基于LRW-1000CAS-VSR-W1k数据集来进行中文唇语数据集识别任务以构建一个全面的唇语识别系统包括数据集准备、模型定义、训练和结果评估。以下是所有相关的代码文件LRW-1000又叫CAS-VSR-W1k)中文唇语识别数据集。目前最大的中文公开唇语识别数据。数据来之不易可以直接使用。可用于训练数字人 wav2lip等基于LRW-1000CAS-VSR-W1k数据集来进行中文唇语识别任务。适合用于训练数字人、Wav2Lip等应用。以下文章的代码仅供参考环境准备确保您已经安装了以下软件和库Python 3.8 或更高版本PyTorch 1.9 或更高版本torchvision 0.10 或更高版本OpenCVnumpypandasmatplotliblibrosa用于音频处理moviepy用于视频处理您可以使用以下命令安装所需的Python库pipinstalltorch torchvision opencv-python numpy pandas matplotlib librosa moviepy数据集准备假设您的数据集已经下载并组织好并且包含训练集、验证集和测试集。以下是数据集的预期结构datasets/ └── lrw_1000/ ├── videos/ │ ├── train/ │ ├── val/ │ └── test/ └── transcripts/ ├── train.txt ├── val.txt └── test.txt数据集文件说明videos/目录包含所有的视频文件。transcripts/目录包含每个视频的转录文本。模型选择我们将使用一个预训练的模型进行微调。这里我们选择一个常用的唇语识别模型例如AV-Sync或者Vid2Speech。为了简化流程我们将使用AV-Sync模型。下载预训练模型首先我们需要下载预训练的AV-Sync模型权重。可以从 AV-Sync GitHub 获取预训练模型。安装 AV-Sync克隆 AV-Sync 仓库并设置环境gitclone https://github.com/microsoft/av-sync.gitcdav-sync pipinstall-rrequirements.txt配置文件创建一个配置文件来定义训练参数和其他设置。以下是一个示例配置文件config.yaml[titleConfiguration File for Lip Reading]# Configuration file for lip reading task using AV-Sync# Data pathsdata_root:../datasets/lrw_1000/# Model settingsmodel_name:av_syncpretrained_weights:./pretrained_weights/av_sync.pth# Training settingsbatch_size:16num_workers:4learning_rate:0.001num_epochs:50log_interval:10# Dataset settingstrain_split:train.txtval_split:val.txttest_split:test.txt# Other settingsdevice:cuda# or cpu数据加载和预处理我们需要编写数据加载和预处理脚本。以下是一个示例脚本dataset.py:[titleDataset Loader and Preprocessor]importosimportcv2importnumpyasnpimporttorchfromtorch.utils.dataimportDatasetimportlibrosafrommoviepy.editorimportVideoFileClipclassLRWDataset(Dataset):def__init__(self,data_root,split_file,transformNone):self.data_rootdata_root self.split_filesplit_file self.transformtransform self.samplesself._load_samples()def_load_samples(self):samples[]withopen(os.path.join(self.data_root,transcripts,self.split_file),r)asf:forlineinf:video_path,transcriptline.strip().split( ,1)samples.append((video_path,transcript))returnsamplesdef__len__(self):returnlen(self.samples)def__getitem__(self,idx):video_path,transcriptself.samples[idx]video_framesself._extract_video_frames(video_path)audio_featuresself._extract_audio_features(video_path)ifself.transform:video_framesself.transform(video_frames)returnvideo_frames,audio_features,transcriptdef_extract_video_frames(self,video_path):frames[]capcv2.VideoCapture(os.path.join(self.data_root,videos,video_path))whileTrue:ret,framecap.read()ifnotret:breakframecv2.cvtColor(frame,cv2.COLOR_BGR2RGB)frames.append(frame)cap.release()returnnp.array(frames)def_extract_audio_features(self,video_path):video_clipVideoFileClip(os.path.join(self.data_root,videos,video_path))audio_signalvideo_clip.audio.to_soundarray(fps16000)mel_spectrogramlibrosa.feature.melspectrogram(yaudio_signal.mean(axis1),sr16000,n_mels80)log_mel_spectrogramlibrosa.power_to_db(mel_spectrogram,refnp.max)returnlog_mel_spectrogram.T# Example usageif__name____main__:datasetLRWDataset(data_root../datasets/lrw_1000/,split_filetrain.txt)sampledataset[0]print(sample[0].shape,sample[1].shape,sample[2])模型定义我们将使用AV-Sync模型进行微调。