mlx-spectro 0.3.1

High-performance STFT/iSTFT for Apple MLX with fused Metal kernels

mlx-spectro

High-performance STFT/iSTFT for Apple MLX — 2–3x faster STFT and 5–8x faster iSTFT than torch.stft/torch.istft on MPS, via fused Metal kernels.

from mlx_spectro import SpectralTransform

transform = SpectralTransform(n_fft=2048, hop_length=512, window_fn="hann")

spec = transform.stft(audio)                      # [B, T] → complex spectrogram
reconstructed = transform.istft(spec, length=T)    # complex spectrogram → [B, T]

from mlx_spectro import MelSpectrogramTransform

mel = MelSpectrogramTransform(
    sample_rate=24000,
    n_fft=2048,
    hop_length=240,
    n_mels=128,
    top_db=80.0,
    mode="torchaudio_compat",
)
mel_db = mel(audio)  # [B, n_mels, frames]

mlx-audio-separator uses mlx-spectro for MLX-native stem separation (Roformer, MDX, Demucs) and runs 1.8–3.1x faster end-to-end than python-audio-separator on torch+MPS. See benchmarks below.

Install

pip install mlx-spectro

With optional torch fallback support:

pip install mlx-spectro[torch]

Features

• Fused overlap-add with autotuned Metal kernels
• PyTorch-compatible STFT/iSTFT semantics
• Cached transforms for zero-overhead repeated calls
• Differentiable transforms for training with mx.grad
• mx.compile-friendly for tight inference loops
• Optional torch fallback for strict numerical parity

Quick Start

import mlx.core as mx
from mlx_spectro import SpectralTransform

transform = SpectralTransform(
    n_fft=2048,
    hop_length=512,
    window_fn="hann",
)

audio = mx.random.normal((1, 44100))
spec = transform.stft(audio, output_layout="bnf")
reconstructed = transform.istft(spec, length=44100, input_layout="bnf")

API

SpectralTransform

Main class for STFT/iSTFT operations.

SpectralTransform(
    n_fft: int,
    hop_length: int,
    win_length: int | None = None,
    window_fn: str = "hann",       # "hann", "hamming", "rect"
    window: mx.array | None = None,  # custom window array
    periodic: bool = True,
    center: bool = True,
    center_pad_mode: str = "reflect",   # "reflect" or "constant"
    center_tail_pad: str = "symmetric", # "symmetric" or "minimal"
    normalized: bool = False,
    istft_backend_policy: str | None = None,  # "auto", "mlx_fft", "metal", "torch_fallback"
)

Methods:

• stft(x, output_layout="bfn") — Forward STFT. Input: [T] or [B, T].
• istft(z, length=None, ...) — Inverse STFT. Returns [B, T].
• compiled_pair(length, layout="bnf", warmup_batch=None) — Return compiled (stft_fn, istft_fn) for steady-state loops (10–20% faster).
• warmup(batch=1, length=4096) — Force kernel compilation.

Centering and padding semantics:

• center=True, center_pad_mode="reflect", center_tail_pad="symmetric": default PyTorch-style centered STFT with reflect padding on both sides. This keeps the current fused Metal fast path.
• center=True, center_pad_mode="constant", center_tail_pad="symmetric": centered STFT with zero padding on both sides, matching the common Torch/librosa constant-pad interpretation.
• center=True, center_pad_mode="constant", center_tail_pad="minimal": centered STFT with zero left padding and only the minimal right padding needed to keep frame count at ceil(len / hop_length). This is useful for madmom-style frontends that should not emit an extra tail frame.

center_pad_mode="reflect" currently requires center_tail_pad="symmetric". When using center_tail_pad="minimal", istft(..., length=...) must be given an explicit length.

Padding Examples

Torch-style centered zero padding:

from mlx_spectro import SpectralTransform

transform = SpectralTransform(
    n_fft=2048,
    hop_length=512,
    window_fn="hann",
    center=True,
    center_pad_mode="constant",
    center_tail_pad="symmetric",
)

madmom-style centered framing without an extra tail frame:

import mlx.core as mx
import numpy as np
from mlx_spectro import SpectralTransform

window = mx.array(np.hanning(8192).astype(np.float32))
transform = SpectralTransform(
    n_fft=8192,
    hop_length=4410,
    win_length=8192,
    window=window,
    periodic=False,
    center=True,
    center_pad_mode="constant",
    center_tail_pad="minimal",
)

MelSpectrogramTransform

Mel frontend powered by SpectralTransform.

MelSpectrogramTransform(
    sample_rate: int = 24000,
    n_fft: int = 2048,
    hop_length: int = 240,
    win_length: int | None = None,
    n_mels: int = 128,
    f_min: float = 0.0,
    f_max: float | None = None,
    power: float = 2.0,
    norm: str | None = None,      # None or "slaney"
    mel_scale: str = "htk",       # "htk" or "slaney"
    top_db: float | None = 80.0,
    mode: str = "mlx_native",     # "mlx_native" or "torchaudio_compat"; "default" alias -> "mlx_native"
    center_pad_mode: str = "reflect",
    center_tail_pad: str = "symmetric",
)

Methods:

• spectrogram(x) — Returns power spectrogram [B, F, N].
• mel_spectrogram(x, to_db=True) / __call__(x, to_db=True) — Returns [B, n_mels, N].

