v1.5

musetalk/utils/audio_processor.py

@@ -0,0 +1,99 @@
+import os
+import math
+import librosa
+import numpy as np
+import torch
+
+from einops import rearrange
+from transformers import AutoFeatureExtractor
+
+class AudioProcessor:
+    def __init__(self, feature_extractor_path="openai/whisper-tiny/"):
+        self.feature_extractor = AutoFeatureExtractor.from_pretrained(feature_extractor_path)
+        
+    def get_audio_feature(self, wav_path, start_index=0):
+        if not os.path.exists(wav_path):
+            return None
+        librosa_output, sampling_rate = librosa.load(wav_path, sr=16000)
+        assert sampling_rate == 16000
+        # Split audio into 30s segments
+        segment_length = 30 * sampling_rate
+        segments = [librosa_output[i:i + segment_length] for i in range(0, len(librosa_output), segment_length)]
+        
+        features = []
+        for segment in segments:
+            audio_feature = self.feature_extractor(
+                segment, 
+                return_tensors="pt",
+                sampling_rate=sampling_rate
+            ).input_features
+            features.append(audio_feature)
+
+        return features, len(librosa_output)
+    
+    def get_whisper_chunk(
+        self, 
+        whisper_input_features, 
+        device, 
+        weight_dtype, 
+        whisper, 
+        librosa_length,
+        fps=25,
+        audio_padding_length_left=2,
+        audio_padding_length_right=2,
+    ):
+        audio_feature_length_per_frame = 2 * (audio_padding_length_left + audio_padding_length_right + 1)
+        whisper_feature = []
+        # Process multiple 30s mel input features
+        for input_feature in whisper_input_features:
+            audio_feats = whisper.encoder(input_feature.to(device), output_hidden_states=True).hidden_states                
+            audio_feats = torch.stack(audio_feats, dim=2).to(weight_dtype)
+            whisper_feature.append(audio_feats)
+            
+        whisper_feature = torch.cat(whisper_feature, dim=1)
+        # Trim the last segment to remove padding
+        sr = 16000
+        audio_fps = 50
+        fps = int(fps)
+        whisper_idx_multiplier = audio_fps / fps
+        num_frames = math.floor((librosa_length / sr)) * fps
+        actual_length = math.floor((librosa_length / sr)) * audio_fps
+        whisper_feature = whisper_feature[:,:actual_length,...]
+        
+        # Calculate padding amount
+        padding_nums = math.floor(whisper_idx_multiplier)
+        # Add padding at start and end
+        whisper_feature = torch.cat([
+            torch.zeros_like(whisper_feature[:, :padding_nums * audio_padding_length_left]), 
+            whisper_feature, 
+            # Add extra padding to prevent out of bounds
+            torch.zeros_like(whisper_feature[:, :padding_nums * 3 * audio_padding_length_right])
+        ], 1)
+        
+        audio_prompts = []
+        for frame_index in range(num_frames):
+            try:
+                audio_index = math.floor(frame_index * whisper_idx_multiplier)
+                audio_clip = whisper_feature[:, audio_index: audio_index + audio_feature_length_per_frame]
+                assert audio_clip.shape[1] == audio_feature_length_per_frame
+                audio_prompts.append(audio_clip)
+            except Exception as e:
+                print(f"Error occurred: {e}")
+                print(f"whisper_feature.shape: {whisper_feature.shape}")
+                print(f"audio_clip.shape: {audio_clip.shape}")
+                print(f"num frames: {num_frames}, fps: {fps}, whisper_idx_multiplier: {whisper_idx_multiplier}")
+                print(f"frame_index: {frame_index}, audio_index: {audio_index}-{audio_index + audio_feature_length_per_frame}")
+                exit()
+                
+        audio_prompts = torch.cat(audio_prompts, dim=0)  # T, 10, 5, 384
+        audio_prompts = rearrange(audio_prompts, 'b c h w -> b (c h) w')
+        return audio_prompts
+
+if __name__ == "__main__":
+    audio_processor = AudioProcessor()
+    wav_path = "/cfs-workspace/users/gozhong/codes/musetalk_opensource2/data/audio/2.wav"
+    audio_feature, librosa_feature_length = audio_processor.get_audio_feature(wav_path)
+    print("Audio Feature shape:", audio_feature.shape)
+    print("librosa_feature_length:", librosa_feature_length)
+    
+    

musetalk/utils/blending.py

@@ -2,9 +2,6 @@ from PIL import Image
 import numpy as np
 import cv2
 import copy
-from face_parsing import FaceParsing
-
-fp = FaceParsing()
 
 def get_crop_box(box, expand):
     x, y, x1, y1 = box
@@ -14,46 +11,98 @@ def get_crop_box(box, expand):
     crop_box = [x_c-s, y_c-s, x_c+s, y_c+s]
     return crop_box, s
 
