Time and Duration and Epoch

本文详细介绍了Go语言中处理当前时间、指定时间、时间字段、比较、间隔计算、格式化以及时间戳的用法,包括年月日、时分秒、时区等,并展示了关键的时间操作技巧和示例。

当前时间

now := time.Now()
fmt.Println("now:", now)

now: 2022-04-16 10:50:52.283976 +0800 CST m=+0.000111228

指定时间

then := time.Date(2009, 11, 17, 20, 34, 58, 651387237, time.UTC)
fmt.Println("then:", then)

then: 2009-11-17 20:34:58.651387237 +0000 UTC

参数分别为:年,月,日,时,分,秒,纳秒,时区。

时间字段

now := time.Now()
fmt.Println("now:", now)
fmt.Println("year:", now.Year())
fmt.Println("month:", now.Month())
fmt.Println("day:", now.Day())
fmt.Println("hour:", now.Hour())
fmt.Println("minute:", now.Minute())
fmt.Println("second:", now.Second())
fmt.Println("nanosecond:", now.Nanosecond())
fmt.Println("location:", now.Location())
fmt.Println("weekday:", now.Weekday())

now: 2022-04-16 11:10:21.76101 +0800 CST m=+0.000108970
year: 2022
month: April
day: 16
hour: 11
minute: 10
second: 21
nanosecond: 761010000
location: Local
weekday: Saturday

时间比较

now := time.Now()
then := time.Date(2009, 11, 17, 20, 34, 58, 651387237, time.UTC)
fmt.Println("then before now:", then.Before(now))
fmt.Println("then after now:", then.After(now))
fmt.Println("then equal now:", then.Equal(now))

then before now: true
then after now: false
then equal now: false

时间间隔

	now := time.Now()
	then := time.Date(2009, 11, 17, 20, 34, 58, 651387237, time.UTC)
	diff := now.Sub(then)
	fmt.Println("diff:", diff)
	fmt.Println("hours:", diff.Hours())
	fmt.Println("minutes:", diff.Minutes())
	fmt.Println("seconds:", diff.Seconds())
	fmt.Println("nanoseconds:", diff.Nanoseconds())

diff: 108774h59m46.516185763s
hours: 108774.99625449604
minutes: 6.526499775269763e+06
seconds: 3.9158998651618576e+08
nanoseconds: 391589986516185763

时间计算

now := time.Now()
fmt.Println("now:", now)
fmt.Println("now add 3 hour", now.Add(3*time.Hour))
fmt.Println("now add -3 hour", now.Add(-3*time.Hour))

now: 2022-04-16 11:47:08.20477 +0800 CST m=+0.000109580
now add 3 hour 2022-04-16 14:47:08.20477 +0800 CST m=+10800.000109580
now add -3 hour 2022-04-16 08:47:08.20477 +0800 CST m=-10799.999890420

时间戳

now := time.Now()
fmt.Println("now:", now)
fmt.Println("now unix:", now.Unix())
fmt.Println("now unix milli:", now.UnixMilli())
fmt.Println("now unix nano:", now.UnixNano())

now: 2022-04-16 13:18:19.23993 +0800 CST m=+0.000111504
now unix: 1650086299
now unix milli: 1650086299239
now unix nano: 1650086299239930000

时间戳转换

fmt.Println("time from unix:", time.Unix(1650080700, 0))
fmt.Println("time from unix nano:", time.Unix(0, 1650080700587122000))

time from unix: 2022-04-16 11:45:00 +0800 CST
time from unix nano: 2022-04-16 11:45:00.587122 +0800 CST

时间格式化

以下是格式化的说明:

