Python3 modifying wav audio data correctly

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Learning how to modify different types of audio files, .wav, .mp3, etcetera using Python3 using the wave module. Specifically .wav file format, in this regard for this question. Presently, I know there are ISO standards for audio formats, and any references for this subject are greatly appreciated regarding audio standards for the .wav file format as well on a side note.

But in terms of my question, simply ignoring the RIFF, FMT headers, in a .wav file using the Python3 wave module import.

Is there a more efficient way to skip the RIFF headers, other containers, and go straight to the data container to modify its contents?

This crude example simply is converting a two-channel audio .wav file to a single-channel audio .wav file while modifying all values to (0, 0).

import wave
import struct

# Open Files
inf = wave.open(r"piano2.wav", 'rb')
outf = wave.open(r"output.wav", 'wb')

# Input Parameters
ip = list(inf.getparams())
print('Input Parameters:', ip)
# Example Output: Input Parameters: [2, 2, 48000, 302712, 'NONE', 'not compressed']

# Output Parameters
op = ip[:]
op[0] = 1
outf.setparams(op)

number_of_channels, sample_width, frame_rate, number_of_frames, comp_type, comp_name = ip

format = '<{}h'.format(number_of_channels)
print('# Channels:', format)

# Read >> Second
for index in range(number_of_frames):
    frame = inf.readframes(1)
    data = struct.unpack(format, frame)

    # Here, I change data to (0, 0), testing purposes
    print('Before Audio Data:', data)
    print('After Modifying Audio Data', (0, 0))

    # Change Audio Data
    data = (0, 0)

    value = data[0]
    value = (value * 2) // 3
    outf.writeframes(struct.pack('<h', value))

# Close In File
inf.close()
# Close Out File
outf.close()

Is there a better practice or reference material if simply just modifying data segments of .wav files?

  • Say you wanted to literally add a sound at a specific timestamp, that would be a more appropriate result to my question.
audio python-3.x python
1 Answers

Performance comparison

Let's examine first 3 ways to read WAVE files.

The slowest one - wave module

As you might have noticed already, wave module can be painfully slow. Consider this code:

import wave
import struct

wavefile = wave.open('your.wav', 'r') # check e.g. freesound.org for samples

length = wavefile.getnframes()
for i in range(0, length):
    wavedata = wavefile.readframes(1)
    data = struct.unpack("<h", wavedata)

For a WAVE as defined below:

Input File     : 'audio.wav'
Channels       : 1
Sample Rate    : 48000
Precision      : 16-bit
Duration       : 00:09:35.71 = 27634080 samples ~ 43178.2 CDDA sectors
File Size      : 55.3M
Bit Rate       : 768k
Sample Encoding: 16-bit Signed Integer PCM

it took on average 27.7s to load the full audio. The flip side to the wave module it is that is available out of the box and will work on any system.

The convenient one - audiofile

A much more convenient and faster solution is e.g. audiofile. According to the project description, its focus is on reading speed.

import audiofile as af

signal, sampling_rate = af.read(audio.wav)

This gave me on average 33 ms to read the mentioned file.

The fastest one - numpy

If we decide to skip header (as OP asks) and go solely for speed, numpy is a great choice:

import numpy as np

byte_length = np.fromfile(filename, dtype=np.int32, count=1, offset=40)[0]
data = np.fromfile(filename, dtype=np.int16, count=byte_length // np.dtype(np.int16).itemsize, offset=44)

The header structure (that tells us what offset to use) is defined here.

The execution of that code takes ~6 ms, 5x less than the audioread. Naturally it comes with a price / preconditions: we need to know in advance what is the data type.

Modifying the audio

Once you have the audio in a numpy array, you can modify it at will, you can also decide to stream the file rather than reading everything at once. Be warned though: since sound is a wave, in a typical scenario simply injecting new data at arbitrary time t will lead to distortion of that audio (unless it was silence).

As for writing the stream back, "modifying the container" would be terribly slow in Python. That's why you should either use arrays or switch to a more suitable language (e.g. C).

If we go with arrays, we should mind that numpy knows nothing about the WAVE format and therefore we'd have to define the header ourselves and write individual bytes. Perfectly feasible exercise, but clunky. Luckily, scipy provides a convenient function that has the benefits of numpy speed (it uses numpy underneath), while making the code much more readable:

from scipy.io.wavfile import write

fs = np.fromfile('audio.wav', dtype=np.int32, count=1, offset=24)[0] # we need sample rate

with open('audio_out.wav', 'a') as fout:
    new_data = data.append(np.zeros(2 * fs)) # append 2 seconds of zeros
    write(fout, fs, new_data)

It could be done in a loop, where you read a chunk with numpy / scipy, modify the array (data) and write to the file (with a for append).

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