It's not mine but an author (Qu Lefan) claim, anyway since it's Python souce is available you can run/compile it on linux (but I think it should work on Wine) too.

GH-code explaination:

However it really needs an english GUI:


Last but not least, it would be really interesting to set up a comparative test (scientific - null test - not perceptual) to understand the real effectiveness those solutions.

 

Last edited: Sep 11, 2025

Have you tried running it on paperspace or run pod?

 

Results:

I don't understand why ceiling appears. Apparently the effect has hard cutoff for bands it works with? Don't know

Second test with the following settings:

Modulation times: 8

Attenuation amplitude: 1,00

Preprocessing high-pass filter cutoff frequency (Hz): 500

Post-processing high-pass filter cutoff frequency (Hz): 10000

Filter order: 11

Hz: 44100



Translation:


1 - Open audio file (ALAC, MP3, WAV, FLAC, Ogg, Aiff, Aif, AAC, WMA, mka)

2 - Clean selected audio

3 - Change output path

4 - Start process

5 - Modulation times

6 - Attenuation amplitude

7 - Preprocessing high-pass filter cutoff frequency (Hz)

8 - Post-processing high-pass filter cutoff frequency (Hz)

9 - Filter order

10 - Select sample rate

11 - Status Bar

12 - Select output format ALAC or FLAC

13 - Cancel process

So, what do the experts think about the quality relative to the other tools like Stereo Tool, DSEE-HX & Nvidia's Diffusion audio restoration ?

 

I really got under the skin of Stereo Tool this time after reading your posts and created three presets that seems to work fine except something I configured incorrectly that effects the transients and the result is more punchy sound. If anyone reading this and uses Stereo tool and has any idea what I might be doing wrong, please help.

 

Here we go, here's another (dated ?) contender on the scene:
Emiya Engine (MATLAB/VST2 plugin)


Since their sources are open, I've also asked GH-Copilot (GPT-5) if is possible to combine those solutions (GH-account needed):

https://github.com/copilot/share/42064124-4804-8431-9101-0840c4484919

Last but not least, deton24 shared his (crazy!) Make your own remaster document/guide, where he reviewed most of these tools.

I am increasingly convinced that an objective (scientific, not perceptual) comparison between the various solutions would be truly interesting and useful.

 

not a delossifier but if we are on it,

thoughts on axiom?

 

Nice tool, anyway in this specific field, I would love to evaluate DeltaWave Audio Null Comparator results (ground truth - read source file - vs delossyfied).

 

Thanks. I tried your python script that you got Gemini to create it for you but it errors out. I tried to get Gemini to sort it out but that wasn't successful either. Gemini is a moron so maybe it's incompetence that it couldn't resolve error or maybe there's something inherenly wrong with the core of the script.



Would it be possible for you to refine the script so I can give it another try ?

Thanks.

 

As claimed, it's not "mine" but GH-copilot generated (for fun).

If you want better results with so-called "vibe-coding" try Claude instead.

 

I've played a bit with Emiya Engine's VST2 plugin...

Original WAV:


MP3 from original:


Enhanced from MP3:


MP3 - Enhanced null:

 

I vibe coded a version of this plugin for mac if anyone wants to test it. No gui just algo.

https://pixeldrain.com/u/C3PdLA8P


(not the most sexiest rn)


I noticed that AkkoMode works better but adds some noise. I also added a dry / wet knob to blend it the signal.

 

...what about combine all ?
Emya + DSRE + FFTW (dunno in what order is better to apply them) as suggested by GH-copilot: https://github.com/copilot/share/42064124-4804-8431-9101-0840c4484919

 

@curtified I'm 100% behind that idea but only Windows here

 

you can get the windows one here:

 

I can try.. IDK if that codebase is realtime

 

No, I meant the one that combines the three like forart suggested.

 

...that's why Python - at least in experimental stage - is the way to go, IMHO...

Here's a GPT-5 optimization of GH-copilot "mixup" code: https://chatgpt.com/share/68ceeeef-3d70-800f-9074-4a45cd77cf2e

Code:

# enhanced_pipeline.py
import numpy as np
import resampy
import librosa
import scipy.signal as signal
import pyfftw
from typing import Optional, Dict, Tuple

# Ensure float32 for speed by default
DTYPE = np.float32

# -------------------------
# Utilities: shape, norm, filters
# -------------------------
def _ensure_2d(x: np.ndarray) -> Tuple[np.ndarray, bool]:
    """Return (x2d, was_1d). We use shape (n_samples, n_channels)."""
    x = np.asarray(x)
    if x.ndim == 1:
        return x.reshape(-1, 1).astype(DTYPE), True
    elif x.ndim == 2:
        # either (n,) or (channels, n)? assume (n, ch) per pipeline contract
        return x.astype(DTYPE), False
    else:
        raise ValueError("Input must be 1D or 2D with shape (n_samples, n_channels).")

def normalize_peak(x: np.ndarray, eps=1e-12) -> np.ndarray:
    """Peak normalization per-channel to ±1."""
    peak = np.max(np.abs(x), axis=0, keepdims=True)
    return x / (peak + eps)

def rms(x: np.ndarray, axis=0, eps=1e-12):
    return np.sqrt(np.mean(x**2, axis=0) + eps)