// Year: “2006” “06”
// Month: “Jan” “January”
// Textual day of the week: “Mon” “Monday”
// Numeric day of the month: “2” “_2” “02”
// Numeric day of the year: “__2” “002”
// Hour: “15” “3” “03” (PM or AM)
// Minute: “4” “04”
// Second: “5” “05”
// AM/PM mark: “PM”
//
// Numeric time zone offsets format as follows:
// “-0700” ±hhmm
// “-07:00” ±hh:mm
// “-07” ±hh
// Replacing the sign in the format with a Z triggers
// the ISO 8601 behavior of printing Z instead of an
// offset for the UTC zone. Thus:
// “Z0700” Z or ±hhmm
// “Z07:00” Z or ±hh:mm
// “Z07” Z or ±hh

now := time.Now()
fmt.Println(now.Format(time.RFC3339))
fmt.Println(now.Format("3:04PM"))
fmt.Println(now.Format("Mon Jan _2 15:04:05 2006"))
fmt.Println(now.Format("2006-01-02T15:04:05.999999-07:00"))

2022-04-16T14:24:19+08:00
2:24PM
Sat Apr 16 14:24:19 2022
2022-04-16T14:24:19.73223+08:00

now := time.Now()
fmt.Printf("%d-%02d-%02dT%02d:%02d:%02d-00:00\n", now.Year(), now.Month(), now.Day(), now.Hour(), now.Minute(), now.Second())

2022-04-16T14:31:59-00:00

时间字符串解析

layout := "3 04 PM"
t, _ := time.Parse(layout, "8 41 PM")
fmt.Println(t)

0000-01-01 20:41:00 +0000 UTC

t, _ := time.Parse(time.RFC3339, "2012-11-01T22:08:41+00:00")
fmt.Println(t)

2012-11-01 22:08:41 +0000 +0000

解析失败时,error会被返回。

layout := "Mon Jan _2 15:04:05 2006"
_, e := time.Parse(layout, "8:41PM")
fmt.Println(e)

parsing time “8:41PM” as “Mon Jan _2 15:04:05 2006”: cannot parse “8:41PM” as “Mon”

