RingFormer/models.py

import torch
import torch.nn.functional as F
import torch.nn as nn
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
from utils import init_weights, get_padding
import numpy as np 
from stft import TorchSTFT
import torchaudio
from nnAudio import features
from einops import rearrange
from norm2d import NormConv2d
from utils import get_padding
from munch import Munch
from conformer import Conformer

LRELU_SLOPE = 0.1


def get_2d_padding(kernel_size, dilation=(1, 1)):
    return (
        ((kernel_size[0] - 1) * dilation[0]) // 2,
        ((kernel_size[1] - 1) * dilation[1]) // 2,
    )



class ResBlock1(torch.nn.Module):
    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
        super(ResBlock1, self).__init__()
        self.h = h
        self.convs1 = nn.ModuleList([
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
                               padding=get_padding(kernel_size, dilation[0]))),
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
                               padding=get_padding(kernel_size, dilation[1]))),
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
                               padding=get_padding(kernel_size, dilation[2])))
        ])
        self.convs1.apply(init_weights)

        self.convs2 = nn.ModuleList([
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
                               padding=get_padding(kernel_size, 1))),
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
                               padding=get_padding(kernel_size, 1))),
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
                               padding=get_padding(kernel_size, 1)))
        ])
        self.convs2.apply(init_weights)
        
        self.alpha1 = nn.ParameterList([nn.Parameter(torch.ones(1, channels, 1)) for i in range(len(self.convs1))])
        self.alpha2 = nn.ParameterList([nn.Parameter(torch.ones(1, channels, 1)) for i in range(len(self.convs2))])


    def forward(self, x):
        for c1, c2, a1, a2 in zip(self.convs1, self.convs2, self.alpha1, self.alpha2):
            xt = x + (1 / a1) * (torch.sin(a1 * x) ** 2)  # Snake1D
            xt = c1(xt)
            xt = xt + (1 / a2) * (torch.sin(a2 * xt) ** 2)  # Snake1D
            xt = c2(xt)
            x = xt + x
        return x

    def remove_weight_norm(self):
        for l in self.convs1:
            remove_weight_norm(l)
        for l in self.convs2:
            remove_weight_norm(l)

class ResBlock1_old(torch.nn.Module):
    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
        super(ResBlock1, self).__init__()
        self.h = h
        self.convs1 = nn.ModuleList([
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
                               padding=get_padding(kernel_size, dilation[0]))),
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
                               padding=get_padding(kernel_size, dilation[1]))),
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
                               padding=get_padding(kernel_size, dilation[2])))
        ])
        self.convs1.apply(init_weights)

        self.convs2 = nn.ModuleList([
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
                               padding=get_padding(kernel_size, 1))),
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
                               padding=get_padding(kernel_size, 1))),
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
                               padding=get_padding(kernel_size, 1)))
        ])
        self.convs2.apply(init_weights)

    def forward(self, x):
        for c1, c2 in zip(self.convs1, self.convs2):
            xt = F.leaky_relu(x, LRELU_SLOPE)
            xt = c1(xt)
            xt = F.leaky_relu(xt, LRELU_SLOPE)
            xt = c2(xt)
            x = xt + x
        return x

    def remove_weight_norm(self):
        for l in self.convs1:
            remove_weight_norm(l)
        for l in self.convs2:
            remove_weight_norm(l)


class ResBlock2(torch.nn.Module):
    def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)):
        super(ResBlock2, self).__init__()
        self.h = h
        self.convs = nn.ModuleList([
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
                               padding=get_padding(kernel_size, dilation[0]))),
            weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
                               padding=get_padding(kernel_size, dilation[1])))
        ])
        self.convs.apply(init_weights)

    def forward(self, x):
        for c in self.convs:
            xt = F.leaky_relu(x, LRELU_SLOPE)
            xt = c(xt)
            x = xt + x
        return x

    def remove_weight_norm(self):
        for l in self.convs:
            remove_weight_norm(l)