# Stable highpass via SOS
def highpass_sos(x: np.ndarray, cutoff_hz: float, sr: float, order: int = 8) -> np.ndarray:
    if cutoff_hz <= 0:
        return x
    sos = signal.butter(order, cutoff_hz / (sr / 2), btype='high', output='sos')
    # axis=0 (time axis)
    return signal.sosfiltfilt(sos, x, axis=0).astype(DTYPE)

# -------------------------
# DSRE: Single Sideband (vectorized)
# -------------------------
def dsre_enhance(x: np.ndarray, sr: float, m=8, decay=1.25,
                 pre_hp=3000.0, post_hp=16000.0, hp_order=8,
                 mix=1.0) -> np.ndarray:
    """
    Vectorized DSRE: compute analytic signal for all channels (hilbert axis=0)
    and modulate with a bank of offsets; then mix back with original.
    mix: wet/dry mix of enhancement (0..1).
    """
    x2d, was_1d = _ensure_2d(x)
    x_hp = highpass_sos(x2d, pre_hp, sr, order=hp_order)

    N = x_hp.shape[0]
    t = np.arange(N, dtype=DTYPE) / float(sr)  # time axis
    analytic = signal.hilbert(x_hp, axis=0)  # shape (N, ch), complex

    # prepare summed modulation
    d_res = np.zeros_like(x_hp, dtype=DTYPE)
    for i in range(m):
        shift_hz = (i + 1) * sr / (m * 2.0)  # original intent preserved
        phase = np.exp(2j * np.pi * shift_hz * t)[:, None]
        comp = analytic * phase
        weight = np.exp(-(i + 1) * decay)
        d_res += comp.real * weight

    # post highpass
    d_res = highpass_sos(d_res, post_hp, sr, order=hp_order)

    # mix safely using RMS matching
    target_rms = rms(x2d, axis=0)
    res_rms = rms(d_res, axis=0)
    gain = (target_rms / (res_rms + 1e-12))[None, :]
    d_res *= gain  # match energy so enhancement isn't too loud

    out = (1.0 - mix) * x2d + mix * (x2d + d_res)  # allow configurable mix
    return out if not was_1d else out[:, 0]

# -------------------------
# FFT-based spectral upsampling (zero-pad in freq domain) using pyfftw
# -------------------------
def spectral_upsample_fft(x: np.ndarray, upscale: int = 2, threads: int = 1) -> np.ndarray:
    """
    Upsample by integer factor using frequency-domain zero-padding.
    For real input signals: use rfft/irfft.
    x: (N, channels)
    Returns: (N*upscale, channels)
    """
    x2d, was_1d = _ensure_2d(x)
    N, C = x2d.shape
    newN = N * upscale

    # prepare output
    y = np.zeros((newN, C), dtype=DTYPE)

    # use rfft/irfft for each channel; build FFTW plans reuse per channel
    for ch in range(C):
        real_in = x2d[:, ch].astype(DTYPE)
        # rfft length
        rlen = N // 2 + 1
        # forward rfft using fftw
        a = pyfftw.empty_aligned(N, dtype='float32')
        b = pyfftw.empty_aligned(rlen, dtype='complex64')
        fft = pyfftw.builders.rfft(a, threads=threads)
        ifft = pyfftw.builders.irfft(b, n=newN, threads=threads)

        # copy into aligned array
        a[:] = real_in
        spec = fft()  # shape (rlen,)
        # create zero-padded spectrum for newN
        new_rlen = newN // 2 + 1
        spec_padded = np.zeros(new_rlen, dtype=np.complex64)
        # copy low frequencies
        copy_len = min(rlen, new_rlen)
        spec_padded[:copy_len] = spec[:copy_len]
        # Note: for odd/even lengths, proper scaling in irfft handled by ifft

        b[:] = spec_padded
        y[:, ch] = ifft().astype(DTYPE)

    return y if not was_1d else y[:, 0]

# -------------------------
# AkkoMode jitter (smoothed envelope)
# -------------------------
def akkomode_jitter(x: np.ndarray, sr: float, depth_low=0.02, depth_high=0.12,
                    env_cutoff_hz=6.0) -> np.ndarray:
    """
    Generate a smooth random envelope per channel (lowpass filtered noise)
    and apply small amplitude modulation for 'jitter' that is musical and not high-frequency noise.
    depth_low/high in fractional amplitude (0..1).
    """
    x2d, was_1d = _ensure_2d(x)
    N, C = x2d.shape
    # generate per-channel random noise and lowpass filter it to make an envelope
    env = np.zeros((N, C), dtype=DTYPE)
    # lowpass SOS
    sos = signal.butter(4, env_cutoff_hz / (sr / 2), btype='low', output='sos')
    for ch in range(C):
        noise = np.random.randn(N).astype(DTYPE)
        # smooth envelope
        env[:, ch] = signal.sosfiltfilt(sos, noise).astype(DTYPE)
        # normalize envelope to [-1,1]
        env[:, ch] /= (np.max(np.abs(env[:, ch])) + 1e-12)