import numpy as np import os from glob import glob import trimesh import open3d as o3d import torch def farthest_point_sample(pointcloud, num_points): """最远点采样(FPS),用于统一输出点数""" pointcloud = np.array(pointcloud) num_all = pointcloud.shape[0] if num_all <= num_points: return pointcloud farthest_pts = np.zeros((num_points, 3)) farthest_pts[0] = pointcloud[np.random.randint(num_all)] distances = np.sum((pointcloud - farthest_pts[0]) ** 2, axis=1) for i in range(1, num_points): idx = np.argmax(distances) farthest_pts[i] = pointcloud[idx] distances = np.minimum(distances, np.sum((pointcloud - farthest_pts[i]) ** 2, axis=1)) return farthest_pts class UnifiedPointCloudDataset: def __init__(self, data_dirs): self.data_dirs = data_dirs self.file_paths = [] for data_dir in data_dirs: self.file_paths.extend(glob(os.path.join(data_dir, "*.npy"))) def __len__(self): return len(self.file_paths) def __getitem__(self, idx): file_path = self.file_paths[idx] points = np.load(file_path) # 直接加载预处理好的 .npy 文件 return torch.tensor(points, dtype=torch.float32) import os import numpy as np import trimesh import open3d as o3d def preprocess_and_save(input_path, output_path, voxel_size=0.02): ext = os.path.splitext(input_path)[1].lower() print(f"Processing: {input_path}") if ext == '.ply': pcd = trimesh.load(input_path) elif ext == '.stl': pcd = trimesh.load(input_path) elif ext == '.obj': pcd = trimesh.load(input_path) else: raise ValueError(f"Unsupported format: {ext}") # 转换为 open3d 点云并进行体素下采样 pcd_o3d = o3d.geometry.PointCloud() pcd_o3d.points = o3d.utility.Vector3dVector(pcd.vertices) pcd_o3d = pcd_o3d.voxel_down_sample(voxel_size=voxel_size) # 直接保存下采样后的点云 sampled_points = np.array(pcd_o3d.points) np.save(output_path, sampled_points) print(f"Saved: {output_path}") def run_preprocess(data_root, save_root, voxel_size=0.02): os.makedirs(save_root, exist_ok=True) for root, _, files in os.walk(data_root): for file in files: if file.lower().endswith(('.ply', '.stl', '.obj')): file_path = os.path.join(root, file) rel_path = os.path.relpath(file_path, data_root) save_path = os.path.join(save_root, os.path.splitext(rel_path)[0] + ".npy") os.makedirs(os.path.dirname(save_path), exist_ok=True) preprocess_and_save(file_path, save_path, voxel_size) if __name__ == '__main__': DATA_ROOT = r"D:\桌面\point\data1\part1" SAVE_ROOT = r"D:\桌面\point\preprocessed_data" run_preprocess(DATA_ROOT, SAVE_ROOT) import torch import torch.nn as nn class PointMLP(nn.Module): def __init__(self, input_points=4096, output_points=1024): super(PointMLP, self).__init__() self.input_points = input_points self.output_points = output_points # Encoder self.encoder = nn.Sequential( nn.Conv1d(3, 64, 1), nn.BatchNorm1d(64), nn.ReLU(), nn.Conv1d(64, 128, 1), nn.BatchNorm1d(128), nn.ReLU(), nn.Conv1d(128, 256, 1), nn.BatchNorm1d(256), nn.ReLU(), nn.Conv1d(256, 512, 1), nn.BatchNorm1d(512), nn.ReLU(), nn.Conv1d(512, 1024, 1), nn.BatchNorm1d(1024), nn.ReLU(), ) # Decoder self.decoder = nn.Sequential( nn.Linear(1024, 1024), nn.BatchNorm1d(1024), nn.ReLU(), nn.Linear(1024, 512), nn.BatchNorm1d(512), nn.ReLU(), nn.Linear(512, output_points * 3) ) def forward(self, x): batch_size = x.size(0) x = x.transpose(1, 2) features = self.encoder(x) global_feature = torch.max(features, dim=2)[0] output = self.decoder(global_feature) return output.view(batch_size, self.output_points, 3) import torch from torch import nn def chamfer_distance(pred, target): pred = pred.unsqueeze(1) target = target.unsqueeze(2) dist = torch.sum((pred - target) ** 2, dim=-1) dist1, _ = torch.min(dist, dim=1) dist2, _ = torch.min(dist, dim=2) loss = (torch.mean(dist1) + torch.mean(dist2)) / 2 return loss class ChamferLoss(nn.Module): def __init__(self): super(ChamferLoss, self).__init__() def forward(self, pred, target): return chamfer_distance(pred, target) import torch import torch.nn as nn from torch.utils.data import DataLoader from torch.cuda.amp import autocast, GradScaler import os import time import numpy as np from pointmlp import PointMLP from loss import ChamferLoss from dataset import UnifiedPointCloudDataset # ========== 配置参数 ========== DATA_ROOT = r"D:\桌面\point\preprocessed_data" # 预处理后的 .npy 数据目录 NUM_PARTS = 20 DATA_DIRS = [os.path.join(DATA_ROOT, f"part{i}") for i in range(1, NUM_PARTS + 1)] # 模型配置 NUM_POINTS = 4096 # 输入点数(预处理已统一) OUTPUT_POINTS = 1024 # 输出点数 BATCH_SIZE = 8 # 可根据显存调整 LR = 0.0005 EPOCHS = 50 # =========================================== def train(): dataset = UnifiedPointCloudDataset(DATA_DIRS) dataloader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, drop_last=True, num_workers=4) model = PointMLP(input_points=NUM_POINTS, output_points=OUTPUT_POINTS) optimizer = torch.optim.Adam(model.parameters(), lr=LR) criterion = ChamferLoss() device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) os.makedirs("checkpoints", exist_ok=True) # 初始化 GradScaler(用于 AMP) scaler = GradScaler() print(f"✅ 开始训练,总共 {len(dataset)} 个点云文件,使用 batch size {BATCH_SIZE}") for epoch in range(EPOCHS): epoch_start_time = time.time() model.train() total_loss = 0 print(f"\n{'=' * 20} Epoch [{epoch + 1}/{EPOCHS}] 开始 {'=' * 20}") batch_times = [] for batch_idx, points in enumerate(dataloader): batch_start_time = time.time() points = points.to(device) # 混合精度训练上下文 with autocast(): output = model(points) loss = criterion(output, points) # 使用 scaler 进行反向传播和优化 scaler.scale(loss).backward() scaler.step(optimizer) scaler.update() optimizer.zero_grad() total_loss += loss.item() batch_duration = time.time() - batch_start_time batch_times.append(batch_duration) if (batch_idx + 1) % 10 == 0: avg_batch_time = sum(batch_times[-10:]) / len(batch_times[-10:]) print(f"Epoch [{epoch + 1}/{EPOCHS}], Batch [{batch_idx + 1}/{len(dataloader)}], " f"Loss: {loss.item():.4f}, 耗时: {batch_duration:.2f}s, 平均: {avg_batch_time:.2f}s") avg_loss = total_loss / len(dataloader) epoch_duration = time.time() - epoch_start_time print(f"{'=' * 20} Epoch [{epoch + 1}/{EPOCHS}] 完成 {'=' * 20}") print(f"平均损失: {avg_loss:.4f}") print(f"总耗时: {epoch_duration:.2f}s") print(f"平均每个batch耗时: {sum(batch_times) / len(batch_times):.2f}s") print(f"最后保存模型: checkpoints/pointmlp_epoch_{epoch + 1}.pth") print("-" * 50) torch.save(model.state_dict(), f"checkpoints/pointmlp_epoch_{epoch + 1}.pth") if __name__ == '__main__': print("🚀 训练程序启动") start_time = time.time() train() total_duration = time.time() - start_time print(f"\n🏁 训练程序结束,总运行时间: {total_duration:.2f}s") 这是我的所有代码,之前预处理使用的是体素下采样和FPS下采样,同意输出点4096个,供模型进行训练,但是处理速度太慢了,我现在只使用了体素下采样,去掉了FPS,相应的你给我改一下其他的代码,因为我觉得只使用4096个点训练的模型不完整,因为我一个点云数据就有100万个点,怎么不得保留5%到10%的点才能完整的表现出原来数据的特征。你就我现在只使用体素下采样的预处理,对其他的代码进行修改
