feat: 增加LatentSync

LatentSync/eval/detectors/README.md

+# Face detector
+
+This face detector is adapted from `https://github.com/cs-giung/face-detection-pytorch`.

LatentSync/eval/detectors/__init__.py

+from .s3fd import S3FD
\ No newline at end of file

LatentSync/eval/detectors/s3fd/__init__.py

+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

LatentSync/eval/detectors/s3fd/box_utils.py

+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

LatentSync/eval/detectors/s3fd/nets.py

+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

LatentSync/eval/draw_syncnet_lines.py

+# 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)

LatentSync/eval/eval_fvd.py

+# 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)

LatentSync/eval/eval_sync_conf.py

+# 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()

LatentSync/eval/eval_sync_conf.sh

+#!/bin/bash
+python -m eval.eval_sync_conf --video_path "RD_Radio1_000_006_out.mp4"

LatentSync/eval/eval_syncnet_acc.py

+# 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)

LatentSync/eval/eval_syncnet_acc.sh

+#!/bin/bash
+
+python -m eval.eval_syncnet_acc --config_path "configs/syncnet/syncnet_16_pixel.yaml"

LatentSync/eval/fvd.py

+# 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()

LatentSync/eval/hyper_iqa.py

+# 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)

LatentSync/eval/inference_videos.py

+# 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)

LatentSync/eval/syncnet/__init__.py

+from .syncnet_eval import SyncNetEval

LatentSync/eval/syncnet/syncnet.py

+# 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

LatentSync/eval/syncnet/syncnet_eval.py

+# 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)

LatentSync/eval/syncnet_detect.py

+# 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

LatentSync/inference.sh

+#!/bin/bash
+
+python -m scripts.inference \
+    --unet_config_path "configs/unet/second_stage.yaml" \
+    --inference_ckpt_path "checkpoints/latentsync_unet.pt" \
+    --guidance_scale 1.0 \
+    --video_path "assets/demo1_video.mp4" \
+    --audio_path "assets/demo1_audio.wav" \
+    --video_out_path "video_out.mp4"

LatentSync/latentsync/data/syncnet_dataset.py

+# 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