    # map envelope to depth range
    depths = depth_low + (depth_high - depth_low) * ((env + 1.0) / 2.0)  # [depth_low..depth_high]
    out = x2d * (1.0 + depths)  # gentle amplitude modulation
    return out if not was_1d else out[:, 0]

# -------------------------
# CopyBand: correct STFT band shift (no wrap by default)
# -------------------------
def copyband_stft(x: np.ndarray, sr: float,
                  band_hpfc=6000.0, band_sft=16000.0,
                  band_gain=1.2, n_fft: Optional[int] = 2048,
                  hop_length: Optional[int] = None,
                  zero_fill: bool = True) -> np.ndarray:
    """
    Shift a band upward: extract frequency band [band_hpfc, band_sft),
    shift it upward by (band_sft - band_hpfc) and add gain.
    zero_fill=True -> zero-fill shifted-in bins (no wraparound).
    """
    x2d, was_1d = _ensure_2d(x)
    if hop_length is None:
        hop_length = n_fft // 4

    N, C = x2d.shape
    out = np.zeros_like(x2d, dtype=DTYPE)
    for ch in range(C):
        channel = x2d[:, ch]
        S = librosa.stft(channel, n_fft=n_fft, hop_length=hop_length)
        # S shape: (n_fft//2+1, n_frames)
        freqs = np.linspace(0, sr/2, S.shape[0])
        # compute bin indices
        low_bin = int(np.searchsorted(freqs, band_hpfc, side='left'))
        high_bin = int(np.searchsorted(freqs, band_sft, side='left'))
        shift_bins = high_bin - low_bin  # shift upward

        # create copy
        S2 = S.copy()
        if shift_bins == 0:
            S2[low_bin:high_bin, :] *= band_gain
        else:
            # zero out original band or attenuate
            S2[low_bin:high_bin, :] *= 0.0
            target_start = low_bin + shift_bins
            if target_start < S.shape[0]:
                # fill target band with shifted content (zero-fill if needed)
                end = min(target_start + (high_bin - low_bin), S.shape[0])
                insert_len = end - target_start
                S2[target_start:end, :] += S[low_bin:low_bin+insert_len, :] * band_gain
                # if not zero_fill, allow wrap-around (rarely desired)
                if not zero_fill and insert_len < (high_bin - low_bin):
                    wrap_len = (high_bin - low_bin) - insert_len
                    S2[:wrap_len, :] += S[low_bin+insert_len:high_bin, :] * band_gain
            else:
                # shifted beyond Nyquist -> discard or optionally wrap
                pass
        ych = librosa.istft(S2, hop_length=hop_length, length=len(channel))
        out[:, ch] = ych.astype(DTYPE)

    return out if not was_1d else out[:, 0]

# -------------------------
# Resampling helper (safe axis)
# -------------------------
def resample_audio(x: np.ndarray, orig_sr: int, target_sr: int) -> np.ndarray:
    x2d, was_1d = _ensure_2d(x)
    # resampy expects shape (n,) or (channels, nsamples) with axis default -1 -> use transpose
    # We'll resample each channel separately (explicit, memory-friendly)
    N, C = x2d.shape
    out_len = int(np.ceil(N * target_sr / orig_sr))
    out = np.zeros((out_len, C), dtype=DTYPE)
    for ch in range(C):
        out[:, ch] = resampy.resample(x2d[:, ch], orig_sr, target_sr)
    return out if was_1d else out

# -------------------------
# Pipeline
# -------------------------
def enhance_audio(x: np.ndarray, sr: int,
                  dsre_params: Optional[Dict] = None,
                  fft_params: Optional[Dict] = None,
                  akko_params: Optional[Dict] = None,
                  copyband_params: Optional[Dict] = None,
                  target_sr: Optional[int] = None,
                  upsc

 

Last edited: Sep 20, 2025 at 11:23 PM

Here's a Claude-5 optimized (both for output quality and performances) commandline - Python - parametric audio enhancer that integrates all:

Code:

#!/usr/bin/env python3
"""
audio_enhancer.py (Optimized Version)

Command-line audio enhancement tool with performance and quality optimizations.

Usage:
    python audio_enhancer.py input.wav [--mix 100] [--threads auto]

Produces: input_enhanced.wav in the same folder (32-bit float WAV).

