<enhance>: modified inference codes

1. Can set bbox_shift in configs/inference/test.yaml
2. Do not need to pip install whisper now

.gitignore

@@ -4,7 +4,7 @@
 .vscode/
 *.pyc
 .ipynb_checkpoints
-models/
+models
 results/
-data/audio/*.WAV
+data/audio/*.wav
 data/video/*.mp4

README.md

@@ -175,11 +175,6 @@ We recommend a python version >=3.10 and cuda version =11.7. Then build environm
 ```shell
 pip install -r requirements.txt
 ```
-### whisper
-install whisper to extract audio feature (only encoder)
-```
-pip install --editable ./musetalk/whisper
-```
 
 ### mmlab packages
 ```bash
@@ -256,13 +251,13 @@ As a complete solution to virtual human generation, you are suggested to first a
 
 # Note
 
-If you want to launch online video chats, you are suggested to generate videos using MuseV and apply necessary pre-processing such as face detection in advance. During online chatting, only UNet and the VAE decoder are involved, which makes MuseTalk real-time.
+If you want to launch online video chats, you are suggested to generate videos using MuseV and apply necessary pre-processing such as face detection and face parsing in advance. During online chatting, only UNet and the VAE decoder are involved, which makes MuseTalk real-time.
 
 
 # Acknowledgement
-1. We thank open-source components like [whisper](https://github.com/isaacOnline/whisper/tree/extract-embeddings), [dwpose](https://github.com/IDEA-Research/DWPose), [face-alignment](https://github.com/1adrianb/face-alignment), [face-parsing](https://github.com/zllrunning/face-parsing.PyTorch), [S3FD](https://github.com/yxlijun/S3FD.pytorch). 
-1. MuseTalk has referred much to [diffusers](https://github.com/huggingface/diffusers).
-1. MuseTalk has been built on `HDTF` datasets.
+1. We thank open-source components like [whisper](https://github.com/openai/whisper), [dwpose](https://github.com/IDEA-Research/DWPose), [face-alignment](https://github.com/1adrianb/face-alignment), [face-parsing](https://github.com/zllrunning/face-parsing.PyTorch), [S3FD](https://github.com/yxlijun/S3FD.pytorch). 
+1. MuseTalk has referred much to [diffusers](https://github.com/huggingface/diffusers) and [isaacOnline/whisper](https://github.com/isaacOnline/whisper/tree/extract-embeddings).
+1. MuseTalk has been built on [HDTF](https://github.com/MRzzm/HDTF) datasets.
 
 Thanks for open-sourcing!
 

configs/inference/test.yaml

@@ -1,9 +1,10 @@
 task_0:
- video_path: "data/video/monalisa.mp4"
- audio_path: "data/audio/monalisa.wav"
+ video_path: "data/video/yongen.mp4"
+ audio_path: "data/audio/yongen.wav"
 
 task_1:
  video_path: "data/video/sun.mp4"
  audio_path: "data/audio/sun.wav"
+ bbox_shift: -7
   
 

musetalk/utils/blending.py

@@ -52,7 +52,6 @@ def get_image(image,face,face_box,upper_boundary_ratio = 0.5,expand=1.2):
     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.save("./debug_mask.png")
     
     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)

musetalk/whisper/audio2feature.py

@@ -1,7 +1,5 @@
 import os
-#import whisper
-from whisper import load_model
-#import whisper.whispher as whiisper
+from .whisper import load_model
 import soundfile as sf
 import numpy as np
 import time
@@ -9,11 +7,12 @@ import sys
 sys.path.append("..")
 
 class Audio2Feature():
-    def __init__(self, whisper_model_type="tiny",model_path="./checkpoints/wisper_tiny.pt"):
+    def __init__(self, 
+                 whisper_model_type="tiny",
+                 model_path="./models/whisper/tiny.pt"):
         self.whisper_model_type = whisper_model_type
         self.model = load_model(model_path) #
 
-
     def get_sliced_feature(self,feature_array, vid_idx, audio_feat_length= [2,2],fps = 25):
         """
         Get sliced features based on a given index

musetalk/whisper/requirements.txt

@@ -1,6 +0,0 @@
-numpy
-torch
-tqdm
-more-itertools
-transformers>=4.19.0
-ffmpeg-python==0.2.0

musetalk/whisper/setup.py

@@ -1,24 +0,0 @@
-import os
-
-import pkg_resources
-from setuptools import setup, find_packages
-
-setup(
-    name="whisper",
-    py_modules=["whisper"],
-    version="1.0",
-    description="",
-    author="OpenAI",
-    packages=find_packages(exclude=["tests*"]),
-    install_requires=[
-        str(r)
-        for r in pkg_resources.parse_requirements(
-            open(os.path.join(os.path.dirname(__file__), "requirements.txt"))
-        )
-    ],
-    entry_points = {
-        'console_scripts': ['whisper=whisper.transcribe:cli'],
-    },
-    include_package_data=True,
-    extras_require={'dev': ['pytest']},
-)

musetalk/whisper/whisper.egg-info/PKG-INFO

@@ -1,5 +0,0 @@