-def face_seg(image):
-    seg_image = fp(image)
+def face_seg(image, mode="jaw", fp=None):
+    """
+    对图像进行面部解析，生成面部区域的掩码。
+
+    Args:
+        image (PIL.Image): 输入图像。
+
+    Returns:
+        PIL.Image: 面部区域的掩码图像。
+    """
+    seg_image = fp(image, mode=mode)  # 使用 FaceParsing 模型解析面部
     if seg_image is None:
-        print("error, no person_segment")
+        print("error, no person_segment")  # 如果没有检测到面部，返回错误
         return None
 
-    seg_image = seg_image.resize(image.size)
+    seg_image = seg_image.resize(image.size)  # 将掩码图像调整为输入图像的大小
     return seg_image
 
-def get_image(image,face,face_box,upper_boundary_ratio = 0.5,expand=1.2):
-    #print(image.shape)
-    #print(face.shape)
+
+def get_image(image, face, face_box, upper_boundary_ratio=0.5, expand=1.5, mode="raw", fp=None):
+    """
+    将裁剪的面部图像粘贴回原始图像，并进行一些处理。
+
+    Args:
+        image (numpy.ndarray): 原始图像（身体部分）。
+        face (numpy.ndarray): 裁剪的面部图像。
+        face_box (tuple): 面部边界框的坐标 (x, y, x1, y1)。
+        upper_boundary_ratio (float): 用于控制面部区域的保留比例。
+        expand (float): 扩展因子，用于放大裁剪框。
+        mode: 融合mask构建方式 
+
+    Returns:
+        numpy.ndarray: 处理后的图像。
+    """
+    # 将 numpy 数组转换为 PIL 图像
+    body = Image.fromarray(image[:, :, ::-1])  # 身体部分图像(整张图)
+    face = Image.fromarray(face[:, :, ::-1])  # 面部图像
+
+    x, y, x1, y1 = face_box  # 获取面部边界框的坐标
+    crop_box, s = get_crop_box(face_box, expand)  # 计算扩展后的裁剪框
+    x_s, y_s, x_e, y_e = crop_box  # 裁剪框的坐标
+    face_position = (x, y)  # 面部在原始图像中的位置
+
+    # 从身体图像中裁剪出扩展后的面部区域（下巴到边界有距离）
+    face_large = body.crop(crop_box)
+        
+    ori_shape = face_large.size  # 裁剪后图像的原始尺寸
+
+    # 对裁剪后的面部区域进行面部解析，生成掩码
+    mask_image = face_seg(face_large, mode=mode, fp=fp)
     
+    mask_small = mask_image.crop((x - x_s, y - y_s, x1 - x_s, y1 - y_s))  # 裁剪出面部区域的掩码
+    
+    mask_image = Image.new('L', ori_shape, 0)  # 创建一个全黑的掩码图像
+    mask_image.paste(mask_small, (x - x_s, y - y_s, x1 - x_s, y1 - y_s))  # 将面部掩码粘贴到全黑图像上
+    
+    
+    # 保留面部区域的上半部分（用于控制说话区域）
+    width, height = mask_image.size
+    top_boundary = int(height * upper_boundary_ratio)  # 计算上半部分的边界
+    modified_mask_image = Image.new('L', ori_shape, 0)  # 创建一个新的全黑掩码图像
+    modified_mask_image.paste(mask_image.crop((0, top_boundary, width, height)), (0, top_boundary))  # 粘贴上半部分掩码
+    
+    
+    # 对掩码进行高斯模糊，使边缘更平滑
+    blur_kernel_size = int(0.05 * ori_shape[0] // 2 * 2) + 1  # 计算模糊核大小
+    mask_array = cv2.GaussianBlur(np.array(modified_mask_image), (blur_kernel_size, blur_kernel_size), 0)  # 高斯模糊
+    #mask_array = np.array(modified_mask_image)
+    mask_image = Image.fromarray(mask_array)  # 将模糊后的掩码转换回 PIL 图像
+    
+    # 将裁剪的面部图像粘贴回扩展后的面部区域
+    face_large.paste(face, (x - x_s, y - y_s, x1 - x_s, y1 - y_s))
+    
+    body.paste(face_large, crop_box[:2], mask_image)
+    
+    # 不用掩码，完全用infer
+    #face_large.save("debug/checkpoint_6_face_large.png")
+
+    body = np.array(body)  # 将 PIL 图像转换回 numpy 数组
+
+    return body[:, :, ::-1]  # 返回处理后的图像（BGR 转 RGB）
+
+def get_image_blending(image,face,face_box,mask_array,crop_box):
     body = Image.fromarray(image[:,:,::-1])
     face = Image.fromarray(face[:,:,::-1])
 
