents(self, batch_size, num_frames, num_channels_latents, height, width, dtype, device, generator): + shape = ( + batch_size, + num_channels_latents, + 1, + height // self.vae_scale_factor, + width // self.vae_scale_factor, + ) + rand_device = "cpu" if device.type == "mps" else device + latents = torch.randn(shape, generator=generator, device=rand_device, dtype=dtype).to(device) + latents = latents.repeat(1, 1, num_frames, 1, 1) + + # scale the initial noise by the standard deviation required by the scheduler + latents = latents * self.scheduler.init_noise_sigma + return latents + + def prepare_mask_latents( + self, mask, masked_image, height, width, dtype, device, generator, do_classifier_free_guidance + ): + # resize the mask to latents shape as we concatenate the mask to the latents + # we do that before converting to dtype to avoid breaking in case we're using cpu_offload + # and half precision + mask = torch.nn.functional.interpolate( + mask, size=(height // self.vae_scale_factor, width // self.vae_scale_factor) + ) + masked_image = masked_image.to(device=device, dtype=dtype) + + # encode the mask image into latents space so we can concatenate it to the latents + masked_image_latents = self.vae.encode(masked_image).latent_dist.sample(generator=generator) + masked_image_latents = (masked_image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor + + # aligning device to prevent device errors when concating it with the latent model input + masked_image_latents = masked_image_latents.to(device=device, dtype=dtype) + mask = mask.to(device=device, dtype=dtype) + + # assume batch size = 1 + mask = rearrange(mask, "f c h w -> 1 c f h w") + masked_image_latents = rearrange(masked_image_latents, "f c h w -> 1 c f h w") + + mask = torch.cat([mask] * 2) if do_classifier_free_guidance else mask + masked_image_latents = ( + torch.cat([masked_image_latents] * 2) if do_classifier_free_guidance else masked_image_latents + ) + return mask, masked_image_latents + + def prepare_image_latents(self, images, device, dtype, generator, do_classifier_free_guidance): + images = images.to(device=device, dtype=dtype) + image_latents = self.vae.encode(images).latent_dist.sample(generator=generator) + image_latents = (image_latents - self.vae.config.shift_factor) * self.vae.config.scaling_factor + image_latents = rearrange(image_latents, "f c h w -> 1 c f h w") + image_latents = torch.cat([image_latents] * 2) if do_classifier_free_guidance else image_latents + + return image_latents + + def set_progress_bar_config(self, **kwargs): + if not hasattr(self, "_progress_bar_config"): + self._progress_bar_config = {} + self._progress_bar_config.update(kwargs) + + @staticmethod + def paste_surrounding_pixels_back(decoded_latents, pixel_values, masks, device, weight_dtype): + # Paste the surrounding pixels back, because we only want to change the mouth region + pixel_values = pixel_values.to(device=device, dtype=weight_dtype) + masks = masks.to(device=device, dtype=weight_dtype) + combined_pixel_values = decoded_latents * masks + pixel_values * (1 - masks) + return combined_pixel_values + + @staticmethod + def pixel_values_to_images(pixel_values: torch.Tensor): + pixel_values = rearrange(pixel_values, "f c h w -> f h w c") + pixel_values = (pixel_values / 2 + 0.5).clamp(0, 1) + images = (pixel_values * 255).to(torch.uint8) + images = images.cpu().numpy() + return images + + def affine_transform_video(self, video_path): + video_frames = read_video(video_path, use_decord=False) + faces = [] + boxes = [] + affine_matrices = [] + print(f"Affine transforming {len(video_frames)} faces...") + for frame in tqdm.tqdm(video_frames): + face, box, affine_matrix = self.image_processor.affine_transform(frame) + faces.append(face) + boxes.append(box) + affine_matrices.append(affine_matrix) + + faces = torch.stack(faces) + return faces, video_frames, boxes, affine_matrices + + def restore_video(self, faces, video_frames, boxes, affine_matrices): + video_frames = video_frames[: faces.shape[0]] + out_frames = [] + for index, face in enumerate(faces): + x1, y1, x2, y2 = boxes[index] + height = int(y2 - y1) + width = int(x2 - x1) + face = torchvision.transforms.functional.resize(face, size=(height, width), antialias=True) + face = rearrange(face, "c h w -> h w c") + face = (face / 2 + 0.5).clamp(0, 1) + face = (face * 255).to(torch.uint8).cpu().numpy() + out_frame = self.image_processor.restorer.restore_img(video_frames[index], face, affine_matrices[index]) + out_frames.append(out_frame) + return np.stack(out_frames, axis=0) + + @torch.no_grad() + def __call__( + self, + video_path: str, + audio_path: str, + video_out_path: str, + video_mask_path: str = None, + num_frames: int = 16, + video_fps: int = 25, + audio_sample_rate: int = 16000, + height: Optional[int] = None, + width: Optional[int] = None, + num_inference_steps: int = 20, + guidance_scale: float = 1.5, + weight_dtype: Optional[torch.dtype] = torch.float16, + eta: float = 0.0, + mask: str = "fix_mask", + generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, + callback: Optional[Callable[[int, int, torch.FloatTensor], None]] = None, + callback_steps: Optional[int] = 1, + **kwargs, + ): + is_train = self.unet.training + self.unet.eval() + + # 0. Define call parameters + batch_size = 1 + device = self._execution_device + self.image_processor = ImageProcessor(height, mask=mask, device="cuda") + self.set_progress_bar_config(desc=f"Sample frames: {num_frames}") + + video_frames, original_video_frames, boxes, affine_matrices = self.affine_transform_video(video_path) + audio_samples = read_audio(audio_path) + + # 1. Default height and width to unet + height = height or self.unet.config.sample_size * self.vae_scale_factor + width = width or self.unet.config.sample_size * self.vae_scale_factor + + # 2. Check inputs + self.check_inputs(height, width, callback_steps) + + # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) + # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` + # corresponds to doing no classifier free guidance. + do_classifier_free_guidance = guidance_scale > 1.0 + + # 3. set timesteps + self.scheduler.set_timesteps(num_inference_steps, device=device) + timesteps = self.scheduler.timesteps + + # 4. Prepare extra step kwargs. + extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) + + self.video_fps = video_fps + + if self.unet.add_audio_layer: + whisper_feature = self.audio_encoder.audio2feat(audio_path) + whisper_chunks = self.audio_encoder.feature2chunks(feature_array=whisper_feature, fps=video_fps) + + num_inferences = min(len(video_frames), len(whisper_chunks)) // num_frames + else: + num_inferences = len(video_frames) // num_frames + + synced_video_frames = [] + masked_video_frames = [] + + num_channels_latents = self.vae.config.latent_channels + + # Prepare latent variables + all_latents = self.prepare_latents( + batch_size, + num_frames * num_inferences, + num_channels_latents, + height, + width, + weight_dtype, + device, + generator, + ) + + for i in tqdm.tqdm(range(num_inferences), desc="Doing inference..."): + if self.unet.add_audio_layer: + audio_embeds = torch.stack(whisper_chunks[i * num_frames : (i + 1) * num_frames]) + audio_embeds = audio_embeds.to(device, dtype=weight_dtype) + if do_classifier_free_guidance: + empty_audio_embeds = torch.zeros_like(audio_embeds) + audio_embeds = torch.cat([empty_audio_embeds, audio_embeds]) + else: + audio_embeds = None + inference_video_frames = video_frames[i * num_frames : (i + 1) * num_frames] + latents = all_latents[:, :, i * num_frames : (i + 1) * num_frames] + pixel_values, masked_pixel_values, masks = self.image_processor.prepare_masks_and_masked_images( + inference_video_frames, affine_transform=False + ) + + # 7. Prepare mask latent variables + mask_latents, masked_image_latents = self.prepare_mask_latents( + masks, + masked_pixel_values, + height, + width, + weight_dtype, + device, + generator, + do_classifier_free_guidance, + ) + + # 8. Prepare image latents + image_latents = self.prepare_image_latents( + pixel_values, + device, + weight_dtype, + generator, + do_classifier_free_guidance, + ) + + # 9. Denoising loop + num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order + with self.progress_bar(total=num_inference_steps) as progress_bar: + for j, t in enumerate(timesteps): + # expand the latents if we are doing classifier free guidance + latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents + + # concat latents, mask, masked_image_latents in the channel dimension + latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) + latent_model_input = torch.cat( + [latent_model_input, mask_latents, masked_image_latents, image_latents], dim=1 + ) + + # predict the noise residual + noise_pred = self.unet(latent_model_input, t, encoder_hidden_states=audio_embeds).sample + + # perform guidance + if do_classifier_free_guidance: + noise_pred_uncond, noise_pred_audio = noise_pred.chunk(2) + noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_audio - noise_pred_uncond) + + # compute the previous noisy sample x_t -> x_t-1 + latents = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs).prev_sample + + # call the callback, if provided + if j == len(timesteps) - 1 or ((j + 1) > num_warmup_steps and (j + 1) % self.scheduler.order == 0): + progress_bar.update() + if callback is not None and j % callback_steps == 0: + callback(j, t, latents) + + # Recover the pixel values + decoded_latents = self.decode_latents(latents) + decoded_latents = self.paste_surrounding_pixels_back( + decoded_latents, pixel_values, 1 - masks, device, weight_dtype + ) + synced_video_frames.append(decoded_latents) + masked_video_frames.append(masked_pixel_values) + + synced_video_frames = self.restore_video( + torch.cat(synced_video_frames), original_video_frames, boxes, affine_matrices + ) + masked_video_frames = self.restore_video( + torch.cat(masked_video_frames), original_video_frames, boxes, affine_matrices + ) + + audio_samples_remain_length = int(synced_video_frames.shape[0] / video_fps * audio_sample_rate) + audio_samples = audio_samples[:audio_samples_remain_length].cpu().numpy() + + if is_train: + self.unet.train() + + temp_dir = "temp" + if os.path.exists(temp_dir): + shutil.rmtree(temp_dir) + os.makedirs(temp_dir, exist_ok=True) + + write_video(os.path.join(temp_dir, "video.mp4"), synced_video_frames, fps=25) + # write_video(video_mask_path, masked_video_frames, fps=25) + + sf.write(os.path.join(temp_dir, "audio.wav"), audio_samples, audio_sample_rate) + + command = f"ffmpeg -y -loglevel error -nostdin -i {os.path.join(temp_dir, 'video.mp4')} -i {os.path.join(temp_dir, 'audio.wav')} -c:v libx264 -c:a aac -q:v 0 -q:a 0 {video_out_path}" + subprocess.run(command, shell=True) diff --git a/latentsync/trepa/__init__.py b/latentsync/trepa/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..086988b92917f827832abd545dda13015cbd0453 --- /dev/null +++ b/latentsync/trepa/__init__.py @@ -0,0 +1,64 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +import torch.nn.functional as F +import torch.nn as nn +from einops import rearrange +from .third_party.VideoMAEv2.utils import load_videomae_model + + +class TREPALoss: + def __init__( + self, + device="cuda", + ckpt_path="/mnt/bn/maliva-gen-ai-v2/chunyu.li/checkpoints/vit_g_hybrid_pt_1200e_ssv2_ft.pth", + ): + self.model = load_videomae_model(device, ckpt_path).eval().to(dtype=torch.float16) + self.model.requires_grad_(False) + self.bce_loss = nn.BCELoss() + + def __call__(self, videos_fake, videos_real, loss_type="mse"): + batch_size = videos_fake.shape[0] + num_frames = videos_fake.shape[2] + videos_fake = rearrange(videos_fake.clone(), "b c f h w -> (b f) c h w") + videos_real = rearrange(videos_real.clone(), "b c f h w -> (b f) c h w") + + videos_fake = F.interpolate(videos_fake, size=(224, 224), mode="bilinear") + videos_real = F.interpolate(videos_real, size=(224, 224), mode="bilinear") + + videos_fake = rearrange(videos_fake, "(b f) c h w -> b c f h w", f=num_frames) + videos_real = rearrange(videos_real, "(b f) c h w -> b c f h w", f=num_frames) + + # Because input pixel range is [-1, 1], and model expects pixel range to be [0, 1] + videos_fake = (videos_fake / 2 + 0.5).clamp(0, 1) + videos_real = (videos_real / 2 + 0.5).clamp(0, 1) + + feats_fake = self.model.forward_features(videos_fake) + feats_real = self.model.forward_features(videos_real) + + feats_fake = F.normalize(feats_fake, p=2, dim=1) + feats_real = F.normalize(feats_real, p=2, dim=1) + + return F.mse_loss(feats_fake, feats_real) + + +if __name__ == "__main__": + # input shape: (b, c, f, h, w) + videos_fake = torch.randn(2, 3, 16, 256, 256, requires_grad=True).to(device="cuda", dtype=torch.float16) + videos_real = torch.randn(2, 3, 16, 256, 256, requires_grad=True).to(device="cuda", dtype=torch.float16) + + trepa_loss = TREPALoss(device="cuda") + loss = trepa_loss(videos_fake, videos_real) + print(loss) diff --git a/latentsync/trepa/third_party/VideoMAEv2/__init__.py b/latentsync/trepa/third_party/VideoMAEv2/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/latentsync/trepa/third_party/VideoMAEv2/utils.py b/latentsync/trepa/third_party/VideoMAEv2/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..0506435b06cb871fe952b801fe409db0eb976bab --- /dev/null +++ b/latentsync/trepa/third_party/VideoMAEv2/utils.py @@ -0,0 +1,81 @@ +import os +import torch +import requests +from tqdm import tqdm +from torchvision import transforms +from .videomaev2_finetune import vit_giant_patch14_224 + +def to_normalized_float_tensor(vid): + return vid.permute(3, 0, 1, 2).to(torch.float32) / 255 + + +# NOTE: for those functions, which generally expect mini-batches, we keep them +# as non-minibatch so that they are applied as if they were 4d (thus image). +# this way, we only apply the transformation in the spatial domain +def resize(vid, size, interpolation='bilinear'): + # NOTE: using bilinear interpolation because we don't work on minibatches + # at this level + scale = None + if isinstance(size, int): + scale = float(size) / min(vid.shape[-2:]) + size = None + return torch.nn.functional.interpolate( + vid, + size=size, + scale_factor=scale, + mode=interpolation, + align_corners=False) + + +class ToFloatTensorInZeroOne(object): + def __call__(self, vid): + return to_normalized_float_tensor(vid) + + +class Resize(object): + def __init__(self, size): + self.size = size + def __call__(self, vid): + return resize(vid, self.size) + +def preprocess_videomae(videos): + transform = transforms.Compose( + [ToFloatTensorInZeroOne(), + Resize((224, 224))]) + return torch.stack([transform(f) for f in torch.from_numpy(videos)]) + + +def load_videomae_model(device, ckpt_path=None): + if ckpt_path is None: + current_dir = os.path.dirname(os.path.abspath(__file__)) + ckpt_path = os.path.join(current_dir, 'vit_g_hybrid_pt_1200e_ssv2_ft.pth') + + if not os.path.exists(ckpt_path): + # download the ckpt to the path + ckpt_url = 'https://pjlab-gvm-data.oss-cn-shanghai.aliyuncs.com/internvideo/videomaev2/vit_g_hybrid_pt_1200e_ssv2_ft.pth' + response = requests.get(ckpt_url, stream=True, allow_redirects=True) + total_size = int(response.headers.get("content-length", 0)) + block_size = 1024 + + with tqdm(total=total_size, unit="B", unit_scale=True) as progress_bar: + with open(ckpt_path, "wb") as fw: + for data in response.iter_content(block_size): + progress_bar.update(len(data)) + fw.write(data) + + model = vit_giant_patch14_224( + img_size=224, + pretrained=False, + num_classes=174, + all_frames=16, + tubelet_size=2, + drop_path_rate=0.3, + use_mean_pooling=True) + + ckpt = torch.load(ckpt_path, map_location='cpu') + for model_key in ['model', 'module']: + if model_key in ckpt: + ckpt = ckpt[model_key] + break + model.load_state_dict(ckpt) + return model.to(device) \ No newline at end of file diff --git a/latentsync/trepa/third_party/VideoMAEv2/videomaev2_finetune.py b/latentsync/trepa/third_party/VideoMAEv2/videomaev2_finetune.py new file mode 100644 index 0000000000000000000000000000000000000000..0eb2c4043368a72664f494891860e1fde618bd17 --- /dev/null +++ b/latentsync/trepa/third_party/VideoMAEv2/videomaev2_finetune.py @@ -0,0 +1,539 @@ +# -------------------------------------------------------- +# Based on BEiT, timm, DINO and DeiT code bases +# https://github.com/microsoft/unilm/tree/master/beit +# https://github.com/rwightman/pytorch-image-models/tree/master/timm +# https://github.com/facebookresearch/deit +# https://github.com/facebookresearch/dino +# --------------------------------------------------------' +from functools import partial + +import math +import warnings +import numpy as np +import collections.abc +import torch +import torch.nn as nn +import torch.nn.functional as F +import torch.utils.checkpoint as cp +from itertools import repeat + + +def _no_grad_trunc_normal_(tensor, mean, std, a, b): + # Cut & paste from PyTorch official master until it's in a few official releases - RW + # Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf + def norm_cdf(x): + # Computes standard normal cumulative distribution function + return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0 + + if (mean < a - 2 * std) or (mean > b + 2 * std): + warnings.warn( + "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. " + "The distribution of values may be incorrect.", + stacklevel=2, + ) + + with torch.no_grad(): + # Values are generated by using a truncated uniform distribution and + # then using the inverse CDF for the normal distribution. + # Get upper and lower cdf values + l = norm_cdf((a - mean) / std) + u = norm_cdf((b - mean) / std) + + # Uniformly fill tensor with values from [l, u], then translate to + # [2l-1, 2u-1]. + tensor.uniform_(2 * l - 1, 2 * u - 1) + + # Use inverse cdf transform for normal distribution to get truncated + # standard normal + tensor.erfinv_() + + # Transform to proper mean, std + tensor.mul_(std * math.sqrt(2.0)) + tensor.add_(mean) + + # Clamp to ensure it's in the proper range + tensor.clamp_(min=a, max=b) + return tensor + + +def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0): + r"""Fills the input Tensor with values drawn from a truncated + normal distribution. The values are effectively drawn from the + normal distribution :math:`\mathcal{N}(\text{mean}, \text{std}^2)` + with values outside :math:`[a, b]` redrawn until they are within + the bounds. The method used for generating the random values works + best when :math:`a \leq \text{mean} \leq b`. + Args: + tensor: an n-dimensional `torch.Tensor` + mean: the mean of the normal distribution + std: the standard deviation of the normal distribution + a: the minimum cutoff value + b: the maximum cutoff value + Examples: + >>> w = torch.empty(3, 5) + >>> nn.init.trunc_normal_(w) + """ + return _no_grad_trunc_normal_(tensor, mean, std, a, b) + + +def _ntuple(n): + def parse(x): + if isinstance(x, collections.abc.Iterable): + return x + return tuple(repeat(x, n)) + + return parse + + +to_2tuple = _ntuple(2) + + +def drop_path(x, drop_prob: float = 0.0, training: bool = False): + """ + Adapted from timm codebase + """ + if drop_prob == 0.0 or not training: + return x + keep_prob = 1 - drop_prob + shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets + random_tensor = keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device) + random_tensor.floor_() # binarize + output = x.div(keep_prob) * random_tensor + return output + + +def _cfg(url="", **kwargs): + return { + "url": url, + "num_classes": 400, + "input_size": (3, 224, 224), + "pool_size": None, + "crop_pct": 0.9, + "interpolation": "bicubic", + "mean": (0.5, 0.5, 0.5), + "std": (0.5, 0.5, 0.5), + **kwargs, + } + + +class DropPath(nn.Module): + """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).""" + + def __init__(self, drop_prob=None): + super(DropPath, self).__init__() + self.drop_prob = drop_prob + + def forward(self, x): + return drop_path(x, self.drop_prob, self.training) + + def extra_repr(self) -> str: + return "p={}".format(self.drop_prob) + + +class Mlp(nn.Module): + + def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0): + super().__init__() + out_features = out_features or in_features + hidden_features = hidden_features or in_features + self.fc1 = nn.Linear(in_features, hidden_features) + self.act = act_layer() + self.fc2 = nn.Linear(hidden_features, out_features) + self.drop = nn.Dropout(drop) + + def forward(self, x): + x = self.fc1(x) + x = self.act(x) + # x = self.drop(x) + # commit this for the orignal BERT implement + x = self.fc2(x) + x = self.drop(x) + return x + + +class CosAttention(nn.Module): + + def __init__( + self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0, attn_head_dim=None + ): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + if attn_head_dim is not None: + head_dim = attn_head_dim + all_head_dim = head_dim * self.num_heads + # self.scale = qk_scale or head_dim**-0.5 + # DO NOT RENAME [self.scale] (for no weight decay) + if qk_scale is None: + self.scale = nn.Parameter(torch.log(10 * torch.ones((num_heads, 1, 1))), requires_grad=True) + else: + self.scale = qk_scale + + self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False) + if qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(all_head_dim)) + self.v_bias = nn.Parameter(torch.zeros(all_head_dim)) + else: + self.q_bias = None + self.v_bias = None + + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(all_head_dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.shape + qkv_bias = None + if self.q_bias is not None: + qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias)) + qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) + qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + attn = F.normalize(q, dim=-1) @ F.normalize(k, dim=-1).transpose(-2, -1) + + # torch.log(torch.tensor(1. / 0.01)) = 4.6052 + logit_scale = torch.clamp(self.scale, max=4.6052).exp() + + attn = attn * logit_scale + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, -1) + + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class Attention(nn.Module): + + def __init__( + self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0, attn_head_dim=None + ): + super().__init__() + self.num_heads = num_heads + head_dim = dim // num_heads + if attn_head_dim is not None: + head_dim = attn_head_dim + all_head_dim = head_dim * self.num_heads + self.scale = qk_scale or head_dim**-0.5 + + self.qkv = nn.Linear(dim, all_head_dim * 3, bias=False) + if qkv_bias: + self.q_bias = nn.Parameter(torch.zeros(all_head_dim)) + self.v_bias = nn.Parameter(torch.zeros(all_head_dim)) + else: + self.q_bias = None + self.v_bias = None + + self.attn_drop = nn.Dropout(attn_drop) + self.proj = nn.Linear(all_head_dim, dim) + self.proj_drop = nn.Dropout(proj_drop) + + def forward(self, x): + B, N, C = x.shape + qkv_bias = None + if self.q_bias is not None: + qkv_bias = torch.cat((self.q_bias, torch.zeros_like(self.v_bias, requires_grad=False), self.v_bias)) + qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias) + qkv = qkv.reshape(B, N, 3, self.num_heads, -1).permute(2, 0, 3, 1, 4) + q, k, v = qkv[0], qkv[1], qkv[2] # make torchscript happy (cannot use tensor as tuple) + + q = q * self.scale + attn = q @ k.transpose(-2, -1) + + attn = attn.softmax(dim=-1) + attn = self.attn_drop(attn) + + x = (attn @ v).transpose(1, 2).reshape(B, N, -1) + + x = self.proj(x) + x = self.proj_drop(x) + return x + + +class Block(nn.Module): + + def __init__( + self, + dim, + num_heads, + mlp_ratio=4.0, + qkv_bias=False, + qk_scale=None, + drop=0.0, + attn_drop=0.0, + drop_path=0.0, + init_values=None, + act_layer=nn.GELU, + norm_layer=nn.LayerNorm, + attn_head_dim=None, + cos_attn=False, + ): + super().__init__() + self.norm1 = norm_layer(dim) + if cos_attn: + self.attn = CosAttention( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + attn_head_dim=attn_head_dim, + ) + else: + self.attn = Attention( + dim, + num_heads=num_heads, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + attn_drop=attn_drop, + proj_drop=drop, + attn_head_dim=attn_head_dim, + ) + # NOTE: drop path for stochastic depth, we shall see if this is better than dropout here + self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity() + self.norm2 = norm_layer(dim) + mlp_hidden_dim = int(dim * mlp_ratio) + self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop) + + if init_values > 0: + self.gamma_1 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True) + self.gamma_2 = nn.Parameter(init_values * torch.ones((dim)), requires_grad=True) + else: + self.gamma_1, self.gamma_2 = None, None + + def forward(self, x): + if self.gamma_1 is None: + x = x + self.drop_path(self.attn(self.norm1(x))) + x = x + self.drop_path(self.mlp(self.norm2(x))) + else: + x = x + self.drop_path(self.gamma_1 * self.attn(self.norm1(x))) + x = x + self.drop_path(self.gamma_2 * self.mlp(self.norm2(x))) + return x + + +class PatchEmbed(nn.Module): + """Image to Patch Embedding""" + + def __init__(self, img_size=224, patch_size=16, in_chans=3, embed_dim=768, num_frames=16, tubelet_size=2): + super().__init__() + img_size = to_2tuple(img_size) + patch_size = to_2tuple(patch_size) + num_spatial_patches = (img_size[0] // patch_size[0]) * (img_size[1] // patch_size[1]) + num_patches = num_spatial_patches * (num_frames // tubelet_size) + + self.img_size = img_size + self.tubelet_size = tubelet_size + self.patch_size = patch_size + self.num_patches = num_patches + self.proj = nn.Conv3d( + in_channels=in_chans, + out_channels=embed_dim, + kernel_size=(self.tubelet_size, patch_size[0], patch_size[1]), + stride=(self.tubelet_size, patch_size[0], patch_size[1]), + ) + + def forward(self, x, **kwargs): + B, C, T, H, W = x.shape + assert ( + H == self.img_size[0] and W == self.img_size[1] + ), f"Input image size ({H}*{W}) doesn't match model ({self.img_size[0]}*{self.img_size[1]})." + # b, c, l -> b, l, c + # [1, 1408, 8, 16, 16] -> [1, 1408, 2048] -> [1, 2048, 1408] + x = self.proj(x).flatten(2).transpose(1, 2) + return x + + +# sin-cos position encoding +# https://github.com/jadore801120/attention-is-all-you-need-pytorch/blob/master/transformer/Models.py#L31 +def get_sinusoid_encoding_table(n_position, d_hid): + """Sinusoid position encoding table""" + + # TODO: make it with torch instead of numpy + def get_position_angle_vec(position): + return [position / np.power(10000, 2 * (hid_j // 2) / d_hid) for hid_j in range(d_hid)] + + sinusoid_table = np.array([get_position_angle_vec(pos_i) for pos_i in range(n_position)]) + sinusoid_table[:, 0::2] = np.sin(sinusoid_table[:, 0::2]) # dim 2i + sinusoid_table[:, 1::2] = np.cos(sinusoid_table[:, 1::2]) # dim 2i+1 + + return torch.tensor(sinusoid_table, dtype=torch.float, requires_grad=False).unsqueeze(0) + + +class VisionTransformer(nn.Module): + """Vision Transformer with support for patch or hybrid CNN input stage""" + + def __init__( + self, + img_size=224, + patch_size=16, + in_chans=3, + num_classes=1000, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4.0, + qkv_bias=False, + qk_scale=None, + drop_rate=0.0, + attn_drop_rate=0.0, + drop_path_rate=0.0, + head_drop_rate=0.0, + norm_layer=nn.LayerNorm, + init_values=0.0, + use_learnable_pos_emb=False, + init_scale=0.0, + all_frames=16, + tubelet_size=2, + use_mean_pooling=True, + with_cp=False, + cos_attn=False, + ): + super().__init__() + self.num_classes = num_classes + # num_features for consistency with other models + self.num_features = self.embed_dim = embed_dim + self.tubelet_size = tubelet_size + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=in_chans, + embed_dim=embed_dim, + num_frames=all_frames, + tubelet_size=tubelet_size, + ) + num_patches = self.patch_embed.num_patches + self.with_cp = with_cp + + if use_learnable_pos_emb: + self.pos_embed = nn.Parameter(torch.zeros(1, num_patches, embed_dim)) + else: + # sine-cosine positional embeddings is on the way + self.pos_embed = get_sinusoid_encoding_table(num_patches, embed_dim) + + self.pos_drop = nn.Dropout(p=drop_rate) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)] # stochastic depth decay rule + self.blocks = nn.ModuleList( + [ + Block( + dim=embed_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[i], + norm_layer=norm_layer, + init_values=init_values, + cos_attn=cos_attn, + ) + for i in range(depth) + ] + ) + self.norm = nn.Identity() if use_mean_pooling else norm_layer(embed_dim) + self.fc_norm = norm_layer(embed_dim) if use_mean_pooling else None + self.head_dropout = nn.Dropout(head_drop_rate) + self.head = nn.Linear(embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + if use_learnable_pos_emb: + trunc_normal_(self.pos_embed, std=0.02) + + self.apply(self._init_weights) + + self.head.weight.data.mul_(init_scale) + self.head.bias.data.mul_(init_scale) + self.num_frames = all_frames + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + trunc_normal_(m.weight, std=0.02) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def get_num_layers(self): + return len(self.blocks) + + @torch.jit.ignore + def no_weight_decay(self): + return {"pos_embed", "cls_token"} + + def get_classifier(self): + return self.head + + def reset_classifier(self, num_classes, global_pool=""): + self.num_classes = num_classes + self.head = nn.Linear(self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + def interpolate_pos_encoding(self, t): + T = 8 + t0 = t // self.tubelet_size + if T == t0: + return self.pos_embed + dim = self.pos_embed.shape[-1] + patch_pos_embed = self.pos_embed.permute(0, 2, 1).reshape(1, dim, 8, 16, 16) + # we add a small number to avoid floating point error in the interpolation + # see discussion at https://github.com/facebookresearch/dino/issues/8 + t0 = t0 + 0.1 + patch_pos_embed = nn.functional.interpolate( + patch_pos_embed, + scale_factor=(t0 / T, 1, 1), + mode="trilinear", + ) + assert int(t0) == patch_pos_embed.shape[-3] + patch_pos_embed = patch_pos_embed.reshape(1, dim, -1).permute(0, 2, 1) + return patch_pos_embed + + def forward_features(self, x): + # [1, 3, 16, 224, 224] + B = x.size(0) + T = x.size(2) + + # [1, 2048, 1408] + x = self.patch_embed(x) + + if self.pos_embed is not None: + x = x + self.interpolate_pos_encoding(T).expand(B, -1, -1).type_as(x).to(x.device).clone().detach() + x = self.pos_drop(x) + + for blk in self.blocks: + if self.with_cp: + x = cp.checkpoint(blk, x) + else: + x = blk(x) + + # return self.fc_norm(x) + + if self.fc_norm is not None: + return self.fc_norm(x.mean(1)) + else: + return self.norm(x[:, 0]) + + def forward(self, x): + x = self.forward_features(x) + x = self.head_dropout(x) + x = self.head(x) + return x + + +def vit_giant_patch14_224(pretrained=False, **kwargs): + model = VisionTransformer( + patch_size=14, + embed_dim=1408, + depth=40, + num_heads=16, + mlp_ratio=48 / 11, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs, + ) + model.default_cfg = _cfg() + return model diff --git a/latentsync/trepa/third_party/VideoMAEv2/videomaev2_pretrain.py b/latentsync/trepa/third_party/VideoMAEv2/videomaev2_pretrain.py new file mode 100644 index 0000000000000000000000000000000000000000..5fbc31aa1a65cd68e24550f6a778411c4e16dc9a --- /dev/null +++ b/latentsync/trepa/third_party/VideoMAEv2/videomaev2_pretrain.py @@ -0,0 +1,469 @@ +# -------------------------------------------------------- +# Based on BEiT, timm, DINO and DeiT code bases +# https://github.com/microsoft/unilm/tree/master/beit +# https://github.com/rwightman/pytorch-image-models/tree/master/timm +# https://github.com/facebookresearch/deit +# https://github.com/facebookresearch/dino +# --------------------------------------------------------' +from functools import partial + +import torch +import torch.nn as nn +import torch.utils.checkpoint as cp + +from .videomaev2_finetune import ( + Block, + PatchEmbed, + _cfg, + get_sinusoid_encoding_table, +) + +from .videomaev2_finetune import trunc_normal_ as __call_trunc_normal_ + +def trunc_normal_(tensor, mean=0., std=1.): + __call_trunc_normal_(tensor, mean=mean, std=std, a=-std, b=std) + + +class PretrainVisionTransformerEncoder(nn.Module): + """ Vision Transformer with support for patch or hybrid CNN input stage + """ + + def __init__(self, + img_size=224, + patch_size=16, + in_chans=3, + num_classes=0, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4., + qkv_bias=False, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0., + norm_layer=nn.LayerNorm, + init_values=None, + tubelet_size=2, + use_learnable_pos_emb=False, + with_cp=False, + all_frames=16, + cos_attn=False): + super().__init__() + self.num_classes = num_classes + # num_features for consistency with other models + self.num_features = self.embed_dim = embed_dim + self.patch_embed = PatchEmbed( + img_size=img_size, + patch_size=patch_size, + in_chans=in_chans, + embed_dim=embed_dim, + num_frames=all_frames, + tubelet_size=tubelet_size) + num_patches = self.patch_embed.num_patches + self.with_cp = with_cp + + if use_learnable_pos_emb: + self.pos_embed = nn.Parameter( + torch.zeros(1, num_patches + 1, embed_dim)) + else: + # sine-cosine positional embeddings + self.pos_embed = get_sinusoid_encoding_table( + num_patches, embed_dim) + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth) + ] # stochastic depth decay rule + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[i], + norm_layer=norm_layer, + init_values=init_values, + cos_attn=cos_attn) for i in range(depth) + ]) + self.norm = norm_layer(embed_dim) + self.head = nn.Linear( + embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + if use_learnable_pos_emb: + trunc_normal_(self.pos_embed, std=.02) + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + nn.init.xavier_uniform_(m.weight) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def get_num_layers(self): + return len(self.blocks) + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token'} + + def get_classifier(self): + return self.head + + def reset_classifier(self, num_classes, global_pool=''): + self.num_classes = num_classes + self.head = nn.Linear( + self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + def forward_features(self, x, mask): + x = self.patch_embed(x) + + x = x + self.pos_embed.type_as(x).to(x.device).clone().detach() + + B, _, C = x.shape + x_vis = x[~mask].reshape(B, -1, C) # ~mask means visible + + for blk in self.blocks: + if self.with_cp: + x_vis = cp.checkpoint(blk, x_vis) + else: + x_vis = blk(x_vis) + + x_vis = self.norm(x_vis) + return x_vis + + def forward(self, x, mask): + x = self.forward_features(x, mask) + x = self.head(x) + return x + + +class PretrainVisionTransformerDecoder(nn.Module): + """ Vision Transformer with support for patch or hybrid CNN input stage + """ + + def __init__(self, + patch_size=16, + num_classes=768, + embed_dim=768, + depth=12, + num_heads=12, + mlp_ratio=4., + qkv_bias=False, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0., + norm_layer=nn.LayerNorm, + init_values=None, + num_patches=196, + tubelet_size=2, + with_cp=False, + cos_attn=False): + super().__init__() + self.num_classes = num_classes + assert num_classes == 3 * tubelet_size * patch_size**2 + # num_features for consistency with other models + self.num_features = self.embed_dim = embed_dim + self.patch_size = patch_size + self.with_cp = with_cp + + dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth) + ] # stochastic depth decay rule + self.blocks = nn.ModuleList([ + Block( + dim=embed_dim, + num_heads=num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop=drop_rate, + attn_drop=attn_drop_rate, + drop_path=dpr[i], + norm_layer=norm_layer, + init_values=init_values, + cos_attn=cos_attn) for i in range(depth) + ]) + self.norm = norm_layer(embed_dim) + self.head = nn.Linear( + embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + self.apply(self._init_weights) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + nn.init.xavier_uniform_(m.weight) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def get_num_layers(self): + return len(self.blocks) + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token'} + + def get_classifier(self): + return self.head + + def reset_classifier(self, num_classes, global_pool=''): + self.num_classes = num_classes + self.head = nn.Linear( + self.embed_dim, num_classes) if num_classes > 0 else nn.Identity() + + def forward(self, x, return_token_num): + for blk in self.blocks: + if self.with_cp: + x = cp.checkpoint(blk, x) + else: + x = blk(x) + + if return_token_num > 0: + # only return the mask tokens predict pixels + x = self.head(self.norm(x[:, -return_token_num:])) + else: + # [B, N, 3*16^2] + x = self.head(self.norm(x)) + return x + + +class PretrainVisionTransformer(nn.Module): + """ Vision Transformer with support for patch or hybrid CNN input stage + """ + + def __init__( + self, + img_size=224, + patch_size=16, + encoder_in_chans=3, + encoder_num_classes=0, + encoder_embed_dim=768, + encoder_depth=12, + encoder_num_heads=12, + decoder_num_classes=1536, # decoder_num_classes=768 + decoder_embed_dim=512, + decoder_depth=8, + decoder_num_heads=8, + mlp_ratio=4., + qkv_bias=False, + qk_scale=None, + drop_rate=0., + attn_drop_rate=0., + drop_path_rate=0., + norm_layer=nn.LayerNorm, + init_values=0., + use_learnable_pos_emb=False, + tubelet_size=2, + num_classes=0, # avoid the error from create_fn in timm + in_chans=0, # avoid the error from create_fn in timm + with_cp=False, + all_frames=16, + cos_attn=False, + ): + super().