-Metadata-Version: 2.1
-Name: whisper
-Version: 1.0
-Author: OpenAI
-Provides-Extra: dev

musetalk/whisper/whisper.egg-info/SOURCES.txt

@@ -1,18 +0,0 @@
-setup.py
-whisper/__init__.py
-whisper/__main__.py
-whisper/audio.py
-whisper/decoding.py
-whisper/model.py
-whisper/tokenizer.py
-whisper/transcribe.py
-whisper/utils.py
-whisper.egg-info/PKG-INFO
-whisper.egg-info/SOURCES.txt
-whisper.egg-info/dependency_links.txt
-whisper.egg-info/entry_points.txt
-whisper.egg-info/requires.txt
-whisper.egg-info/top_level.txt
-whisper/normalizers/__init__.py
-whisper/normalizers/basic.py
-whisper/normalizers/english.py

musetalk/whisper/whisper.egg-info/dependency_links.txt

@@ -1 +0,0 @@
-

musetalk/whisper/whisper.egg-info/entry_points.txt

@@ -1,2 +0,0 @@
-[console_scripts]
-whisper = whisper.transcribe:cli

musetalk/whisper/whisper.egg-info/requires.txt

@@ -1,9 +0,0 @@
-numpy
-torch
-tqdm
-more-itertools
-transformers>=4.19.0
-ffmpeg-python==0.2.0
-
-[dev]
-pytest

musetalk/whisper/whisper.egg-info/top_level.txt

@@ -1 +0,0 @@
-whisper

scripts/inference.py

@@ -13,6 +13,7 @@ from musetalk.utils.utils import get_file_type,get_video_fps,datagen
 from musetalk.utils.preprocessing import get_landmark_and_bbox,read_imgs,coord_placeholder
 from musetalk.utils.blending import get_image
 from musetalk.utils.utils import load_all_model
+import shutil
 
 # load model weights
 audio_processor,vae,unet,pe  = load_all_model()
@@ -26,6 +27,7 @@ def main(args):
     for task_id in inference_config:
         video_path = inference_config[task_id]["video_path"]
         audio_path = inference_config[task_id]["audio_path"]
+        bbox_shift = inference_config[task_id].get("bbox_shift", args.bbox_shift)
 
         input_basename = os.path.basename(video_path).split('.')[0]
         audio_basename  = os.path.basename(audio_path).split('.')[0]
@@ -42,7 +44,7 @@ def main(args):
         if get_file_type(video_path)=="video":
             save_dir_full = os.path.join(args.result_dir, input_basename)
             os.makedirs(save_dir_full,exist_ok = True)
-            cmd = f"ffmpeg -i {video_path} -start_number 0 {save_dir_full}/%08d.png"
+            cmd = f"ffmpeg -v fatal -i {video_path} -start_number 0 {save_dir_full}/%08d.png"
             os.system(cmd)
             input_img_list = sorted(glob.glob(os.path.join(save_dir_full, '*.[jpJP][pnPN]*[gG]')))
             fps = get_video_fps(video_path)
@@ -62,7 +64,7 @@ def main(args):
             frame_list = read_imgs(input_img_list)
         else:
             print("extracting landmarks...time consuming")
-            coord_list, frame_list = get_landmark_and_bbox(input_img_list,args.bbox_shift)
+            coord_list, frame_list = get_landmark_and_bbox(input_img_list, bbox_shift)
             with open(crop_coord_save_path, 'wb') as f:
                 pickle.dump(coord_list, f)
                 
@@ -117,24 +119,26 @@ def main(args):
         print(cmd_img2video)
         os.system(cmd_img2video)
         
-        cmd_combine_audio = f"ffmpeg -i {audio_path} -i temp.mp4 {output_vid_name} -y"
+        cmd_combine_audio = f"ffmpeg -y -v fatal -i {audio_path} -i temp.mp4 {output_vid_name}"
         print(cmd_combine_audio)
         os.system(cmd_combine_audio)
         
-        os.system("rm temp.mp4")
-        os.system(f"rm -rf {result_img_save_path}")
+        os.remove("temp.mp4")
+        shutil.rmtree(result_img_save_path)
         print(f"result is save to {output_vid_name}")
 
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
-    parser.add_argument("--inference_config",type=str, default="configs/inference/test_img.yaml")
-    parser.add_argument("--bbox_shift",type=int, default=0)
+    parser.add_argument("--inference_config", type=str, default="configs/inference/test_img.yaml")
+    parser.add_argument("--bbox_shift", type=int, default=0)
     parser.add_argument("--result_dir", default='./results', help="path to output")
 
-    parser.add_argument("--fps",type=int, default=25)
-    parser.add_argument("--batch_size",type=int, default=8)
-    parser.add_argument("--output_vid_name",type=str,default='')
-    parser.add_argument("--use_saved_coord",action="store_true", help='use saved coordinate to save time')
+    parser.add_argument("--fps", type=int, default=25)
+    parser.add_argument("--batch_size", type=int, default=8)
+    parser.add_argument("--output_vid_name", type=str,default='')
+    parser.add_argument("--use_saved_coord",
+                        action="store_true",
+                        help='use saved coordinate to save time')
 
 
     args = parser.parse_args()