Understanding Audio Buffers in Python

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These buffers are then processed in real-time by our trusty Python code.

So how does it work exactly? Well, let’s say we have some audio coming in at a rate of 44100 samples per second (which is pretty standard for CD quality sound). We want to process this data in chunks that are easy for our computer to handle, so we set up a buffer size of 2048 samples. This means that every time new audio comes in, it gets added to the current buffer until it reaches 2048 samples (or “frames”), at which point we start processing that chunk and move on to the next one.

Here’s some code to illustrate this:

# Import necessary libraries
import numpy as np # for array manipulation
from scipy.io import wavfile # for reading/writing WAV files

# Set up buffer size (in frames) and sample rate
buffer_size = 2048 # number of samples to process at a time
sample_rate = 44100 # number of samples per second

# Initialize variables to hold audio data and current frame index
audio_data = np.zeros(buffer_size, dtype=np.int16) # create empty array for storing audio data
frame_index = 0 # keep track of which frame we're currently processing

# Open WAV file for reading (assuming it's in the same directory as our script)
with open('input.wav', 'rb') as f:
    # Read header information to get sample rate and number of channels
    header = wavfile.read(f)[1] # returns sample rate, number of channels, and number of frames
    num_channels, bits_per_sample = header[3], header[4] # extract number of channels and bits per sample from header

    # Set up variables for storing audio data and current frame index
    frames = [] # create empty list to hold processed frames (in 16-bit format)
    while True:
        # Read next chunk of audio data into buffer
        num_bytes = sample_rate * buffer_size // 1000 # calculate number of bytes per second at given sample rate and buffer size
        chunk = f.read(num_bytes) # read specified number of bytes from file

        # Convert raw binary data to numpy array (in 16-bit format)
        audio_data[:] = np.frombuffer(chunk, dtype=np.int16).reshape(-1, num_channels) # reshape data into array with specified number of channels

        # Process current buffer of audio data as needed...
        # ...and add resulting frames to list for later use (if desired)
        processed_frames = do_some_processing(audio_data) # function to process audio data
        if len(processed_frames) > 0:
            frames.append(np.int16(processed_frames)) # convert results back to numpy array in 16-bit format for writing to output file (if desired)

        # Update frame index and check if we've reached end of input file
        frame_index += len(audio_data) // num_channels # increment frame index by number of frames processed this time around
        if frame_index >= header[2]: # check if we've read all the way to the end of the WAV file (assuming it has a fixed length)
            break # exit loop if so, otherwise continue reading and processing audio data as needed.

Buffering in Python for real-time audio processing. It may seem like a lot at first glance, but once you get the hang of it, it’s actually pretty straightforward. And who knows? Maybe someday we’ll all be using this technique to create our own custom sound effects and music on the fly!

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