__init__() + self.encoder = PretrainVisionTransformerEncoder( + img_size=img_size, + patch_size=patch_size, + in_chans=encoder_in_chans, + num_classes=encoder_num_classes, + embed_dim=encoder_embed_dim, + depth=encoder_depth, + num_heads=encoder_num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop_rate=drop_rate, + attn_drop_rate=attn_drop_rate, + drop_path_rate=drop_path_rate, + norm_layer=norm_layer, + init_values=init_values, + tubelet_size=tubelet_size, + use_learnable_pos_emb=use_learnable_pos_emb, + with_cp=with_cp, + all_frames=all_frames, + cos_attn=cos_attn) + + self.decoder = PretrainVisionTransformerDecoder( + patch_size=patch_size, + num_patches=self.encoder.patch_embed.num_patches, + num_classes=decoder_num_classes, + embed_dim=decoder_embed_dim, + depth=decoder_depth, + num_heads=decoder_num_heads, + mlp_ratio=mlp_ratio, + qkv_bias=qkv_bias, + qk_scale=qk_scale, + drop_rate=drop_rate, + attn_drop_rate=attn_drop_rate, + drop_path_rate=drop_path_rate, + norm_layer=norm_layer, + init_values=init_values, + tubelet_size=tubelet_size, + with_cp=with_cp, + cos_attn=cos_attn) + + self.encoder_to_decoder = nn.Linear( + encoder_embed_dim, decoder_embed_dim, bias=False) + + self.mask_token = nn.Parameter(torch.zeros(1, 1, decoder_embed_dim)) + + self.pos_embed = get_sinusoid_encoding_table( + self.encoder.patch_embed.num_patches, decoder_embed_dim) + + trunc_normal_(self.mask_token, std=.02) + + def _init_weights(self, m): + if isinstance(m, nn.Linear): + nn.init.xavier_uniform_(m.weight) + if isinstance(m, nn.Linear) and m.bias is not None: + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + def get_num_layers(self): + return len(self.blocks) + + @torch.jit.ignore + def no_weight_decay(self): + return {'pos_embed', 'cls_token', 'mask_token'} + + def forward(self, x, mask, decode_mask=None): + decode_vis = mask if decode_mask is None else ~decode_mask + + x_vis = self.encoder(x, mask) # [B, N_vis, C_e] + x_vis = self.encoder_to_decoder(x_vis) # [B, N_vis, C_d] + B, N_vis, C = x_vis.shape + + # we don't unshuffle the correct visible token order, + # but shuffle the pos embedding accorddingly. + expand_pos_embed = self.pos_embed.expand(B, -1, -1).type_as(x).to( + x.device).clone().detach() + pos_emd_vis = expand_pos_embed[~mask].reshape(B, -1, C) + pos_emd_mask = expand_pos_embed[decode_vis].reshape(B, -1, C) + + # [B, N, C_d] + x_full = torch.cat( + [x_vis + pos_emd_vis, self.mask_token + pos_emd_mask], dim=1) + # NOTE: if N_mask==0, the shape of x is [B, N_mask, 3 * 16 * 16] + x = self.decoder(x_full, pos_emd_mask.shape[1]) + + return x + + +def pretrain_videomae_small_patch16_224(pretrained=False, **kwargs): + model = PretrainVisionTransformer( + img_size=224, + patch_size=16, + encoder_embed_dim=384, + encoder_depth=12, + encoder_num_heads=6, + encoder_num_classes=0, + decoder_num_classes=1536, # 16 * 16 * 3 * 2 + decoder_embed_dim=192, + decoder_num_heads=3, + mlp_ratio=4, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + if pretrained: + checkpoint = torch.load(kwargs["init_ckpt"], map_location="cpu") + model.load_state_dict(checkpoint["model"]) + return model + + +def pretrain_videomae_base_patch16_224(pretrained=False, **kwargs): + model = PretrainVisionTransformer( + img_size=224, + patch_size=16, + encoder_embed_dim=768, + encoder_depth=12, + encoder_num_heads=12, + encoder_num_classes=0, + decoder_num_classes=1536, # 16 * 16 * 3 * 2 + decoder_embed_dim=384, + decoder_num_heads=6, + mlp_ratio=4, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + if pretrained: + checkpoint = torch.load(kwargs["init_ckpt"], map_location="cpu") + model.load_state_dict(checkpoint["model"]) + return model + + +def pretrain_videomae_large_patch16_224(pretrained=False, **kwargs): + model = PretrainVisionTransformer( + img_size=224, + patch_size=16, + encoder_embed_dim=1024, + encoder_depth=24, + encoder_num_heads=16, + encoder_num_classes=0, + decoder_num_classes=1536, # 16 * 16 * 3 * 2 + decoder_embed_dim=512, + decoder_num_heads=8, + mlp_ratio=4, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + if pretrained: + checkpoint = torch.load(kwargs["init_ckpt"], map_location="cpu") + model.load_state_dict(checkpoint["model"]) + return model + + +def pretrain_videomae_huge_patch16_224(pretrained=False, **kwargs): + model = PretrainVisionTransformer( + img_size=224, + patch_size=16, + encoder_embed_dim=1280, + encoder_depth=32, + encoder_num_heads=16, + encoder_num_classes=0, + decoder_num_classes=1536, # 16 * 16 * 3 * 2 + decoder_embed_dim=512, + decoder_num_heads=8, + mlp_ratio=4, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + if pretrained: + checkpoint = torch.load(kwargs["init_ckpt"], map_location="cpu") + model.load_state_dict(checkpoint["model"]) + return model + + +def pretrain_videomae_giant_patch14_224(pretrained=False, **kwargs): + model = PretrainVisionTransformer( + img_size=224, + patch_size=14, + encoder_embed_dim=1408, + encoder_depth=40, + encoder_num_heads=16, + encoder_num_classes=0, + decoder_num_classes=1176, # 14 * 14 * 3 * 2, + decoder_embed_dim=512, + decoder_num_heads=8, + mlp_ratio=48 / 11, + qkv_bias=True, + norm_layer=partial(nn.LayerNorm, eps=1e-6), + **kwargs) + model.default_cfg = _cfg() + if pretrained: + checkpoint = torch.load(kwargs["init_ckpt"], map_location="cpu") + model.load_state_dict(checkpoint["model"]) + return model diff --git a/latentsync/trepa/third_party/__init__.py b/latentsync/trepa/third_party/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/latentsync/trepa/utils/__init__.py b/latentsync/trepa/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/latentsync/trepa/utils/data_utils.py b/latentsync/trepa/utils/data_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..2bd0c04af8848f768c23fa00780dbf276f29c421 --- /dev/null +++ b/latentsync/trepa/utils/data_utils.py @@ -0,0 +1,321 @@ +import os +import math +import os.path as osp +import random +import pickle +import warnings + +import glob +import numpy as np +from PIL import Image + +import torch +import torch.utils.data as data +import torch.nn.functional as F +import torch.distributed as dist +from torchvision.datasets.video_utils import VideoClips + +IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG'] +VID_EXTENSIONS = ['.avi', '.mp4', '.webm', '.mov', '.mkv', '.m4v'] + + +def get_dataloader(data_path, image_folder, resolution=128, sequence_length=16, sample_every_n_frames=1, + batch_size=16, num_workers=8): + data = VideoData(data_path, image_folder, resolution, sequence_length, sample_every_n_frames, batch_size, num_workers) + loader = data._dataloader() + return loader + + +def is_image_file(filename): + return any(filename.endswith(extension) for extension in IMG_EXTENSIONS) + + +def get_parent_dir(path): + return osp.basename(osp.dirname(path)) + + +def preprocess(video, resolution, sequence_length=None, in_channels=3, sample_every_n_frames=1): + # video: THWC, {0, ..., 255} + assert in_channels == 3 + video = video.permute(0, 3, 1, 2).float() / 255. # TCHW + t, c, h, w = video.shape + + # temporal crop + if sequence_length is not None: + assert sequence_length <= t + video = video[:sequence_length] + + # skip frames + if sample_every_n_frames > 1: + video = video[::sample_every_n_frames] + + # scale shorter side to resolution + scale = resolution / min(h, w) + if h < w: + target_size = (resolution, math.ceil(w * scale)) + else: + target_size = (math.ceil(h * scale), resolution) + video = F.interpolate(video, size=target_size, mode='bilinear', + align_corners=False, antialias=True) + + # center crop + t, c, h, w = video.shape + w_start = (w - resolution) // 2 + h_start = (h - resolution) // 2 + video = video[:, :, h_start:h_start + resolution, w_start:w_start + resolution] + video = video.permute(1, 0, 2, 3).contiguous() # CTHW + + return {'video': video} + + +def preprocess_image(image): + # [0, 1] => [-1, 1] + img = torch.from_numpy(image) + return img + + +class VideoData(data.Dataset): + """ Class to create dataloaders for video datasets + + Args: + data_path: Path to the folder with video frames or videos. + image_folder: If True, the data is stored as images in folders. + resolution: Resolution of the returned videos. + sequence_length: Length of extracted video sequences. + sample_every_n_frames: Sample every n frames from the video. + batch_size: Batch size. + num_workers: Number of workers for the dataloader. + shuffle: If True, shuffle the data. + """ + + def __init__(self, data_path: str, image_folder: bool, resolution: int, sequence_length: int, + sample_every_n_frames: int, batch_size: int, num_workers: int, shuffle: bool = True): + super().__init__() + self.data_path = data_path + self.image_folder = image_folder + self.resolution = resolution + self.sequence_length = sequence_length + self.sample_every_n_frames = sample_every_n_frames + self.batch_size = batch_size + self.num_workers = num_workers + self.shuffle = shuffle + + def _dataset(self): + ''' + Initializes and return the dataset. + ''' + if self.image_folder: + Dataset = FrameDataset + dataset = Dataset(self.data_path, self.sequence_length, + resolution=self.resolution, sample_every_n_frames=self.sample_every_n_frames) + else: + Dataset = VideoDataset + dataset = Dataset(self.data_path, self.sequence_length, + resolution=self.resolution, sample_every_n_frames=self.sample_every_n_frames) + return dataset + + def _dataloader(self): + ''' + Initializes and returns the dataloader. + ''' + dataset = self._dataset() + if dist.is_initialized(): + sampler = data.distributed.DistributedSampler( + dataset, num_replicas=dist.get_world_size(), rank=dist.get_rank() + ) + else: + sampler = None + dataloader = data.DataLoader( + dataset, + batch_size=self.batch_size, + num_workers=self.num_workers, + pin_memory=True, + sampler=sampler, + shuffle=sampler is None and self.shuffle is True + ) + return dataloader + + +class VideoDataset(data.Dataset): + """ + Generic dataset for videos files stored in folders. + Videos of the same class are expected to be stored in a single folder. Multiple folders can exist in the provided directory. + The class depends on `torchvision.datasets.video_utils.VideoClips` to load the videos. + Returns BCTHW videos in the range [0, 1]. + + Args: + data_folder: Path to the folder with corresponding videos stored. + sequence_length: Length of extracted video sequences. + resolution: Resolution of the returned videos. + sample_every_n_frames: Sample every n frames from the video. + """ + + def __init__(self, data_folder: str, sequence_length: int = 16, resolution: int = 128, sample_every_n_frames: int = 1): + super().__init__() + self.sequence_length = sequence_length + self.resolution = resolution + self.sample_every_n_frames = sample_every_n_frames + + folder = data_folder + files = sum([glob.glob(osp.join(folder, '**', f'*{ext}'), recursive=True) + for ext in VID_EXTENSIONS], []) + + warnings.filterwarnings('ignore') + cache_file = osp.join(folder, f"metadata_{sequence_length}.pkl") + if not osp.exists(cache_file): + clips = VideoClips(files, sequence_length, num_workers=4) + try: + pickle.dump(clips.metadata, open(cache_file, 'wb')) + except: + print(f"Failed to save metadata to {cache_file}") + else: + metadata = pickle.load(open(cache_file, 'rb')) + clips = VideoClips(files, sequence_length, + _precomputed_metadata=metadata) + + self._clips = clips + # instead of uniformly sampling from all possible clips, we sample uniformly from all possible videos + self._clips.get_clip_location = self.get_random_clip_from_video + + def get_random_clip_from_video(self, idx: int) -> tuple: + ''' + Sample a random clip starting index from the video. + + Args: + idx: Index of the video. + ''' + # Note that some videos may not contain enough frames, we skip those videos here. + while self._clips.clips[idx].shape[0] <= 0: + idx += 1 + n_clip = self._clips.clips[idx].shape[0] + clip_id = random.randint(0, n_clip - 1) + return idx, clip_id + + def __len__(self): + return self._clips.num_videos() + + def __getitem__(self, idx): + resolution = self.resolution + while True: + try: + video, _, _, idx = self._clips.get_clip(idx) + except Exception as e: + print(idx, e) + idx = (idx + 1) % self._clips.num_clips() + continue + break + + return dict(**preprocess(video, resolution, sample_every_n_frames=self.sample_every_n_frames)) + + +class FrameDataset(data.Dataset): + """ + Generic dataset for videos stored as images. The loading will iterates over all the folders and subfolders + in the provided directory. Each leaf folder is assumed to contain frames from a single video. + + Args: + data_folder: path to the folder with video frames. The folder + should contain folders with frames from each video. + sequence_length: length of extracted video sequences + resolution: resolution of the returned videos + sample_every_n_frames: sample every n frames from the video + """ + + def __init__(self, data_folder, sequence_length, resolution=64, sample_every_n_frames=1): + self.resolution = resolution + self.sequence_length = sequence_length + self.sample_every_n_frames = sample_every_n_frames + self.data_all = self.load_video_frames(data_folder) + self.video_num = len(self.data_all) + + def __getitem__(self, index): + batch_data = self.getTensor(index) + return_list = {'video': batch_data} + + return return_list + + def load_video_frames(self, dataroot: str) -> list: + ''' + Loads all the video frames under the dataroot and returns a list of all the video frames. + + Args: + dataroot: The root directory containing the video frames. + + Returns: + A list of all the video frames. + + ''' + data_all = [] + frame_list = os.walk(dataroot) + for _, meta in enumerate(frame_list): + root = meta[0] + try: + frames = sorted(meta[2], key=lambda item: int(item.split('.')[0].split('_')[-1])) + except: + print(meta[0], meta[2]) + if len(frames) < max(0, self.sequence_length * self.sample_every_n_frames): + continue + frames = [ + os.path.join(root, item) for item in frames + if is_image_file(item) + ] + if len(frames) > max(0, self.sequence_length * self.sample_every_n_frames): + data_all.append(frames) + + return data_all + + def getTensor(self, index: int) -> torch.Tensor: + ''' + Returns a tensor of the video frames at the given index. + + Args: + index: The index of the video frames to return. + + Returns: + A BCTHW tensor in the range `[0, 1]` of the video frames at the given index. + + ''' + video = self.data_all[index] + video_len = len(video) + + # load the entire video when sequence_length = -1, whiel the sample_every_n_frames has to be 1 + if self.sequence_length == -1: + assert self.sample_every_n_frames == 1 + start_idx = 0 + end_idx = video_len + else: + n_frames_interval = self.sequence_length * self.sample_every_n_frames + start_idx = random.randint(0, video_len - n_frames_interval) + end_idx = start_idx + n_frames_interval + img = Image.open(video[0]) + h, w = img.height, img.width + + if h > w: + half = (h - w) // 2 + cropsize = (0, half, w, half + w) # left, upper, right, lower + elif w > h: + half = (w - h) // 2 + cropsize = (half, 0, half + h, h) + + images = [] + for i in range(start_idx, end_idx, + self.sample_every_n_frames): + path = video[i] + img = Image.open(path) + + if h != w: + img = img.crop(cropsize) + + img = img.resize( + (self.resolution, self.resolution), + Image.ANTIALIAS) + img = np.asarray(img, dtype=np.float32) + img /= 255. + img_tensor = preprocess_image(img).unsqueeze(0) + images.append(img_tensor) + + video_clip = torch.cat(images).permute(3, 0, 1, 2) + return video_clip + + def __len__(self): + return self.video_num diff --git a/latentsync/trepa/utils/metric_utils.py b/latentsync/trepa/utils/metric_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..444a4a40165736b498e261e539475d7e9aee9f6d --- /dev/null +++ b/latentsync/trepa/utils/metric_utils.py @@ -0,0 +1,161 @@ +# Adapted from https://github.com/universome/stylegan-v/blob/master/src/metrics/metric_utils.py +import os +import random +import torch +import pickle +import numpy as np + +from typing import List, Tuple + +def seed_everything(seed): + random.seed(seed) + os.environ['PYTHONHASHSEED'] = str(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + + +class FeatureStats: + ''' + Class to store statistics of features, including all features and mean/covariance. + + Args: + capture_all: Whether to store all the features. + capture_mean_cov: Whether to store mean and covariance. + max_items: Maximum number of items to store. + ''' + def __init__(self, capture_all: bool = False, capture_mean_cov: bool = False, max_items: int = None): + ''' + ''' + self.capture_all = capture_all + self.capture_mean_cov = capture_mean_cov + self.max_items = max_items + self.num_items = 0 + self.num_features = None + self.all_features = None + self.raw_mean = None + self.raw_cov = None + + def set_num_features(self, num_features: int): + ''' + Set the number of features diminsions. + + Args: + num_features: Number of features diminsions. + ''' + if self.num_features is not None: + assert num_features == self.num_features + else: + self.num_features = num_features + self.all_features = [] + self.raw_mean = np.zeros([num_features], dtype=np.float64) + self.raw_cov = np.zeros([num_features, num_features], dtype=np.float64) + + def is_full(self) -> bool: + ''' + Check if the maximum number of samples is reached. + + Returns: + True if the storage is full, False otherwise. + ''' + return (self.max_items is not None) and (self.num_items >= self.max_items) + + def append(self, x: np.ndarray): + ''' + Add the newly computed features to the list. Update the mean and covariance. + + Args: + x: New features to record. + ''' + x = np.asarray(x, dtype=np.float32) + assert x.ndim == 2 + if (self.max_items is not None) and (self.num_items + x.shape[0] > self.max_items): + if self.num_items >= self.max_items: + return + x = x[:self.max_items - self.num_items] + + self.set_num_features(x.shape[1]) + self.num_items += x.shape[0] + if self.capture_all: + self.all_features.append(x) + if self.capture_mean_cov: + x64 = x.astype(np.float64) + self.raw_mean += x64.sum(axis=0) + self.raw_cov += x64.T @ x64 + + def append_torch(self, x: torch.Tensor, rank: int, num_gpus: int): + ''' + Add the newly computed PyTorch features to the list. Update the mean and covariance. + + Args: + x: New features to record. + rank: Rank of the current GPU. + num_gpus: Total number of GPUs. + ''' + assert isinstance(x, torch.Tensor) and x.ndim == 2 + assert 0 <= rank < num_gpus + if num_gpus > 1: + ys = [] + for src in range(num_gpus): + y = x.clone() + torch.distributed.broadcast(y, src=src) + ys.append(y) + x = torch.stack(ys, dim=1).flatten(0, 1) # interleave samples + self.append(x.cpu().numpy()) + + def get_all(self) -> np.ndarray: + ''' + Get all the stored features as NumPy Array. + + Returns: + Concatenation of the stored features. + ''' + assert self.capture_all + return np.concatenate(self.all_features, axis=0) + + def get_all_torch(self) -> torch.Tensor: + ''' + Get all the stored features as PyTorch Tensor. + + Returns: + Concatenation of the stored features. + ''' + return torch.from_numpy(self.get_all()) + + def get_mean_cov(self) -> Tuple[np.ndarray, np.ndarray]: + ''' + Get the mean and covariance of the stored features. + + Returns: + Mean and covariance of the stored features. + ''' + assert self.capture_mean_cov + mean = self.raw_mean / self.num_items + cov = self.raw_cov / self.num_items + cov = cov - np.outer(mean, mean) + return mean, cov + + def save(self, pkl_file: str): + ''' + Save the features and statistics to a pickle file. + + Args: + pkl_file: Path to the pickle file. + ''' + with open(pkl_file, 'wb') as f: + pickle.dump(self.__dict__, f) + + @staticmethod + def load(pkl_file: str) -> 'FeatureStats': + ''' + Load the features and statistics from a pickle file. + + Args: + pkl_file: Path to the pickle file. + ''' + with open(pkl_file, 'rb') as f: + s = pickle.load(f) + obj = FeatureStats(capture_all=s['capture_all'], max_items=s['max_items']) + obj.__dict__.update(s) + print('Loaded %d features from %s' % (obj.num_items, pkl_file)) + return obj diff --git a/latentsync/utils/affine_transform.py b/latentsync/utils/affine_transform.py new file mode 100644 index 0000000000000000000000000000000000000000..0e18107133ab23452f004afd25c62c58f817c750 --- /dev/null +++ b/latentsync/utils/affine_transform.py @@ -0,0 +1,138 @@ +# Adapted from https://github.com/guanjz20/StyleSync/blob/main/utils.py + +import numpy as np +import cv2 + + +def transformation_from_points(points1, points0, smooth=True, p_bias=None): + points2 = np.array(points0) + points2 = points2.astype(np.float64) + points1 = points1.astype(np.float64) + c1 = np.mean(points1, axis=0) + c2 = np.mean(points2, axis=0) + points1 -= c1 + points2 -= c2 + s1 = np.std(points1) + s2 = np.std(points2) + points1 /= s1 + points2 /= s2 + U, S, Vt = np.linalg.svd(np.matmul(points1.T, points2)) + R = (np.matmul(U, Vt)).T + sR = (s2 / s1) * R + T = c2.reshape(2, 1) - (s2 / s1) * np.matmul(R, c1.reshape(2, 1)) + M = np.concatenate((sR, T), axis=1) + if smooth: + bias = points2[2] - points1[2] + if p_bias is None: + p_bias = bias + else: + bias = p_bias * 0.2 + bias * 0.8 + p_bias = bias + M[:, 2] = M[:, 2] + bias + return M, p_bias + + +class AlignRestore(object): + def __init__(self, align_points=3): + if align_points == 3: + self.upscale_factor = 1 + self.crop_ratio = (2.8, 2.8) + self.face_template = np.array([[19 - 2, 30 - 10], [56 + 2, 30 - 10], [37.5, 45 - 5]]) + self.face_template = self.face_template * 2.8 + # self.face_size = (int(100 * self.crop_ratio[0]), int(100 * self.crop_ratio[1])) + self.face_size = (int(75 * self.crop_ratio[0]), int(100 * self.crop_ratio[1])) + self.p_bias = None + + def process(self, img, lmk_align=None, smooth=True, align_points=3): + aligned_face, affine_matrix = self.align_warp_face(img, lmk_align, smooth) + restored_img = self.restore_img(img, aligned_face, affine_matrix) + cv2.imwrite("restored.jpg", restored_img) + cv2.imwrite("aligned.jpg", aligned_face) + return aligned_face, restored_img + + def align_warp_face(self, img, lmks3, smooth=True, border_mode="constant"): + affine_matrix, self.p_bias = transformation_from_points(lmks3, self.face_template, smooth, self.p_bias) + if border_mode == "constant": + border_mode = cv2.BORDER_CONSTANT + elif border_mode == "reflect101": + border_mode = cv2.BORDER_REFLECT101 + elif border_mode == "reflect": + border_mode = cv2.BORDER_REFLECT + cropped_face = cv2.warpAffine( + img, affine_matrix, self.face_size, borderMode=border_mode, borderValue=[127, 127, 127] + ) + return cropped_face, affine_matrix + + def align_warp_face2(self, img, landmark, border_mode="constant"): + affine_matrix = cv2.estimateAffinePartial2D(landmark, self.face_template)[0] + if border_mode == "constant": + border_mode = cv2.BORDER_CONSTANT + elif border_mode == "reflect101": + border_mode = cv2.BORDER_REFLECT101 + elif border_mode == "reflect": + border_mode = cv2.BORDER_REFLECT + cropped_face = cv2.warpAffine( + img, affine_matrix, self.face_size, borderMode=border_mode, borderValue=(135, 133, 132) + ) + return cropped_face, affine_matrix + + def restore_img(self, input_img, face, affine_matrix): + h, w, _ = input_img.shape + h_up, w_up = int(h * self.upscale_factor), int(w * self.upscale_factor) + upsample_img = cv2.resize(input_img, (w_up, h_up), interpolation=cv2.INTER_LANCZOS4) + inverse_affine = cv2.invertAffineTransform(affine_matrix) + inverse_affine *= self.upscale_factor + if self.upscale_factor > 1: + extra_offset = 0.5 * self.upscale_factor + else: + extra_offset = 0 + inverse_affine[:, 2] += extra_offset + inv_restored = cv2.warpAffine(face, inverse_affine, (w_up, h_up)) + mask = np.ones((self.face_size[1], self.face_size[0]), dtype=np.float32) + inv_mask = cv2.warpAffine(mask, inverse_affine, (w_up, h_up)) + inv_mask_erosion = cv2.erode( + inv_mask, np.ones((int(2 * self.upscale_factor), int(2 * self.upscale_factor)), np.uint8) + ) + pasted_face = inv_mask_erosion[:, :, None] * inv_restored + total_face_area = np.sum(inv_mask_erosion) + w_edge = int(total_face_area**0.5) // 20 + erosion_radius = w_edge * 2 + inv_mask_center = cv2.erode(inv_mask_erosion, np.ones((erosion_radius, erosion_radius), np.uint8)) + blur_size = w_edge * 2 + inv_soft_mask = cv2.GaussianBlur(inv_mask_center, (blur_size + 1, blur_size + 1), 0) + inv_soft_mask = inv_soft_mask[:, :, None] + upsample_img = inv_soft_mask * pasted_face + (1 - inv_soft_mask) * upsample_img + if np.max(upsample_img) > 256: + upsample_img = upsample_img.astype(np.uint16) + else: + upsample_img = upsample_img.astype(np.uint8) + return upsample_img + + +class laplacianSmooth: + def __init__(self, smoothAlpha=0.3): + self.smoothAlpha = smoothAlpha + self.pts_last = None + + def smooth(self, pts_cur): + if self.pts_last is None: + self.pts_last = pts_cur.copy() + return pts_cur.copy() + x1 = min(pts_cur[:, 0]) + x2 = max(pts_cur[:, 0]) + y1 = min(pts_cur[:, 1]) + y2 = max(pts_cur[:, 1]) + width = x2 - x1 + pts_update = [] + for i in range(len(pts_cur)): + x_new, y_new = pts_cur[i] + x_old, y_old = self.pts_last[i] + tmp = (x_new - x_old) ** 2 + (y_new - y_old) ** 2 + w = np.exp(-tmp / (width * self.smoothAlpha)) + x = x_old * w + x_new * (1 - w) + y = y_old * w + y_new * (1 - w) + pts_update.append([x, y]) + pts_update = np.array(pts_update) + self.pts_last = pts_update.copy() + + return pts_update diff --git a/latentsync/utils/audio.py b/latentsync/utils/audio.py new file mode 100644 index 0000000000000000000000000000000000000000..4403bd5c1e2e39f7f0724c8edbfe27e8dad66b91 --- /dev/null +++ b/latentsync/utils/audio.py @@ -0,0 +1,194 @@ +# Adapted from https://github.com/Rudrabha/Wav2Lip/blob/master/audio.py + +import librosa +import librosa.filters +import numpy as np +from scipy import signal +from scipy.io import wavfile +from omegaconf import OmegaConf +import torch + +audio_config_path = "configs/audio.yaml" + +config = OmegaConf.load(audio_config_path) + + +def load_wav(path, sr): + return librosa.core.load(path, sr=sr)[0] + + +def save_wav(wav, path, sr): + wav *= 32767 / max(0.01, np.max(np.abs(wav))) + # proposed by @dsmiller + wavfile.write(path, sr, wav.astype(np.int16)) + + +def save_wavenet_wav(wav, path, sr): + librosa.output.write_wav(path, wav, sr=sr) + + +def preemphasis(wav, k, preemphasize=True): + if preemphasize: + return signal.lfilter([1, -k], [1], wav) + return wav + + +def inv_preemphasis(wav, k, inv_preemphasize=True): + if inv_preemphasize: + return signal.lfilter([1], [1, -k], wav) + return wav + + +def get_hop_size(): + hop_size = config.audio.hop_size + if hop_size is None: + assert config.audio.frame_shift_ms is not None + hop_size = int(config.audio.frame_shift_ms / 1000 * config.audio.sample_rate) + return hop_size + + +def linearspectrogram(wav): + D = _stft(preemphasis(wav, config.audio.preemphasis, config.audio.preemphasize)) + S = _amp_to_db(np.abs(D)) - config.audio.ref_level_db + + if config.audio.signal_normalization: + return _normalize(S) + return S + + +def melspectrogram(wav): + D = _stft(preemphasis(wav, config.audio.preemphasis, config.audio.preemphasize)) + S = _amp_to_db(_linear_to_mel(np.abs(D))) - config.audio.ref_level_db + + if config.audio.signal_normalization: + return _normalize(S) + return S + + +def _lws_processor(): + import lws + + return lws.lws(config.audio.n_fft, get_hop_size(), fftsize=config.audio.win_size, mode="speech") + + +def _stft(y): + if config.audio.use_lws: + return _lws_processor(config.audio).stft(y).T + else: + return librosa.stft(y=y, n_fft=config.audio.n_fft, hop_length=get_hop_size(), win_length=config.audio.win_size) + + +########################################################## +# Those are only correct when using lws!!! (This was messing with Wavenet quality for a long time!) +def num_frames(length, fsize, fshift): + """Compute number of time frames of spectrogram""" + pad = fsize - fshift + if length % fshift == 0: + M = (length + pad * 2 - fsize) // fshift + 1 + else: + M = (length + pad * 2 - fsize) // fshift + 2 + return M + + +def pad_lr(x, fsize, fshift): + """Compute left and right padding""" + M = num_frames(len(x), fsize, fshift) + pad = fsize - fshift + T = len(x) + 2 * pad + r = (M - 1) * fshift + fsize - T + return pad, pad + r + + +########################################################## +# Librosa correct padding +def librosa_pad_lr(x, fsize, fshift): + return 0, (x.shape[0] // fshift + 1) * fshift - x.shape[0] + + +# Conversions +_mel_basis = None + + +def _linear_to_mel(spectogram): + global _mel_basis + if _mel_basis is None: + _mel_basis = _build_mel_basis() + return np.dot(_mel_basis, spectogram) + + +def _build_mel_basis(): + assert config.audio.fmax <= config.audio.sample_rate // 2 + return librosa.filters.mel( + sr=config.audio.sample_rate, + n_fft=config.audio.n_fft, + n_mels=config.audio.num_mels, + fmin=config.audio.fmin, + fmax=config.audio.fmax, + ) + + +def _amp_to_db(x): + min_level = np.exp(config.audio.min_level_db / 20 * np.log(10)) + return 20 * np.log10(np.maximum(min_level, x)) + + +def _db_to_amp(x): + return np.power(10.0, (x) * 0.05) + + +def _normalize(S): + if config.audio.allow_clipping_in_normalization: + if config.audio.symmetric_mels: + return np.clip( + (2 * config.audio.max_abs_value) * ((S - config.audio.min_level_db) / (-config.audio.min_level_db)) + - config.audio.max_abs_value, + -config.audio.max_abs_value, + config.audio.max_abs_value, + ) + else: + return np.clip( + config.audio.max_abs_value * ((S - config.audio.min_level_db) / (-config.audio.min_level_db)), + 0, + config.audio.max_abs_value, + ) + + assert S.max() <= 0 and S.min() - config.audio.min_level_db >= 0 + if config.audio.symmetric_mels: + return (2 * config.audio.max_abs_value) * ( + (S - config.audio.min_level_db) / (-config.audio.min_level_db) + ) - config.audio.max_abs_value + else: + return config.audio.max_abs_value * ((S - config.audio.min_level_db) / (-config.audio.min_level_db)) + + +def _denormalize(D): + if config.audio.allow_clipping_in_normalization: + if config.audio.symmetric_mels: + return ( + (np.clip(D, -config.audio.max_abs_value, config.audio.max_abs_value) + config.audio.max_abs_value) + * -config.audio.min_level_db + / (2 * config.audio.max_abs_value) + ) + config.audio.min_level_db + else: + return ( + np.clip(D, 0, config.audio.max_abs_value) * -config.audio.min_level_db / config.audio.max_abs_value + ) + config.audio.min_level_db + + if config.audio.symmetric_mels: + return ( + (D + config.audio.max_abs_value) * -config.audio.min_level_db / (2 * config.audio.max_abs_value) + ) + config.audio.min_level_db + else: + return (D * -config.audio.min_level_db / config.audio.max_abs_value) + config.audio.min_level_db + + +def get_melspec_overlap(audio_samples, melspec_length=52): + mel_spec_overlap = melspectrogram(audio_samples.numpy()) + mel_spec_overlap = torch.from_numpy(mel_spec_overlap) + i = 0 + mel_spec_overlap_list = [] + while i + melspec_length < mel_spec_overlap.shape[1] - 3: + mel_spec_overlap_list.append(mel_spec_overlap[:, i : i + melspec_length].unsqueeze(0)) + i += 3 + mel_spec_overlap = torch.stack(mel_spec_overlap_list) + return mel_spec_overlap diff --git a/latentsync/utils/av_reader.py b/latentsync/utils/av_reader.py new file mode 100644 index 0000000000000000000000000000000000000000..15432445d795dfc5f77d4ccd442eec40d412826a --- /dev/null +++ b/latentsync/utils/av_reader.py @@ -0,0 +1,157 @@ +# We modified the original AVReader class of decord to solve the problem of memory leak. +# For more details, refer to: https://github.com/dmlc/decord/issues/208 + +import numpy as np +from decord.video_reader import VideoReader +from decord.audio_reader import AudioReader + +from decord.ndarray import cpu +from decord import ndarray as _nd +from decord.bridge import bridge_out + + +class AVReader(object): + """Individual audio video reader with convenient indexing function. + + Parameters + ---------- + uri: str + Path of file. + ctx: decord.Context + The context to decode the file, can be decord.cpu() or decord.gpu(). + sample_rate: int, default is -1 + Desired output sample rate of the audio, unchanged if `-1` is specified. + mono: bool, default is True + Desired output channel layout of the audio. `True` is mono layout. `False` is unchanged. + width : int, default is -1 + Desired output width of the video, unchanged if `-1` is specified. + height : int, default is -1 + Desired output height of the video, unchanged if `-1` is specified. + num_threads : int, default is 0 + Number of decoding thread, auto if `0` is specified. + fault_tol : int, default is -1 + The threshold of corupted and recovered frames. This is to prevent silent fault + tolerance when for example 50% frames of a video cannot be decoded and duplicate + frames are returned. You may find the fault tolerant feature sweet in many cases, + but not for training models. Say `N = # recovered frames` + If `fault_tol` < 0, nothing will happen. + If 0 < `fault_tol` < 1.0, if N > `fault_tol * len(video)`, raise `DECORDLimitReachedError`. + If 1 < `fault_tol`, if N > `fault_tol`, raise `DECORDLimitReachedError`. + """ + + def __init__( + self, uri, ctx=cpu(0), sample_rate=44100, mono=True, width=-1, height=-1, num_threads=0, fault_tol=-1 + ): + self.__audio_reader = AudioReader(uri, ctx, sample_rate, mono) + self.__audio_reader.add_padding() + if hasattr(uri, "read"): + uri.seek(0) + self.__video_reader = VideoReader(uri, ctx, width, height, num_threads, fault_tol) + self.__video_reader.seek(0) + + def __len__(self): + """Get length of the video. Note that sometimes FFMPEG reports inaccurate number of frames, + we always follow what FFMPEG reports. + Returns + ------- + int + The number of frames in the video file. + """ + return len(self.__video_reader) + + def __getitem__(self, idx): + """Get audio samples and video frame at `idx`. + + Parameters + ---------- + idx : int or slice + The frame index, can be negative which means it will index backwards, + or slice of frame indices. + + Returns + ------- + (ndarray/list of ndarray, ndarray) + First element is samples of shape CxS or a list of length N containing samples of shape CxS, + where N is the number of frames, C is the number of channels, + S is the number of samples of the corresponding frame. + + Second element is Frame of shape HxWx3 or batch of image frames with shape NxHxWx3, + where N is the length of the slice. + """ + assert self.__video_reader is not None and self.__audio_reader is not None + if isinstance(idx, slice): + return self.get_batch(range(*idx.indices(len(self.__video_reader)))) + if idx < 0: + idx += len(self.__video_reader) + if idx >= len(self.__video_reader) or idx < 0: + raise IndexError("Index: {} out of bound: {}".format(idx, len(self.__video_reader))) + audio_start_idx, audio_end_idx = self.__video_reader.get_frame_timestamp(idx) + audio_start_idx = self.__audio_reader._time_to_sample(audio_start_idx) + audio_end_idx = self.__audio_reader._time_to_sample(audio_end_idx) + results = (self.__audio_reader[audio_start_idx:audio_end_idx], self.__video_reader[idx]) + self.__video_reader.seek(0) + return results + + def get_batch(self, indices): + """Get entire batch of audio samples and video frames. + + Parameters + ---------- + indices : list of integers + A list of frame indices. If negative indices detected, the indices will be indexed from backward + Returns + ------- + (list of ndarray, ndarray) + First element is a list of length N containing samples of shape CxS, + where N is the number of frames, C is the number of channels, + S is the number of samples of the corresponding frame. + + Second element is Frame of shape HxWx3 or batch of image frames with shape NxHxWx3, + where N is the length of the slice. + + """ + assert self.__video_reader is not None and self.__audio_reader is not None + indices = self._validate_indices(indices) + audio_arr = [] + prev_video_idx = None + prev_audio_end_idx = None + for idx in list(indices): + frame_start_time, frame_end_time = self.