Key Optimizations:
- Full CPU multithreading support with optimal thread allocation
- Vectorized operations and memory-efficient processing
- Quality improvements: better filters, window functions, overlap-add
- Smart caching and pre-allocation to minimize memory allocations
- Optional GPU acceleration support
- Advanced spectral processing with better phase preservation

Dependencies:
    numpy, scipy, soundfile, librosa, resampy, pyfftw (recommended), numba (optional)
"""

import argparse
import os
import sys
import warnings
from dataclasses import dataclass
from typing import Tuple, Optional
from concurrent.futures import ThreadPoolExecutor
import multiprocessing as mp

import numpy as np
import soundfile as sf
import librosa
import resampy
import scipy.signal as signal
from scipy.ndimage import uniform_filter1d

# Optional: use pyfftw for faster FFTs if available
try:
    import pyfftw
    pyfftw.config.NUM_THREADS = mp.cpu_count()
    pyfftw.config.PLANNER_EFFORT = 'FFTW_MEASURE'
    _HAS_PYFFTW = True
except ImportError:
    pyfftw = None
    _HAS_PYFFTW = False

# Optional: use numba for JIT compilation
try:
    from numba import jit, prange
    _HAS_NUMBA = True
except ImportError:
    # Create dummy decorator if numba is not available
    def jit(*args, **kwargs):
        def decorator(func):
            return func
        return decorator
    prange = range
    _HAS_NUMBA = False

# Global configuration
DTYPE = np.float32
DEFAULT_CHUNK_SIZE = 32768  # Process in chunks for memory efficiency
_fft_cache = {}  # Cache for FFT planning

@dataclass
class AutoParams:
    target_sr: int
    dsre_m: int
    dsre_decay: float
    dsre_pre_hp: float
    dsre_post_hp: float
    dsre_mix: float
    akko_depth_low: float
    akko_depth_high: float
    akko_env_hz: float
    copyband_hpfc: float
    copyband_sft: float
    copyband_gain: float
    upsample_factor: int
    n_threads: int


def get_optimal_threads() -> int:
    """Determine optimal number of threads for processing."""
    cpu_count = mp.cpu_count()
    # Leave one core free for system tasks, but use at least 1
    return max(1, cpu_count - 1)


def _ensure_2d(x: np.ndarray) -> Tuple[np.ndarray, bool]:
    """Ensure input is 2D with optimized memory layout."""
    x = np.asarray(x, dtype=DTYPE, order='C')  # Ensure C-contiguous for better performance
    if x.ndim == 1:
        return x.reshape(-1, 1), True
    elif x.ndim == 2:
        return x, False
    else:
        raise ValueError("Input must be 1D or 2D (n_samples, n_channels)")


@jit(nopython=True if _HAS_NUMBA else False, parallel=True)
def normalize_peak_numba(x: np.ndarray, eps: float = 1e-12) -> np.ndarray:
    """Optimized peak normalization with numba."""
    if x.ndim == 1:
        peak = np.max(np.abs(x))
        return x / (peak + eps)
    else:
        for ch in prange(x.shape[1]):
            peak = np.max(np.abs(x[:, ch]))
            x[:, ch] = x[:, ch] / (peak + eps)
        return x


def normalize_peak(x: np.ndarray, eps: float = 1e-12) -> np.ndarray:
    """Peak normalization with fallback."""
    if _HAS_NUMBA:
        return normalize_peak_numba(x.copy(), eps)
    else:
        if x.ndim == 1:
            peak = np.max(np.abs(x))
            return x / (peak + eps)
        else:
            peak = np.max(np.abs(x), axis=0, keepdims=True)
            return x / (peak + eps)


def get_sos_filter(cutoff_hz: float, sr: int, filter_type: str, order: int = 8) -> Optional[np.ndarray]:
    """Get SOS filter coefficients with caching."""
    cache_key = (cutoff_hz, sr, filter_type, order)
    if cache_key not in _fft_cache:
        if cutoff_hz <= 0 or cutoff_hz >= sr/2 - 1:
            return None
        try:
            sos = signal.butter(order, cutoff_hz / (sr / 2), btype=filter_type, output='sos')
            _fft_cache[cache_key] = sos
        except Exception:
            return None
    return _fft_cache[cache_key]


def apply_sos_filter(x: np.ndarray, sos: np.ndarray, n_threads: int = 1) -> np.ndarray:
    """Apply SOS filter with optional threading for multi-channel audio."""
    if x.ndim == 1:
        return signal.sosfiltfilt(sos, x)
  
    if n_threads == 1 or x.shape[1] == 1:
        return signal.sosfiltfilt(sos, x, axis=0)
  
    # Multi-threaded processing for multi-channel audio
    def filter_channel(ch_data):
        return signal.sosfiltfilt(sos, ch_data)
  
    with ThreadPoolExecutor(max_workers=min(n_threads, x.shape[1])) as executor:
        results = list(executor.map(filter_channel, x.T))
  
    return np.column_stack(results)


def highpass_sos(x: np.ndarray, cutoff_hz: float, sr: int, order: int = 8, n_threads: int = 1) -> np.ndarray:
    """High-pass filter with threading support."""
    sos = get_sos_filter(cutoff_hz, sr, 'high', order)
    if sos is None:
        return x
    return apply_sos_filter(x, sos, n_threads)


def lowpass_sos(x: np.ndarray, cutoff_hz: float, sr: int, order: int = 6, n_threads: int = 1) -> np.ndarray:
    """Low-pass filter with threading support."""
    sos = get_sos_filter(cutoff_hz, sr, 'low', order)
    if sos is None:
        return x
    return apply_sos_filter(x, sos, n_threads)


def get_fft_planner(n: int, dtype: np.dtype = np.complex64, threads: int = 1):
    """Get cached FFT planner for better performance."""
    cache_key = (n, dtype, threads, 'rfft')
    if cache_key not in _fft_cache and _HAS_PYFFTW:
        try:
            a = pyfftw.empty_aligned(n, dtype='float32')
            b = pyfftw.empty_aligned(n//2+1, dtype='complex64')
            fft_forward = pyfftw.builders.rfft(a, threads=threads)
            fft_backward = pyfftw.builders.irfft(b, n=n, threads=threads)
            _fft_cache[cache_key] = (fft_forward, fft_backward, a, b)
        except Exception:
            _fft_cache[cache_key] = None
    return _fft_cache.get(cache_key)


