diff --git "a/EmoTalk/EmoTalk\344\275\277\347\224\250\346\226\207\346\241\243.docx" "b/EmoTalk/EmoTalk\344\275\277\347\224\250\346\226\207\346\241\243.docx" new file mode 100644 index 00000000..57ea9d1e Binary files /dev/null and "b/EmoTalk/EmoTalk\344\275\277\347\224\250\346\226\207\346\241\243.docx" differ diff --git a/EmoTalk/README.md b/EmoTalk/README.md new file mode 100644 index 00000000..556cae16 --- /dev/null +++ b/EmoTalk/README.md @@ -0,0 +1,91 @@ +# EmoTalk大作业 + +原仓库链接：[psyai-net/EmoTalk_release: This is the official source for our ICCV 2023 paper "EmoTalk: Speech-Driven Emotional Disentanglement for 3D Face Animation" (github.com)](https://github.com/psyai-net/EmoTalk_release) + +封装后的可运行项目：https://pan.baidu.com/s/1ZO7TercF4GeucMflwcy9zw?pwd=jdkv + +### 本项目用于记录EmoTalk小组大作业的所有工作，包括以下内容： + +- **论文学习**：学习相关方法并进行记录。 + +- **部署环境**：对原仓库代码进行修改以适应不同环境。 + +- **测试指标**：包括定量测试老师提供的指标和定性测试论文的评价指标。 + +- **改进创新**：将原仓库的推理模型改为训练，成功复现论文的训练。 + + + +## 项目结构和组成 +```plaintext +实验报告.docx #二、实验报告，按照北理学报格式 +code/ # 我们的代码工作汇总 + ├── EmoTalk_win/ # 修改后的Windows下可运行项目 + ├── test/ # 测试老师测试集的LSE-C，LSE-D评价指标，以及定性评价论文提供的评价指标 + ├── train/ # 将原仓库的推理模型改为训练模型 +EmoTalk使用文档.docx #3.3、封装后的可运行项目配置文档 +``` + + +# 文件说明 + +## 1. 实验报告.docx +包括模型简介，实验困难及解决方案，模型的定性及定量评价结果，可能改进方法，组内评价 + +## 2. Code +我们的主要代码工作汇总，包括以下子模块： + +### 2.1 EmoTalk +修改后的可运行项目，Windows环境下使用。 + +**注意事项**： + +- 需要手动下载以下模型并放置到指定目录： + - **`wav2vec2-large-xlsr-53-english`** 和 **`wav2vec-english-speech-emotion-recognition`**，下载后存放至 `models/` 文件夹。 + - **`EmoTalk.pth`**，下载后存放至 `pretrain_model/` 文件夹。 +- 其他具体信息参考[环境部署教程](code/EmoTalk_win#readme)。 + +### 2.2 test +用于测试老师提供的测试集，计算LSE-C，LSE-D评价指标。另外定性评价了论文的几项指标。 + +### 2.3 train +将原仓库的推理模型修改为训练模型，复现论文训练功能。 + +## 3. EmoTalk使用文档.docx + +详细讲解了如何运行文件以生成视频和评价指标 + +1. **Dockers镜像下载** + +> 通过代码仓库或百度网盘下载docker镜像百度网盘https://pan.baidu.com/s/1ZO7TercF4GeucMflwcy9zw?pwd=jdkv + +2. **Docker镜像配置** + +> 下载后在终端加载docker镜像： +> +> ``` +> docker load \< /path/to/EmoTalk.tar +> ``` +> +> 加载完毕后，查看镜像是否已经导入：docker images + +3. **运行EmoTalk.py** + +> ``` +> docker run --gpus all -v :/app/videos -v :/app/result -it emotalk bash +> ``` +> +> 将输入视频和输出结果的路径挂载到容器上，注意一定要添加 \--gpus all +> +> 在容器中执行 +> +> ``` +> python EmoTalk.py \ +> ``` +> +> 运行成功，输出LSE-D和LESE-C +> +> 输出文件保存在输出路径 + + + diff --git a/EmoTalk/code/EmoTalk_win/Dockerfile b/EmoTalk/code/EmoTalk_win/Dockerfile new file mode 100644 index 00000000..dedbee31 --- /dev/null +++ b/EmoTalk/code/EmoTalk_win/Dockerfile @@ -0,0 +1,54 @@ +FROM nvidia/cudagl:11.3.1-devel-ubuntu20.04 +MAINTAINER "Jungwoo Choi" + +ARG DEBIAN_FRONTEND=noninteractive +ENV TZ=Asia/Seoul + +ADD requirements.txt /tmp/requirements.txt +RUN \ + # Fix CUDA apt error + rm -f /etc/apt/sources.list.d/cuda.list && \ + rm -f /etc/apt/sources.list.d/nvidia-ml.list && \ + apt-get update && apt-get install -y gnupg2 software-properties-common && \ + apt-key del 7fa2af80 && \ + apt-get update && apt-get install -y --no-install-recommends wget && \ + wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-keyring_1.0-1_all.deb && \ + dpkg -i cuda-keyring_1.0-1_all.deb && \ + # Install Start + apt update && \ + add-apt-repository -y ppa:savoury1/ffmpeg4 && \ + apt -y install python3.8 python3.8-distutils libgl1-mesa-glx libglib2.0-0 git wget zsh vim openssh-server curl ffmpeg && \ + # Python Library + update-alternatives --install /usr/bin/python python /usr/bin/python3.8 1 && \ + wget https://bootstrap.pypa.io/get-pip.py && \ + python3 get-pip.py && \ + pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113 && \ + pip install -r /tmp/requirements.txt && \ + # zsh option + chsh -s /bin/zsh && \ + sh -c "$(wget -O- https://raw.githubusercontent.com/ohmyzsh/ohmyzsh/master/tools/install.sh)" && \ + # add zsh-autosuggestions, zsh-syntax-highlighting plugin + git clone https://github.com/zsh-users/zsh-autosuggestions ~/.oh-my-zsh/custom/plugins/zsh-autosuggestions && \ + git clone https://github.com/zsh-users/zsh-syntax-highlighting.git ~/.oh-my-zsh/custom/plugins/zsh-syntax-highlighting && \ + # Modify .zshrc whth Perl + perl -pi -w -e 's/ZSH_THEME=.*/ZSH_THEME="af-magic"/g;' ~/.zshrc && \ + perl -pi -w -e 's/plugins=.*/plugins=(git ssh-agent zsh-autosuggestions zsh-syntax-highlighting)/g;' ~/.zshrc && \ + # Set ssh id and password, default is id = root, password = root. + # I recommand changing this for more security + # PermitRootLogin : yes - for ssh connection + echo 'root:root' |chpasswd && \ + sed -ri 's/^#?PermitRootLogin\s+.*/PermitRootLogin yes/' /etc/ssh/sshd_config && \ + sed -ri 's/UsePAM yes/#UsePAM yes/g' /etc/ssh/sshd_config && \ + mkdir /root/.ssh && \ + mkdir /var/run/sshd && \ + # install language pack for timeline issue. + apt-get install -y language-pack-en && update-locale && \ + # Clean up + apt-get clean && \ + apt-get autoclean && \ + apt-get autoremove -y && \ + rm -rf /var/lib/cache/* && \ + rm -rf /var/lib/log/* + +WORKDIR /workspace +CMD ["echo", "nvidia/cudagl:11.3.1-devel-ubuntu20.04 is ready!", 'zsh'] diff --git a/EmoTalk/code/EmoTalk_win/LICENSE b/EmoTalk/code/EmoTalk_win/LICENSE new file mode 100644 index 00000000..53339cf3 --- /dev/null +++ b/EmoTalk/code/EmoTalk_win/LICENSE @@ -0,0 +1,13 @@ +Copyright (c) 2023 Psyche AI Inc. + +This work is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). To view a copy of this license, visit http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. + +Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, and distribute the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: + +1. Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. + +2. NonCommercial — You may not use the material for commercial purposes. + +3. No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. diff --git a/EmoTalk/code/EmoTalk_win/audio/angry1.wav b/EmoTalk/code/EmoTalk_win/audio/angry1.wav new file mode 100644 index 00000000..72bd1800 Binary files /dev/null and b/EmoTalk/code/EmoTalk_win/audio/angry1.wav differ diff --git a/EmoTalk/code/EmoTalk_win/audio/angry2.wav b/EmoTalk/code/EmoTalk_win/audio/angry2.wav new file mode 100644 index 00000000..8d0f1305 Binary files /dev/null and b/EmoTalk/code/EmoTalk_win/audio/angry2.wav differ diff --git a/EmoTalk/code/EmoTalk_win/audio/disgust.wav b/EmoTalk/code/EmoTalk_win/audio/disgust.wav new file mode 100644 index 00000000..35dac6ab Binary files /dev/null and b/EmoTalk/code/EmoTalk_win/audio/disgust.wav differ diff --git a/EmoTalk/code/EmoTalk_win/audio/fearful.wav b/EmoTalk/code/EmoTalk_win/audio/fearful.wav new file mode 100644 index 00000000..7f5c0bc8 Binary files /dev/null and b/EmoTalk/code/EmoTalk_win/audio/fearful.wav differ diff --git a/EmoTalk/code/EmoTalk_win/audio/happy.wav b/EmoTalk/code/EmoTalk_win/audio/happy.wav new file mode 100644 index 00000000..a2eca39a Binary files /dev/null and b/EmoTalk/code/EmoTalk_win/audio/happy.wav differ diff --git a/EmoTalk/code/EmoTalk_win/audio/malaya.wav b/EmoTalk/code/EmoTalk_win/audio/malaya.wav new file mode 100644 index 00000000..d35ad0bd Binary files /dev/null and b/EmoTalk/code/EmoTalk_win/audio/malaya.wav differ diff --git a/EmoTalk/code/EmoTalk_win/audio/sad.wav b/EmoTalk/code/EmoTalk_win/audio/sad.wav new file mode 100644 index 00000000..d7e3f7df Binary files /dev/null and b/EmoTalk/code/EmoTalk_win/audio/sad.wav differ diff --git a/EmoTalk/code/EmoTalk_win/audio/ted1.wav b/EmoTalk/code/EmoTalk_win/audio/ted1.wav new file mode 100644 index 00000000..de05bbae Binary files /dev/null and b/EmoTalk/code/EmoTalk_win/audio/ted1.wav differ diff --git a/EmoTalk/code/EmoTalk_win/audio/ted2.wav b/EmoTalk/code/EmoTalk_win/audio/ted2.wav new file mode 100644 index 00000000..737f345f Binary files /dev/null and b/EmoTalk/code/EmoTalk_win/audio/ted2.wav differ diff --git a/EmoTalk/code/EmoTalk_win/blender.sh b/EmoTalk/code/EmoTalk_win/blender.sh new file mode 100644 index 00000000..cbfe6513 --- /dev/null +++ b/EmoTalk/code/EmoTalk_win/blender.sh @@ -0,0 +1,4 @@ +wget https://ftp.nluug.nl/pub/graphics/blender/release/Blender3.4/blender-3.4.1-linux-x64.tar.xz +tar -xf blender-3.4.1-linux-x64.tar.xz +mv blender-3.4.1-linux-x64 blender && rm blender-3.4.1-linux-x64.tar.xz + diff --git