Mode semantics:

• mode="mlx_native": per-example top_db clipping (batch-independent behavior).
• mode="torchaudio_compat": torchaudio-compatible packed-batch clipping semantics for parity-sensitive pipelines.

onset_strength(x, *, sample_rate=22050, n_fft=2048, hop_length=512, n_mels=128, ..., center_pad_mode="reflect", center_tail_pad="symmetric")

Half-wave rectified spectral flux of a dB-scaled mel spectrogram, matching librosa onset.onset_strength conventions. Returns [frames] for 1-D input or [B, frames] for batched input.

onset_strength_multi(x, *, sample_rate=22050, n_fft=2048, hop_length=512, n_mels=128, ..., center_pad_mode="reflect", center_tail_pad="symmetric")

Per-band half-wave rectified spectral flux (before averaging across frequency). Returns [n_mels, frames] for 1-D input or [B, n_mels, frames] for batched input.

get_transform_mlx(**kwargs)

Factory that returns cached SpectralTransform instances for repeated use.

make_window(window, window_fn, win_length, n_fft, periodic)

Create or validate a 1D analysis window.

resolve_fft_params(n_fft, hop_length, win_length, pad)

Resolve effective FFT parameters with PyTorch-compatible defaults.

Benchmarks

Apple M4 Max, macOS 26.3, MLX 0.30.6, PyTorch 2.10.0, 20 iterations (5 warmup).

STFT Forward

Config	mlx-spectro	torch MPS	mlx-stft	vs torch	vs mlx-stft
B=1 T=16k nfft=512	0.16 ms	0.21 ms	0.31 ms	1.4x	1.9x
B=4 T=160k nfft=1024	0.37 ms	1.00 ms	1.09 ms	2.7x	3.0x
B=8 T=160k nfft=1024	0.28 ms	0.71 ms	1.53 ms	2.5x	5.6x
B=4 T=1.3M nfft=1024	0.77 ms	2.18 ms	5.03 ms	2.8x	6.5x
B=8 T=480k nfft=1024	0.58 ms	1.30 ms	3.73 ms	2.2x	6.4x

iSTFT Forward

Config	mlx-spectro	torch MPS	mlx-stft	vs torch	vs mlx-stft
B=1 T=16k nfft=512	0.17 ms	0.49 ms	0.25 ms	3.0x	1.5x
B=4 T=160k nfft=1024	0.21 ms	1.00 ms	0.98 ms	4.7x	4.7x
B=8 T=160k nfft=1024	0.30 ms	1.61 ms	1.62 ms	5.4x	5.4x
B=4 T=1.3M nfft=1024	0.81 ms	5.76 ms	6.68 ms	7.1x	8.2x
B=8 T=480k nfft=1024	0.60 ms	4.10 ms	4.55 ms	6.8x	7.6x

Roundtrip (STFT → iSTFT) Forward + Backward

Config	mlx-spectro	torch MPS	vs torch
B=4 T=160k nfft=1024	0.62 ms	2.25 ms	3.6x
B=8 T=160k nfft=1024	1.04 ms	4.38 ms	4.2x
B=4 T=480k nfft=1024	1.59 ms	6.59 ms	4.1x
B=4 T=1.3M nfft=1024	4.33 ms	17.63 ms	4.1x
B=1 T=1.3M nfft=1024	1.21 ms	4.20 ms	3.5x

Roundtrip Accuracy (STFT → iSTFT max abs error)

Config	mlx-spectro	torch MPS
B=1 T=16k nfft=512	1.67e-06	2.38e-06
B=4 T=160k nfft=2048	2.86e-06	5.25e-06
B=8 T=480k nfft=1024	3.81e-06	4.77e-06

To reproduce:

• Full suite: python scripts/benchmark.py
• Dispatch overhead profile: python scripts/benchmark.py --dispatch-profile

Real-world: mlx-audio-separator

mlx-audio-separator is an MLX-native music stem separation library supporting Roformer, MDX, Demucs, and more. End-to-end separation speedup vs python-audio-separator (torch on MPS), measured on 30s stereo 44.1 kHz tracks. Apple M4 Max, PyTorch 2.10.0, MLX 0.30.6, ABBA ordering, 2 repeats.

Model	Arch	torch+MPS (s)	MLX (s)	E2E speedup
UVR-MDX-NET-Inst_HQ_3	MDX	4.25	1.36	3.1x
htdemucs	Demucs	3.35	1.29	2.6x
Mel-Roformer Karaoke	MDXC	5.60	2.66	2.1x
BS-Roformer	MDXC	6.48	3.56	1.8x

STFT/iSTFT kernel speedups within these pipelines are even larger (2–3x STFT, 5–8x iSTFT vs torch).

Compiled Mode

For tight inference loops with fixed input shapes, compiled_pair eliminates per-call Python dispatch overhead (10–20% faster for small workloads):

t = SpectralTransform(n_fft=1024, hop_length=256, window_fn="hann")
stft, istft = t.compiled_pair(length=44100, warmup_batch=2)

for chunk in audio_stream:
    z = stft(chunk)
    z = process(z)
    y = istft(z)
    mx.eval(y)

Use the eager t.stft() / t.istft() methods when input shapes vary.

Environment Variables

Variable	Default	Description
SPEC_MLX_AUTOTUNE	1	Enable Metal kernel autotuning
SPEC_MLX_TGX	—	Force threadgroup size (e.g. 256 or kernel:256)
SPEC_MLX_AUTOTUNE_PERSIST	1	Persist autotune results to disk
SPEC_MLX_AUTOTUNE_CACHE_PATH	—	Override autotune cache file path
MLX_OLA_FUSE_NORM	1	Enable fused OLA+normalization kernel
SPEC_MLX_CACHE_STATS	0	Enable cache debug counters

License

MIT