class SineGen(torch.nn.Module):
    """ Definition of sine generator
    SineGen(samp_rate, harmonic_num = 0,
            sine_amp = 0.1, noise_std = 0.003,
            voiced_threshold = 0,
            flag_for_pulse=False)
    samp_rate: sampling rate in Hz
    harmonic_num: number of harmonic overtones (default 0)
    sine_amp: amplitude of sine-wavefrom (default 0.1)
    noise_std: std of Gaussian noise (default 0.003)
    voiced_thoreshold: F0 threshold for U/V classification (default 0)
    flag_for_pulse: this SinGen is used inside PulseGen (default False)
    Note: when flag_for_pulse is True, the first time step of a voiced
        segment is always sin(np.pi) or cos(0)
    """

    def __init__(self, samp_rate, upsample_scale, harmonic_num=0,
                 sine_amp=0.1, noise_std=0.003,
                 voiced_threshold=0,
                 flag_for_pulse=False):
        super(SineGen, self).__init__()
        self.sine_amp = sine_amp
        self.noise_std = noise_std
        self.harmonic_num = harmonic_num
        self.dim = self.harmonic_num + 1
        self.sampling_rate = samp_rate
        self.voiced_threshold = voiced_threshold
        self.flag_for_pulse = flag_for_pulse
        self.upsample_scale = upsample_scale

    def _f02uv(self, f0):
        # generate uv signal
        uv = (f0 > self.voiced_threshold).type(torch.float32)
        return uv

    def _f02sine(self, f0_values):
        """ f0_values: (batchsize, length, dim)
            where dim indicates fundamental tone and overtones
        """
        # convert to F0 in rad. The interger part n can be ignored
        # because 2 * np.pi * n doesn't affect phase
        rad_values = (f0_values / self.sampling_rate) % 1

        # initial phase noise (no noise for fundamental component)
        rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \
                              device=f0_values.device)
        rand_ini[:, 0] = 0
        rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini

        # instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad)
        if not self.flag_for_pulse:
#             # for normal case

#             # To prevent torch.cumsum numerical overflow,
#             # it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1.
#             # Buffer tmp_over_one_idx indicates the time step to add -1.
#             # This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi
#             tmp_over_one = torch.cumsum(rad_values, 1) % 1
#             tmp_over_one_idx = (padDiff(tmp_over_one)) < 0
#             cumsum_shift = torch.zeros_like(rad_values)
#             cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0

#             phase = torch.cumsum(rad_values, dim=1) * 2 * np.pi
            rad_values = torch.nn.functional.interpolate(rad_values.transpose(1, 2), 
                                                         scale_factor=1/self.upsample_scale, 
                                                         mode="linear").transpose(1, 2)
    
#             tmp_over_one = torch.cumsum(rad_values, 1) % 1
#             tmp_over_one_idx = (padDiff(tmp_over_one)) < 0
#             cumsum_shift = torch.zeros_like(rad_values)
#             cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
    
            phase = torch.cumsum(rad_values, dim=1) * 2 * np.pi
            phase = torch.nn.functional.interpolate(phase.transpose(1, 2) * self.upsample_scale, 
                                                    scale_factor=self.upsample_scale, mode="linear").transpose(1, 2)
            sines = torch.sin(phase)
            
        else:
            # If necessary, make sure that the first time step of every
            # voiced segments is sin(pi) or cos(0)
            # This is used for pulse-train generation

            # identify the last time step in unvoiced segments
            uv = self._f02uv(f0_values)
            uv_1 = torch.roll(uv, shifts=-1, dims=1)
            uv_1[:, -1, :] = 1
            u_loc = (uv < 1) * (uv_1 > 0)

            # get the instantanouse phase
            tmp_cumsum = torch.cumsum(rad_values, dim=1)
            # different batch needs to be processed differently
            for idx in range(f0_values.shape[0]):
                temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :]
                temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :]
                # stores the accumulation of i.phase within
                # each voiced segments
                tmp_cumsum[idx, :, :] = 0
                tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum

            # rad_values - tmp_cumsum: remove the accumulation of i.phase
            # within the previous voiced segment.
            i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1)