@jit(nopython=True if _HAS_NUMBA else False, parallel=True)
def dsre_core_computation(analytic: np.ndarray, t: np.ndarray, m: int, sr: int, decay: float) -> np.ndarray:
    """Core DSRE computation optimized with numba."""
    N, C = analytic.shape
    d_res = np.zeros_like(analytic, dtype=np.complex64)
  
    for i in prange(m):
        shift_hz = (i + 1) * sr / (m * 2.0)
        weight = np.exp(-(i + 1) * decay)
      
        for ch in prange(C):
            phase = np.exp(2j * np.pi * shift_hz * t)
            d_res[:, ch] += analytic[:, ch] * phase * weight
  
    return d_res.real


def dsre_enhance(x: np.ndarray, sr: int, m: int = 6, decay: float = 1.1,
                pre_hp: float = 300.0, post_hp: float = 10000.0, mix: float = 0.6,
                hp_order: int = 6, n_threads: int = 1) -> np.ndarray:
    """Enhanced DSRE with optimizations."""
    x2d, was_1d = _ensure_2d(x)
  
    # Pre-filter
    x_hp = highpass_sos(x2d, pre_hp, sr, order=hp_order, n_threads=n_threads)
  
    N = x_hp.shape[0]
    t = np.arange(N, dtype=DTYPE) / float(sr)
  
    # Compute analytic signal with threading for multi-channel
    if x_hp.shape[1] == 1 or n_threads == 1:
        analytic = signal.hilbert(x_hp, axis=0)
    else:
        def compute_hilbert(ch_data):
            return signal.hilbert(ch_data)
      
        with ThreadPoolExecutor(max_workers=min(n_threads, x_hp.shape[1])) as executor:
            hilbert_results = list(executor.map(compute_hilbert, x_hp.T))
        analytic = np.column_stack(hilbert_results)
  
    # Core DSRE computation
    if _HAS_NUMBA:
        d_res = dsre_core_computation(analytic, t, m, sr, decay)
    else:
        d_res = np.zeros_like(x_hp)
        for i in range(m):
            shift_hz = (i + 1) * sr / (m * 2.0)
            phase = np.exp(2j * np.pi * shift_hz * t)[:, None]
            weight = np.exp(-(i + 1) * decay)
            d_res += (analytic * phase).real * weight
  
    # Post-filter
    d_res = highpass_sos(d_res, post_hp, sr, order=hp_order, n_threads=n_threads)
  
    # Energy matching with improved RMS calculation
    def rms_stable(a):
        return np.sqrt(np.mean(a**2, axis=0) + 1e-12)
  
    target_rms = rms_stable(x2d)
    res_rms = rms_stable(d_res)
    gain = np.where(res_rms > 1e-12, target_rms / res_rms, 1.0)[None, :]
    d_res *= gain
  
    # Mix with improved blending
    out = (1.0 - mix) * x2d + mix * (x2d + d_res)
    return out if not was_1d else out[:, 0]


def akkomode_jitter(x: np.ndarray, sr: int, depth_low: float = 0.02, depth_high: float = 0.12,
                   env_cutoff_hz: float = 6.0, n_threads: int = 1) -> np.ndarray:
    """Optimized akko-mode jitter with better envelope smoothing."""
    x2d, was_1d = _ensure_2d(x)
    N, C = x2d.shape
  
    # Pre-compute filter once
    sos = get_sos_filter(env_cutoff_hz, sr/2, 'low', 4)  # Note: sr/2 for proper normalization
    if sos is None:
        return x
  
    # Generate smooth envelopes for each channel
    def generate_envelope():
        # Use better random seed for reproducible but varied results
        np.random.seed(None)  # Reset to time-based seed
        noise = np.random.randn(N).astype(DTYPE)
        # Apply smoothing filter
        smoothed = signal.sosfiltfilt(sos, noise)
        # Normalize to [-1, 1] range more stably
        peak = np.max(np.abs(smoothed))
        if peak > 1e-12:
            smoothed /= peak
        return smoothed
  
    if n_threads == 1 or C == 1:
        envelopes = np.column_stack([generate_envelope() for _ in range(C)])
    else:
        with ThreadPoolExecutor(max_workers=min(n_threads, C)) as executor:
            envelopes = np.column_stack(list(executor.map(lambda _: generate_envelope(), range(C))))
  
