为什么我的Wav阅读中有这么多零？
我正在尝试制作语音识别算法。我有一个wav包含 +-20分钟语音的文件。我已经将其读取为一个numpy数组，每个1024值的每个chunck都是一行。由于wave模块的文件readframes方法的不一样，并非所有块，都有相同的长度，使用numpy.padd < /代码>功能以使阵列具有同质形状。这些桨仅附加在数组后面，因此不会引起以下内容。
我注意到数组中有一些仅包含零的列。这些列总是成对出现的，并且总是由包含正常值的两个列分开。第六列似乎是这种模式的例外：有时确实包含一个列。这些列是真实记录的Speach，还是由于某种原因将它们放在我的计算机之间？知道这一点很重要，因为我不知道要训练计算机生成的值的算法。我应该删除这些值还是保留它们更好？阵列不必再播放的音频了。
这是阵列中的样本。我无法附加完整的数组，因为这对此很大。可以找到一个更完整的版本在这里。
[78, 1, 0, 0, 79, 1, 0, 0, 12, 1, 0, 0, 185, 0, 0, 0, 177, 0, 0, 0, 28, 1, 0, 0, 245, 1, 0, 0, 38, 3, 0, 0, 106, 4, 0, 0, 81, 5, 0, 0, 148, 5, 0, 0, 74, 5, 0, 0, 168, 4, 0, 0, 229, 3, 0, 0, 83, 3, 0, 0, 31, 3, 0, 0, 26, 3, 0, 0, 33, 3, 0, 0, 40, 3, 0, 0, 22, 3, 0, 0, 246, 2, 0, 0, 211, 2, 0, 0, 136, 2, 0, 0, 240, 1, 0, 0, 247, 0, 0, 0, 176, 255, 0, 0, 97, 254, 0, 0, 69, 253, 0, 0, 131, 252, 0, 0, 54, 252, 0, 0, 81, 252, 0, 0, 188, 252, 0, 0, 79, 253, 0, 0, 207, 253, 0, 0, 48, 254, 0, 0, 97, 254, 0, 0, 73, 254, 0, 0, 6, 254, 0, 0, 175, 253, 0, 0, 90, 253, 0, 0, 58, 253, 0, 0, 73, 253, 0, 0, 101, 253, 0, 0, 132, 253, 0, 0, 147, 253, 0, 0, 164, 253, 0, 0, 199, 253, 0, 0, 224, 253, 0, 0, 8, 254, 0, 0, 97, 254, 0, 0, 228, 254, 0, 0, 163, 255, 0, 0, 138, 0, 0, 0, 89, 1, 0, 0, 251, 1, 0, 0, 45, 2, 0, 0, 157, 1, 0, 0, 95, 0, 0, 0, 161, 254, 0, 0, 185, 252, 0, 0, 56, 251, 0, 0, 134, 250, 0, 0, 226, 250, 0, 0, 51, 252, 0, 0, 246, 253, 0, 0, 175, 255, 0, 0, 208, 0, 0, 0, 240, 0, 0, 0, 96, 0, 0, 0, 124, 255, 0, 0, 119, 254, 0, 0, 223, 253, 0, 0, 243, 253, 0, 0, 120, 254, 0, 0, 77, 255, 0, 0, 254, 255, 0, 0, 253, 255, 0, 0, 63, 255, 0, 0, 224, 253, 0, 0, 61, 252, 0, 0, 27, 251, 0, 0, 35, 251, 0, 0, 180, 252, 0, 0, 154, 255, 0, 0, 1, 3, 0, 0, 6, 6, 0, 0, 189, 7, 0, 0, 116, 7, 0, 0, 111, 5, 0, 0, 118, 2, 0, 0, 116, 255, 0, 0, 133, 253, 0, 0, 55, 253, 0, 0, 124, 254, 0, 0, 17, 1, 0, 0, 19, 4, 0, 0, 87, 6, 0, 0, 42, 7, 0, 0, 53, 6, 0, 0, 195, 3, 0, 0]
[240, 0, 0, 0, 235, 254, 0, 0, 155, 254, 0, 0, 87, 0, 0, 0, 80, 3, 0, 0, 25, 6, 0, 0, 137, 7, 0, 0, 253, 6, 0, 0, 146, 4, 0, 0, 57, 1, 0, 0, 35, 254, 0, 0, 73, 252, 0, 0, 56, 252, 0, 0, 185, 253, 0, 0, 251, 255, 0, 0, 60, 2, 0, 0, 204, 3, 0, 0, 18, 4, 0, 0, 15, 3, 0, 0, 21, 1, 0, 0, 137, 254, 0, 0, 95, 252, 0, 0, 99, 251, 0, 0, 137, 251, 0, 0, 210, 252, 0, 0, 46, 255, 0, 0, 161, 1, 0, 0, 40, 3, 0, 0, 102, 3, 0, 0, 95, 2, 0, 0, 127, 