spleeter

Pros of librosa

• More comprehensive audio analysis toolkit with a wider range of features
• Better documentation and extensive examples for various audio processing tasks
• Actively maintained with frequent updates and community support

Cons of librosa

• Slower processing speed for certain operations compared to Spleeter
• Lacks specialized source separation capabilities out of the box
• Steeper learning curve for beginners due to its extensive feature set

Code Comparison

librosa

import librosa

y, sr = librosa.load('audio.wav')
tempo, beat_frames = librosa.beat.beat_track(y=y, sr=sr)
mfcc = librosa.feature.mfcc(y=y, sr=sr)

Spleeter

from spleeter.separator import Separator

separator = Separator('spleeter:2stems')
separator.separate_to_file('audio.mp3', 'output/')

Librosa offers a more versatile approach to audio analysis, providing functions for various tasks like tempo detection and feature extraction. Spleeter, on the other hand, focuses specifically on source separation, offering a simpler API for splitting audio into different stems. While Librosa requires more code for complex operations, it provides greater flexibility. Spleeter excels in its specialized task with a more straightforward implementation for source separation.

Pros of Essentia

• More comprehensive audio analysis toolkit with a wider range of features
• Supports multiple programming languages (C++, Python, JavaScript)
• Actively maintained with regular updates and contributions

Cons of Essentia

• Steeper learning curve due to its extensive functionality
• Requires more setup and configuration compared to Spleeter
• May be overkill for simple audio separation tasks

Code Comparison

Spleeter (Python):

from spleeter.separator import Separator

separator = Separator('spleeter:2stems')
separator.separate_to_file('audio_example.mp3', 'output/')

Essentia (Python):

import essentia.standard as es

audio = es.MonoLoader(filename='audio_example.mp3')()
w = es.Windowing(type='hann')
spectrum = es.Spectrum()
mfcc = es.MFCC()

for frame in es.FrameGenerator(audio, frameSize=1024, hopSize=512):
    mfcc_bands, mfcc_coeffs = mfcc(spectrum(w(frame)))

This comparison highlights the different focus of each library. Spleeter is designed specifically for audio source separation, making it simpler to use for that task. Essentia, on the other hand, offers a broader range of audio analysis tools, requiring more code but providing greater flexibility for various audio processing tasks.

Pros of madmom

• Broader range of music information retrieval (MIR) tasks, including beat tracking, onset detection, and chord recognition
• More flexible and customizable for advanced users and researchers
• Provides both offline and real-time processing capabilities

Cons of madmom

• Steeper learning curve and more complex setup compared to Spleeter
• Less focused on source separation, which is Spleeter's primary function
• May require more computational resources for certain tasks

Code Comparison

madmom example:

from madmom.features.beats import RNNBeatProcessor
from madmom.features.tempo import TempoEstimationProcessor

beats = RNNBeatProcessor()(audio_file)
tempo = TempoEstimationProcessor()(beats)

Spleeter example:

from spleeter.separator import Separator

separator = Separator('spleeter:2stems')
separator.separate_to_file(audio_file, output_path)

Both libraries offer Python-based interfaces, but madmom's API is more diverse and requires more specific function calls for different MIR tasks. Spleeter, on the other hand, provides a simpler interface focused primarily on source separation.

Pros of Pedalboard

• More versatile, offering a wide range of audio effects and processing capabilities
• Designed for real-time audio processing, making it suitable for live applications
• Provides a user-friendly API for audio manipulation

Cons of Pedalboard

• Lacks specific source separation functionality, which is Spleeter's primary focus
• May require more manual configuration for complex audio processing tasks

Code Comparison

Spleeter (source separation):

from spleeter.separator import Separator

separator = Separator('spleeter:2stems')
separator.separate_to_file('input.mp3', 'output_directory')

Pedalboard (audio effects):

from pedalboard import Pedalboard, Reverb, Delay

board = Pedalboard([Reverb(), Delay(delay_seconds=0.5)])
with AudioFile('input.mp3') as f:
    audio = f.read(f.frames)
    effected = board(audio, f.samplerate)

Both libraries offer Python-based audio processing, but they serve different purposes. Spleeter focuses on source separation, while Pedalboard provides a broader range of audio effects and processing tools. The choice between them depends on the specific audio manipulation requirements of your project.

Pros of nnAudio

• Focuses on audio processing and spectrograms using neural networks
• Provides GPU acceleration for faster processing
• Offers a wider range of audio processing functions beyond source separation

Cons of nnAudio

• Less specialized in music source separation compared to Spleeter
• May require more setup and configuration for specific tasks
• Potentially steeper learning curve for users unfamiliar with neural network approaches

Code Comparison

nnAudio example:

import torch
from nnAudio import Spectrogram
spec_layer = Spectrogram.STFT()
spec = spec_layer(torch.randn(1, 16000))

Spleeter example:

from spleeter.separator import Separator
separator = Separator('spleeter:2stems')
prediction = separator.separate_numpy(waveform)

Summary

nnAudio is a more general-purpose audio processing library with GPU acceleration, offering a wide range of functions for spectrograms and audio analysis. Spleeter, on the other hand, is specifically designed for music source separation, making it easier to use for that particular task. nnAudio may require more setup but provides greater flexibility, while Spleeter offers a more straightforward approach to source separation with pre-trained models.

Pros of pytorch/audio

• More comprehensive audio processing toolkit with broader functionality
• Seamless integration with PyTorch ecosystem for deep learning
• Active development and regular updates from a large community

Cons of pytorch/audio

• Steeper learning curve for beginners
• May require more setup and configuration for specific tasks
• Potentially higher computational requirements for some operations

Code Comparison

spleeter:

from spleeter.separator import Separator

separator = Separator('spleeter:2stems')
separator.separate_to_file('audio_example.mp3', 'output/')

pytorch/audio:

import torchaudio

waveform, sample_rate = torchaudio.load('audio_example.mp3')
spectrogram = torchaudio.transforms.Spectrogram()(waveform)
mel_spectrogram = torchaudio.transforms.MelSpectrogram()(waveform)

Summary

spleeter is focused on audio source separation, offering a straightforward API for splitting audio into stems. It's easy to use but has a narrower scope. pytorch/audio provides a more comprehensive set of audio processing tools, integrating well with the PyTorch ecosystem. While it offers greater flexibility and functionality, it may require more expertise to utilize effectively. The choice between the two depends on the specific requirements of your audio processing task.