mlx-stft 0.1.2

An implementation of STFT and Inverse STFT in mlx

Short-Time Fourier Transform (STFT)

This module implements Forward and inverse Short-Time Fourier Transform (STFT) in Python using MLX.

It is designed to inherit from the nn.Module, which allows it to be used either as a standalone module or as part of a larger neural network architecture.

Installation

You can install mlx_stft directly from pypi:

pip install mlx_stft

Or you can install it directly from the source code:

git clone https://github.com/nuniz/mlx_stft.git
cd mlx_stft
python setup.py install

Features

• Multiple Backends: FFT-based or convolution-based implementations
• JIT Compilation: Compiled versions (CompiledSTFT, CompiledISTFT)
• Spectrum Modes: Support for both one-sided and dual-sided spectrums

Usage

from mlx_stft import STFT, ISTFT

# Forward transform
stft = STFT(n_fft=1024, hop_length=256, onesided=True)
spectrum = stft(x)

# Inverse transform
istft = ISTFT(n_fft=1024, hop_length=256, onesided=True)
reconstructed = istft(spectrum, length=x.shape[-1])

Compiled (JIT) Versions

from mlx_stft import CompiledSTFT, CompiledISTFT

# Use compiled versions for better performance
stft = CompiledSTFT(n_fft=1024, hop_length=256, onesided=True)
istft = CompiledISTFT(n_fft=1024, hop_length=256, onesided=True)

Arguments

STFT

n_fft: Number of Fourier transform points.
win_length: Length of the STFT window (default: same as n_fft).
hop_length: Number of audio samples between adjacent STFT columns.
window: Type of window function to apply (default is "hann").
onesided: Whether to return only the non-redundant part of the spectrum (default is False).
return_db: Whether to return the result in decibels (default is False).
use_fft: Force FFT-based computation. If None, auto-selects based on n_fft

ISTFT

n_fft: Number of Fourier transform points.
win_length: Length of the STFT window (default: same as n_fft).
hop_length: Number of audio samples between adjacent STFT columns.
window: Type of window function to apply (default is "hann").
onesided: Whether the input spectrum is one-sided (default is False).
use_fft: Force FFT-based computation. If None, auto-selects based on n_fft.
center: Whether to center the frames (default is True).

Performance

Benchmarks on Apple M4 (16GB RAM) with n_fft=1024, signal_length=16000, batch_size=1:

Transform	Compiled	Onesided	Time (ms)	Throughput
STFT	No	Yes	0.64 ± 0.07	25.0 M samples/s
STFT	Yes	Yes	0.44 ± 0.03	36.6 M samples/s
iSTFT	No	Yes	0.86 ± 0.13	18.7 M samples/s
iSTFT	Yes	Yes	0.82 ± 0.12	19.5 M samples/s
Roundtrip	Yes	Yes	0.83 ± 0.07	19.2 M samples/s

Run your benchmarks:

python scripts/benchmark.py --quick  # Quick benchmark
python scripts/benchmark.py          # Full benchmark with multiple configurations

Example

The STFT is illustrated in the picture below using the NOIZEUS dataset's sp09.wav file.

One-Sided STFT

Dual-Sided STFT