From b7a6e43a4c28a1648163c62899b94500a96aac1c Mon Sep 17 00:00:00 2001
From: RVC-Boss <129054828+RVC-Boss@users.noreply.github.com>
Date: Thu, 27 Feb 2025 16:07:44 +0800
Subject: [PATCH] =?UTF-8?q?=E6=94=AF=E6=8C=8124k=E9=9F=B3=E9=A2=91?=
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支持24k音频超分48k采样率
---
 tools/AP_BWE_main/models/model.py | 455 ++++++++++++++++++++++++++++++
 1 file changed, 455 insertions(+)
 create mode 100644 tools/AP_BWE_main/models/model.py

diff --git a/tools/AP_BWE_main/models/model.py b/tools/AP_BWE_main/models/model.py
new file mode 100644
index 0000000..0c235d6
--- /dev/null
+++ b/tools/AP_BWE_main/models/model.py
@@ -0,0 +1,455 @@
+import torch
+import torch.nn.functional as F
+import torch.nn as nn
+from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
+# from utils import init_weights, get_padding
+def get_padding(kernel_size, dilation=1):
+    return int((kernel_size*dilation - dilation)/2)
+def init_weights(m, mean=0.0, std=0.01):
+    classname = m.__class__.__name__
+    if classname.find("Conv") != -1:
+        m.weight.data.normal_(mean, std)
+
+import numpy as np
+from typing import Tuple, List
+
+LRELU_SLOPE = 0.1
+
+class ConvNeXtBlock(nn.Module):
+    """ConvNeXt Block adapted from https://github.com/facebookresearch/ConvNeXt to 1D audio signal.
+
+    Args:
+        dim (int): Number of input channels.
+        intermediate_dim (int): Dimensionality of the intermediate layer.
+        layer_scale_init_value (float, optional): Initial value for the layer scale. None means no scaling.
+            Defaults to None.
+        adanorm_num_embeddings (int, optional): Number of embeddings for AdaLayerNorm.
+            None means non-conditional LayerNorm. Defaults to None.
+    """
+
+    def __init__(
+        self,
+        dim: int,
+        layer_scale_init_value= None,
+        adanorm_num_embeddings = None,
+    ):
+        super().__init__()
+        self.dwconv = nn.Conv1d(dim, dim, kernel_size=7, padding=3, groups=dim)  # depthwise conv
+        self.adanorm = adanorm_num_embeddings is not None
+        
+        self.norm = nn.LayerNorm(dim, eps=1e-6)
+        self.pwconv1 = nn.Linear(dim, dim*3)  # pointwise/1x1 convs, implemented with linear layers
+        self.act = nn.GELU()
+        self.pwconv2 = nn.Linear(dim*3, dim)
+        self.gamma = (
+            nn.Parameter(layer_scale_init_value * torch.ones(dim), requires_grad=True)
+            if layer_scale_init_value > 0
+            else None
+        )
+
+    def forward(self, x, cond_embedding_id = None) :
+        residual = x
+        x = self.dwconv(x)
+        x = x.transpose(1, 2)  # (B, C, T) -> (B, T, C)
+        if self.adanorm:
+            assert cond_embedding_id is not None
+            x = self.norm(x, cond_embedding_id)
+        else:
+            x = self.norm(x)
+        x = self.pwconv1(x)
+        x = self.act(x)
+        x = self.pwconv2(x)
+        if self.gamma is not None:
+            x = self.gamma * x
+        x = x.transpose(1, 2)  # (B, T, C) -> (B, C, T)
+
+        x = residual + x
+        return x
+
+
+class APNet_BWE_Model(torch.nn.Module):
+    def __init__(self, h):
+        super(APNet_BWE_Model, self).__init__()
+        self.h = h
+        self.adanorm_num_embeddings = None
+        layer_scale_init_value =  1 / h.ConvNeXt_layers
+
+        self.conv_pre_mag = nn.Conv1d(h.n_fft//2+1, h.ConvNeXt_channels, 7, 1, padding=get_padding(7, 1))
+        self.norm_pre_mag = nn.LayerNorm(h.ConvNeXt_channels, eps=1e-6)
+        self.conv_pre_pha = nn.Conv1d(h.n_fft//2+1, h.ConvNeXt_channels, 7, 1, padding=get_padding(7, 1))
+        self.norm_pre_pha = nn.LayerNorm(h.ConvNeXt_channels, eps=1e-6)
+
+        self.convnext_mag = nn.ModuleList(
+            [