            # get the sines
            sines = torch.cos(i_phase * 2 * np.pi)
        return sines

    def forward(self, f0):
        """ sine_tensor, uv = forward(f0)
        input F0: tensor(batchsize=1, length, dim=1)
                  f0 for unvoiced steps should be 0
        output sine_tensor: tensor(batchsize=1, length, dim)
        output uv: tensor(batchsize=1, length, 1)
        """
        f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim,
                             device=f0.device)
        # fundamental component
        fn = torch.multiply(f0, torch.FloatTensor([[range(1, self.harmonic_num + 2)]]).to(f0.device))

        # generate sine waveforms
        sine_waves = self._f02sine(fn) * self.sine_amp

        # generate uv signal
        # uv = torch.ones(f0.shape)
        # uv = uv * (f0 > self.voiced_threshold)
        uv = self._f02uv(f0)

        # noise: for unvoiced should be similar to sine_amp
        #        std = self.sine_amp/3 -> max value ~ self.sine_amp
        # .       for voiced regions is self.noise_std
        noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
        noise = noise_amp * torch.randn_like(sine_waves)

        # first: set the unvoiced part to 0 by uv
        # then: additive noise
        sine_waves = sine_waves * uv + noise
        return sine_waves, uv, noise


class SourceModuleHnNSF(torch.nn.Module):
    """ SourceModule for hn-nsf
    SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
                 add_noise_std=0.003, voiced_threshod=0)
    sampling_rate: sampling_rate in Hz
    harmonic_num: number of harmonic above F0 (default: 0)
    sine_amp: amplitude of sine source signal (default: 0.1)
    add_noise_std: std of additive Gaussian noise (default: 0.003)
        note that amplitude of noise in unvoiced is decided
        by sine_amp
    voiced_threshold: threhold to set U/V given F0 (default: 0)
    Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
    F0_sampled (batchsize, length, 1)
    Sine_source (batchsize, length, 1)
    noise_source (batchsize, length 1)
    uv (batchsize, length, 1)
    """

    def __init__(self, sampling_rate, upsample_scale, harmonic_num=0, sine_amp=0.1,
                 add_noise_std=0.003, voiced_threshod=0):
        super(SourceModuleHnNSF, self).__init__()

        self.sine_amp = sine_amp
        self.noise_std = add_noise_std

        # to produce sine waveforms
        self.l_sin_gen = SineGen(sampling_rate, upsample_scale, harmonic_num,
                                 sine_amp, add_noise_std, voiced_threshod)

        # to merge source harmonics into a single excitation
        self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
        self.l_tanh = torch.nn.Tanh()

    def forward(self, x):
        """
        Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
        F0_sampled (batchsize, length, 1)
        Sine_source (batchsize, length, 1)
        noise_source (batchsize, length 1)
        """
        # source for harmonic branch
        with torch.no_grad():
            sine_wavs, uv, _ = self.l_sin_gen(x)
        sine_merge = self.l_tanh(self.l_linear(sine_wavs))

        # source for noise branch, in the same shape as uv
        noise = torch.randn_like(uv) * self.sine_amp / 3
        return sine_merge, noise, uv
def padDiff(x):
    return F.pad(F.pad(x, (0,0,-1,1), 'constant', 0) - x, (0,0,0,-1), 'constant', 0)

            

class Generator(torch.nn.Module):
    def __init__(self, h, F0_model):
        super(Generator, self).__init__()
        self.h = h
        self.num_kernels = len(h.resblock_kernel_sizes)
        self.num_upsamples = len(h.upsample_rates)
        self.conv_pre = weight_norm(Conv1d(128, h.upsample_initial_channel, 7, 1, padding=3))
        