以下是一个示例模型定义model.py:[titleModel Definition for Lip Reading]importtorchimporttorch.nnasnnimporttorchvision.modelsasmodelsclassAVSync(nn.Module):def__init__(self,num_classes1000,pretrainedTrue):super(AVSync,self).__init__()self.visual_modelmodels.resnet18(pretrainedpretrained)self.visual_model.fcnn.Linear(self.visual_model.fc.in_features,256)self.audio_modelnn.Sequential(nn.Conv1d(80,128,kernel_size3,stride1,padding1),nn.ReLU(),nn.MaxPool1d(kernel_size2,stride2),nn.Conv1d(128,256,kernel_size3,stride1,padding1),nn.ReLU(),nn.MaxPool1d(kernel_size2,stride2),nn.Flatten(),nn.Linear(256*25,256),nn.ReLU())self.fusion_layernn.Sequential(nn.Linear(512,256),nn.ReLU(),nn.Linear(256,num_classes))defforward(self,visual_input,audio_input):visual_outputself.visual_model(visual_input)audio_outputself.audio_model(audio_input)fusion_outputtorch.cat((visual_output,audio_output),dim1)outputself.fusion_layer(fusion_output)returnoutput# Example usageif__name____main__:modelAVSync(num_classes1000)visual_inputtorch.randn(16,3,224,224)# Batch size of 16, 3 channels, 224x224 imagesaudio_inputtorch.randn(16,80,100)# Batch size of 16, 80 mel bins, 100 framesoutputmodel(visual_input,audio_input)print(output.shape)训练脚本编写训练脚本来训练模型。以下是一个示例训练脚本train.py:[titleTraining Script for Lip Reading]importosimporttorchimporttorch.optimasoptimfromtorch.utils.dataimportDataLoaderfromdatasetimportLRWDatasetfrommodelimportAVSyncimportyamlfromtqdmimporttqdmdefload_config(config_path):withopen(config_path,r)asf:configyaml.safe_load(f)returnconfigdefmain():configload_config(config.yaml)devicetorch.device(config[device])# Create datasets and dataloaderstrain_datasetLRWDataset(data_rootconfig[data_root],split_fileconfig[train_split])val_datasetLRWDataset(data_rootconfig[data_root],split_fileconfig[val_split])train_loaderDataLoader(train_dataset,batch_sizeconfig[batch_size],shuffleTrue,num_workersconfig[num_workers])val_loaderDataLoader(val_dataset,batch_sizeconfig[batch_size],shuffleFalse,num_workersconfig[num_workers])# Create modelmodelAVSync(num_classes1000,pretrainedTrue)model.to(device)# Load pretrained weightsifconfig[pretrained_weights]:state_dicttorch.load(config[pretrained_weights])model.load_state_dict(state_dict,strictFalse)# Define loss function and optimizercriterionnn.CrossEntropyLoss()optimizeroptim.Adam(model.parameters(),lrconfig[learning_rate])# Training loopforepochinrange(config[num_epochs]):model.train()running_loss0.0fori,(video_frames,audio_features,transcripts)inenumerate(tqdm(train_loader)):video_framesvideo_frames.permute(0,3,1,2).to(device)# Reshape to (batch_size, C, H, W)audio_featuresaudio_features.transpose(1,2).to(device)# Reshape to (batch_size, C, L)optimizer.zero_grad()outputsmodel(video_frames,audio_features)targetstorch.tensor([int(transcript)fortranscriptintranscripts],dtypetorch.long).to(device)losscriterion(outputs,targets)loss.backward()optimizer.step()running_lossloss.item()ifi%config[log_interval]0:print(fEpoch [{epoch1}/{config[num_epochs]}], Step [{i1}/{len(train_loader)}], Loss:{running_loss/config[log_interval]:.4f})running_loss0.0# Validationmodel.eval()correct0total0withtorch.no_grad():forvideo_frames,audio_features,transcriptsintqdm(val_loader):video_framesvideo_frames.permute(0,3,1,2).to(device)# Reshape to (batch_size, C, H, W)audio_featuresaudio_features.transpose(1,2).to(device)# Reshape to (batch_size, C, L)outputsmodel(video_frames,audio_features)_,predictedtorch.max(outputs.data,1)targetstorch.tensor([int(transcript)fortranscriptintranscripts],dtypetorch.long).to(device)totaltargets.size(0)correct(predictedtargets).sum().item()accuracy100*correct/totalprint(fValidation Accuracy:{accuracy:.2f}%)# Save the trained modeltorch.save(model.state_dict(),runs/train/exp/best.pt)if__name____main__:main()结果评估训练完成后可以使用以下脚本评估模型性能[titleEvaluation Script for Lip Reading]importosimporttorchfromtorch.utils.dataimportDataLoaderfromdatasetimportLRWDatasetfrommodelimportAVSyncimportyamlfromtqdmimporttqdmdefload_config(config_path):withopen(config_path,r)asf:configyaml.safe_load(f)returnconfigdefevaluate_model(model,dataloader,device):model.eval()correct0total0withtorch.no_grad():forvideo_frames,audio_features,transcriptsintqdm(dataloader):video_framesvideo_frames.permute(0,3,1,2).to(device)# Reshape to (batch_size, C, H, W)audio_featuresaudio_features.transpose(1,2).to(device)# Reshape to (batch_size, C, L)outputsmodel(video_frames,audio_features)_,predictedtorch.max(outputs.data,1)targetstorch.tensor([int(transcript)fortranscriptintranscripts],dtypetorch.long).to(device)totaltargets.size(0)correct(predictedtargets).sum().item()accuracy100*correct/totalprint(fTest Accuracy:{accuracy:.2f}%)defmain():configload_config(config.yaml)devicetorch.device(config[device])# Create dataset and dataloadertest_datasetLRWDataset(data_rootconfig[data_root],split_fileconfig[test_split])test_loaderDataLoader(test_dataset,batch_sizeconfig[batch_size],shuffleFalse,num_workersconfig[num_workers])# Create modelmodelAVSync(num_classes1000,pretrainedFalse)model.to(device)# Load trained weightsmodel.load_state_dict(torch.load(runs/train/exp/best.pt))# Evaluate modelevaluate_model(model,test_loader,device)if__name____main__:main()使用说明配置路径确保datasets/lrw_1000/目录结构正确。确保config.yaml中的路径和设置正确。运行脚本在终端中依次运行训练脚本和评估脚本。注意事项根据需要调整超参数和训练设置。可以通过修改config.yaml中的模型架构和其他参数来优化模型性能。示例假设您的数据集文件夹结构如下datasets/ └── lrw_1000/ ├── videos/ │ ├── train/ │ ├── val/ │ └── test/ └── transcripts/ ├── train.txt ├── val.txt └── test.txt并且transcripts/目录中的.txt文件包含每个视频的路径和对应的类别索引。运行上述脚本后您可以查看训练日志和最终的模型权重文件。总结通过上述步骤我们可以构建一个全面的唇语识别系统包括数据集准备、模型定义、训练和结果评估。以下是所有相关的代码文件配置文件(config.yaml)数据加载和预处理脚本(dataset.py)模型定义脚本(model.py)训练脚本(train.py)评估脚本(eval.py)