-    x, y, x1, y1 = face_box 
-    #print(x1-x,y1-y)
-    crop_box, s = get_crop_box(face_box, expand)
+    x, y, x1, y1 = face_box
     x_s, y_s, x_e, y_e = crop_box
-    face_position = (x, y)
-
     face_large = body.crop(crop_box)
-    ori_shape = face_large.size
 
-    mask_image = face_seg(face_large)
-    mask_small = mask_image.crop((x-x_s, y-y_s, x1-x_s, y1-y_s))
-    mask_image = Image.new('L', ori_shape, 0)
-    mask_image.paste(mask_small, (x-x_s, y-y_s, x1-x_s, y1-y_s))
-
-    # keep upper_boundary_ratio of talking area
-    width, height = mask_image.size
-    top_boundary = int(height * upper_boundary_ratio)
-    modified_mask_image = Image.new('L', ori_shape, 0)
-    modified_mask_image.paste(mask_image.crop((0, top_boundary, width, height)), (0, top_boundary))
-
-    blur_kernel_size = int(0.1 * ori_shape[0] // 2 * 2) + 1
-    mask_array = cv2.GaussianBlur(np.array(modified_mask_image), (blur_kernel_size, blur_kernel_size), 0)
     mask_image = Image.fromarray(mask_array)
-    
+    mask_image = mask_image.convert("L")
     face_large.paste(face, (x-x_s, y-y_s, x1-x_s, y1-y_s))
     body.paste(face_large, crop_box[:2], mask_image)
     body = np.array(body)
@@ -84,17 +133,3 @@ def get_image_prepare_material(image,face_box,upper_boundary_ratio = 0.5,expand=
     blur_kernel_size = int(0.1 * ori_shape[0] // 2 * 2) + 1
     mask_array = cv2.GaussianBlur(np.array(modified_mask_image), (blur_kernel_size, blur_kernel_size), 0)
     return mask_array,crop_box
-
-def get_image_blending(image,face,face_box,mask_array,crop_box):
-    body = image
-    x, y, x1, y1 = face_box
-    x_s, y_s, x_e, y_e = crop_box
-    face_large = copy.deepcopy(body[y_s:y_e, x_s:x_e])
-    face_large[y-y_s:y1-y_s, x-x_s:x1-x_s]=face
-
-    mask_image = cv2.cvtColor(mask_array,cv2.COLOR_BGR2GRAY)
-    mask_image = (mask_image/255).astype(np.float32)
-
-    body[y_s:y_e, x_s:x_e] = cv2.blendLinear(face_large,body[y_s:y_e, x_s:x_e],mask_image,1-mask_image)
-
-    return body

musetalk/utils/face_parsing/__init__.py

@@ -8,9 +8,53 @@ from .model import BiSeNet
 import torchvision.transforms as transforms
 
 class FaceParsing():
-    def __init__(self):
+    def __init__(self, left_cheek_width=80, right_cheek_width=80):
         self.net = self.model_init()
         self.preprocess = self.image_preprocess()
+        # Ensure all size parameters are integers
+        cone_height = 21
+        tail_height = 12
+        total_size = cone_height + tail_height
+        
+        # Create kernel with explicit integer dimensions
+        kernel = np.zeros((total_size, total_size), dtype=np.uint8)
+        center_x = total_size // 2  # Ensure center coordinates are integers
+        
+        # Cone part
+        for row in range(cone_height):
+            if row < cone_height//2:
+                continue
+            width = int(2 * (row - cone_height//2) + 1)
+            start = int(center_x - (width // 2))
+            end = int(center_x + (width // 2) + 1)
+            kernel[row, start:end] = 1
+
+        # Vertical extension part
+        if cone_height > 0:
+            base_width = int(kernel[cone_height-1].sum())
+        else:
+            base_width = 1
+        
+        for row in range(cone_height, total_size):
+            start = max(0, int(center_x - (base_width//2)))
+            end = min(total_size, int(center_x + (base_width//2) + 1))
+            kernel[row, start:end] = 1
+        self.kernel = kernel
+        
+        # Modify cheek erosion kernel to be flatter ellipse
+        self.cheek_kernel = cv2.getStructuringElement(
+            cv2.MORPH_ELLIPSE, (35, 3))
+        
+        # Add cheek area mask (protect chin area)
+        self.cheek_mask = self._create_cheek_mask(left_cheek_width=left_cheek_width, right_cheek_width=right_cheek_width)
+        
+    def _create_cheek_mask(self, left_cheek_width=80, right_cheek_width=80):
+        """Create cheek area mask (1/4 area on both sides)"""
+        mask = np.zeros((512, 512), dtype=np.uint8)
+        center = 512 // 2
+        cv2.rectangle(mask, (0, 0), (center - left_cheek_width, 512), 255, -1)    # Left cheek
+        cv2.rectangle(mask, (center + right_cheek_width, 0), (512, 512), 255, -1)  # Right cheek
+        return mask
 
     def model_init(self, 
                    resnet_path='./models/face-parse-bisent/resnet18-5c106cde.pth', 
@@ -30,7 +74,7 @@ class FaceParsing():
             transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
         ])
 