        

        resblock = ResBlock1 if h.resblock == '1' else ResBlock2

        self.m_source = SourceModuleHnNSF(
                    sampling_rate=h.sampling_rate,
                    upsample_scale=np.prod(h.upsample_rates) * h.gen_istft_hop_size,
                    harmonic_num=8, voiced_threshod=10)
        
        self.f0_upsamp = torch.nn.Upsample(
            scale_factor=np.prod(h.upsample_rates) * h.gen_istft_hop_size)
        self.noise_convs = nn.ModuleList()
        self.noise_res = nn.ModuleList()
        
        self.F0_model = F0_model
        
        self.ups = nn.ModuleList()
        for i, (u, k) in enumerate(zip(h.upsample_rates, h.upsample_kernel_sizes)):
            self.ups.append(weight_norm(
                ConvTranspose1d(h.upsample_initial_channel//(2**i),
                                h.upsample_initial_channel//(2**(i+1)),
                                k,
                                u,
                                padding=(k-u)//2)))

            c_cur = h.upsample_initial_channel // (2 ** (i + 1))
            
            if i + 1 < len(h.upsample_rates):  #
                stride_f0 = np.prod(h.upsample_rates[i + 1:])
                self.noise_convs.append(Conv1d(
                    h.gen_istft_n_fft + 2, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=(stride_f0+1) // 2))
                self.noise_res.append(resblock(h, c_cur, 7, [1,3,5]))
            else:
                self.noise_convs.append(Conv1d(h.gen_istft_n_fft + 2, c_cur, kernel_size=1))
                self.noise_res.append(resblock(h, c_cur, 11, [1,3,5]))
                
        self.alphas = nn.ParameterList()
        self.alphas.append(nn.Parameter(torch.ones(1, h.upsample_initial_channel, 1)))
        self.resblocks = nn.ModuleList()
        for i in range(len(self.ups)):
            ch = h.upsample_initial_channel//(2**(i+1))
            self.alphas.append(nn.Parameter(torch.ones(1, ch, 1)))
            for j, (k, d) in enumerate(
                zip(h.resblock_kernel_sizes, h.resblock_dilation_sizes)):
                self.resblocks.append(resblock(h, ch, k, d))

        
        self.conformers = nn.ModuleList()
        self.post_n_fft = h.gen_istft_n_fft
        self.conv_post = weight_norm(Conv1d(128, self.post_n_fft + 2, 7, 1, padding=3))
        
        for i in range(len(self.ups)):
            ch = h.upsample_initial_channel // (2**i)
            self.conformers.append(
                Conformer(
                    dim=ch,
                    depth=2,
                    dim_head=64,
                    heads=8,
                    ff_mult=4,
                    conv_expansion_factor=2,
                    conv_kernel_size=31,
                    attn_dropout=0.1,
                    ff_dropout=0.1,
                    conv_dropout=0.1,
                    # device=self.device
                )
            )
            
        self.ups.apply(init_weights)
        self.conv_post.apply(init_weights)
        self.reflection_pad = torch.nn.ReflectionPad1d((1, 0))
        self.stft = TorchSTFT(filter_length=h.gen_istft_n_fft,
                              hop_length=h.gen_istft_hop_size,
                              win_length=h.gen_istft_n_fft)



    def forward(self, x):
        
      
        
        f0, _, _ = self.F0_model(x.unsqueeze(1))
        if len(f0.shape) == 1:
            f0 = f0.unsqueeze(0)
        
        f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2)  # bs,n,t

        har_source, _, _ = self.m_source(f0)
        har_source = har_source.transpose(1, 2).squeeze(1)
        har_spec, har_phase = self.stft.transform(har_source)
        har = torch.cat([har_spec, har_phase], dim=1)
        

        x = self.conv_pre(x)
   
        for i in range(self.num_upsamples):

            x = x + (1 / self.alphas[i]) * (torch.sin(self.alphas[i] * x) ** 2)
            x = rearrange(x, "b f t -> b t f")
        
            x = self.conformers[i](x)

            x = rearrange(x, "b t f -> b f t")
            