07-26
(01) wangtianze24@b0215dbf77d3:~/project_all_wang/qqAttnSleep-main/AttnSleep-main/prepare_datasets$ python prepare_physionet.py --data_dir /home/wangtianze24/sleep_edf20/SLEEP_EDF_20 --output_dir data_edf_20_npz/fpzcz --select_ch "EEG Fpz-Cz" Extracting EDF parameters from /home/wangtianze24/sleep_edf20/SLEEP_EDF_20/SC4001E0-PSG.edf... EDF file detected Setting channel info structure... Creating raw.info structure... Reading 0 ... 7949999 = 0.000 ... 79499.990 secs... /home/wangtianze24/project_all_wang/qqAttnSleep-main/AttnSleep-main/prepare_datasets/prepare_physionet.py:95: DeprecationWarning: scaling_time is deprecated in version 0.20 and will be removed in version 0.21. To replicate old behavior, use time_format="ms" to get time in milliseconds, or use time_format=None and scale the time column (in seconds) after DataFrame creation. raw_ch_df = raw.to_data_frame(scaling_time=100.0)[select_ch] Traceback (most recent call last): File "/home/wangtianze24/project_all_wang/qqAttnSleep-main/AttnSleep-main/prepare_datasets/prepare_physionet.py", line 218, in <module> main() File "/home/wangtianze24/project_all_wang/qqAttnSleep-main/AttnSleep-main/prepare_datasets/prepare_physionet.py", line 131, in main label_epoch = np.ones(duration_epoch, dtype=np.int) * label File "/home/wangtianze24/anaconda3/envs/01/lib/python3.9/site-packages/numpy/__init__.py", line 305, in __getattr__ raise AttributeError(__former_attrs__[attr]) AttributeError: module 'numpy' has no attribute 'int'. `np.int` was a deprecated alias for the builtin `int`. To avoid this error in existing code, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information. The aliases was originally deprecated in NumPy 1.20; for more details and guidance see the original release note at: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
03-22
import torch import torch.nn as nn from torch.utils.data import DataLoader from torch.cuda.amp import autocast, GradScaler import os import time import numpy as np from pointmlp import PointMLP from loss import ChamferLoss from dataset import UnifiedPointCloudDataset import os from glob import glob DATA_ROOT = r"G:\point\preprocessed_data" # 预处理后的 .npy 数据目录 NUM_PARTS = 1 DATA_DIRS = [os.path.join(DATA_ROOT, f"part{i}") for i in range(1, NUM_PARTS + 1)] # ========== 递归查找所有 .npy 文件,只执行一次 ========== def find_all_npy_files(data_dir): """递归查找所有子目录中的 .npy 文件""" npy_files = [] for root, dirs, files in os.walk(data_dir): for file in files: if file.endswith(".npy"): npy_files.append(os.path.join(root, file)) return npy_files # 只搜索一次 print("🔍 正在搜索数据目录:") all_file_paths = [] for data_dir in DATA_DIRS: if not os.path.exists(data_dir): print(f"⚠️ 路径不存在: {data_dir}") continue print(f"📁 搜索路径: {data_dir}") files = find_all_npy_files(data_dir) print(f" - 找到 {len(files)} 个 .npy 文件") all_file_paths.extend(files) # 筛选符合点数范围的文件(可选) filtered_paths = [] min_points, max_points = 1000, 50000 for path in all_file_paths: try: points = np.load(path) if min_points <= points.shape[0] <= max_points: filtered_paths.append(path) else: print(f"📉 文件点数不符合要求,跳过: {path} ({points.shape[0]} points)") except Exception as e: print(f"❌ 加载文件失败: {path}, 错误: {e}") all_file_paths = filtered_paths print(f"✅ 最终筛选后,保留 {len(all_file_paths)} 个有效文件") # 模型配置 NUM_POINTS = 4096 # 输入点数(预处理已统一) OUTPUT_POINTS = 1024 # 输出点数 BATCH_SIZE = 8 # 可根据显存调整 LR = 0.0005 EPOCHS = 50 # =========================================== def pad_or_truncate(points, target_length): """统一长度,超出裁剪,不足补零""" num_points = points.shape[0] if num_points > target_length: return points[:target_length] else: pad = np.zeros((target_length - num_points, 3)) return np.vstack([points, pad]) def collate_fn(batch): """改进版:先转成 numpy array,统一 pad 后再构造 tensor""" max_len = max(len(points) for points in batch) padded_batch = [pad_or_truncate(points, max_len) for points in batch] array_batch = np.stack(padded_batch) # 构造连续内存的 numpy array return torch.from_numpy(array_batch).float() # 构造连续内存的 tensor def train(): dataset = UnifiedPointCloudDataset(file_paths=all_file_paths) dataloader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, drop_last=True, num_workers=4, collate_fn=collate_fn, pin_memory=True) model = PointMLP(output_points=OUTPUT_POINTS) optimizer = torch.optim.Adam(model.parameters(), lr=LR) criterion = ChamferLoss() device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) if torch.backends.cudnn.is_available(): torch.backends.cudnn.benchmark = True scaler = GradScaler() os.makedirs("checkpoints", exist_ok=True) print(f"✅ 开始训练,总共 {len(dataset)} 个点云文件,使用 batch size {BATCH_SIZE}") for epoch in range(EPOCHS): epoch_start_time = time.time() model.train() total_loss = 0 batch_times = [] torch.cuda.empty_cache() print(f"\n{'=' * 20} Epoch [{epoch + 1}/{EPOCHS}] 开始 {'=' * 20}") for batch_idx, points in enumerate(dataloader): # ✅ 在这里加入调试信息 print(f"Batch {batch_idx}: shape={points.shape}, contiguous={points.is_contiguous()}") batch_start_time = time.time() points = points.to(device, non_blocking=True) # 送入 GPU with autocast(): # 启动混合精度计算 output = model(points) loss = criterion(output, points) # 混合精度反向传播 scaler.scale(loss).backward() scaler.step(optimizer) scaler.update() optimizer.zero_grad() total_loss += loss.item() batch_duration = time.time() - batch_start_time batch_times.append(batch_duration) if (batch_idx + 1) % 10 == 0: avg_batch_time = sum(batch_times[-10:]) / len(batch_times[-10:]) print(f"Epoch [{epoch + 1}/{EPOCHS}], Batch [{batch_idx + 1}/{len(dataloader)}], " f"Loss: {loss.item():.4f}, 耗时: {batch_duration:.2f}s, 平均: {avg_batch_time:.2f}s") avg_loss = total_loss / len(dataloader) epoch_duration = time.time() - epoch_start_time print(f"{'=' * 20} Epoch [{epoch + 1}/{EPOCHS}] 完成 {'=' * 20}") print(f"平均损失: {avg_loss:.4f}") print(f"总耗时: {epoch_duration:.2f}s") print(f"平均每个batch耗时: {sum(batch_times) / len(batch_times):.2f}s") print(f"最后保存模型: checkpoints/pointmlp_epoch_{epoch + 1}.pth") print("-" * 50) torch.save(model.state_dict(), f"checkpoints/pointmlp_epoch2_{epoch + 1}.pth") if __name__ == '__main__': print("🚀 训练程序启动") start_time = time.time() train() total_duration = time.time() - start_time print(f"\n🏁 训练程序结束,总运行时间: {total_duration:.2f}s") import numpy as np import os from glob import glob import trimesh import open3d as o3d import torch def