make n_fft and n_mels complete configurable for soundstream for multi spectral recon loss

import functools

from itertools import cycle

from pathlib import Path

from functools import partial, wraps

from itertools import zip_longest

import torch

from torch import nn, einsum

def default(val, d):

    return val if exists(val) else d

def cast_tuple(t, l = 1):

    return ((t,) * l) if not isinstance(t, tuple) else t

# tensor helpers

def l2norm(t, dim = -1):

        enc_cycle_dilations = (1, 3, 9),

        dec_cycle_dilations = (1, 3, 9),

        multi_spectral_window_powers_of_two = tuple(range(6, 12)),

        multi_spectral_n_mels = (8, 16, 32, 64, 64, 64, 64),

        multi_spectral_n_ffts = 512,

        multi_spectral_n_mels = 64,

        recon_loss_weight = 1.,

        multi_spectral_recon_loss_weight = 1.,

        adversarial_loss_weight = 1.,

        self.mel_spec_transforms = nn.ModuleList([])

        self.mel_spec_recon_alphas = []

        for powers, n_mels in zip(multi_spectral_window_powers_of_two, multi_spectral_n_mels):

        num_transforms = len(multi_spectral_window_powers_of_two)

        multi_spectral_n_ffts = cast_tuple(multi_spectral_n_ffts, num_transforms)

        multi_spectral_n_mels = cast_tuple(multi_spectral_n_mels, num_transforms)

        for powers, n_fft, n_mels in zip_longest(multi_spectral_window_powers_of_two, multi_spectral_n_ffts, multi_spectral_n_mels):

            win_length = 2 ** powers

            alpha = (win_length / 2) ** 0.5

            calculated_n_fft = default(max(n_fft, win_length), win_length)  # @AndreyBocharnikov said this is usually win length, but overridable

            # if any audio experts have an opinion about these settings, please submit a PR

            melspec_transform = T.MelSpectrogram(

                sample_rate = target_sample_hz,

                n_fft = win_length,

                n_fft = calculated_n_fft,

                win_length = win_length,

                hop_length = win_length // 4,

                n_mels = n_mels,

setup(

  name = 'audiolm-pytorch',

  packages = find_packages(exclude=[]),

  version = '0.11.8',

  version = '0.11.9',

  license='MIT',

  description = 'AudioLM - Language Modeling Approach to Audio Generation from Google Research - Pytorch',

  author = 'Phil Wang',