            # x = F.leaky_relu(x, LRELU_SLOPE)
            x_source = self.noise_convs[i](har)
            x_source = self.noise_res[i](x_source)
            
            x = self.ups[i](x)
            if i == self.num_upsamples - 1:
                x = self.reflection_pad(x)
                
            x = x + x_source
            
            
            xs = None
            for j in range(self.num_kernels):
                if xs is None:
                    xs = self.resblocks[i*self.num_kernels+j](x)
                else:
                    xs += self.resblocks[i*self.num_kernels+j](x)
            x = xs / self.num_kernels
        # x = F.leaky_relu(x)
        

        x = x + (1 / self.alphas[i + 1]) * (torch.sin(self.alphas[i + 1] * x) ** 2)
        
        x = self.conv_post(x)
        spec = torch.exp(x[:,:self.post_n_fft // 2 + 1, :]).to(x.device)
        phase = torch.sin(x[:, self.post_n_fft // 2 + 1:, :]).to(x.device)

        return spec, phase

    def remove_weight_norm(self):
        print("Removing weight norm...")
        for l in self.ups:
            remove_weight_norm(l)
        for l in self.resblocks:
            l.remove_weight_norm()
        remove_weight_norm(self.conv_pre)
        remove_weight_norm(self.conv_post)



def stft(x, fft_size, hop_size, win_length, window):
    """Perform STFT and convert to magnitude spectrogram.
    Args:
        x (Tensor): Input signal tensor (B, T).
        fft_size (int): FFT size.
        hop_size (int): Hop size.
        win_length (int): Window length.
        window (str): Window function type.
    Returns:
        Tensor: Magnitude spectrogram (B, #frames, fft_size // 2 + 1).
    """
    x_stft = torch.stft(x, fft_size, hop_size, win_length, window,
            return_complex=True)
    real = x_stft[..., 0]
    imag = x_stft[..., 1]

    # NOTE(kan-bayashi): clamp is needed to avoid nan or inf
    return torch.abs(x_stft).transpose(2, 1)

class SpecDiscriminator(nn.Module):
    """docstring for Discriminator."""

    def __init__(self, fft_size=1024, shift_size=120, win_length=600, window="hann_window", use_spectral_norm=False):
        super(SpecDiscriminator, self).__init__()
        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
        self.fft_size = fft_size
        self.shift_size = shift_size
        self.win_length = win_length
        self.window = getattr(torch, window)(win_length)
        self.discriminators = nn.ModuleList([
            norm_f(nn.Conv2d(1, 32, kernel_size=(3, 9), padding=(1, 4))),
            norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1,2), padding=(1, 4))),
            norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1,2), padding=(1, 4))),
            norm_f(nn.Conv2d(32, 32, kernel_size=(3, 9), stride=(1,2), padding=(1, 4))),
            norm_f(nn.Conv2d(32, 32, kernel_size=(3, 3), stride=(1,1), padding=(1, 1))),
        ])

        self.out = norm_f(nn.Conv2d(32, 1, 3, 1, 1))

    def forward(self, y):

        fmap = []
        y = y.squeeze(1)
        y = stft(y, self.fft_size, self.shift_size, self.win_length, self.window.to(y.get_device()))
        y = y.unsqueeze(1)
        for i, d in enumerate(self.discriminators):
            y = d(y)
            y = F.leaky_relu(y, LRELU_SLOPE)
            fmap.append(y)

        y = self.out(y)
        fmap.append(y)

        return torch.flatten(y, 1, -1), fmap

# class MultiResSpecDiscriminator(torch.nn.Module):

#     def __init__(self,
#                  fft_sizes=[1024, 2048, 512],
#                  hop_sizes=[120, 240, 50],
#                  win_lengths=[600, 1200, 240],
#                  window="hann_window"):

#         super(MultiResSpecDiscriminator, self).__init__()
#         self.discriminators = nn.ModuleList([
#             SpecDiscriminator(fft_sizes[0], hop_sizes[0], win_lengths[0], window),
#             SpecDiscriminator(fft_sizes[1], hop_sizes[1], win_lengths[1], window),
#             SpecDiscriminator(fft_sizes[2], hop_sizes[2], win_lengths[2], window)
#             ])