__video_reader.get_frame_timestamp(idx) + # timestamp and sample conversion could have some error that could cause non-continuous audio + # we detect if retrieving continuous frame and make the audio continuous + if prev_video_idx and idx == prev_video_idx + 1: + audio_start_idx = prev_audio_end_idx + else: + audio_start_idx = self.__audio_reader._time_to_sample(frame_start_time) + audio_end_idx = self.__audio_reader._time_to_sample(frame_end_time) + audio_arr.append(self.__audio_reader[audio_start_idx:audio_end_idx]) + prev_video_idx = idx + prev_audio_end_idx = audio_end_idx + results = (audio_arr, self.__video_reader.get_batch(indices)) + self.__video_reader.seek(0) + return results + + def _get_slice(self, sl): + audio_arr = np.empty(shape=(self.__audio_reader.shape()[0], 0), dtype="float32") + for idx in list(sl): + audio_start_idx, audio_end_idx = self.__video_reader.get_frame_timestamp(idx) + audio_start_idx = self.__audio_reader._time_to_sample(audio_start_idx) + audio_end_idx = self.__audio_reader._time_to_sample(audio_end_idx) + audio_arr = np.concatenate( + (audio_arr, self.__audio_reader[audio_start_idx:audio_end_idx].asnumpy()), axis=1 + ) + results = (bridge_out(_nd.array(audio_arr)), self.__video_reader.get_batch(sl)) + self.__video_reader.seek(0) + return results + + def _validate_indices(self, indices): + """Validate int64 integers and convert negative integers to positive by backward search""" + assert self.__video_reader is not None and self.__audio_reader is not None + indices = np.array(indices, dtype=np.int64) + # process negative indices + indices[indices < 0] += len(self.__video_reader) + if not (indices >= 0).all(): + raise IndexError("Invalid negative indices: {}".format(indices[indices < 0] + len(self.__video_reader))) + if not (indices < len(self.__video_reader)).all(): + raise IndexError("Out of bound indices: {}".format(indices[indices >= len(self.__video_reader)])) + return indices diff --git a/latentsync/utils/image_processor.py b/latentsync/utils/image_processor.py new file mode 100644 index 0000000000000000000000000000000000000000..f8180af26a714eddf7099428bdbbe4d5f9f0db01 --- /dev/null +++ b/latentsync/utils/image_processor.py @@ -0,0 +1,342 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from torchvision import transforms +import cv2 +from einops import rearrange +import mediapipe as mp +import torch +import numpy as np +from typing import Union +from .affine_transform import AlignRestore, laplacianSmooth +import face_alignment + +""" +If you are enlarging the image, you should prefer to use INTER_LINEAR or INTER_CUBIC interpolation. If you are shrinking the image, you should prefer to use INTER_AREA interpolation. +https://stackoverflow.com/questions/23853632/which-kind-of-interpolation-best-for-resizing-image +""" + + +def load_fixed_mask(resolution: int) -> torch.Tensor: + mask_image = cv2.imread("latentsync/utils/mask.png") + mask_image = cv2.cvtColor(mask_image, cv2.COLOR_BGR2RGB) + mask_image = cv2.resize(mask_image, (resolution, resolution), interpolation=cv2.INTER_AREA) / 255.0 + mask_image = rearrange(torch.from_numpy(mask_image), "h w c -> c h w") + return mask_image + + +class ImageProcessor: + def __init__(self, resolution: int = 512, mask: str = "fix_mask", device: str = "cpu", mask_image=None): + self.resolution = resolution + self.resize = transforms.Resize( + (resolution, resolution), interpolation=transforms.InterpolationMode.BILINEAR, antialias=True + ) + self.normalize = transforms.Normalize([0.5], [0.5], inplace=True) + self.mask = mask + + if mask in ["mouth", "face", "eye"]: + self.face_mesh = mp.solutions.face_mesh.FaceMesh(static_image_mode=True) # Process single image + if mask == "fix_mask": + self.face_mesh = None + self.smoother = laplacianSmooth() + self.restorer = AlignRestore() + + if mask_image is None: + self.mask_image = load_fixed_mask(resolution) + else: + self.mask_image = mask_image + + if device != "cpu": + self.fa = face_alignment.FaceAlignment( + face_alignment.LandmarksType.TWO_D, flip_input=False, device=device + ) + self.face_mesh = None + else: + # self.face_mesh = mp.solutions.face_mesh.FaceMesh(static_image_mode=True) # Process single image + self.face_mesh = None + self.fa = None + + def detect_facial_landmarks(self, image: np.ndarray): + height, width, _ = image.shape + results = self.face_mesh.process(image) + if not results.multi_face_landmarks: # Face not detected + raise RuntimeError("Face not detected") + face_landmarks = results.multi_face_landmarks[0] # Only use the first face in the image + landmark_coordinates = [ + (int(landmark.x * width), int(landmark.y * height)) for landmark in face_landmarks.landmark + ] # x means width, y means height + return landmark_coordinates + + def preprocess_one_masked_image(self, image: torch.Tensor) -> np.ndarray: + image = self.resize(image) + + if self.mask == "mouth" or self.mask == "face": + landmark_coordinates = self.detect_facial_landmarks(image) + if self.mask == "mouth": + surround_landmarks = mouth_surround_landmarks + else: + surround_landmarks = face_surround_landmarks + + points = [landmark_coordinates[landmark] for landmark in surround_landmarks] + points = np.array(points) + mask = np.ones((self.resolution, self.resolution)) + mask = cv2.fillPoly(mask, pts=[points], color=(0, 0, 0)) + mask = torch.from_numpy(mask) + mask = mask.unsqueeze(0) + elif self.mask == "half": + mask = torch.ones((self.resolution, self.resolution)) + height = mask.shape[0] + mask[height // 2 :, :] = 0 + mask = mask.unsqueeze(0) + elif self.mask == "eye": + mask = torch.ones((self.resolution, self.resolution)) + landmark_coordinates = self.detect_facial_landmarks(image) + y = landmark_coordinates[195][1] + mask[y:, :] = 0 + mask = mask.unsqueeze(0) + else: + raise ValueError("Invalid mask type") + + image = image.to(dtype=torch.float32) + pixel_values = self.normalize(image / 255.0) + masked_pixel_values = pixel_values * mask + mask = 1 - mask + + return pixel_values, masked_pixel_values, mask + + def affine_transform(self, image: torch.Tensor) -> np.ndarray: + # image = rearrange(image, "c h w-> h w c").numpy() + if self.fa is None: + landmark_coordinates = np.array(self.detect_facial_landmarks(image)) + lm68 = mediapipe_lm478_to_face_alignment_lm68(landmark_coordinates) + else: + detected_faces = self.fa.get_landmarks(image) + if detected_faces is None: + raise RuntimeError("Face not detected") + lm68 = detected_faces[0] + + points = self.smoother.smooth(lm68) + lmk3_ = np.zeros((3, 2)) + lmk3_[0] = points[17:22].mean(0) + lmk3_[1] = points[22:27].mean(0) + lmk3_[2] = points[27:36].mean(0) + # print(lmk3_) + face, affine_matrix = self.restorer.align_warp_face( + image.copy(), lmks3=lmk3_, smooth=True, border_mode="constant" + ) + box = [0, 0, face.shape[1], face.shape[0]] # x1, y1, x2, y2 + face = cv2.resize(face, (self.resolution, self.resolution), interpolation=cv2.INTER_CUBIC) + face = rearrange(torch.from_numpy(face), "h w c -> c h w") + return face, box, affine_matrix + + def preprocess_fixed_mask_image(self, image: torch.Tensor, affine_transform=False): + if affine_transform: + image, _, _ = self.affine_transform(image) + else: + image = self.resize(image) + pixel_values = self.normalize(image / 255.0) + masked_pixel_values = pixel_values * self.mask_image + return pixel_values, masked_pixel_values, self.mask_image[0:1] + + def prepare_masks_and_masked_images(self, images: Union[torch.Tensor, np.ndarray], affine_transform=False): + if isinstance(images, np.ndarray): + images = torch.from_numpy(images) + if images.shape[3] == 3: + images = rearrange(images, "b h w c -> b c h w") + if self.mask == "fix_mask": + results = [self.preprocess_fixed_mask_image(image, affine_transform=affine_transform) for image in images] + else: + results = [self.preprocess_one_masked_image(image) for image in images] + + pixel_values_list, masked_pixel_values_list, masks_list = list(zip(*results)) + return torch.stack(pixel_values_list), torch.stack(masked_pixel_values_list), torch.stack(masks_list) + + def process_images(self, images: Union[torch.Tensor, np.ndarray]): + if isinstance(images, np.ndarray): + images = torch.from_numpy(images) + if images.shape[3] == 3: + images = rearrange(images, "b h w c -> b c h w") + images = self.resize(images) + pixel_values = self.normalize(images / 255.0) + return pixel_values + + def close(self): + if self.face_mesh is not None: + self.face_mesh.close() + + +def mediapipe_lm478_to_face_alignment_lm68(lm478, return_2d=True): + """ + lm478: [B, 478, 3] or [478,3] + """ + # lm478[..., 0] *= W + # lm478[..., 1] *= H + landmarks_extracted = [] + for index in landmark_points_68: + x = lm478[index][0] + y = lm478[index][1] + landmarks_extracted.append((x, y)) + return np.array(landmarks_extracted) + + +landmark_points_68 = [ + 162, + 234, + 93, + 58, + 172, + 136, + 149, + 148, + 152, + 377, + 378, + 365, + 397, + 288, + 323, + 454, + 389, + 71, + 63, + 105, + 66, + 107, + 336, + 296, + 334, + 293, + 301, + 168, + 197, + 5, + 4, + 75, + 97, + 2, + 326, + 305, + 33, + 160, + 158, + 133, + 153, + 144, + 362, + 385, + 387, + 263, + 373, + 380, + 61, + 39, + 37, + 0, + 267, + 269, + 291, + 405, + 314, + 17, + 84, + 181, + 78, + 82, + 13, + 312, + 308, + 317, + 14, + 87, +] + + +# Refer to https://storage.googleapis.com/mediapipe-assets/documentation/mediapipe_face_landmark_fullsize.png +mouth_surround_landmarks = [ + 164, + 165, + 167, + 92, + 186, + 57, + 43, + 106, + 182, + 83, + 18, + 313, + 406, + 335, + 273, + 287, + 410, + 322, + 391, + 393, +] + +face_surround_landmarks = [ + 152, + 377, + 400, + 378, + 379, + 365, + 397, + 288, + 435, + 433, + 411, + 425, + 423, + 327, + 326, + 94, + 97, + 98, + 203, + 205, + 187, + 213, + 215, + 58, + 172, + 136, + 150, + 149, + 176, + 148, +] + +if __name__ == "__main__": + image_processor = ImageProcessor(512, mask="fix_mask") + video = cv2.VideoCapture("/mnt/bn/maliva-gen-ai-v2/chunyu.li/HDTF/original/val/RD_Radio57_000.mp4") + while True: + ret, frame = video.read() + # if not ret: + # break + + # cv2.imwrite("image.jpg", frame) + + frame = rearrange(torch.Tensor(frame).type(torch.uint8), "h w c -> c h w") + # face, masked_face, _ = image_processor.preprocess_fixed_mask_image(frame, affine_transform=True) + face, _, _ = image_processor.affine_transform(frame) + + break + + face = (rearrange(face, "c h w -> h w c").detach().cpu().numpy()).astype(np.uint8) + cv2.imwrite("face.jpg", face) + + # masked_face = (rearrange(masked_face, "c h w -> h w c").detach().cpu().numpy()).astype(np.uint8) + # cv2.imwrite("masked_face.jpg", masked_face) diff --git a/latentsync/utils/mask.png b/latentsync/utils/mask.png new file mode 100644 index 0000000000000000000000000000000000000000..eb8fdd1f4e1c59a55f9b3604a71946b9833f3ca2 Binary files /dev/null and b/latentsync/utils/mask.png differ diff --git a/latentsync/utils/util.py b/latentsync/utils/util.py new file mode 100644 index 0000000000000000000000000000000000000000..e6fd47a793ebb69f8d2519ef0a1b4a33c9a9a406 --- /dev/null +++ b/latentsync/utils/util.py @@ -0,0 +1,365 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import imageio +import numpy as np +import json +from typing import Union +import matplotlib.pyplot as plt + +import torch +import torch.nn as nn +import torch.nn.functional as F +import torchvision +import torch.distributed as dist +from torchvision import transforms + +from tqdm import tqdm +from einops import rearrange +import cv2 +from decord import AudioReader, VideoReader +import shutil +import subprocess + + +# Machine epsilon for a float32 (single precision) +eps = np.finfo(np.float32).eps + + +def read_json(filepath: str): + with open(filepath) as f: + json_dict = json.load(f) + return json_dict + + +def read_video(video_path: str, change_fps=True, use_decord=True): + if change_fps: + temp_dir = "temp" + if os.path.exists(temp_dir): + shutil.rmtree(temp_dir) + os.makedirs(temp_dir, exist_ok=True) + command = ( + f"ffmpeg -loglevel error -y -nostdin -i {video_path} -r 25 -crf 18 {os.path.join(temp_dir, 'video.mp4')}" + ) + subprocess.run(command, shell=True) + target_video_path = os.path.join(temp_dir, "video.mp4") + else: + target_video_path = video_path + + if use_decord: + return read_video_decord(target_video_path) + else: + return read_video_cv2(target_video_path) + + +def read_video_decord(video_path: str): + vr = VideoReader(video_path) + video_frames = vr[:].asnumpy() + vr.seek(0) + return video_frames + + +def read_video_cv2(video_path: str): + # Open the video file + cap = cv2.VideoCapture(video_path) + + # Check if the video was opened successfully + if not cap.isOpened(): + print("Error: Could not open video.") + return np.array([]) + + frames = [] + + while True: + # Read a frame + ret, frame = cap.read() + + # If frame is read correctly ret is True + if not ret: + break + + # Convert BGR to RGB + frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) + + frames.append(frame_rgb) + + # Release the video capture object + cap.release() + + return np.array(frames) + + +def read_audio(audio_path: str, audio_sample_rate: int = 16000): + if audio_path is None: + raise ValueError("Audio path is required.") + ar = AudioReader(audio_path, sample_rate=audio_sample_rate, mono=True) + + # To access the audio samples + audio_samples = torch.from_numpy(ar[:].asnumpy()) + audio_samples = audio_samples.squeeze(0) + + return audio_samples + + +def write_video(video_output_path: str, video_frames: np.ndarray, fps: int): + height, width = video_frames[0].shape[:2] + out = cv2.VideoWriter(video_output_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (width, height)) + # out = cv2.VideoWriter(video_output_path, cv2.VideoWriter_fourcc(*"vp09"), fps, (width, height)) + for frame in video_frames: + frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR) + out.write(frame) + out.release() + + +def init_dist(backend="nccl", **kwargs): + """Initializes distributed environment.""" + rank = int(os.environ["RANK"]) + num_gpus = torch.cuda.device_count() + if num_gpus == 0: + raise RuntimeError("No GPUs available for training.") + local_rank = rank % num_gpus + torch.cuda.set_device(local_rank) + dist.init_process_group(backend=backend, **kwargs) + + return local_rank + + +def zero_rank_print(s): + if dist.is_initialized() and dist.get_rank() == 0: + print("### " + s) + + +def zero_rank_log(logger, message: str): + if dist.is_initialized() and dist.get_rank() == 0: + logger.info(message) + + +def make_audio_window(audio_embeddings: torch.Tensor, window_size: int): + audio_window = [] + end_idx = audio_embeddings.shape[1] - window_size + 1 + for i in range(end_idx): + audio_window.append(audio_embeddings[:, i : i + window_size, :]) + audio_window = torch.stack(audio_window) + audio_window = rearrange(audio_window, "f b w d -> b f w d") + return audio_window + + +def check_video_fps(video_path: str): + cam = cv2.VideoCapture(video_path) + fps = cam.get(cv2.CAP_PROP_FPS) + if fps != 25: + raise ValueError(f"Video FPS is not 25, it is {fps}. Please convert the video to 25 FPS.") + + +def tailor_tensor_to_length(tensor: torch.Tensor, length: int): + if len(tensor) == length: + return tensor + elif len(tensor) > length: + return tensor[:length] + else: + return torch.cat([tensor, tensor[-1].repeat(length - len(tensor))]) + + +def save_videos_grid(videos: torch.Tensor, path: str, rescale=False, n_rows=6, fps=8): + videos = rearrange(videos, "b c f h w -> f b c h w") + outputs = [] + for x in videos: + x = torchvision.utils.make_grid(x, nrow=n_rows) + x = x.transpose(0, 1).transpose(1, 2).squeeze(-1) + if rescale: + x = (x + 1.0) / 2.0 # -1,1 -> 0,1 + x = (x * 255).numpy().astype(np.uint8) + outputs.append(x) + + os.makedirs(os.path.dirname(path), exist_ok=True) + imageio.mimsave(path, outputs, fps=fps) + + +def interpolate_features(features: torch.Tensor, output_len: int) -> torch.Tensor: + features = features.cpu().numpy() + input_len, num_features = features.shape + + input_timesteps = np.linspace(0, 10, input_len) + output_timesteps = np.linspace(0, 10, output_len) + output_features = np.zeros((output_len, num_features)) + for feat in range(num_features): + output_features[:, feat] = np.interp(output_timesteps, input_timesteps, features[:, feat]) + return torch.from_numpy(output_features) + + +# DDIM Inversion +@torch.no_grad() +def init_prompt(prompt, pipeline): + uncond_input = pipeline.tokenizer( + [""], padding="max_length", max_length=pipeline.tokenizer.model_max_length, return_tensors="pt" + ) + uncond_embeddings = pipeline.text_encoder(uncond_input.input_ids.to(pipeline.device))[0] + text_input = pipeline.tokenizer( + [prompt], + padding="max_length", + max_length=pipeline.tokenizer.model_max_length, + truncation=True, + return_tensors="pt", + ) + text_embeddings = pipeline.text_encoder(text_input.input_ids.to(pipeline.device))[0] + context = torch.cat([uncond_embeddings, text_embeddings]) + + return context + + +def reversed_forward(ddim_scheduler, pred_noise, timesteps, x_t): + # Compute alphas, betas + alpha_prod_t = ddim_scheduler.alphas_cumprod[timesteps] + beta_prod_t = 1 - alpha_prod_t + + # 3. compute predicted original sample from predicted noise also called + # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf + if ddim_scheduler.config.prediction_type == "epsilon": + beta_prod_t = beta_prod_t[:, None, None, None, None] + alpha_prod_t = alpha_prod_t[:, None, None, None, None] + pred_original_sample = (x_t - beta_prod_t ** (0.5) * pred_noise) / alpha_prod_t ** (0.5) + else: + raise NotImplementedError("This prediction type is not implemented yet") + + # Clip "predicted x_0" + if ddim_scheduler.config.clip_sample: + pred_original_sample = torch.clamp(pred_original_sample, -1, 1) + return pred_original_sample + + +def next_step( + model_output: Union[torch.FloatTensor, np.ndarray], + timestep: int, + sample: Union[torch.FloatTensor, np.ndarray], + ddim_scheduler, +): + timestep, next_timestep = ( + min(timestep - ddim_scheduler.config.num_train_timesteps // ddim_scheduler.num_inference_steps, 999), + timestep, + ) + alpha_prod_t = ddim_scheduler.alphas_cumprod[timestep] if timestep >= 0 else ddim_scheduler.final_alpha_cumprod + alpha_prod_t_next = ddim_scheduler.alphas_cumprod[next_timestep] + beta_prod_t = 1 - alpha_prod_t + next_original_sample = (sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5 + next_sample_direction = (1 - alpha_prod_t_next) ** 0.5 * model_output + next_sample = alpha_prod_t_next**0.5 * next_original_sample + next_sample_direction + return next_sample + + +def get_noise_pred_single(latents, t, context, unet): + noise_pred = unet(latents, t, encoder_hidden_states=context)["sample"] + return noise_pred + + +@torch.no_grad() +def ddim_loop(pipeline, ddim_scheduler, latent, num_inv_steps, prompt): + context = init_prompt(prompt, pipeline) + uncond_embeddings, cond_embeddings = context.chunk(2) + all_latent = [latent] + latent = latent.clone().detach() + for i in tqdm(range(num_inv_steps)): + t = ddim_scheduler.timesteps[len(ddim_scheduler.timesteps) - i - 1] + noise_pred = get_noise_pred_single(latent, t, cond_embeddings, pipeline.unet) + latent = next_step(noise_pred, t, latent, ddim_scheduler) + all_latent.append(latent) + return all_latent + + +@torch.no_grad() +def ddim_inversion(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt=""): + ddim_latents = ddim_loop(pipeline, ddim_scheduler, video_latent, num_inv_steps, prompt) + return ddim_latents + + +def plot_loss_chart(save_path: str, *args): + # Creating the plot + plt.figure() + for loss_line in args: + plt.plot(loss_line[1], loss_line[2], label=loss_line[0]) + plt.xlabel("Step") + plt.ylabel("Loss") + plt.legend() + + # Save the figure to a file + plt.savefig(save_path) + + # Close the figure to free memory + plt.close() + + +CRED = "\033[91m" +CEND = "\033[0m" + + +def red_text(text: str): + return f"{CRED}{text}{CEND}" + + +log_loss = nn.BCELoss(reduction="none") + + +def cosine_loss(vision_embeds, audio_embeds, y): + sims = nn.functional.cosine_similarity(vision_embeds, audio_embeds) + # sims[sims!=sims] = 0 # remove nan + # sims = sims.clamp(0, 1) + loss = log_loss(sims.unsqueeze(1), y).squeeze() + return loss + + +def save_image(image, save_path): + # input size (C, H, W) + image = (image / 2 + 0.5).clamp(0, 1) + image = (image * 255).to(torch.uint8) + image = transforms.ToPILImage()(image) + # Save the image copy + image.save(save_path) + + # Close the image file + image.close() + + +def gather_loss(loss, device): + # Sum the local loss across all processes + local_loss = loss.item() + global_loss = torch.tensor(local_loss, dtype=torch.float32).to(device) + dist.all_reduce(global_loss, op=dist.ReduceOp.SUM) + + # Calculate the average loss across all processes + global_average_loss = global_loss.item() / dist.get_world_size() + return global_average_loss + + +def gather_video_paths_recursively(input_dir): + print(f"Recursively gathering video paths of {input_dir} ...") + paths = [] + gather_video_paths(input_dir, paths) + return paths + + +def gather_video_paths(input_dir, paths): + for file in sorted(os.listdir(input_dir)): + if file.endswith(".mp4"): + filepath = os.path.join(input_dir, file) + paths.append(filepath) + elif os.path.isdir(os.path.join(input_dir, file)): + gather_video_paths(os.path.join(input_dir, file), paths) + + +def count_video_time(video_path): + video = cv2.VideoCapture(video_path) + + frame_count = video.get(cv2.CAP_PROP_FRAME_COUNT) + fps = video.get(cv2.CAP_PROP_FPS) + return frame_count / fps diff --git a/latentsync/whisper/audio2feature.py b/latentsync/whisper/audio2feature.py new file mode 100644 index 0000000000000000000000000000000000000000..4e4333772d95c9ec819edca5c2ce6159f8f8e0f4 --- /dev/null +++ b/latentsync/whisper/audio2feature.py @@ -0,0 +1,166 @@ +# Adapted from https://github.com/TMElyralab/MuseTalk/blob/main/musetalk/whisper/audio2feature.py + +from .whisper import load_model +import numpy as np +import torch +import os + + +class Audio2Feature: + def __init__( + self, + model_path="checkpoints/whisper/tiny.pt", + device=None, + audio_embeds_cache_dir=None, + num_frames=16, + ): + self.model = load_model(model_path, device) + self.audio_embeds_cache_dir = audio_embeds_cache_dir + self.num_frames = num_frames + self.embedding_dim = self.model.dims.n_audio_state + + def get_sliced_feature(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 = [] + + 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 = torch.cat(selected_feature, dim=0) + selected_feature = selected_feature.reshape(-1, self.embedding_dim) # 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, self.embedding_dim) # 50*384 + selected_feature = torch.from_numpy(selected_feature) + return selected_feature, selected_idx + + def feature2chunks(self, feature_array, fps, audio_feat_length=[2, 2]): + whisper_chunks = [] + whisper_idx_multiplier = 50.0 / fps + i = 0 + print(f"video in {fps} FPS, audio idx in 50FPS") + + while True: + 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: str): + # 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 = torch.from_numpy(np.concatenate(embed_list, axis=0)) + return concatenated_array + + def audio2feat(self, audio_path): + if self.audio_embeds_cache_dir == "" or self.audio_embeds_cache_dir is None: + return self._audio2feat(audio_path) + + audio_embeds_cache_path = os.path.join(self.audio_embeds_cache_dir, os.path.basename(audio_path) + ".pt") + + if os.path.isfile(audio_embeds_cache_path): + try: + audio_feat = torch.load(audio_embeds_cache_path) + except Exception as e: + print(f"{type(e).__name__} - {e} - {audio_embeds_cache_path}") + os.remove(audio_embeds_cache_path) + audio_feat = self._audio2feat(audio_path) + torch.save(audio_feat, audio_embeds_cache_path) + else: + audio_feat = self._audio2feat(audio_path) + torch.save(audio_feat, audio_embeds_cache_path) + + return audio_feat + + def crop_overlap_audio_window(self, audio_feat, start_index): + selected_feature_list = [] + for i in range(start_index, start_index + self.num_frames): + selected_feature, selected_idx = self.get_sliced_feature( + feature_array=audio_feat, vid_idx=i, audio_feat_length=[2, 2], fps=25 + ) + selected_feature_list.append(selected_feature) + mel_overlap = torch.stack(selected_feature_list) + return mel_overlap + + +if __name__ == "__main__": + audio_encoder = Audio2Feature(model_path="checkpoints/whisper/tiny.pt") + audio_path = "assets/demo1_audio.wav" + array = audio_encoder.audio2feat(audio_path) + print(array.shape) + fps = 25 + whisper_idx_multiplier = 50.0 / fps + + i = 0 + print(f"video in {fps} FPS, audio idx in 50FPS") + while True: + start_idx = int(i * whisper_idx_multiplier) + selected_feature, selected_idx = audio_encoder.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/latentsync/whisper/whisper/__init__.py b/latentsync/whisper/whisper/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..60c88fbf2a0472cbe111aee1c4f5980317951ead --- /dev/null +++ b/latentsync/whisper/whisper/__init__.py @@ -0,0 +1,119 @@ +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/latentsync/whisper/whisper/__main__.py b/latentsync/whisper/whisper/__main__.py new file mode 100644 index 0000000000000000000000000000000000000000..bc9b04a39283f71082f46ec8bb11fe15f8cf75b2 --- /dev/null +++ b/latentsync/whisper/whisper/__main__.py @@ -0,0 +1,4 @@ +from .transcribe import cli + + +cli() diff --git a/latentsync/whisper/whisper/assets/gpt2/merges.txt b/latentsync/whisper/whisper/assets/gpt2/merges.txt new file mode 100644 index 0000000000000000000000000000000000000000..6636bda4a1fd7a63653dffb22683b8162c8de956 --- /dev/null +++ b/latentsync/whisper/whisper/assets/gpt2/merges.txt @@ -0,0 +1,50001 @@ +#version: 0.2 - Trained by `huggingface/tokenizers` +ฤ  t +ฤ  a +h e +i n +r e +o n +ฤ t he +e r +ฤ  s +a t +ฤ  w +ฤ  o +e n +ฤ  c +i t +i s +a n +o r +e s +ฤ  b +e d +ฤ  f +in g +ฤ  p +o u +ฤ a n +a l +a r +ฤ t o +ฤ  m +ฤ o f +ฤ  in +ฤ  d +ฤ  h +ฤ an d +i c +a s +l e +ฤ t h +i on +o m +l l +en t +ฤ  n +ฤ  l +s t +ฤ  re +v e +ฤ  e +r o +l y +ฤ b e +ฤ  g +ฤ  T +c t +ฤ  S +i d +o t +ฤ  I +u t +e t +ฤ  A +ฤ  is +ฤ  on +i m +a m +o w +a y +a d +s e +ฤ th at +ฤ  C +i g +ฤ f or +a c +ฤ  y +v er +u r +ฤ  u +l d +ฤ s t +ฤ  M +' s +ฤ  he +ฤ  it +at ion +it h +i r +c e +ฤ y ou +i l +ฤ  B +ฤ w h +o l +ฤ  P +ฤ w ith +ฤ  1 +t er +c h +ฤ a s +ฤ w e +ฤ  ( +n d +i ll +ฤ  D +i f +ฤ  2 +a g +er s +k e +ฤ  " +ฤ  H +e m +ฤ c on +ฤ  W +ฤ  R +he r +ฤ w as +ฤ  r +o d +ฤ  F +u l +at e +ฤ a t +r i +p p +o re +ฤ T he +ฤ s e +u s +ฤ p ro +ฤ h a +u m +ฤ a re +ฤ d e +a in +an d +ฤ o r +ig h +es t +is t +a b +r om +ฤ  N +t h +ฤ c om +ฤ  G +u n +o p +0 0 +ฤ  L +ฤ n ot +es s +ฤ e x +ฤ  v +re s +ฤ  E +e w +it y +an t +ฤ b y +e l +o s +or t +o c +q u +ฤ f rom +ฤ ha ve +ฤ s u +i ve +ou ld +ฤ s h +ฤ th is +n t +r a +p e +igh t +ar t +m ent +ฤ a l +u st +en d +- - +al l +ฤ  O +ac k +ฤ c h +ฤ  le +i es +re d +ar d +รข ฤข +ou t +ฤ  J +ฤ a b +e ar +i v +al ly +ou r +o st +g h +p t +ฤ p l +as t +ฤ c an +a k +om e +u d +T he +ฤ h is +ฤ d o +ฤ g o +ฤ h as +g e +' t +ฤ  U +r ou +ฤ s a +ฤ  j +ฤ b ut +ฤ w or +ฤ a ll +e ct +ฤ  k +am e +ฤ w ill +o k +ฤ w he +ฤ the y +id e +0 1 +f f +ic h +p l +t her +ฤ t r +. . +ฤ in t +i e +u re +ag e +ฤ n e +i al +a p +in e +ic e +ฤ m e +ฤ o ut +an s +on e +on g +ion s +ฤ wh o +ฤ  K +ฤ u p +ฤ the ir +ฤ a d +ฤ  3 +ฤ u s +at ed +ou s +ฤ m ore +u e +o g +ฤ S t +in d +i ke +ฤ s o +im e +p er +. 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14388, "ฤ รฌฤฟฤบ": 14389, "ฤ twelve": 14390, "ฤ Champions": 14391, "รฉฤฟล€รฅยธยธ": 14392, "ร‘ฤฃรยป": 14393, "ฤ 2005": 14394, "pm": 14395, "ฤ onde": 14396, "ฤ diffรƒยฉ": 14397, "ฤ Chall": 14398, "ฤ difficulties": 14399, "ฤ garage": 14400, "ฤ dรƒยก": 14401, "รƒยผnk": 14402, "ฤ รซยฌยผ": 14403, "ฤ tran": 14404, "ฤ submitted": 14405, "zw": 14406, "ร™ฤชร˜ยง": 14407, "ฤ ark": 14408, "ฤ รฌฤฆยฑ": 14409, "ฤ grocery": 14410, "รยพรยฝรยฐ": 14411, "iere": 14412, "ฤ aest": 14413, "ฤ exhibition": 14414, "ฤ rรƒยฉs": 14415, "ฤ consistency": 14416, "ฤ cookie": 14417, "รยฝรยตรยน": 14418, "ฤ replacement": 14419, "รฆยฒยน": 14420, "ฤ Sem": 14421, "ฤ รฌฤคยฌรฌฤผยฉ": 14422, "800": 14423, "ฤ genes": 14424, "ฤ transaction": 14425, "ฤ EL": 14426, "ฤ durante": 14427, "ibles": 14428, "ฤ Eat": 14429, "tail": 14430, "issance": 14431, "ฤ toss": 14432, "ฤ survived": 14433, "ฤ offices": 14434, "ฤ supportive": 14435, "Where": 14436, "ฤ toutes": 14437, "ฤ รซยงฤซ": 14438, "ฤ jokes": 14439, "ieron": 14440, "apers": 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14495, "รฆล‚ยท": 14496, "EST": 14497, "รฃฤฃฤครฃฤคฤฌ": 14498, "ฤ Wang": 14499, "ฤ NAT": 14500, "ฤ aug": 14501, "ฤ desktop": 14502, "ฤ eternal": 14503, "ฤ รฌฤคยฌรฌฤญยค": 14504, "ฤ Constitution": 14505, "รฌฤคยฌรซ": 14506, "ร—ฤปร—ฤพ": 14507, "pres": 14508, "ฤ รยขร‘ฤญ": 14509, "ฤ interf": 14510, "ฤ lists": 14511, "ฤ fights": 14512, "ften": 14513, "ฤ Iowa": 14514, "ฤ motivated": 14515, "ฤ Hosp": 14516, "ฤ elsewhere": 14517, "ฤ paths": 14518, "ฤ instances": 14519, "Bl": 14520, "range": 14521, "รกยปยฑ": 14522, "ฤ Sit": 14523, "mana": 14524, "ฤ รฌฤญฤพรฌล€ฤณ": 14525, "ฤ mรƒยฌnh": 14526, "ansas": 14527, "ฤ sna": 14528, "ฤ philosoph": 14529, "ฤ passe": 14530, "ร†ยฐรกยปฤฟi": 14531, "akh": 14532, "ental": 14533, "ฤ ihn": 14534, "ructor": 14535, "ฤ รยฒรยฐร‘ฤช": 14536, "ฤ generous": 14537, "ฤ pivot": 14538, "รยฟรยพรยป": 14539, "ฤ jamais": 14540, "ฤ coment": 14541, "ฤ Lew": 14542, "odzi": 14543, "ฤ Xbox": 14544, "ฤ รยฒรยพรยด": 14545, "ฤ consent": 14546, "ฤซรฌล€ยฅ": 14547, "ฤ dispar": 14548, 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"รฉฤคฤฆรฆฤพฤซ": 15569, "ฤ Jessica": 15570, "ฤ Joy": 15571, "ฤ sophistic": 15572, "รยตรยฝรยธรยธ": 15573, "รฐฤฟฤบ": 15574, "ฤ chili": 15575, "ฤ Type": 15576, "ฤ proteins": 15577, "ฤ presenting": 15578, "alia": 15579, "รฌฤผยธ": 15580, "ฤ Major": 15581, "ฤ molecule": 15582, "umer": 15583, "ฤ collapse": 15584, "ฤ Anyways": 15585, "ฤ Mountain": 15586, "anted": 15587, "รฃฤขฤฒ": 15588, "ฤ รยฒรยธรยดรยตรยพ": 15589, "รฆยฐยด": 15590, "Aud": 15591, "ฤ conqu": 15592, "ฤ voll": 15593, "ฤ knit": 15594, "ฤ membr": 15595, "ฤ Market": 15596, "ฤ dari": 15597, "ฤ calculated": 15598, "รยณรยธ": 15599, "ฤ shrimp": 15600, "ฤ Mu": 15601, "ฤ รยฟร‘ฤขรยพร‘ฤค": 15602, "ฤ รฌฤบฤฃรฌฤฅฤฃ": 15603, "ฤ productivity": 15604, "ฤ cognitive": 15605, "ฤ Heb": 15606, "ictions": 15607, "รชยฒยฝ": 15608, "ฤ crรƒยฉ": 15609, "fรƒยถr": 15610, "ฤ praying": 15611, "ashi": 15612, "ฤ Tik": 15613, "รƒยณr": 15614, "wen": 15615, "ร‘ฤฎร‘ฤฐ": 15616, "ixo": 15617, "ฤ (\"": 15618, "ฤ ร‘ฤครยตรยป": 15619, "ฤ รฌฤธยดรซฤธยค": 15620, "ฤ รยฟรยตร‘ฤขรยตรยด": 15621, "ฤ Drive": 15622, "รฃฤขฤณ": 15623, "ฤ Equ": 15624, "ฤ equilibrium": 15625, "ฤ describes": 15626, "รยฝรยตรยต": 15627, "42": 15628, "ฤ Current": 15629, "yy": 15630, "ฤ absorb": 15631, "ฤ soldier": 15632, "ders": 15633, "ฤ testimony": 15634, "ฤ decline": 15635, "ฤพรซยกฤพ": 15636, "gage": 15637, "ฤ inspire": 15638, "lapping": 15639, "ฤ spinning": 15640, "ฤ slavery": 15641, "ฤ facial": 15642, "ฤ traditions": 15643, "รƒยกrios": 15644, "ฤ Hospital": 15645, "ฤ nest": 15646, "ฤ รซฤชฤฆ": 15647, "ฤ toi": 15648, "ฤ fears": 15649, "รฌฤงยจ": 15650, "ฤ Muh": 15651, "ฤ graduation": 15652, "ฤ impacted": 15653, "ฤ aunt": 15654, "ฤ Lets": 15655, "ฤ aluminum": 15656, "ฤ dominant": 15657, "ฤ Davis": 15658, "ฤ Navy": 15659, "ฤ compt": 15660, "oples": 15661, "ฤ estava": 15662, "รจยฅ": 15663, "ฤ scal": 15664, "ฤ preserve": 15665, "ฤ Opp": 15666, "ฤ practically": 15667, "ฤ magnitude": 15668, "ฤ fitting": 15669, "ฤ coordinate": 15670, "ฤ furniture": 15671, "ฤ Famil": 15672, "ฤ explosion": 15673, "ฤ documentary": 15674, "ฤ Script": 15675, "ฤ portray": 15676, "mat": 15677, "ฤ scheduled": 15678, "ฤ dynamics": 15679, "phy": 15680, "aky": 15681, "ฤ UI": 15682, "Che": 15683, "ฤ continuously": 15684, "ฤ Prov": 15685, "รฅยฐฤณ": 15686, "ร‘ฤฅรยท": 15687, "rah": 15688, "ฤ gerne": 15689, "proof": 15690, "ฤ secretary": 15691, "ฤ Patreon": 15692, "scream": 15693, "ฤ Kids": 15694, "รกยปฤตi": 15695, "ฤ kg": 15696, "ฤ uncertainty": 15697, "ฤ รยบรยฐรยถรยด": 15698, "ฤ mitig": 15699, "ฤ reads": 15700, "รฅยทยฒ": 15701, "ฤ Ru": 15702, "ฤ priest": 15703, "ฤ รยฝรยตรยด": 15704, "ฤ limitations": 15705, "ฤ float": 15706, "600": 15707, "ฤ Toy": 15708, "ฤ Jimmy": 15709, "ฤ offensive": 15710, "eni": 15711, "ฤ Xi": 15712, "ฤ eyebr": 15713, "ฤ Turk": 15714, "ฤ accidentally": 15715, "ฤ ohne": 15716, "ฤ Saud": 15717, "95": 15718, "ฤ Dutch": 15719, "รยฐรยฝร‘ฤฃ": 15720, "ฤ Seattle": 15721, "ฤ รซฤตยฑ": 15722, "check": 15723, "kร„ฤป": 15724, "ฤ contributions": 15725, "ฤ beside": 15726, "ฤ quindi": 15727, "ฤ flew": 15728, "รฆฤนยถ": 15729, "ร˜ยฐร˜ยง": 15730, "ฤ LO": 15731, "ฤ waist": 15732, "ฤ EV": 15733, "ฤ holidays": 15734, "jon": 15735, "ฤ misunder": 15736, "ร‘ฤฑรยฝ": 15737, "ฤ bout": 15738, "ฤ dimin": 15739, "รกยบยฝ": 15740, "รƒยณl": 15741, "ฤ Grace": 15742, "ฤ inputs": 15743, "ฤ deny": 15744, "ฤ forming": 15745, "ฤ Bild": 15746, "ฤ adequ": 15747, "ฤ folk": 15748, "ฤ rejected": 15749, "semb": 15750, "ฤ frustrated": 15751, "open": 15752, "ฤ Better": 15753, "ilon": 15754, "ฤ towel": 15755, "ฤ differential": 15756, "ฤ sacred": 15757, "ฤ sail": 15758, "รฉฤฉฤฎ": 15759, "entimes": 15760, "ฤ gentleman": 15761, "ฤ iconic": 15762, "ฤ comparing": 15763, "ฤ sagt": 15764, "ฤ texts": 15765, "ฤ grandma": 15766, "ฤ rolls": 15767, "ฤ contents": 15768, "รคยธฤฏรฅยฅยฝ": 15769, "รยพร‘ฤฃร‘ฤฃ": 15770, "ฤ suspension": 15771, "roit": 15772, "ยฆยผ": 15773, "ฤ assez": 15774, "ฤ dort": 15775, "ฤ Math": 15776, "ฤ Victor": 15777, "ฤ JavaScript": 15778, "รคยธฤฏรฅยฐฤฏ": 15779, 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16152, "ฤ righteous": 16153, "ฤ Magic": 16154, "ฤ relates": 16155, "ฤ trucks": 16156, "ฤ 1960": 16157, "รฅฤชยฅ": 16158, "ฤ appet": 16159, "ฤ snacks": 16160, "ฤ Summer": 16161, "ฤ yรƒยผz": 16162, "ฤ pris": 16163, "ฤ Mexican": 16164, "ฤ transparen": 16165, "ฤ minority": 16166, "ฤ verte": 16167, "ฤ lassen": 16168, "46": 16169, "รยปรยตรยบ": 16170, "รƒยฉp": 16171, "ฤ ร‘ฤฆรยธรยปร‘ฤฎ": 16172, "ฤ iyi": 16173, "ฤ span": 16174, "รญฤทฤบรฌยงฤข": 16175, "ฤ indicated": 16176, "quar": 16177, "ฤ scholarship": 16178, "ฤ LGBT": 16179, "ฤ historically": 16180, "รƒยณร…ฤค": 16181, "ฤ minist": 16182, "ฤ penet": 16183, "ฤ Rap": 16184, "ฤ conservation": 16185, "รงฤฝยด": 16186, "ฤ Honey": 16187, "ฤ Bei": 16188, "idel": 16189, "ฤ responsibilities": 16190, "ฤ messy": 16191, "ฤ Except": 16192, "ORE": 16193, "ฤ initiatives": 16194, "ฤ junior": 16195, "ฤ designers": 16196, "ฤ exploration": 16197, "ฤ sponsor": 16198, "ฤ mobility": 16199, "ฤ integ": 16200, "lando": 16201, "ฤ bark": 16202, "ฤ indicates": 16203, 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"รฌยฒยด": 16260, "ฤ Action": 16261, "รฅล": 16262, "ฤ penalty": 16263, "ฤ Kaz": 16264, "'?": 16265, "ฤ cried": 16266, "ฤ canvas": 16267, "fte": 16268, "ฤ exclud": 16269, "ยธรซยกฤพ": 16270, "ฤ emphasis": 16271, "ฤ enzy": 16272, "ฤ Hou": 16273, "ฤ overseas": 16274, "รƒลƒamos": 16275, "รฅยธยซ": 16276, "รƒยถglich": 16277, "ฤ headphones": 16278, "cn": 16279, "ฤ Age": 16280, "ฤ akan": 16281, "ฤ characteristic": 16282, "รญฤทฤบรซยฉยด": 16283, "gets": 16284, "ฤ รซยถฤช": 16285, "ฤ rival": 16286, "ฤ borders": 16287, "emente": 16288, "emรƒยกs": 16289, "ฤ yol": 16290, "ฤ compe": 16291, "enders": 16292, "ร„ยฑndan": 16293, "ฤ mรƒยถglich": 16294, "ฤ bubbles": 16295, "natural": 16296, "ฤ armed": 16297, "ฤ elabor": 16298, "ฤ รฌฤฟยดรซยฒฤช": 16299, "ฤ washed": 16300, "รŽยฟรฤงรŽยผรŽยต": 16301, "รจยซฤญ": 16302, "ฤ flavors": 16303, "ฤ existe": 16304, "ฤ prest": 16305, "ฤ Thema": 16306, "รยพรยฟร‘ฤขรยพร‘ฤฃ": 16307, "eron": 16308, "UE": 16309, "eri": 16310, "ฤ concer": 16311, "ฤ aixรƒยฒ": 16312, "รฅฤงยฉ": 16313, "ฤ protective": 16314, "ฤ รยทรยฝรยฐร‘ฤฐ": 16315, "ฤ รซฤคล‚": 16316, "ฤ III": 16317, "ฤ meer": 16318, "ฤ Shop": 16319, "lli": 16320, "ฤ Order": 16321, "ฤ MY": 16322, "ฤ Ghost": 16323, "รฃฤคฤครฃฤฃฤจ": 16324, "adel": 16325, "ฤ stole": 16326, "ฤ releasing": 16327, "ฤ Comment": 16328, "ฤ trains": 16329, "รซยชฤง": 16330, "ฤ wissen": 16331, "ensed": 16332, "ฤ descend": 16333, "ฤ fier": 16334, "ฤ radi": 16335, "ฤ persu": 16336, "รงยข": 16337, "ฤ รยผรยฝ": 16338, "ฤ Dest": 16339, "ฤ worries": 16340, "itet": 16341, "bas": 16342, "ฤ stab": 16343, "name": 16344, "oric": 16345, "ฤ Close": 16346, "ฤ alumni": 16347, "ฤ Self": 16348, "ffe": 16349, "itating": 16350, "atherine": 16351, "ฤ Rights": 16352, "ฤ ellos": 16353, "ฤ warrant": 16354, "ฤ nerve": 16355, "ฤ vegetable": 16356, "ฤ Teil": 16357, "ฤ รชยฐฤปรฌฤฟยด": 16358, "RY": 16359, "ฤ sustainability": 16360, "ฤ steht": 16361, "ฤ brid": 16362, "adaร…ล": 16363, "ฤ tv": 16364, "ฤ duration": 16365, "ฤ pessoa": 16366, "ฤ metrics": 16367, 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16530, "ฤ fortune": 16531, "ฤ cleaner": 16532, "ฤ Robin": 16533, "ฤ saus": 16534, "ฤ Geld": 16535, "ฤ kat": 16536, "obs": 16537, "ฤ olur": 16538, "ฤ matt": 16539, "ฤ questa": 16540, "ฤ suggestion": 16541, "encer": 16542, "รยพร‘ฤฃร‘ฤค": 16543, "ฤ radar": 16544, "ฤ รฌล€ยก": 16545, "isha": 16546, "ร ยฎยจ": 