CODEWITHSUNDEEP
pythonnumpyhexnumpy-ndarraypyaudio
ViennaMike
ViennaMike

Reputation: 2337

Read left channel of wav data into numpy array

I'm using pyaudio to take input from a microphone or read a wav file, and analyze the stream while playing it. I want to only analyze the right channel if the input is stereo. I've been able to extract the data and convert to integers using loops:

        levels = []
        length = len(data)
        if channels == 1:
            for i in range(length//2):
                volume = abs(struct.unpack('<h', data[i:i+2])[0])
                levels.append(volume)
        elif channels == 2:
            for i in range(length//4):
                j = 4 * i + 2
                volume = abs(struct.unpack('<h', data[j:j+2])[0])
                levels.append(volume)

I think this working correctly, I know it runs without error on a laptop and Raspberry Pi 3, but it appears to consume too much time to run on a Raspberry Pi Zero when simultaneously streaming the output to a speaker. I figure that eliminating the loop and using numpy may help. I assume I need to use np.ndarray to do this, and the first parameter will be (CHUNK,) where CHUNK is my chunk size for analyzing the audio (I'm using 1024). And the format would be '<h', as in the struct code above, I think. But I'm at a loss as to how to code it correctly for each of the two cases (mono and right channel only for stereo). How do I create the numpy arrays for each of the two cases?

Upvotes: 0

Views: 1156

Answers (1)

zvone
zvone

Reputation: 19362

You are here reading 16-bit integers from a binary file. It seems that you are first reading the data into data variable with something like data = f.read(), which is here not visible. Then you do:

for i in range(length//2):
    volume = abs(struct.unpack('<h', data[i:i+2])[0])
    levels.append(volume)

BTW, that code is wrong, it shoud be abs(struct.unpack('<h', data[2*i:2*i+2])[0]), otherwise you are overlapping bytes from different values.

To do the same with numpy, you should just do this (instead of both f.read()and the whole loop):

data = np.fromfile(f, dtype='<i2')

This is over 100 times faster than the manual thing above in my test on 5 MB of data.

In the second case, you have interleaved left-right-left-right values. Again you can read them all (assuming you have enough memory) and then access only one half:

data = np.fromfile(f, dtype='<i2')
left = data[::2]
right = data[1::2]

This processes everything, even though you need just one half, but it is still much much faster.

EDIT: If the data not coming from a file, np.fromfile can be replaced with np.frombuffer. Then you have this:

channel_data = np.frombuffer(data, dtype='<i2')
if channels == 2:
    channel_data = channel_data[1::2]
levels = np.abs(channel_data)

Upvotes: 1

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