    # Apply depth modulation
    depths = depth_low + (depth_high - depth_low) * ((envelopes + 1.0) / 2.0)
    out = x2d * (1.0 + depths)
  
    return out if not was_1d else out[:, 0]


def copyband_stft_optimized(x: np.ndarray, sr: int, band_hpfc: float = 6000.0,
                          band_sft: float = 16000.0, band_gain: float = 1.2,
                          n_fft: int = 2048, hop_length: Optional[int] = None,
                          zero_fill: bool = True, n_threads: int = 1) -> np.ndarray:
    """Optimized copyband STFT with better windowing and phase preservation."""
    x2d, was_1d = _ensure_2d(x)
  
    if hop_length is None:
        hop_length = n_fft // 4  # 75% overlap for better quality
  
    N, C = x2d.shape
  
    # Use Hann window for better spectral properties
    window = 'hann'
  
    def process_channel(ch_idx):
        channel = x2d[:, ch_idx]
      
        # Use librosa with optimized parameters
        S = librosa.stft(channel, n_fft=n_fft, hop_length=hop_length, window=window)
        freqs = np.linspace(0, sr/2, S.shape[0])
      
        # More precise frequency bin calculation
        low_bin = np.searchsorted(freqs, band_hpfc, side='left')
        high_bin = np.searchsorted(freqs, band_sft, side='left')
        shift_bins = high_bin - low_bin
      
        S2 = S.copy()
      
        if shift_bins > 0:
            # Clear original band
            S2[low_bin:high_bin, :] = 0
          
            # Copy to new location with gain
            target_start = low_bin + shift_bins
            if target_start < S.shape[0]:
                end = min(target_start + shift_bins, S.shape[0])
                insert_len = end - target_start
                S2[target_start:end, :] = S[low_bin:low_bin+insert_len, :] * band_gain
              
                # Handle wrapping if requested and needed
                if not zero_fill and insert_len < shift_bins:
                    wrap_len = shift_bins - insert_len
                    wrap_end = min(wrap_len, S.shape[0])
                    S2[:wrap_end, :] = S[low_bin+insert_len:low_bin+insert_len+wrap_end, :] * band_gain
        else:
            # Just apply gain if no shifting
            S2[low_bin:high_bin, :] *= band_gain
      
        # Reconstruct with same parameters
        return librosa.istft(S2, hop_length=hop_length, window=window, length=len(channel))
  
    # Process channels
    if n_threads == 1 or C == 1:
        results = [process_channel(ch) for ch in range(C)]
    else:
        with ThreadPoolExecutor(max_workers=min(n_threads, C)) as executor:
            results = list(executor.map(process_channel, range(C)))
  
    out = np.column_stack(results).astype(DTYPE)
    return out if not was_1d else out[:, 0]


def spectral_upsample_fft_optimized(x: np.ndarray, upscale: int = 2, n_threads: int = 1) -> np.ndarray:
    """Optimized spectral upsampling with caching and threading."""
    x2d, was_1d = _ensure_2d(x)
    N, C = x2d.shape
    newN = N * upscale
  
    def upsample_channel(ch_idx):
        real_in = x2d[:, ch_idx]
      
        # Try to use cached FFT planner
        planner_result = get_fft_planner(N, threads=1) if _HAS_PYFFTW else None
      
        if planner_result and _HAS_PYFFTW:
            fft_forward, fft_backward, a, b = planner_result
          
            # Check if planner dimensions match
            if a.shape[0] == N:
                a[:] = real_in
                spec = fft_forward()
            else:
                spec = np.fft.rfft(real_in)
        else:
            spec = np.fft.rfft(real_in)
      
        # Zero-pad in frequency domain
        new_spec = np.zeros(newN//2+1, dtype=spec.dtype)
        copy_len = min(spec.shape[0], new_spec.shape[0])
        new_spec[:copy_len] = spec[:copy_len]
      
        # IFFT back to time domain
        if planner_result and _HAS_PYFFTW and newN == b.shape[0]:
            b[:] = new_spec
            return fft_backward().astype(DTYPE)
        else:
            return np.fft.irfft(new_spec, n=newN).astype(DTYPE)
  
    # Process channels
    if n_threads == 1 or C == 1:
        results = [upsample_channel(ch) for ch in range(C)]
    else:
        with ThreadPoolExecutor(max_workers=min(n_threads, C)) as executor:
            results = list(executor.map(upsample_channel, range(C)))
  
    y = np.column_stack(results)
    return y if not was_1d else y[:, 0]


def analyze_and_select_params(x: np.ndarray, sr: int, n_threads: Optional[int] = None) -> AutoParams:
    """Enhanced parameter selection with better analysis."""
    if n_threads is None:
        n_threads = get_optimal_threads()
  
    x2d, _ = _ensure_2d(x)
    mono = np.mean(x2d, axis=1)
  
    # More robust spectral analysis
    if len(mono) < 1024:
        centroid = 2000.0
        spectral_rolloff = 8000.0
    else:
        try:
            # Compute multiple spectral features
            cent = librosa.feature.spectral_centroid(y=mono.astype(float), sr=sr)
            rolloff = librosa.feature.spectral_rolloff(y=mono.astype(float), sr=sr, roll_percent=0.85)
          
            centroid = float(np.median(cent)) if cent.size else 2000.0
            spectral_rolloff = float(np.median(rolloff)) if rolloff.size else 8000.0
        except Exception:
            centroid = 2000.0
            spectral_rolloff = 8000.0
  