16547, "รฃฤคฤตรฃฤฃยช": 16548, "jes": 16549, "ฤ veel": 16550, "รฌฤคยฐ": 16551, "ฤ authors": 16552, "รฃฤขฤฐ": 16553, "plan": 16554, "ฤ collaborative": 16555, "ฤ instinct": 16556, "ฤ farming": 16557, "auge": 16558, "Edu": 16559, "ฤ membership": 16560, "ฤ simultaneously": 16561, "ฤ bake": 16562, "ฤ kรƒยค": 16563, "ฤ lectures": 16564, "ร‘ฤฉรยตร‘ฤฃ": 16565, "ฤ prendre": 16566, "ฤ collaps": 16567, "ฤ Saya": 16568, "ฤ Fut": 16569, "ฤ yog": 16570, "ฤ Rather": 16571, "ร˜ยฑร™ฤฌ": 16572, "ฤ camps": 16573, "รยพรยปรยพรยด": 16574, "ฤ simulation": 16575, "ฤ Mak": 16576, "Laughs": 16577, "ฤ grey": 16578, "ฤ sentences": 16579, "yen": 16580, "ฤ Unless": 16581, "Je": 16582, "ฤ Satan": 16583, 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16684, "ฤ รฌฤนฤฆ": 16685, "ฤ sous": 16686, "ฤ sphere": 16687, "BY": 16688, "ฤ Ped": 16689, "igned": 16690, "ฤ wheat": 16691, "ฤ unders": 16692, "ฤ evolve": 16693, "ฤ declar": 16694, "ฤ lightly": 16695, "ฤ identifying": 16696, "รฆฤฆฤฑรฆฤขฤฟ": 16697, "ฤ legendary": 16698, "ฤ genuine": 16699, "ฤ grind": 16700, "ฤ Une": 16701, "geben": 16702, "ฤ bicy": 16703, "ฤ jumps": 16704, "ฤ province": 16705, "ziร„ฤป": 16706, "ฤ ร—ฤฒร—ล‚ร—ฤป": 16707, "ฤ hoc": 16708, "ฤ รยฑรยป": 16709, "ฤ Grad": 16710, "ฤ revenge": 16711, "ฤ ร˜ยงร™ฤฆร˜ยช": 16712, "ooh": 16713, "รฆฤญฤพ": 16714, "รยฐร‘ฤจรยธรยธ": 16715, "รฅยนยณ": 16716, "ฤ electro": 16717, "ฤ รซฤฒฤฒ": 16718, "รฃฤฃยงรฃฤฃยฏ": 16719, "ฤ fals": 16720, "riel": 16721, "oker": 16722, "ฤ Excellent": 16723, "ฤ Morgan": 16724, "ฤ brick": 16725, "ฤ substantial": 16726, "ฤ pollution": 16727, "ฤ Tรƒยผr": 16728, "ฤ Evet": 16729, "ฤ lung": 16730, "รฃฤฃฤธ": 16731, "ร—ฤปร—ยฉ": 16732, "ommes": 16733, "ฤ realizing": 16734, "ฤ humble": 16735, "ฤ Lock": 16736, "ฤ bod": 16737, "ฤ รฌฤธยธ": 16738, "ฤ peers": 16739, "uzz": 16740, "ฤ embedded": 16741, "ฤ claro": 16742, "ฤ aggreg": 16743, "ฤ employers": 16744, "ฤ Raj": 16745, "ฤ รฃฤฃยจ": 16746, "ฤ Yi": 16747, "ฤ jeu": 16748, "aters": 16749, "ฤ strikes": 16750, "nos": 16751, "autres": 16752, "dr": 16753, "opher": 16754, "ฤ Apparently": 16755, "รญฤบฤฆ": 16756, "ฤ infant": 16757, "ร˜ยงร˜ยจ": 16758, "ร‘ฤคร‘ฤญ": 16759, "รญฤฝ": 16760, "รšยฏ": 16761, "ฤ redes": 16762, "acaร„ลร„ยฑm": 16763, "ฤ DAVID": 16764, "ฤ Chicken": 16765, "ฤ perspectives": 16766, "ฤ viewer": 16767, "ฤ shar": 16768, "ฤ รยฟร‘ฤขรยพรยธรยท": 16769, "ligt": 16770, "eros": 16771, "itable": 16772, "รยธรยปรยพร‘ฤฃร‘ฤฎ": 16773, "ฤ difรƒลƒ": 16774, "ยดรซฤฏยฐ": 16775, "ฤ retired": 16776, "ฤ thats": 16777, "zenie": 16778, "beiten": 16779, "ฤ mycket": 16780, "ฤ Rab": 16781, "ฤ inflamm": 16782, "รฌยฐยฎ": 16783, "ฤ dum": 16784, "ฤ daddy": 16785, "รฆฤพล": 16786, "ฤ immers": 16787, "ฤ playlist": 16788, "ร ยฏฤจ": 16789, "ฤ traum": 16790, "ฤ refuse": 16791, "step": 16792, "ร ยฎฤผ": 16793, "cup": 16794, "ฤ pops": 16795, "rimin": 16796, "ayร„ยฑm": 16797, "ฤ ald": 16798, "ฤ unnecess": 16799, "ฤ dah": 16800, "ฤ Irish": 16801, "ฤ compr": 16802, "laร…ล": 16803, "TP": 16804, "ฤ translated": 16805, "Sc": 16806, "ceร„ลim": 16807, "ยดฤฒ": 16808, "ฤ drei": 16809, "ฤ รยปร‘ฤฐรยดรยตรยน": 16810, "ฤ quiero": 16811, "ฤ hele": 16812, "zlich": 16813, "ฤ apples": 16814, "ฤ districts": 16815, "ฤ credits": 16816, "ฤ asp": 16817, "ฤ รซฤญยจ": 16818, "oral": 16819, "รฅยฝยฑ": 16820, "ฤ stepping": 16821, "ฤ Va": 16822, "ฤ gains": 16823, "65": 16824, "ฤ nuestra": 16825, "eday": 16826, "assador": 16827, "ฤ Lind": 16828, "ฤ crops": 16829, "ciendo": 16830, "igue": 16831, "ฤ bana": 16832, "Am": 16833, "ฤ pent": 16834, "ฤ addiction": 16835, "ฤ packaging": 16836, "รƒยคd": 16837, "ยชยจ": 16838, "ฤ perquรƒยจ": 16839, "ฤ campaigns": 16840, "ฤ steep": 16841, "ฤ neue": 16842, "ฤ embarrassed": 16843, "ฤ distinction": 16844, "itzer": 16845, "รฅฤณฤฌ": 16846, "ฤ registration": 16847, "ฤ llam": 16848, "ฤ Almighty": 16849, "liest": 16850, "ฤ uz": 16851, "nak": 16852, "รงยบ": 16853, "ฤ teraz": 16854, "iamente": 16855, "ฤ transactions": 16856, "ฤ cรƒยดt": 16857, "ฤ switched": 16858, "ฤ combo": 16859, "ฤ prayers": 16860, "ฤ internship": 16861, "ฤ addresses": 16862, "ฤ charity": 16863, "ฤ WOO": 16864, "ฤ bait": 16865, "รจยฟฤฉ": 16866, "ฤ รฏยฟยฝ": 16867, "ฤ fica": 16868, "ฤ Tyler": 16869, "aru": 16870, "ฤ atoms": 16871, "ฤ Level": 16872, "ฤ รยฟรยพร‘ฤครยพรยผ": 16873, "ฤ fame": 16874, "ulk": 16875, "ฤ teaches": 16876, "ฤ rebuild": 16877, "รยตรยดร‘ฤฎ": 16878, "ฤ Indonesia": 16879, "ushi": 16880, "ฤ Short": 16881, "ฤ ensuring": 16882, "fs": 16883, "ele": 16884, "ฤ marginal": 16885, "ฤ conclude": 16886, "amt": 16887, "ฤ verify": 16888, "ฤ McDonald": 16889, "ฤ skal": 16890, "ฤ reconst": 16891, "ฤ Mann": 16892, "ฤ basement": 16893, "ฤ transformed": 16894, "ฤ occasionally": 16895, "zone": 16896, "ฤ Dans": 16897, "ฤ รยบรยฐรยบรยพรยน": 16898, "ฤ diagnosed": 16899, "ฤ รฤฆรŽยฑ": 16900, "ฤ commands": 16901, "ฤ presidential": 16902, "ฤ abb": 16903, "ฤ bracket": 16904, "ฤ Lem": 16905, "รƒยฅng": 16906, "ฤ favorites": 16907, "ฤ revol": 16908, "ฤ รญฤฌยน": 16909, "ฤ harass": 16910, "รฉฤง": 16911, "ฤ cleans": 16912, "stรƒยคnd": 16913, "ฤ knocked": 16914, "ฤ peoples": 16915, "ฤ musicians": 16916, "ฤ mutual": 16917, "ฤ Cold": 16918, "88": 16919, "zej": 16920, "atie": 16921, "ฤ Honor": 16922, "ฤ obsessed": 16923, "ฤ MUSIC": 16924, "ฤ Break": 16925, "รƒยบng": 16926, "ฤ modify": 16927, "ฤ sรƒยถyle": 16928, "ฤ ร—ล€ร—ฤถ": 16929, "ฤ Online": 16930, "fo": 16931, "ฤ Miller": 16932, "ฤ liking": 16933, "ฤ inhab": 16934, "ฤ gratitude": 16935, "ฤ Journal": 16936, "arness": 16937, "John": 16938, "ฤ Git": 16939, "รฅฤซฤฝ": 16940, "ฤ sincere": 16941, "ฤ Sci": 16942, "ฤ Eli": 16943, "ฤ symbols": 16944, "ฤ manually": 16945, "รŽยตรฤค": 16946, "ฤ รยฒร‘ฤธรยด": 16947, "ฤ Fat": 16948, "ฤ labels": 16949, "ฤ sophisticated": 16950, "umps": 16951, "ฤ releases": 16952, "ฤ 47": 16953, "ฤ OM": 16954, "รชยฐฤขรซ": 16955, "ฤ Bien": 16956, "ฤ Ref": 16957, "รจยจฤบ": 16958, "ฤ Sta": 16959, "ฤ Egg": 16960, "ฤ indicator": 16961, "pson": 16962, "ฤ nasร„ยฑl": 16963, "Right": 16964, "ฤ convey": 16965, "ฤ knot": 16966, "ฤ connects": 16967, "ulas": 16968, "ฤ preced": 16969, "ฤ inequality": 16970, "amiento": 16971, "ฤ reply": 16972, "OY": 16973, "ฤ dismiss": 16974, "ฤ รซฤฒฤพ": 16975, "รงฤฆยก": 16976, "ฤ ร‘ฤงรยพร‘ฤขรยพร‘ฤชรยพ": 16977, "ฤ mรƒยฉd": 16978, "ฤ randomly": 16979, "ฤ Ont": 16980, "uard": 16981, "ฤ pulls": 16982, "ฤ ร‘ฤครยตรยฟรยตร‘ฤขร‘ฤฎ": 16983, "ฤ Need": 16984, "ฤ Soft": 16985, "ฤ strengths": 16986, "ฤ goed": 16987, "umen": 16988, "รฆลƒยป": 16989, "ฤ รญฤฐยธ": 16990, "ฤ รยดรยพรยฑ": 16991, "ฤ clarity": 16992, "ฤ Ai": 16993, "ฤ balloon": 16994, "ฤ Pand": 16995, "ฤ รฌฤทฤฆรซฤญ": 16996, "ฤ shiny": 16997, "ฤ smallest": 16998, "onia": 16999, "hill": 17000, "oting": 17001, "ฤ eing": 17002, "ฤ merely": 17003, "ฤ seus": 17004, 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17059, "ฤ รˆฤปi": 17060, "ฤ muchos": 17061, "ฤ watches": 17062, "ortunate": 17063, "ฤ Juan": 17064, "รฌล€ฤธรฌฤทฤฆ": 17065, "ร‘ฤขรยต": 17066, "ei": 17067, "ionen": 17068, "ฤ experimental": 17069, "ฤ daughters": 17070, "ร ยธฤฝ": 17071, "ฤ mentally": 17072, "becca": 17073, "aware": 17074, "รฌฤฆฤฟ": 17075, "ฤ whatsoever": 17076, "ฤ enables": 17077, "ฤ Low": 17078, "oid": 17079, "ร ยธฤฌ": 17080, "รƒยณd": 17081, "ร˜ยบ": 17082, "ฤ constructed": 17083, "ฤ Ladies": 17084, "ฤ accused": 17085, "ฤ รยฐรยฝ": 17086, "Dan": 17087, "ฤ spawn": 17088, "ฤ containers": 17089, "ฤ artistic": 17090, "ร„ยฑp": 17091, "ฤ discl": 17092, "ฤ autres": 17093, "inas": 17094, "ฤ Nation": 17095, "ฤ nag": 17096, "bean": 17097, "whe": 17098, "ฤพรซฤฑฤฆ": 17099, "ฤ Seoul": 17100, "ฤ รญฤฑยฌ": 17101, "ฤ Nich": 17102, "ฤ complement": 17103, "ฤ interven": 17104, "ฤ Model": 17105, "ฤ Orange": 17106, "namon": 17107, "ฤ calculation": 17108, "see": 17109, "ฤ ustedes": 17110, "ฤ leb": 17111, "ฤ doct": 17112, "ร‘ฤธรยฝ": 17113, 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17219, "plete": 17220, "ฤ placing": 17221, "ฤ luego": 17222, "illance": 17223, "ฤ รยพรยฑร‘ฤซ": 17224, "ฤ provision": 17225, "ฤ lion": 17226, "tra": 17227, "boards": 17228, "ฤ behaviour": 17229, "hey": 17230, "ฤ subscription": 17231, "ฤ protagon": 17232, "รฃฤฅยฃ": 17233, "ฤ vara": 17234, "ฤ ร…ลu": 17235, "ฤ haha": 17236, "ฤ teaspoon": 17237, "รฆล": 17238, "avoir": 17239, "ฤ crypto": 17240, "ฤ ร‘ฤฃร‘ฤครยฐร‘ฤข": 17241, "ฤ Store": 17242, "abs": 17243, "ฤ Students": 17244, "ฤ laund": 17245, "into": 17246, "ฤ approached": 17247, "ยฐฤพ": 17248, "ร‘ฤฅร‘ฤฐร‘ฤซ": 17249, "ฤ Labor": 17250, "otes": 17251, "iatric": 17252, "ฤ groรƒล": 17253, "utive": 17254, "ฤ รยธรยด": 17255, "ฤ Gib": 17256, "ฤ placement": 17257, "ฤ difรƒลƒcil": 17258, "ฤ frog": 17259, "ฤ รยฒร‘ฤฃรยตร‘ฤง": 17260, "ฤ Jr": 17261, "azed": 17262, "ร‘ฤฅร‘ฤซ": 17263, "ฤ รชยผ": 17264, "frame": 17265, "รยฐรยตร‘ฤชร‘ฤฎ": 17266, "ฤ lockdown": 17267, "รฅฤณยณ": 17268, "ฤ medi": 17269, "ฤ ร—ฤถร—ล€ร—": 17270, "รยตรยฝรยธรยน": 17271, 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"ฤ recip": 17325, "ฤ Christianity": 17326, "ฤ รชยดฤพ": 17327, "ordon": 17328, "ร—ฤทร—ยฉ": 17329, "ฤ slash": 17330, "ฤ Pf": 17331, "ร‘ฤฅรยดร‘ฤฎ": 17332, "ร—ฤทร—ฤฟ": 17333, "ฤ Perry": 17334, "ฤ mamy": 17335, "ฤ backgrounds": 17336, "ฤ ร ยฎฤฐร ยฎยฉ": 17337, "ฤ pendant": 17338, "ฤ Columbia": 17339, "ฤ inverse": 17340, "ฤ ร‘ฤฉรยตร‘ฤขรยตรยท": 17341, "ฤ sv": 17342, "ฤ digging": 17343, "41": 17344, "chem": 17345, "ฤ navigation": 17346, "ฤ Shin": 17347, "ฤ Front": 17348, "PD": 17349, "ฤ bearing": 17350, "ฤ Wasser": 17351, "ฤ wax": 17352, "ฤ CHRIS": 17353, "ching": 17354, "ฤ pressed": 17355, "El": 17356, "ฤ Dal": 17357, "onsin": 17358, "ฤ binding": 17359, "ร‘ฤฃรยบรยพรยน": 17360, "poons": 17361, "ฤ mock": 17362, "arest": 17363, "รยบร‘ฤขรยฐ": 17364, "MM": 17365, "ฤ corrupt": 17366, "storm": 17367, "ฤ refres": 17368, "ฤ Coach": 17369, "llรƒยค": 17370, "ฤ THIS": 17371, "ฤ parag": 17372, "ฤ รฌฤตยฐ": 17373, "pool": 17374, "ฤ billions": 17375, "ฤ รชยนฤข": 17376, "group": 17377, "ฤ welcoming": 17378, "cellence": 17379, "ฤ Duke": 17380, "รชยธยด": 17381, "ฤ primera": 17382, "รฌล‚ยธ": 17383, "ฤ pond": 17384, "ฤ statue": 17385, "ฤ รชยตยฌรซ": 17386, "ฤ hatch": 17387, "ฤ instrumental": 17388, "ฤ residential": 17389, "รฌยปยค": 17390, "ฤ accepting": 17391, "oshi": 17392, "date": 17393, "ฤ รฌฤถยจ": 17394, "ฤ planted": 17395, "ฤ joking": 17396, "ฤ รฌฤฆฤพ": 17397, "ฤ hated": 17398, "ฤ ร‘ฤขรยฐร‘ฤฃร‘ฤฃรยบ": 17399, "ฤ slept": 17400, "ฤ packages": 17401, "ฤ islands": 17402, "esen": 17403, "ร„ลร„ยฑ": 17404, "ฤ diagon": 17405, "ฤ Osc": 17406, "ฤ mesh": 17407, "ฤ scales": 17408, "arity": 17409, "ฤ Defense": 17410, "รฃฤฃยกรฃฤคฤฉ": 17411, "ฤ Lewis": 17412, "ฤ ร‘ฤฃรยตรยณรยพรยดรยฝร‘ฤฑ": 17413, "ฤ flies": 17414, "uinely": 17415, "ฤ Consider": 17416, "ฤ stark": 17417, "hew": 17418, "ฤ Asรƒลƒ": 17419, "ยณยดรซ": 17420, "ฤ propose": 17421, "ฤ รญฤทฤบรซยฉยด": 17422, "odo": 17423, "ฤ Normally": 17424, "ฤ heeft": 17425, "ฤ Harris": 17426, "gro": 17427, "ฤ Blood": 17428, "base": 17429, "ฤ iOS": 17430, "ฤ touches": 17431, "ฤ inspir": 17432, "ฤ ร—ฤต": 17433, "ฤ binary": 17434, "ฤ รฌยถฤถ": 17435, "ฤ serial": 17436, "ฤ ion": 17437, "ฤ unemployment": 17438, "ฤ odds": 17439, "ฤ Fab": 17440, "ฤ FBI": 17441, "BRUN": 17442, "ฤ weights": 17443, "รŽยฝรŽยฟ": 17444, "atile": 17445, "ฤ nurses": 17446, "ฤ involvement": 17447, "ฤ รญฤถยผ": 17448, "ฤ governance": 17449, "ฤ รขฤคยฌ": 17450, "ร‘ฤขร‘ฤฅรยฟ": 17451, "ierra": 17452, "รญฤบฤท": 17453, "ฤ Jerry": 17454, "ฤ beard": 17455, "ฤ salvation": 17456, "ฤ Along": 17457, "gentle": 17458, "ฤ Ki": 17459, "bol": 17460, "ฤ Plat": 17461, "ฤ hasht": 17462, "รจยฟฤณ": 17463, "ฤ ware": 17464, "ฤ partie": 17465, "ycz": 17466, "ฤ intr": 17467, "Fih": 17468, "nent": 17469, "ฤ cheat": 17470, "ilen": 17471, "ฤ รซยฏ": 17472, "orie": 17473, "ฤ fรƒยกcil": 17474, "etric": 17475, "ฤ affecting": 17476, "unciation": 17477, "ฤ affairs": 17478, "ฤ bee": 17479, "ฤ viewing": 17480, "ฤ orang": 17481, "ฤ Lan": 17482, "ฤ รยกร‘ฤค": 17483, "รคยธฤธ": 17484, "ฤ Mes": 17485, "ฤฅฤฃ": 17486, "erie": 17487, "ฤ espa": 17488, "ฤ interpre": 17489, "ฤ possess": 17490, "ฤ purely": 17491, "rito": 17492, "found": 17493, "asma": 17494, "รฌล‚ฤฃรฌฤฟยธ": 17495, "ฤ examine": 17496, "ฤ ร‘ฤฅรยผ": 17497, "ฤ besch": 17498, "ฤ Tomorrow": 17499, "ฤ Block": 17500, "ฤ variant": 17501, "ฤ preference": 17502, "ฤ coaches": 17503, "ฤ medications": 17504, "ฤ รญฤบฤฆ": 17505, "ฤ empire": 17506, "รซฤฆยค": 17507, "ฤ Illinois": 17508, "ฤ crispy": 17509, "ฤ thรƒยฌ": 17510, "ฤ bees": 17511, "77": 17512, "ฤ glow": 17513, "รจยบ": 17514, "ฤ Studies": 17515, "รฅฤฒฤฆ": 17516, "ฤ Challenge": 17517, "ฤ unlikely": 17518, "รยง": 17519, "ร„ยฑyorsun": 17520, "DIE": 17521, "ฤ minimize": 17522, "izard": 17523, "ฤ รƒยบn": 17524, "ฤ encontrar": 17525, "ฤ Kill": 17526, "รฅยป": 17527, "ฤ vanilla": 17528, "ฤ Grant": 17529, "ฤ GT": 17530, "sea": 17531, "ฤ sought": 17532, "รยฒรยพรยด": 17533, "ฤ nรƒยคm": 17534, "ฤ Aunt": 17535, "OWN": 17536, "ฤ pumpkin": 17537, "stellen": 17538, "ฤ rag": 17539, "รยตรยณรยดรยฐ": 17540, "ฤ storyt": 17541, "ฤ forum": 17542, "รฆยฉล": 17543, "ฤ estaba": 17544, "uche": 17545, "ฤ congress": 17546, "ฤ Rey": 17547, "ฤ dramatically": 17548, "ฤ Sport": 17549, "ฤ Yellow": 17550, "ฤ รชยณฤฆรฌฤจฤฏ": 17551, "ฤ disgusting": 17552, "ฤ Recent": 17553, "ฤ acquired": 17554, "ฤ cables": 17555, "รงฤถฤผ": 17556, "din": 17557, "ฤ visto": 17558, "ฤ communicating": 17559, "ร‘ฤฃร‘ฤครยฐรยฒรยปร‘ฤฑ": 17560, "รยตร‘ฤฃร‘ฤครยพ": 17561, "รฃฤฅยปรฃฤฅยปรฃฤฅยป": 17562, "ฤ rรƒยฉg": 17563, "ฤ socks": 17564, "ฤ proces": 17565, "because": 17566, "ฤ utter": 17567, "ฤ colocar": 17568, "ฤ newest": 17569, "ฤ gramm": 17570, "รจยกยจ": 17571, "รคยธฤฏรงลยฅรฉฤฃฤต": 17572, "ฤ shifting": 17573, "ฤ carrier": 17574, "ฤ ร‘ฤฃรยบรยพร‘ฤข": 17575, "ฤ Schw": 17576, "ฤ executed": 17577, "ฤ maintained": 17578, "ฤ รฤจ": 17579, "ฤ Moses": 17580, "ฤ disse": 17581, "ฤ horr": 17582, "รฃฤขฤพ": 17583, "ฤ rally": 17584, "ฤ allem": 17585, "ฤ Eventually": 17586, "ฤ diyor": 17587, "lvania": 17588, 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"ฤ tranqu": 17640, "ฤ advers": 17641, "ฤ Temple": 17642, "iffs": 17643, "igence": 17644, "รจฤฉยชรฅยทยฑ": 17645, "ฤ kรƒยถnnte": 17646, "ฤ ร„ฤณรƒยณ": 17647, "Did": 17648, "ฤ photographs": 17649, "ฤ AWS": 17650, "ร‘ฤจรยธร‘ฤฑ": 17651, "ฤ guards": 17652, "ฤ appointed": 17653, "ฤ Gil": 17654, "ฤ รยผรยพรยผ": 17655, "ฤ cod": 17656, "ฤ Unlike": 17657, "ฤ evenly": 17658, "isconsin": 17659, "ฤ estou": 17660, "ฤ mnie": 17661, "ฤ Exec": 17662, "ฤ MV": 17663, "ฤ Eine": 17664, "รคยฟยก": 17665, "ฤ Roger": 17666, "ฤ Fac": 17667, "ฤ List": 17668, "ฤ fuer": 17669, "รยฐรยตร‘ฤครยต": 17670, "omed": 17671, "ฤ attraction": 17672, "รจฤซยฒ": 17673, "ฤ terrain": 17674, "ฤ Drop": 17675, "ฤ corporations": 17676, "ฤ sciences": 17677, "ฤ throne": 17678, "รฃฤฃฤฆรฃฤฃล": 17679, "ฤ aj": 17680, "ฤ Rot": 17681, "รงฤซยน": 17682, "ฤ supporters": 17683, "ฤ Bere": 17684, "Here": 17685, "ฤ diferentes": 17686, "ฤ significance": 17687, "รฤฅรŽยท": 17688, "รฆฤชฤณรจยฆยบรฅยพฤน": 17689, "ฤ clamp": 17690, "ฤ รซฤฎฤขรซ": 17691, 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"ฤ Tamam": 18224, "ฤ oscill": 18225, "ฤ letzten": 18226, "ฤ repeatedly": 18227, "ฤ jungle": 18228, "ฤ Push": 18229, "hai": 18230, "ฤ รŽยท": 18231, "ฤ deadly": 18232, "ร‘ฤฑรยถ": 18233, "wiร„ฤง": 18234, "ฤ Common": 18235, "ฤ รŽฤท": 18236, "ฤ skate": 18237, "TC": 18238, "ฤ Mini": 18239, "ฤ hobby": 18240, "รกยบยงn": 18241, "ฤ routes": 18242, "ฤ amigos": 18243, "ฤ conjun": 18244, "ฤ partnerships": 18245, "ฤ novo": 18246, "ฤ aver": 18247, "ฤ pouvez": 18248, "bridge": 18249, "ฤ preoc": 18250, "him": 18251, "ฤ turb": 18252, "ฤ sob": 18253, "ฤ Snap": 18254, "ฤ รฌยฐยธ": 18255, "minute": 18256, "ฤ traject": 18257, "ujร„ฤป": 18258, "ฤ eager": 18259, "ฤ regulatory": 18260, "ฤ banking": 18261, "bling": 18262, "ร‘ฤชร‘ฤฎ": 18263, "aร…ยผ": 18264, "ฤ bizarre": 18265, "itated": 18266, "dire": 18267, "ฤ threatened": 18268, "ฤ shining": 18269, "ฤ nesse": 18270, "ฤ corps": 18271, "ฤ ร‘ฤฃร‘ฤฅ": 18272, "ฤ teles": 18273, "ฤ temp": 18274, "tem": 18275, "ฤ รยบรยฐรยฝ": 18276, "ฤ fever": 18277, "New": 18278, 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"ฤ passengers": 18436, "ฤ cรƒยดtรƒยฉ": 18437, "ฤ rever": 18438, "ฤ technological": 18439, "ฤ allen": 18440, "ฤ raining": 18441, "avi": 18442, "ฤ salty": 18443, "ฤ typing": 18444, "ฤ ร‘ฤครยต": 18445, "ฤ tilt": 18446, "ฤ รฌยนฤบ": 18447, "ฤ รยพร‘ฤข": 18448, "ฤ รยฟร‘ฤขร‘ฤฑรยผ": 18449, "ฤ rou": 18450, "ฤ arena": 18451, "arat": 18452, "รฅฤชยซ": 18453, "HHHH": 18454, "ฤ manufacturers": 18455, "ฤ Edward": 18456, "ฤ tuck": 18457, "ฤ blows": 18458, "ingo": 18459, "ฤ Marc": 18460, "รฌฤทฤฆรฌฤฆฤพ": 18461, "Mich": 18462, "ฤ Clean": 18463, "รจยด": 18464, "esto": 18465, "ฤ Pack": 18466, "ฤ shaft": 18467, "BRUNO": 18468, "ฤ aven": 18469, "uur": 18470, "ร‘ฤฃรยบรยพรยปร‘ฤฎรยบรยพ": 18471, "รชยดฤข": 18472, "ฤ automated": 18473, "ฤ venture": 18474, "ฤ surveillance": 18475, "ฤ Grow": 18476, "ฤ Emer": 18477, "ฤ รยดรยพร‘ฤข": 18478, "ฤ investor": 18479, "ฤ Yok": 18480, "ฤ latter": 18481, "ฤ NI": 18482, "ฤ functioning": 18483, "ฤ Hamilton": 18484, "ฤ 51": 18485, "ฤ murdered": 18486, "ฤ anchor": 18487, "ฤ cuc": 18488, "ฤ SCP": 18489, "ฤ Madam": 18490, "ฤ constraints": 18491, "ฤ barn": 18492, "anken": 18493, "ฤ รซยงฤฐรฌฤฟฤข": 18494, "ฤ Motor": 18495, "ฤ Doing": 18496, "ฤ amen": 18497, "etts": 18498, "ฤ instructor": 18499, "egt": 18500, "ako": 18501, "ฤ posture": 18502, "ivia": 18503, "ฤ Polish": 18504, "ฤ รยดรยฒรยฐ": 18505, "ฤ colorful": 18506, "ฤ elbow": 18507, "ฤ parle": 18508, "ฤ passer": 18509, "ฤ condem": 18510, "ortal": 18511, "ฤ fertil": 18512, "ร˜ยงร˜ยฏ": 18513, "ฤ Colomb": 18514, "ฤ alignment": 18515, "ฤ astronaut": 18516, "ฤ Mut": 18517, "ฤ salmon": 18518, "ฤ structured": 18519, "ล€ร—ยจ": 18520, "ฤ clicks": 18521, "ฤ miej": 18522, "รฆฤถยฟ": 18523, "รฃฤฃฤฆรฃฤคฤฆ": 18524, "ฤ Round": 18525, "ฤ rainbow": 18526, "ฤ VA": 18527, "รฃฤฃฤถรฃฤฃฤธ": 18528, "รฌยงฤช": 18529, "otz": 18530, ",": 21732, "ฤ chords": 21733, "ฤ Sanders": 21734, "ฤ รซยถฤฆรซ": 21735, "Ben": 21736, "ฤ darรƒยผber": 21737, "ilians": 21738, "ฤ ordering": 21739, "ฤ Manh": 21740, "ฤ kilogram": 21741, "ฤ karร…ล": 21742, "ฤ grasp": 21743, "ฤ ghosts": 21744, "alen": 21745, "ฤ Jedi": 21746, "ฤ รยฑรยปรยธ": 21747, "ฤ downloaded": 21748, "ฤ conducting": 21749, "ฤ Hak": 21750, "ฤ researcher": 21751, "ilan": 21752, "good": 21753, "ฤ Hannah": 21754, "ฤ dรƒยผร…ลรƒยผn": 21755, "ฤ Messiah": 21756, "uity": 21757, "iona": 21758, "ฤ probable": 21759, "ฤ YE": 21760, "ฤ independently": 21761, "ฤ buffer": 21762, "burn": 21763, "ourd": 21764, "ฤ McK": 21765, "ฤ lingu": 21766, "ujemy": 21767, "รยตร‘ฤขร‘ฤค": 21768, "ฤ intuitive": 21769, "ฤ cracks": 21770, "appropri": 21771, "nty": 21772, "ฤ geen": 21773, "ฤ lend": 21774, "ฤ certification": 21775, "IDS": 21776, "unter": 21777, "pees": 21778, "ฤ trump": 21779, "ฤ bankrupt": 21780, "ฤ feas": 21781, "รจฤน": 21782, "ฤ duร…ยผ": 21783, "รฆยธฤง": 21784, "ฤ viruses": 21785, "ฤ 58": 21786, "god": 21787, "ฤ รยถรยตรยป": 21788, "ฤ stalk": 21789, "Ind": 21790, "achi": 21791, "ฤ CF": 21792, "ฤ Cond": 21793, "ฤ sanct": 21794, "ฤ conten": 21795, "ฤ freed": 21796, "ฤ RT": 21797, "ฤ mentors": 21798, "รฌยกยฑ": 21799, "ฤ portable": 21800, "ฤ Paulo": 21801, "rane": 21802, "HAHA": 21803, "ฤ Section": 21804, "รงฤจ": 21805, "hyun": 21806, "ฤ รŽลƒรฤฉ": 21807, "ฤ Pub": 21808, "ฤ Independ": 21809, "ฤ compounds": 21810, "ฤ ร‘ฤฃร‘ฤญ": 21811, "ฤ messaging": 21812, "ฤ dedication": 21813, "ฤ noticing": 21814, "ฤ devoted": 21815, "ร‘ฤฐร‘ฤคร‘ฤฃร‘ฤฑ": 21816, "ฤ snakes": 21817, "ฤ battlefield": 21818, "pers": 21819, "ฤ dela": 21820, "92": 21821, "ฤ hai": 21822, "illรƒยค": 21823, "รƒยฉrer": 21824, "every": 21825, "ฤ responsive": 21826, "ร—ฤปร—ฤท": 21827, "opf": 21828, "รฉฤซ": 21829, "ฤฌยธ": 21830, "Because": 21831, "ฤ tourism": 21832, "ฤ รชยทยธรชยฒฤฎ": 21833, "ร—ฤทร—ยฆ": 21834, "ฤ cans": 21835, "stรƒยผt": 21836, "ฤ donne": 21837, "ฤ Dios": 21838, "ฤ Uber": 21839, "actory": 21840, "ฤ oriented": 21841, "ฤ Herm": 21842, "ฤ patron": 21843, "urf": 21844, "bei": 21845, "ฤ programa": 21846, "ฤ Ohh": 21847, "gener": 21848, "ฤ fist": 21849, "ฤ Wendy": 21850, "ฤ anda": 21851, "ฤ guessed": 21852, "ฤ freak": 21853, "รคยธลƒรฅฤพฤญ": 21854, "ฤ Kings": 21855, "chool": 21856, "ฤ offline": 21857, "ฤ Indiana": 21858, "ฤ Alliance": 21859, "ฤ 53": 21860, "ฤ particul": 21861, "ฤ Focus": 21862, "ฤ inhabit": 21863, "ฤ รชยฐฤปรฌฤฟฤขรซฤฏยฐ": 21864, "ฤ McG": 21865, "owski": 21866, "ฤ รฌฤฟยดรชยฑยด": 21867, "ฤ paร…ฤฆst": 21868, "รยพรยฝรยธ": 21869, "itta": 21870, "ฤ confirmation": 21871, "ฤ Brooklyn": 21872, "ฤ noodle": 21873, "fund": 21874, "itud": 21875, "ฤ grandparents": 21876, "ฤ barbecue": 21877, "รŽยตรŽยนรฤค": 21878, "ฤ รก": 21879, "ฤ ballot": 21880, "ฤ Veter": 21881, "ฤ pipes": 21882, "igious": 21883, "ฤ Graph": 21884, "ested": 21885, "ฤ รซยธฤฎรซ": 21886, "ฤ KE": 21887, "รฃฤฃยกรฃฤคฤฉรฃฤฃยฃรฃฤฃยจ": 21888, "ฤ eins": 21889, "ฤ hatred": 21890, "รฃฤฃฤณรฃฤฃยฉ": 21891, "ฤ dang": 21892, "eeee": 21893, "ฤ archae": 21894, "ฤ Jesse": 21895, "ฤ detected": 21896, "ฤ seni": 21897, "burgh": 21898, "ฤ displacement": 21899, "ฤ dop": 21900, "ฤ conditioning": 21901, "ฤ รยฝรยตร‘ฤฃรยบรยพรยปร‘ฤฎรยบรยพ": 21902, "ฤ disturbing": 21903, "PH": 21904, "ฤ thinner": 21905, "ฤ wounded": 21906, "ฤ Cuando": 21907, "ฤ cushion": 21908, "ฤ whites": 21909, "ฤ preferences": 21910, "ฤ รฌยคฤขรซยนฤฆ": 21911, "ฤ kaร…ยผ": 21912, "ฤ Gate": 21913, "ฤ Path": 21914, "dles": 21915, "ร ยธฤฆร ยธยฃ": 21916, "imore": 21917, "ฤ รซยณยดรฌฤนยฌ": 21918, "ฤ disciplines": 21919, "รกยปฤฑ": 21920, "ฤ mesma": 21921, "ฤ รฌฤฅฤชรซ": 21922, "ฤ รฌฤญยฌ": 21923, "ฤ ging": 21924, "ฤ umbrella": 21925, "IGHT": 21926, "ฤ pension": 21927, "ฤ combining": 21928, "SS": 21929, "ฤ rectangle": 21930, "รกยปฤฉt": 21931, "ฤ proxim": 21932, "ฤ Cow": 21933, "ยธฤฎ": 21934, "ฤ intentional": 21935, "รฆฤทฤป": 21936, "ฤ decid": 21937, "ฤ ร‘ฤฃรยบรยฐรยถ": 21938, "ฤ Uma": 21939, "iasm": 21940, "buz": 21941, "ฤ debris": 21942, "ฤ cass": 21943, "ฤ Prop": 21944, "iska": 21945, "รซล‚ยฅ": 21946, "esterol": 21947, "ussian": 21948, "รฌฤฟยดรซล€ฤณ": 21949, "ฤ unlimited": 21950, "ฤ admire": 21951, "ฤ tightly": 21952, 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22004, "ฤ Product": 22005, "ฤ Buck": 22006, "ฤ Aww": 22007, "seen": 22008, "ฤ void": 22009, "ฤ Franklin": 22010, "ฤ advocacy": 22011, "ฤ Sep": 22012, "ฤ coolest": 22013, "ฤ ร‘ฤฃร‘ฤขรยฐรยทร‘ฤฅ": 22014, "ฤ Quand": 22015, "ฤ 900": 22016, "ฤ Trad": 22017, "dies": 22018, "ฤ hash": 22019, "รฆฤชฤณรฅยฐยฑ": 22020, "รคยนลรฆฤบยฏ": 22021, "ฤ pots": 22022, "ฤ sadly": 22023, "ฤ viable": 22024, "ฤ Tiger": 22025, "ฤ ONE": 22026, "ฤ neurons": 22027, "owanie": 22028, "ร„ฤน": 22029, "ฤ Shar": 22030, "ฤ Landes": 22031, "ฤ conferences": 22032, "รจยฉยฒ": 22033, "ฤ credential": 22034, "ฤ lime": 22035, "inee": 22036, "xit": 22037, "pay": 22038, "ฤ incons": 22039, "ฤ >>:": 22040, "รจยชฤฏ": 22041, "ฤ รญล€ฤบรซ": 22042, "ฤ lesser": 22043, "ฤ spill": 22044, "ฤ premise": 22045, "ฤ 365": 22046, "ฤ Host": 22047, "ฤ tomar": 22048, "ร—ฤฒร—ฤพ": 22049, "รซยฒฤช": 22050, "ฤ Whats": 22051, "ฤ lightweight": 22052, "ฤ Map": 22053, "fia": 22054, "ellschaft": 22055, "ฤ vendors": 22056, "uesto": 22057, "ฤ Mister": 22058, 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22108, "ฤ tror": 22109, "ฤ organisms": 22110, "ฤ falta": 22111, "ฤ hometown": 22112, "รงยฑ": 22113, "ฤ รญฤญ": 22114, "ฤ cheg": 22115, "ฤ รฌยก": 22116, "ฤ comma": 22117, "isรƒยฉ": 22118, "ฤ likelihood": 22119, "avored": 22120, "ฤ geldi": 22121, "รยฝรยธรยบรยพรยฒ": 22122, "ฤ medio": 22123, "ฤ jakie": 22124, "ฤ Jup": 22125, "ฤ greenhouse": 22126, "ฤ spit": 22127, "รยบรยพรยต": 22128, "ฤ รยบรยฐรยถ": 22129, "ฤ Gram": 22130, "ฤ Conference": 22131, "ฤ deficit": 22132, "sร„ยฑn": 22133, "inse": 22134, "uร„ล": 22135, "ฤ richt": 22136, "ฤ coincidence": 22137, "รฅฤฑฤฏ": 22138, "ฤ europ": 22139, "ฤ butterfly": 22140, "pread": 22141, "ฤ รฌฤธยผ": 22142, "รจฤขยถ": 22143, "ฤ wavel": 22144, "ฤ Infin": 22145, "ฤ Planet": 22146, "ฤ selfie": 22147, "ientras": 22148, "ฤ arrog": 22149, "oser": 22150, "idal": 22151, "ล‚ร—ฤนร—ล‚ร—ฤท": 22152, "รƒยผtรƒยผn": 22153, "ฤ freshman": 22154, "ฤ Machine": 22155, "รฤฅรฤฆ": 22156, "ฤ Dia": 22157, "รฌฤฟยดรซฤญยค": 22158, "รฃฤฃฤตรฃฤฃฤจ": 22159, "nea": 22160, 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"ร ยนฤฃร ยธยฅ": 22214, "ฤ influential": 22215, "รƒยคsident": 22216, "รฉยค": 22217, "ฤ รฌฤฆล‚รซ": 22218, "ฤ volumes": 22219, "รฆฤฒ": 22220, "ฤ outras": 22221, "ฤ Twitch": 22222, "ฤ founding": 22223, "ฤ awhile": 22224, "ฤ coil": 22225, "รชยฐฤป": 22226, "ฤ cรกยบยฃ": 22227, "ฤ Throw": 22228, "ฤ Hence": 22229, "ommt": 22230, "ฤ Benjamin": 22231, "รยณรยปร‘ฤฑรยด": 22232, "Time": 22233, "obic": 22234, "ฤ mour": 22235, "ฤ dread": 22236, "ฤ Lรƒล‚": 22237, "ฤ Chile": 22238, "ฤ preval": 22239, "ฤ vain": 22240, "ฤ artร„ยฑk": 22241, "ฤ preserved": 22242, "ฤ รยพร‘ฤครยด": 22243, "ฤ warehouse": 22244, "ฤ beste": 22245, "ฤ Several": 22246, "ฤ Situation": 22247, "ฤ cardboard": 22248, "Tod": 22249, "erna": 22250, "ฤ garant": 22251, "ฤ gesture": 22252, "ฤ hen": 22253, "ฤ spelling": 22254, "osexual": 22255, "ฤ anne": 22256, "ฤ mice": 22257, "ฤ Meine": 22258, "card": 22259, "ฤ rebell": 22260, "ฤ certo": 22261, "ฤ รฌฤพล‚รซ": 22262, "ฤ verschied": 22263, "ฤ Bos": 22264, "ฤ invention": 22265, "ฤ trze": 22266, "ฤ maniรƒยจre": 22267, "ฤ Chad": 22268, "ฤ spre": 22269, "ฤ organisations": 22270, "ฤ poorly": 22271, "ฤ anterior": 22272, "ฤ stair": 22273, "รยบร‘ฤข": 22274, "ฤ atomic": 22275, "ฤ sympath": 22276, "ฤ continually": 22277, "ฤ kleine": 22278, "รƒยจte": 22279, "รยธร‘ฤซ": 22280, "รŽยฟรฤค": 22281, "peut": 22282, "ฤ reposit": 22283, "ฤ entra": 22284, "Em": 22285, "ฤ financing": 22286, "ฤ รยผรยฝรยพรยณ": 22287, "ฤ thesis": 22288, "ฤ Computer": 22289, "eau": 22290, "ฤ Tree": 22291, "ฤ bride": 22292, "onsieur": 22293, "shire": 22294, "wic": 22295, "DE": 22296, "ฤ รฌฤชฤบรซ": 22297, "ฤ acom": 22298, "ฤ PO": 22299, "ersch": 22300, "ฤ รยฟรยพรยผรยพร‘ฤซ": 22301, "ฤ Armen": 22302, "ฤ รฌยฃยฝ": 22303, "ฤ zor": 22304, "ฤ prints": 22305, "ฤ Dass": 22306, "รฆยธยฏ": 22307, "ฤ durable": 22308, "ฤ Transport": 22309, "รฌล€ฤฒรชยฐฤข": 22310, "ฤ รยปรยตรยณ": 22311, "ฤ dรƒยฉt": 22312, "รƒยดle": 22313, "amous": 22314, "YN": 22315, "ฤ cliff": 22316, "ฤ grammar": 22317, "ฤ รลรยพร‘ฤฏร‘ฤครยพรยผร‘ฤฅ": 22318, "ฤ lรƒล‚m": 22319, "esch": 22320, "ฤ miserable": 22321, "ฤ volts": 22322, "ฤ Cad": 22323, "ukan": 22324, "ร‘ฤครยธรยฒ": 22325, "rust": 22326, "ฤ รฌฤบยฌรซฤฟยผ": 22327, "ฤ verk": 22328, "ฤ chickens": 22329, "ฤ Yoo": 22330, "ฤ outfits": 22331, "code": 22332, "ฤ hierarchy": 22333, "netes": 22334, "ฤ counterpart": 22335, "ฤ tรƒยดi": 22336, "ฤ ted": 22337, "ฤ Bart": 22338, "ฤ รซฤฟยผ": 22339, "ฤ Genau": 22340, "ฤ incoming": 22341, "ฤ ABC": 22342, "rique": 22343, "ฤ รยพร‘ฤครยฟ": 22344, "qual": 22345, "ฤ incentive": 22346, "ฤ ihren": 22347, "ร—ล‚ร—ฤป": 22348, "loe": 22349, "ฤ 1930": 22350, "ฤ barg": 22351, "ฤ diction": 22352, "ฤ รƒยถnce": 22353, "INS": 22354, "ฤ reh": 22355, "isiaj": 22356, "mouth": 22357, "ฤ scoring": 22358, "lร„ยฑk": 22359, "ฤ รฌฤทฤฆรฌยฃยผ": 22360, "ORIA": 22361, "ฤ Estados": 22362, "ฤ companion": 22363, "ฤ assemble": 22364, "ฤ punished": 22365, "ฤ ital": 22366, "ฤ prevents": 22367, "istes": 22368, "ฤ Kentucky": 22369, "ฤ locate": 22370, "ฤ fasting": 22371, "รฃฤฃยจรฆฤขฤฟ": 22372, "ฤฅฤข": 22373, "ฤ Seb": 22374, "ฤ Crown": 22375, "opia": 22376, "ฤ whip": 22377, "usz": 22378, "รยบรยฐรยผรยธ": 22379, "ฤ databases": 22380, "รฅลƒฤน": 22381, "ฤ prosec": 22382, "ฤ 1997": 22383, "ฤ รฌฤคยดรฌยงฤฟ": 22384, "ฤ Solar": 22385, "ฤ Pues": 22386, "ฤ Zen": 22387, "ollo": 22388, "ฤ Guru": 22389, "ฤ squeez": 22390, "ฤ รฤนรยฐ": 22391, "ฤ ร„ฤฏ": 22392, "ceptions": 22393, "cca": 22394, "izable": 22395, "mand": 22396, "ฤ breakthrough": 22397, "ฤ tablespoon": 22398, "ฤ SEC": 22399, "ikh": 22400, "ฤ Sรƒยฃo": 22401, "ฤ รยฟรยปรยพ": 22402, "amen": 22403, "ฤ prac": 22404, "ฤ darling": 22405, "ฤ taller": 22406, "ฤ rendering": 22407, "ฤ รฌฤผยฐรซยฆยฌรชยฐฤข": 22408, "ฤ รฤฆรŽยทรฤค": 22409, "ฤ mรƒยฃ": 22410, "ฤ esos": 22411, "uerdo": 22412, "ฤ ร‘ฤฃร‘ฤฉรยธร‘ฤค": 22413, "aller": 22414, "รฌฤนฤชรฌฤธยดรฌฤผฤถ": 22415, "ฤ millones": 22416, "lerin": 22417, "ฤ pegar": 22418, "onne": 22419, "ฤ enrollment": 22420, "ฤ liegt": 22421, "ฤ boa": 22422, "wiร„ฤป": 22423, "bsp": 22424, "ฤ cycling": 22425, "ฤ Bernie": 22426, "ฤ 1989": 22427, "ฤ รยดรยฐรยปร‘ฤฎ": 22428, "ฤ Dakota": 22429, "ฤ ร‘ฤฃรยฒร‘ฤฑรยท": 22430, "ฤ CP": 22431, "ฤ stare": 22432, "รญฤคยค": 22433, "ฤ prosperity": 22434, "ฤ arrangements": 22435, "ฤ arriving": 22436, "mรƒยค": 22437, "ฤ kayak": 22438, "ipt": 22439, "ฤ pardon": 22440, "ฤ relat": 22441, "ฤ verste": 22442, "ฤ Fig": 22443, "ฤ foil": 22444, "ฤ Talking": 22445, "peare": 22446, "ฤ noi": 22447, "ฤ รยฟร‘ฤขรยธร‘ฤช": 22448, "ฤ hockey": 22449, "ฤ ado": 22450, "ฤ OUT": 22451, "67": 22452, "ฤ hormones": 22453, "ฤ Avenue": 22454, "ฤ Superman": 22455, "ฤ prescription": 22456, "ubernetes": 22457, "CL": 22458, "otive": 22459, "NIS": 22460, "ienen": 22461, "ฤ sadness": 22462, "ฤ Vit": 22463, "Ty": 22464, "ฤ starter": 22465, "ฤ bede": 22466, "ฤ foundations": 22467, "ฤ sore": 22468, "รฅยบฤน": 22469, "ร‘ฤซรยตร‘ฤฃร‘ฤครยฒ": 22470, "รฌฤผยฐรซ": 22471, "ฤ ร‘ฤฉร‘ฤฅรยฒ": 22472, "link": 22473, "ฤ maneu": 22474, "working": 22475, "รƒล‚n": 22476, "ฤ Attack": 22477, "ฤ Cart": 22478, "veis": 22479, "ฤ Resp": 22480, "ensing": 22481, "ฤ รฌยขฤญรฌฤทฤฆรฌฤผฤถ": 22482, "ฤ escuch": 22483, "ฤ RNA": 22484, "ฤคยด": 22485, "ฤ adop": 22486, "ฤ bending": 22487, "ร˜ยนร˜ยฏ": 22488, "ฤ manages": 22489, "usp": 22490, "ฤ tart": 22491, "ฤ router": 22492, "Bo": 22493, "ฤ establishing": 22494, "ฤ balancing": 22495, "ฤ athletic": 22496, "ฤ Slo": 22497, "ฤ fills": 22498, "ฤ รยฝรยฐรยฑ": 22499, "ฤ รยดรยฐรยป": 22500, "ฤ posso": 22501, "ฤ Vielen": 22502, "ฤ critics": 22503, "ฤ lawsuit": 22504, "ฤ Isaac": 22505, "ฤ ร‘ฤฆรยธรยปร‘ฤฎรยผ": 22506, "ฤ tras": 22507, "ฤ praw": 22508, "ฤ Crazy": 22509, "ฤ neu": 22510, "ฤ kull": 22511, "ฤ tumor": 22512, "ฤ APP": 22513, "gate": 22514, "ฤ ARE": 22515, "98": 22516, "ฤ Steam": 22517, "ฤ fucked": 22518, "lage": 22519, "ฤ รขฤปยฌ": 22520, "ฤ MD": 22521, "fy": 22522, "ฤ shells": 22523, "ฤ Seems": 22524, "izers": 22525, "ฤ ranges": 22526, "ฤ Antonio": 22527, "ATION": 22528, "ฤ Baba": 22529, "ฤ รฌฤฅฤซ": 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"ฤ รฌฤคยฌรซล€ฤณ": 22581, "ฤ atm": 22582, "ฤ protects": 22583, "ฤ imped": 22584, "HO": 22585, "ฤ cima": 22586, "ฤ toch": 22587, "abis": 22588, "ฤ sendo": 22589, "laus": 22590, "ฤ curl": 22591, "ฤ Num": 22592, "ฤ sponsors": 22593, "ฤ dรƒยฉbut": 22594, "ฤ Alexa": 22595, "ฤ Bรƒยผr": 22596, "ฤ Amer": 22597, "ฤ cope": 22598, "ฤ รยธรยทรยฒ": 22599, "jal": 22600, "ฤ 1995": 22601, "apat": 22602, "resse": 22603, "ฤ Prize": 22604, "ฤ Claire": 22605, "ฤ Brandon": 22606, "ฤ wszystko": 22607, "ฤ valued": 22608, "ร ยธฤปร ยธยฐ": 22609, "ฤ sect": 22610, "ฤ secretly": 22611, "ฤ diamonds": 22612, "ฤ Evan": 22613, "ฤ RPG": 22614, "รฃฤฃยซรฃฤฃยช": 22615, "ฤชรซฤฑฤฆ": 22616, "ฤ Universal": 22617, "ฤ doubts": 22618, "ฤ Pin": 22619, "wiร„ฤงz": 22620, "ฤผยฉ": 22621, "ฤ albo": 22622, "ฤ braucht": 22623, "AUL": 22624, "ฤ Mobile": 22625, "grades": 22626, "ฤ schem": 22627, "why": 22628, "ฤ Nicht": 22629, "pi": 22630, "gle": 22631, "ฤ chorus": 22632, "ฤ gly": 22633, "ฤ reinforce": 22634, "ฤ muff": 22635, "ฤ Shen": 22636, "ฤ Hola": 22637, "ร‘ฤฅรยณ": 22638, "videmment": 22639, "vial": 22640, "acious": 22641, "laimed": 22642, "ฤ Rico": 22643, "ฤ vegg": 22644, "ฤ illustration": 22645, "ฤ Butter": 22646, "owad": 22647, "ฤ eux": 22648, "ฤ enfants": 22649, "ฤ Leader": 22650, "ฤ Village": 22651, "etically": 22652, "ร™ฤจร™ฤฌ": 22653, "ฤ stew": 22654, "ฤ surprises": 22655, "ฤ cue": 22656, "ฤ Grandma": 22657, "ฤ Celsius": 22658, "ฤ Richt": 22659, "enc": 22660, "ฤ petition": 22661, "ฤ herb": 22662, "ฤ wicked": 22663, "ฤ schle": 22664, "ocaly": 22665, "ฤ transf": 22666, "ฤ tokens": 22667, "ฤ Gray": 22668, "ฤ BBC": 22669, "IK": 22670, "ฤ 1500": 22671, "zn": 22672, "ฤ Nev": 22673, "ฤ koy": 22674, "ฤ zar": 22675, "ฤ bullshit": 22676, "ฤ Colombia": 22677, "ulative": 22678, "ฤ widespread": 22679, "yect": 22680, "kit": 22681, "ฤ empresa": 22682, "ฤ nour": 22683, "ฤ burns": 22684, "atin": 22685, "aired": 22686, "ฤ revolutionary": 22687, "ฤ รยณรยพรยดร‘ฤฅ": 22688, "ฤ Logan": 22689, "ฤ 1996": 22690, "ฤ Graham": 22691, "reb": 22692, "ฤ NHS": 22693, "รฆฤพฤฝ": 22694, "ฤ costumes": 22695, "ฤ nawet": 22696, "ฤ lovers": 22697, "ฤ Lucy": 22698, "ฤ Indigenous": 22699, "รญฤทฤบรชยธยฐ": 22700, "ฤ immunity": 22701, "ยฅยดรซ": 22702, "uito": 22703, "ฤ excessive": 22704, "ฤ donations": 22705, "ฤ ร—ฤถร—ยจ": 22706, "ฤ รฌยฒยซ": 22707, "รฉฤซฤฆ": 22708, "ฤ drying": 22709, "melon": 22710, "ฤ surveys": 22711, "ฤ รซยฌยดรฌฤฌยจ": 22712, "รฉยขยจ": 22713, "aaa": 22714, "ฤ probe": 22715, "ancial": 22716, "ฤ louder": 22717, "ฤ hotels": 22718, "รƒยผร„ล": 22719, "agner": 22720, "ฤ origins": 22721, "ฤ รซยงฤชรฌยงฤขรซยงฤซ": 22722, "ฤ **": 22723, "ฤ strangers": 22724, "ฤ Haus": 22725, "comed": 22726, "ฤ anthrop": 22727, "ฤ uso": 22728, "ฤ รฌฤทฤฆรฌยงฤฃ": 22729, "ฤ Yuan": 22730, "ฤ รญฤทฤฆรฌฤผฤถ": 22731, "pler": 22732, "ressive": 22733, "ฤ spraw": 22734, "ฤ Stew": 22735, "ฤ 1994": 22736, "ฤ elders": 22737, "ฤ meinen": 22738, "ฤ junt": 22739, "ฤ acoust": 22740, "ฤ Wohn": 22741, "ฤ bananas": 22742, "ฤ projection": 22743, "ฤ Stick": 22744, "legt": 22745, "speed": 22746, "ฤ cร…ยฉng": 22747, "ฤ Wort": 22748, "ฤ Baltimore": 22749, "ฤ ร‘ฤจรยตรยป": 22750, "ฤ dunno": 22751, "รฅยผยท": 22752, "?,": 22753, "รฃฤฅฤซรฃฤฅยณ": 22754, "ฤ Local": 22755, "osto": 22756, "รลƒ": 22757, "รยพรยดรยฐ": 22758, "ฤ Portuguese": 22759, "ฤ theirs": 22760, "ฤ dรƒยฉm": 22761, "รฅฤฑยฆ": 22762, "ฤ drauf": 22763, "ฤ Buddhist": 22764, "erta": 22765, "Ge": 22766, "ฤ carrot": 22767, "ฤ Wonderful": 22768, "ฤ soak": 22769, "ฤ chairman": 22770, "ggi": 22771, "ICA": 22772, "fried": 22773, "ฤ flick": 22774, "ฤ Throughout": 22775, "ฤ รฌฤผยฐรซ": 22776, "ฤ cough": 22777, "ฤ fluffy": 22778, "school": 22779, "ฤ ripped": 22780, "--------": 22781, "ฤ Zukunft": 22782, "ฤ รยฝรยตรยฑ": 22783, "ฤ sto": 22784, "ฤ BO": 22785, "pent": 22786, "ฤ Lawrence": 22787, "รฤซรฤค": 22788, "sticks": 22789, "ฤ Eins": 22790, "ฤ ร‘ฤขร‘ฤญ": 22791, "ฤ Strong": 22792, "ฤ caramel": 22793, "ฤ spite": 22794, "azar": 22795, "รฉฤฅยฝรฆฤบยฏ": 22796, "ฤ critically": 22797, "ฤ obra": 22798, "owitz": 22799, "ฤ Zone": 22800, "ฤ ร‘ฤขรยตรยบ": 22801, "ฤ sug": 22802, "arded": 22803, "ฤ gรƒยฌ": 22804, "ffentlich": 22805, "anche": 22806, "ร˜ล": 22807, "astically": 22808, "รฌฤฟยผรซ": 22809, "รยปรยฐรยฒ": 22810, "ฤ simplest": 22811, "ฤ Friend": 22812, "ฤ quello": 22813, "ฤ ambition": 22814, "ฤ abbiamo": 22815, "รฅยบฤท": 22816, "ฤ ร‘ฤฆรยพร‘ฤขรยผ": 22817, "ฤ Essa": 22818, "ฤ educators": 22819, "ฤ statistical": 22820, "รฉฤขฤปรฉฤคฤฌ": 22821, "ฤ changer": 22822, "ฤ atau": 22823, "รƒยฉtais": 22824, "ฤ Shakespeare": 22825, "รซฤฒฤบ": 22826, "ฤ triggers": 22827, "ฤ realiz": 22828, "ฤ celui": 22829, "wheel": 22830, "ฤ loyalty": 22831, "ฤ screams": 22832, "kehr": 22833, "ฤ Mega": 22834, "east": 22835, "ฤ tops": 22836, "ฤ Totally": 22837, "ountain": 22838, "lord": 22839, "ฤ violation": 22840, "ฤ GA": 22841, "ฤ nicer": 22842, "ฤ Fresh": 22843, "ฤ Melissa": 22844, "function": 22845, "ฤ rape": 22846, "ฤ exceptions": 22847, "ฤ silicon": 22848, "ฤ liberty": 22849, "ฤ households": 22850, "รฃฤฃฤฏรฃฤฃยพรฃฤฃฤป": 22851, "ฤ CA": 22852, "ฤ รล€รยฑ": 22853, "ฤ lib": 22854, "ล€ฤฎ": 22855, "cific": 22856, "ฤ tropical": 22857, "ฤ investigating": 22858, "HD": 22859, "ฤ adapter": 22860, "ฤ Pitt": 22861, "ancia": 22862, "ฤ Shell": 22863, "friendly": 22864, "ฤ conclusions": 22865, "ฤ turtle": 22866, "ฤ decomp": 22867, "ฤ animations": 22868, "ฤ ร‘ฤฃรยตรยบ": 22869, "insi": 22870, "ฤ retention": 22871, "kie": 22872, "ฤ injection": 22873, "ฤ Madison": 22874, "รฌยฐยฐ": 22875, "ฤ vient": 22876, "ฤ varied": 22877, "ฤ violin": 22878, "ฤ Bil": 22879, "ฤ luckily": 22880, "ฤ htt": 22881, "lรƒยค": 22882, "ฤ ranch": 22883, "รงฤพฤญรงฤพฤญ": 22884, "ฤ sรƒยณlo": 22885, "รฌฤทฤง": 22886, "ฤ Derek": 22887, "ฤ Scripture": 22888, "รยพร‘ฤขรยฐ": 22889, "ฤ classrooms": 22890, "avil": 22891, "formed": 22892, "ฤ beforehand": 22893, "ฤ Gem": 22894, "prech": 22895, "ฤ lin": 22896, "ฤ greens": 22897, "ร‘ฤจรยตรยฒ": 22898, "ฤ Mercedes": 22899, "ฤ drought": 22900, "gasps": 22901, "ฤ abortion": 22902, "ฤ terribly": 22903, "ฤ sposรƒยณb": 22904, "ฤ secured": 22905, "ฤ atrรƒยกs": 22906, "ฤ wavelength": 22907, "ฤ grains": 22908, "ective": 22909, "ฤ spacecraft": 22910, "ฤ tours": 22911, "ฤ profes": 22912, "ฤ surgeon": 22913, "ฤ Pie": 22914, "ฤ ideally": 22915, "arner": 22916, "UP": 22917, "opard": 22918, "sce": 22919, "ฤ immense": 22920, "ฤ Ort": 22921, "roller": 22922, "ฤ Dallas": 22923, "ฤ Nicholas": 22924, "ฤ sulf": 22925, "ฤ Toyota": 22926, "ฤ quantities": 22927, "ceans": 22928, "ฤ cui": 22929, "anรƒยงa": 22930, "ฤ CAN": 22931, "itzerland": 22932, "รฅฤฆยฟ": 22933, "ฤ zou": 22934, "ฤ Cyber": 22935, "legen": 22936, "ฤ Init": 22937, "edu": 22938, "ฤ apert": 22939, "ฤ adjac": 22940, "ouv": 22941, "รจฤขฤฎรคยธฤถ": 22942, "rs": 22943, "ฤ cabbage": 22944, "ฤ wheelchair": 22945, "inyl": 22946, "ฤ Dynam": 22947, "ฤ รฌฤทฤฆรซฤญฤชรซฤฟยผ": 22948, "ฤ ling": 22949, "hl": 22950, "ฤ รยผรยพรยณร‘ฤฅ": 22951, "ฤ crisp": 22952, "ฤ mij": 22953, "ฤ dug": 22954, "nin": 22955, "ฤ bloss": 22956, "ฤ belonging": 22957, "ฤ loudly": 22958, "ฤ minerals": 22959, "ฤ concluded": 22960, "ฤ searched": 22961, "96": 22962, "ฤ Meet": 22963, "ฤ SEO": 22964, "ฤ รยกรยบ": 22965, "ฤ Hob": 22966, "otta": 22967, "ฤ propaganda": 22968, "ฤ cinnamon": 22969, "ฤ hunter": 22970, "ฤ gemeins": 22971, "ฤ sculpture": 22972, "ulsion": 22973, "ฤ vรƒยคl": 22974, "ฤ magazines": 22975, "ฤ controversy": 22976, "รคยธฤขรฆยจยฃ": 22977, "ฤ sequences": 22978, "รฃฤฃฤฆรฃฤคฤญ": 22979, "ฤ รญฤผฤฎ": 22980, "ฤ deleted": 22981, "รคยฝยฟ": 22982, "ฤฒรซฤฑฤฆ": 22983, "ฤ varying": 22984, "รฃฤฅฤจ": 22985, "ฤ mounting": 22986, "ฤ affair": 22987, "ฤ pathways": 22988, "รฆยฆ": 22989, "ฤ digo": 22990, "รคยบยฎ": 22991, "ฤ รยดรยพรยบ": 22992, "Alex": 22993, "ฤ tobacco": 22994, "ฤ CV": 22995, "ฤ bothered": 22996, "ฤ ambient": 22997, "inky": 22998, "ฤ SL": 22999, "ฤ hates": 23000, "ฤ jeร…ยผeli": 23001, "ฤ congreg": 23002, "ฤ elas": 23003, "ฤ deuts": 23004, "ฤ Studios": 23005, "chร„ฤป": 23006, "ฤ documented": 23007, "ฤ Cruz": 23008, "ฤ Len": 23009, "ฤ Douglas": 23010, "ฤ Portugal": 23011, "enti": 23012, "ฤ spouse": 23013, "ฤ analys": 23014, "avia": 23015, "ฤ edited": 23016, "ฤ lรกยบยกi": 23017, "built": 23018, "ฤ ville": 23019, "adora": 23020, "ฤ bracelet": 23021, "ฤ sushi": 23022, "ฤ pm": 23023, "ฤ trails": 23024, "ฤ lug": 23025, "ฤ รƒยถver": 23026, "ฤ sorrow": 23027, "ฤ colony": 23028, "adox": 23029, "ฤ serie": 23030, "anyak": 23031, "ฤ ร˜ยท": 23032, "ฤ Gulf": 23033, "รฆฤบยฏรคยธฤฏรฆฤบยฏ": 23034, "ฤ PV": 23035, "ฤ Samuel": 23036, "ฤ Kit": 23037, "ฤ Ral": 23038, "ontin": 23039, "expl": 23040, "ฤ entries": 23041, "ฤ activists": 23042, "Ps": 23043, "ฤ sant": 23044, "ฤ ร‘ฤครยพร‘ฤฉ": 23045, "ฤ Bruno": 23046, "keley": 23047, "ฤ tutto": 23048, "รฉฤถ": 23049, "ฤ vintage": 23050, "ฤ terrified": 23051, "ฤ รยฟรยพร‘ฤง": 23052, "usive": 23053, "owers": 23054, "รยฐรยนร‘ฤค": 23055, "รซฤฑฤป": 23056, "ฤ twisted": 23057, "ฤ Thought": 23058, "ฤ tah": 23059, "ฤ shrink": 23060, "ฤ sheer": 23061, "lit": 23062, "ฤ dalam": 23063, "ฤ dib": 23064, "ฤ vard": 23065, "owane": 23066, "ฤ dobr": 23067, "ฤ Rena": 23068, "ฤ ร‘ฤฃรยฒรยพร‘ฤฐ": 23069, "ฤ paรƒลƒses": 23070, "ฤ Era": 23071, "รฃฤฃยฎรฃฤฃยง": 23072, "ฤ BUT": 23073, "sighs": 23074, "ฤ รชยทยธรชยฑยฐ": 23075, "ฤ groรƒลen": 23076, "ฤ รซยนยจรซยฆยฌ": 23077, "ฤ nerves": 23078, "ฤ constit": 23079, "ฤ preocup": 23080, "ฤ Gay": 23081, "ฤ Xu": 23082, "keeper": 23083, "heure": 23084, "..)": 23085, "ฤ Calm": 23086, "ฤ Unidos": 23087, "ฤ รฌฤฟยดรชยฒฤฅ": 23088, "ฤ Aqui": 23089, "ฤ รฌล‚ฤพรฌฤฟยผ": 23090, "dร„ยฑr": 23091, "รฌยฆฤบ": 23092, "your": 23093, "ฤ ร‘ฤฏร‘ฤครยธรยผ": 23094, "2020": 23095, "ฤ rund": 23096, "ฤ HO": 23097, "ฤ Catherine": 23098, "ieli": 23099, "ฤ fusion": 23100, "ฤ ideology": 23101, "ฤ foram": 23102, "shaped": 23103, "ฤ รญฤฝฤฆรซ": 23104, "ฤ wt": 23105, "ฤ retr": 23106, "ฤ prรƒยฉc": 23107, "ฤ รชยฐฤณ": 23108, "ฤ openly": 23109, "vity": 23110, "รชยตยฌรฌฤผฤถ": 23111, "ฤ obstacle": 23112, "ฤ boo": 23113, "ฤ seiner": 23114, "icorn": 23115, 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23796, "ฤ autonomous": 23797, "รฃฤงฤฐรฃฤงฤฐ": 23798, "sover": 23799, "รฆยฒฤดรฉฤฎยฏ": 23800, "รฅยพฤชรฅยฅยฝ": 23801, "ฤ reflex": 23802, "ฤ gardens": 23803, "ฤ dated": 23804, "รฌยฑ": 23805, "amiร„ฤป": 23806, "ฤ continuity": 23807, "ฤ citizenship": 23808, "ฤ schwer": 23809, "ฤ zak": 23810, "table": 23811, "ฤ ร‘ฤฃร‘ฤฉ": 23812, "รจยงฤฃ": 23813, "ฤ รฤฅรŽยต": 23814, "ฤ generates": 23815, "รชยตยฌรซฤคฤบ": 23816, "รƒยถh": 23817, "รƒยณm": 23818, "alam": 23819, "ฤ JUDY": 23820, "ฤ Bug": 23821, "ฤ รฃฤฃยฆ": 23822, "ฤ drones": 23823, "ฤ รƒยกgua": 23824, "acaks": 23825, "รฆฤผ": 23826, "ฤ รฤผรยพรยฝ": 23827, "ร—ฤธร—ฤถ": 23828, "ฤ strive": 23829, "ฤ Altern": 23830, "ฤ nearest": 23831, "ฤ proyect": 23832, "tera": 23833, "ฤ ASHLEY": 23834, "ฤ worm": 23835, "ฤ replay": 23836, "ฤ tara": 23837, "ฤ Indians": 23838, "รฃฤคยฐ": 23839, "icaid": 23840, "ฤ รฌฤชฤพ": 23841, "ฤ appealing": 23842, "ฤ Wes": 23843, "ฤ mentions": 23844, "ฤ รยดรยตรยปรยต": 23845, "ฤ kw": 23846, "ฤ fragile": 23847, "isz": 23848, "kรƒยณw": 23849, "hang": 23850, "color": 23851, "ฤ presidente": 23852, "87": 23853, "รยตร‘ฤฆ": 23854, "รงฤชยธ": 23855, "ฤ รยดรยพรยฑรยฐรยฒ": 23856, "ฤ Nelson": 23857, "รƒยกfic": 23858, "ฤ MICHAEL": 23859, "ฤ mechanic": 23860, "ฤ metres": 23861, "ฤ oczywiร…ฤฝcie": 23862, "ฤ Cind": 23863, "ฤ ogsรƒยฅ": 23864, "ฤ landsca": 23865, "ACE": 23866, "ฤ headlines": 23867, "ฤ catalyst": 23868, "ฤ Catch": 23869, "inkles": 23870, "ฤ pills": 23871, "ordo": 23872, "ฤ immigrant": 23873, "ฤ examination": 23874, "ฤ accidents": 23875, "zร„ฤงd": 23876, "ฤ quiere": 23877, "ฤ nella": 23878, "ฤ 67": 23879, "ฤ passa": 23880, "ฤ superfic": 23881, "istor": 23882, "ฤ nov": 23883, "รซฤญยต": 23884, "ฤ mandate": 23885, "isons": 23886, "ฤ Virtual": 23887, "ฤ selber": 23888, "ฤ counseling": 23889, "ฤ NBA": 23890, "ฤ sept": 23891, "ฤ believer": 23892, "ฤ marvel": 23893, "ฤ Integr": 23894, "ฤ รยผร‘ฤธ": 23895, "ฤ orph": 23896, "ฤ backward": 23897, "ฤ Generation": 23898, "ฤ Pict": 23899, "ฤ ร‘ฤครยพร‘ฤค": 23900, "ฤ tapi": 23901, "prochen": 23902, "ฤ hallway": 23903, "hte": 23904, "ฤ ร›ฤฃร›ฤด": 23905, "ฤ Zum": 23906, "รจฤขฤฃรฅยธยซ": 23907, "achment": 23908, "iquer": 23909, "folg": 23910, "ฤ Eddie": 23911, "ฤ Kil": 23912, "ฤ wellness": 23913, "stock": 23914, "รจยผฤฅ": 23915, "ฤ kaรƒยง": 23916, "ฤ terrorism": 23917, "ฤ pointer": 23918, "Of": 23919, "heric": 23920, "ฤ Ultimately": 23921, "ฤ meses": 23922, "ฤ Trade": 23923, "ฤ pint": 23924, "ฤ tuition": 23925, "ฤ disagre": 23926, "ฤ รชยฒฤฎรฌล€ฤฆ": 23927, "ฤ manuscript": 23928, "ฤ roomm": 23929, "ฤ outputs": 23930, "รยตร‘ฤจรยธ": 23931, "ฤ ries": 23932, "ฤ salud": 23933, "otzdem": 23934, "ฤ masses": 23935, "ฤ byร…ฤคa": 23936, "ฤ clearing": 23937, "ฤ discourse": 23938, "atson": 23939, "ฤ folded": 23940, "ฤ Jar": 23941, "ร™ฤฆร™ฤซ": 23942, "900": 23943, "ฤ ร‘ฤฅร‘ฤฃรยฟ": 23944, "ฤ prophecy": 23945, "ฤ interfere": 23946, "รยธร‘ฤงรยพรยด": 23947, "ร ยนฤฎ": 23948, "ฤ thri": 23949, "ฤ ร—ล€ร—ยฉ": 23950, "ฤ lazร„ยฑm": 23951, "ฤ 1992": 23952, "ฤ futuro": 23953, "ฤ locking": 23954, "ฤ embargo": 23955, "ฤ Neither": 23956, "ivamente": 23957, "ฤ mรƒยฅste": 23958, "ฤ mik": 23959, "ฤ collector": 23960, "รยตรยบรยพร‘ฤครยพร‘ฤข": 23961, "ฤ Gand": 23962, "ฤ sentir": 23963, "ฤ Might": 23964, "รฅยกฤถ": 23965, "ฤ ganzen": 23966, "UC": 23967, "ฤ relating": 23968, "SD": 23969, "ฤ mosquito": 23970, "GR": 23971, "ฤ hollow": 23972, "รขฤบฤง": 23973, "ฤ Walker": 23974, "ฤ affiliate": 23975, "ฤ duplicate": 23976, "รยฝรยตรยผ": 23977, "ฤ grape": 23978, "ฤ Organization": 23979, "ฤ synt": 23980, "Joe": 23981, "ฤ geg": 23982, "ฤ revealing": 23983, "ฤ Ethan": 23984, "outer": 23985, "ฤ yay": 23986, "รฉยซฤถ": 23987, "รยปรยฐร‘ฤข": 23988, "ฤ reportedly": 23989, "ฤ ihrer": 23990, "ฤ recognise": 23991, "ฤ bumper": 23992, "ฤ Randy": 23993, "ฤ Venus": 23994, "tles": 23995, "ฤ appetite": 23996, "ฤ glucose": 23997, "ฤ chodzi": 23998, "ฤ Furthermore": 23999, "tir": 24000, "ฤ conta": 24001, "ฤ intuition": 24002, "ฤ altitude": 24003, "ฤ chunks": 24004, "ฤ Joshua": 24005, "ร„ยฑร„ลร„ยฑm": 24006, "rylic": 24007, "leans": 24008, "ฤ รญฤถยผรซ": 24009, "LL": 24010, "Que": 24011, "ฤ gor": 24012, "ฤ รยทรยฝรยฐร‘ฤฉรยธร‘ฤค": 24013, "ฤ poems": 24014, "ฤ excel": 24015, "ฤ explored": 24016, "ฤ popul": 24017, "ฤ incluso": 24018, "stรƒยค": 24019, "ฤ Gavin": 24020, "alling": 24021, "ฤ รฤฆรŽยฟรŽยฝ": 24022, "รฉยฉ": 24023, "arbeit": 24024, "ฤ Gas": 24025, "ฤ glorious": 24026, "rieben": 24027, "ฤ spam": 24028, "ฤ indoor": 24029, "ฤ thrust": 24030, "ฤ Ald": 24031, "ฤ Prior": 24032, "ฤ onboard": 24033, "รฃฤฃล‚รฃฤฃฤทรฃฤฃฤฆ": 24034, "oca": 24035, "ASH": 24036, "ยฃล‚": 24037, "ฤ Christine": 24038, "ฤ drawer": 24039, "ฤ noon": 24040, "ฤ รฌล€ฤบรซ": 24041, "ฤ permanently": 24042, "รฆยทยฑ": 24043, "ฤ รยฝรยฐรยฟร‘ฤขรยธรยผรยตร‘ฤข": 24044, "ฤ podcasts": 24045, "erapeut": 24046, "prit": 24047, "ฤ stainless": 24048, "ฤ รšยฉร›ฤด": 24049, "ฤ familia": 24050, "ฤ ร‘ฤขรยฐรยทร‘ฤข": 24051, "unto": 24052, "ฤ ร‘ฤฃร‘ฤครยพรยป": 24053, "ฤ hรƒยค": 24054, "ฤ Hai": 24055, "ฤ PB": 24056, "izon": 24057, "ฤ konnte": 24058, "ฤ bรƒยผyรƒยผk": 24059, "ฤ utilizar": 24060, "รšฤจ": 24061, "ฤ aquesta": 24062, "ฤ mixer": 24063, "udent": 24064, "รยปรยตรยบร‘ฤฃ": 24065, "ร…ฤคu": 24066, "ฤ ร‘ฤฃรยธร‘ฤฃร‘ฤครยตรยผ": 24067, "ฤ รยฝรยพร‘ฤขรยผ": 24068, "ฤ fatal": 24069, "ฤ considerations": 24070, "ฤ validation": 24071, "ฤ oli": 24072, "ฤ kardeร…ล": 24073, "ฤ GLORIA": 24074, "ฤ pall": 24075, "รยตร‘ฤฃร‘ฤครยต": 24076, "ฤ rectang": 24077, "ฤ medieval": 24078, "allahi": 24079, "asti": 24080, "ฤ Syrian": 24081, "ฤ shear": 24082, "ฤ debug": 24083, "ฤ Mai": 24084, "ฤ knocking": 24085, "ฤ Lex": 24086, "ardan": 24087, "rov": 24088, "ฤ memorial": 24089, "รฆยฐยฃ": 24090, "ooky": 24091, "ฤ stuffed": 24092, "ฤ passรƒยฉ": 24093, "ฤ wig": 24094, "ฤคล‚": 24095, "ฤ prรƒยณxima": 24096, "ฤ 1991": 24097, "ฤ รยผรยตรยถรยดร‘ฤฅ": 24098, "ฤ nuestros": 24099, "ฤ Beast": 24100, "ฤ smo": 24101, "atched": 24102, "ologia": 24103, "ฤ รยผรยพรยด": 24104, "ฤ gee": 24105, "ฤ conceptual": 24106, "ฤ รƒยด": 24107, "ฤ decreases": 24108, "ฤ queries": 24109, "รยพรยปร‘ฤฎร‘ฤช": 24110, "ฤ Apart": 24111, "ฤ exempl": 24112, "รฅยฑยฑ": 24113, "ฤ fled": 24114, "ฤ OFF": 24115, "ggak": 24116, "ฤ bead": 24117, "hir": 24118, "lies": 24119, "ฤ Clearly": 24120, "ร„ยฑlar": 24121, "ฤ chess": 24122, "ฤ whichever": 24123, "ฤ 96": 24124, "รกยบยฑ": 24125, "ฤ respects": 24126, "ฤ รยผรยพร‘ฤข": 24127, "ฤ organism": 24128, "ฤ grandpa": 24129, "ฤ Vie": 24130, "รจยทลรคยฝล‚": 24131, "ฤ flooding": 24132, "ฤ upgraded": 24133, "ร‘ฤณร‘ฤข": 24134, "ฤ cheeks": 24135, "ฤ conquer": 24136, "ฤ stubborn": 24137, "ฤ puzzles": 24138, "ฤ auction": 24139, "ฤ relying": 24140, "ฤ PROF": 24141, "ฤ Esper": 24142, "ฤ รฤพรยฃ": 24143, "ฤ hype": 24144, "ฤ possibil": 24145, "ฤ imprison": 24146, "ฤ Ern": 24147, "รฌฤนฤชรฌฤฌยตรซฤญฤชรซฤญยค": 24148, "ฤ envie": 24149, "ฤ resurrection": 24150, "รคยธฤฏรจยกฤฎ": 24151, "ฤ sper": 24152, "ฤ Venezuela": 24153, "som": 24154, "ฤ รฌล€ล‚รชยน": 24155, "ฤ nouvelle": 24156, "ฤ closes": 24157, "ฤ 1940": 24158, "ฤ qua": 24159, "ฤ Jared": 24160, "ฤ Pir": 24161, "ฤ inde": 24162, "ฤ scrub": 24163, "uku": 24164, "ฤ requiring": 24165, "ฤ รยฒรยฐรยผรยธ": 24166, "ฤ considerable": 24167, "รฅฤฒฤฝ": 24168, "ilia": 24169, "ฤ inne": 24170, "ฤ meinem": 24171, "ฤ hardship": 24172, "ฤ traps": 24173, "roc": 24174, "ฤ รฌฤฆยครซ": 24175, "ฤ researching": 24176, "ฤ Margaret": 24177, "ฤ penny": 24178, "ฤ bร„ยฑrak": 24179, "ร‘ฤณรยป": 24180, "ฤ wool": 24181, "ฤ rhet": 24182, "ฤ flatten": 24183, "รงฤฉ": 24184, "ร ยนฤขร ยธยฃ": 24185, "ฤ pied": 24186, "ฤ Chap": 24187, "ฤ underm": 24188, "ฤ fret": 24189, "ฤ crashed": 24190, "ฤ Frauen": 24191, "ร˜ยฐร™ฤฉ": 24192, "ivan": 24193, "ฤ literary": 24194, "latego": 24195, "ฤ spรƒยคter": 24196, "ฤ similarities": 24197, "รขฤจ": 24198, "ฤ Coron": 24199, "ฤ Creek": 24200, "ฤ bosses": 24201, "ฤ accompanied": 24202, "ฤ debates": 24203, "ฤ assembled": 24204, "ฤ รƒฤฃ": 24205, "ฤ Vai": 24206, "ฤ tract": 24207, "ฤ simplement": 24208, "ฤ Arin": 24209, "ฤ vulnerability": 24210, "ฤ hormone": 24211, "IEL": 24212, "OOK": 24213, "ฤ relay": 24214, "ฤ Andrea": 24215, "ril": 24216, "ฤ necessity": 24217, "aceutical": 24218, "ร‘ฤฐร‘ฤซ": 24219, "ousing": 24220, "nahmen": 24221, "ฤ footprint": 24222, "map": 24223, "ฤ Tier": 24224, "annya": 24225, "intend": 24226, "รฅฤธยฎ": 24227, "รฅยข": 24228, "ฤ decorate": 24229, "ฤ zombies": 24230, "ฤ Hyd": 24231, "ฤ Suz": 24232, "ฤ campuses": 24233, "ฤ Emb": 24234, "ฤ throttle": 24235, "ฤ admin": 24236, "ฤ oportun": 24237, "ฤ mirrors": 24238, "ฤ identities": 24239, "ฤ Clin": 24240, "ฤ รซยนฤฆรซ": 24241, "รกยนยฃ": 24242, "ฤ Ott": 24243, "ฤ blues": 24244, "ฤ impressions": 24245, "-,": 24246, "ฤ vague": 24247, "afe": 24248, "ฤ inferior": 24249, "erald": 24250, "ฤ medicines": 24251, "ฤ pregunta": 24252, "osely": 24253, "ฤ tรƒยฉlรƒยฉ": 24254, "ฤ Month": 24255, "ฤ Leaders": 24256, "ฤ Egyptian": 24257, "ฤ ration": 24258, "kers": 24259, "heits": 24260, "ฤ recht": 24261, "Play": 24262, "ฤ eg": 24263, "ฤ polls": 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"ฤ ecological": 31054, "ฤ Mae": 31055, "ฤ ร—ล€ร—ฤฒร—ฤทร—ฤต": 31056, "ฤ รซฤคฤบรซฤฑฤฆ": 31057, "รยปรยพรยฝ": 31058, "yss": 31059, "ฤ pergunt": 31060, "ฤ prix": 31061, "izzard": 31062, "ฤ cancers": 31063, "ฤ 91": 31064, "susp": 31065, "ฤ Item": 31066, "ร…ลa": 31067, "ฤ pest": 31068, "ฤ takร„ฤง": 31069, "ฤ lymph": 31070, "ฤ Patri": 31071, "fill": 31072, "ฤ reconna": 31073, "ฤ optimism": 31074, "ฤ mimic": 31075, "ฤ รฌยฒฤพ": 31076, "ฤ Madame": 31077, "ocy": 31078, "lining": 31079, "รฅฤณฤฌรจยจยด": 31080, "erme": 31081, "ฤ folders": 31082, "ฤ czร…ฤค": 31083, "uchar": 31084, "ฤ curso": 31085, "ฤ breach": 31086, "รยฝรยธร‘ฤคร‘ฤฎ": 31087, "ฤ pamiร„ฤป": 31088, "ฤ elig": 31089, "ฤ autop": 31090, "Flow": 31091, "ฤ programmed": 31092, "ฤ Process": 31093, "ฤ figur": 31094, "ฤ SF": 31095, "ฤ Eles": 31096, "ฤ programmes": 31097, "ฤ dizzy": 31098, "รฌฤญฤพรชยฐฤฆ": 31099, "ฤ รยปรยธรยฑรยพ": 31100, "ฤ sniff": 31101, "ฤ Sebastian": 31102, "ฤ Hye": 31103, "ฤ 4000": 31104, "ฤ permite": 31105, "รฆยขฤฟ": 31106, "ฤ รยทรยฐร‘ฤซ": 31107, "ฤ guit": 31108, "ฤ Dais": 31109, "ฤ accordance": 31110, "ฤ modular": 31111, "ogeneous": 31112, "รฆฤญฤฏ": 31113, "ฤ pouquinho": 31114, "ฤ artillery": 31115, "ฤ lubric": 31116, "ฤ volcan": 31117, "ฤ NH": 31118, "รฐลยค": 31119, "ฤ dean": 31120, "Rh": 31121, "ฤ ministre": 31122, "รฅฤฟฤฒ": 31123, "ฤ Inv": 31124, "ฤ Bulgar": 31125, "ฤ Daten": 31126, "รจฤฐ": 31127, "Im": 31128, "ฤ originated": 31129, "ฤ Nixon": 31130, "integr": 31131, "ฤ lacks": 31132, "ฤ Nacht": 31133, "รฌฤธยดรซฤคฤบ": 31134, "camera": 31135, "ฤ radish": 31136, "kiye": 31137, "ฤ anges": 31138, "ฤ prรƒยฉf": 31139, "juk": 31140, "ฤ Bee": 31141, "ฤ BU": 31142, "ฤ รยฒรยพร‘ฤฃรยฟ": 31143, "ฤ BT": 31144, "รƒยชmes": 31145, "ฤ Stรƒยผck": 31146, "ฤ Ink": 31147, "รฆฤชฤธรจฤขฤง": 31148, "ฤ Sergeant": 31149, "ฤ Multip": 31150, "ฤ hiรƒยงbir": 31151, "ฤ รยกรยฐรยผ": 31152, "ฤ Dรƒยฉ": 31153, "olph": 31154, "รฌฤธยธ": 31155, "ฤ impat": 31156, "ฤ รฌฤทฤฌรชยณล‚": 31157, "ฤ ร‘ฤครยฐรยบรยพรยณรยพ": 31158, "ฤ รยฝรยฐรยฒรยตร‘ฤขรยฝรยพรยต": 31159, "ฤ unpredictable": 31160, "ฤ mend": 31161, "ฤ รฌฤนฤจรฌฤธยดรฌฤผฤถ": 31162, "ฤ jakieร…ฤฝ": 31163, "ฤ anni": 31164, "ฤ donnรƒยฉ": 31165, "ฤ Kirsty": 31166, "ฤ rectangular": 31167, "ฤ empezar": 31168, "ฤ Exchange": 31169, "รชยฐฤถ": 31170, "ฤ รƒยฉconom": 31171, "รฃฤฃฤตรฃฤคฤต": 31172, "elin": 31173, "reibt": 31174, "ฤ ร—ฤถร—ยค": 31175, "ฤ cemetery": 31176, "ฤ espaรƒยฑol": 31177, "olin": 31178, "รยปร‘ฤฐรยด": 31179, "ฤ grรƒยขce": 31180, "allen": 31181, "ฤ Philos": 31182, "ฤ Erst": 31183, "ฤ รฌฤฅฤช": 31184, "ฤ Vid": 31185, "Give": 31186, "OH": 31187, "รŽยผรŽยฟ": 31188, "ฤ Pare": 31189, "ฤ metabolism": 31190, "ฤ maple": 31191, "ฤ axle": 31192, "ฤ Dy": 31193, "ฤ komme": 31194, "รฤฐรŽยฝ": 31195, "ฤ greatness": 31196, "ฤ verified": 31197, "ฤ spรƒยฉ": 31198, "ฤ Fahrenheit": 31199, "ฤ Bren": 31200, "ฤ Confeder": 31201, "ฤ histoire": 31202, "ฤ eliminating": 31203, "ฤ Adding": 31204, "ฤ Abi": 31205, "รฆฤฟฤฐ": 31206, "ฤ hospitality": 31207, "tim": 31208, 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"รคยปฤธรงฤผฤฆ": 31309, "ฤ Arist": 31310, "ฤ withstand": 31311, "ฤ qualitative": 31312, "ฤ Kraft": 31313, "ฤ ร‘ฤฏรยปรยตรยบร‘ฤค": 31314, "ฤ Bead": 31315, "รยตรยบร‘ฤครยธรยฒ": 31316, "ฤ crushing": 31317, "รฌยณฤฒ": 31318, "ฤ navy": 31319, "ร™ฤชรšยบ": 31320, "sho": 31321, "ฤ oak": 31322, "ippers": 31323, "ฤ soils": 31324, "ฤ pigment": 31325, "ฤ evitar": 31326, "รฃฤฅฤฉ": 31327, "ฤ fuse": 31328, "ฤ Dale": 31329, ":\"": 31330, "ฤ complรƒยจtement": 31331, "ฤ kel": 31332, "ร ยนฤจ": 31333, "ฤ quatre": 31334, "ฤ UM": 31335, "ฤ รซยงฤฒรซ": 31336, "รฆล‚ยน": 31337, "รƒลƒr": 31338, "ฤ leisure": 31339, "ฤ Housing": 31340, "ฤ folds": 31341, "estion": 31342, "ARS": 31343, "ฤ mash": 31344, "urpose": 31345, "ฤ accumulated": 31346, "ฤ Stuff": 31347, "รจยชล€": 31348, "ฤ tapes": 31349, "ฤ ร‘ฤฃรยธรยปร‘ฤฎรยฝรยพ": 31350, "ฤ LOVE": 31351, "ฤ 1982": 31352, "ฤ scars": 31353, "ฤ capitalist": 31354, "ฤ Ned": 31355, "ฤ soften": 31356, "ฤ notably": 31357, "ฤ forcรƒยฉment": 31358, "ฤ Raum": 31359, "ฤ รยฝรยตรยพรยฑร‘ฤงรยพรยด": 31360, "ฤ trademark": 31361, "ฤ fertig": 31362, "ฤ ?!": 31363, "รฆฤนล‚": 31364, "ฤ reinforced": 31365, "ฤ recharge": 31366, "ฤ Putting": 31367, "ฤ villains": 31368, "ฤ handic": 31369, "ฤ advertisement": 31370, "ร˜ยชร™ฤฌ": 31371, "ฤ ร‘ฤฃร‘ฤฅรยผ": 31372, "ฤ Riley": 31373, "ร—ฤทร—ฤณร—": 31374, "รคยบยฌ": 31375, "Os": 31376, "ร˜ยงร˜ยฒ": 31377, "Boy": 31378, "ฤ squish": 31379, "ocket": 31380, "ฤ testify": 31381, "รฆยผฤถ": 31382, "ฤ ร—ฤพร—ล€ร—": 31383, "ฤ รยผรยฐร‘ฤฃร‘ฤฃ": 31384, "manuel": 31385, "ฤ Arkansas": 31386, "iffe": 31387, "ฤ analysts": 31388, "ฤ Deaf": 31389, "ฤ jรƒยณ": 31390, "ฤ groceries": 31391, "ฤ Wheel": 31392, "ฤ ร‘ฤขรยธร‘ฤฃ": 31393, "ฤ cรƒยฒn": 31394, "ฤ Cob": 31395, "ฤ prisons": 31396, "รƒยจve": 31397, "ฤ Cabinet": 31398, "ฤ posed": 31399, "ฤ guerre": 31400, "ฤ Lloyd": 31401, "ฤ clerk": 31402, "ฤ crises": 31403, "ฤ Sho": 31404, "ฤ Ore": 31405, "ฤ Football": 31406, "ฤ Advis": 31407, "ฤ Zheng": 31408, "รจฤฏ": 31409, "ฤ AMY": 31410, "ฤ unfor": 31411, "ฤ monaster": 31412, "ฤ compile": 31413, "ฤ immortal": 31414, "atable": 31415, "ฤ parano": 31416, "ฤ tiver": 31417, "ฤ Steph": 31418, "ฤ Fuรƒล": 31419, "ฤ discontin": 31420, "ฤ ripe": 31421, "ฤ hacking": 31422, "ฤ siendo": 31423, "ฤ seguro": 31424, "altres": 31425, "ฤ anderes": 31426, "ฤ รซยฆยฌรซ": 31427, "ฤ exports": 31428, "รฆลƒยฅ": 31429, "ฤ tabii": 31430, "ฤ รชยธยฐรซฤญยครซ": 31431, "ฤ bothering": 31432, "ฤ pickle": 31433, "ฤ BRIAN": 31434, "ฤ altar": 31435, "ฤ รยฟร‘ฤขรยธรยฑ": 31436, "ฤ transferring": 31437, "ฤ Vors": 31438, "ฤ ร™ฤฉร™ฤช": 31439, "ฤ Za": 31440, "ฤ Frances": 31441, "ฤ browse": 31442, "emit": 31443, "ฤ chewing": 31444, "ฤ Freddy": 31445, "ฤ editors": 31446, "รƒยคlle": 31447, "ฤ รญฤฎฤข": 31448, "ฤ Sque": 31449, "ฤ Cultural": 31450, "awk": 31451, "ฤ Sache": 31452, "ฤ Carbon": 31453, "รกยบยฏt": 31454, "FL": 31455, "ฤ NGO": 31456, "peร…ฤค": 31457, "ฤ Sou": 31458, "ฤ hvor": 31459, "unintelligible": 31460, "ฤ รซยฒฤท": 31461, "ฤ ร‚ยฐ": 31462, "iin": 31463, "ฤ ร—ยขร—ฤฟ": 31464, "ฤ derriรƒยจre": 31465, "ฤ czym": 31466, "ฤ Apost": 31467, "ฤ regarder": 31468, "ฤ agrade": 31469, "ฤ Candy": 31470, "ฤ mare": 31471, "ฤ introduces": 31472, "birds": 31473, "ฤ uniquely": 31474, "ฤ muk": 31475, "ฤ cooker": 31476, "ฤ crews": 31477, "ฤ jeito": 31478, "ERT": 31479, "ยถฤฆรซ": 31480, "nisse": 31481, "ฤ ef": 31482, "ฤ carte": 31483, "ฤ Yak": 31484, "ฤ PAT": 31485, "รยธรยฝรยพ": 31486, "bokki": 31487, "ฤ mates": 31488, "ฤ distint": 31489, "ฤ รฌยฝฤถรซยกฤพรซฤคฤบ": 31490, "ฤ yร„ยฑl": 31491, "ฤ รŽยบรŽยฌรŽยฝ": 31492, "ฤ configurations": 31493, "enga": 31494, "recht": 31495, "Happy": 31496, "รฃฤคฤฆรฃฤฃยฃรฃฤฃยฆ": 31497, "invest": 31498, "ฤ reconstruct": 31499, "ฤ ร‘ฤฏร‘ฤครยพรยผร‘ฤฅ": 31500, "ฤ mosque": 31501, "raum": 31502, "ฤ voyez": 31503, "ฤ NBC": 31504, "ฤ รฌล€ฤฒรฌฤญล‚": 31505, "ฤ sturdy": 31506, "ฤ รยบรยฐรยฟ": 31507, "ฤ ansch": 31508, "alid": 31509, "ฤ masih": 31510, "ฤ REP": 31511, "ฤ รฌยฝฤถรซ": 31512, "ฤ deduct": 31513, "ฤ salir": 31514, "wurf": 31515, "ilot": 31516, "ฤ Mutter": 31517, "olds": 31518, "ฤ FEMA": 31519, "ฤ Bib": 31520, "ฤ neighboring": 31521, "ฤ bliss": 31522, "ฤ รญฤบยผ": 31523, "รยปรยธร‘ฤฃร‘ฤฎ": 31524, "ฤ ร‘ฤคร‘ฤขรยตรยฑ": 31525, "ฤ รฅยฐยฑรฆฤบยฏ": 31526, "ฤ grenade": 31527, "ฤ egal": 31528, "ฤ finely": 31529, "ฤ petals": 31530, "ฤ keer": 31531, "ฤ chyba": 31532, "ฤ skipping": 31533, "ฤ thirteen": 31534, "ฤ gravy": 31535, "ฤ SAT": 31536, "61": 31537, "ฤ รยฝรยพรยณ": 31538, "ฤ mins": 31539, "ITE": 31540, "ฤ sozial": 31541, "รญฤทฤบรซยฉยดรฌฤฆฤพ": 31542, "ruktur": 31543, "ฤ รยฒรยพรยทรยผรยพรยถ": 31544, "ฤ รยพรยฟร‘ฤฑร‘ฤคร‘ฤฎ": 31545, "ฤ arth": 31546, "ฤ Cuban": 31547, "ฤ treasures": 31548, "ฤ fertilizer": 31549, "ฤ awakening": 31550, "ฤ รซยฐยฑรฌฤญล‚": 31551, "ฤ rall": 31552, "ฤ depict": 31553, "ฤ Pablo": 31554, "ฤ nineteen": 31555, "ฤ watt": 31556, "ฤ entirety": 31557, "KS": 31558, "ฤ Woods": 31559, "Sch": 31560, "ฤ รšยฉร™ฤช": 31561, "ฤ Dry": 31562, "รฃฤฃล€": 31563, "uve": 31564, "ฤ reconstruction": 31565, "ฤ anatomy": 31566, "ฤชรซยฅยผ": 31567, "ฤ baba": 31568, "ฤ listener": 31569, "ฤ sharpen": 31570, "ฤ Peru": 31571, "ฤ รยฒร‘ฤญรยท": 31572, "ฤ recreation": 31573, "ฤ initiate": 31574, "ฤ calor": 31575, "ฤ Naj": 31576, "gee": 31577, "ฤ Feels": 31578, "ฤ Snapchat": 31579, "ฤ Tet": 31580, "ฤ Nest": 31581, "ฤ Daf": 31582, "ฤ Finish": 31583, "ฤ ร‘ฤครยฐรยบรยธรยผ": 31584, "รƒยบc": 31585, "izens": 31586, "ฤ spins": 31587, "ฤ embry": 31588, "ฤ passages": 31589, "ฤ cient": 31590, "ฤ justification": 31591, "รคยปฤธรจยชยช": 31592, "ฤ olmaz": 31593, "ฤ flooded": 31594, "ฤ emoji": 31595, "ฤ embracing": 31596, "ฤ discard": 31597, "ฤ Basic": 31598, "agog": 31599, "ฤ รฌฤพฤฆรญฤทยด": 31600, "ฤ asylum": 31601, "erin": 31602, "ฤ fim": 31603, "ฤ ninja": 31604, "ฤ automate": 31605, "ฤ allergic": 31606, "รƒยฟรƒยฟรƒยฟรƒยฟ": 31607, "amam": 31608, "ฤ รยผรยฐร‘ฤข": 31609, "ฤ Oi": 31610, "รƒยคus": 31611, "ฤ induct": 31612, "ฤ BEN": 31613, "ฤ zร…ฤค": 31614, "ฤ kaร…ยผdy": 31615, "ฤ AMP": 31616, "nร„ฤฝ": 31617, "Sure": 31618, "ฤ quil": 31619, "ฤ espec": 31620, "rok": 31621, "BSCRI": 31622, "ฤ liebe": 31623, "pus": 31624, "achsen": 31625, "ฤ cricket": 31626, "รซฤฌฤฒ": 31627, "ฤ Frame": 31628, "ekkรƒยผr": 31629, "arb": 31630, "ฤ pร…ฤป": 31631, "รยธร‘ฤฃร‘ฤฃ": 31632, "ฤ zeggen": 31633, "ฤ doubles": 31634, "ฤ Dre": 31635, "test": 31636, "insp": 31637, "boys": 31638, "ฤ mรƒยฃo": 31639, "ฤ Verse": 31640, "ฤ muscular": 31641, "ฤ MALE": 31642, "ฤ dulu": 31643, "ฤ occasional": 31644, "Lo": 31645, "conomic": 31646, "ฤ vak": 31647, "ฤ remedy": 31648, "รฅยคล‚": 31649, "ฤ รขฤปยชรขฤปยชรขฤปยช": 31650, "vem": 31651, "ฤ รƒยถnem": 31652, "ฤ karร…ลร„ยฑ": 31653, "ฤ Sharp": 31654, "hur": 31655, "ฤ รซยฐยฉรซยฒฤท": 31656, "ฤ grandson": 31657, "ฤ aktiv": 31658, "ฤ Thrones": 31659, "ฤ รฌฤทฤชรฌฤนฤฒ": 31660, "ฤ tots": 31661, "ฤ subd": 31662, "ฤ Paula": 31663, "ฤ graves": 31664, "ฤ Brent": 31665, "ฤ รยฝรยธรยบร‘ฤครยพ": 31666, "ฤ sรƒยถz": 31667, "ฤ crec": 31668, "ฤ Vladimir": 31669, "รงฤธยซ": 31670, "ฤ รยฟรยพรยน": 31671, "ฤ \"-": 31672, "ฤ psy": 31673, "atri": 31674, "idan": 31675, "ฤ aรƒยบn": 31676, "ฤ standardized": 31677, "รฌยนฤบรซ": 31678, "ฤ รยบร‘ฤขรยพรยฒ": 31679, "ฤ Zhu": 31680, "something": 31681, "ฤ 750": 31682, "ฤ mujeres": 31683, "ฤ ait": 31684, "รฉฤนยด": 31685, "agu": 31686, "ฤ corrected": 31687, "ikka": 31688, "eled": 31689, "ฤ Career": 31690, "owym": 31691, "ฤ roommate": 31692, "ฤ descendants": 31693, "ฤ Napoleon": 31694, "ฤ รฤถรยพ": 31695, "รญฤธฤชรฌฤธยดรฌฤผฤถ": 31696, "ฤ bunun": 31697, "ฤ Micha": 31698, "รงยทฤผ": 31699, "ฤ descob": 31700, "PI": 31701, "ฤ palabra": 31702, "ฤ tracked": 31703, "ฤ dependence": 31704, "ฤ Barack": 31705, "รฅฤฃฤฉ": 31706, "ฤ fertility": 31707, "ฤ Southwest": 31708, "ฤ incomplete": 31709, "ฤ comunic": 31710, "ฤ compris": 31711, "ฤ Restaur": 31712, "ฤ acron": 31713, "รŽยบรŽยฑ": 31714, "ฤ apprentices": 31715, "ฤ musst": 31716, "ฤ Abr": 31717, "ฤ pentru": 31718, "ฤ Consort": 31719, "ฤ Avec": 31720, "ฤ dumplings": 31721, "LR": 31722, "ฤ wszystkie": 31723, "ฤ swamp": 31724, "รยฝรยตรยฒ": 31725, "uggle": 31726, "ฤ watercolor": 31727, "ฤ proton": 31728, "ฤ Espaรƒยฑa": 31729, "ocking": 31730, "รยพรยฒรยฐรยป": 31731, "ฤ takim": 31732, "Very": 31733, "ฤ dementia": 31734, "ฤ ร…ลeyi": 31735, "Jac": 31736, "ฤ MacBook": 31737, "ฤ Liv": 31738, "fficients": 31739, "ฤ Hunt": 31740, "ฤ overlay": 31741, "รฆฤฆลรจยฆยบ": 31742, "ฤ Skype": 31743, "punkt": 31744, "ฤ confined": 31745, "ฤ Adrian": 31746, "ร˜ยฑร™ฤฅ": 31747, "ฤ Jeep": 31748, "ฤ enquanto": 31749, "ฤ anest": 31750, "รยพร‘ฤครยฒรยตร‘ฤค": 31751, "ฤ รยผรยตรยฝร‘ฤฎ": 31752, "ฤ irrigation": 31753, "รกยปฤณn": 31754, "ฤ eighteen": 31755, "ฤ Pon": 31756, "ฤ rescued": 31757, "ฤ 1983": 31758, "rรƒยผ": 31759, "jae": 31760, "ฤ Jeong": 31761, "ฤ amazingly": 31762, "ฤ FDP": 31763, "ฤ backstage": 31764, "cue": 31765, "ฤ รฤฅรฤฆรŽยทรŽยฝ": 31766, "ฤ ร˜ยงร™ฤฆร˜ยต": 31767, "ฤ livestock": 31768, "ฤ Warner": 31769, "ฤ majors": 31770, "รฃฤฅฤฃรฃฤฅยฃ": 31771, "ฤ cooperative": 31772, "ฤ Brady": 31773, "rained": 31774, "rieb": 31775, "ฤ ร—ฤณร—ล€ร—": 31776, "ฤ รยดรยพรยฒรยพรยปร‘ฤฎรยฝรยพ": 31777, "ฤ FE": 31778, "ฤ leaked": 31779, "ฤ Mercury": 31780, "ฤ persuade": 31781, "ฤ transformer": 31782, "ฤ Norweg": 31783, "ฤ รฌฤนยฌรซลยฌ": 31784, "ฤ zrobiร„ฤฉ": 31785, "ฤ cardiovascular": 31786, "ฤ Crash": 31787, "ฤ gossip": 31788, "รยฐร‘ฤฃร‘ฤคร‘ฤฎ": 31789, "ฤ รฌยชยฝ": 31790, "ฤ swept": 31791, "ฤ Horn": 31792, "ฤ Atรƒยฉ": 31793, "ฤ bukan": 31794, "ฤ Kaw": 31795, "KY": 31796, "ฤ Stories": 31797, "Gary": 31798, "ฤ gardening": 31799, "ฤ Quickly": 31800, "ฤ Falcon": 31801, "ฤ ovat": 31802, "cร„ยฑ": 31803, "ฤ Complet": 31804, "ฤ Date": 31805, "ฤ รยฟร‘ฤขรยธรยผ": 31806, "ฤ lรƒยคuft": 31807, "ฤ Audrey": 31808, "ฤ Went": 31809, "ฤ pelรƒลƒcul": 31810, "ฤ carriage": 31811, "ฤ unacceptable": 31812, "nymi": 31813, "ฤ ร‘ฤฃรยปร‘ฤญร‘ฤช": 31814, "ฤ terre": 31815, "uellement": 31816, "EEEE": 31817, "ฤ pharmac": 31818, "hรƒยตes": 31819, "ฤ zich": 31820, "ฤ migrate": 31821, "ฤ Fry": 31822, "รƒยฑana": 31823, "ฤ Muito": 31824, "EOVER": 31825, "ฤ fortress": 31826, "ฤ Compan": 31827, "ฤ JSON": 31828, "ordnung": 31829, "ฤ warto": 31830, "ฤ ungef": 31831, "รฌฤงฤถรฌฤฆฤพ": 31832, "ฤ ร‘ฤขรยพรยบ": 31833, "ฤ paddle": 31834, "Jared": 31835, "ฤ submitting": 31836, "ฤ latch": 31837, "ฤ fug": 31838, "ฤ รยบรยพร‘ฤฃ": 31839, "ฤ Ef": 31840, "ฤ launches": 31841, "ฤ ft": 31842, "otechn": 31843, "ฤ travelled": 31844, "ร˜ยงร™ฤฃ": 31845, "รฉฤฃฤท": 31846, "ฤ proch": 31847, "ฤ dedim": 31848, "83": 31849, "ฤ rebound": 31850, "ฤ LU": 31851, "path": 31852, "ฤ ร‘ฤฃรยฟร‘ฤขรยฐรยฒ": 31853, "ฤ รƒยถl": 31854, "ฤ รญฤคยค": 31855, "ฤ privat": 31856, "ฤ tractor": 31857, "ฤ Attention": 31858, "Ser": 31859, "ฤ coses": 31860, "รƒยกria": 31861, "pal": 31862, "ฤ รฌฤฟฤข": 31863, "ฤ successor": 31864, "ฤ connectors": 31865, "ฤ ร‘ฤฅร‘ฤฃร‘ฤครยฐรยฝรยพรยฒ": 31866, "ฤ genocide": 31867, "ฤ sufficiently": 31868, "ฤ Aixรƒยฒ": 31869, "ฤ stabilize": 31870, "ฤ congest": 31871, "ฤ carving": 31872, "ฤ zost": 31873, "ฤ รยฑร‘ฤญร‘ฤฃร‘ฤคร‘ฤขรยพ": 31874, "ฤ shortest": 31875, "ฤ livel": 31876, "ฤ 89": 31877, "รฉฤฃฤฌ": 31878, "ฤ erk": 31879, "ฤ portraits": 31880, "ร ยฅฤข": 31881, "รจฤบ": 31882, "boat": 31883, "llah": 31884, "ANC": 31885, "ฤ empirical": 31886, "ฤ Echo": 31887, "ฤ Nederland": 31888, "รจยฟฤปรคยนฤช": 31889, "Net": 31890, "ฤ cuidado": 31891, "ฤ Roma": 31892, "ฤ calf": 31893, "ฤ giants": 31894, "ฤ Explorer": 31895, "ฤ Collect": 31896, "alition": 31897, "ฤ Destiny": 31898, "ฤ ausge": 31899, "ฤ Edu": 31900, "ฤ Clo": 31901, "ฤ earrings": 31902, "ฤ Track": 31903, "ฤ ROS": 31904, "ฤ Belle": 31905, "รงฤปยพ": 31906, "ฤ pueda": 31907, "ฤ daytime": 31908, "ฤ supplier": 31909, "ฤ SV": 31910, "ฤ Exhale": 31911, "ฤ galera": 31912, "course": 31913, "ฤ centimeter": 31914, "ฤ Bast": 31915, "mud": 31916, "ฤ sangat": 31917, "ฤ Physical": 31918, "ฤ privately": 31919, "ฤ trata": 31920, "lynn": 31921, "illi": 31922, "ฤ รซยฉฤถรฌฤฟยดรญฤฃยฌรฌฤนฤง": 31923, "ฤ crystall": 31924, 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"ฤ Hiรƒยง": 33410, "ยชยป": 33411, "ฤ editorial": 33412, "ฤ seize": 33413, "ฤ kร„ยฑr": 33414, "ฤ mouve": 33415, "ฤ tierra": 33416, "ฤ testosterone": 33417, "ฤ rh": 33418, "ฤ Kingston": 33419, "ELLE": 33420, "ฤ Representative": 33421, "ฤ 1974": 33422, "ฤ iba": 33423, "Ts": 33424, "ฤ sorta": 33425, "ฤ (?)": 33426, "ฤ ร˜ยชร™ฤช": 33427, "ฤ รซฤคยดรซล‚ยค": 33428, "ฤ bekommt": 33429, "ฤ spiritually": 33430, "ฤ distorted": 33431, "Mad": 33432, "ฤ reim": 33433, "รƒยกnh": 33434, "ฤ Ottoman": 33435, "ฤ Relig": 33436, "ฤ Els": 33437, "ฤ retained": 33438, "ฤ Laughs": 33439, "รฆฤขยป": 33440, "ฤ SAS": 33441, "ฤ รยบรยพรยปรยธร‘ฤฉรยตร‘ฤฃร‘ฤครยฒรยพ": 33442, "ร—ฤทร—ยชร—ยจ": 33443, "ฤ innovate": 33444, "ฤ kork": 33445, "ฤ ร‘ฤขรยฐร‘ฤฃร‘ฤฃรยบรยฐรยทร‘ฤญรยฒ": 33446, "ondere": 33447, "ivi": 33448, "aye": 33449, "ounty": 33450, "ฤ รยฟรยพรยปร‘ฤฅร‘ฤฉรยฐรยตร‘ฤคร‘ฤฃร‘ฤฑ": 33451, "ฤ buns": 33452, "รฅฤงยซ": 33453, "ฤ yรƒยผzden": 33454, "ฤ surgeries": 33455, "ร˜ยฃร™ฤจ": 33456, "ฤ bankruptcy": 33457, 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33817, "ฤ reboot": 33818, "ฤ paj": 33819, "ฤ รซยณยดรฌฤนยฌรซ": 33820, "ฤ thicken": 33821, "ฤ Reality": 33822, "idรƒยคn": 33823, "Na": 33824, "ฤ รชยฒฤฅรฌฤฟฤข": 33825, "!!)": 33826, "ฤ routines": 33827, "ฤ รยพรยดรยฝรยพรยณรยพ": 33828, "ฤ exting": 33829, "ฤ รฌยฆฤฟ": 33830, "ฤ sulfur": 33831, "ฤ carve": 33832, "ฤ asteroid": 33833, "ฤ Warrior": 33834, "ฤ photographers": 33835, "ฤ pell": 33836, "ฤ crossover": 33837, "รฆฤชฤณรงลยฅรฉฤฃฤต": 33838, "ฤ hacemos": 33839, "ฤ Nej": 33840, "ฤ settling": 33841, "ฤ irm": 33842, "ฤ Books": 33843, "ientรƒยดt": 33844, "ฤ espacio": 33845, "ฤ Scholars": 33846, "ฤ doomed": 33847, "ฤ IRS": 33848, "wohl": 33849, "ฤ segue": 33850, "ฤ รซฤชฤฆรชยฐฤข": 33851, "ฤ pratic": 33852, "BT": 33853, "ฤ Considering": 33854, "ฤ Buffalo": 33855, "ฤ trainings": 33856, "ฤ gebru": 33857, "ฤ Gleich": 33858, "ฤ pirates": 33859, "ฤ envelop": 33860, "ฤ reopen": 33861, "imat": 33862, "ฤ tee": 33863, "ฤ sued": 33864, "feh": 33865, "ฤ ร—ฤถร—ยง": 33866, "ฤ diets": 33867, "ฤ juntos": 33868, "asto": 33869, "ฤ misunderstood": 33870, "ฤ ruim": 33871, "ฤ classify": 33872, "ฤ รยฟร‘ฤขรยพรยดร‘ฤฅรยบ": 33873, "ฤ inse": 33874, "ฤ illustrated": 33875, "ฤ corrosion": 33876, "ฤ accred": 33877, "ฤ Auntie": 33878, "ฤ รยฟร‘ฤขรยธรยฒรยตร‘ฤค": 33879, "ฤ LIVE": 33880, "ฤ rek": 33881, "ฤ receipt": 33882, "รฅฤชยฐรฅยบฤท": 33883, "ฤ Barbie": 33884, "ฤ Snake": 33885, "turn": 33886, "Jeff": 33887, "รฃฤฃฤฌรฃฤฃฤฌ": 33888, "ฤทฤฆ": 33889, "VOICEOVER": 33890, "coll": 33891, "ฤ runners": 33892, "รฌล‚ฤพรซ": 33893, "osos": 33894, "moon": 33895, "ฤ keynote": 33896, "ฤ Instit": 33897, "SPEAK": 33898, "ฤ plugs": 33899, "ฤ curv": 33900, "ฤ Yuri": 33901, "ฤ Theres": 33902, "ฤ Ps": 33903, "ฤ รŽยผรฤขรŽยฟ": 33904, "ฤ converter": 33905, "ฤ refine": 33906, "ฤ badass": 33907, "ฤ รŽยฟรŽยน": 33908, "ฤ regen": 33909, "azzi": 33910, "ร™ฤฌร™ฤฃ": 33911, "ฤ seized": 33912, "ฤ iรƒยงer": 33913, "ilee": 33914, "ฤ upstream": 33915, "ฤ buds": 33916, "ฤ pim": 33917, "ฤ รญฤทฤบรซยฃยจ": 33918, "ฤ alluded": 33919, "ฤ themed": 33920, "ฤ consisting": 33921, "ฤ bons": 33922, "unuz": 33923, "ฤ รยฟร‘ฤขรยพรยฒรยพรยด": 33924, "ฤ Lovely": 33925, "ร ยฅฤญ": 33926, "ฤ parach": 33927, "ฤ Staats": 33928, "รฉฤผฤฌ": 33929, "ฤ selective": 33930, "ฤ fase": 33931, "ฤ Georget": 33932, "ฤ cocaine": 33933, "ฤ reproduction": 33934, "ฤ Lara": 33935, "ฤ LD": 33936, "ฤ gh": 33937, "Jon": 33938, "ฤ lรƒยฅ": 33939, "ฤ รซฤณฤฒรซ": 33940, "ฤ typed": 33941, "ฤ Bana": 33942, "รซฤตฤพรซ": 33943, "ฤ savory": 33944, "ฤ Zomb": 33945, "standen": 33946, "ฤ pedestrian": 33947, "ฤ diffรƒยฉrents": 33948, "ฤ รฌฤญยธ": 33949, "รจฤซยฏ": 33950, "ฤ complained": 33951, "รงยฆฤฑ": 33952, "ฤ รฤผร‘ฤครยพ": 33953, "ฤ ร—ฤพร—ยค": 33954, "aliร…ฤฝmy": 33955, "ฤ mortar": 33956, "ฤ verdict": 33957, "ฤ suficiente": 33958, "ฤ Million": 33959, "mittel": 33960, "inals": 33961, "ฤ ร˜ยงร™ฤฆร˜ยฎ": 33962, "รยฐร‘ฤฐร‘ฤฃร‘ฤฎ": 33963, "ฤ miร„ฤปdzy": 33964, "ฤ Ole": 33965, "ฤ invert": 33966, "czyร„ฤฉ": 33967, "รยพรยทรยผรยพรยถรยฝรยพ": 33968, "starter": 33969, "ฤ auditor": 33970, "ฤ Scout": 33971, "chien": 33972, "ฤ Sverige": 33973, "uffled": 33974, "ฤ zehn": 33975, "ฤ Auckland": 33976, "ฤ argent": 33977, "ฤ 1976": 33978, "ฤ Hoe": 33979, "ฤ bothers": 33980, "ฤ socialist": 33981, "ฤ pliers": 33982, "ฤ emergen": 33983, "ฤ XP": 33984, "รยตร‘ฤขรยพรยฒ": 33985, "More": 33986, "ฤ Levi": 33987, "ฤ Anders": 33988, "ibilidad": 33989, "ฤ Parents": 33990, "ฤ induced": 33991, "รฌฤธยดรฌยค": 33992, "ฤ balances": 33993, "ฤ รยฒร‘ฤญร‘ฤช": 33994, "ฤ submarine": 33995, "Start": 33996, "ฤ dries": 33997, "ฤ volver": 33998, "ฤ ticking": 33999, "cott": 34000, "ฤ faj": 34001, "prรƒยฉs": 34002, "ฤ Sabb": 34003, "ฤ รยทรยฐร‘ฤฉ": 34004, "ฤ รยฟรยพรยบร‘ฤฅรยฟ": 34005, "ฤ baptized": 34006, "ฤ Brilliant": 34007, "ฤ รฤณรยพรยณ": 34008, "ฤ mots": 34009, "bits": 34010, "ฤ lattice": 34011, "รฆฤชฤณรจยทลรคยฝล‚": 34012, "ฤ coriander": 34013, "ฤ residency": 34014, "ync": 34015, "ฤ pierwszy": 34016, "ฤ Knock": 34017, "ฤ Zap": 34018, "ฤ รฤทรยฒ": 34019, "รชยฒยฌ": 34020, "รฅยฐฤฑรฅยฟฤฅ": 34021, "ฤ uneven": 34022, "ฤ Jas": 34023, "odor": 34024, "รงยฟฤด": 34025, "74": 34026, "ฤ Site": 34027, "ฤ aconteceu": 34028, "ympt": 34029, "ฤ trilogy": 34030, "ฤ lantern": 34031, "ฤ Zucker": 34032, "vari": 34033, "welling": 34034, "ฤ Potato": 34035, "gomery": 34036, "ฤ reacted": 34037, "ฤ Chron": 34038, "ฤ jede": 34039, "beeld": 34040, "ฤ twent": 34041, "ฤ lact": 34042, "รฆยจฤค": 34043, "ฤ rรƒยฉse": 34044, "ฤ relent": 34045, "ฤ furnace": 34046, "ฤ widget": 34047, "ฤ earthquakes": 34048, "ฤ Adjust": 34049, "ilit": 34050, "ฤ ร˜ยฃร™ฤช": 34051, "ฤ hearings": 34052, "ฤ defendant": 34053, "irsiniz": 34054, "ฤ bask": 34055, "cja": 34056, "ฤพยจ": 34057, "ฤ rifles": 34058, "ฤ instal": 34059, "ฤ Forgive": 34060, "pical": 34061, "ฤ รล€ร‘ฤฉรยตรยฝร‘ฤฎ": 34062, "ฤ petites": 34063, "ฤ hp": 34064, "ฤ renowned": 34065, "ฤ Inn": 34066, "ฤ รฌยฃยผรฌฤฆยธรฌฤผฤถ": 34067, "ฤ emphasized": 34068, "รฉฤนยฎรฉยขฤบ": 34069, "ฤ รฌล€ฤชรฌยฃล‚": 34070, "ฤ รชยฒฤฅรฌฤพยผรซยกฤพ": 34071, "รฃฤคฤจ": 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"ฤ camino": 34124, "Eric": 34125, "ฤ Blind": 34126, "รจฤชฤฉ": 34127, "รฅยนยดรงฤผฤฆ": 34128, "ฤ Discovery": 34129, "รฌยธล‚": 34130, "รงฤชยถ": 34131, "ฤ interpreter": 34132, "ฤ bred": 34133, "ฤ Psalm": 34134, "ฤ defended": 34135, "รฌฤซยฌ": 34136, "ฤ Erfahr": 34137, "ฤ Peach": 34138, "ฤ moons": 34139, "ฤ Ost": 34140, "ฤ spรƒยฉcial": 34141, "ฤ arriver": 34142, "ฤ Wis": 34143, "uci": 34144, "ฤ robotics": 34145, "IVE": 34146, "ฤ siege": 34147, "arla": 34148, "ฤ separates": 34149, "ฤ TC": 34150, "รญฤฑยฐ": 34151, "quisite": 34152, "ฤ parentheses": 34153, "รยธรยบรยต": 34154, "รงยซฤป": 34155, "ฤ trous": 34156, "รฅยปยบ": 34157, "ฤ ร‘ฤฃรยธรยปร‘ฤฎ": 34158, "ฤ beers": 34159, "ฤ รยฟรยปรยฐร‘ฤค": 34160, "รฃฤฃฤปรฃฤฃฤถรฃฤฃฤฆ": 34161, "ฤ sola": 34162, "ฤ dรƒยจs": 34163, "mingham": 34164, "ikte": 34165, "ฤ oops": 34166, "ฤ twitch": 34167, "รฅยฐฤฉ": 34168, "รฤช": 34169, "ฤ Shouldn": 34170, "uvre": 34171, "ฤ leer": 34172, "criptions": 34173, "ฤ eyeshadow": 34174, "ฤ Guo": 34175, "ฤ Powell": 34176, 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34743, "ฤ Wrestle": 34744, "ฤ Palestinians": 34745, "ฤ GORD": 34746, "iyet": 34747, "ฤ ร‘ฤขรยฐรยดรยธ": 34748, "ฤ versuchen": 34749, "ฤ transistor": 34750, "ฤ รลร‘ฤขรยพร‘ฤฃร‘ฤครยพ": 34751, "ฤ รยฟรยพรยฝร‘ฤขรยฐรยฒ": 34752, "ฤ rhyme": 34753, "ฤ Vermont": 34754, "platz": 34755, "รจยฎยฐ": 34756, "ฤ ร„ยฐร…ลte": 34757, "ฤ Hag": 34758, "ฤ รฤบรยผ": 34759, "ฤ ร‘ฤขรยฐร‘ฤฃร‘ฤฃรยบรยฐรยท": 34760, "ฤ metros": 34761, "ฤ Infinity": 34762, "wolf": 34763, "ibal": 34764, "ftig": 34765, "ฤ รšฤจ": 34766, "ฤ รญฤบยนรฌฤญฤพ": 34767, "ฤ oggi": 34768, "ฤ disposit": 34769, "ฤ รยฟร‘ฤขรยธรยป": 34770, "ฤ รยฒร‘ฤญรยฟรยพรยป": 34771, "ฤ thรƒยดi": 34772, "ฤ KENN": 34773, "ฤ handing": 34774, "actus": 34775, "ฤ tacos": 34776, "ฤ formerly": 34777, "ฤ Corinthians": 34778, "รฃฤฃยซรฃฤฃยฏ": 34779, "ร‘ฤจร‘ฤธร‘ฤน": 34780, "ฤ padre": 34781, "ฤ congregation": 34782, "รฆฤณ": 34783, "fert": 34784, "ฤ subir": 34785, "aiser": 34786, "qua": 34787, "araoh": 34788, "ฤ Curry": 34789, "ฤ รฌฤทฤฌรซฤฌฤถ": 34790, 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"edor": 34897, "ฤ conceived": 34898, "gere": 34899, "ฤ disappearing": 34900, "ฤ Sull": 34901, "ฤ รฌฤนยฐรซ": 34902, "ฤ hoffe": 34903, "ฤ detox": 34904, "รญฤถฤฎ": 34905, "ฤ retir": 34906, "ฤ รซฤฃฤฟรซฤค": 34907, "ฤ pergunta": 34908, "ฤ BOY": 34909, "รงยฒยพ": 34910, "ฤ penn": 34911, "รฆฤฟยฅรคยบฤจ": 34912, "hรƒยฉs": 34913, "hon": 34914, "ฤ catastrophic": 34915, "ฤ aust": 34916, "ฤ torso": 34917, "ฤ รฌฤธยดรซฤฌฤฒ": 34918, "ฤ รฌฤคยฌรซล€ฤฎรซฤตยครฌฤฟยด": 34919, "ฤ marvelous": 34920, "ฤ Harley": 34921, "achine": 34922, "ฤ tiรกยบยฟ": 34923, "itto": 34924, "ฤ Iรƒลƒm": 34925, "ylon": 34926, "ฤ shutdown": 34927, ".''": 34928, "ฤ apologies": 34929, "ฤ Communication": 34930, "ฤ รยณรยพรยฒรยพร‘ฤขร‘ฤฐ": 34931, "รฃฤฃฤครฃฤฅยผ": 34932, "รขฤฆยข": 34933, "รƒลƒveis": 34934, "acun": 34935, "ฤ retaining": 34936, "ฤ contradiction": 34937, "ฤ ADAM": 34938, "COM": 34939, "Bryan": 34940, "ฤ Monsieur": 34941, "ฤ adapting": 34942, "รยจรฤฒ": 34943, "ฤ Scr": 34944, "รƒยคndert": 34945, "ฤ plaus": 34946, 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"ฤ decisive": 34998, "ฤ exempel": 34999, "{\\": 35000, "ฤ Rรƒยผck": 35001, "rite": 35002, "ฤ Zeus": 35003, "ฤ calorie": 35004, "ฤ attractions": 35005, "ฤ Hinter": 35006, "ฤ uhm": 35007, "ฤ รญฤฎฤฒ": 35008, "ฤ rulers": 35009, "ฤ discouraged": 35010, "ฤ acontecer": 35011, "ฤ accents": 35012, "ฤ Optim": 35013, "ฤ Alg": 35014, "kids": 35015, "2021": 35016, "ฤ Lindsay": 35017, "ฤ filmmakers": 35018, "prowad": 35019, "ฤ terug": 35020, "รซฤญยด": 35021, "ฤ Sommer": 35022, "2018": 35023, "ฤ borrowing": 35024, "ฤ Transfer": 35025, "รยฝรยพรยฟ": 35026, "arias": 35027, "ฤ headphone": 35028, "รฌยผฤพ": 35029, "ฤ translating": 35030, "ฤ aufge": 35031, "ร ยฎยชร ยฎล": 35032, "weis": 35033, "avant": 35034, "paid": 35035, "baby": 35036, "ฤ toughest": 35037, "ฤ repeats": 35038, "ฤ Teresa": 35039, "Lord": 35040, "ฤ acabar": 35041, "ฤ Ride": 35042, "dir": 35043, "ฤ leng": 35044, "ฤ dwa": 35045, "ฤ headaches": 35046, "ฤ nรกยปยฏa": 35047, "ฤ รยฝรยฐร‘ฤฃร‘ฤครยพร‘ฤฑร‘ฤซ": 35048, "ฤ boils": 35049, 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"arnya": 38039, "ฤ รยก": 38040, "ฤ seid": 38041, "ฤ anak": 38042, "ฤ acabou": 38043, "รญฤทฤณ": 38044, "ฤ notorious": 38045, "ฤ Godzilla": 38046, "ฤ overcoming": 38047, "ฤ Pend": 38048, "ฤ olabilir": 38049, "รƒยผlme": 38050, "ฤ erhalten": 38051, "รฃฤคฤซรฃฤฃฤฆ": 38052, "รชยทยน": 38053, "ฤ Meter": 38054, "ฤ staan": 38055, "Ol": 38056, "ฤ chats": 38057, "ฤ Buenos": 38058, "รƒลƒve": 38059, "aluable": 38060, "ฤ strategically": 38061, "ฤ comprised": 38062, "ฤ รยฟรยตร‘ฤขร‘ฤฃรยพรยฝรยฐรยถ": 38063, "ฤ wann": 38064, "ฤ Cen": 38065, "รยฝรยธร‘ฤครยต": 38066, "ลฤฃ": 38067, "ฤ ร‘ฤครยพรยฑรยพรยน": 38068, "iad": 38069, "ฤ kardeร…ลim": 38070, "ฤ Congressman": 38071, "reaming": 38072, "homme": 38073, "ฤ communaut": 38074, "ฤ alcoholic": 38075, "ฤ pickled": 38076, "ฤ acord": 38077, "position": 38078, "egรƒยณl": 38079, "ฤ troubling": 38080, "ฤ Marcheg": 38081, "ฤ zumindest": 38082, "ฤ seamlessly": 38083, "ฤ olun": 38084, "ฤ TVs": 38085, "ฤ รยฟร‘ฤขรยฐรยบร‘ฤครยธร‘ฤฉรยตร‘ฤฃรยบรยธ": 38086, "ฤ backend": 38087, "รฃฤฃฤตรฃฤคฤตรฃฤฃยซรฃฤฃยกรฃฤฃยฏ": 38088, "idable": 38089, "ฤ gadget": 38090, "ฤ faรƒยงo": 38091, "ฤ Marchegiani": 38092, "ฤ รซยฐยค": 38093, "ฤ accidental": 38094, "ฤ LP": 38095, "ฤ eldest": 38096, "ฤ Admiral": 38097, "ฤ nร„ฤฅm": 38098, "lever": 38099, "ฤ pastel": 38100, "ฤ fondo": 38101, "Connie": 38102, "ฤ tercer": 38103, "ฤ pact": 38104, "ฤ Monte": 38105, "ฤ meats": 38106, "ฤ SMS": 38107, "ฤ Australians": 38108, "รงยผ": 38109, "Rhett": 38110, "ฤ exactement": 38111, "ฤ รซยนยผ": 38112, "ฤ MOD": 38113, "รงยก": 38114, "ฤ Rapt": 38115, "ฤ Noch": 38116, "ฤ abort": 38117, "ฤ Naval": 38118, "ฤ Fuji": 38119, "INTER": 38120, "ฤ รยฝรยพรยฒร‘ฤญรยน": 38121, "ฤ miejsce": 38122, "ฤ ICU": 38123, "ฤ Graduate": 38124, "ฤ Glen": 38125, "ardi": 38126, "ฤ รˆฤบ": 38127, "ฤ solder": 38128, "ฤ professions": 38129, "ฤ orthog": 38130, "omn": 38131, "introdu": 38132, "ฤ Denise": 38133, "รฌล€ฤฒรซยฅยผ": 38134, "ฤ correspondence": 38135, "AMA": 38136, "ฤ inflict": 38137, "ฤ fand": 38138, "ฤ Gรƒยผ": 38139, "ฤ ร‘ฤฉรยตร‘ฤค": 38140, "ฤ traced": 38141, "ฤ patents": 38142, "ฤ ambush": 38143, "ฤ lotta": 38144, "ffer": 38145, "ฤ Wagner": 38146, "ฤ imperson": 38147, "ฤ extrรƒยชmement": 38148, "ร™ฤคร˜ยช": 38149, "conduct": 38150, "Att": 38151, "ฤ Mueller": 38152, "ฤ Alicia": 38153, "ฤ cyc": 38154, "ฤ hacker": 38155, "ฤ tys": 38156, "ฤ hail": 38157, "ฤ รยทรยฐร‘ฤฑรยฒ": 38158, "ฤ passo": 38159, "ฤ รฌยถฤถรชยฐฤข": 38160, "ฤ รŽฤช": 38161, "ฤ packaged": 38162, "ฤ Cynthia": 38163, "heet": 38164, "รคยธลƒรฅฤฝยฝ": 38165, "ฤ Nissan": 38166, "ฤ Questo": 38167, "รฉยจ": 38168, "did": 38169, "ฤ รŽยผรŽยนรŽยฑ": 38170, "ฤ Ellis": 38171, "ฤ Analysis": 38172, "cemos": 38173, "ฤ aseg": 38174, "ฤ Myster": 38175, "ฤ Cao": 38176, "ฤ tuv": 38177, "ฤ Industry": 38178, "รฌยฃยผรชยณล‚": 38179, "otal": 38180, "ฤ pequeรƒยฑo": 38181, "bras": 38182, "ฤ comprehend": 38183, "ฤ Simpson": 38184, "ร‘ฤฃร‘ฤครยฒรยธรยต": 38185, "ocracy": 38186, "รยธร‘ฤฉรยตร‘ฤฃรยบรยธ": 38187, "ฤ Mush": 38188, "ฤ Laurie": 38189, "ฤ triangular": 38190, "ฤ Presents": 38191, "ฤ Kunden": 38192, "รงยดยน": 38193, "รฆลƒยฆ": 38194, "ฤ Iss": 38195, "ฤ Deck": 38196, "รกยปฤฅn": 38197, "ฤ Darkness": 38198, "ฤ inflammatory": 38199, "eremiah": 38200, "ฤ warmed": 38201, "veyard": 38202, "ฤ Memory": 38203, "etty": 38204, "ฤ taxpayers": 38205, "ร ยธฤต": 38206, "ร˜ยก": 38207, "ฤ practise": 38208, "รซฤญยฌรซ": 38209, "ฤ drilled": 38210, "mรƒยผร…ล": 38211, "logo": 38212, "ฤ Fach": 38213, "ยครซยกฤพ": 38214, "ฤ รƒยผbrigens": 38215, "ฤ konnten": 38216, "ฤ normalmente": 38217, "ฤ argues": 38218, "ilingual": 38219, "ยฐรซยฅยผ": 38220, "egal": 38221, "ฤ travaill": 38222, "ovy": 38223, "รยฐร‘ฤครยพ": 38224, "ฤ ruth": 38225, "ฤ Lights": 38226, "ฤ consisted": 38227, "ร—ฤณร—ยจร—ฤปร—ฤฟ": 38228, "ฤ stereotype": 38229, "ฤ payer": 38230, "ฤ Ree": 38231, "ฤ Airbnb": 38232, "ฤ drowned": 38233, "ฤ Zoe": 38234, "ฤ canopy": 38235, "ฤ barr": 38236, "ฤ รยฝรยพร‘ฤฉ": 38237, "ฤ pagan": 38238, "ฤ jars": 38239, "ฤ rรƒยช": 38240, "erver": 38241, "รฆฤชยฟ": 38242, "ieben": 38243, "ฤ espect": 38244, "ฤ Fi": 38245, "ฤ unwilling": 38246, "ฤ technician": 38247, "รกยบยทt": 38248, "member": 38249, "ฤ Canal": 38250, "ร˜ยณร™ฤง": 38251, "ฤ lieber": 38252, "ฤ inference": 38253, "ฤ honoring": 38254, "รฅฤณยต": 38255, "ฤ Campaign": 38256, "ฤ lineage": 38257, "ฤ Stress": 38258, "ฤ victories": 38259, "ฤ deja": 38260, "ร—ยฃ": 38261, "รƒยชtes": 38262, "blick": 38263, "ฤ รยผรยตรยฝรยตรยต": 38264, "oths": 38265, "ฤ Couple": 38266, "Jason": 38267, "ฤ Nicolas": 38268, "รยตรยบร‘ฤฃ": 38269, "lib": 38270, "ฤ herramient": 38271, "ฤ ร—ฤฒร—ฤทร—ล€ร—ยจ": 38272, "ฤ รยฒรยธรยดรยธรยผ": 38273, "millimeter": 38274, "ฤ silhouette": 38275, "ฤ driveway": 38276, "ฤ cherish": 38277, "รฃฤงล‚รฃฤงล‚": 38278, "ฤ ransom": 38279, "ฤ interdisciplinary": 38280, "ฤ Portal": 38281, "ฤ trag": 38282, "thood": 38283, "ฤ tedious": 38284, "ฤ glossy": 38285, "ฤ prรƒยฉpar": 38286, "ฤ Cay": 38287, "ฤ Took": 38288, "ฤ Bottom": 38289, "ฤ zig": 38290, "รฅยซ": 38291, "รฅฤฏยฑ": 38292, "represented": 38293, "ร ยนฤขร ยธยฅร ยธยข": 38294, "ฤ desarrollo": 38295, "รฌฤฆฤพรซ": 38296, "ฤ viscos": 38297, "ฤ milligram": 38298, "ฤ Gund": 38299, "ฤ ferment": 38300, "drum": 38301, "ฤ drawers": 38302, "Laugh": 38303, "ฤ pelos": 38304, "ฤ pavement": 38305, "ฤ memoir": 38306, "avait": 38307, "ฤ 2050": 38308, "ยครซยฅยผ": 38309, "ฤ razรƒยณn": 38310, "ฤ flourish": 38311, "ฤ stern": 38312, "รคยธฤช": 38313, "ฤ Chung": 38314, "ฤ serpent": 38315, "ฤ Gentlemen": 38316, "รงฤพลรงฤผฤฆรฅยพฤช": 38317, "kook": 38318, "ฤ lut": 38319, "importe": 38320, "parent": 38321, "ฤ wsz": 38322, "ฤ scree": 38323, "ฤ Mitarbeiter": 38324, "รฅยทยด": 38325, "mut": 38326, "ฤ รฌฤธฤบรชยธยฐรซยฅยผ": 38327, "ฤ semble": 38328, "ฤ OW": 38329, "ฤ investigator": 38330, "ฤ Cheryl": 38331, "ฤ Gerald": 38332, "ฤ prere": 38333, "ฤ compares": 38334, "nyt": 38335, "ฤ diferenรƒยงa": 38336, "?-": 38337, "ฤ quรƒยก": 38338, "ร—ยจร—ฤป": 38339, "Sen": 38340, "ฤ heps": 38341, "ฤ gratuit": 38342, "ฤ consort": 38343, "ฤ STOP": 38344, "ฤ Protestant": 38345, "ฤ electrode": 38346, "รขฤน": 38347, "ฤ securely": 38348, "รยธร‘ฤฉรยตร‘ฤฃรยบรยพรยน": 38349, "ฤ tรƒยครƒยค": 38350, "ฤ registers": 38351, "ฤ Heavenly": 38352, "ogly": 38353, "issรƒยค": 38354, "ฤ Physics": 38355, "ฤ Merkel": 38356, "ฤ rรƒยฉv": 38357, "รฉฤปยข": 38358, "ฤ erased": 38359, "ฤ Sacramento": 38360, "ฤ coffin": 38361, "ฤ exacer": 38362, "ฤ lanz": 38363, "ฤ poets": 38364, "ulif": 38365, "ฤ รฌยนฤบรซ": 38366, "ฤ Nerd": 38367, "ฤ NCT": 38368, "ฤ Hour": 38369, "nehmer": 38370, "ล€ฤบรซฤฑฤฆ": 38371, "ฤ Princi": 38372, "Sw": 38373, "mies": 38374, "armed": 38375, "ฤ Beatles": 38376, "ฤ propagation": 38377, "ฤ exchanged": 38378, "ฤ cumulative": 38379, "ฤ รฌยงฤณรฌฤนฤฒ": 38380, "ฤ defeating": 38381, "รฆฤฌยฑ": 38382, "bels": 38383, "ฤ wes": 38384, "ฤ Odyssey": 38385, "รคยฝล‚รฆฤฅยณ": 38386, "avior": 38387, "ฤ รฌฤพฤฆรฌฤนฤฒ": 38388, "ฤ brit": 38389, "ฤ hijo": 38390, "DAY": 38391, "ฤ ร˜ยงร™ฤฆร˜ยชร™ฤฌ": 38392, "ฤ รยกรยตร‘ฤขรยณ": 38393, "ร‘ฤฅรยบรยฐ": 38394, "edsiร„ฤป": 38395, "ฤ impos": 38396, "ฤ ellas": 38397, "ฤ firearms": 38398, "ฤ NR": 38399, "ฤ ร—ฤณร—ฤฒ": 38400, "ฤ รลรยพรยบรยฐ": 38401, "awi": 38402, "ฤ รฌฤฆยฑรชยณยต": 38403, "ฤ pupils": 38404, "ฤ Tack": 38405, "ฤ frase": 38406, "ฤ Ship": 38407, "ฤ stad": 38408, "รคยธฤพ": 38409, "ฤ Greater": 38410, "unun": 38411, "immung": 38412, "grown": 38413, "ฤ NXT": 38414, "ฤ Americas": 38415, "fox": 38416, "ฤ manten": 38417, "รฉล‚ฤฒรฅฤคฤป": 38418, "ฤ ร‘ฤฃรยพรยบ": 38419, "ฤ rikt": 38420, "lectric": 38421, "deep": 38422, "ฤ รยทรยฝรยฐรยตร‘ฤชร‘ฤฎ": 38423, "ฤ benut": 38424, "ฤ Infrast": 38425, "ฤ Emir": 38426, "ฤ รยพร‘ฤครยฟร‘ฤขรยฐรยฒ": 38427, "ฤ Kimchi": 38428, "ฤ Finnish": 38429, "ยดรฌล‚ฤฃ": 38430, "inaire": 38431, "ฤ oike": 38432, "รฆยธฤงรฆยฅฤผ": 38433, "ฤ hostage": 38434, "ฤ Button": 38435, "ร™ฤคร™ฤฌ": 38436, "eking": 38437, "ฤ Kazakh": 38438, "ฤ comforting": 38439, "ฤ sog": 38440, "ฤ greeted": 38441, "guitar": 38442, "payer": 38443, "ฤ relational": 38444, "ฤ construir": 38445, "รงฤซยนรฅฤชยฅ": 38446, "opian": 38447, "ฤ Volume": 38448, "ieth": 38449, "ร‘ฤฃร‘ฤครยฒรยพรยผ": 38450, "urrection": 38451, "liร…ฤฝmy": 38452, "ฤ hemisphere": 38453, "ฤ Bean": 38454, "IGN": 38455, "ฤ kรƒยถtรƒยผ": 38456, "ฤ Fallout": 38457, "ฤ brace": 38458, "รงยนยผรงยบฤฎ": 38459, "รฤขรŽยฌ": 38460, "ฤ HAS": 38461, "ฤ gรƒยฉ": 38462, "ฤ characterize": 38463, "รกยบยทc": 38464, "ฤ Milky": 38465, "ฤ tumors": 38466, "ฤ nuit": 38467, "ฤ Gaz": 38468, "ฤ รฌล€ฤชรซฤญยครซฤฌฤถ": 38469, "ฤ รยณรยฐร‘ฤข": 38470, "essment": 38471, "ฤ Abe": 38472, "ฤ รซยฝฤณ": 38473, "ฤ Einsatz": 38474, "JIN": 38475, "jรƒยค": 38476, "Cry": 38477, "ฤ Promised": 38478, "ฤ ร‘ฤฃรยตร‘ฤขรยด": 38479, "okus": 38480, "ฤ scalable": 38481, "ฤ รยฟรยพร‘ฤฃรยผรยพร‘ฤคร‘ฤขรยตร‘ฤคร‘ฤฎ": 38482, "รƒยผcklich": 38483, "ฤ realism": 38484, "ฤ mayo": 38485, "ฤ juvenile": 38486, "ฤ headlights": 38487, "ฤ gรƒยถrรƒยผร…ล": 38488, "ฤ Reform": 38489, "ฤ halves": 38490, "czne": 38491, "ฤ breakup": 38492, "ร…ยผej": 38493, "ฤ rรƒยคtt": 38494, "Day": 38495, "ฤ รฌฤฟยผรซยณยธ": 38496, "ฤ muerte": 38497, "ฤ tunes": 38498, "ฤ Smile": 38499, "record": 38500, "ฤ recherche": 38501, "atisfied": 38502, "ฤ pozi": 38503, "ฤ celebrations": 38504, "isexual": 38505, "ฤ ROB": 38506, "thirds": 38507, "ฤ Fortune": 38508, "ฤ ร‘ฤครยพรยน": 38509, "ฤ branded": 38510, "loo": 38511, "ฤ dud": 38512, "ฤ randomized": 38513, "ฤ combin": 38514, "รคยธฤขรคยบฤฝ": 38515, "ieran": 38516, "czenia": 38517, "ฤฏรฃฤฅยซ": 38518, "ฤ curator": 38519, "ฤ artery": 38520, "ฤ ร‘ฤฅร‘ฤช": 38521, "ฤ ร‘ฤฉรยธร‘ฤค": 38522, "ฤ subsidies": 38523, "ฤ blossom": 38524, "ฤ Twilight": 38525, "ฤ hyvรƒยค": 38526, "ฤ Pompe": 38527, "ฤ Cisco": 38528, "ฤ รลร‘ฤขรยพ": 38529, "ฤ biri": 38530, "ฤ gern": 38531, "ฤ rebuilt": 38532, "ฤ wcze": 38533, "ฤ benefici": 38534, "ฤ drummer": 38535, "ฤ solids": 38536, "ฤ diyorsun": 38537, "รฃฤฃฤครฃฤคฤฌรฃฤฃฤฎรฃฤฃยจรฃฤฃฤจรฃฤฃฤถรฃฤฃฤธรฃฤฃฤฆรฃฤฃยพรฃฤฃฤนรฃฤฃล": 38538, "lated": 38539, "ฤ muddy": 38540, "ฤ holog": 38541, "ฤ claps": 38542, "ฤ Rings": 38543, "ฤ Okey": 38544, "ฤ Brave": 38545, "ฤ valuation": 38546, "ฤ migrant": 38547, "ฤ intermitt": 38548, "ฤ eigene": 38549, "iliary": 38550, "รฃฤฅยผรฃฤฅฤช": 38551, "markt": 38552, "kr": 38553, "ฤ Rib": 38554, "รกยปฤปi": 38555, "ฤ accusations": 38556, "ฤ arab": 38557, "wash": 38558, "ฤ Bardzo": 38559, "ฤ ugh": 38560, "esters": 38561, "ophren": 38562, "ฤ alimentos": 38563, "ฤ Uz": 38564, "ร–ฤค": 38565, "ฤ 650": 38566, "ฤ รยฟร‘ฤขรยธรยตร‘ฤง": 38567, "FI": 38568, "ฤ sampai": 38569, "ฤ parlรƒยฉ": 38570, "hesion": 38571, "ฤ sร„ยฑr": 38572, "ฤ apparatus": 38573, "ฤ correlated": 38574, "ฤ Principal": 38575, "ฤ corr": 38576, "ฤ Official": 38577, "รยธร‘ฤฉรยตร‘ฤฃรยบรยธรยต": 38578, "ฤ terminals": 38579, "Should": 38580, "ฤ vacun": 38581, "ฤ stellt": 38582, "ฤ mooi": 38583, "etzung": 38584, "ฤ รยบร‘ฤขรยฐ": 38585, "ฤ dai": 38586, "ฤ รยฟรยพรยถ": 38587, "Team": 38588, "ฤ PPE": 38589, "ฤ รล€ร‘ฤฃ": 38590, "ฤ Leah": 38591, "ฤ Ivy": 38592, "yst": 38593, "ฤ uhhh": 38594, "ฤ nighttime": 38595, "ฤ trendy": 38596, "ฤ securities": 38597, "ฤ continents": 38598, "ฤ firsthand": 38599, "ฤ Veron": 38600, "ฤ รซฤคยฎ": 38601, "ฤ browsing": 38602, "ฤ Cada": 38603, "tro": 38604, "ฤ tramp": 38605, "reib": 38606, "ฤ erstmal": 38607, "irler": 38608, "ฤ psic": 38609, "ฤ getir": 38610, "ฤ NP": 38611, "ฤ dzieci": 38612, "รยพรยฑร‘ฤขรยฐรยท": 38613, "ฤ magician": 38614, "ฤ scrutiny": 38615, "ฤ slab": 38616, "ฤ OT": 38617, "isty": 38618, "iries": 38619, "orest": 38620, "ฤ tasked": 38621, "ฤ morally": 38622, "รฌฤทยผรฌยงฤข": 38623, "ustered": 38624, "ฤ fools": 38625, "ฤ irrespons": 38626, "ฤ einf": 38627, "ฤ viรกยปฤฉc": 38628, "ฤ scor": 38629, "ฤ pillows": 38630, "ฤ Gegen": 38631, "ฤ tutte": 38632, "ฤ quarterly": 38633, "ฤ didnt": 38634, "ฤ Gym": 38635, "ฤ Ether": 38636, "ฤ ร˜ยซ": 38637, "รยปรยธร‘ฤชรยบรยพรยผ": 38638, "ฤ signaling": 38639, "ฤ Node": 38640, "ฤ Doncs": 38641, "ฤ yah": 38642, "ฤ Kanal": 38643, "ฤ fading": 38644, "etin": 38645, "ฤ influencers": 38646, "ฤ medals": 38647, "ฤ engineered": 38648, "ฤ fermented": 38649, "รชยฒล‚รฌยงฤขรซยงฤฎ": 38650, "ฤ Beethoven": 38651, "ร—ล€ร—ยฉ": 38652, "inental": 38653, "ฤ รฌฤทฤฎรซล‚ยค": 38654, "รƒยผtfen": 38655, "alnya": 38656, "ฤ overe": 38657, "ฤ denkt": 38658, "รยฐรยบร‘ฤครยตร‘ฤข": 38659, "ฤ รขฤบ": 38660, "ฤ necesit": 38661, "ฤ generators": 38662, "grass": 38663, "ฤ รยฟรยพรยดร‘ฤฅรยผ": 38664, "lieรƒลen": 38665, "Bar": 38666, "ฤพรซฤฑฤป": 38667, "ฤ รยดรยตร‘ฤครยตรยน": 38668, "ฤ sucking": 38669, "ฤ stencil": 38670, "ฤ primo": 38671, "ฤ Breath": 38672, "strom": 38673, "ฤ immensely": 38674, "ฤ appreh": 38675, "รฌล‚ฤทรฌฤฟยด": 38676, "Pop": 38677, "ฤ jong": 38678, "ฤ Giul": 38679, "ฤ ADHD": 38680, "ฤ hรƒยถren": 38681, "ฤ elo": 38682, "ivent": 38683, "ฤ rus": 38684, "ฤ outrageous": 38685, "ฤ mastered": 38686, "ฤ รฌยปยค": 38687, "ร™ฤชร™ฤฃ": 38688, "ipes": 38689, "ฤ Rudy": 38690, "Jacob": 38691, "ฤ bullish": 38692, "ฤ tapped": 38693, "ฤ faud": 38694, "izophren": 38695, "ฤ ร‘ฤฃรยพร‘ฤง": 38696, "ฤ Darling": 38697, "ฤ 1963": 38698, "ฤ Prevention": 38699, "ยฒฤถ": 38700, "ฤ abdominal": 38701, "stones": 38702, "ฤ avaient": 38703, "รกยปฤทi": 38704, "make": 38705, "ฤ sare": 38706, "ฤ Instant": 38707, "รยบรยฐรยผ": 38708, "ฤ keeper": 38709, "ฤ blankets": 38710, "รฃฤฃยงรฃฤฃฤนรฃฤคฤฉรฃฤฃฤจ": 38711, "ฤ sweats": 38712, "ฤ Minneapolis": 38713, "รฅฤงยจรฉฤฅยจ": 38714, "ฤ genommen": 38715, "ฤ fasten": 38716, "ฤ Brussels": 38717, "รฅฤณยผ": 38718, "ฤ cafeter": 38719, "ฤ absorbing": 38720, "ฤ hago": 38721, "ฤ Elmo": 38722, "ฤ gusto": 38723, "ฤ Yap": 38724, "Mรƒยบsica": 38725, "ฤ tert": 38726, "ฤ banda": 38727, "ฤ mily": 38728, "ฤ thereafter": 38729, "ฤ Stockholm": 38730, "ฤ Carson": 38731, "ฤ calibration": 38732, "avaร…ล": 38733, "ansa": 38734, "ikke": 38735, "ฤ foresee": 38736, "ฤ qualche": 38737, "ฤ deste": 38738, "รฆยค": 38739, "รƒยผnรƒยผz": 38740, "ฤ forge": 38741, "Dis": 38742, "esten": 38743, "ฤ รŽยดรŽยนรŽยฑ": 38744, "ฤ encaps": 38745, "ฤ Gespr": 38746, "ฤ chercher": 38747, "ickets": 38748, "ร‘ฤครยพร‘ฤขร‘ฤญ": 38749, "Cr": 38750, "ฤ รยขรยฐรยบรยถรยต": 38751, "ฤ rabbits": 38752, "ฤ Dot": 38753, "heiten": 38754, "ฤ causal": 38755, "ฤ Foster": 38756, "ajร„ฤงc": 38757, "ฤ bereit": 38758, "ฤ ayudar": 38759, "รฉยซฤป": 38760, "รฃฤฃยณ": 38761, "song": 38762, "comb": 38763, "ฤ fringe": 38764, "ฤ cybersecurity": 38765, "ฤ รซฤพยจ": 38766, "ฤ kier": 38767, "ฤ beschรƒยคft": 38768, "ฤ รยบรยพรยฝร‘ฤจรยต": 38769, "ฤ facilit": 38770, "ฤ Namen": 38771, "ฤ bilateral": 38772, "tx": 38773, "ฤ Wissenschaft": 38774, "ฤ nuances": 38775, "ฤ ripping": 38776, "ฤ fy": 38777, "ฤ Sicherheit": 38778, "ฤ Ghana": 38779, "olon": 38780, "ฤ topped": 38781, "ฤ Morocco": 38782, "ฤ radial": 38783, "ฤ LEE": 38784, "ฤ Andreas": 38785, "edd": 38786, "ฤ รฌฤนยดรซ": 38787, "ฤ Airlines": 38788, "รฃฤฃฤตรฃฤคฤฏ": 38789, "ฤ valores": 38790, "รชยทฤพ": 38791, "Hy": 38792, "ฤ รยทรยฐรยดรยฐร‘ฤฉ": 38793, "ฤ Kendall": 38794, "ฤ ร‘ฤงรยฐร‘ฤข": 38795, "ฤ Vamp": 38796, "ฤ python": 38797, "ฤ manageable": 38798, "ฤ Gente": 38799, "oise": 38800, "iciary": 38801, "ฤ imposs": 38802, "ฤ Bunny": 38803, "iesta": 38804, "Andrew": 38805, "ฤ sert": 38806, "ฤ Cec": 38807, "zzarella": 38808, "ฤ automobile": 38809, "ฤ Tiere": 38810, "allows": 38811, "รฅฤจฤจ": 38812, "ฤ รซยฐฤข": 38813, "ฤ Scorp": 38814, "ฤ Jelly": 38815, "agara": 38816, "ฤ Stretch": 38817, "ฤ redef": 38818, "ฤ exacerb": 38819, "ฤ SHA": 38820, "รƒยฉf": 38821, "orsa": 38822, "ฤ flawed": 38823, "ฤ Noel": 38824, "?!?": 38825, "ฤ procent": 38826, "ฤ menstru": 38827, "ฤ รยฟร‘ฤขรยพร‘ฤฉ": 38828, "ฤ infants": 38829, "รฐลฤฐยต": 38830, "pause": 38831, "ฤ Racing": 38832, "ฤ 1948": 38833, "ฤ superintendent": 38834, "idores": 38835, "idy": 38836, "brahim": 38837, "ฤ unlucky": 38838, "ฤ perk": 38839, "anci": 38840, "ฤ รซยงฤฎรซฤคฤบ": 38841, "ฤ รฤพรยพร‘ฤฃรยบรยฒ": 38842, "ฤ finans": 38843, "ฤ diferencia": 38844, "ล‚ฤชรฌฤฟยด": 38845, "รฉฤงฤฏ": 38846, "ORY": 38847, "ฤ Tac": 38848, "ร›ฤฎร˜ยง": 38849, "ฤ desem": 38850, "ฤ รยฒรยฐรยถรยฝรยพ": 38851, "ฤ JU": 38852, "ฤ รฌล€ฤชรฌล€ฤธรฌฤทฤฆรฌฤผฤถ": 38853, "ฤ รŽฤฟ": 38854, "ฤ informations": 38855, "ฤ HEL": 38856, "hst": 38857, "ฤ รยฟรยพรยณรยพรยฒรยพร‘ฤข": 38858, "ฤ voiture": 38859, "ฤ reus": 38860, "รƒยคndig": 38861, "ฤ รยฟรยพร‘ฤงรยพรยถ": 38862, "jing": 38863, "ฤ dru": 38864, "altra": 38865, "ฤ produits": 38866, "ฤ kite": 38867, "ฤ eyeball": 38868, "ฤ Belt": 38869, "ฤ Restaurant": 38870, "ฤ gamb": 38871, "ฤ porridge": 38872, "itters": 38873, "ฤ converts": 38874, "ฤ yardร„ยฑm": 38875, "ฤ mรƒยกximo": 38876, "wirtschaft": 38877, "ฤ รญฤทฤบรซฤคฤบรซ": 38878, "ฤ รฌยคฤข": 38879, "ฤ iceberg": 38880, "ฤ vorbei": 38881, "ฤ 256": 38882, "ocratic": 38883, "ฤ reckless": 38884, "onner": 38885, "ฤ mรƒยบs": 38886, "ฤ logically": 38887, "ฤ Prison": 38888, "ฤ Netz": 38889, "ฤ vacant": 38890, "ฤ nimmt": 38891, "ฤ HARR": 38892, "ฤ รยทรยพรยฒ": 38893, "ฤ Dee": 38894, "ringe": 38895, "niest": 38896, "ฤ Rules": 38897, "รฌฤฌยครซลยฝ": 38898, "cussions": 38899, "ฤ floral": 38900, "ฤ constrained": 38901, "ฤ differentiation": 38902, "ฤ Quebec": 38903, "ฤ ร›ฤฃร›ฤฎรšยบ": 38904, "ฤ pรƒยบblica": 38905, "itel": 38906, "ฤ accommodations": 38907, "ฤ Grรƒยผ": 38908, "รญฤพ": 38909, "ฤ pickles": 38910, "รยธร‘ฤฉรยตร‘ฤฃรยบรยธร‘ฤง": 38911, "ฤ commissions": 38912, "ฤ Baek": 38913, "ฤ รƒยงocuร„ล": 38914, "ฤ Medium": 38915, "ฤ periodically": 38916, "ฤ wonderfully": 38917, "ฤ staffing": 38918, "รฌฤฝฤฒรซ": 38919, "rire": 38920, "fle": 38921, "ฤ McL": 38922, "ฤ ร‘ฤครยตรยฟ": 38923, "ฤ รยฟรยตร‘ฤขรยตรยบ": 38924, "รยฝรยพรยปรยพรยณ": 38925, "ฤ รญฤฃยฌรชยฒฤฎ": 38926, "รงฤปยผรงฤฑยพ": 38927, "ฤ prosperous": 38928, "ฤ Spiritual": 38929, "ฤ Chick": 38930, "DIA": 38931, "ฤ รลร‘ฤขรยธรยฒรยตร‘ฤค": 38932, "ฤ perรƒลƒ": 38933, "ร‘ฤฎร‘ฤฐร‘ฤค": 38934, "ฤ consultants": 38935, "ฤ Earl": 38936, "รคยปฤฌรฅยนยด": 38937, "ฤ ruining": 38938, "รยพร‘ฤขรยต": 38939, "ฤ penser": 38940, "ฤ takiej": 38941, "ฤ strengthened": 38942, "ฤ Liquid": 38943, "รยพรยฝรยตร‘ฤจ": 38944, "รยฐรยฒรยฐร‘ฤคร‘ฤฎ": 38945, "ฤ camer": 38946, "ฤ disagreement": 38947, "ฤ bathing": 38948, "ฤ Yosh": 38949, "aal": 38950, "prechen": 38951, "RISADAS": 38952, "ฤ superstar": 38953, "รฆฤฃลƒ": 38954, "รยปร‘ฤฑร‘ฤคร‘ฤฎ": 38955, "ฤ nib": 38956, "ฤ Therm": 38957, "ฤ DANIEL": 38958, "ฤ paw": 38959, "ฤ liquids": 38960, "ฤ capacit": 38961, "arken": 38962, "ฤ vagina": 38963, "ฤ mashed": 38964, "ฤ emerges": 38965, "yscy": 38966, "ฤ unrelated": 38967, "ฤ Guild": 38968, "ฤ inverted": 38969, "itives": 38970, "Tra": 38971, "ฤ begr": 38972, "ฤ alte": 38973, "รฌยงฤท": 38974, "รฃฤคฤฃรฃฤฃยฆ": 38975, "ฤ ร‘ฤขรยฐรยทร‘ฤขรยฐรยฑรยพร‘ฤค": 38976, "finder": 38977, "ฤ รยดรยฐรยปรยตรยต": 38978, "ฤ รยฑรยปรยฐรยณรยพรยดรยฐร‘ฤข": 38979, "walker": 38980, "ฤ crater": 38981, "assadors": 38982, "rences": 38983, "inski": 38984, "ฤ KIM": 38985, "ฤ Elliot": 38986, "2017": 38987, "ฤ Sr": 38988, "inka": 38989, "anov": 38990, "ฤ รฌล€ฤบรซยชยป": 38991, "ฤ proprietary": 38992, "displaystyle": 38993, "ฤ ร‘ฤฃรยธรยผ": 38994, "ฤ รยธรยทรยฑ": 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39044, "รญฤจยตรซล‚ยน": 39045, "รฃฤฃยฃรฃฤฃยก": 39046, "ฤ ร‘ฤฅรยดรยฐร‘ฤข": 39047, "ฤ introductory": 39048, "ฤ Ici": 39049, "รซฤฎฤขรซ": 39050, "akat": 39051, "ฤ trench": 39052, "ฤ proceeded": 39053, "ฤ Coin": 39054, "ฤ derecho": 39055, "ฤ Rede": 39056, "รฆยฏฤฝ": 39057, "รยฐรยฝรยฝร‘ฤญรยน": 39058, "ฤ incarcerated": 39059, "ฤ Richmond": 39060, "Rock": 39061, "ฤ Pav": 39062, "ฤ Karma": 39063, "uges": 39064, "ฤ conteรƒยบ": 39065, "รซยนฤฆ": 39066, "ฤ รชยทยธรซยงฤฎ": 39067, "ฤ Gone": 39068, "ฤ wspรƒยณร…ฤค": 39069, "ฤ Rahmen": 39070, "unken": 39071, "ฤ รฌยคฤณรฌฤผฤถรญฤทฤพ": 39072, "ฤ ib": 39073, "ฤ attaching": 39074, "Hay": 39075, "ฤ suka": 39076, "รฌฤฏยน": 39077, "ฤ pivotal": 39078, "ฤ Respect": 39079, "รƒลƒda": 39080, "IB": 39081, "ฤ Verantwort": 39082, "wiet": 39083, "ฤ forensic": 39084, "ร‘ฤขรยธร‘ฤฃร‘ฤค": 39085, "ฤ รยฟร‘ฤขรยธรยฝร‘ฤจรยธรยฟรยต": 39086, "ฤ markings": 39087, "ฤ kettle": 39088, "ฤ Opera": 39089, "ฤ Doctors": 39090, "ฤ shredded": 39091, "ฤ recuer": 39092, "ฤ vigil": 39093, "ฤ Fail": 39094, "ฤ entrev": 39095, "ฤ รยดร‘ฤฅร‘ฤช": 39096, "ฤ outbreaks": 39097, "รจยตยฐรฅฤฒยง": 39098, "ฤ รฤขรŽยฟ": 39099, "ฤ rogue": 39100, "angled": 39101, "ฤ yearly": 39102, "ฤ Creed": 39103, "ฤ wam": 39104, "ฤ lotus": 39105, "รชยณยผรซ": 39106, "รฃฤขฤฃรฃฤขฤฃ": 39107, "ฤ Spit": 39108, "ฤ Itu": 39109, "ฤ strains": 39110, "ฤ stamped": 39111, "ฤ plaint": 39112, "ฤ potion": 39113, "ฤ consolidation": 39114, "รจยฉฤท": 39115, "รยพร‘ฤฉรยบร‘ฤฅ": 39116, "ฤ vlogging": 39117, "ฤ slate": 39118, "ฤ Auft": 39119, "ฤ Incor": 39120, "รกยปยซng": 39121, "ยงฤฒ": 39122, "enh": 39123, "ฤ heiรƒล": 39124, "ฤ domest": 39125, "ฤ Strom": 39126, "รฅฤฏยณ": 39127, "akis": 39128, "ฤ fragen": 39129, "ฤ finer": 39130, "ฤ Sug": 39131, "ฤ uphill": 39132, "ฤ รƒยฉรƒยฉn": 39133, "รขฤขยฆ)": 39134, "ฤ ร‘ฤฃรยพรยฟ": 39135, "ฤ Corey": 39136, "ฤ siebie": 39137, "ฤ muse": 39138, "ฤ cloves": 39139, "ฤ pous": 39140, "ฤ Finanz": 39141, "ฤ Route": 39142, "amat": 39143, "ฤ mutually": 39144, 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"ฤ gels": 39196, "ฤ electroly": 39197, "รจฤต": 39198, "ฤ รยผรยฝรยพรยน": 39199, "ฤ episod": 39200, "ฤ Dieses": 39201, "ฤ ATP": 39202, "ฤ ediyorum": 39203, "ฤ expresses": 39204, "ฤ exhibits": 39205, "Comm": 39206, "ฤ รยบร‘ฤขร‘ฤฅรยฟ": 39207, "ฤ matar": 39208, "ฤ 2025": 39209, "ฤ Artem": 39210, "vasive": 39211, "rรƒล‚": 39212, "ฤ beร…ล": 39213, "รฉยปฤฅ": 39214, "ฤ lizard": 39215, "ฤ fille": 39216, "ฤ รฌยงฤชรซยฌยธ": 39217, "ฤ รยผรยพร‘ฤซ": 39218, "ฤ tรƒยผr": 39219, "ฤ culprit": 39220, "ฤ woven": 39221, "ฤ ANY": 39222, "nim": 39223, "ฤ tay": 39224, "ฤ promin": 39225, "ฤ acompa": 39226, "ฤ idรƒยฉ": 39227, "ฤ boiler": 39228, "ฤ Themen": 39229, "ฤ avenue": 39230, "ฤ Mud": 39231, "ฤ รยฝรยพรยฒร‘ฤญรยต": 39232, "ฤ witnessing": 39233, "ฤ lance": 39234, "ฤ CHAN": 39235, "ฤ Bever": 39236, "ร˜ยชร™ฤง": 39237, "ฤ chemotherapy": 39238, "King": 39239, "ฤ bร„ฤปdร„ฤป": 39240, "ฤ atual": 39241, "ฤ tive": 39242, "ฤ talkin": 39243, "ฤ quedar": 39244, "ieรƒล": 39245, "edel": 39246, "ฤ รฌฤธยดรฌล‚ฤพ": 39247, "ฤ jogar": 39248, "ฤ รƒยถr": 39249, "ฤ undertaking": 39250, "ฤ Strength": 39251, "ฤ milhรƒยตes": 39252, "ฤ Wine": 39253, "ฤ Molt": 39254, "รจยฎยฒ": 39255, "รฃฤฃฤณรฃฤคฤฎ": 39256, "ฤ undermine": 39257, "ฤ Archives": 39258, "vana": 39259, "mercial": 39260, "MC": 39261, "ฤ caste": 39262, "รยฟร‘ฤข": 39263, "ฤ legislators": 39264, "ulators": 39265, "รƒยชnio": 39266, "ฤ รซฤฏยฐรซ": 39267, "ฤ ร‘ฤงรยพร‘ฤครยธร‘ฤครยต": 39268, "ฤ รยฝรยตรยบ": 39269, "ฤ surn": 39270, "ฤ consci": 39271, "ฤ POW": 39272, "ฤ culinary": 39273, "ฤ KAT": 39274, "ฤ Folks": 39275, "ร‘ฤญรยฒรยฐรยตรยผ": 39276, "ฤ รยฒรยพรยบ": 39277, "รฃฤฃฤณรฃฤคฤญ": 39278, "service": 39279, "pts": 39280, "ฤ รยฟรยพรยฑรยตรยด": 39281, "รฆฤบยฏรฅฤทฤฌ": 39282, "ฤ tents": 39283, "ฤ nord": 39284, "STE": 39285, "ฤ republican": 39286, "ฤ wyk": 39287, "ฤ minions": 39288, "รจฤปฤท": 39289, "ฤ memang": 39290, "jest": 39291, "ฤ comparative": 39292, "ฤ tyle": 39293, "carbon": 39294, "bedingt": 39295, "ksen": 39296, "ฤ negativity": 39297, "ฤ sjรƒยคlv": 39298, "ฤ dรƒยบ": 39299, "รฆฤซฤขรฆฤพฤซ": 39300, "ฤ recalled": 39301, "cra": 39302, "ฤ Tada": 39303, "ฤ ร‘ฤขร‘ฤฅรยบรยธ": 39304, "ฤ รยพรยฟร‘ฤขรยตรยดรยตรยป": 39305, "ฤ procrast": 39306, "ฤ jogos": 39307, "ฤ Oo": 39308, "ฤ Hearts": 39309, "ฤ รƒยฉch": 39310, "ฤ ksiร„ฤงร…ยผ": 39311, "ฤ coarse": 39312, "ฤ Tube": 39313, "ฤ Greens": 39314, "ฤ รƒยฉn": 39315, "ฤ dumbbell": 39316, "ฤ ร‘ฤครยธ": 39317, "ฤ querer": 39318, "ร˜ยงร˜ลƒ": 39319, "รฤฅรŽยตรŽยน": 39320, "ฤ รยฟร‘ฤขรยฐรยฒรยธรยปร‘ฤฎรยฝรยพ": 39321, "ฤ รยฟรยฐรยฟ": 39322, "ฤ compra": 39323, "ฤ tรƒยฉr": 39324, "ฤ Antes": 39325, "ฤ optimum": 39326, "ฤ biscuit": 39327, "รŽยบรŽยน": 39328, "aczego": 39329, "ฤ รฌฤญฤพรชยฐฤฆรฌฤฟยด": 39330, "ฤ Marines": 39331, "vero": 39332, "ฤ vaccinations": 39333, "ฤ petty": 39334, "riters": 39335, "ฤ รยฐรยป": 39336, "country": 39337, "ฤ counters": 39338, "ฤ attendant": 39339, "ฤ Hui": 39340, "รฃฤฃยจรฃฤฃฤฆรฃฤฃฤจรฃฤฃฤตรฃฤฃยจรฃฤฃยง": 39341, "cka": 39342, "ร‘ฤฃร‘ฤครยฒรยตรยฝรยฝร‘ฤญรยน": 39343, "guy": 39344, "ฤ tricked": 39345, "ฤ RED": 39346, "ฤ thrilling": 39347, "รฤขรŽยฟรŽยน": 39348, "ฤ piggy": 39349, "ฤ anunci": 39350, "ORTER": 39351, "ฤ Value": 39352, "ฤ rond": 39353, "ฤ ADA": 39354, "ฤ poser": 39355, "hores": 39356, "ฤ Roland": 39357, "ฤตยฏ": 39358, "ฤ noir": 39359, "ฤ ร—ยฉร—ฤฒร—": 39360, "รซยฐฤพ": 39361, "iemand": 39362, "ฤ รยฟรยพร‘ฤครยตร‘ฤข": 39363, "รชยณยณ": 39364, "ฤ รชยฑยฑ": 39365, "ฤ formatting": 39366, "ฤ Led": 39367, "รจยงฤขรงฤพยพ": 39368, "ฤ killers": 39369, "ฤ ร„ฤณรกยบยฅy": 39370, "ฤ haar": 39371, "again": 39372, "!>[": 45687, "minster": 45688, "ฤ รยฒรยปรยธ": 45689, "ฤ identifier": 45690, "ฤ Lambda": 45691, "ฤ tros": 45692, "ฤ flawless": 45693, "ฤ detrimental": 45694, "ฤ bunlarร„ยฑ": 45695, "War": 45696, "ฤ regiรƒยฃo": 45697, "รงฤพลรงฤผฤฆรฆฤบยฏ": 45698, "ฤ Bike": 45699, "cessors": 45700, "ฤ cรƒยนng": 45701, "ฤ RN": 45702, "ฤ รชยฝฤฅ": 45703, "ฤ kรƒยผรƒยงรƒยผk": 45704, "ฤ Beginning": 45705, "รญฤบยธรซ": 45706, 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"Group": 48711, "ฤ resiliency": 48712, "ฤ nogle": 48713, "ฤ CNC": 48714, "prรƒยผ": 48715, "ฤ physicists": 48716, "รยฝรยพรยบ": 48717, "LI": 48718, "ฤ stuffs": 48719, "ฤ sistemas": 48720, "ฤ interfering": 48721, "ฤ Marvin": 48722, "รƒยฉrcito": 48723, "ฤ รฌฤนฤจรชยณล‚": 48724, "ฤ sonic": 48725, "ฤ equiv": 48726, "ฤ abord": 48727, "ฤ Ramen": 48728, "ฤ 09": 48729, "medim": 48730, "atiques": 48731, "ฤ รยดรยตรยปรยฐร‘ฤฐร‘ฤค": 48732, "ฤ unanimously": 48733, "ฤ skirts": 48734, "ฤ รญฤฌยนรซยณฤฆ": 48735, "ฤ Prix": 48736, "kami": 48737, "ฤ fruition": 48738, "ฤ birthdays": 48739, "รยธรยบรยพรยผ": 48740, "ฤ inaugural": 48741, "ฤ correlate": 48742, "ฤ Tory": 48743, "ฤ รซฤคฤบรฌฤฃ": 48744, "ฤ dew": 48745, "ฤ Precis": 48746, "ihi": 48747, "ฤ รซยฌยธรฌล‚ฤพรชยฐฤข": 48748, "ฤ citing": 48749, "ฤ Lana": 48750, "ฤ Kag": 48751, "ฤ playthrough": 48752, "ฤ Protocol": 48753, "frist": 48754, "hovah": 48755, "ฤ merciful": 48756, "ฤ bilingual": 48757, "ฤ Guitar": 48758, "rh": 48759, "ฤ glamorous": 48760, "ฤ Vikings": 48761, "ฤ Ooooh": 48762, "รญฤทฤบรซฤฌฤถรซฤฏยฐ": 48763, "ฤ Uganda": 48764, "ฤ collapses": 48765, "entry": 48766, "ฤ antioxidants": 48767, "รซฤคฤบรซ": 48768, "ร‘ฤชรยฐร‘ฤฑ": 48769, "ฤ trivia": 48770, "ฤ gรƒยคller": 48771, "ฤ fungi": 48772, "ฤ milks": 48773, "ฤ dicht": 48774, "รŽยผรŽยท": 48775, "poke": 48776, "ฤ รยฒร‘ฤญรยฟร‘ฤฅร‘ฤฃรยบ": 48777, "ฤ feeder": 48778, "ฤ Alcohol": 48779, "hower": 48780, "ฤ deserving": 48781, "ฤ Rebel": 48782, "iosis": 48783, "ฤ 103": 48784, "ฤ handout": 48785, "ฤ enm": 48786, "ฤ landlords": 48787, "ฤ geology": 48788, "rils": 48789, "ฤ cobra": 48790, "ฤ Vold": 48791, "ฤ Panch": 48792, "ฤ GREG": 48793, "ฤ pross": 48794, "ฤ bracelets": 48795, "ฤ Vega": 48796, "ฤ rozum": 48797, "รฆยฌยพ": 48798, "รยฐรยทรยด": 48799, "ฤ Lynd": 48800, "ฤ Honors": 48801, "ฤ surrendered": 48802, "ฤ librarians": 48803, "125": 48804, "ฤ ร‘ฤฃรยธรยณ": 48805, "ฤ uniformly": 48806, "ฤ Eagles": 48807, "รฌฤทฤป": 48808, "รยธร‘ฤครยฐรยฝ": 48809, "andid": 48810, "ฤ รฌล‚ฤชรซฤฎฤข": 48811, "ฤ ร˜ยถ": 48812, "ฤ arrests": 48813, "ฤ CSV": 48814, "ฤ Azerbaijan": 48815, "ortic": 48816, "ฤ DX": 48817, "ฤ Adventures": 48818, "ฤ abus": 48819, "ฤ Fau": 48820, "ฤ schlimm": 48821, "ฤ rattling": 48822, "ฤ consumes": 48823, "ฤ Tolkien": 48824, "ฤ resurrected": 48825, "ฤ XY": 48826, "รญฤฌยธรชยฐฤข": 48827, "ฤ รยฒร‘ฤญร‘ฤฃร‘ฤคร‘ฤฅรยฟ": 48828, "ฤ Angie": 48829, "ร…ยผenia": 48830, "Mic": 48831, "ฤ Sheila": 48832, "achtet": 48833, "ฤ overst": 48834, "ฤ lรƒยข": 48835, "ฤ ineffective": 48836, "รฆฤฟยก": 48837, "รฆฤขฤฐรคยนฤชรคยบฤจ": 48838, "รฅยฟฤป": 48839, "ฤ wichtiger": 48840, "ฤ vino": 48841, "ฤ pum": 48842, "ฤ angled": 48843, "ฤ Pione": 48844, "ฤ Mรกยปยน": 48845, "รฃฤฃฤฟรฃฤคฤฎรฃฤฃยฏ": 48846, "woร…ฤฝร„ฤฉ": 48847, "draw": 48848, "ร ยธยฑร ยนฤช": 48849, "markets": 48850, "ฤ cafes": 48851, "ฤ Cem": 48852, "รขฤฟยค": 48853, "ฤ Suit": 48854, "MK": 48855, "ฤ emphasizes": 48856, "ฤ tortilla": 48857, "ฤ mejorar": 48858, "ฤ Surviv": 48859, "casting": 48860, "ฤ educaciรƒยณn": 48861, "ฤ Gum": 48862, "uely": 48863, "ฤ รฌฤนยฌรชยธยฐรซฤฌฤถ": 48864, "ฤ stretchy": 48865, "enรƒยงa": 48866, "ฤ withhold": 48867, "ฤ exiting": 48868, "ฤ enthalpy": 48869, "ฤ Transit": 48870, "ร„ยฑlmร„ยฑร…ล": 48871, "alies": 48872, "ฤ salvar": 48873, "ฤ leaned": 48874, "ฤ groรƒลes": 48875, "ฤ fitt": 48876, "รยฐรยบรยธ": 48877, "Sarah": 48878, "ฤ hostel": 48879, "ฤ fingerna": 48880, "ฤ nadziejร„ฤป": 48881, "wives": 48882, "Rec": 48883, "ฤ spool": 48884, "รยฐร‘ฤครยพรยฒ": 48885, "ฤ Enemy": 48886, "ฤ fury": 48887, "ฤ detta": 48888, "ฤ Fay": 48889, "รฉฤผยจ": 48890, "ร‘ฤฑร‘ฤฐร‘ฤค": 48891, "ฤ aproximadamente": 48892, "ฤ silos": 48893, "ฤ magist": 48894, "ฤ cree": 48895, "ฤ Krank": 48896, "ฤ DOWN": 48897, "ฤ startled": 48898, "ฤ reborn": 48899, "ฤ Umwelt": 48900, "ฤ Suzanne": 48901, "รยฝรยธร‘ฤจร‘ฤญ": 48902, "outez": 48903, "ฤ JAC": 48904, "yards": 48905, "radas": 48906, "rau": 48907, "ipts": 48908, "hail": 48909, "ฤ paragraphs": 48910, "ฤ meglio": 48911, "ฤ isolating": 48912, "ฤ aceite": 48913, "ฤ Harsh": 48914, "ฤ cyst": 48915, "ฤ Blockchain": 48916, "ฤ ร‘ฤงรยพร‘ฤขรยพร‘ฤชรยธรยน": 48917, "ฤ virtuous": 48918, "ฤ investigaciรƒยณn": 48919, "ฤ devoir": 48920, "ฤ masturb": 48921, "ฤ Sale": 48922, "ร™ฤฌร˜ยฑร˜ยฉ": 48923, "ฤ รŽยง": 48924, "ฤ Straรƒลen": 48925, "ฤ dikk": 48926, "ฤ afore": 48927, "ฤ Jungkook": 48928, "ฤ chociaร…ยผ": 48929, "ฤ Debatte": 48930, "ฤ weirdly": 48931, "ฤ viaje": 48932, "regist": 48933, "Help": 48934, "ฤ kinderen": 48935, "ฤ formulated": 48936, "ฤ enfim": 48937, "ฤ Towards": 48938, "รยบรยพร‘ฤน": 48939, "ivering": 48940, "ฤ รยดรยตร‘ฤครยธ": 48941, "charger": 48942, "ฤ purl": 48943, "ฤ academically": 48944, "ฤ Nurse": 48945, "ฤ deleting": 48946, "ayo": 48947, "ฤ refusal": 48948, "ฤ depicts": 48949, "ฤ Dracula": 48950, "ฤ toasted": 48951, "ฤ Zombie": 48952, "ฤ Superior": 48953, "ฤ Bold": 48954, "ฤ quizzes": 48955, "ฤ gle": 48956, "450": 48957, "ฤ comeรƒยงo": 48958, "ynn": 48959, "ฤ verst": 48960, "ฤ Olaf": 48961, "ฤ pomoc": 48962, "ฤ Sask": 48963, "รซฤบ": 48964, "ฤ TCP": 48965, "ฤ Property": 48966, "รญฤทฤบรฌยฃล‚": 48967, "ร ยธฤพร ยธยก": 48968, "boom": 48969, "aros": 48970, "ฤ ร‘ฤขรยพร‘ฤฃร‘ฤฃรยธรยน": 48971, "ฤ รยฑร‘ฤญรยฒรยฐรยตร‘ฤค": 48972, "รฅฤฉยบรฅฤฐยป": 48973, "ฤ รฌฤฟยดรฌฤทยผรชยธยฐรซยฅยผ": 48974, "ฤ combien": 48975, "vacc": 48976, "ฤ ebenfalls": 48977, "para": 48978, "ฤ รยทรยผ": 48979, "ฤ desperation": 48980, "ordre": 48981, "ฤ ร—ยฉร—ฤพร—ฤป": 48982, "ฤ generously": 48983, "ฤ รล€รยบ": 48984, "ฤ orbiting": 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/latentsync/whisper/whisper/decoding.py b/latentsync/whisper/whisper/decoding.py new file mode 100644 index 0000000000000000000000000000000000000000..183505481e40a4f3779933e5fde8086d79e4cc39 --- /dev/null +++ b/latentsync/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/latentsync/whisper/whisper/model.py b/latentsync/whisper/whisper/model.py new file mode 100644 index 0000000000000000000000000000000000000000..acb6a0be88109a884b2c4d0e2d50daaf00870307 --- /dev/null +++ b/latentsync/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/latentsync/whisper/whisper/normalizers/__init__.py b/latentsync/whisper/whisper/normalizers/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..0b10d5a9d6389368fe7f3bac38e3b3cd9cc1470f --- /dev/null +++ b/latentsync/whisper/whisper/normalizers/__init__.py @@ -0,0 +1,2 @@ +from .basic import BasicTextNormalizer +from .english import EnglishTextNormalizer diff --git a/latentsync/whisper/whisper/normalizers/basic.py b/latentsync/whisper/whisper/normalizers/basic.py new file mode 100644 index 0000000000000000000000000000000000000000..ef8d249bfca2cd10bccdd56950504581e2598560 --- /dev/null +++ b/latentsync/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 = { + "ล“": "oe", + "ล’": "OE", + "รธ": "o", + "ร˜": "O", + "รฆ": "ae", + "ร†": "AE", + "รŸ": "ss", + "แบž": "SS", + "ฤ‘": "d", + "ฤ": "D", + "รฐ": "d", + "ร": "D", + "รพ": "th", + "รž": "th", + "ล‚": "l", + "ล": "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/latentsync/whisper/whisper/normalizers/english.json b/latentsync/whisper/whisper/normalizers/english.json new file mode 100644 index 0000000000000000000000000000000000000000..bd84ae73faeb4dfa75214b99c4b392a51be90336 --- /dev/null +++ b/latentsync/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/latentsync/whisper/whisper/normalizers/english.py b/latentsync/whisper/whisper/normalizers/english.py new file mode 100644 index 0000000000000000000000000000000000000000..d5c2bb4ebef1a04b9ab14970c10c3fb0d4e948d4 --- /dev/null +++ b/latentsync/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": "ยฃ", + "pounds": "ยฃ", + "euro": "โ‚ฌ", + "euros": "โ‚ฌ", + "dollar": "$", + "dollars": "$", + "cent": "ยข", + "cents": "ยข", + } + self.prefixes = set( + list(self.preceding_prefixers.values()) + list(self.following_prefixers.values()) + ) + self.suffixers = { + "per": {"cent": "%"}, + "percent": "%", + } + self.specials = {"and", "double", "triple", "point"} + + self.words = set( + [ + key + for mapping in [ + self.zeros, + self.ones, + self.ones_suffixed, + self.tens, + self.tens_suffixed, + self.multipliers, + self.multipliers_suffixed, + self.preceding_prefixers, + self.following_prefixers, + self.suffixers, + self.specials, + ] + for key in mapping + ] + ) + self.literal_words = {"one", "ones"} + + def process_words(self, words: List[str]) -> Iterator[str]: + prefix: Optional[str] = None + value: Optional[Union[str, int]] = None + skip = False + + def to_fraction(s: str): + try: + return Fraction(s) + except ValueError: + return None + + def output(result: Union[str, int]): + nonlocal prefix, value + result = str(result) + if prefix is not None: + result = prefix + result + value = None + prefix = None + return result + + if len(words) == 0: + return + + for prev, current, next in windowed([None] + words + [None], 3): + if skip: + skip = False + continue + + next_is_numeric = next is not None and re.match(r"^\d+(\.\d+)?$", 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"ยข{int(m.group(1))}" + except ValueError: + return m.string + + # apply currency postprocessing; "$2 and ยข7" -> "$2.07" + s = re.sub(r"([โ‚ฌยฃ$])([0-9]+) (?:and )?ยข([0-9]{1,2})\b", combine_cents, s) + s = re.sub(r"[โ‚ฌยฃ$]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]|$)", r" \1", s) # remove periods not followed by numbers + s = remove_symbols_and_diacritics(s, keep=".%$ยขโ‚ฌยฃ") # 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"[.$ยขโ‚ฌยฃ]([^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/latentsync/whisper/whisper/tokenizer.py b/latentsync/whisper/whisper/tokenizer.py new file mode 100644 index 0000000000000000000000000000000000000000..88142c43c9ca0d951a36b5b30baf54dd7bc7efbf --- /dev/null +++ b/latentsync/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. + + - โ™ชโ™ชโ™ช + - ( SPEAKING FOREIGN LANGUAGE ) + - [DAVID] Hey there, + + keeping basic punctuations like commas, periods, question marks, exclamation points, etc. + """ + symbols = list("\"#()*+/:;<=>@[\\]^_`{|}~ใ€Œใ€ใ€Žใ€") + symbols += "<< >> <<< >>> -- --- -( -[ (' (\" (( )) ((( ))) [[ ]] {{ }} โ™ชโ™ช โ™ชโ™ชโ™ช".