+                ConvNeXtBlock(
+                    dim=h.ConvNeXt_channels,
+                    layer_scale_init_value=layer_scale_init_value,
+                    adanorm_num_embeddings=self.adanorm_num_embeddings,
+                )
+                for _ in range(h.ConvNeXt_layers)
+            ]
+        )
+
+        self.convnext_pha = nn.ModuleList(
+            [
+                ConvNeXtBlock(
+                    dim=h.ConvNeXt_channels,
+                    layer_scale_init_value=layer_scale_init_value,
+                    adanorm_num_embeddings=self.adanorm_num_embeddings,
+                )
+                for _ in range(h.ConvNeXt_layers)
+            ]
+        )
+
+        self.norm_post_mag = nn.LayerNorm(h.ConvNeXt_channels, eps=1e-6)
+        self.norm_post_pha = nn.LayerNorm(h.ConvNeXt_channels, eps=1e-6)
+        self.apply(self._init_weights)
+        self.linear_post_mag = nn.Linear(h.ConvNeXt_channels, h.n_fft//2+1)
+        self.linear_post_pha_r = nn.Linear(h.ConvNeXt_channels, h.n_fft//2+1)
+        self.linear_post_pha_i = nn.Linear(h.ConvNeXt_channels, h.n_fft//2+1)
+
+    def _init_weights(self, m):
+        if isinstance(m, (nn.Conv1d, nn.Linear)):
+            nn.init.trunc_normal_(m.weight, std=0.02)
+            nn.init.constant_(m.bias, 0)
+
+    def forward(self, mag_nb, pha_nb):
+
+        x_mag = self.conv_pre_mag(mag_nb)
+        x_pha = self.conv_pre_pha(pha_nb)
+        x_mag = self.norm_pre_mag(x_mag.transpose(1, 2)).transpose(1, 2)
+        x_pha = self.norm_pre_pha(x_pha.transpose(1, 2)).transpose(1, 2)
+
+        for conv_block_mag, conv_block_pha in zip(self.convnext_mag, self.convnext_pha):
+            x_mag = x_mag + x_pha
+            x_pha = x_pha + x_mag
+            x_mag = conv_block_mag(x_mag, cond_embedding_id=None)
+            x_pha = conv_block_pha(x_pha, cond_embedding_id=None)
+
+        x_mag = self.norm_post_mag(x_mag.transpose(1, 2))
+        mag_wb = mag_nb + self.linear_post_mag(x_mag).transpose(1, 2)
+
+        x_pha = self.norm_post_pha(x_pha.transpose(1, 2))
+        x_pha_r = self.linear_post_pha_r(x_pha)
+        x_pha_i = self.linear_post_pha_i(x_pha)
+        pha_wb = torch.atan2(x_pha_i, x_pha_r).transpose(1, 2)
+
+        com_wb = torch.stack((torch.exp(mag_wb)*torch.cos(pha_wb), 
+                           torch.exp(mag_wb)*torch.sin(pha_wb)), dim=-1)
+        
+        return mag_wb, pha_wb, com_wb
+
+
+
+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(nn.Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
+            norm_f(nn.Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
+            norm_f(nn.Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
+            norm_f(nn.Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
+            norm_f(nn.Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
+        ])
+        self.conv_post = norm_f(nn.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 i,l in enumerate(self.convs):
+            x = l(x)
+            x = F.leaky_relu(x, LRELU_SLOPE)
+            if i > 0:
+                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 MultiResolutionAmplitudeDiscriminator(nn.Module):
+    def __init__(
+        self,
+        resolutions: Tuple[Tuple[int, int, int]] = ((512, 128, 512), (1024, 256, 1024), (2048, 512, 2048)),
+        num_embeddings: int = None,
+    ):
+        super().__init__()
+        self.discriminators = nn.ModuleList(
+            [DiscriminatorAR(resolution=r, num_embeddings=num_embeddings) for r in resolutions]
+        )
+
+    def forward(
+        self, y: torch.Tensor, y_hat: torch.Tensor, bandwidth_id: torch.Tensor = None
+    ) -> Tuple[List[torch.Tensor], List[torch.Tensor], List[List[torch.Tensor]], List[List[torch.Tensor]]]:
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+
+        for d in self.discriminators:
+            y_d_r, fmap_r = d(x=y, cond_embedding_id=bandwidth_id)
+            y_d_g, fmap_g = d(x=y_hat, cond_embedding_id=bandwidth_id)
+            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 DiscriminatorAR(nn.Module):
+    def __init__(
+        self,
+        resolution: Tuple[int, int, int],
+        channels: int = 64,
+        in_channels: int = 1,
+        num_embeddings: int = None,
+    ):
+        super().__init__()
+        self.resolution = resolution
+        self.in_channels = in_channels
+        self.convs = nn.ModuleList(
+            [
+                weight_norm(nn.Conv2d(in_channels, channels, kernel_size=(7, 5), stride=(2, 2), padding=(3, 2))),
+                weight_norm(nn.Conv2d(channels, channels, kernel_size=(5, 3), stride=(2, 1), padding=(2, 1))),
+                weight_norm(nn.Conv2d(channels, channels, kernel_size=(5, 3), stride=(2, 2), padding=(2, 1))),
+                weight_norm(nn.Conv2d(channels, channels, kernel_size=3, stride=(2, 1), padding=1)),
+                weight_norm(nn.Conv2d(channels, channels, kernel_size=3, stride=(2, 2), padding=1)),
+            ]
+        )
+        if num_embeddings is not None:
+            self.emb = torch.nn.Embedding(num_embeddings=num_embeddings, embedding_dim=channels)
+            torch.nn.init.zeros_(self.emb.weight)
+        self.conv_post = weight_norm(nn.Conv2d(channels, 1, (3, 3), padding=(1, 1)))
+
+    def forward(
+        self, x: torch.Tensor, cond_embedding_id: torch.Tensor = None
+    ) -> Tuple[torch.Tensor, List[torch.Tensor]]:
+        fmap = []
+        x=x.squeeze(1)
+        
+        x = self.spectrogram(x)
+        x = x.unsqueeze(1)
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, LRELU_SLOPE)
+            fmap.append(x)
+        if cond_embedding_id is not None:
+            emb = self.emb(cond_embedding_id)
+            h = (emb.view(1, -1, 1, 1) * x).sum(dim=1, keepdims=True)
+        else:
+            h = 0
+        x = self.conv_post(x)
+        fmap.append(x)
+        x += h
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+    def spectrogram(self, x: torch.Tensor) -> torch.Tensor:
+        n_fft, hop_length, win_length = self.resolution
+        amplitude_spectrogram = torch.stft(
+            x,
+            n_fft=n_fft,
+            hop_length=hop_length,
+            win_length=win_length,
+            window=None,  # interestingly rectangular window kind of works here
+            center=True,
+            return_complex=True,
+        ).abs()
+
+        return amplitude_spectrogram
+
+
+class MultiResolutionPhaseDiscriminator(nn.Module):
+    def __init__(
+        self,
+        resolutions: Tuple[Tuple[int, int, int]] = ((512, 128, 512), (1024, 256, 1024), (2048, 512, 2048)),
+        num_embeddings: int = None,
+    ):
+        super().__init__()
+        self.discriminators = nn.ModuleList(
+            [DiscriminatorPR(resolution=r, num_embeddings=num_embeddings) for r in resolutions]
+        )
+
+    def forward(
+        self, y: torch.Tensor, y_hat: torch.Tensor, bandwidth_id: torch.Tensor = None
+    ) -> Tuple[List[torch.Tensor], List[torch.Tensor], List[List[torch.Tensor]], List[List[torch.Tensor]]]:
+        y_d_rs = []
+        y_d_gs = []
+        fmap_rs = []
+        fmap_gs = []
+
+        for d in self.discriminators:
+            y_d_r, fmap_r = d(x=y, cond_embedding_id=bandwidth_id)
+            y_d_g, fmap_g = d(x=y_hat, cond_embedding_id=bandwidth_id)
+            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 DiscriminatorPR(nn.Module):
+    def __init__(
+        self,
+        resolution: Tuple[int, int, int],
+        channels: int = 64,