#     def forward(self, y, y_hat):
#         y_d_rs = []
#         y_d_gs = []
#         fmap_rs = []
#         fmap_gs = []
#         for i, d in enumerate(self.discriminators):
#             y_d_r, fmap_r = d(y)
#             y_d_g, fmap_g = d(y_hat)
#             y_d_rs.append(y_d_r)
#             fmap_rs.append(fmap_r)
#             y_d_gs.append(y_d_g)
#             fmap_gs.append(fmap_g)

#         return y_d_rs, y_d_gs, fmap_rs, fmap_gs
        

class DiscriminatorP(torch.nn.Module):
    def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
        super(DiscriminatorP, self).__init__()
        self.period = period
        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
        self.convs = nn.ModuleList([
            norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
            norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
            norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
            norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
            norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
        ])
        self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))

    def forward(self, x):
        fmap = []

        # 1d to 2d
        b, c, t = x.shape
        if t % self.period != 0: # pad first
            n_pad = self.period - (t % self.period)
            x = F.pad(x, (0, n_pad), "reflect")
            t = t + n_pad
        x = x.view(b, c, t // self.period, self.period)

        for l in self.convs:
            x = l(x)
            x = F.leaky_relu(x, LRELU_SLOPE)
            fmap.append(x)
        x = self.conv_post(x)
        fmap.append(x)
        x = torch.flatten(x, 1, -1)

        return x, fmap


class MultiPeriodDiscriminator(torch.nn.Module):
    def __init__(self):
        super(MultiPeriodDiscriminator, self).__init__()
        self.discriminators = nn.ModuleList([
            DiscriminatorP(2),
            DiscriminatorP(3),
            DiscriminatorP(5),
            DiscriminatorP(7),
            DiscriminatorP(11),
        ])

    def forward(self, y, y_hat):
        y_d_rs = []
        y_d_gs = []
        fmap_rs = []
        fmap_gs = []
        for i, d in enumerate(self.discriminators):
            y_d_r, fmap_r = d(y)
            y_d_g, fmap_g = d(y_hat)
            y_d_rs.append(y_d_r)
            fmap_rs.append(fmap_r)
            y_d_gs.append(y_d_g)
            fmap_gs.append(fmap_g)

        return y_d_rs, y_d_gs, fmap_rs, fmap_gs


class DiscriminatorS(torch.nn.Module):
    def __init__(self, use_spectral_norm=False):
        super(DiscriminatorS, self).__init__()
        norm_f = weight_norm if use_spectral_norm == False else spectral_norm
        self.convs = nn.ModuleList([
            norm_f(Conv1d(1, 128, 15, 1, padding=7)),
            norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)),
            norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)),
            norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)),
            norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
            norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)),
            norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
        ])
        self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))

    def forward(self, x):
        fmap = []
        for l in self.convs:
            x = l(x)
            x = F.leaky_relu(x, LRELU_SLOPE)
            fmap.append(x)
        x = self.conv_post(x)
        fmap.append(x)
        x = torch.flatten(x, 1, -1)

        return x, fmap


class MultiScaleDiscriminator(torch.nn.Module):
    def __init__(self):
        super(MultiScaleDiscriminator, self).__init__()
        self.discriminators = nn.ModuleList([
            DiscriminatorS(use_spectral_norm=True),
            DiscriminatorS(),
            DiscriminatorS(),
        ])
        self.meanpools = nn.ModuleList([
            AvgPool1d(4, 2, padding=2),
            AvgPool1d(4, 2, padding=2)
        ])

    def forward(self, y, y_hat):
        y_d_rs = []
        y_d_gs = []
        fmap_rs = []
        fmap_gs = []
        for i, d in enumerate(self.discriminators):
            if i != 0:
                y = self.meanpools[i-1](y)
                y_hat = self.meanpools[i-1](y_hat)
            y_d_r, fmap_r = d(y)
            y_d_g, fmap_g = d(y_hat)
            y_d_rs.append(y_d_r)
            fmap_rs.append(fmap_r)
            y_d_gs.append(y_d_g)
            fmap_gs.append(fmap_g)