    # Better parameter scaling based on audio content
    pre_hp = max(200.0, min(500.0, centroid * 0.25))
    post_hp = min(max(6000.0, spectral_rolloff * 0.8), sr/2 * 0.95)
  
    # Enhanced DSRE parameters
    if sr >= 96000:
        m, decay, mix = 10, 1.3, 0.5
    elif sr >= 48000:
        m, decay, mix = 8, 1.2, 0.55
    elif sr >= 44100:
        m, decay, mix = 6, 1.1, 0.6
    else:
        m, decay, mix = 4, 1.0, 0.65
  
    # Dynamic range aware jitter
    rms_val = np.sqrt(np.mean(mono.astype(float)**2) + 1e-12)
    dynamic_range = np.max(np.abs(mono)) / (rms_val + 1e-12)
  
    if dynamic_range > 10:  # High dynamic range
        depth_low, depth_high = 0.005, 0.02
    elif dynamic_range > 5:  # Medium dynamic range
        depth_low, depth_high = 0.01, 0.04
    else:  # Compressed audio
        depth_low, depth_high = 0.02, 0.06
  
    # Smarter copyband parameters
    copyband_hpfc = max(4000.0, min(8000.0, centroid * 0.9))
    copyband_sft = min(copyband_hpfc * 2.2, sr/2 * 0.9)
  
    # Intelligent target sample rate selection
    if sr < 44100:
        target_sr = 48000
    elif sr == 44100:
        target_sr = 48000  # Slight upsample for better processing
    else:
        target_sr = sr
  
    return AutoParams(
        target_sr=int(target_sr),
        dsre_m=m,
        dsre_decay=decay,
        dsre_pre_hp=float(pre_hp),
        dsre_post_hp=float(post_hp),
        dsre_mix=float(mix),
        akko_depth_low=float(depth_low),
        akko_depth_high=float(depth_high),
        akko_env_hz=4.0,  # Slightly higher for better envelope
        copyband_hpfc=float(copyband_hpfc),
        copyband_sft=float(copyband_sft),
        copyband_gain=1.15,  # Slightly more conservative
        upsample_factor=1,
        n_threads=n_threads,
    )


def enhance_pipeline(x: np.ndarray, sr: int, params: AutoParams,
                    do_copyband: bool = True, do_akko: bool = True,
                    do_dsre: bool = True, do_upsample: bool = False) -> Tuple[np.ndarray, int]:
    """Main enhancement pipeline with optimizations."""
    print(f"Processing with {params.n_threads} threads...")
  
    # Resample if necessary
    if sr != params.target_sr:
        print(f"Resampling {sr} -> {params.target_sr}")
        # Use high-quality resampling
        x = resampy.resample(x.T, sr, params.target_sr, axis=0, filter='kaiser_best').T
        sr = params.target_sr
  
    # Apply enhancements in optimal order
    if do_dsre:
        print("Applying DSRE enhancement...")
        x = dsre_enhance(x, sr, m=params.dsre_m, decay=params.dsre_decay,
                        pre_hp=params.dsre_pre_hp, post_hp=params.dsre_post_hp,
                        mix=params.dsre_mix, n_threads=params.n_threads)
  
    if do_akko:
        print("Applying Akko-mode jitter...")
        x = akkomode_jitter(x, sr, depth_low=params.akko_depth_low,
                           depth_high=params.akko_depth_high,
                           env_cutoff_hz=params.akko_env_hz, n_threads=params.n_threads)
  
    if do_copyband:
        print("Applying copyband STFT enhancement...")
        x = copyband_stft_optimized(x, sr, band_hpfc=params.copyband_hpfc,
                                  band_sft=params.copyband_sft,
                                  band_gain=params.copyband_gain, n_threads=params.n_threads)
  
    if do_upsample and params.upsample_factor > 1:
        print(f"Spectral upsampling by factor {params.upsample_factor}...")
        x = spectral_upsample_fft_optimized(x, upscale=params.upsample_factor, n_threads=params.n_threads)
        sr = int(sr * params.upsample_factor)
  
    # Final normalization
    x = normalize_peak(x)
    return x, sr


def build_output_path(inp_path: str) -> str:
    """Build output path with _enhanced suffix."""
    base, ext = os.path.splitext(inp_path)
    return f"{base}_enhanced{ext}"


def main():
    parser = argparse.ArgumentParser(
        description='Enhanced audio processing tool with performance optimizations',
        formatter_class=argparse.ArgumentDefaultsHelpFormatter
    )
    parser.add_argument('input', help='Input audio file')
    parser.add_argument('--no-copyband', action='store_true', help='Disable copyband STFT enhancement')
    parser.add_argument('--no-akko', action='store_true', help='Disable akkomode jitter')
    parser.add_argument('--no-dsre', action='store_true', help='Disable DSRE enhancement')
    parser.add_argument('--upsample', type=int, default=0, help='Spectral upsample factor (0=disabled)')
    parser.add_argument('--out', default=None, help='Output path (auto-generated if not specified)')
    parser.add_argument('--mix', type=float, default=100.0, help='Wet percentage (0-100)')
    parser.add_argument('--threads', default='auto', help='Number of threads (auto, or integer)')
  
    args = parser.parse_args()
  
