reshapedBuffer = audioBuffer.reshape((64, 8160)) audio = AudioSegment( data=reshapedBuffer.tobytes(), sample_width=2, # 16位音频，每个采样点占用2个字节 frame_rate=192000, channels=64 )发生错误，PermissionError: [Errno 13] Permission denied: 'C:\\Users\\ADMINI~1\\AppData\\Local\\Temp\\tmpfbfjoq9r.wav'

根据您提供的错误信息，看起来在导出音频文件时出现了权限错误。这可能是由于您的程序没有足够的权限在指定的路径创建临时文件。

您可以尝试以下解决方法：

1. 确保您的程序运行时具有足够的权限来在指定路径创建临时文件。如果您在Windows操作系统上运行程序，请尝试以管理员身份运行程序。

2. 检查您的防火墙或安全软件设置，确保它们不会阻止程序创建临时文件。

3. 尝试更改导出音频文件的路径，以避免在受限制的目录下创建临时文件。您可以使用其他位置或指定不同的文件名。

另外，请确保您已经安装了所需的库（例如pydub）并导入所需的模块。

如果问题仍然存在，请提供更多关于您的操作系统和完整的代码片段，以便我可以更好地帮助您解决问题。

相关问题

def formatData(X, y, i): # Format the MFCC data X_data = [] y_data = [] if i==0: # 12 features (MFCC) for sample, label in zip(X, y): features = extractFeatures(sample) mfcc = np.mean(features, axis=0).reshape((1, n_mfcc)) # mean X_data.append(mfcc) y_data.append(label) X_data = np.reshape(X_data, (-1, n_mfcc)) else: # 24 features (MFCC + variance) for sample, label in zip(X, y): features = extractFeatures(sample) mfcc = np.mean(features, axis=0).reshape((1, n_mfcc)) # mean var = np.var(features, axis=0).reshape((1, n_mfcc)) # added variance X_data.append(np.hstack((mfcc, var))) y_data.append(label) X_data = np.reshape(X_data, (-1, 2*n_mfcc)) return np.array(X_data), np.array(y_data)

这段代码是一个数据预处理函数，用于将原始的音频数据转换为可以用于机器学习模型训练的数据格式。具体含义如下：

- `X`：输入数据，包括MFCC特征。
- `y`：标签数据，包括各个音频文件所属的乐器类别。
- `i`：选择特征数量的标志位，如果为0则只使用MFCC的平均值，如果为1则使用MFCC的平均值和方差。

下面是函数的具体实现：

1. 遍历所有输入样本，处理每个样本的MFCC特征。
2. 对于每个样本，提取MFCC特征并计算其平均值或平均值和方差。
3. 将处理后的数据添加到`X_data`和`y_data`中。
4. 根据标志位`i`的不同，将`X_data`格式化为12个MFCC特征或者24个MFCC特征和方差。
5. 返回格式化后的`X_data`和`y_data`。

这个函数是一个非常重要的预处理步骤，可以将原始的音频数据转换为可用于机器学习的格式。在这个函数中，使用了`extractFeatures`函数提取MFCC特征，并使用`numpy`库计算平均值和方差。最终得到的数据格式可以直接用于训练和评估机器学习模型。

没有GPU，优化程序class point_cloud_generator(): def init(self, rgb_file, depth_file, save_ply, camera_intrinsics=[312.486, 243.928, 382.363, 382.363]): self.rgb_file = rgb_file self.depth_file = depth_file self.save_ply = save_ply self.rgb = cv2.imread(rgb_file) self.depth = cv2.imread(self.depth_file, -1) print("your depth image shape is:", self.depth.shape) self.width = self.rgb.shape[1] self.height = self.rgb.shape[0] self.camera_intrinsics = camera_intrinsics self.depth_scale = 1000 def compute(self): t1 = time.time() depth = np.asarray(self.depth, dtype=np.uint16).T self.Z = depth / self.depth_scale fx, fy, cx, cy = self.camera_intrinsics X = np.zeros((self.width, self.height)) Y = np.zeros((self.width, self.height)) for i in range(self.width): X[i, :] = np.full(X.shape[1], i) self.X = ((X - cx / 2) * self.Z) / fx for i in range(self.height): Y[:, i] = np.full(Y.shape[0], i) self.Y = ((Y - cy / 2) * self.Z) / fy data_ply = np.zeros((6, self.width * self.height)) data_ply[0] = self.X.T.reshape(-1)[:self.width * self.height] data_ply[1] = -self.Y.T.reshape(-1)[:self.width * self.height] data_ply[2] = -self.Z.T.reshape(-1)[:self.width * self.height] img = np.array(self.rgb, dtype=np.uint8) data_ply[3] = img[:, :, 0:1].reshape(-1)[:self.width * self.height] data_ply[4] = img[:, :, 1:2].reshape(-1)[:self.width * self.height] data_ply[5] = img[:, :, 2:3].reshape(-1)[:self.width * self.height] self.data_ply = data_ply t2 = time.time() print('calcualte 3d point cloud Done.', t2 - t1) def write_ply(self): start = time.time() float_formatter = lambda x: "%.4f" % x points = [] for i in self.data_ply

It that the code is generating a point cloud from an RGB-D image pair. Since you mentioned that you do not have a GPU, one possible optimization could be to use the `numba` library to speed up the computation. Here is how you can modify the code to use `numba`:

1. Import the `numba` library by adding the following line at the top of your code:

   import numba

2. Add the `@numba.jit(nopython=True)` decorator to the `compute` method to enable `numba` JIT compilation:

   @numba.jit(nopython=True)
   def compute(self):
       # rest of the code

3. Modify the `for` loops in the `compute` method to use `numba`'s `prange` function to parallelize the computation across multiple CPU cores:

   for i in numba.prange(self.width):
       # rest of the code

   for i in numba.prange(self.height):
       # rest of the code

4. Add the `@numba.jit(nopython=True)` decorator to the `write_ply` method as well, since it is also computationally intensive:

   @numba.jit(nopython=True)
   def write_ply(self):
       # rest of the code

With these modifications, the `compute` and `write_ply` methods should run faster. However, keep in mind that `numba`'s JIT compilation can take some time during the first function call, so subsequent calls should be faster. Also, note that `numba`'s `prange` function only works with integer indices, so you may need to convert floating-point indices to integers before using them in the loops.