The Optimization That Reduced Memory Consumption by 99%

Every time auto-editor processes a media file with audio, auto-editor reads, does analysis, then writes new the audio file. The most common type of files auto-editor processes are mp4 containers with an AAC audio track, but many other audio codecs could be used as well. Therefore, Auto-Editor converts them into WAV files.

However, the data needs to be in a form that can be viewed or changed by the Python runtime. That's where comes in. reads WAV file data and returns a data structure that Python can use. I won't spoil what is used in the final version, but first, let's examine a naïve approach.

Python's List

# A 2-channel stereo audio samples
samples = [[0, 0, 345, 578, 345 ...], [0, 0, 235, 456, 234 ...]]

The code above represents samples as a native Python list, however, this is incredibly memory inefficient. A typical audio file is has a sample rate of 44.1kHz. That means that one second of audio needs to store 44,100 numbers. Let's see how well Python stores all this data.

>>> sys.getsizeof([0] * 44100) + 44100 * 28
1587656  # 1,587,656 bytes -> 1.5 megabytes

That's pretty bad.

The reason why memory usage is so high is that the lists can't store int objects directly. They store a 4 byte reference to a Python Object, which in this case is a 28 byte sized int. In order to reduce memory, we'll need to pick a more suitable data structure.

Numpy Arrays

Using numpy arrays allows us to pack fixed-size data extremely tightly and efficiently.

>>> sys.getsizeof(np.zeros([44100, 1], dtype=np.int32))
176528  # 176,528 bytes -> 176 kilobytes

The memory used is much more reasonable. By using numpy arrays, we see a 10x improvement in memory.

However, even with numpy arrays, working with large audio files can still consume a lot of memory. For example, a 6-hour stereo audio file can take up around 2GB of memory. Since auto-editor reads and writes big audio files. It's important that auto-editor doesn't have these big files in memory at the same time, but at one point, that was exactly what was happening.

In commit 9657cfa, I had to give a hint to Python's Garbage Collector using the del keyword because the input audio samples weren't being cleaned up soon enough. This reduced peak memory consumption by half, but I actually figured out a substantial optimization that made this patch irrelevant.

I knew before writing even a single piece of audio rendering code that lists were unsuitable to the task, but I what didn't know that applying this optimization would save so much memory, and would make my fix obsolete in the process.

Memory Maps

The optimization was swapping numpy.array to numpy.memmap when reading and writing audio WAV files. memory-maps are a way of reading and writing files as if it were in memory, but can be lazy-loaded from storage. Otherwise, numpy.memmap has basically the same interface as numpy.array.

Memory-maps really shine in our use-case because since we've already paid for the storage cost by storing audio data in the WAV format, we've reduced the memory used in this operation to almost zero!


Thinking about your data structures is essential if you care about performance. Sometimes researching more than what is typically used/well known. The best way to catch these issues is to test your program with very large input and profile the memory, storage, and time used.

Creating fast, efficient programs is everyone's responsibly.

Other Paths

Python has a built-in alternative to numpy arrays, which is the array.array datatype. However, there are many problems. One is that you don't have nice guaranteed, only platform-dependent intlong and long long values. Another is that numpy has speed optimizations like SIMD and array.array doesn't. array.array is really only a thin layer other a fixed size C array, but C Compilers don't include these arithmetic optimizations like numpy does.