    # Validate input file
    if not os.path.exists(args.input):
        print(f"Error: Input file not found: {args.input}")
        sys.exit(1)
  
    # Determine thread count
    if args.threads == 'auto':
        n_threads = get_optimal_threads()
    else:
        try:
            n_threads = max(1, int(args.threads))
        except ValueError:
            print("Warning: Invalid thread count, using auto detection")
            n_threads = get_optimal_threads()
  
    # Validate and clamp mix parameter
    mix_pct = np.clip(float(args.mix), 0.0, 100.0)
    wet = mix_pct / 100.0
    dry = 1.0 - wet
  
    # Load audio file
    print(f"Loading {args.input}...")
    try:
        data, sr = sf.read(args.input, always_2d=True, dtype='float32')
        print(f"Loaded: {data.shape[0]} samples, {data.shape[1]} channels, {sr} Hz")
    except Exception as e:
        print(f"Error loading audio file: {e}")
        sys.exit(1)
  
    # Analyze and select parameters
    print("Analyzing audio characteristics...")
    params = analyze_and_select_params(data, sr, n_threads)
  
    # Override upsample factor if specified
    if args.upsample >= 2:
        params.upsample_factor = int(args.upsample)
  
    print("\nSelected parameters:")
    for field_name, value in params.__dict__.items():
        print(f"  {field_name}: {value}")
    print(f"Mix ratio: {dry*100:.1f}% dry / {wet*100:.1f}% wet")
  
    # Prepare original for mixing
    working = data
    working_sr = sr
    if sr != params.target_sr:
        print(f"Pre-resampling original for mixing...")
        working = resampy.resample(data.T, sr, params.target_sr, axis=0, filter='kaiser_best').T
        working_sr = params.target_sr
  
    # Run enhancement pipeline
    print("\n" + "="*50)
    print("Running enhancement pipeline...")
    print("="*50)
  
    enhanced, out_sr = enhance_pipeline(
        working, working_sr, params,
        do_copyband=not args.no_copyband,
        do_akko=not args.no_akko,
        do_dsre=not args.no_dsre,
        do_upsample=(params.upsample_factor > 1)
    )
  
    # Handle upsampling for mixing
    if out_sr != working_sr:
        print(f"Upsampling original {working_sr} -> {out_sr} for mixing...")
        working_up = resampy.resample(working.T, working_sr, out_sr, axis=0, filter='kaiser_best').T
    else:
        working_up = working
  
    # Ensure compatible shapes for mixing
    min_len = min(working_up.shape[0], enhanced.shape[0])
    if working_up.shape[0] != enhanced.shape[0]:
        print(f"Length mismatch: trimming to {min_len} samples for mixing")
        working_up = working_up[:min_len]
        enhanced = enhanced[:min_len]
  
    # Mix dry and wet signals
    print("Mixing dry/wet signals...")
    final = (dry * working_up) + (wet * enhanced)
  
    # Safe clipping prevention
    peak = np.max(np.abs(final))
    if peak > 0.999:
        print(f"Peak level: {peak:.3f} - applying safety limiter")
        final = final / peak * 0.995
  
    # Prepare output
    out_path = args.out if args.out else build_output_path(args.input)
  
    # Handle mono output correctly
    if final.ndim == 2 and final.shape[1] == 1:
        final_out = final[:, 0]
    else:
        final_out = final
  
    # Write output file
    print(f"Writing enhanced audio to: {out_path}")
    print(f"Output format: {out_sr} Hz, 32-bit float")
  
    try:
        sf.write(out_path, final_out.astype('float32'), out_sr, subtype='FLOAT')
        print("✓ Enhancement complete!")
      
        # Performance summary
        print(f"\nPerformance summary:")
        print(f"  Threads used: {params.n_threads}")
        print(f"  FFTw acceleration: {'✓' if _HAS_PYFFTW else '✗'}")
        print(f"  Numba JIT: {'✓' if _HAS_NUMBA else '✗'}")
      
    except Exception as e:
        print(f"Error writing output file: {e}")
        sys.exit(1)


if __name__ == '__main__':
    # Set up multiprocessing for Windows compatibility
    mp.set_start_method('spawn', force=True) if sys.platform == 'win32' else None
  
    # Configure numpy for better threading
    try:
        import mkl
        mkl.set_num_threads(get_optimal_threads())
    except ImportError:
        pass
  
    # Suppress common warnings for cleaner output
    warnings.filterwarnings('ignore', category=UserWarning, module='librosa')
    warnings.filterwarnings('ignore', category=RuntimeWarning, module='numpy')
  
    main()

MP3:


Enhanched (100% mix):


Null:

 

...here's the result from another - recently patched - interesting open source competitor on the scene:



Check it out: HRAudioWizard