0, 0, 0, 92, 254, 0, 0, 160, 252, 0, 0, 18, 252, 0, 0, 216, 252, 0, 0, 133, 254, 0, 0, 181, 0, 0, 0, 136, 2, 0, 0, 248, 2, 0, 0, 213, 1, 0, 0, 130, 255, 0, 0, 202, 252, 0, 0, 199, 250, 0, 0, 241, 249, 0, 0, 56, 250, 0, 0, 121, 251, 0, 0, 15, 253, 0, 0, 72, 254, 0, 0, 235, 254, 0, 0, 188, 254, 0, 0, 180, 253, 0, 0, 61, 252, 0, 0, 191, 250, 0, 0, 167, 249, 0, 0, 108, 249, 0, 0, 33, 250, 0, 0, 144, 251, 0, 0, 106, 253, 0, 0, 20, 255, 0, 0, 227, 255, 0, 0, 169, 255, 0, 0, 173, 254, 0, 0, 82, 253, 0, 0, 3, 252, 0, 0, 69, 251, 0, 0, 115, 251, 0, 0, 146, 252, 0, 0, 109, 254, 0, 0, 137, 0, 0, 0, 64, 2, 0, 0, 28, 3, 0, 0, 241, 2, 0, 0, 233, 1, 0, 0, 148, 0, 0, 0, 143, 255, 0, 0, 60, 255, 0, 0, 183, 255, 0, 0, 185, 0, 0, 0, 203, 1, 0, 0, 149, 2, 0, 0, 208, 2, 0, 0, 97, 2, 0, 0, 127, 1, 0, 0, 103, 0, 0, 0, 66, 255, 0, 0, 74, 254, 0, 0, 172, 253, 0, 0, 121, 253, 0, 0, 158, 253, 0, 0, 218, 253, 0, 0, 242, 253, 0, 0, 207, 253, 0, 0, 97, 253, 0, 0, 191, 252, 0, 0, 91, 252, 0, 0, 144, 252, 0, 0, 82, 253, 0, 0, 100, 254, 0, 0, 122, 255, 0, 0, 34, 0, 0, 0]
[1, 0, 0, 0, 65, 255, 0, 0, 89, 254, 0, 0, 151, 253, 0, 0, 47, 253, 0, 0, 69, 253, 0, 0, 221, 253, 0, 0, 237, 254, 0, 0, 76, 0, 0, 0, 166, 1, 0, 0, 187, 2, 0, 0, 133, 3, 0, 0, 3, 4, 0, 0, 86, 4, 0, 0, 179, 4, 0, 0, 47, 5, 0, 0, 198, 5, 0, 0, 96, 6, 0, 0, 197, 6, 0, 0, 210, 6, 0, 0, 145, 6, 0, 0, 37, 6, 0, 0, 217, 5, 0, 0, 226, 5, 0, 0, 73, 6, 0, 0, 35, 7, 0, 0, 86, 8, 0, 0, 105, 9, 0, 0, 11, 10, 0, 0, 252, 9, 0, 0, 222, 8, 0, 0, 224, 6, 0, 0, 151, 4, 0, 0, 73, 2, 0, 0, 111, 0, 0, 0, 152, 255, 0, 0, 125, 255, 0, 0, 130, 255, 0, 0, 105, 255, 0, 0, 214, 254, 0, 0, 135, 253, 0, 0, 223, 251, 0, 0, 108, 250, 0, 0, 144, 249, 0, 0, 117, 249, 0, 0, 255, 249, 0, 0, 244, 250, 0, 0, 239, 251, 0, 0, 101, 252, 0, 0, 38, 252, 0, 0, 99, 251, 0, 0, 105, 250, 0, 0, 195, 249, 0, 0, 239, 249, 0, 0, 4, 251, 0, 0, 216, 252, 0, 0, 218, 254, 0, 0, 61, 0, 0, 0, 161, 0, 0, 0, 19, 0, 0, 0, 227, 254, 0, 0, 190, 253, 0, 0, 69, 253, 0, 0, 179, 253, 0, 0, 209, 254, 0, 0, 21, 0, 0, 0, 250, 0, 0, 0, 35, 1, 0, 0, 76, 0, 0, 0, 162, 254, 0, 0, 204, 252, 0, 0, 104, 251, 0, 0, 239, 250, 0, 0, 177, 251, 0, 0, 127, 253, 0, 0, 162, 255, 0, 0, 42, 1, 0, 0, 95, 1, 0, 0, 246, 255, 0, 0, 23, 253, 0, 0, 154, 249, 0, 0, 220, 246, 0, 0, 235, 245, 0, 0, 32, 247, 0, 0, 33, 250, 0, 0, 214, 253, 0, 0, 203, 0, 0, 0, 227, 1, 0, 0, 234, 0, 0, 0, 137, 254, 0, 0, 202, 251, 0, 0, 251, 249, 0, 0, 42, 250, 0, 0, 77, 252, 0, 0, 154, 255, 0, 0, 24, 3, 0, 0, 162, 5, 0, 0, 107, 6, 0, 0, 139, 5, 0, 0, 154, 3, 0, 0]
I'm trying to make a speech recognition algorithm. I've a wav file containing +-20 minutes of speech. I've read it into a numpy array, each chunck of 1024 values is a row. As somehow not all chunks, provided by the wave module's file's readframes method, are of the same length, some rows are padded with zeros using the numpy.padd function in order to make the array have a homogenous shape. These paddings are only appended behind the array, and thus cannot cause the following.
I've noticed that there are columns in the array that are only containing zeros. These columns are always occuring in pairs and are always seperated by two columns containing normal values. The sixth column seems to be an exception on this pattern: It does also contain ones sometimes. Are these columns real recorded speach or are they put in between by my computer for some reason? This is important to know, as I don't wan't my algorithm to train on computer generated values. Should I delete those values or is it better to keep them? The array does not have to be playable as audio anymore.
Here's a sample out of the array. I can't attach the complete array, as it is way to big for that. A more complete version can be found here.
[78, 1, 0, 0, 79, 1, 0, 0, 12, 1, 0, 0, 185, 0, 0, 0, 177, 0, 0, 0, 28, 1, 0, 0, 245, 1, 0, 0, 38, 3, 0, 0, 106, 4, 0, 0, 81, 5, 0, 0, 148, 5, 0, 0, 74, 5, 0, 0, 168, 4, 0, 0, 229, 3, 0, 0, 83, 3, 0, 0, 31, 3, 0, 0, 26, 3, 0, 0, 33, 3, 0, 0, 40, 3, 0, 0, 22, 3, 0, 0, 246, 2, 0, 0, 211, 2, 0, 0, 136, 2, 0, 0, 240, 1, 0, 0, 247, 0, 0, 0, 176, 255, 0, 0, 97, 254, 0, 0, 69, 253, 0, 0, 131, 252, 0, 0, 54, 252, 0, 0, 81, 252, 0, 0, 188, 252, 0, 0, 79, 253, 0, 0, 207, 253, 0, 0, 48, 254, 0, 0, 97, 254, 0, 0, 73, 254, 0, 0, 6, 254, 0, 0, 175, 253, 0, 0, 90, 253, 0, 0, 58, 253, 0, 0, 73, 253, 0, 0, 101, 253, 0, 0, 132, 253, 0, 0, 147, 253, 0, 0, 164, 253, 0, 0, 199, 253, 0, 0, 224, 253, 0, 0, 8, 254, 0, 0, 97, 254, 0, 0, 228, 254, 0, 0, 163, 255, 0, 0, 138, 0, 0, 0, 89, 1, 0, 0, 251, 1, 0, 0, 45, 2, 0, 0, 157, 1, 0, 0, 95, 0, 0, 0, 161, 