farthest_point_sample(pointcloud, num_points): """最远点采样(FPS),用于统一输出点数""" pointcloud = np.array(pointcloud) num_all = pointcloud.shape[0] if num_all <= num_points: return pointcloud farthest_pts = np.zeros((num_points, 3)) farthest_pts[0] = pointcloud[np.random.randint(num_all)] distances = np.sum((pointcloud - farthest_pts[0]) ** 2, axis=1) for i in range(1, num_points): idx = np.argmax(distances) farthest_pts[i] = pointcloud[idx] distances = np.minimum(distances, np.sum((pointcloud - farthest_pts[i]) ** 2, axis=1)) return farthest_pts import os import numpy as np from glob import glob class UnifiedPointCloudDataset: def __init__(self, data_dirs=None, file_paths=None, min_points=1000, max_points=1000000): """ 点云数据集类,支持从文件夹递归加载 .npy 文件,或直接传入文件路径列表 参数: data_dirs (list): 文件夹路径列表,用于搜索 .npy 文件 file_paths (list): 已知的 .npy 文件路径列表(优先使用) min_points (int): 点云最小点数阈值 max_points (int): 点云最大点数阈值 """ self.file_paths = [] # 优先使用传入的 file_paths if file_paths is not None and len(file_paths) > 0: self.file_paths = file_paths print(f"✅ 使用传入的 {len(self.file_paths)} 个 .npy 文件") elif data_dirs is not None and len(data_dirs) > 0: print("🔍 正在搜索数据目录:") for data_dir in data_dirs: if not os.path.exists(data_dir): print(f"⚠️ 路径不存在: {data_dir}") continue print(f"📁 搜索路径: {data_dir}") files = glob(os.path.join(data_dir, "**/*.npy"), recursive=True) print(f" - 找到 {len(files)} 个 .npy 文件") self.file_paths.extend(files) else: raise ValueError("必须提供 data_dirs 或 file_paths 中的一个") # 如果还没有文件,直接返回空数据集 if not self.file_paths: print("⚠️ 未找到任何 .npy 文件") return # 过滤掉点数不符合要求的文件 filtered_paths = [] for path in self.file_paths: try: points = np.load(path) if min_points <= points.shape[0] <= max_points: filtered_paths.append(path) else: print(f"📉 文件点数不符合要求,跳过: {path} ({points.shape[0]} points)") except Exception as e: print(f"❌ 加载文件失败: {path}, 错误: {e}") self.file_paths = filtered_paths print(f"✅ 最终筛选后,保留 {len(self.file_paths)} 个有效文件") def __len__(self): return len(self.file_paths) def __getitem__(self, idx): file_path = self.file_paths[idx] points = np.load(file_path) return torch.tensor(points, dtype=torch.float32)
07-26
本文旨在系统阐述利用MATLAB平台执行多模态语音分离任务的方法,重点围绕LRS3数据集的数据生成流程展开。LRS3(长时RGB+音频语音数据集)作为一个规模庞大的视频与音频集合,整合了丰富的视觉与听觉信息,适用于语音识别、语音分离及情感分析等多种研究场景。MATLAB凭借其高效的数值计算能力与完备的编程环境,成为处理此类多模态任务的适宜工具。 多模态语音分离的核心在于综合利用视觉与听觉等多种输入信息来解析语音信号。具体而言,该任务的目标是从混合音频中分离出不同说话人的声音,并借助视频中的唇部运动信息作为辅助线索。LRS3数据集包含大量同步的视频与音频片段,提供RGB视频、单声道音频及对应的文本转录,为多模态语音处理算法的开发与评估提供了重要平台。其高质量与大容量使其成为该领域的关键资源。 在相关资源包中,主要包含以下两部分内容: 1. 说明文档:该文件详细阐述了项目的整体结构、代码运行方式、预期结果以及可能遇到的问题与解决方案。在进行数据处理或模型训练前,仔细阅读此文档对正确理解与操作代码至关重要。 2. 专用于语音分离任务的LRS3数据集版本:解压后可获得原始的视频、音频及转录文件,这些数据将由MATLAB脚本读取并用于生成后续训练与测试所需的数据。 基于MATLAB的多模态语音分离通常遵循以下步骤: 1. 数据预处理:从LRS3数据集中提取每段视频的音频特征与视觉特征。音频特征可包括梅尔频率倒谱系数、感知线性预测系数等;视觉特征则涉及唇部运动的检测与关键点定位。 2. 特征融合:将提取的音频特征与视觉特征相结合,构建多模态表示。融合方式可采用简单拼接、加权融合或基于深度学习模型的复杂方法。 3. 模型构建:设计并实现用于语音分离的模型。传统方法可采用自适应滤波器或矩阵分解,而深度学习方法如U-Net、Transformer等在多模态学习中表现优异。 4. 训练与优化:使用预处理后的数据对模型进行训练,并通过交叉验证与超参数调整来优化模型性能。 5. 评估与应用:采用信号失真比、信号干扰比及信号伪影比等标准指标评估模型性能。若结果满足要求,该模型可进一步应用于实际语音分离任务。 借助MATLAB强大的矩阵运算功能与信号处理工具箱,上述步骤得以有效实施。需注意的是,多模态任务常需大量计算资源,处理大规模数据集时可能需要对代码进行优化或借助GPU加速。所提供的MATLAB脚本为多模态语音分离研究奠定了基础,通过深入理解与运用这些脚本,研究者可更扎实地掌握语音分离的原理,从而提升其在实用场景中的性能表现。 资源来源于网络分享,仅用于学习交流使用,请勿用于商业,如有侵权请联系我删除!
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