a/EmoTalk/code/EmoTalk_win/blender/README.md b/EmoTalk/code/EmoTalk_win/blender/README.md new file mode 100644 index 00000000..33c62ee5 --- /dev/null +++ b/EmoTalk/code/EmoTalk_win/blender/README.md @@ -0,0 +1,3 @@ +通过百度网盘分享的文件：blender.zip +链接：https://pan.baidu.com/s/1O2ZoDuh2IGq43ElTfEnESA?pwd=7ssp +提取码：7ssp \ No newline at end of file diff --git a/EmoTalk/code/EmoTalk_win/demo.py b/EmoTalk/code/EmoTalk_win/demo.py new file mode 100644 index 00000000..77438910 --- /dev/null +++ b/EmoTalk/code/EmoTalk_win/demo.py @@ -0,0 +1,138 @@ +import librosa +import numpy as np +import argparse +from scipy.signal import savgol_filter +import torch +from model import EmoTalk +import random +import os, subprocess +import shlex + + +@torch.no_grad() +def test(args): + result_path = args.result_path + os.makedirs(result_path, exist_ok=True) + eye1 = np.array([0.36537236, 0.950235724, 0.95593375, 0.916715622, 0.367256105, 0.119113259, 0.025357503]) + eye2 = np.array([0.234776169, 0.909951985, 0.944758058, 0.777862132, 0.191071674, 0.235437036, 0.089163929]) + eye3 = np.array([0.870040774, 0.949833691, 0.949418545, 0.695911646, 0.191071674, 0.072576277, 0.007108896]) + eye4 = np.array([0.000307991, 0.556701422, 0.952656746, 0.942345619, 0.425857186, 0.148335218, 0.017659493]) + model = EmoTalk(args) + model.load_state_dict(torch.load(args.model_path, map_location=torch.device(args.device)), strict=False) + model = model.to(args.device) + model.eval() + wav_path = args.wav_path + file_name = wav_path.split('/')[-1].split('.')[0] + speech_array, sampling_rate = librosa.load(os.path.join(wav_path), sr=16000) + audio = torch.FloatTensor(speech_array).unsqueeze(0).to(args.device) + level = torch.tensor([1]).to(args.device) + person = torch.tensor([0]).to(args.device) + prediction = model.predict(audio, level, person) + prediction = prediction.squeeze().detach().cpu().numpy() + if args.post_processing: + output = np.zeros((prediction.shape[0], prediction.shape[1])) + for i in range(prediction.shape[1]): + output[:, i] = savgol_filter(prediction[:, i], 5, 2) + output[:, 8] = 0 + output[:, 9] = 0 + i = random.randint(0, 60) + while i < output.shape[0] - 7: + eye_num = random.randint(1, 4) + if eye_num == 1: + output[i:i + 7, 8] = eye1 + output[i:i + 7, 9] = eye1 + elif eye_num == 2: + output[i:i + 7, 8] = eye2 + output[i:i + 7, 9] = eye2 + elif eye_num == 3: + output[i:i + 7, 8] = eye3 + output[i:i + 7, 9] = eye3 + else: + output[i:i + 7, 8] = eye4 + output[i:i + 7, 9] = eye4 + time1 = random.randint(60, 180) + i = i + time1 + np.save(os.path.join(result_path, "{}.npy".format(file_name)), output) # with postprocessing (smoothing and blinking) + else: + np.save(os.path.join(result_path, "{}.npy".format(file_name)), prediction) # without post-processing + + +def render_video(args): + wav_name = args.wav_path.split('/')[-1].split('.')[0] + image_path = os.path.join(args.result_path, wav_name) + os.makedirs(image_path, exist_ok=True) + image_temp = image_path + "/%d.png" + output_path = os.path.join(args.result_path, wav_name + ".mp4") + blender_path = args.blender_path + python_path = "./render.py" + blend_path = "./render.blend" + + current_file_path = os.path.abspath(__file__) + directory_of_current_file = os.path.dirname(current_file_path) + result_path = directory_of_current_file + args.result_path + + cmd = '{} -t 64 -b {} -P {} -- "{}" "{}" '.format(blender_path, blend_path, python_path, result_path, wav_name) + cmd = shlex.split(cmd) + p = subprocess.Popen(cmd, shell=False, stdout=subprocess.PIPE, stderr=subprocess.STDOUT) + while p.poll() is None: + line = p.stdout.readline() + line = line.strip() + if line: + print('[{}]'.format(line)) + if p.returncode == 0: + print('Subprogram success') + else: + print('Subprogram failed') + + # If the operating system is Windows + try: + ffmpeg_cmd = [ + 'ffmpeg', '-r', '30', '-i', image_temp, '-i', args.wav_path, + '-pix_fmt', 'yuv420p', '-s', '512x768', output_path, '-y' + ] + ffmpeg_cmd_str = ' '.join(ffmpeg_cmd) + subprocess.run(ffmpeg_cmd_str, check=True, shell=True) + + rm_cmd = ['rmdir', '/S', '/Q', f'"{image_path}"'] + rm_cmd_str = ' '.join(rm_cmd) + subprocess.run(rm_cmd_str, check=True, shell=True) + + except subprocess.CalledProcessError as e: + print(f"Command failed with return code {e.returncode}: {e.cmd}") + if e.output: + print(f"Output: {e.output}") + if e.stderr: + print(f"Error: {e.stderr}") + except Exception as e: + print(f"An unexpected error occurred: {e}") + + # cmd = 'ffmpeg -r 30 -i "{}" -i "{}" -pix_fmt yuv420p -s 512x768 "{}" -y'.format(image_temp, args.wav_path, output_path) + # subprocess.call(cmd, shell=True) + # + # cmd = 'rm -rf "{}"'.format(image_path) + # subprocess.call(cmd, shell=True) + + +def main(): + parser = argparse.ArgumentParser( + description='EmoTalk: Speech-driven Emotional Disentanglement for 3D Face Animation') + parser.add_argument("--wav_path", type=str, default="./audio/angry1.wav", help='path of the test data') + parser.add_argument("--bs_dim", type=int, default=52, help='number of blendshapes:52') + parser.add_argument("--feature_dim", type=int, default=832, help='number of feature dim') + parser.add_argument("--period", type=int, default=30, help='number of period') + parser.add_argument("--device", type=str, default="cuda", help='device') + parser.add_argument("--model_path", type=str, default="./pretrain_model/EmoTalk.pth", + help='path of the trained models') + parser.add_argument("--result_path", type=str, default="./result/", help='path of the result') + parser.add_argument("--max_seq_len", type=int, default=5000, help='max sequence length') + parser.add_argument("--num_workers", type=int, default=0) + parser.add_argument("--batch_size", type=int, default=1) + parser.add_argument("--post_processing", type=bool, default=True, help='whether to use post processing') + parser.add_argument("--blender_path", type=str, default="./blender/blender", help='path of blender') + args = parser.parse_args() + test(args) + render_video(args) + + +if __name__ == "__main__": + main() diff --git a/EmoTalk/code/EmoTalk_win/images/media/image1.png b/EmoTalk/code/EmoTalk_win/images/media/image1.png new file mode 100644 index 00000000..a56258a9 Binary files /dev/null and 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torch +import torch.nn as nn +import numpy as np +import math +from transformers import Wav2Vec2Processor, Wav2Vec2FeatureExtractor +from wav2vec import Wav2Vec2Model, Wav2Vec2ForSpeechClassification +from utils import init_biased_mask, enc_dec_mask + + +class EmoTalk(nn.Module): + def __init__(self, args): + super(EmoTalk, self).__init__() + self.feature_dim = args.feature_dim + self.bs_dim = args.bs_dim + self.device = args.device + self.batch_size = args.batch_size + self.audio_encoder_cont = Wav2Vec2Model.from_pretrained("models/wav2vec2-large-xlsr-53-english") + self.processor = Wav2Vec2Processor.from_pretrained("models/wav2vec2-large-xlsr-53-english") + self.audio_encoder_cont.feature_extractor._freeze_parameters() + self.audio_encoder_emo = Wav2Vec2ForSpeechClassification.from_pretrained( + "models/wav2vec-english-speech-emotion-recognition") + self.feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained( + "models/wav2vec-english-speech-emotion-recognition") + self.audio_encoder_emo.wav2vec2.feature_extractor._freeze_parameters() + self.max_seq_len = args.max_seq_len + self.audio_feature_map_cont = nn.Linear(1024, 512) + self.audio_feature_map_emo = nn.Linear(1024, 832) + self.audio_feature_map_emo2 = nn.Linear(832, 256) + self.relu = nn.ReLU() + self.biased_mask1 = init_biased_mask(n_head=4, max_seq_len=args.max_seq_len, period=args.period) + self.one_hot_level = np.eye(2) + self.obj_vector_level = nn.Linear(2, 32) + self.one_hot_person = np.eye(24) + self.obj_vector_person = nn.Linear(24, 32) + decoder_layer = nn.TransformerDecoderLayer(d_model=args.feature_dim, nhead=4, dim_feedforward=args.feature_dim, + batch_first=True) + self.transformer_decoder = nn.TransformerDecoder(decoder_layer, num_layers=1) + self.bs_map_r = nn.Linear(self.feature_dim, self.bs_dim) + nn.init.constant_(self.bs_map_r.weight, 