        return y_d_rs, y_d_gs, fmap_rs, fmap_gs





########################### from ringformer

multiscale_subband_cfg = {
    "hop_lengths": [1024, 512, 512],  # Doubled to maintain similar time resolution
    "sampling_rate": 44100,           # New sampling rate
    "filters": 32,                    # Kept same as it controls initial feature dimension
    "max_filters": 1024,              # Kept same as it's a maximum limit
    "filters_scale": 1,               # Kept same as it's a scaling factor
    "dilations": [1, 2, 4],          # Kept same as they control receptive field growth
    "in_channels": 1,                 # Kept same (mono audio)
    "out_channels": 1,                # Kept same (mono audio)
    "n_octaves": [10, 10, 10],       # Increased by 1 to handle higher frequency range
    "bins_per_octaves": [24, 36, 48], # Kept same as they control frequency resolution
}



# multiscale_subband_cfg = {
#     "hop_lengths": [512, 256, 256],
#     "sampling_rate": 24000,
#     "filters": 32,
#     "max_filters": 1024,
#     "filters_scale": 1,
#     "dilations": [1, 2, 4],
#     "in_channels": 1,
#     "out_channels": 1,
#     "n_octaves": [9, 9, 9],
#     "bins_per_octaves": [24, 36, 48],
# }

class DiscriminatorCQT(nn.Module): 
    def __init__(self, cfg, hop_length, n_octaves, bins_per_octave):
        super(DiscriminatorCQT, self).__init__()
        self.cfg = cfg

        self.filters = cfg.filters
        self.max_filters = cfg.max_filters
        self.filters_scale = cfg.filters_scale
        self.kernel_size = (3, 9)
        self.dilations = cfg.dilations
        self.stride = (1, 2)

        self.in_channels = cfg.in_channels
        self.out_channels = cfg.out_channels
        self.fs = cfg.sampling_rate
        self.hop_length = hop_length
        self.n_octaves = n_octaves
        self.bins_per_octave = bins_per_octave

        self.cqt_transform = features.cqt.CQT2010v2(
            sr=self.fs * 2,
            hop_length=self.hop_length,
            n_bins=self.bins_per_octave * self.n_octaves,
            bins_per_octave=self.bins_per_octave,
            output_format="Complex",
            pad_mode="constant",
        )

        self.conv_pres = nn.ModuleList()
        for i in range(self.n_octaves):
            self.conv_pres.append(
                NormConv2d(
                    self.in_channels * 2,
                    self.in_channels * 2,
                    kernel_size=self.kernel_size,
                    padding=get_2d_padding(self.kernel_size),
                )
            )

        self.convs = nn.ModuleList()

        self.convs.append(
            NormConv2d(
                self.in_channels * 2,
                self.filters,
                kernel_size=self.kernel_size,
                padding=get_2d_padding(self.kernel_size),
            )
        )

        in_chs = min(self.filters_scale * self.filters, self.max_filters)
        for i, dilation in enumerate(self.dilations):
            out_chs = min(
                (self.filters_scale ** (i + 1)) * self.filters, self.max_filters
            )
            self.convs.append(
                NormConv2d(
                    in_chs,
                    out_chs,
                    kernel_size=self.kernel_size,
                    stride=self.stride,
                    dilation=(dilation, 1),
                    padding=get_2d_padding(self.kernel_size, (dilation, 1)),
                    norm="weight_norm",
                )
            )
            in_chs = out_chs
        out_chs = min(
            (self.filters_scale ** (len(self.dilations) + 1)) * self.filters,
            self.max_filters,
        )
        self.convs.append(
            NormConv2d(
                in_chs,
                out_chs,
                kernel_size=(self.kernel_size[0], self.kernel_size[0]),
                padding=get_2d_padding((self.kernel_size[0], self.kernel_size[0])),
                norm="weight_norm",
            )
        )

        self.conv_post = NormConv2d(
            out_chs,
            self.out_channels,
            kernel_size=(self.kernel_size[0], self.kernel_size[0]),
            padding=get_2d_padding((self.kernel_size[0], self.kernel_size[0])),
            norm="weight_norm",
        )

