AudioSample

by deepdub.ai

AudioSample is an optimized numpy-like audio manipulation library, created for researchers, used by developers.

It is an advanced audio manipulation library designed to provide researchers and developers with efficient, numpy-like tools for audio processing. It supports complex audio operations with ease and offers a familiar syntax for those accustomed to numpy.

AudioSample is perfect for data loading and ETLs, because its fast and has a low memory footprint due to lazy actions.

Features

Seamless Audio Operations: Perform a wide range of audio manipulations, including mixing, filtering, and transformations.
Integration with Numpy: Leverage numpy's syntax and capabilities for intuitive audio handling.
Integration with Torch: Export audio directly to and from torch tensors.
High Performance: Optimized for speed and efficiency, suitable for research and production environments. Most actions are lazy, so no operation done until absolutely necessary.
Extensive I/O Support: Easily read from and write to various audio formats. Utilizes PyAv - to support multiple ranges.

Installation

To install AudioSample, use pip:

to install all prerequisites:

pip install audiosample[all] 
#linux/WSL:
pip install audiosample[all] 

#Possible extras are:
[av] - only av
[torch] - add torch
[tests] - include everything for tests.
[noui] - install without jupyter support.

#Mac OS:
brew install portaudio
#linux/WSL:
apt-get install portaudio19-dev
[play] - bare, with ability to play audio in console. (uses pyaudio)

Usage

Here's a quick example of how to load, process, and save audio using AudioSample:

import audiosample as ap
import numpy as np

# Create a 1 second audio sample with 44100 samples per second and 2 channels
au = ap.AudioSample.from_numpy(np.random.rand(2, 48000), rate=48000)
beep = ap.AudioSample().beep(1).to_stereo()
out = au.gain(-12) * beep
out.write("beep_with_overlayed_noise.mp3")
out = au.gain(-10) + au.silence(1) + beep
out.write("noise_then_silence_then_beep.mp3")

Additional Operations

Resampling: Fast resampling of audio.
Normalization: Easily normalize audio levels.
Mixing: Easily mix multiple audio sources together. Using * sign
Concat Easily concat audio sources. Using + sign
Playback: Play audio directly in Jupyter notebooks or from the command line.

Documentation

Bench Marks

AudioSample outperforms PyDub

open concatenation and save.

longbeep is a 100s long wav file of beep.

import pydub
from audiosample import AudioSample
def test_audiosample():
    au = AudioSample()
    for i in range(0, 100):
        au += AudioSample("longbeep.wav")[50:51]
    au.write("out.wav")

def test_pydub():
    au = pydub.AudioSegment.empty()
    for i in range(0, 100):
        au += pydub.AudioSegment.from_file("longbeep.wav")[50:51]
    au.export("out.wav")

%timeit test_audiosample()
#52.9 ms ± 1.89 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

%timeit test_pydub()
#376 ms ± 15.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

AudioSample mix vs. PyDub overlay

def test_audiosample():
    au = AudioSample().silence(1)
    for i in range(0, 100):
        au *= AudioSample("longbeep.wav")[50:51]
    au.write("out.wav")
def test_pydub():
    au = pydub.AudioSegment.silent(1)
    for i in range(0, 100):
        au = au.overlay(pydub.AudioSegment.from_file("longbeep.wav")[50:51], 0)
    au.export("out.wav")

In [3]: %timeit test_audiosample()
12.7 ms ± 265 μs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [4]: %timeit test_pydub()
398 ms ± 26.2 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

AudioSample outperforms SoundFile

verylongbeep.wav - is a 3200s file. (293M)

import soundfile as sf
from audiosample import AudioSample

def test_audiosample():
    out = AudioSample("verylongbeep.wav")[1500:1501].as_numpy()

def test_soundfile():
    with sf.SoundFile("verylongbeep.wav") as f:
        f.seek(48000*1500)
        out = f.read(48000)

In [5]: %timeit test_audiosample()
35.8 μs ± 1.69 μs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)
In [6]: %timeit test_soundfile()
140 μs ± 8.89 μs per loop (mean ± std. dev. of 7 runs, 10,000 loops each)

For detailed instructions and API references, type help(AudioSample)

Examples

Explore the examples notebook to see practical applications of AudioSample in action.

License

AudioSample is released under the MIT License.

Contributing

Contributions are welcome! Please follow the contributing guidelines to submit changes.

About Deepdub

AudioSample is developed by Deepdub, a company specializing in AI-driven audio solutions. Deepdub focuses on enhancing media experiences through cutting-edge technology, enabling content creators to reach global audiences with high-quality, localized audio.

Support

If you have questions or need help, please open an issue on GitHub.

deepdub-ai/audiosample