0) + nn.init.constant_(self.bs_map_r.bias, 0) + + def forward(self, data): + frame_num11 = data["target11"].shape[1] + frame_num12 = data["target12"].shape[1] + inputs12 = self.processor(torch.squeeze(data["input12"]), sampling_rate=16000, return_tensors="pt", + padding="longest").input_values.to(self.device) + hidden_states_cont1 = self.audio_encoder_cont(inputs12, frame_num=frame_num11).last_hidden_state + hidden_states_cont12 = self.audio_encoder_cont(inputs12, frame_num=frame_num12).last_hidden_state + inputs21 = self.feature_extractor(torch.squeeze(data["input21"]), sampling_rate=16000, padding=True, + return_tensors="pt").input_values.to(self.device) + inputs12 = self.feature_extractor(torch.squeeze(data["input12"]), sampling_rate=16000, padding=True, + return_tensors="pt").input_values.to(self.device) + + output_emo1 = self.audio_encoder_emo(inputs21, frame_num=frame_num11) + output_emo2 = self.audio_encoder_emo(inputs12, frame_num=frame_num12) + + hidden_states_emo1 = output_emo1.hidden_states + hidden_states_emo2 = output_emo2.hidden_states + + label1 = output_emo1.logits + onehot_level = self.one_hot_level[data["level"]] + onehot_level = torch.from_numpy(onehot_level).to(self.device).float() + onehot_person = self.one_hot_person[data["person"]] + onehot_person = torch.from_numpy(onehot_person).to(self.device).float() + if data["target11"].shape[0] == 1: + obj_embedding_person = self.obj_vector_person(onehot_person).unsqueeze(0) + obj_embedding_level = self.obj_vector_level(onehot_level).unsqueeze(0) + else: + obj_embedding_level = self.obj_vector_level(onehot_level).unsqueeze(0).permute(1, 0, 2) + obj_embedding_person = self.obj_vector_person(onehot_person).unsqueeze(0).permute(1, 0, 2) + + obj_embedding_level11 = obj_embedding_level.repeat(1, frame_num11, 1) + obj_embedding_level12 = obj_embedding_level.repeat(1, frame_num12, 1) + obj_embedding_person11 = obj_embedding_person.repeat(1, frame_num11, 1) + obj_embedding_person12 = obj_embedding_person.repeat(1, frame_num12, 1) + hidden_states_cont1 = self.audio_feature_map_cont(hidden_states_cont1) + hidden_states_emo11_832 = self.audio_feature_map_emo(hidden_states_emo1) + hidden_states_emo11_256 = self.relu(self.audio_feature_map_emo2(hidden_states_emo11_832)) + + hidden_states11 = torch.cat( + [hidden_states_cont1, hidden_states_emo11_256, obj_embedding_level11, obj_embedding_person11], dim=2) + hidden_states_cont12 = self.audio_feature_map_cont(hidden_states_cont12) + hidden_states_emo12_832 = self.audio_feature_map_emo(hidden_states_emo2) + hidden_states_emo12_256 = self.relu(self.audio_feature_map_emo2(hidden_states_emo12_832)) + + hidden_states12 = torch.cat( + [hidden_states_cont12, hidden_states_emo12_256, obj_embedding_level12, obj_embedding_person12], dim=2) + if data["target11"].shape[0] == 1: + tgt_mask11 = self.biased_mask1[:, :hidden_states11.shape[1], :hidden_states11.shape[1]].clone().detach().to( + device=self.device) + tgt_mask22 = self.biased_mask1[:, :hidden_states12.shape[1], :hidden_states12.shape[1]].clone().detach().to( + device=self.device) + + memory_mask11 = enc_dec_mask(self.device, hidden_states11.shape[1], hidden_states11.shape[1]) + memory_mask12 = enc_dec_mask(self.device, hidden_states12.shape[1], hidden_states12.shape[1]) + bs_out11 = self.transformer_decoder(hidden_states11, hidden_states_emo11_832, tgt_mask=tgt_mask11, + memory_mask=memory_mask11) + bs_out12 = self.transformer_decoder(hidden_states12, hidden_states_emo12_832, tgt_mask=tgt_mask22, + memory_mask=memory_mask12) + bs_output11 = self.bs_map_r(bs_out11) + bs_output12 = self.bs_map_r(bs_out12) + + return bs_output11, bs_output12, label1 + + def predict(self, audio, level, person): + frame_num11 = math.ceil(audio.shape[1] / 16000 * 30) + inputs12 = self.processor(torch.squeeze(audio), sampling_rate=16000, return_tensors="pt", + padding="longest").input_values.to(self.device) + hidden_states_cont1 = self.audio_encoder_cont(inputs12, frame_num=frame_num11).last_hidden_state + inputs12 = self.feature_extractor(torch.squeeze(audio), sampling_rate=16000, padding=True, + return_tensors="pt").input_values.to(self.device) + output_emo1 = self.audio_encoder_emo(inputs12, frame_num=frame_num11) + hidden_states_emo1 = output_emo1.hidden_states + + onehot_level = self.one_hot_level[level] + onehot_level = torch.from_numpy(onehot_level).to(self.device).float() + onehot_person = self.one_hot_person[person] + onehot_person = torch.from_numpy(onehot_person).to(self.device).float() + if audio.shape[0] == 1: + obj_embedding_person = self.obj_vector_person(onehot_person).unsqueeze(0) + obj_embedding_level = self.obj_vector_level(onehot_level).unsqueeze(0) + else: + obj_embedding_level = self.obj_vector_level(onehot_level).unsqueeze(0).permute(1, 0, 2) + obj_embedding_person = self.obj_vector_person(onehot_person).unsqueeze(0).permute(1, 0, 2) + + obj_embedding_level11 = obj_embedding_level.repeat(1, frame_num11, 1) + obj_embedding_person11 = obj_embedding_person.repeat(1, frame_num11, 1) + hidden_states_cont1 = self.audio_feature_map_cont(hidden_states_cont1) + hidden_states_emo11_832 = self.audio_feature_map_emo(hidden_states_emo1) + hidden_states_emo11_256 = self.relu( + self.audio_feature_map_emo2(hidden_states_emo11_832)) + + hidden_states11 = torch.cat( + [hidden_states_cont1, hidden_states_emo11_256, obj_embedding_level11, obj_embedding_person11], dim=2) + if audio.shape[0] == 1: + tgt_mask11 = self.biased_mask1[:, :hidden_states11.shape[1], + :hidden_states11.shape[1]].clone().detach().to(device=self.device) + + memory_mask11 = enc_dec_mask(self.device, hidden_states11.shape[1], hidden_states11.shape[1]) + bs_out11 = self.transformer_decoder(hidden_states11, hidden_states_emo11_832, tgt_mask=tgt_mask11, + memory_mask=memory_mask11) + bs_output11 = self.bs_map_r(bs_out11) + + return bs_output11 diff --git a/EmoTalk/code/EmoTalk_win/models/README.md b/EmoTalk/code/EmoTalk_win/models/README.md new file mode 100644 index 00000000..14673c79 --- /dev/null +++ b/EmoTalk/code/EmoTalk_win/models/README.md @@ -0,0 +1,3 @@ +通过网盘分享的文件：models.zip +链接: https://pan.baidu.com/s/1gMMB5o_yIL-5Teiv1WgzMw?pwd=9aw4 提取码: 9aw4 +--来自百度网盘超级会员v5的分享 \ No newline at end of file diff --git a/EmoTalk/code/EmoTalk_win/pretrain_model/Put the pretrained model here b/EmoTalk/code/EmoTalk_win/pretrain_model/Put the pretrained model here new file mode 100644 index 00000000..e69de29b diff --git a/EmoTalk/code/EmoTalk_win/pretrain_model/README.md b/EmoTalk/code/EmoTalk_win/pretrain_model/README.md new file mode 100644 index 00000000..654d3aa2 --- /dev/null +++ b/EmoTalk/code/EmoTalk_win/pretrain_model/README.md @@ -0,0 +1,3 @@ +通过百度网盘分享的文件：EmoTalk.pth +链接：https://pan.baidu.com/s/1Dy4g3O2LlVgiqNpzRob8hg?pwd=fycx +提取码：fycx diff --git a/EmoTalk/code/EmoTalk_win/readme.md b/EmoTalk/code/EmoTalk_win/readme.md new file mode 100644 index 00000000..0acaa90b --- /dev/null +++ b/EmoTalk/code/EmoTalk_win/readme.md @@ -0,0 +1,123 @@ +此readme节选自实验报告的模型简介和困难解决部分 + +# **1 模型简介** + +## **1.1模型解决的问题** + +解开语音中的不同情绪，从而产生与之匹配的 3D 面部表情。 + +目前，基于学习的 3D面部动画因其可以产生高质量的动画效果和降低商业成本而备受欢迎，而EmoTalk项目提出的模型相比于当前主流研究，主要解决了3D面部动画中情绪的缺失问题。它不仅实现了各种情绪表达，而且超越了现有的最先进的方法。 + +以 Ji 等人的工作为基础，提出了一种改进的情感解缠编码器用于 3D面部动画生成。情绪解缠的过程是将情感与内容分开，这使神经网络能够学习更具体的特征，是语音处理中的关键挑战之一。 + +## **1.2模型使用的方法** + +提出端到端的神经网络，将其用于语音驱动的情感增强 3D面部动画，方法的具体实现可以参考这张图理解。 + +![](./images/media/image1.png){width="5.769444444444445in" +height="2.2305555555555556in"} + +图中左侧的Emotion Disentanling Encoder（情绪解缠器），可以使用深度学习网络提取输入语音信号的内容特征和情感特征，解缠也就是确保两种特征彼此独立。 + +EmoTalk模型以多种方式简化和增强了原始的解缠模块。情绪解缠部分的改进包括使用带有预训练的音频特征提取器 +wav2vec2.0，简化解开过程，以及使用端到端形式的模块直接输出面部动画所需的 52 个blendshape系数，让模型在训练过程中获得更好的约束。为了保证内容和情感之间的解开，模型还利用伪训练对将不同的情感和内容作为输入，经过处理生成交叉重构的blendshape +系数，并要求网络重建相应的真实样本作为输出。 + +图中中间的Emotion-guided feature fusiondecoder（情感引导特征融合器）使用基于 Transformer的特征融合模块，将内容和情感特征结合。 + +情感引导特征融合器可以使用音频中的情感信息将音频映射到 3D面部动画系数，旨在生成更具表现力的面部动画。Transformer结构允许全局特征的交互，有效提升融合精度和生成多样性。 + +采用一个由交叉重建损失、自我重建损失、速度损失和分类损失四部分组成的损失函数，来训练神经网络（对应图右侧：计算经过解缠和引导融合训练得到的blendshape系数和真实数据集blendshape之间的loss）。最终提取的blendshape系数可以驱动3D模型的面部运动，即通过控制面部关键点变化，生成对应的表情与嘴型。 + +## **1.3模型效果：** + +定性评价：生成的3D动画表现出更真实的情感变化。 + +定量评价：相较于现有方法，该方法在情感准确性、面部运动自然性等指标上表现优异。 + +用户研究：主观评价显示生成动画更接近人类期望。 + +# **2 实验困难及解决方案** + +## **2.1 无法连接hugging face。** + +解决方案: 从hugging face下载模型到本地（我们放在根目录的models文件夹下）离线使用，需要下载的模型如下： + + + + + +并修改model.py的EmoTalk类。 + +![](./images/media/image5.png){width="5.249990157480315in" +height="3.4580653980752407in"} + +图1：修改前model.py + +![](./images/media/image6.png){width="5.768055555555556in" +height="3.9375in"} + +图2：修改后报错消失，项目可以正确调用预训练模型 + +## **2.2 Render无法找到正确的相对路径，会将result的中间结果存放到C盘默认路径下。** + +![](./images/media/image7.png){width="3.361626202974628in" +height="0.8891754155730534in"} + +![](./images/media/image8.png){width="5.768055555555556in" +height="0.6569444444444444in"} + +解决方案：修改render.py的render_video函数确保中间结果的相对路径正确解析。 + +![](./images/media/image9.png){width="5.768055555555556in" +height="3.9in"} + +图3：修改前render.py + +![](./images/media/image10.png){width="5.768055555555556in" +height="3.4125in"} + +图:4：修改后 + +最终可以看到路径正确转移到了项目的result文件夹下。 + +![](./images/media/image11.png){width="5.768055555555556in" +height="2.877083333333333in"} + +## **2. 3 项目使用了Linux环境而我们使用了Windows环境，blender和ffmpeg的配置、执行代码需要修改。** + +首先需要下载Windows版本的blender并解压到根目录下，下载Windows版本的ffmpeg并修改环境变量。 + +然后修改render.py的render_video函数，确保ffmpeg可以在Windows下正确合成视频和删除目录。 + +![](./images/media/image12.png){width="5.768055555555556in" +height="4.201388888888889in"} + +图5：将原先的Linux专用代码修改为Windows下可运行代码 + +## **2.4 小结** + +![](./images/media/image13.png){width="2.7836034558180227in" +height="1.869028871391076in"} + +图6：最终项目结构 + +Emotalk的环境部署大体上还算顺利但也碰到了一些头痛的问题，很高兴我们能齐心协力解决，主要的步骤总结如下： + +1. 从github克隆代码并正确按照指令配置虚拟环境和安装库函数，其中需要手动下载的有hugging + face的wav2vec2-large-xlsr-53-english模型和wav2vec-english-speech-emotion-recognition模型（均存放在models子文件夹下），以及作者仓库提供的EmoTalk.pth预训练参数（存放在pretrain_model子文件夹下）。 + +2. 下载解压Windows版本的blender并解压到根目录（blender文件夹），下载Windows版本的ffmpeg并配置环境变量，将render.py中涉及视频合成等步骤的代码修改为Windows版本的。 + +3. 修改render.py中result的相对路径相关代码，确保项目在任意工作目录下都可以正确存储中间结果，保证代码的稳定执行。 + +最终示例代码运行成功截图： + + np.ndarray: + bsz, all_sz = shape + mask = np.full((bsz, all_sz), False) + + all_num_mask = int( + mask_prob * all_sz / float(mask_length) + + np.random.rand() + ) + all_num_mask = max(min_masks, all_num_mask) + mask_idcs = [] + padding_mask = attention_mask.ne(1) if attention_mask is not None else None + for i in range(bsz): + if padding_mask is not None: + sz = all_sz - padding_mask[i].long().sum().item() + num_mask = int( + mask_prob * sz / float(mask_length) + + np.random.rand() + ) + num_mask = max(min_masks, num_mask) + else: + sz = all_sz + num_mask = all_num_mask + + lengths = np.full(num_mask, mask_length) + + if sum(lengths) == 0: + lengths[0] = min(mask_length, sz - 1) + + min_len = min(lengths) + if sz - min_len <= num_mask: + min_len = sz - num_mask - 1 + + mask_idc = np.random.choice(sz - min_len, num_mask, replace=False) + mask_idc = np.asarray([mask_idc[j] + offset for j in range(len(mask_idc)) for offset in range(lengths[j])]) + mask_idcs.append(np.unique(mask_idc[mask_idc < sz])) + + min_len = min([len(m) for m in mask_idcs]) + for i, mask_idc in enumerate(mask_idcs): + if len(mask_idc) > min_len: + mask_idc = np.random.choice(mask_idc, min_len, replace=False) + mask[i, mask_idc] = True + return mask + + +# linear interpolation layer +def linear_interpolation(features, input_fps, output_fps, output_len=None): + features = features.transpose(1, 2) + seq_len = features.shape[2] / float(input_fps) + if output_len is None: + output_len = int(seq_len * output_fps) + output_features = F.interpolate(features, size=output_len, align_corners=True, mode='linear') + return output_features.transpose(1, 2) + + +class Wav2Vec2Model(Wav2Vec2Model): + def __init__(self, config): + super().__init__(config) + self.lm_head = nn.Linear(1024, 32) + + def forward( + self, + input_values, + attention_mask=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + frame_num=None + ): + self.config.output_attentions = True + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + hidden_states = self.feature_extractor(input_values) + hidden_states = hidden_states.transpose(1, 2) + + hidden_states = linear_interpolation(hidden_states, 50, 30, output_len=frame_num) + + if attention_mask is not None: + output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)) + attention_mask = torch.zeros( + hidden_states.shape[:2], dtype=hidden_states.dtype, device=hidden_states.device + ) + attention_mask[ + (torch.arange(attention_mask.shape[0], device=hidden_states.device), output_lengths - 1) + ] = 1 + attention_mask = attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool() + + hidden_states = self.feature_projection(hidden_states)[0] + + encoder_outputs = self.encoder( + hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = encoder_outputs[0] + if not return_dict: + return (hidden_states,) + encoder_outputs[1:] + + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +@dataclass +class SpeechClassifierOutput(ModelOutput): + loss: Optional[torch.FloatTensor] = None + logits: torch.FloatTensor = None + hidden_states: Optional[Tuple[torch.FloatTensor]] = None + attentions: Optional[Tuple[torch.FloatTensor]] = None + + +class Wav2Vec2ClassificationHead(nn.Module): + """Head for wav2vec classification task.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.dropout = nn.Dropout(config.final_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +class Wav2Vec2ForSpeechClassification(Wav2Vec2PreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.pooling_mode = config.pooling_mode + self.config = config + + self.wav2vec2 = Wav2Vec2Model(config) + self.classifier = Wav2Vec2ClassificationHead(config) + + self.init_weights() + + def freeze_feature_extractor(self): + self.wav2vec2.feature_extractor._freeze_parameters() + + def merged_strategy( + self, + hidden_states, + mode="mean" + ): + if mode == "mean": + outputs = torch.mean(hidden_states, dim=1) + elif mode == "sum": + outputs = torch.sum(hidden_states, dim=1) + elif mode == "max": + outputs = torch.max(hidden_states, dim=1)[0] + else: + raise Exception( + "The pooling method hasn't been defined! Your pooling mode must be one of these ['mean', 'sum', 'max']") + + return outputs + + def forward( + self, + input_values, + attention_mask=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + labels=None, + frame_num=None, + ): + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + outputs = self.wav2vec2( + input_values, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = outputs[0] + hidden_states1 = linear_interpolation(hidden_states, 50, 30, output_len=frame_num) + hidden_states = self.merged_strategy(hidden_states1, mode=self.pooling_mode) + logits = self.classifier(hidden_states) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SpeechClassifierOutput( + loss=loss, + logits=logits, + hidden_states=hidden_states1, + attentions=outputs.attentions, + ) diff --git a/EmoTalk/code/README.md b/EmoTalk/code/README.md new file mode 100644 index 00000000..03be9330 --- /dev/null +++ b/EmoTalk/code/README.md @@ -0,0 +1,5 @@ +**EmoTalk_win**是经过环境配置后的Windows下可运行项目，无法上传的大模型和文件可使用文件夹下提供的百度网盘链接下载 + +**test**包括测试老师提供的测试集，计算LSE-C，LSE-D评价指标，以及定性评价论文的几项指标。 + +**train**包括我们重新复现论文训练的代码、已完成的文档，会尽快发到助教老师邮箱 diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/65.xlsx" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/65.xlsx" new file mode 100644 index 00000000..02335a5b Binary files /dev/null and "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/65.xlsx" differ diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/65_distance.txt" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/65_distance.txt" new file mode 100644 index 00000000..1e9cd567 --- /dev/null +++ "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/65_distance.txt" @@ -0,0 +1,390 @@ +8.640448688140487832e+00 +8.376425943243383188e+00 +8.705282766788899451e+00 +8.499068231928541550e+00 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+-1.909956634011058529e+00,2.039023164807436928e+01,4.925083091030375471e+01 +-1.806707718400530460e+00,1.947617450750200163e+01,4.723273302010217378e+01 diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/cal.py" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/cal.py" new file mode 100644 index 00000000..90b55031 --- /dev/null +++ "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/cal.py" @@ -0,0 +1,31 @@ +import numpy as np +import pandas as pd + +# 设置文件路径 +npy_file_path = './anno.npy' # .npy 文件路径 +excel_file_path = './67.xlsx' # Excel 文件路径 + +# 读取 .npy 文件 +data_npy = np.load(npy_file_path) + +# 读取 Excel 文件，确保 header=None，因为没有标题行 +data_excel = pd.read_excel(excel_file_path, header=None) # 不使用标题行 + +# 将 Excel 数据转换为 NumPy 数组 +data_excel_np = data_excel.to_numpy() + +# 确保矩阵的维度是正确的 +assert data_npy.shape == (390, 52), "npy 文件的形状应该是 (390, 52)" +assert data_excel_np.shape == (52, 3), "Excel 文件应该有 52 行和 3 列" + +# 矩阵乘法 +result = np.dot(data_npy, data_excel_np) + +# 输出结果 +print("Result of the matrix multiplication (shape: {0}):".format(result.shape)) +print(result) + +# 如果需要将结果保存到文件中 +output_file_path = './anno67.npy' +np.save(output_file_path, result) # 保存结果为 .npy 文件 +print(f"Result saved to {output_file_path}") diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/cal2.py" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/cal2.py" new file mode 100644 index 00000000..6b24e056 --- /dev/null +++ "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/cal2.py" @@ -0,0 +1,22 @@ +import numpy as np + +# 定义文件名 +differences_file = 'differences_test.npy' + +# 加载 .npy 文件 +differences_data = np.load(differences_file) + +# 确保数据的列数正确 +if differences_data.shape[1] < 3: + raise ValueError("The data must have at least three columns.") + +# 计算每行的距离（第一个和第二个元素的平方和的平方根） +distances = np.sqrt(differences_data[:, 0]**2 + differences_data[:, 1]**2) + +# 输出为 .npy 文件 +np.save('calculated_distances.npy', distances) + +# 输出为 .txt 文件 +np.savetxt('calculated_distances.txt', distances, delimiter=',', fmt='%.18e') + +print("Distances calculated and saved to calculated_distances.npy and calculated_distances.txt") diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/cal_dis.py" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/cal_dis.py" new file mode 100644 index 00000000..14637e72 --- /dev/null +++ "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/cal_dis.py" @@ -0,0 +1,39 @@ +import numpy as np + +# 定义文件名 +annotation_file = 'test65.npy' +test_file = 'test67.npy' + +# 加载 .npy 文件 +annotation_data = np.load(annotation_file) +test_data = np.load(test_file) + +# 确保数据有足够的行 +if annotation_data.shape[0] < 390 or test_data.shape[0] < 390: + raise ValueError("Both files must have at least 390 rows.") + +# 取前 153 行 +annotation_subset = annotation_data[:390] +test_subset = test_data[:390] + +# 提取 x 和 z 坐标 (假设 x 在第 0 列, z 在第 1 列) +# x_coords_annotation = annotation_subset[:, 0] +# y_coords_annotation = annotation_subset[:, 1] +# x_coords_test = test_subset[:, 0] +# y_coords_test = test_subset[:, 1] + +# 计算投影距离 (只考虑 x 和 z 坐标) +# 距离计算公式: distance = sqrt((x2 - x1)^2 + (y2 - y1)^2) +# distances = np.sqrt((x_coords_test - x_coords_annotation) ** 2 + (y_coords_test - y_coords_annotation) ** 2) + +y_coords_annotation = annotation_subset[:, 1] +y_coords_test = test_subset[:, 1] +distances = np.sqrt((y_coords_test - y_coords_annotation) ** 2) +# 输出为 .npy 文件 +np.save('mouth_test.npy', distances) + +# 输出为 .txt 文件 +np.savetxt('mouth_test.txt', distances, delimiter=',', fmt='%.18e') + +print("Projected distance calculations saved to projected_distances.npy and projected_distances.txt") + diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/diff.py" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/diff.py" new file mode 100644 index 00000000..33d6d309 --- /dev/null +++ "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/diff.py" @@ -0,0 +1,25 @@ +import numpy as np + +# 定义文件名 +test_file_1 = 'test65.npy' +test_file_2 = 'test67.npy' + +# 加载 .npy 文件 +test_data_1 = np.load(test_file_1) +test_data_2 = np.load(test_file_2) + +# 确保两个数据的行数相同 +if test_data_1.shape[0] != test_data_2.shape[0]: + raise ValueError("Both files must have the same number of rows.") + +# 计算每一行的差异 +# 假设 test_data_1 和 test_data_2 都是 n 行 3 列的数组 +differences = test_data_1 - test_data_2 # 每一元素相减 + +# 输出为 .npy 文件 +np.save('differences_test.npy', differences) + +# 输出为 .txt 文件 +np.savetxt('differences_test.txt', differences, delimiter=',', fmt='%.18e') + +print("Differences calculated and saved to differences.npy and differences.txt") diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/differences_anno.npy" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/differences_anno.npy" new file mode 100644 index 00000000..98515eea Binary files /dev/null and "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/differences_anno.npy" differ diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/differences_anno.txt" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/differences_anno.txt" new file mode 100644 index 00000000..4084677f --- /dev/null +++ "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/differences_anno.txt" @@ -0,0 +1,390 @@ +5.606353423484708998e-01,-1.022914622551337871e+01,-1.825086089685072466e+00 +5.008810265153179619e-01,-1.022957776431666588e+01,-1.806198024878170827e+00 +6.523118669508233269e-01,-1.067509662111333668e+01,-1.887058719803178519e+00 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"b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/distance_67.npy" new file mode 100644 index 00000000..be49efbe Binary files /dev/null and "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/distance_67.npy" differ diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/distance_67.txt" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/distance_67.txt" new file mode 100644 index 00000000..e83b0d04 --- /dev/null +++ "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/distance_67.txt" @@ -0,0 +1,390 @@ +1.240563951304702961e+01 +1.176782762558256756e+01 +1.234012138523667623e+01 +1.234780363146143145e+01 +1.129618782546451961e+01 +9.152248878747650807e+00 +7.348602188584317574e+00 +6.974047866519547334e+00 +6.919266735179363970e+00 +5.963507117958882553e+00 +4.806023280143514320e+00 +3.893945316081054209e+00 +4.031650174667862352e+00 +3.300562630178638823e+00 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"b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/mean.py" new file mode 100644 index 00000000..a15cbbf9 --- /dev/null +++ "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/mean.py" @@ -0,0 +1,17 @@ +import numpy as np + +# 定义距离文件名 +distances_file = 'distance_67.npy' + +# 加载距离数据 +distances = np.load(distances_file) + +# 确保距离数据的长度为 98 +if distances.shape[0] != 390: + raise ValueError("The distance data must contain exactly 390 entries.") + +# 计算平均值 +average_distance = np.mean(distances) + +# 输出平均值 +print("Average projected distance of the first 390 entries:", average_distance) \ No newline at end of file diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/mouth_anno.npy" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/mouth_anno.npy" new file mode 100644 index 00000000..f5a54d39 Binary files /dev/null and "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/mouth_anno.npy" differ diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/mouth_anno.txt" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/mouth_anno.txt" new file mode 100644 index 00000000..20bfa67d --- /dev/null +++ "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/mouth_anno.txt" @@ -0,0 +1,390 @@ +1.022914622551337871e+01 +1.022957776431666588e+01 +1.067509662111333668e+01 +1.092974045857008036e+01 +1.067266875200683529e+01 +9.844003683933117088e+00 +8.978876299273643724e+00 +8.708092131295167349e+00 +8.716415166451596974e+00 +8.436472650679075258e+00 +8.409444624322155448e+00 +8.769020418875857814e+00 +9.219439711886327160e+00 +9.234101848504714383e+00 +8.451664407407371016e+00 +7.978871621290338112e+00 +8.162875444300164318e+00 +8.545187346189081268e+00 +8.586828888690334338e+00 +8.056393458442796529e+00 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+1.031000541918860236e+01 +1.072735514258681278e+01 +1.100893387506766175e+01 +1.125143543282148428e+01 +1.153435809379620025e+01 +1.285258248335644993e+01 +1.473198216216239587e+01 +1.478705278298955861e+01 +1.458631744268285502e+01 +1.281167705355996489e+01 +1.191483175009185658e+01 +1.151944672030958117e+01 +1.129132970444202400e+01 +1.091311814762376642e+01 +1.027140048508946180e+01 +9.582141604416477776e+00 +8.949240027921625540e+00 +8.260499463565565748e+00 +7.714920312665558555e+00 +7.687884868823324780e+00 +8.190821880061019300e+00 +8.637018478810624345e+00 +8.720871976729515751e+00 +8.597758881031682066e+00 +8.478110311980561420e+00 +8.324368078645935753e+00 +7.976412961568438931e+00 +7.650262094544828528e+00 +7.289920260844759525e+00 diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/npyread.py" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/npyread.py" new file mode 100644 index 00000000..9ea0fbe0 --- /dev/null +++ "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/npyread.py" @@ -0,0 +1,18 @@ +import numpy as np +import os + +# 定义要转换的 .npy 文件名 +npy_files = ['anno65.npy', 'anno67.npy', 'test65.npy', 'test67.npy'] + +# 遍历每个 .npy 文件并进行转换 +for npy_file in npy_files: + # 加载 .npy 文件 + data = np.load(npy_file) + + # 生成对应的 txt 文件名 + txt_file = os.path.splitext(npy_file)[0] + '.txt' + + # 将数据保存为 .txt 文件 + np.savetxt(txt_file, data, delimiter=',', fmt='%.18e') # 使用科学计数法格式输出 + + print(f"Converted {npy_file} to {txt_file}") diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/predicted.py" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/predicted.py" new file mode 100644 index 00000000..2aabdf34 --- /dev/null +++ "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/predicted.py" @@ -0,0 +1,18 @@ +import numpy as np +from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score + +# 加载预测值和实际值 +predictions = np.load('selected_mouth_test.npy') # 预测值 +actuals = np.load('selected_mouth_anno.npy') # 实际值 + +# 计算指标 +mae = mean_absolute_error(actuals, predictions) # 平均绝对误差 +mse = mean_squared_error(actuals, predictions) # 均方误差 +rmse = np.sqrt(mse) # 均方根误差 +r2 = r2_score(actuals, predictions) # R^2决定系数 + +# 输出结果 +print(f'Mean Absolute Error (MAE): {mae}') +print(f'Mean Squared Error (MSE): {mse}') +print(f'Root Mean Squared Error (RMSE): {rmse}') +print(f'R^2 Score: {r2}') diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/select.py" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/select.py" new file mode 100644 index 00000000..cf0f4f46 --- /dev/null +++ "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/select.py" @@ -0,0 +1,16 @@ +import numpy as np + +# 加载原始的.npy文件 +data = np.load('mouth_test.npy') + +# 确保数据至少有400行，否则无法从第15行开始取376个数 +if data.shape[0] < 390: + raise ValueError("数据不足400行，无法取出376个数。") + +# 从第15行开始取376个数 +start_index = 14 # 因为索引是从0开始的 +end_index = start_index + 376 +selected_data = data[start_index:end_index] + +# 将选取的数据保存到新的.npy文件 +np.save('selected_mouth_test.npy', selected_data) diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/selected_mouth_anno.npy" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/selected_mouth_anno.npy" new file mode 100644 index 00000000..105de25d Binary files /dev/null and "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/selected_mouth_anno.npy" differ diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/selected_mouth_test.npy" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/selected_mouth_test.npy" new file mode 100644 index 00000000..36576efd Binary files /dev/null and "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/selected_mouth_test.npy" differ diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/test.npy" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/test.npy" new file mode 100644 index 00000000..e7c09773 Binary files /dev/null and "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/test.npy" differ diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/test65.npy" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/test65.npy" new file mode 100644 index 00000000..b458153a Binary files /dev/null and "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/test65.npy" differ diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/test65.txt" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/test65.txt" new file mode 100644 index 00000000..4649f5a4 --- /dev/null +++ "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/HDTF/test65.txt" @@ -0,0 +1,390 @@ +1.228252688609617060e-01,1.285289166592923848e+01,4.464833124921261742e+01 +9.265138351364013425e-02,1.266370888884251400e+01,4.503668489393533036e+01 +7.653061663367596856e-02,1.269484421822855325e+01,4.555244728399119936e+01 +7.123434795673888598e-02,1.299665308114220252e+01,4.635726414258252959e+01 +8.891951122653901640e-02,1.334435222831953638e+01,4.671741223200644555e+01 +1.101309458084120396e-01,1.350612709901183628e+01,4.645301359729005242e+01 +1.256753043085989396e-01,1.352813638847698385e+01,4.624781736517942221e+01 +1.308937177176451860e-01,1.372344908739732361e+01,4.730585366787461510e+01 +1.272815114380978219e-01,1.392728046216998727e+01,4.889375030838061775e+01 +1.192873685414157781e-01,1.385168554735421864e+01,4.953003739417043505e+01 +1.221375607404210639e-01,1.361851527360541780e+01,4.924611126223751967e+01 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+-4.580565599638406482e-01,3.828479639272248392e+01,8.967616110657209560e+01 +-4.593626170942508580e-01,3.844377846988456326e+01,9.006916179176127457e+01 +-4.655486034663357531e-01,3.848691207420043270e+01,9.015843995241260700e+01 +-4.584533633963566168e-01,3.878133959074288128e+01,9.082525548235167889e+01 +-4.612225997033090330e-01,3.921215506709874177e+01,9.184049499318561516e+01 +-4.721582809400990954e-01,3.969122028137662994e+01,9.297791369927213623e+01 +-4.899633676282501016e-01,4.024748046634212528e+01,9.428528596281120144e+01 +-5.099771656643120599e-01,4.096779817564007686e+01,9.593242330966745612e+01 diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/cal.py" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/cal.py" new file mode 100644 index 00000000..5934afce --- /dev/null +++ "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/cal.py" @@ -0,0 +1,31 @@ +import numpy as np +import pandas as pd + +# 设置文件路径 +npy_file_path = './test.npy' # .npy 文件路径 +excel_file_path = './65.xlsx' # Excel 文件路径 + +# 读取 .npy 文件 +data_npy = np.load(npy_file_path) + +# 读取 Excel 文件，确保 header=None，因为没有标题行 +data_excel = pd.read_excel(excel_file_path, header=None) # 不使用标题行 + +# 将 Excel 数据转换为 NumPy 数组 +data_excel_np = data_excel.to_numpy() + +# 确保矩阵的维度是正确的 +assert data_npy.shape == (147, 52), "npy 文件的形状应该是 (146, 52)" +assert data_excel_np.shape == (52, 3), "Excel 文件应该有 52 行和 3 列" + +# 矩阵乘法 +result = np.dot(data_npy, data_excel_np) + +# 输出结果 +print("Result of the matrix multiplication (shape: {0}):".format(result.shape)) +print(result) + +# 如果需要将结果保存到文件中 +output_file_path = './test65.npy' +np.save(output_file_path, result) # 保存结果为 .npy 文件 +print(f"Result saved to {output_file_path}") diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/cal2.py" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/cal2.py" new file mode 100644 index 00000000..6b24e056 --- /dev/null +++ "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/cal2.py" @@ -0,0 +1,22 @@ +import numpy as np + +# 定义文件名 +differences_file = 'differences_test.npy' + +# 加载 .npy 文件 +differences_data = np.load(differences_file) + +# 确保数据的列数正确 +if differences_data.shape[1] < 3: + raise ValueError("The data must have at least three columns.") + +# 计算每行的距离（第一个和第二个元素的平方和的平方根） +distances = np.sqrt(differences_data[:, 0]**2 + differences_data[:, 1]**2) + +# 输出为 .npy 文件 +np.save('calculated_distances.npy', distances) + +# 输出为 .txt 文件 +np.savetxt('calculated_distances.txt', distances, delimiter=',', fmt='%.18e') + +print("Distances calculated and saved to calculated_distances.npy and calculated_distances.txt") diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/cal_dis.py" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/cal_dis.py" new file mode 100644 index 00000000..73eab8c6 --- /dev/null +++ "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/cal_dis.py" @@ -0,0 +1,39 @@ +import numpy as np + +# 定义文件名 +annotation_file = 'test67.npy' +test_file = 'anno67.npy' + +# 加载 .npy 文件 +annotation_data = np.load(annotation_file) +test_data = np.load(test_file) + +# 确保数据有足够的行 +if annotation_data.shape[0] < 146 or test_data.shape[0] < 146: + raise ValueError("Both files must have at least 98 rows.") + +# 取前 146 行 +annotation_subset = annotation_data[:146] +test_subset = test_data[:146] + +# 提取 x 和 z 坐标 (假设 x 在第 0 列, z 在第 1 列) +x_coords_annotation = annotation_subset[:, 0] +y_coords_annotation = annotation_subset[:, 1] +x_coords_test = test_subset[:, 0] +y_coords_test = test_subset[:, 1] + +# 计算投影距离 (只考虑 x 和 z 坐标) +# 距离计算公式: distance = sqrt((x2 - x1)^2 + (y2 - y1)^2) +distances = np.sqrt((x_coords_test - x_coords_annotation) ** 2 + (y_coords_test - y_coords_annotation) ** 2) + +#y_coords_annotation = annotation_subset[:, 1] +#y_coords_test = test_subset[:, 1] +#distances = np.sqrt((y_coords_test - y_coords_annotation) ** 2) +# 输出为 .npy 文件 +np.save('67diff.npy', distances) + +# 输出为 .txt 文件 +np.savetxt('distance_test.txt', distances, delimiter=',', fmt='%.18e') + +print("Projected distance calculations saved to projected_distances.npy and projected_distances.txt") + diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/diff.py" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/diff.py" new file mode 100644 index 00000000..e95ed5b3 --- /dev/null +++ "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/diff.py" @@ -0,0 +1,25 @@ +import numpy as np + +# 定义文件名 +test_file_1 = 'annotation65.npy' +test_file_2 = 'annotation67.npy' + +# 加载 .npy 文件 +test_data_1 = np.load(test_file_1) +test_data_2 = np.load(test_file_2) + +# 确保两个数据的行数相同 +if test_data_1.shape[0] != test_data_2.shape[0]: + raise ValueError("Both files must have the same number of rows.") + +# 计算每一行的差异 +# 假设 test_data_1 和 test_data_2 都是 n 行 3 列的数组 +differences = test_data_1 - test_data_2 # 每一元素相减 + +# 输出为 .npy 文件 +np.save('differences_anno.npy', differences) + +# 输出为 .txt 文件 +np.savetxt('differences_anno.txt', differences, delimiter=',', fmt='%.18e') + +print("Differences calculated and saved to differences.npy and differences.txt") diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/distance_anno.npy" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/distance_anno.npy" new file mode 100644 index 00000000..ff7a8915 Binary files /dev/null and "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/distance_anno.npy" differ diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/distance_anno.txt" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/distance_anno.txt" new file mode 100644 index 00000000..cd3b893d --- /dev/null +++ "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/distance_anno.txt" @@ -0,0 +1,146 @@ +8.976768340844769156e+00 +9.039477181169489839e+00 +9.203064587339998326e+00 +9.319761940714336390e+00 +9.312361570131056254e+00 +9.163607668561279951e+00 +8.982316061506011806e+00 +8.834229038165993586e+00 +8.937178530697297063e+00 +8.848025349019236430e+00 +8.895758940362462397e+00 +9.066255764271268447e+00 +9.275980583572826177e+00 +9.533693988855574730e+00 +9.807420008477691908e+00 +1.006721301761783494e+01 +1.005402564561578060e+01 +9.974874686482898767e+00 +9.854812674744653833e+00 +9.803926103412617721e+00 +9.750549433000500699e+00 +9.634716170865644358e+00 +9.522534436655519130e+00 +9.726902497221679766e+00 +9.949064641017319133e+00 +1.027354611685483832e+01 +1.053817261108832781e+01 +1.074001649625015276e+01 +1.103706965857668720e+01 +1.136322230552811163e+01 +1.165335416282853842e+01 +1.191046717540719513e+01 +1.215276589028031040e+01 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"a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/distance_test.npy" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/distance_test.npy" new file mode 100644 index 00000000..f7f96c86 Binary files /dev/null and "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/distance_test.npy" differ diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/distance_test.txt" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/distance_test.txt" new file mode 100644 index 00000000..d6b904ed --- /dev/null +++ "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/distance_test.txt" @@ -0,0 +1,146 @@ +4.253476679402439231e+00 +4.761160923760558994e+00 +4.406484708050890653e+00 +2.187154494788700720e+00 +1.941839474433892709e+00 +2.489620104778178966e+00 +2.618154388315089509e+00 +2.252450839118064430e+00 +2.405846998094790745e+00 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+2.557062713346240024e+00 +2.465824312352038028e+00 +2.527965549138883716e+00 +2.829507031899753056e+00 +2.890813846325442693e+00 +2.395468837146337737e+00 +2.592569860335740550e+00 +2.713489259388293995e+00 +3.005455578124306193e+00 +2.947078627989139221e+00 +2.850648677562310773e+00 +2.736436921287040303e+00 +3.252854637705841157e+00 +3.073861764349504977e+00 +3.133598234476831834e+00 +2.776616010853616867e+00 +2.982609990099556629e+00 +3.062988479902522432e+00 +3.418280710123056121e+00 +3.872417605517468608e+00 +4.409507678899593053e+00 +5.072648300658111786e+00 +5.961272993232282325e+00 diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/mean.py" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/mean.py" new file mode 100644 index 00000000..b1663d92 --- /dev/null +++ "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/mean.py" @@ -0,0 +1,17 @@ +import numpy as np + +# 定义距离文件名 +distances_file = '65diff.npy' + +# 加载距离数据 +distances = np.load(distances_file) + +# 确保距离数据的长度为 146 +if distances.shape[0] != 146: + raise ValueError("The distance data must contain exactly 146 entries.") + +# 计算平均值 +average_distance = np.mean(distances) + +# 输出平均值 +print("Average projected distance of the first 146 entries:", average_distance) \ No newline at end of file diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/npyread.py" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/npyread.py" new file mode 100644 index 00000000..9ea0fbe0 --- /dev/null +++ "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/npyread.py" @@ -0,0 +1,18 @@ +import numpy as np +import os + +# 定义要转换的 .npy 文件名 +npy_files = ['anno65.npy', 'anno67.npy', 'test65.npy', 'test67.npy'] + +# 遍历每个 .npy 文件并进行转换 +for npy_file in npy_files: + # 加载 .npy 文件 + data = np.load(npy_file) + + # 生成对应的 txt 文件名 + txt_file = os.path.splitext(npy_file)[0] + '.txt' + + # 将数据保存为 .txt 文件 + np.savetxt(txt_file, data, delimiter=',', fmt='%.18e') # 使用科学计数法格式输出 + + print(f"Converted {npy_file} to {txt_file}") diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/predicted.py" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/predicted.py" new file mode 100644 index 00000000..5029c65c --- /dev/null +++ "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/predicted.py" @@ -0,0 +1,18 @@ +import numpy as np +from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score + +# 加载预测值和实际值 +predictions = np.load('distance_test.npy') # 预测值 +actuals = np.load('distance_anno.npy') # 实际值 + +# 计算指标 +mae = mean_absolute_error(actuals, predictions) # 平均绝对误差 +mse = mean_squared_error(actuals, predictions) # 均方误差 +rmse = np.sqrt(mse) # 均方根误差 +r2 = r2_score(actuals, predictions) # R^2决定系数 + +# 输出结果 +print(f'Mean Absolute Error (MAE): {mae}') +print(f'Mean Squared Error (MSE): {mse}') +print(f'Root Mean Squared Error (RMSE): {rmse}') +print(f'R^2 Score: {r2}') diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/test.npy" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/test.npy" new file mode 100644 index 00000000..2ec9c05b Binary files /dev/null and "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/test.npy" differ diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/test65.npy" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/test65.npy" new file mode 100644 index 00000000..4c64bda2 Binary files /dev/null and "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/test65.npy" differ diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/test65.txt" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/test65.txt" new file mode 100644 index 00000000..3e7e719d --- /dev/null +++ "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/RAVDESS/test65.txt" @@ -0,0 +1,147 @@ +9.065001307359314697e-02,1.787105757388946259e+01,6.387415127392314673e+01 +8.835169940974380021e-02,1.823808837351170098e+01,6.516741979811794749e+01 +8.921213456617689552e-02,1.826523280431612051e+01,6.525402044042654381e+01 +9.547856972767258810e-02,1.699834828848711510e+01,6.080002737911247834e+01 +9.519892839655261230e-02,1.429032711708857839e+01,5.132820165530559109e+01 +9.473431499358264674e-02,1.370421153135010073e+01,4.922141226185151197e+01 +9.567018459952231213e-02,1.333172228880256327e+01,4.782635325791966352e+01 +9.701598004717322532e-02,1.333790694661739451e+01,4.775235420286192323e+01 +9.856291704408351451e-02,1.342358210752231074e+01,4.796704105551587816e+01 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"b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/README.md" @@ -0,0 +1,34 @@ +此readme节选自《实验报告》的《3.1定量评估结果》 + +**定性评估结果** + +定性评估方面，我们的工作主要关注模型在口型同步和情感表达方面的能力。 + +1.口型同步 + +本模型数据库的构建是基于作者提出的52个标准表情FLAME Head Templates完成的，在blender中这52个Flame Head作为Shape Key +混合输出最终的图像，因此我们选取Flame Head中上下唇各一个顶点（65号与67号）作为关键点，计算这两点之间的距离来衡量每一帧表情的张口距离，并将输出视频的张口距离和原标注集的数据进行对比。 + +![](./image/media/image1.png){width="3.109027777777778in" +height="2.861111111111111in"} + +图9：blender标注唇关键点 + +通过分别对来自原作者数据集中的两个音频HDTF.wav和RAVDESS.wav进行评估后得到以下结论： + +![](./image/media/image2.png){width="5.15625in" +height="2.361111111111111in"} + +![](./image/media/image3.png){width="5.322222222222222in" +height="3.0972222222222223in"} + +统计图中的横轴代表图像帧号，纵轴代表两个关键点之间的距离，结果显示，生成视频的张口距离趋势变化与原标注数据基本一致，证明本模型在口型同步方面有较好的性能，统计每一帧的平均误差，RAVDESS.mp4生成视频的平均误差为10.76%，HDTF.mp4生成视频的平均误差为15.66%。 + +2.情感表达 + +本模型在情感表达方面也具有较好的性能，能提取语音中的情感特征，增强生成的3D面部动画。使用原项目中有明显情绪特征的语音生成视频，能体现出不同情绪下生成面部表情的区别。 + +![](./image/media/image4.jpeg){width="4.7347222222222225in" +height="3.5479166666666666in"} + +图 10：不同情感下的表情(Happy, Angry, Disgust, Sad) diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/image/media/image1.png" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/image/media/image1.png" new file mode 100644 index 00000000..31428d47 Binary files /dev/null and "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/image/media/image1.png" differ diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/image/media/image2.png" "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/image/media/image2.png" new file mode 100644 index 00000000..ffdad66f Binary files /dev/null and "b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/image/media/image2.png" differ diff --git "a/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/image/media/image3.png" 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"b/EmoTalk/code/test/\345\256\232\346\200\247\350\257\204\344\273\267/\350\257\204\344\273\267.xlsx" differ diff --git "a/EmoTalk/code/test/\345\256\232\351\207\217\350\257\204\344\273\267/EmoTalk.py" "b/EmoTalk/code/test/\345\256\232\351\207\217\350\257\204\344\273\267/EmoTalk.py" new file mode 100644 index 00000000..625b975f --- /dev/null +++ "b/EmoTalk/code/test/\345\256\232\351\207\217\350\257\204\344\273\267/EmoTalk.py" @@ -0,0 +1,186 @@ +import os +import sys +import subprocess +import numpy as np +from moviepy.editor import VideoFileClip, concatenate_videoclips +import soundfile as sf +import librosa +from tqdm import tqdm # 导入 tqdm + +def extract_audio(input_video_file, output_audio_directory, sample_rate=16000): + os.makedirs(output_audio_directory, exist_ok=True) + input_filename = os.path.basename(input_video_file) + output_audio_file = os.path.splitext(input_filename)[0] + ".wav" + output_audio_file_path = os.path.join(output_audio_directory, output_audio_file) + + print(f"Extracting audio from video file: {input_video_file}") + print(f"Saving audio to: {output_audio_file_path}") + + try: + video = VideoFileClip(input_video_file) + audio = video.audio + audio = audio.set_fps(sample_rate) + audio.write_audiofile(output_audio_file_path, codec='pcm_s16le') + video.close() + print(f"Audio saved: {output_audio_file_path}") + return output_audio_file_path + except Exception as e: + print(f"Error: {e}") + return None + +def split_audio(audio_path, segment_duration=60): + print(f"Splitting audio file: {audio_path}") + speech_array, sr = librosa.load(audio_path, sr=None) + segments = [] + for start in range(0, len(speech_array), segment_duration * sr): + segment = speech_array[start:start + segment_duration * sr] + if len(segment) > 0: + segments.append(segment) + print(f"Audio split into {len(segments)} segments.") + return segments, sr + +def save_audio_segments(segments, sr, output_dir): + os.makedirs(output_dir, exist_ok=True) + segment_files = [] + for i, segment in enumerate(segments): + segment_file = os.path.join(output_dir, f"segment_{i}.wav") + print(f"Saving segment {i} to: {segment_file}") + sf.write(segment_file, segment, sr) # 使用 soundfile 保存音频 + segment_files.append(segment_file) + return segment_files + +def run_emotalk(audio_path): + absolute_audio_path = os.path.abspath(audio_path) # 获取音频文件的绝对路径 + print(f"Processing audio files: {absolute_audio_path}") + try: + subprocess.run(["python", "demo.py", "--wav_path", absolute_audio_path], check=True) + except subprocess.CalledProcessError as e: + print(f"An error occurred while running demo.py: {e}") + +def combine_videos(segment_file_paths, output_directory): + video_clips = [] + print(f"Combining video clips into final video in: {output_directory}") + + for segment_file in segment_file_paths: + video_path = os.path.splitext(segment_file)[0] + '.mp4' # 生成相应的mp4文件名 + print(f"Checking if video exists at: {video_path}") + if os.path.exists(video_path): + video_clips.append(VideoFileClip(video_path)) + else: + print(f"Video file not found: {video_path}") + + if video_clips: + final_video = concatenate_videoclips(video_clips) + final_video_path = os.path.join(output_directory, "final_output.mp4") + print(f"Final video will be saved at: {final_video_path}") + try: + final_video.write_videofile(final_video_path, codec='libx264', audio_codec='aac') + print(f"Video saved successfully: {final_video_path}") + except Exception as e: + print(f"Error during video saving: {e}") + return final_video_path + return None + +def clean_up_files(segment_files, output_directory): + print(f"Cleaning up files in directory: {output_directory}") + for segment_file in segment_files: + if os.path.exists(segment_file): + os.remove(segment_file) + print(f"Deleted audio files: {segment_file}") + + # 删除生成的分段视频文件（如果有的话） + video_files = [os.path.splitext(segment_file)[0] + '.mp4' for segment_file in segment_files] + for video_file in video_files: + if os.path.exists(video_file): + os.remove(video_file) + print(f"Deleted video files: {video_file}") + +# 计算评分 +def calculate_scores(result_video_path, syncnet_dir=".\syncnet_python"): + print(f"Calculating scores for video: {result_video_path}") + try: + # 切换到 syncnet_python 文件夹并执行命令 + os.chdir(syncnet_dir) + + # 执行视频评分命令，计算 LSE-D 和 LSE-C + video_file = "../result/final_output.mp4" + command_run_pipeline = [ + "python", "run_pipeline.py", "--videofile", video_file, "--reference", "wav2lip", "--data_dir", "tmp_dir" + ] + subprocess.run(command_run_pipeline, check=True) + + command_calculate_scores = [ + "python", "calculate_scores_real_videos.py", "--videofile", video_file, "--reference", "wav2lip", "--data_dir", "tmp_dir" + ] + result = subprocess.run(command_calculate_scores, capture_output=True, text=True, check=True) + + # 输出评分到 all_scores.txt 文件 + scores_output = result.stdout + with open("all_scores.txt", "w") as f: + f.write(scores_output) + + lse_c = None + lse_d = None + + # 从输出中提取 LSE-D 和 LSE-C + scores = scores_output.split() + if len(scores) >= 2: + lse_d = scores[0].strip() + lse_c = scores[1].strip() + else: + raise ValueError("Unexpected output format: not enough scores.") + + print(f"LSE-D: {lse_d}") + print(f"LSE-C: {lse_c}") + + return lse_d, lse_c + except subprocess.CalledProcessError as e: + print(f"An error occurred while calculating scores: {e}") + return None, None + +def main(): + # 打印当前工作目录 + current_directory = os.path.abspath(os.getcwd()) + print(f"Current working directory: {current_directory}") + + if len(sys.argv) != 2: + print("usage: python EmoTalk.py