hunterHearsPy 1.4.0

Audio processing.

hunterHearsPy

A comprehensive collection of Python utilities for audio processing.

Audio Processing Made Simple

Load and Save Audio Files

Read audio files with automatic stereo conversion and sample rate control:

from hunterHearsPy import readAudioFile, writeWAV

# Load audio with sample rate conversion
waveform = readAudioFile('input.wav', sampleRate=44100)

# Save in WAV format (always 32-bit float)
writeWAV('output.wav', waveform)

Process Multiple Audio Files at Once

Load and process batches of audio files:

from hunterHearsPy import loadWaveforms

# Load multiple files with consistent formatting
array_waveforms = loadWaveforms(['file1.wav', 'file2.wav', 'file3.wav'])

# The result is a unified array with shape (channels, samples, file_count)

Work with Spectrograms

Convert between waveforms and spectrograms:

from hunterHearsPy import stft, halfsine

# Create a spectrogram with a half-sine window
spectrogram = stft(waveform, windowingFunction=halfsine(1024))

# Convert back to a waveform
reconstructed = stft(spectrogram, inverse=True, lengthWaveform=original_length)

Process Audio in the Frequency Domain

Create functions that operate on spectrograms:

from hunterHearsPy import waveformSpectrogramWaveform

def boost_low_frequencies(spectrogram):
    # Boost frequencies below 500 Hz
    spectrogram[:, :10, :] *= 2.0
    return spectrogram

# Create a processor that handles the STFT/ISTFT automatically
processor = waveformSpectrogramWaveform(boost_low_frequencies)

# Apply the processor to a waveform
processed_waveform = processor(original_waveform)

Development

I want:

• consistent waveforms,
• consistent spectrograms, and
• efficient, reliable transformations.

Therefore, I want one package, this package, to manage those objectives with a single source of truth for configurable universal settings.

• I need good default universal settings.
• I need an easy way to change a universal setting.
• I want ad hoc overrides for some or all universal settings.

I don't know a good way to implement user-configurable universal settings. Therefore, as I normally do, I will use my HARDCODED system as a placeholder.

Semiotics

• channel, rarely channels.
• time is more generic than samples and often preferred.
• array is generic.
• when talking about a NumPy ndarray, write ndarray not array.
• Use sampleRate unless I have a compelling reason not to.

Preferred packages

• NumPy
• scipy
• hunterMakesPy
• tqdm (for status messages)
• cytoolz via the Z0Z_tools package (until it finds a forever home)
• more_itertools
• astToolKit
• pytest

Probably won't need

• analyzeAudio
• gmpy2
• numba
• platformdirs
• sympy
• torch-einops-kit

Not using PyTorch for "business" logic

• But, I must have torch compatibility.
• At a minimum, a transformation to and from torch that the user must call.
• astToolkit easily creates real torch APIs and identifiers, however, for the windowingFunctions module in windowingFunctionsTensor.

Not using librosa

• Dependency bloated.
• Slow.
• Less precise than I want.
• I generally dislike the API and identifiers.

Disfavored packages

• pandas

More vectorization

• Few or no for loops.
• Few or no for object comprehensions.

Installation

pip install hunterHearsPy

My recovery