NumPy-like audio manipulation library

4 months ago 3

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.

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.

Support up to numpy 2.2.0
Streaming input, streaming output:
- AudioSample now supports receiving a python generator for input Generator[Union[bytes,numpy,AudioSample]]
- Warning: It currently still stores everything in memory so this can't live forever.
- Plugin functionality is not supported in stream mode.
- streaming mode requires PyAV (See example below):
Constructor supports numpy buffers (same as calling AudioSample.from_numpy use force_read_sample_rate to set sample rate.)

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)

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")

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.

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)

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

Streaming code example below:

def chunkify(buffer: bytes): CHUNK_SIZE = 1000 for i in range(0, len(buffer), CHUNK_SIZE): yield buffer[i:i+CHUNK_SIZE] testmp3 = open('test.mp3','rb').read() collect = b'' for chunk in AudioSample(chunkify(testmp3), force_read_sample_rate=48000, force_sample_rate=8000).as_data_stream(force_out_format='mulaw') collect += chunk open('test.mulaw','wb').write(chunk)

AudioSample is released under the MIT License.

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

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.

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

Read Entire Article

NumPy-like audio manipulation library

AudioSample outperforms PyDub

open concatenation and save.

AudioSample mix vs. PyDub overlay

AudioSample outperforms SoundFile

Streaming code example below:

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