Generate Audio with Python


I’ve been intrigued by the concept of using computers to generate audio for a long time. It turns out that you can generate audio with nothing but the standard library of Python.

The approach I used relies heavily on the itertools module. Essentially, I use itertools to create infinite generators and then take some data from these generators to produce the audio. The resultant sequence of floats in the range [-1.0, 1.0] is converted to 16 bit PCM audio (i.e., a sequence of signed 16 bit integers in the range [-32767, 32767]) and then written to a .wav file using the wave module.

If you’re not familiar with iterators and the itertools module, this post may be somewhat hard to follow. itertools really opens up some interesting possibilities in Python, making it more like Lisp or Haskell. In truth, if you’re relying on itertools as much as I am in this post, you might as well just use Lisp or Haskell and receive a nice performance boost. The reason I didn’t is quite simply because I wanted to generate some audio in Python.

To follow the code examples below, you probably need to perform the following imports:

Generating Waves

You may remember from your physics class that sound consists of waves. Many instruments produce tones that are basically a combination of pure sine waves. Thus, we need a way to produce sine waves if we want to generate audio. My first approach was something like this:

This computes a sine wave of infinite length at the specified frequency, and returns an infinite generator which samples the wave 44,100 times per second.

The problem with this approach is that it is inefficient. Sine waves are periodic functions, meaning that they repeat themselves after a certain period. This means that we can pre-calculate the function for one period, and then return an iterator which simply cycles these pre-computed values indefinitely:

This resulted in a substantial performance improvement on my machine, but this is Python after all so a discussion of performance is perhaps a moot point.

Generating Noise

Sometimes you want to generate noise. The simplest kind of noise is called white noise, which is completely random audio data.

The main downside to this approach is that random values need to be calculated 44,100 times per second. Using the itertools module, we can pre-calculate one second of white noise, and then just cycle that data:

Combining Functions

As I mentioned earlier, complex sounds can be modeled as combinations of pure sine waves. If you’re generating a stereo audio file, you can have different audio functions in each channel. The way I chose to represent this concept is as follows:

c1 is the left channel, c2 is the right channel. Each channel is an iterable containing the functions that comprise that channel. All channels are then combined into a single iterable, channels.

If you play the same sound through both channels of a stereo audio file, the sound will seem to come from the center of the soundstage.

You can also control the location of the sound by altering the amplitude of the waves. This example will make a 440.0 Hz sine wave which is slightly left of center:

Additionally, you can have more than one function playing at the same time. Here’s an example of a 200.0 Hz tone in the left channel, a 205.0 tone in the right channel, and some white noise in the background:

That’s a binaural beat.

Computing Samples

Recall from your physics class that waves combine with each other to produce new waves. We can compute this new wave by simply adding the waves together.

Now that we have defined the audio channels, we need to compute the sum of the functions in the channel at each sample in the file. Since our waves are represented as generators, we want to create a new generator which calculates the sum of each sample in the input generators. Essentially we want a function that accepts audio channels in the format described above and returns a generator which yields tuples where element 0 is the sum of the functions in the left channel at that point, and element 1 is the sum of the functions in the right channel at that point.

This calls for use of the imap and izip functions.

Note that if nsamples is specified, we return a sequence of finite length (using the islice function). Otherwise, we return a sequence of infinite length. Since we are using iterators, sequences of infinite length can be represented more or less elegantly and efficiently.

Writing a Wavefile

The next step is to use the wave module to create a .wav file. The first thing to do is to generate the wave header, which is some information at the beginning of the wavefile that describes the contents of the file. The information we need to generate this header is as follows:

To open a wavefile for writing with the wave module, do this:

The 'NONE' and 'not compressed' just indicate that we are creating an uncompressed wavefile (nothing else is supported by the wave module at the time of writing).

Now the wavefile is ready for our audio data. 16 bit audio is encoded as a series of signed 16 bit integers. The first thing to do is to scale our sequence of floats in the range [-1.0, 1.0] to signed 16 bit integers (in the range [-32767, 32767]). For example:

We’re writing a binary format, so we need the struct module to convert our audio data to the correct binary encoding. Specifically, we need the struct.pack function. The struct.pack function uses format strings to designate how to pack the data. A signed 16 bit integer is also known as a signed short, so we want to use the format string ‘h’ (the format string for a signed short). Thus, to pack the integer 1000 into a signed short:

Now, we are going to be creating stereo audio files, so we need to consider how .wav files represent multiple channels. It turns out that .wav files look something like this:


Where L1 is the first sample in the left channel, R1 is the first sample in the right channel, and so on. In other words, the channels are interleaved.

Finally, we want to keep performance in mind. On one extreme, we write data to the file every time we compute a sample. This is memory-efficient, but incurs a severe performance penalty due to the overhead of writing to the file. On the other extreme, we pre-compute the entire file and write all of the samples at once. This does not incur the aforementioned performance penalty, but it has two major problems. First, it requires a huge amount of memory, since the entire .wav file will be loaded into memory. Second, it means you can’t stream the audio as it is generated, which means you can’t play the audio in realtime (by writing to stdout and piping to aplay, for example).

Thus, we take the third approach: buffer chunks of the audio stream and write each chunk as it is computed. This offers the advantages of both techniques.