+        in_channels: int = 1,
+        num_embeddings: int = None,
+    ):
+        super().__init__()
+        self.resolution = resolution
+        self.in_channels = in_channels
+        self.convs = nn.ModuleList(
+            [
+                weight_norm(nn.Conv2d(in_channels, channels, kernel_size=(7, 5), stride=(2, 2), padding=(3, 2))),
+                weight_norm(nn.Conv2d(channels, channels, kernel_size=(5, 3), stride=(2, 1), padding=(2, 1))),
+                weight_norm(nn.Conv2d(channels, channels, kernel_size=(5, 3), stride=(2, 2), padding=(2, 1))),
+                weight_norm(nn.Conv2d(channels, channels, kernel_size=3, stride=(2, 1), padding=1)),
+                weight_norm(nn.Conv2d(channels, channels, kernel_size=3, stride=(2, 2), padding=1)),
+            ]
+        )
+        if num_embeddings is not None:
+            self.emb = torch.nn.Embedding(num_embeddings=num_embeddings, embedding_dim=channels)
+            torch.nn.init.zeros_(self.emb.weight)
+        self.conv_post = weight_norm(nn.Conv2d(channels, 1, (3, 3), padding=(1, 1)))
+
+    def forward(
+        self, x: torch.Tensor, cond_embedding_id: torch.Tensor = None
+    ) -> Tuple[torch.Tensor, List[torch.Tensor]]:
+        fmap = []
+        x=x.squeeze(1)
+        
+        x = self.spectrogram(x)
+        x = x.unsqueeze(1)
+        for l in self.convs:
+            x = l(x)
+            x = F.leaky_relu(x, LRELU_SLOPE)
+            fmap.append(x)
+        if cond_embedding_id is not None:
+            emb = self.emb(cond_embedding_id)
+            h = (emb.view(1, -1, 1, 1) * x).sum(dim=1, keepdims=True)
+        else:
+            h = 0
+        x = self.conv_post(x)
+        fmap.append(x)
+        x += h
+        x = torch.flatten(x, 1, -1)
+
+        return x, fmap
+
+    def spectrogram(self, x: torch.Tensor) -> torch.Tensor:
+        n_fft, hop_length, win_length = self.resolution
+        phase_spectrogram = torch.stft(
+            x,
+            n_fft=n_fft,
+            hop_length=hop_length,
+            win_length=win_length,
+            window=None,  # interestingly rectangular window kind of works here
+            center=True,
+            return_complex=True,
+        ).angle()
+
+        return phase_spectrogram
+
+
+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
+
+
+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(torch.clamp(1 - dr, min=0))
+            g_loss = torch.mean(torch.clamp(1 + dg, min=0))
+            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(torch.clamp(1 - dg, min=0))
+            gen_losses.append(l)
+            loss += l
+
+    return loss, gen_losses
+
+
+def phase_losses(phase_r, phase_g):
+
+    ip_loss = torch.mean(anti_wrapping_function(phase_r - phase_g))
+    gd_loss = torch.mean(anti_wrapping_function(torch.diff(phase_r, dim=1) - torch.diff(phase_g, dim=1)))
+    iaf_loss = torch.mean(anti_wrapping_function(torch.diff(phase_r, dim=2) - torch.diff(phase_g, dim=2)))
+
+    return ip_loss, gd_loss, iaf_loss
+
+def anti_wrapping_function(x):
+
+    return torch.abs(x - torch.round(x / (2 * np.pi)) * 2 * np.pi)
+
+def stft_mag(audio, n_fft=2048, hop_length=512):
+    hann_window = torch.hann_window(n_fft).to(audio.device)
+    stft_spec = torch.stft(audio, n_fft, hop_length, window=hann_window, return_complex=True)
+    stft_mag = torch.abs(stft_spec)
+    return(stft_mag)
+
+def cal_snr(pred, target):
+    snr = (20 * torch.log10(torch.norm(target, dim=-1) / torch.norm(pred - target, dim=-1).clamp(min=1e-8))).mean()
+    return snr
+
+def cal_lsd(pred, target):
+    sp = torch.log10(stft_mag(pred).square().clamp(1e-8))
+    st = torch.log10(stft_mag(target).square().clamp(1e-8))
+    return (sp - st).square().mean(dim=1).sqrt().mean()