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("โ™ฉโ™ชโ™ซโ™ฌโ™ญโ™ฎโ™ฏ") + 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/latentsync/whisper/whisper/transcribe.py b/latentsync/whisper/whisper/transcribe.py new file mode 100644 index 0000000000000000000000000000000000000000..745e77554b90c63db69ce7da40156676c7a990de --- /dev/null +++ b/latentsync/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: + # seekๆ˜ฏๅผ€ๅง‹็š„ๅธงๆ•ฐ + 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/latentsync/whisper/whisper/utils.py b/latentsync/whisper/whisper/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..87c91f2e969c4d2ebfc311266bced76a52cb3ef3 --- /dev/null +++ b/latentsync/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/lipsync.py b/lipsync.py index 2fe2b0389b32eaf5c452599974a6a5b2c153e9c2..158483095019801ff0425ebac7e8d727eb39a5bd 100644 --- a/lipsync.py +++ b/lipsync.py @@ -1,19 +1,64 @@ import gc import os import traceback +from glob import glob import torch -from DeepCache import DeepCacheSDHelper +from omegaconf import OmegaConf +from diffusers import AutoencoderKL, DDIMScheduler +from huggingface_hub import snapshot_download +from diffusers.utils.import_utils import is_xformers_available +from accelerate.utils import set_seed +from latentsync.models.unet import UNet3DConditionModel from latentsync.pipelines.lipsync_pipeline import LipsyncPipeline +from latentsync.whisper.audio2feature import Audio2Feature torch.backends.cudnn.benchmark = True torch.backends.cudnn.deterministic = False +os.makedirs("checkpoints", exist_ok=True) +snapshot_download(repo_id="ByteDance/LatentSync-1.6", local_dir="./checkpoints") + +unet_config_path = "configs/unet/stage2_512.yaml" +config = OmegaConf.load(unet_config_path) + +inference_ckpt_path = "checkpoints/latentsync_unet.pt" + +scheduler = DDIMScheduler.from_pretrained("configs") + +if config.model.cross_attention_dim == 768: + whisper_model_path = "checkpoints/whisper/small.pt" +elif config.model.cross_attention_dim == 384: + whisper_model_path = "checkpoints/whisper/tiny.pt" +else: + raise NotImplementedError("cross_attention_dim must be 768 or 384") + +audio_encoder = Audio2Feature( + model_path=whisper_model_path, device="cuda", num_frames=config.data.num_frames +) + +vae = AutoencoderKL.from_pretrained( + "stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16 +) +vae.config.scaling_factor = 0.18215 +vae.config.shift_factor = 0 + +unet, _ = UNet3DConditionModel.from_pretrained( + OmegaConf.to_container(config.model), + inference_ckpt_path, + device="cpu", +) + +unet = unet.to(dtype=torch.float16) + +if is_xformers_available(): + unet.enable_xformers_memory_efficient_attention() + pipeline = LipsyncPipeline( - vae=None, - audio_encoder=None, - unet=None, - scheduler=None, + vae=vae, + audio_encoder=audio_encoder, + unet=unet, + scheduler=scheduler, ).to("cuda") @@ -105,21 +150,16 @@ def apply_lipsync(video_input_path, audio_path, video_out_path, crop_size=256): optimal_params = get_optimal_params(total_memory / 1024**3) print(f"get_optimal_params output: {optimal_params}") - num_frames = optimal_params["num_frames"] - num_inference_steps = optimal_params["num_inference_steps"] + num_frames = config.data.num_frames + num_inference_steps = config.run.inference_steps - print(f"\nOptimized parameters based on VRAM:") + print(f"\nParameters from config:") print(f" num_frames: {num_frames}") print(f" num_inference_steps: {num_inference_steps}") - - print("Loading DeepCache helper...") - helper = DeepCacheSDHelper(pipe=pipeline) - helper.set_params(cache_interval=3, cache_branch_id=0) - helper.enable() - print("DeepCache enabled (cache_interval=3)") + print(f" resolution: {config.data.resolution}") print(f"Initial seed: {torch.initial_seed()}") - print(f"GPU Memory After DeepCache: {get_gpu_memory_info()}") + print(f"GPU Memory After model load: {get_gpu_memory_info()}") print("\nStarting pipeline inference...") print( @@ -134,8 +174,8 @@ def apply_lipsync(video_input_path, audio_path, video_out_path, crop_size=256): audio_path=audio_path, video_out_path=video_out_path, video_mask_path=video_out_path.replace(".mp4", "_mask.mp4"), - num_frames=16, - num_inference_steps=20, + num_frames=num_frames, + num_inference_steps=num_inference_steps, guidance_scale=1.0, weight_dtype=torch.float16, width=crop_size, diff --git a/packages.txt b/packages.txt new file mode 100644 index 0000000000000000000000000000000000000000..e60324b17ebd623db93bec6ae872f9fe43a7bb83 --- /dev/null +++ b/packages.txt @@ -0,0 +1,2 @@ +ffmpeg +libgl1 diff --git a/preprocess/affine_transform.py b/preprocess/affine_transform.py new file mode 100644 index 0000000000000000000000000000000000000000..a9e37fbca1469917788cd4ef33b4660fe17acfff --- /dev/null +++ b/preprocess/affine_transform.py @@ -0,0 +1,137 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from latentsync.utils.util import read_video, write_video +from latentsync.utils.image_processor import ImageProcessor +import torch +from einops import rearrange +import os +import tqdm +import subprocess +from multiprocessing import Process +import shutil + +paths = [] + + +def gather_video_paths(input_dir, output_dir): + for video in sorted(os.listdir(input_dir)): + if video.endswith(".mp4"): + video_input = os.path.join(input_dir, video) + video_output = os.path.join(output_dir, video) + if os.path.isfile(video_output): + continue + paths.append((video_input, video_output)) + elif os.path.isdir(os.path.join(input_dir, video)): + gather_video_paths(os.path.join(input_dir, video), os.path.join(output_dir, video)) + + +class FaceDetector: + def __init__(self, resolution: int = 512, device: str = "cpu"): + self.image_processor = ImageProcessor(resolution, "fix_mask", device) + + def affine_transform_video(self, video_path): + video_frames = read_video(video_path, change_fps=False) + results = [] + for frame in video_frames: + frame, _, _ = self.image_processor.affine_transform(frame) + results.append(frame) + results = torch.stack(results) + + results = rearrange(results, "f c h w -> f h w c").numpy() + return results + + def close(self): + self.image_processor.close() + + +def combine_video_audio(video_frames, video_input_path, video_output_path, process_temp_dir): + video_name = os.path.basename(video_input_path)[:-4] + audio_temp = os.path.join(process_temp_dir, f"{video_name}_temp.wav") + video_temp = os.path.join(process_temp_dir, f"{video_name}_temp.mp4") + + write_video(video_temp, video_frames, fps=25) + + command = f"ffmpeg -y -loglevel error -i {video_input_path} -q:a 0 -map a {audio_temp}" + subprocess.run(command, shell=True) + + os.makedirs(os.path.dirname(video_output_path), exist_ok=True) + command = f"ffmpeg -y -loglevel error -i {video_temp} -i {audio_temp} -c:v libx264 -c:a aac -map 0:v -map 1:a -q:v 0 -q:a 0 {video_output_path}" + subprocess.run(command, shell=True) + + os.remove(audio_temp) + os.remove(video_temp) + + +def func(paths, process_temp_dir, device_id, resolution): + os.makedirs(process_temp_dir, exist_ok=True) + face_detector = FaceDetector(resolution, f"cuda:{device_id}") + + for video_input, video_output in paths: + if os.path.isfile(video_output): + continue + try: + video_frames = face_detector.affine_transform_video(video_input) + except Exception as e: # Handle the exception of face not detcted + print(f"Exception: {e} - {video_input}") + continue + + os.makedirs(os.path.dirname(video_output), exist_ok=True) + combine_video_audio(video_frames, video_input, video_output, process_temp_dir) + print(f"Saved: {video_output}") + + face_detector.close() + + +def split(a, n): + k, m = divmod(len(a), n) + return (a[i * k + min(i, m) : (i + 1) * k + min(i + 1, m)] for i in range(n)) + + +def affine_transform_multi_gpus(input_dir, output_dir, temp_dir, resolution, num_workers): + print(f"Recursively gathering video paths of {input_dir} ...") + gather_video_paths(input_dir, output_dir) + num_devices = torch.cuda.device_count() + if num_devices == 0: + raise RuntimeError("No GPUs found") + + if os.path.exists(temp_dir): + shutil.rmtree(temp_dir) + os.makedirs(temp_dir, exist_ok=True) + + split_paths = list(split(paths, num_workers * num_devices)) + + processes = [] + + for i in range(num_devices): + for j in range(num_workers): + process_index = i * num_workers + j + process = Process( + target=func, args=(split_paths[process_index], os.path.join(temp_dir, f"process_{i}"), i, resolution) + ) + process.start() + processes.append(process) + + for process in processes: + process.join() + + +if __name__ == "__main__": + input_dir = "/mnt/bn/maliva-gen-ai-v2/chunyu.li/avatars/resampled/train" + output_dir = "/mnt/bn/maliva-gen-ai-v2/chunyu.li/avatars/affine_transformed/train" + temp_dir = "temp" + resolution = 256 + num_workers = 10 # How many processes per device + + affine_transform_multi_gpus(input_dir, output_dir, temp_dir, resolution, num_workers) diff --git a/preprocess/data_processing_pipeline.py b/preprocess/data_processing_pipeline.py new file mode 100644 index 0000000000000000000000000000000000000000..f543a5554cbfafbf50cba43ad99fc04627ecc574 --- /dev/null +++ b/preprocess/data_processing_pipeline.py @@ -0,0 +1,85 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +import os +from preprocess.affine_transform import affine_transform_multi_gpus +from preprocess.remove_broken_videos import remove_broken_videos_multiprocessing +from preprocess.detect_shot import detect_shot_multiprocessing +from preprocess.filter_high_resolution import filter_high_resolution_multiprocessing +from preprocess.resample_fps_hz import resample_fps_hz_multiprocessing +from preprocess.segment_videos import segment_videos_multiprocessing +from preprocess.sync_av import sync_av_multi_gpus +from preprocess.filter_visual_quality import filter_visual_quality_multi_gpus +from preprocess.remove_incorrect_affined import remove_incorrect_affined_multiprocessing + + +def data_processing_pipeline( + total_num_workers, per_gpu_num_workers, resolution, sync_conf_threshold, temp_dir, input_dir +): + print("Removing broken videos...") + remove_broken_videos_multiprocessing(input_dir, total_num_workers) + + print("Resampling FPS hz...") + resampled_dir = os.path.join(os.path.dirname(input_dir), "resampled") + resample_fps_hz_multiprocessing(input_dir, resampled_dir, total_num_workers) + + print("Detecting shot...") + shot_dir = os.path.join(os.path.dirname(input_dir), "shot") + detect_shot_multiprocessing(resampled_dir, shot_dir, total_num_workers) + + print("Segmenting videos...") + segmented_dir = os.path.join(os.path.dirname(input_dir), "segmented") + segment_videos_multiprocessing(shot_dir, segmented_dir, total_num_workers) + + print("Filtering high resolution...") + high_resolution_dir = os.path.join(os.path.dirname(input_dir), "high_resolution") + filter_high_resolution_multiprocessing(segmented_dir, high_resolution_dir, resolution, total_num_workers) + + print("Affine transforming videos...") + affine_transformed_dir = os.path.join(os.path.dirname(input_dir), "affine_transformed") + affine_transform_multi_gpus( + high_resolution_dir, affine_transformed_dir, temp_dir, resolution, per_gpu_num_workers // 2 + ) + + print("Removing incorrect affined videos...") + remove_incorrect_affined_multiprocessing(affine_transformed_dir, total_num_workers) + + print("Syncing audio and video...") + av_synced_dir = os.path.join(os.path.dirname(input_dir), f"av_synced_{sync_conf_threshold}") + sync_av_multi_gpus(affine_transformed_dir, av_synced_dir, temp_dir, per_gpu_num_workers, sync_conf_threshold) + + print("Filtering visual quality...") + high_visual_quality_dir = os.path.join(os.path.dirname(input_dir), "high_visual_quality") + filter_visual_quality_multi_gpus(av_synced_dir, high_visual_quality_dir, per_gpu_num_workers) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--total_num_workers", type=int, default=100) + parser.add_argument("--per_gpu_num_workers", type=int, default=20) + parser.add_argument("--resolution", type=int, default=256) + parser.add_argument("--sync_conf_threshold", type=int, default=3) + parser.add_argument("--temp_dir", type=str, default="temp") + parser.add_argument("--input_dir", type=str, required=True) + args = parser.parse_args() + + data_processing_pipeline( + args.total_num_workers, + args.per_gpu_num_workers, + args.resolution, + args.sync_conf_threshold, + args.temp_dir, + args.input_dir, + ) diff --git a/preprocess/detect_shot.py b/preprocess/detect_shot.py new file mode 100644 index 0000000000000000000000000000000000000000..ca3e185b2d67c06373982782bb4a21a56427a44a --- /dev/null +++ b/preprocess/detect_shot.py @@ -0,0 +1,62 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import subprocess +import tqdm +from multiprocessing import Pool + +paths = [] + + +def gather_paths(input_dir, output_dir): + for video in sorted(os.listdir(input_dir)): + if video.endswith(".mp4"): + video_input = os.path.join(input_dir, video) + video_output = os.path.join(output_dir, video) + if os.path.isfile(video_output): + continue + paths.append([video_input, output_dir]) + elif os.path.isdir(os.path.join(input_dir, video)): + gather_paths(os.path.join(input_dir, video), os.path.join(output_dir, video)) + + +def detect_shot(video_input, output_dir): + os.makedirs(output_dir, exist_ok=True) + video = os.path.basename(video_input)[:-4] + command = f"scenedetect --quiet -i {video_input} detect-adaptive --threshold 2 split-video --filename '{video}_shot_$SCENE_NUMBER' --output {output_dir}" + # command = f"scenedetect --quiet -i {video_input} detect-adaptive --threshold 2 split-video --high-quality --filename '{video}_shot_$SCENE_NUMBER' --output {output_dir}" + subprocess.run(command, shell=True) + + +def multi_run_wrapper(args): + return detect_shot(*args) + + +def detect_shot_multiprocessing(input_dir, output_dir, num_workers): + print(f"Recursively gathering video paths of {input_dir} ...") + gather_paths(input_dir, output_dir) + + print(f"Detecting shot of {input_dir} ...") + with Pool(num_workers) as pool: + for _ in tqdm.tqdm(pool.imap_unordered(multi_run_wrapper, paths), total=len(paths)): + pass + + +if __name__ == "__main__": + input_dir = "/mnt/bn/maliva-gen-ai-v2/chunyu.li/ads/high-resolution" + output_dir = "/mnt/bn/maliva-gen-ai-v2/chunyu.li/ads/shot" + num_workers = 50 + + detect_shot_multiprocessing(input_dir, output_dir, num_workers) diff --git a/preprocess/filter_high_resolution.py b/preprocess/filter_high_resolution.py new file mode 100644 index 0000000000000000000000000000000000000000..9c8f2b8873ff86ffe9be934a9bc3f9bd5adbc335 --- /dev/null +++ b/preprocess/filter_high_resolution.py @@ -0,0 +1,112 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import mediapipe as mp +from latentsync.utils.util import read_video +import os +import tqdm +import shutil +from multiprocessing import Pool + +paths = [] + + +def gather_video_paths(input_dir, output_dir, resolution): + for video in sorted(os.listdir(input_dir)): + if video.endswith(".mp4"): + video_input = os.path.join(input_dir, video) + video_output = os.path.join(output_dir, video) + if os.path.isfile(video_output): + continue + paths.append([video_input, video_output, resolution]) + elif os.path.isdir(os.path.join(input_dir, video)): + gather_video_paths(os.path.join(input_dir, video), os.path.join(output_dir, video), resolution) + + +class FaceDetector: + def __init__(self, resolution=256): + self.face_detection = mp.solutions.face_detection.FaceDetection( + model_selection=0, min_detection_confidence=0.5 + ) + self.resolution = resolution + + def detect_face(self, image): + height, width = image.shape[:2] + # Process the image and detect faces. + results = self.face_detection.process(image) + + if not results.detections: # Face not detected + raise Exception("Face not detected") + + if len(results.detections) != 1: + return False + detection = results.detections[0] # Only use the first face in the image + + bounding_box = detection.location_data.relative_bounding_box + face_width = int(bounding_box.width * width) + face_height = int(bounding_box.height * height) + if face_width < self.resolution or face_height < self.resolution: + return False + return True + + def detect_video(self, video_path): + video_frames = read_video(video_path, change_fps=False) + if len(video_frames) == 0: + return False + for frame in video_frames: + if not self.detect_face(frame): + return False + return True + + def close(self): + self.face_detection.close() + + +def filter_video(video_input, video_out, resolution): + if os.path.isfile(video_out): + return + face_detector = FaceDetector(resolution) + try: + save = face_detector.detect_video(video_input) + except Exception as e: + # print(f"Exception: {e} Input video: {video_input}") + face_detector.close() + return + if save: + os.makedirs(os.path.dirname(video_out), exist_ok=True) + shutil.copy(video_input, video_out) + face_detector.close() + + +def multi_run_wrapper(args): + return filter_video(*args) + + +def filter_high_resolution_multiprocessing(input_dir, output_dir, resolution, num_workers): + print(f"Recursively gathering video paths of {input_dir} ...") + gather_video_paths(input_dir, output_dir, resolution) + + print(f"Filtering high resolution videos in {input_dir} ...") + with Pool(num_workers) as pool: + for _ in tqdm.tqdm(pool.imap_unordered(multi_run_wrapper, paths), total=len(paths)): + pass + + +if __name__ == "__main__": + input_dir = "/mnt/bn/maliva-gen-ai/lichunyu/HDTF/original/train" + output_dir = "/mnt/bn/maliva-gen-ai/lichunyu/HDTF/detected/train" + resolution = 256 + num_workers = 50 + + filter_high_resolution_multiprocessing(input_dir, output_dir, resolution, num_workers) diff --git a/preprocess/filter_visual_quality.py b/preprocess/filter_visual_quality.py new file mode 100644 index 0000000000000000000000000000000000000000..28e3c27d17e84634d0ea2fb6cb9e12e26a119a5f --- /dev/null +++ b/preprocess/filter_visual_quality.py @@ -0,0 +1,127 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import tqdm +import torch +import torchvision +import shutil +from multiprocessing import Process +import numpy as np +from decord import VideoReader +from einops import rearrange +from eval.hyper_iqa import HyperNet, TargetNet + + +paths = [] + + +def gather_paths(input_dir, output_dir): + # os.makedirs(output_dir, exist_ok=True) + + for video in tqdm.tqdm(sorted(os.listdir(input_dir))): + if video.endswith(".mp4"): + video_input = os.path.join(input_dir, video) + video_output = os.path.join(output_dir, video) + if os.path.isfile(video_output): + continue + paths.append((video_input, video_output)) + elif os.path.isdir(os.path.join(input_dir, video)): + gather_paths(os.path.join(input_dir, video), os.path.join(output_dir, video)) + + +def read_video(video_path: str): + vr = VideoReader(video_path) + first_frame = vr[0].asnumpy() + middle_frame = vr[len(vr) // 2].asnumpy() + last_frame = vr[-1].asnumpy() + vr.seek(0) + video_frames = np.stack([first_frame, middle_frame, last_frame], axis=0) + video_frames = torch.from_numpy(rearrange(video_frames, "b h w c -> b c h w")) + video_frames = video_frames / 255.0 + return video_frames + + +def func(paths, device_id): + device = f"cuda:{device_id}" + + model_hyper = HyperNet(16, 112, 224, 112, 56, 28, 14, 7).to(device) + model_hyper.train(False) + + # load the pre-trained model on the koniq-10k dataset + model_hyper.load_state_dict((torch.load("checkpoints/auxiliary/koniq_pretrained.pkl"))) + + transforms = torchvision.transforms.Compose( + [ + torchvision.transforms.CenterCrop(size=224), + torchvision.transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + ] + ) + + for video_input, video_output in paths: + try: + video_frames = read_video(video_input) + video_frames = transforms(video_frames) + video_frames = video_frames.clone().detach().to(device) + paras = model_hyper(video_frames) # 'paras' contains the network weights conveyed to target network + + # Building target network + model_target = TargetNet(paras).cuda() + for param in model_target.parameters(): + param.requires_grad = False + + # Quality prediction + pred = model_target(paras["target_in_vec"]) # 'paras['target_in_vec']' is the input to target net + + # quality score ranges from 0-100, a higher score indicates a better quality + quality_score = pred.mean().item() + print(f"Input video: {video_input}\nVisual quality score: {quality_score:.2f}") + + if quality_score >= 40: + os.makedirs(os.path.dirname(video_output), exist_ok=True) + shutil.copy(video_input, video_output) + except Exception as e: + print(e) + + +def split(a, n): + k, m = divmod(len(a), n) + return (a[i * k + min(i, m) : (i + 1) * k + min(i + 1, m)] for i in range(n)) + + +def filter_visual_quality_multi_gpus(input_dir, output_dir, num_workers): + gather_paths(input_dir, output_dir) + num_devices = torch.cuda.device_count() + if num_devices == 0: + raise RuntimeError("No GPUs found") + split_paths = list(split(paths, num_workers * num_devices)) + processes = [] + + for i in range(num_devices): + for j in range(num_workers): + process_index = i * num_workers + j + process = Process(target=func, args=(split_paths[process_index], i)) + process.start() + processes.append(process) + + for process in processes: + process.join() + + +if __name__ == "__main__": + input_dir = "/mnt/bn/maliva-gen-ai-v2/chunyu.li/VoxCeleb2/av_synced_high" + output_dir = "/mnt/bn/maliva-gen-ai-v2/chunyu.li/VoxCeleb2/high_visual_quality" + num_workers = 20 # How many processes per device + + filter_visual_quality_multi_gpus(input_dir, output_dir, num_workers) diff --git a/preprocess/remove_broken_videos.py b/preprocess/remove_broken_videos.py new file mode 100644 index 0000000000000000000000000000000000000000..fc58b38e9ea51ac5ac3efde911a8712885fef096 --- /dev/null +++ b/preprocess/remove_broken_videos.py @@ -0,0 +1,43 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +from multiprocessing import Pool +import tqdm + +from latentsync.utils.av_reader import AVReader +from latentsync.utils.util import gather_video_paths_recursively + + +def remove_broken_video(video_path): + try: + AVReader(video_path) + except Exception: + os.remove(video_path) + + +def remove_broken_videos_multiprocessing(input_dir, num_workers): + video_paths = gather_video_paths_recursively(input_dir) + + print("Removing broken videos...") + with Pool(num_workers) as pool: + for _ in tqdm.tqdm(pool.imap_unordered(remove_broken_video, video_paths), total=len(video_paths)): + pass + + +if __name__ == "__main__": + input_dir = "/mnt/bn/maliva-gen-ai-v2/chunyu.li/multilingual/affine_transformed" + num_workers = 50 + + remove_broken_videos_multiprocessing(input_dir, num_workers) diff --git a/preprocess/remove_incorrect_affined.py b/preprocess/remove_incorrect_affined.py new file mode 100644 index 0000000000000000000000000000000000000000..baf4a85bbf682258f748312da9c109e60d410e53 --- /dev/null +++ b/preprocess/remove_incorrect_affined.py @@ -0,0 +1,81 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import mediapipe as mp +from latentsync.utils.util import read_video, gather_video_paths_recursively +import os +import tqdm +from multiprocessing import Pool + + +class FaceDetector: + def __init__(self): + self.face_detection = mp.solutions.face_detection.FaceDetection( + model_selection=0, min_detection_confidence=0.5 + ) + + def detect_face(self, image): + # Process the image and detect faces. + results = self.face_detection.process(image) + + if not results.detections: # Face not detected + return False + + if len(results.detections) != 1: + return False + return True + + def detect_video(self, video_path): + try: + video_frames = read_video(video_path, change_fps=False) + except Exception as e: + print(f"Exception: {e} - {video_path}") + return False + if len(video_frames) == 0: + return False + for frame in video_frames: + if not self.detect_face(frame): + return False + return True + + def close(self): + self.face_detection.close() + + +def remove_incorrect_affined(video_path): + if not os.path.isfile(video_path): + return + face_detector = FaceDetector() + has_face = face_detector.detect_video(video_path) + if not has_face: + os.remove(video_path) + print(f"Removed: {video_path}") + face_detector.close() + + +def remove_incorrect_affined_multiprocessing(input_dir, num_workers): + video_paths = gather_video_paths_recursively(input_dir) + print(f"Total videos: {len(video_paths)}") + + print(f"Removing incorrect affined videos in {input_dir} ...") + with Pool(num_workers) as pool: + for _ in tqdm.tqdm(pool.imap_unordered(remove_incorrect_affined, video_paths), total=len(video_paths)): + pass + + +if __name__ == "__main__": + input_dir = "/mnt/bn/maliva-gen-ai-v2/chunyu.li/multilingual_dcc/high_visual_quality" + num_workers = 50 + + remove_incorrect_affined_multiprocessing(input_dir, num_workers) diff --git a/preprocess/resample_fps_hz.py b/preprocess/resample_fps_hz.py new file mode 100644 index 0000000000000000000000000000000000000000..77788d11b7c7c86adb064303e2c66befacfa474a --- /dev/null +++ b/preprocess/resample_fps_hz.py @@ -0,0 +1,70 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import subprocess +import tqdm +from multiprocessing import Pool +import cv2 + +paths = [] + + +def gather_paths(input_dir, output_dir): + for video in sorted(os.listdir(input_dir)): + if video.endswith(".mp4"): + video_input = os.path.join(input_dir, video) + video_output = os.path.join(output_dir, video) + if os.path.isfile(video_output): + continue + paths.append([video_input, video_output]) + elif os.path.isdir(os.path.join(input_dir, video)): + gather_paths(os.path.join(input_dir, video), os.path.join(output_dir, video)) + + +def get_video_fps(video_path: str): + cam = cv2.VideoCapture(video_path) + fps = cam.get(cv2.CAP_PROP_FPS) + return fps + + +def resample_fps_hz(video_input, video_output): + os.makedirs(os.path.dirname(video_output), exist_ok=True) + if get_video_fps(video_input) == 25: + command = f"ffmpeg -loglevel error -y -i {video_input} -c:v copy -ar 16000 -q:a 0 {video_output}" + else: + command = f"ffmpeg -loglevel error -y -i {video_input} -r 25 -ar 16000 -q:a 0 {video_output}" + subprocess.run(command, shell=True) + + +def multi_run_wrapper(args): + return resample_fps_hz(*args) + + +def resample_fps_hz_multiprocessing(input_dir, output_dir, num_workers): + print(f"Recursively gathering video paths of {input_dir} ...") + gather_paths(input_dir, output_dir) + + print(f"Resampling FPS and Hz of {input_dir} ...") + with Pool(num_workers) as pool: + for _ in tqdm.tqdm(pool.imap_unordered(multi_run_wrapper, paths), total=len(paths)): + pass + + +if __name__ == "__main__": + input_dir = "/mnt/bn/maliva-gen-ai-v2/chunyu.li/HDTF/segmented/train" + output_dir = "/mnt/bn/maliva-gen-ai-v2/chunyu.li/HDTF/resampled_test" + num_workers = 20 + + resample_fps_hz_multiprocessing(input_dir, output_dir, num_workers) diff --git a/preprocess/segment_videos.py b/preprocess/segment_videos.py new file mode 100644 index 0000000000000000000000000000000000000000..11e33399d8e326675aefb838b924bb235e707b56 --- /dev/null +++ b/preprocess/segment_videos.py @@ -0,0 +1,62 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import subprocess +import tqdm +from multiprocessing import Pool + +paths = [] + + +def gather_paths(input_dir, output_dir): + for video in sorted(os.listdir(input_dir)): + if video.endswith(".mp4"): + video_basename = video[:-4] + video_input = os.path.join(input_dir, video) + video_output = os.path.join(output_dir, f"{video_basename}_%03d.mp4") + if os.path.isfile(video_output): + continue + paths.append([video_input, video_output]) + elif os.path.isdir(os.path.join(input_dir, video)): + gather_paths(os.path.join(input_dir, video), os.path.join(output_dir, video)) + + +def segment_video(video_input, video_output): + os.makedirs(os.path.dirname(video_output), exist_ok=True) + command = f"ffmpeg -loglevel error -y -i {video_input} -map 0 -c:v copy -segment_time 5 -f segment -reset_timestamps 1 -q:a 0 {video_output}" + # command = f'ffmpeg -loglevel error -y -i {video_input} -map 0 -segment_time 5 -f segment -reset_timestamps 1 -force_key_frames "expr:gte(t,n_forced*5)" -crf 18 -q:a 0 {video_output}' + subprocess.run(command, shell=True) + + +def multi_run_wrapper(args): + return segment_video(*args) + + +def segment_videos_multiprocessing(input_dir, output_dir, num_workers): + print(f"Recursively gathering video paths of {input_dir} ...") + gather_paths(input_dir, output_dir) + + print(f"Segmenting videos of {input_dir} ...") + with Pool(num_workers) as pool: + for _ in tqdm.tqdm(pool.imap_unordered(multi_run_wrapper, paths), total=len(paths)): + pass + + +if __name__ == "__main__": + input_dir = "/mnt/bn/maliva-gen-ai-v2/chunyu.li/avatars_new/cut" + output_dir = "/mnt/bn/maliva-gen-ai-v2/chunyu.li/avatars_new/segmented" + num_workers = 50 + + segment_videos_multiprocessing(input_dir, output_dir, num_workers) diff --git a/preprocess/sync_av.py b/preprocess/sync_av.py new file mode 100644 index 0000000000000000000000000000000000000000..6867c88d137c4c2e4b565a60f3efe1e9715b8976 --- /dev/null +++ b/preprocess/sync_av.py @@ -0,0 +1,113 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import tqdm +from eval.syncnet import SyncNetEval +from eval.syncnet_detect import SyncNetDetector +from eval.eval_sync_conf import syncnet_eval +import torch +import subprocess +import shutil +from multiprocessing import Process + +paths = [] + + +def gather_paths(input_dir, output_dir): + # os.makedirs(output_dir, exist_ok=True) + + for video in tqdm.tqdm(sorted(os.listdir(input_dir))): + if video.endswith(".mp4"): + video_input = os.path.join(input_dir, video) + video_output = os.path.join(output_dir, video) + if os.path.isfile(video_output): + continue + paths.append((video_input, video_output)) + elif os.path.isdir(os.path.join(input_dir, video)): + gather_paths(os.path.join(input_dir, video), os.path.join(output_dir, video)) + + +def adjust_offset(video_input: str, video_output: str, av_offset: int, fps: int = 25): + command = f"ffmpeg -loglevel error -y -i {video_input} -itsoffset {av_offset/fps} -i {video_input} -map 0:v -map 1:a -c copy -q:v 0 -q:a 0 {video_output}" + subprocess.run(command, shell=True) + + +def func(sync_conf_threshold, paths, device_id, process_temp_dir): + os.makedirs(process_temp_dir, exist_ok=True) + device = f"cuda:{device_id}" + + syncnet = SyncNetEval(device=device) + syncnet.loadParameters("checkpoints/auxiliary/syncnet_v2.model") + + detect_results_dir = os.path.join(process_temp_dir, "detect_results") + syncnet_eval_results_dir = os.path.join(process_temp_dir, "syncnet_eval_results") + + syncnet_detector = SyncNetDetector(device=device, detect_results_dir=detect_results_dir) + + for video_input, video_output in paths: + try: + av_offset, conf = syncnet_eval( + syncnet, syncnet_detector, video_input, syncnet_eval_results_dir, detect_results_dir + ) + if conf >= sync_conf_threshold and abs(av_offset) <= 6: + os.makedirs(os.path.dirname(video_output), exist_ok=True) + if av_offset == 0: + shutil.copy(video_input, video_output) + else: + adjust_offset(video_input, video_output, av_offset) + except Exception as e: + print(e) + + +def split(a, n): + k, m = divmod(len(a), n) + return (a[i * k + min(i, m) : (i + 1) * k + min(i + 1, m)] for i in range(n)) + + +def sync_av_multi_gpus(input_dir, output_dir, temp_dir, num_workers, sync_conf_threshold): + gather_paths(input_dir, output_dir) + num_devices = torch.cuda.device_count() + if num_devices == 0: + raise RuntimeError("No GPUs found") + split_paths = list(split(paths, num_workers * num_devices)) + processes = [] + + for i in range(num_devices): + for j in range(num_workers): + process_index = i * num_workers + j + process = Process( + target=func, + args=( + sync_conf_threshold, + split_paths[process_index], + i, + os.path.join(temp_dir, f"process_{process_index}"), + ), + ) + process.start() + processes.append(process) + + for process in processes: + process.join() + + +if __name__ == "__main__": + input_dir = "/mnt/bn/maliva-gen-ai-v2/chunyu.li/ads/affine_transformed" + output_dir = "/mnt/bn/maliva-gen-ai-v2/chunyu.li/VoxCeleb2/temp" + temp_dir = "temp" + num_workers = 20 # How many processes per device + sync_conf_threshold = 3 + + sync_av_multi_gpus(input_dir, output_dir, temp_dir, num_workers, sync_conf_threshold) diff --git a/requirements.txt b/requirements.txt index db520d41652d331919138689cb6fa40d3686ea9c..11fe495c6c41e1330559fb80089c278af7f37bce 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,30 +1,53 @@ # --- Core Torch Stack --- -torch -torchvision +torch==2.5.1 +torchvision==0.20.1 +--extra-index-url https://download.pytorch.org/whl/cu121 + +# --- DeepCache (giแปฏ theo yรชu cแบงu) --- +DeepCache # --- ML/HF Dependencies --- -gradio -huggingface-hub<1.0 -transformers==4.52.3 -tokenizers==0.21.0 diffusers==0.33.1 +transformers==4.52.3 +huggingface-hub<1.0 +xformers==0.0.29.post1 +triton==3.1.0 accelerate==1.8.1 +einops==0.7.0 omegaconf==2.3.0 -DeepCache +safetensors>=0.4.3 -# --- Progress & Utilities --- -tqdm -psutil +# --- Audio/Video Processing --- +imageio==2.27.0 +decord==0.6.0 +mediapipe==0.10.14 +python_speech_features==0.6 +librosa==0.10.1 +pydub==0.25.1 +moviepy==1.0.3 +av +soundfile -# --- FFmpeg Wrapper --- -ffmpy +# --- Face Processing --- +opencv-python>=4.8.0 +face-alignment==1.4.1 +scenedetect==0.6.1 +ffmpeg-python==0.2.0 -# --- Model Repos --- -latentsync @ git+https://github.com/OutofAi/LatentSync-batch.git +# --- Metrics/Training --- +torch-fidelity==0.3.0 +torchmetrics==1.3.1 +lpips==0.1.4 -# --- Flash Attention (HuggingFace Spaces) --- -https://huggingface.co/alexnasa/flash-attn-3/resolve/main/128/flash_attn_3-3.0.0b1-cp39-abi3-linux_x86_64.whl +# --- Utilities --- +ninja==1.11.1.1 +pandas==2.0.3 +numpy==1.24.4 +tqdm +psutil +ffmpy -# --- Face Detection --- -mediapipe==0.10.14 -opencv-python>=4.8.0 +# --- Gradio & Spaces --- +gradio +spaces +httpx[socks] diff --git a/scripts/inference.py b/scripts/inference.py new file mode 100644 index 0000000000000000000000000000000000000000..c8409970726d16f63d26d0589876451977ea86bf --- /dev/null +++ b/scripts/inference.py @@ -0,0 +1,99 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import argparse +from omegaconf import OmegaConf +import torch +from diffusers import AutoencoderKL, DDIMScheduler +from latentsync.models.unet import UNet3DConditionModel +from latentsync.pipelines.lipsync_pipeline import LipsyncPipeline +from diffusers.utils.import_utils import is_xformers_available +from accelerate.utils import set_seed +from latentsync.whisper.audio2feature import Audio2Feature + + +def main(config, args): + print(f"Input video path: {args.video_path}") + print(f"Input audio path: {args.audio_path}") + print(f"Loaded checkpoint path: {args.inference_ckpt_path}") + + scheduler = DDIMScheduler.from_pretrained("configs") + + if config.model.cross_attention_dim == 768: + whisper_model_path = "checkpoints/whisper/small.pt" + elif config.model.cross_attention_dim == 384: + whisper_model_path = "checkpoints/whisper/tiny.pt" + else: + raise NotImplementedError("cross_attention_dim must be 768 or 384") + + audio_encoder = Audio2Feature(model_path=whisper_model_path, device="cuda", num_frames=config.data.num_frames) + + vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16) + vae.config.scaling_factor = 0.18215 + vae.config.shift_factor = 0 + + unet, _ = UNet3DConditionModel.from_pretrained( + OmegaConf.to_container(config.model), + args.inference_ckpt_path, # load checkpoint + device="cpu", + ) + + unet = unet.to(dtype=torch.float16) + + # set xformers + if is_xformers_available(): + unet.enable_xformers_memory_efficient_attention() + + pipeline = LipsyncPipeline( + vae=vae, + audio_encoder=audio_encoder, + unet=unet, + scheduler=scheduler, + ).to("cuda") + + if args.seed != -1: + set_seed(args.seed) + else: + torch.seed() + + print(f"Initial seed: {torch.initial_seed()}") + + pipeline( + video_path=args.video_path, + audio_path=args.audio_path, + video_out_path=args.video_out_path, + video_mask_path=args.video_out_path.replace(".mp4", "_mask.mp4"), + num_frames=config.data.num_frames, + num_inference_steps=config.run.inference_steps, + guidance_scale=args.guidance_scale, + weight_dtype=torch.float16, + width=config.data.resolution, + height=config.data.resolution, + ) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--unet_config_path", type=str, default="configs/unet.yaml") + parser.add_argument("--inference_ckpt_path", type=str, required=True) + parser.add_argument("--video_path", type=str, required=True) + parser.add_argument("--audio_path", type=str, required=True) + parser.add_argument("--video_out_path", type=str, required=True) + parser.add_argument("--guidance_scale", type=float, default=1.0) + parser.add_argument("--seed", type=int, default=1247) + args = parser.parse_args() + + config = OmegaConf.load(args.unet_config_path) + + main(config, args) diff --git a/scripts/train_syncnet.py b/scripts/train_syncnet.py new file mode 100644 index 0000000000000000000000000000000000000000..56b983a29764e45469d94dbe0e3c50357252ed9f --- /dev/null +++ b/scripts/train_syncnet.py @@ -0,0 +1,336 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from tqdm.auto import tqdm +import os, argparse, datetime, math +import logging +from omegaconf import OmegaConf +import shutil + +from latentsync.data.syncnet_dataset import SyncNetDataset +from latentsync.models.syncnet import SyncNet +from latentsync.models.syncnet_wav2lip import SyncNetWav2Lip +from latentsync.utils.util import gather_loss, plot_loss_chart +from accelerate.utils import set_seed + +import torch +from diffusers import AutoencoderKL +from diffusers.utils.logging import get_logger +from einops import rearrange +import torch.distributed as dist +from torch.nn.parallel import DistributedDataParallel as DDP +from torch.utils.data.distributed import DistributedSampler +from latentsync.utils.util import init_dist, cosine_loss + +logger = get_logger(__name__) + + +def main(config): + # Initialize distributed training + local_rank = init_dist() + global_rank = dist.get_rank() + num_processes = dist.get_world_size() + is_main_process = global_rank == 0 + + seed = config.run.seed + global_rank + set_seed(seed) + + # Logging folder + folder_name = "train" + datetime.datetime.now().strftime(f"-%Y_%m_%d-%H:%M:%S") + output_dir = os.path.join(config.data.train_output_dir, folder_name) + + # Make one log on every process with the configuration for debugging. + logging.basicConfig( + format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", + datefmt="%m/%d/%Y %H:%M:%S", + level=logging.INFO, + ) + + # Handle the output folder creation + if is_main_process: + os.makedirs(output_dir, exist_ok=True) + os.makedirs(f"{output_dir}/checkpoints", exist_ok=True) + os.makedirs(f"{output_dir}/loss_charts", exist_ok=True) + shutil.copy(config.config_path, output_dir) + + device = torch.device(local_rank) + + if config.data.latent_space: + vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16) + vae.requires_grad_(False) + vae.to(device) + else: + vae = None + + # Dataset and Dataloader setup + train_dataset = SyncNetDataset(config.data.train_data_dir, config.data.train_fileslist, config) + val_dataset = SyncNetDataset(config.data.val_data_dir, config.data.val_fileslist, config) + + train_distributed_sampler = DistributedSampler( + train_dataset, + num_replicas=num_processes, + rank=global_rank, + shuffle=True, + seed=config.run.seed, + ) + + # DataLoaders creation: + train_dataloader = torch.utils.data.DataLoader( + train_dataset, + batch_size=config.data.batch_size, + shuffle=False, + sampler=train_distributed_sampler, + num_workers=config.data.num_workers, + pin_memory=False, + drop_last=True, + worker_init_fn=train_dataset.worker_init_fn, + ) + + num_samples_limit = 640 + + val_batch_size = min( + num_samples_limit // config.data.num_frames, config.data.batch_size + ) # limit batch size to avoid CUDA OOM + + val_dataloader = torch.utils.data.DataLoader( + val_dataset, + batch_size=val_batch_size, + shuffle=False, + num_workers=config.data.num_workers, + pin_memory=False, + drop_last=False, + worker_init_fn=val_dataset.worker_init_fn, + ) + + # Model + syncnet = SyncNet(OmegaConf.to_container(config.model)).to(device) + # syncnet = SyncNetWav2Lip().to(device) + + optimizer = torch.optim.AdamW( + list(filter(lambda p: p.requires_grad, syncnet.parameters())), lr=config.optimizer.lr + ) + + if config.ckpt.resume_ckpt_path != "": + if is_main_process: + logger.info(f"Load checkpoint from: {config.ckpt.resume_ckpt_path}") + ckpt = torch.load(config.ckpt.resume_ckpt_path, map_location=device) + + syncnet.load_state_dict(ckpt["state_dict"]) + global_step = ckpt["global_step"] + train_step_list = ckpt["train_step_list"] + train_loss_list = ckpt["train_loss_list"] + val_step_list = ckpt["val_step_list"] + val_loss_list = ckpt["val_loss_list"] + else: + global_step = 0 + train_step_list = [] + train_loss_list = [] + val_step_list = [] + val_loss_list = [] + + # DDP wrapper + syncnet = DDP(syncnet, device_ids=[local_rank], output_device=local_rank) + + num_update_steps_per_epoch = math.ceil(len(train_dataloader)) + num_train_epochs = math.ceil(config.run.max_train_steps / num_update_steps_per_epoch) + # validation_steps = int(config.ckpt.save_ckpt_steps // 5) + # validation_steps = 100 + + if is_main_process: + logger.info("***** Running training *****") + logger.info(f" Num examples = {len(train_dataset)}") + logger.info(f" Num Epochs = {num_train_epochs}") + logger.info(f" Instantaneous batch size per device = {config.data.batch_size}") + logger.info(f" Total train batch size (w. parallel & distributed) = {config.data.batch_size * num_processes}") + logger.info(f" Total optimization steps = {config.run.max_train_steps}") + + first_epoch = global_step // num_update_steps_per_epoch + num_val_batches = config.data.num_val_samples // (num_processes * config.data.batch_size) + + # Only show the progress bar once on each machine. + progress_bar = tqdm( + range(0, config.run.max_train_steps), initial=global_step, desc="Steps", disable=not is_main_process + ) + + # Support mixed-precision training + scaler = torch.cuda.amp.GradScaler() if config.run.mixed_precision_training else None + + for epoch in range(first_epoch, num_train_epochs): + train_dataloader.sampler.set_epoch(epoch) + syncnet.train() + + for step, batch in enumerate(train_dataloader): + ### >>>> Training >>>> ### + + frames = batch["frames"].to(device, dtype=torch.float16) + audio_samples = batch["audio_samples"].to(device, dtype=torch.float16) + y = batch["y"].to(device, dtype=torch.float32) + + if config.data.latent_space: + max_batch_size = ( + num_samples_limit // config.data.num_frames + ) # due to the limited cuda memory, we split the input frames into parts + if frames.shape[0] > max_batch_size: + assert ( + frames.shape[0] % max_batch_size == 0 + ), f"max_batch_size {max_batch_size} should be divisible by batch_size {frames.shape[0]}" + frames_part_results = [] + for i in range(0, frames.shape[0], max_batch_size): + frames_part = frames[i : i + max_batch_size] + frames_part = rearrange(frames_part, "b f c h w -> (b f) c h w") + with torch.no_grad(): + frames_part = vae.encode(frames_part).latent_dist.sample() * 0.18215 + frames_part_results.append(frames_part) + frames = torch.cat(frames_part_results, dim=0) + else: + frames = rearrange(frames, "b f c h w -> (b f) c h w") + with torch.no_grad(): + frames = vae.encode(frames).latent_dist.sample() * 0.18215 + + frames = rearrange(frames, "(b f) c h w -> b (f c) h w", f=config.data.num_frames) + else: + frames = rearrange(frames, "b f c h w -> b (f c) h w") + + if config.data.lower_half: + height = frames.shape[2] + frames = frames[:, :, height // 2 :, :] + + # audio_embeds = wav2vec_encoder(audio_samples).last_hidden_state + + # Mixed-precision training + with torch.autocast(device_type="cuda", dtype=torch.float16, enabled=config.run.mixed_precision_training): + vision_embeds, audio_embeds = syncnet(frames, audio_samples) + + loss = cosine_loss(vision_embeds.float(), audio_embeds.float(), y).mean() + + optimizer.zero_grad() + + # Backpropagate + if config.run.mixed_precision_training: + scaler.scale(loss).backward() + """ >>> gradient clipping >>> """ + scaler.unscale_(optimizer) + torch.nn.utils.clip_grad_norm_(syncnet.parameters(), config.optimizer.max_grad_norm) + """ <<< gradient clipping <<< """ + scaler.step(optimizer) + scaler.update() + else: + loss.backward() + """ >>> gradient clipping >>> """ + torch.nn.utils.clip_grad_norm_(syncnet.parameters(), config.optimizer.max_grad_norm) + """ <<< gradient clipping <<< """ + optimizer.step() + + progress_bar.update(1) + global_step += 1 + + global_average_loss = gather_loss(loss, device) + train_step_list.append(global_step) + train_loss_list.append(global_average_loss) + + if is_main_process and global_step % config.run.validation_steps == 0: + logger.info(f"Validation at step {global_step}") + val_loss = validation( + val_dataloader, + device, + syncnet, + cosine_loss, + config.data.latent_space, + config.data.lower_half, + vae, + num_val_batches, + ) + val_step_list.append(global_step) + val_loss_list.append(val_loss) + logger.info(f"Validation loss at step {global_step} is {val_loss:0.3f}") + + if is_main_process and global_step % config.ckpt.save_ckpt_steps == 0: + checkpoint_save_path = os.path.join(output_dir, f"checkpoints/checkpoint-{global_step}.pt") + torch.save( + { + "state_dict": syncnet.module.state_dict(), # to unwrap DDP + "global_step": global_step, + "train_step_list": train_step_list, + "train_loss_list": train_loss_list, + "val_step_list": val_step_list, + "val_loss_list": val_loss_list, + }, + checkpoint_save_path, + ) + logger.info(f"Saved checkpoint to {checkpoint_save_path}") + plot_loss_chart( + os.path.join(output_dir, f"loss_charts/loss_chart-{global_step}.png"), + ("Train loss", train_step_list, train_loss_list), + ("Val loss", val_step_list, val_loss_list), + ) + + progress_bar.set_postfix({"step_loss": global_average_loss}) + if global_step >= config.run.max_train_steps: + break + + progress_bar.close() + dist.destroy_process_group() + + +@torch.no_grad() +def validation(val_dataloader, device, syncnet, cosine_loss, latent_space, lower_half, vae, num_val_batches): + syncnet.eval() + + losses = [] + val_step = 0 + while True: + for step, batch in enumerate(val_dataloader): + ### >>>> Validation >>>> ### + + frames = batch["frames"].to(device, dtype=torch.float16) + audio_samples = batch["audio_samples"].to(device, dtype=torch.float16) + y = batch["y"].to(device, dtype=torch.float32) + + if latent_space: + num_frames = frames.shape[1] + frames = rearrange(frames, "b f c h w -> (b f) c h w") + frames = vae.encode(frames).latent_dist.sample() * 0.18215 + frames = rearrange(frames, "(b f) c h w -> b (f c) h w", f=num_frames) + else: + frames = rearrange(frames, "b f c h w -> b (f c) h w") + + if lower_half: + height = frames.shape[2] + frames = frames[:, :, height // 2 :, :] + + with torch.autocast(device_type="cuda", dtype=torch.float16): + vision_embeds, audio_embeds = syncnet(frames, audio_samples) + + loss = cosine_loss(vision_embeds.float(), audio_embeds.float(), y).mean() + + losses.append(loss.item()) + + val_step += 1 + if val_step > num_val_batches: + syncnet.train() + if len(losses) == 0: + raise RuntimeError("No validation data") + return sum(losses) / len(losses) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Code to train the expert lip-sync discriminator") + parser.add_argument("--config_path", type=str, default="configs/syncnet/syncnet_16_vae.yaml") + args = parser.parse_args() + + # Load a configuration file + config = OmegaConf.load(args.config_path) + config.config_path = args.config_path + + main(config) diff --git a/scripts/train_unet.py b/scripts/train_unet.py new file mode 100644 index 0000000000000000000000000000000000000000..ee1a0a620700a0c19cc5bff4aada7f3276c0b5a8 --- /dev/null +++ b/scripts/train_unet.py @@ -0,0 +1,510 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import math +import argparse +import shutil +import datetime +import logging +from omegaconf import OmegaConf + +from tqdm.auto import tqdm +from einops import rearrange + +import torch +import torch.nn.functional as F +import torch.distributed as dist +from torch.utils.data.distributed import DistributedSampler +from torch.nn.parallel import DistributedDataParallel as DDP + +import diffusers +from diffusers import AutoencoderKL, DDIMScheduler +from diffusers.utils.logging import get_logger +from diffusers.optimization import get_scheduler +from diffusers.utils.import_utils import is_xformers_available +from accelerate.utils import set_seed + +from latentsync.data.unet_dataset import UNetDataset +from latentsync.models.unet import UNet3DConditionModel +from latentsync.models.syncnet import SyncNet +from latentsync.pipelines.lipsync_pipeline import LipsyncPipeline +from latentsync.utils.util import ( + init_dist, + cosine_loss, + reversed_forward, +) +from latentsync.utils.util import plot_loss_chart, gather_loss +from latentsync.whisper.audio2feature import Audio2Feature +from latentsync.trepa import TREPALoss +from eval.syncnet import SyncNetEval +from eval.syncnet_detect import SyncNetDetector +from eval.eval_sync_conf import syncnet_eval +import lpips + + +logger = get_logger(__name__) + + +def main(config): + # Initialize distributed training + local_rank = init_dist() + global_rank = dist.get_rank() + num_processes = dist.get_world_size() + is_main_process = global_rank == 0 + + seed = config.run.seed + global_rank + set_seed(seed) + + # Logging folder + folder_name = "train" + datetime.datetime.now().strftime(f"-%Y_%m_%d-%H:%M:%S") + output_dir = os.path.join(config.data.train_output_dir, folder_name) + + # Make one log on every process with the configuration for debugging. + logging.basicConfig( + format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", + datefmt="%m/%d/%Y %H:%M:%S", + level=logging.INFO, + ) + + # Handle the output folder creation + if is_main_process: + diffusers.utils.logging.set_verbosity_info() + os.makedirs(output_dir, exist_ok=True) + os.makedirs(f"{output_dir}/checkpoints", exist_ok=True) + os.makedirs(f"{output_dir}/val_videos", exist_ok=True) + os.makedirs(f"{output_dir}/loss_charts", exist_ok=True) + shutil.copy(config.unet_config_path, output_dir) + shutil.copy(config.data.syncnet_config_path, output_dir) + + device = torch.device(local_rank) + + noise_scheduler = DDIMScheduler.from_pretrained("configs") + + vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", torch_dtype=torch.float16) + vae.config.scaling_factor = 0.18215 + vae.config.shift_factor = 0 + vae_scale_factor = 2 ** (len(vae.config.block_out_channels) - 1) + vae.requires_grad_(False) + vae.to(device) + + syncnet_eval_model = SyncNetEval(device=device) + syncnet_eval_model.loadParameters("checkpoints/auxiliary/syncnet_v2.model") + + syncnet_detector = SyncNetDetector(device=device, detect_results_dir="detect_results") + + if config.model.cross_attention_dim == 768: + whisper_model_path = "checkpoints/whisper/small.pt" + elif config.model.cross_attention_dim == 384: + whisper_model_path = "checkpoints/whisper/tiny.pt" + else: + raise NotImplementedError("cross_attention_dim must be 768 or 384") + + audio_encoder = Audio2Feature( + model_path=whisper_model_path, + device=device, + audio_embeds_cache_dir=config.data.audio_embeds_cache_dir, + num_frames=config.data.num_frames, + ) + + unet, resume_global_step = UNet3DConditionModel.from_pretrained( + OmegaConf.to_container(config.model), + config.ckpt.resume_ckpt_path, # load checkpoint + device=device, + ) + + if config.model.add_audio_layer and config.run.use_syncnet: + syncnet_config = OmegaConf.load(config.data.syncnet_config_path) + if syncnet_config.ckpt.inference_ckpt_path == "": + raise ValueError("SyncNet path is not provided") + syncnet = SyncNet(OmegaConf.to_container(syncnet_config.model)).to(device=device, dtype=torch.float16) + syncnet_checkpoint = torch.load(syncnet_config.ckpt.inference_ckpt_path, map_location=device) + syncnet.load_state_dict(syncnet_checkpoint["state_dict"]) + syncnet.requires_grad_(False) + + unet.requires_grad_(True) + trainable_params = list(unet.parameters()) + + if config.optimizer.scale_lr: + config.optimizer.lr = config.optimizer.lr * num_processes + + optimizer = torch.optim.AdamW(trainable_params, lr=config.optimizer.lr) + + if is_main_process: + logger.info(f"trainable params number: {len(trainable_params)}") + logger.info(f"trainable params scale: {sum(p.numel() for p in trainable_params) / 1e6:.3f} M") + + # Enable xformers + if config.run.enable_xformers_memory_efficient_attention: + if is_xformers_available(): + unet.enable_xformers_memory_efficient_attention() + else: + raise ValueError("xformers is not available. Make sure it is installed correctly") + + # Enable gradient checkpointing + if config.run.enable_gradient_checkpointing: + unet.enable_gradient_checkpointing() + + # Get the training dataset + train_dataset = UNetDataset(config.data.train_data_dir, config) + distributed_sampler = DistributedSampler( + train_dataset, + num_replicas=num_processes, + rank=global_rank, + shuffle=True, + seed=config.run.seed, + ) + + # DataLoaders creation: + train_dataloader = torch.utils.data.DataLoader( + train_dataset, + batch_size=config.data.batch_size, + shuffle=False, + sampler=distributed_sampler, + num_workers=config.data.num_workers, + pin_memory=False, + drop_last=True, + worker_init_fn=train_dataset.worker_init_fn, + ) + + # Get the training iteration + if config.run.max_train_steps == -1: + assert config.run.max_train_epochs != -1 + config.run.max_train_steps = config.run.max_train_epochs * len(train_dataloader) + + # Scheduler + lr_scheduler = get_scheduler( + config.optimizer.lr_scheduler, + optimizer=optimizer, + num_warmup_steps=config.optimizer.lr_warmup_steps, + num_training_steps=config.run.max_train_steps, + ) + + if config.run.perceptual_loss_weight != 0 and config.run.pixel_space_supervise: + lpips_loss_func = lpips.LPIPS(net="vgg").to(device) + + if config.run.trepa_loss_weight != 0 and config.run.pixel_space_supervise: + trepa_loss_func = TREPALoss(device=device) + + # Validation pipeline + pipeline = LipsyncPipeline( + vae=vae, + audio_encoder=audio_encoder, + unet=unet, + scheduler=noise_scheduler, + ).to(device) + pipeline.set_progress_bar_config(disable=True) + + # DDP warpper + unet = DDP(unet, device_ids=[local_rank], output_device=local_rank) + + # We need to recalculate our total training steps as the size of the training dataloader may have changed. + num_update_steps_per_epoch = math.ceil(len(train_dataloader)) + # Afterwards we recalculate our number of training epochs + num_train_epochs = math.ceil(config.run.max_train_steps / num_update_steps_per_epoch) + + # Train! + total_batch_size = config.data.batch_size * num_processes + + if is_main_process: + logger.info("***** Running training *****") + logger.info(f" Num examples = {len(train_dataset)}") + logger.info(f" Num Epochs = {num_train_epochs}") + logger.info(f" Instantaneous batch size per device = {config.data.batch_size}") + logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") + logger.info(f" Total optimization steps = {config.run.max_train_steps}") + global_step = resume_global_step + first_epoch = resume_global_step // num_update_steps_per_epoch + + # Only show the progress bar once on each machine. + progress_bar = tqdm( + range(0, config.run.max_train_steps), + initial=resume_global_step, + desc="Steps", + disable=not is_main_process, + ) + + train_step_list = [] + sync_loss_list = [] + recon_loss_list = [] + + val_step_list = [] + sync_conf_list = [] + + # Support mixed-precision training + scaler = torch.cuda.amp.GradScaler() if config.run.mixed_precision_training else None + + for epoch in range(first_epoch, num_train_epochs): + train_dataloader.sampler.set_epoch(epoch) + unet.train() + + for step, batch in enumerate(train_dataloader): + ### >>>> Training >>>> ### + + if config.model.add_audio_layer: + if batch["mel"] != []: + mel = batch["mel"].to(device, dtype=torch.float16) + + audio_embeds_list = [] + try: + for idx in range(len(batch["video_path"])): + video_path = batch["video_path"][idx] + start_idx = batch["start_idx"][idx] + + with torch.no_grad(): + audio_feat = audio_encoder.audio2feat(video_path) + audio_embeds = audio_encoder.crop_overlap_audio_window(audio_feat, start_idx) + audio_embeds_list.append(audio_embeds) + except Exception as e: + logger.info(f"{type(e).__name__} - {e} - {video_path}") + continue + audio_embeds = torch.stack(audio_embeds_list) # (B, 16, 50, 384) + audio_embeds = audio_embeds.to(device, dtype=torch.float16) + else: + audio_embeds = None + + # Convert videos to latent space + gt_images = batch["gt"].to(device, dtype=torch.float16) + gt_masked_images = batch["masked_gt"].to(device, dtype=torch.float16) + mask = batch["mask"].to(device, dtype=torch.float16) + ref_images = batch["ref"].to(device, dtype=torch.float16) + + gt_images = rearrange(gt_images, "b f c h w -> (b f) c h w") + gt_masked_images = rearrange(gt_masked_images, "b f c h w -> (b f) c h w") + mask = rearrange(mask, "b f c h w -> (b f) c h w") + ref_images = rearrange(ref_images, "b f c h w -> (b f) c h w") + + with torch.no_grad(): + gt_latents = vae.encode(gt_images).latent_dist.sample() + gt_masked_images = vae.encode(gt_masked_images).latent_dist.sample() + ref_images = vae.encode(ref_images).latent_dist.sample() + + mask = torch.nn.functional.interpolate(mask, size=config.data.resolution // vae_scale_factor) + + gt_latents = ( + rearrange(gt_latents, "(b f) c h w -> b c f h w", f=config.data.num_frames) - vae.config.shift_factor + ) * vae.config.scaling_factor + gt_masked_images = ( + rearrange(gt_masked_images, "(b f) c h w -> b c f h w", f=config.data.num_frames) + - vae.config.shift_factor + ) * vae.config.scaling_factor + ref_images = ( + rearrange(ref_images, "(b f) c h w -> b c f h w", f=config.data.num_frames) - vae.config.shift_factor + ) * vae.config.scaling_factor + mask = rearrange(mask, "(b f) c h w -> b c f h w", f=config.data.num_frames) + + # Sample noise that we'll add to the latents + if config.run.use_mixed_noise: + # Refer to the paper: https://arxiv.org/abs/2305.10474 + noise_shared_std_dev = (config.run.mixed_noise_alpha**2 / (1 + config.run.mixed_noise_alpha**2)) ** 0.5 + noise_shared = torch.randn_like(gt_latents) * noise_shared_std_dev + noise_shared = noise_shared[:, :, 0:1].repeat(1, 1, config.data.num_frames, 1, 1) + + noise_ind_std_dev = (1 / (1 + config.run.mixed_noise_alpha**2)) ** 0.5 + noise_ind = torch.randn_like(gt_latents) * noise_ind_std_dev + noise = noise_ind + noise_shared + else: + noise = torch.randn_like(gt_latents) + noise = noise[:, :, 0:1].repeat( + 1, 1, config.data.num_frames, 1, 1 + ) # Using the same noise for all frames, refer to the paper: https://arxiv.org/abs/2308.09716 + + bsz = gt_latents.shape[0] + + # Sample a random timestep for each video + timesteps = torch.randint(0, noise_scheduler.config.num_train_timesteps, (bsz,), device=gt_latents.device) + timesteps = timesteps.long() + + # Add noise to the latents according to the noise magnitude at each timestep + # (this is the forward diffusion process) + noisy_tensor = noise_scheduler.add_noise(gt_latents, noise, timesteps) + + # Get the target for loss depending on the prediction type + if noise_scheduler.config.prediction_type == "epsilon": + target = noise + elif noise_scheduler.config.prediction_type == "v_prediction": + raise NotImplementedError + else: + raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}") + + unet_input = torch.cat([noisy_tensor, mask, gt_masked_images, ref_images], dim=1) + + # Predict the noise and compute loss + # Mixed-precision training + with torch.autocast(device_type="cuda", dtype=torch.float16, enabled=config.run.mixed_precision_training): + pred_noise = unet(unet_input, timesteps, encoder_hidden_states=audio_embeds).sample + + if config.run.recon_loss_weight != 0: + recon_loss = F.mse_loss(pred_noise.float(), target.float(), reduction="mean") + else: + recon_loss = 0 + + pred_latents = reversed_forward(noise_scheduler, pred_noise, timesteps, noisy_tensor) + + if config.run.pixel_space_supervise: + pred_images = vae.decode( + rearrange(pred_latents, "b c f h w -> (b f) c h w") / vae.config.scaling_factor + + vae.config.shift_factor + ).sample + + if config.run.perceptual_loss_weight != 0 and config.run.pixel_space_supervise: + pred_images_perceptual = pred_images[:, :, pred_images.shape[2] // 2 :, :] + gt_images_perceptual = gt_images[:, :, gt_images.shape[2] // 2 :, :] + lpips_loss = lpips_loss_func(pred_images_perceptual.float(), gt_images_perceptual.float()).mean() + else: + lpips_loss = 0 + + if config.run.trepa_loss_weight != 0 and config.run.pixel_space_supervise: + trepa_pred_images = rearrange(pred_images, "(b f) c h w -> b c f h w", f=config.data.num_frames) + trepa_gt_images = rearrange(gt_images, "(b f) c h w -> b c f h w", f=config.data.num_frames) + trepa_loss = trepa_loss_func(trepa_pred_images, trepa_gt_images) + else: + trepa_loss = 0 + + if config.model.add_audio_layer and config.run.use_syncnet: + if config.run.pixel_space_supervise: + syncnet_input = rearrange(pred_images, "(b f) c h w -> b (f c) h w", f=config.data.num_frames) + else: + syncnet_input = rearrange(pred_latents, "b c f h w -> b (f c) h w") + + if syncnet_config.data.lower_half: + height = syncnet_input.shape[2] + syncnet_input = syncnet_input[:, :, height // 2 :, :] + ones_tensor = torch.ones((config.data.batch_size, 1)).float().to(device=device) + vision_embeds, audio_embeds = syncnet(syncnet_input, mel) + sync_loss = cosine_loss(vision_embeds.float(), audio_embeds.float(), ones_tensor).mean() + sync_loss_list.append(gather_loss(sync_loss, device)) + else: + sync_loss = 0 + + loss = ( + recon_loss * config.run.recon_loss_weight + + sync_loss * config.run.sync_loss_weight + + lpips_loss * config.run.perceptual_loss_weight + + trepa_loss * config.run.trepa_loss_weight + ) + + train_step_list.append(global_step) + if config.run.recon_loss_weight != 0: + recon_loss_list.append(gather_loss(recon_loss, device)) + + optimizer.zero_grad() + + # Backpropagate + if config.run.mixed_precision_training: + scaler.scale(loss).backward() + """ >>> gradient clipping >>> """ + scaler.unscale_(optimizer) + torch.nn.utils.clip_grad_norm_(unet.parameters(), config.optimizer.max_grad_norm) + """ <<< gradient clipping <<< """ + scaler.step(optimizer) + scaler.update() + else: + loss.backward() + """ >>> gradient clipping >>> """ + torch.nn.utils.clip_grad_norm_(unet.parameters(), config.optimizer.max_grad_norm) + """ <<< gradient clipping <<< """ + optimizer.step() + + # Check the grad of attn blocks for debugging + # print(unet.module.up_blocks[3].attentions[2].transformer_blocks[0].audio_cross_attn.attn.to_q.weight.grad) + + lr_scheduler.step() + progress_bar.update(1) + global_step += 1 + + ### <<<< Training <<<< ### + + # Save checkpoint and conduct validation + if is_main_process and (global_step % config.ckpt.save_ckpt_steps == 0): + if config.run.recon_loss_weight != 0: + plot_loss_chart( + os.path.join(output_dir, f"loss_charts/recon_loss_chart-{global_step}.png"), + ("Reconstruction loss", train_step_list, recon_loss_list), + ) + if config.model.add_audio_layer: + if sync_loss_list != []: + plot_loss_chart( + os.path.join(output_dir, f"loss_charts/sync_loss_chart-{global_step}.png"), + ("Sync loss", train_step_list, sync_loss_list), + ) + model_save_path = os.path.join(output_dir, f"checkpoints/checkpoint-{global_step}.pt") + state_dict = { + "global_step": global_step, + "state_dict": unet.module.state_dict(), # to unwrap DDP + } + try: + torch.save(state_dict, model_save_path) + logger.info(f"Saved checkpoint to {model_save_path}") + except Exception as e: + logger.error(f"Error saving model: {e}") + + # Validation + logger.info("Running validation... ") + + validation_video_out_path = os.path.join(output_dir, f"val_videos/val_video_{global_step}.mp4") + validation_video_mask_path = os.path.join(output_dir, f"val_videos/val_video_mask.mp4") + + with torch.autocast(device_type="cuda", dtype=torch.float16): + pipeline( + config.data.val_video_path, + config.data.val_audio_path, + validation_video_out_path, + validation_video_mask_path, + num_frames=config.data.num_frames, + num_inference_steps=config.run.inference_steps, + guidance_scale=config.run.guidance_scale, + weight_dtype=torch.float16, + width=config.data.resolution, + height=config.data.resolution, + mask=config.data.mask, + ) + + logger.info(f"Saved validation video output to {validation_video_out_path}") + + val_step_list.append(global_step) + + if config.model.add_audio_layer: + try: + _, conf = syncnet_eval(syncnet_eval_model, syncnet_detector, validation_video_out_path, "temp") + except Exception as e: + logger.info(e) + conf = 0 + sync_conf_list.append(conf) + plot_loss_chart( + os.path.join(output_dir, f"loss_charts/sync_conf_chart-{global_step}.png"), + ("Sync confidence", val_step_list, sync_conf_list), + ) + + logs = {"step_loss": loss.item(), "lr": lr_scheduler.get_last_lr()[0]} + progress_bar.set_postfix(**logs) + + if global_step >= config.run.max_train_steps: + break + + progress_bar.close() + dist.destroy_process_group() + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + + # Config file path + parser.add_argument("--unet_config_path", type=str, default="configs/unet.yaml") + + args = parser.parse_args() + config = OmegaConf.load(args.unet_config_path) + config.unet_config_path = args.unet_config_path + + main(config) diff --git a/tools/count_videos_time.py b/tools/count_videos_time.py new file mode 100644 index 0000000000000000000000000000000000000000..cb37842d23c13810a8a2905d20672c289b353ffb --- /dev/null +++ b/tools/count_videos_time.py @@ -0,0 +1,45 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import matplotlib.pyplot as plt +from latentsync.utils.util import count_video_time, gather_video_paths_recursively +from tqdm import tqdm + + +def plot_histogram(data, fig_path): + # Create histogram + plt.hist(data, bins=30, edgecolor="black") + + # Add titles and labels + plt.title("Histogram of Data Distribution") + plt.xlabel("Video time") + plt.ylabel("Frequency") + + # Save plot as an image file + plt.savefig(fig_path) # Save as PNG file. You can also use 'histogram.jpg', 'histogram.pdf', etc. + + +def main(input_dir, fig_path): + video_paths = gather_video_paths_recursively(input_dir) + video_times = [] + for video_path in tqdm(video_paths): + video_times.append(count_video_time(video_path)) + plot_histogram(video_times, fig_path) + + +if __name__ == "__main__": + input_dir = "validation" + fig_path = "histogram.png" + + main(input_dir, fig_path) diff --git a/tools/download_youtube_videos.py b/tools/download_youtube_videos.py new file mode 100644 index 0000000000000000000000000000000000000000..7cf4529d23ce4e32db0d0ae8126dfca36b04386b --- /dev/null +++ b/tools/download_youtube_videos.py @@ -0,0 +1,113 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import subprocess +from concurrent.futures import ThreadPoolExecutor +import pandas as pd +from tqdm import tqdm + +""" +To use this python file, first install yt-dlp by: + +pip install yt-dlp==2024.5.27 +""" + + +def download_video(video_url, video_path): + get_video_channel_command = f"yt-dlp --print channel {video_url}" + result = subprocess.run(get_video_channel_command, shell=True, capture_output=True, text=True) + channel = result.stdout.strip() + if channel in unwanted_channels: + return + download_video_command = f"yt-dlp -f bestvideo+bestaudio --skip-unavailable-fragments --merge-output-format mp4 '{video_url}' --output '{video_path}' --external-downloader aria2c --external-downloader-args '-x 16 -k 1M'" + try: + subprocess.run(download_video_command, shell=True) # ignore_security_alert_wait_for_fix RCE + except KeyboardInterrupt: + print("Stopped") + exit() + except: + print(f"Error downloading video {video_url}") + + +def download_videos(num_workers, video_urls, video_paths): + with ThreadPoolExecutor(max_workers=num_workers) as executor: + executor.map(download_video, video_urls, video_paths) + + +def read_video_urls(csv_file_path: str, language_column, video_url_column): + video_urls = [] + print("Reading video urls...") + df = pd.read_csv(csv_file_path, sep=",") + for row in tqdm(df.itertuples(), total=len(df)): + language = getattr(row, language_column) + video_url = getattr(row, video_url_column) + if "clip" in video_url: + continue + video_urls.append((language, video_url)) + return video_urls + + +def extract_vid(video_url): + if "watch?v=" in video_url: # ignore_security_alert_wait_for_fix RCE + return video_url.split("watch?v=")[1][:11] + elif "shorts/" in video_url: + return video_url.split("shorts/")[1][:11] + elif "youtu.be/" in video_url: + return video_url.split("youtu.be/")[1][:11] + elif "&v=" in video_url: + return video_url.split("&v=")[1][:11] + else: + print(f"Invalid video url: {video_url}") + return None + + +def main(csv_file_path, language_column, video_url_column, output_dir, num_workers): + os.makedirs(output_dir, exist_ok=True) + all_video_urls = read_video_urls(csv_file_path, language_column, video_url_column) + + video_paths = [] + video_urls = [] + + print("Extracting vid...") + for language, video_url in tqdm(all_video_urls): + vid = extract_vid(video_url) + if vid is None: + continue + video_path = os.path.join(output_dir, language.lower(), f"vid_{vid}.mp4") + if os.path.isfile(video_path): + continue + os.makedirs(os.path.dirname(video_path), exist_ok=True) + video_paths.append(video_path) + video_urls.append(video_url) + + if len(video_paths) == 0: + print("All videos have been downloaded") + exit() + else: + print(f"Downloading {len(video_paths)} videos") + + download_videos(num_workers, video_urls, video_paths) + + +if __name__ == "__main__": + csv_file_path = "dcc.csv" + language_column = "video_language" + video_url_column = "video_link" + output_dir = "/mnt/bn/maliva-gen-ai-v2/chunyu.li/multilingual/raw" + num_workers = 50 + + unwanted_channels = ["TEDx Talks", "DaePyeong Mukbang", "Joeman"] + + main(csv_file_path, language_column, video_url_column, output_dir, num_workers) diff --git a/tools/move_files_recur.py b/tools/move_files_recur.py new file mode 100644 index 0000000000000000000000000000000000000000..a66ba361d46749879a126e6ff8efa7374a56c2ac --- /dev/null +++ b/tools/move_files_recur.py @@ -0,0 +1,48 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import shutil +from tqdm import tqdm + +paths = [] + + +def gather_paths(input_dir, output_dir): + os.makedirs(output_dir, exist_ok=True) + + for video in sorted(os.listdir(input_dir)): + if video.endswith(".mp4"): + video_input = os.path.join(input_dir, video) + video_output = os.path.join(output_dir, video) + if os.path.isfile(video_output): + continue + paths.append([video_input, output_dir]) + elif os.path.isdir(os.path.join(input_dir, video)): + gather_paths(os.path.join(input_dir, video), os.path.join(output_dir, video)) + + +def main(input_dir, output_dir): + print(f"Recursively gathering video paths of {input_dir} ...") + gather_paths(input_dir, output_dir) + + for video_input, output_dir in tqdm(paths): + shutil.move(video_input, output_dir) + + +if __name__ == "__main__": + input_dir = "/mnt/bn/maliva-gen-ai-v2/chunyu.li/multilingual_dcc" + output_dir = "/mnt/bn/maliva-gen-ai-v2/chunyu.li/multilingual" + + main(input_dir, output_dir) diff --git a/tools/occupy_gpu.py b/tools/occupy_gpu.py new file mode 100644 index 0000000000000000000000000000000000000000..f101012e87efce5ae2bef82b6ab4fb06aa3464cd --- /dev/null +++ b/tools/occupy_gpu.py @@ -0,0 +1,60 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +import os +import torch.multiprocessing as mp +import time + + +def check_mem(cuda_device): + devices_info = ( + os.popen('"/usr/bin/nvidia-smi" --query-gpu=memory.total,memory.used --format=csv,nounits,noheader') + .read() + .strip() + .split("\n") + ) + total, used = devices_info[int(cuda_device)].split(",") + return total, used + + +def loop(cuda_device): + cuda_i = torch.device(f"cuda:{cuda_device}") + total, used = check_mem(cuda_device) + total = int(total) + used = int(used) + max_mem = int(total * 0.9) + block_mem = max_mem - used + while True: + x = torch.rand(20, 512, 512, dtype=torch.float, device=cuda_i) + y = torch.rand(20, 512, 512, dtype=torch.float, device=cuda_i) + time.sleep(0.001) + x = torch.matmul(x, y) + + +def main(): + if torch.cuda.is_available(): + num_processes = torch.cuda.device_count() + processes = list() + for i in range(num_processes): + p = mp.Process(target=loop, args=(i,)) + p.start() + processes.append(p) + for p in processes: + p.join() + + +if __name__ == "__main__": + torch.multiprocessing.set_start_method("spawn") + main() diff --git a/tools/remove_outdated_files.py b/tools/remove_outdated_files.py new file mode 100644 index 0000000000000000000000000000000000000000..00b4a60864e736b09e20fa2ed7496d098395447c --- /dev/null +++ b/tools/remove_outdated_files.py @@ -0,0 +1,34 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os +import subprocess + + +def remove_outdated_files(input_dir, begin_date, end_date): + # Remove files from a specific time period + for subdir in os.listdir(input_dir): + if subdir >= begin_date and subdir <= end_date: + subdir_path = os.path.join(input_dir, subdir) + command = f"rm -rf {subdir_path}" + subprocess.run(command, shell=True) + print(f"Deleted: {subdir_path}") + + +if __name__ == "__main__": + input_dir = "/mnt/bn/video-datasets/output/syncnet" + begin_date = "train-2024_06_19-16:25:44" + end_date = "train-2024_08_03-07:39:58" + + remove_outdated_files(input_dir, begin_date, end_date) diff --git a/tools/write_fileslist.py b/tools/write_fileslist.py new file mode 100644 index 0000000000000000000000000000000000000000..2132a2468a0a0f28cc841a408546547524e99ee1 --- /dev/null +++ b/tools/write_fileslist.py @@ -0,0 +1,45 @@ +# Copyright (c) 2024 Bytedance Ltd. and/or its affiliates +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from tqdm import tqdm +from latentsync.utils.util import gather_video_paths_recursively + + +def write_fileslist(fileslist_path): + with open(fileslist_path, "w") as _: + pass + + +def append_fileslist(fileslist_path, video_paths): + with open(fileslist_path, "a") as f: + for video_path in tqdm(video_paths): + f.write(f"{video_path}\n") + + +def process_input_dir(fileslist_path, input_dir): + print(f"Processing input dir: {input_dir}") + video_paths = gather_video_paths_recursively(input_dir) + append_fileslist(fileslist_path, video_paths) + + +if __name__ == "__main__": + fileslist_path = "/mnt/bn/maliva-gen-ai-v2/chunyu.li/fileslist/all_data_v6.txt" + + write_fileslist(fileslist_path) + process_input_dir(fileslist_path, "/mnt/bn/maliva-gen-ai-v2/chunyu.li/VoxCeleb2/high_visual_quality/train") + process_input_dir(fileslist_path, "/mnt/bn/maliva-gen-ai-v2/chunyu.li/HDTF/high_visual_quality/train") + process_input_dir(fileslist_path, "/mnt/bn/maliva-gen-ai-v2/chunyu.li/avatars/high_visual_quality/train") + process_input_dir(fileslist_path, "/mnt/bn/maliva-gen-ai-v2/chunyu.li/multilingual/high_visual_quality") + process_input_dir(fileslist_path, "/mnt/bn/maliva-gen-ai-v2/chunyu.li/celebv_text/high_visual_quality/train") + process_input_dir(fileslist_path, "/mnt/bn/maliva-gen-ai-v2/chunyu.li/youtube/high_visual_quality")