So, putting all of this together, we end up with something like this:


I have compiled these techniques and a few others into a module called wavebender. Here’s the current source code (at the time of writing), but you can always find the latest at

#!/usr/bin/env python
import sys
import wave
import math
import struct
import random
import argparse
from itertools import *

def grouper(n, iterable, fillvalue=None):
    "grouper(3, 'ABCDEFG', 'x') --> ABC DEF Gxx"
    args = [iter(iterable)] * n
    return izip_longest(fillvalue=fillvalue, *args)

def sine_wave(frequency=440.0, framerate=44100, amplitude=0.5):
    Generate a sine wave at a given frequency of infinite length.
    period = int(framerate / frequency)
    if amplitude > 1.0: amplitude = 1.0
    if amplitude < 0.0: amplitude = 0.0
    lookup_table = [float(amplitude) * math.sin(2.0*math.pi*float(frequency)*(float(i%period)/float(framerate))) for i in xrange(period)]
    return (lookup_table[i%period] for i in count(0))

def square_wave(frequency=440.0, framerate=44100, amplitude=0.5):
    for s in sine_wave(frequency, framerate, amplitude):
        if s > 0:
            yield amplitude
        elif s < 0:
            yield -amplitude
            yield 0.0

def damped_wave(frequency=440.0, framerate=44100, amplitude=0.5, length=44100):
    if amplitude > 1.0: amplitude = 1.0
    if amplitude < 0.0: amplitude = 0.0
    return (math.exp(-(float(i%length)/float(framerate))) * s for i, s in enumerate(sine_wave(frequency, framerate, amplitude)))

def white_noise(amplitude=0.5):
    Generate random samples.
    return (float(amplitude) * random.uniform(-1, 1) for i in count(0))

def compute_samples(channels, nsamples=None):
    create a generator which computes the samples.

    essentially it creates a sequence of the sum of each function in the channel
    at each sample in the file for each channel.
    return islice(izip(*(imap(sum, izip(*channel)) for channel in channels)), nsamples)

def write_wavefile(filename, samples, nframes=None, nchannels=2, sampwidth=2, framerate=44100, bufsize=2048):
    "Write samples to a wavefile."
    if nframes is None:
        nframes = -1

    w =, 'w')
    w.setparams((nchannels, sampwidth, framerate, nframes, 'NONE', 'not compressed'))

    max_amplitude = float(int((2 ** (sampwidth * 8)) / 2) - 1)

    # split the samples into chunks (to reduce memory consumption and improve performance)
    for chunk in grouper(bufsize, samples):
        frames = ''.join(''.join(struct.pack('h', int(max_amplitude * sample)) for sample in channels) for channels in chunk if channels is not None)


    return filename

def write_pcm(f, samples, sampwidth=2, framerate=44100, bufsize=2048):
    "Write samples as raw PCM data."
    max_amplitude = float(int((2 ** (sampwidth * 8)) / 2) - 1)

    # split the samples into chunks (to reduce memory consumption and improve performance)
    for chunk in grouper(bufsize, samples):
        frames = ''.join(''.join(struct.pack('h', int(max_amplitude * sample)) for sample in channels) for channels in chunk if channels is not None)


    return filename

def main():
    parser = argparse.ArgumentParser()
    parser.add_argument('-c', '--channels', help="Number of channels to produce", default=2, type=int)
    parser.add_argument('-b', '--bits', help="Number of bits in each sample", choices=(16,), default=16, type=int)
    parser.add_argument('-r', '--rate', help="Sample rate in Hz", default=44100, type=int)
    parser.add_argument('-t', '--time', help="Duration of the wave in seconds.", default=60, type=int)
    parser.add_argument('-a', '--amplitude', help="Amplitude of the wave on a scale of 0.0-1.0.", default=0.5, type=float)
    parser.add_argument('-f', '--frequency', help="Frequency of the wave in Hz", default=440.0, type=float)
    parser.add_argument('filename', help="The file to generate.")
    args = parser.parse_args()

    # each channel is defined by infinite functions which are added to produce a sample.
    channels = ((sine_wave(args.frequency, args.rate, args.amplitude),) for i in range(args.channels))

    # convert the channel functions into waveforms
    samples = compute_samples(channels, args.rate * args.time)

    # write the samples to a file
    if args.filename == '-':
        filename = sys.stdout
        filename = args.filename
    write_wavefile(filename, samples, args.rate * args.time, args.channels, args.bits / 8, args.rate)

if __name__ == "__main__":

You can execute the file directly and it will generate a pure sine tone.



SBaGen is a program which generates binaural beats. It’s reasonably complex, consisting of around 3000 lines of C. You can instruct SBaGen to generate a 200Hz pure sine tone in one channel and a 204Hz pure sine tone in the other channel by doing this:

$ sbagen -i 202+2/10

You can also generate multiple binaural beats simultaneously:

$ sbagen -i 202+2/10 400+20/10

The following emulates sbagen -i, but it’s about 100x slower. No problem for real-time use on a modern computer, but you’re better off using the real deal for serious use.

You can use it like this:

$ ./ 272.2+7.83/10 332+7.83/10 421.3+7.83/10 289.4+7.83/10 367.5+7.83/10 442+7.83/10 295.7+7.83/10 414.7+7.83/10 422+7.83/10 | aplay

That generates a bunch of simultaneous binaural tones and uses aplay to play them in realtime.


Here’s an example of a melody, using itertools and wavebender.


Well, there’s your daily abuse of itertools. For more itertools madness, check out my Project Euler Solutions.