254, 0, 0, 185, 252, 0, 0, 56, 251, 0, 0, 134, 250, 0, 0, 226, 250, 0, 0, 51, 252, 0, 0, 246, 253, 0, 0, 175, 255, 0, 0, 208, 0, 0, 0, 240, 0, 0, 0, 96, 0, 0, 0, 124, 255, 0, 0, 119, 254, 0, 0, 223, 253, 0, 0, 243, 253, 0, 0, 120, 254, 0, 0, 77, 255, 0, 0, 254, 255, 0, 0, 253, 255, 0, 0, 63, 255, 0, 0, 224, 253, 0, 0, 61, 252, 0, 0, 27, 251, 0, 0, 35, 251, 0, 0, 180, 252, 0, 0, 154, 255, 0, 0, 1, 3, 0, 0, 6, 6, 0, 0, 189, 7, 0, 0, 116, 7, 0, 0, 111, 5, 0, 0, 118, 2, 0, 0, 116, 255, 0, 0, 133, 253, 0, 0, 55, 253, 0, 0, 124, 254, 0, 0, 17, 1, 0, 0, 19, 4, 0, 0, 87, 6, 0, 0, 42, 7, 0, 0, 53, 6, 0, 0, 195, 3, 0, 0]
[240, 0, 0, 0, 235, 254, 0, 0, 155, 254, 0, 0, 87, 0, 0, 0, 80, 3, 0, 0, 25, 6, 0, 0, 137, 7, 0, 0, 253, 6, 0, 0, 146, 4, 0, 0, 57, 1, 0, 0, 35, 254, 0, 0, 73, 252, 0, 0, 56, 252, 0, 0, 185, 253, 0, 0, 251, 255, 0, 0, 60, 2, 0, 0, 204, 3, 0, 0, 18, 4, 0, 0, 15, 3, 0, 0, 21, 1, 0, 0, 137, 254, 0, 0, 95, 252, 0, 0, 99, 251, 0, 0, 137, 251, 0, 0, 210, 252, 0, 0, 46, 255, 0, 0, 161, 1, 0, 0, 40, 3, 0, 0, 102, 3, 0, 0, 95, 2, 0, 0, 127, 0, 0, 0, 92, 254, 0, 0, 160, 252, 0, 0, 18, 252, 0, 0, 216, 252, 0, 0, 133, 254, 0, 0, 181, 0, 0, 0, 136, 2, 0, 0, 248, 2, 0, 0, 213, 1, 0, 0, 130, 255, 0, 0, 202, 252, 0, 0, 199, 250, 0, 0, 241, 249, 0, 0, 56, 250, 0, 0, 121, 251, 0, 0, 15, 253, 0, 0, 72, 254, 0, 0, 235, 254, 0, 0, 188, 254, 0, 0, 180, 253, 0, 0, 61, 252, 0, 0, 191, 250, 0, 0, 167, 249, 0, 0, 108, 249, 0, 0, 33, 250, 0, 0, 144, 251, 0, 0, 106, 253, 0, 0, 20, 255, 0, 0, 227, 255, 0, 0, 169, 255, 0, 0, 173, 254, 0, 0, 82, 253, 0, 0, 3, 252, 0, 0, 69, 251, 0, 0, 115, 251, 0, 0, 146, 252, 0, 0, 109, 254, 0, 0, 137, 0, 0, 0, 64, 2, 0, 0, 28, 3, 0, 0, 241, 2, 0, 0, 233, 1, 0, 0, 148, 0, 0, 0, 143, 255, 0, 0, 60, 255, 0, 0, 183, 255, 0, 0, 185, 0, 0, 0, 203, 1, 0, 0, 149, 2, 0, 0, 208, 2, 0, 0, 97, 2, 0, 0, 127, 1, 0, 0, 103, 0, 0, 0, 66, 255, 0, 0, 74, 254, 0, 0, 172, 253, 0, 0, 121, 253, 0, 0, 158, 253, 0, 0, 218, 253, 0, 0, 242, 253, 0, 0, 207, 253, 0, 0, 97, 253, 0, 0, 191, 252, 0, 0, 91, 252, 0, 0, 144, 252, 0, 0, 82, 253, 0, 0, 100, 254, 0, 0, 122, 255, 0, 0, 34, 0, 0, 0]