-    def __call__(self, image, size=(512, 512)):
+    def __call__(self, image, size=(512, 512), mode="jaw"):
         if isinstance(image, str):
             image = Image.open(image)
 
@@ -44,8 +88,25 @@ class FaceParsing():
                 img = torch.unsqueeze(img, 0)
             out = self.net(img)[0]
             parsing = out.squeeze(0).cpu().numpy().argmax(0)
-            parsing[np.where(parsing>13)] = 0
-            parsing[np.where(parsing>=1)] = 255
+            
+            # Add 14:neck, remove 10:nose and 7:8:9
+            if mode == "neck":
+                parsing[np.isin(parsing, [1, 11, 12, 13, 14])] = 255
+                parsing[np.where(parsing!=255)] = 0
+            elif mode == "jaw":
+                face_region = np.isin(parsing, [1])*255
+                face_region = face_region.astype(np.uint8)
+                original_dilated = cv2.dilate(face_region, self.kernel, iterations=1)
+                eroded = cv2.erode(original_dilated, self.cheek_kernel, iterations=2)
+                face_region = cv2.bitwise_and(eroded, self.cheek_mask)
+                face_region = cv2.bitwise_or(face_region, cv2.bitwise_and(original_dilated, ~self.cheek_mask))
+                parsing[(face_region==255) & (~np.isin(parsing, [10]))] = 255         
+                parsing[np.isin(parsing, [11, 12, 13])] = 255
+                parsing[np.where(parsing!=255)] = 0
+            else:
+                parsing[np.isin(parsing, [1, 11, 12, 13])] = 255
+                parsing[np.where(parsing!=255)] = 0
+
         parsing = Image.fromarray(parsing.astype(np.uint8))
         return parsing
 

musetalk/utils/utils.py

@@ -15,13 +15,24 @@ from musetalk.whisper.audio2feature import Audio2Feature
 from musetalk.models.vae import VAE
 from musetalk.models.unet import UNet,PositionalEncoding
 
-def load_all_model():
-    audio_processor = Audio2Feature(model_path="./models/whisper/tiny.pt")
-    vae = VAE(model_path = "./models/sd-vae-ft-mse/")
-    unet = UNet(unet_config="./models/musetalk/musetalk.json",
-                model_path ="./models/musetalk/pytorch_model.bin")
+
+def load_all_model(
+    unet_model_path="./models/musetalk/pytorch_model.bin",
+    vae_type="sd-vae-ft-mse",
+    unet_config="./models/musetalk/musetalk.json",
+    device=None,
+):
+    vae = VAE(
+        model_path = f"./models/{vae_type}/",
+    )
+    print(f"load unet model from {unet_model_path}")
+    unet = UNet(
+        unet_config=unet_config,
+        model_path=unet_model_path,
+        device=device
+    )
     pe = PositionalEncoding(d_model=384)
-    return audio_processor,vae,unet,pe
+    return vae, unet, pe
 
 def get_file_type(video_path):
     _, ext = os.path.splitext(video_path)
@@ -39,10 +50,13 @@ def get_video_fps(video_path):
     video.release()
     return fps
 
-def datagen(whisper_chunks,
-            vae_encode_latents,
-            batch_size=8,
-            delay_frame=0):
+def datagen(
+    whisper_chunks,
+    vae_encode_latents,
+    batch_size=8,
+    delay_frame=0,
+    device="cuda:0",
+):
     whisper_batch, latent_batch = [], []
     for i, w in enumerate(whisper_chunks):
         idx = (i+delay_frame)%len(vae_encode_latents)
@@ -51,14 +65,14 @@ def datagen(whisper_chunks,
         latent_batch.append(latent)
 
         if len(latent_batch) >= batch_size:
-            whisper_batch = np.stack(whisper_batch)
+            whisper_batch = torch.stack(whisper_batch)
             latent_batch = torch.cat(latent_batch, dim=0)
             yield whisper_batch, latent_batch
-            whisper_batch, latent_batch = [], []
+            whisper_batch, latent_batch  = [], []
 
     # the last batch may smaller than batch size
     if len(latent_batch) > 0:
-        whisper_batch = np.stack(whisper_batch)
+        whisper_batch = torch.stack(whisper_batch)
         latent_batch = torch.cat(latent_batch, dim=0)
 
-        yield whisper_batch, latent_batch
+        yield whisper_batch.to(device), latent_batch.to(device)