        self.activation = torch.nn.LeakyReLU(negative_slope=LRELU_SLOPE)
        self.resample = torchaudio.transforms.Resample(
            orig_freq=self.fs, new_freq=self.fs * 2
        )

    def forward(self, x):
        fmap = []

        x = self.resample(x)

        z = self.cqt_transform(x)

        z_amplitude = z[:, :, :, 0].unsqueeze(1)
        z_phase = z[:, :, :, 1].unsqueeze(1)

        z = torch.cat([z_amplitude, z_phase], dim=1)
        z = rearrange(z, "b c w t -> b c t w")

        latent_z = []
        for i in range(self.n_octaves):
            latent_z.append(
                self.conv_pres[i](
                    z[
                        :,
                        :,
                        :,
                        i * self.bins_per_octave : (i + 1) * self.bins_per_octave,
                    ]
                )
            )
        latent_z = torch.cat(latent_z, dim=-1)

        for i, l in enumerate(self.convs):
            latent_z = l(latent_z)

            latent_z = self.activation(latent_z)
            fmap.append(latent_z)

        latent_z = self.conv_post(latent_z)

        return latent_z, fmap
    
    
    
class MultiScaleSubbandCQTDiscriminator(nn.Module): # replacing "MultiResSpecDiscriminator"
    def __init__(self):
        super(MultiScaleSubbandCQTDiscriminator, self).__init__()
        cfg = Munch(multiscale_subband_cfg)
        self.cfg = cfg
        self.discriminators = nn.ModuleList(
            [
                DiscriminatorCQT(
                    cfg,
                    hop_length=cfg.hop_lengths[i],
                    n_octaves=cfg.n_octaves[i],
                    bins_per_octave=cfg.bins_per_octaves[i],
                )
                for i in range(len(cfg.hop_lengths))
            ]
        )

    def forward(self, y, y_hat):
        y_d_rs = []
        y_d_gs = []
        fmap_rs = []
        fmap_gs = []

        for disc in self.discriminators:
            y_d_r, fmap_r = disc(y)
            y_d_g, fmap_g = disc(y_hat)
            y_d_rs.append(y_d_r)
            fmap_rs.append(fmap_r)
            y_d_gs.append(y_d_g)
            fmap_gs.append(fmap_g)

        return y_d_rs, y_d_gs, fmap_rs, fmap_gs

    
    
    #############################



def feature_loss(fmap_r, fmap_g):
    loss = 0
    for dr, dg in zip(fmap_r, fmap_g):
        for rl, gl in zip(dr, dg):
            loss += torch.mean(torch.abs(rl - gl))

    return loss*2


def discriminator_loss(disc_real_outputs, disc_generated_outputs):
    loss = 0
    r_losses = []
    g_losses = []
    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
        r_loss = torch.mean((1-dr)**2)
        g_loss = torch.mean(dg**2)
        loss += (r_loss + g_loss)
        r_losses.append(r_loss.item())
        g_losses.append(g_loss.item())

    return loss, r_losses, g_losses


def generator_loss(disc_outputs):
    loss = 0
    gen_losses = []
    for dg in disc_outputs:
        l = torch.mean((1-dg)**2)
        gen_losses.append(l)
        loss += l

    return loss, gen_losses

def discriminator_TPRLS_loss(disc_real_outputs, disc_generated_outputs):
    loss = 0
    for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
        tau = 0.04
        m_DG = torch.median((dr-dg))
        L_rel = torch.mean((((dr - dg) - m_DG)**2)[dr < dg + m_DG])
        loss += tau - F.relu(tau - L_rel)
    return loss

def generator_TPRLS_loss(disc_real_outputs, disc_generated_outputs):
    loss = 0
    for dg, dr in zip(disc_real_outputs, disc_generated_outputs):
        tau = 0.04
        m_DG = torch.median((dr-dg))
        L_rel = torch.mean((((dr - dg) - m_DG)**2)[dr < dg + m_DG])
        loss += tau - F.relu(tau - L_rel)
    return loss