[1, 0, 0, 0, 65, 255, 0, 0, 89, 254, 0, 0, 151, 253, 0, 0, 47, 253, 0, 0, 69, 253, 0, 0, 221, 253, 0, 0, 237, 254, 0, 0, 76, 0, 0, 0, 166, 1, 0, 0, 187, 2, 0, 0, 133, 3, 0, 0, 3, 4, 0, 0, 86, 4, 0, 0, 179, 4, 0, 0, 47, 5, 0, 0, 198, 5, 0, 0, 96, 6, 0, 0, 197, 6, 0, 0, 210, 6, 0, 0, 145, 6, 0, 0, 37, 6, 0, 0, 217, 5, 0, 0, 226, 5, 0, 0, 73, 6, 0, 0, 35, 7, 0, 0, 86, 8, 0, 0, 105, 9, 0, 0, 11, 10, 0, 0, 252, 9, 0, 0, 222, 8, 0, 0, 224, 6, 0, 0, 151, 4, 0, 0, 73, 2, 0, 0, 111, 0, 0, 0, 152, 255, 0, 0, 125, 255, 0, 0, 130, 255, 0, 0, 105, 255, 0, 0, 214, 254, 0, 0, 135, 253, 0, 0, 223, 251, 0, 0, 108, 250, 0, 0, 144, 249, 0, 0, 117, 249, 0, 0, 255, 249, 0, 0, 244, 250, 0, 0, 239, 251, 0, 0, 101, 252, 0, 0, 38, 252, 0, 0, 99, 251, 0, 0, 105, 250, 0, 0, 195, 249, 0, 0, 239, 249, 0, 0, 4, 251, 0, 0, 216, 252, 0, 0, 218, 254, 0, 0, 61, 0, 0, 0, 161, 0, 0, 0, 19, 0, 0, 0, 227, 254, 0, 0, 190, 253, 0, 0, 69, 253, 0, 0, 179, 253, 0, 0, 209, 254, 0, 0, 21, 0, 0, 0, 250, 0, 0, 0, 35, 1, 0, 0, 76, 0, 0, 0, 162, 254, 0, 0, 204, 252, 0, 0, 104, 251, 0, 0, 239, 250, 0, 0, 177, 251, 0, 0, 127, 253, 0, 0, 162, 255, 0, 0, 42, 1, 0, 0, 95, 1, 0, 0, 246, 255, 0, 0, 23, 253, 0, 0, 154, 249, 0, 0, 220, 246, 0, 0, 235, 245, 0, 0, 32, 247, 0, 0, 33, 250, 0, 0, 214, 253, 0, 0, 203, 0, 0, 0, 227, 1, 0, 0, 234, 0, 0, 0, 137, 254, 0, 0, 202, 251, 0, 0, 251, 249, 0, 0, 42, 250, 0, 0, 77, 252, 0, 0, 154, 255, 0, 0, 24, 3, 0, 0, 162, 5, 0, 0, 107, 6, 0, 0, 139, 5, 0, 0, 154, 3, 0, 0]
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看来该文件包含两个不同的音频通道。由于我只有一个麦克风，因此未记录第二个声通道。这导致此通道默认为所有零。因为
.wav文件使用两个位记录每个频道，所以这导致两个地方填充了零。然后，wave模块将所有不同的通道组合到一个Chunck中，每个通道包含2个正确的位和2个不正确的（0）位，并将100个框架合并为我的Chunck大小，并将其交付给一个数组给出的数组。我可以将零从阵列中完全删除，而无需任何音频质量损失。但是，由于我刚刚删除了第二个通道，因此我还必须将通道量减少到1。
It appeared that the file contained two different sound channels. The second sound channel was not recorded, as I have only one microphone. This caused this channel to be defaulted to all zeros. Because
.wavfiles use two bits to record each channel, this resulted in two places being filled with zeros. Then thewavemodule combined all the different channels into one chunck, each containing 2 correct bits and 2 incorrect (0) bits and combined 100 of these frames, my chunck size, into one array and thus deliver the array given.I could perfectly remove the zeros out of the array without any audio quality loss. However, I also had to reduce the amount of channels to 1, as I just deleted the second channel.