refactor(xtts): remove duplicate hifigan generator

TTS/tts/layers/xtts/hifigan_decoder.py

@@ -1,618 +1,13 @@
 import logging
 
 import torch
-import torchaudio
-from torch import nn
-from torch.nn import Conv1d, ConvTranspose1d
-from torch.nn import functional as F
-from torch.nn.utils.parametrizations import weight_norm
-from torch.nn.utils.parametrize import remove_parametrizations
 from trainer.io import load_fsspec
 
-from TTS.utils.generic_utils import is_pytorch_at_least_2_4
+from TTS.encoder.models.resnet import ResNetSpeakerEncoder
-from TTS.vocoder.models.hifigan_generator import get_padding
+from TTS.vocoder.models.hifigan_generator import HifiganGenerator
 
 logger = logging.getLogger(__name__)
 
-LRELU_SLOPE = 0.1
-
-
-class ResBlock1(torch.nn.Module):
-    """Residual Block Type 1. It has 3 convolutional layers in each convolutional block.
-
-    Network::
-
-        x -> lrelu -> conv1_1 -> conv1_2 -> conv1_3 -> z -> lrelu -> conv2_1 -> conv2_2 -> conv2_3 -> o -> + -> o
-        |--------------------------------------------------------------------------------------------------|
-
-
-    Args:
-        channels (int): number of hidden channels for the convolutional layers.
-        kernel_size (int): size of the convolution filter in each layer.
-        dilations (list): list of dilation value for each conv layer in a block.
-    """
-
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
-        super().__init__()
-        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.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),
-                    )
-                ),
-            ]
-        )
-
-    def forward(self, x):
-        """
-        Args:
-            x (Tensor): input tensor.
-        Returns:
-            Tensor: output tensor.
-        Shapes:
-            x: [B, C, T]
-        """
-        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_parametrizations(l, "weight")
-        for l in self.convs2:
-            remove_parametrizations(l, "weight")
-
-
-class ResBlock2(torch.nn.Module):
-    """Residual Block Type 2. It has 1 convolutional layers in each convolutional block.
-
-    Network::
-
-        x -> lrelu -> conv1-> -> z -> lrelu -> conv2-> o -> + -> o
-        |---------------------------------------------------|
-
-
-    Args:
-        channels (int): number of hidden channels for the convolutional layers.
-        kernel_size (int): size of the convolution filter in each layer.
-        dilations (list): list of dilation value for each conv layer in a block.
-    """
-
-    def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
-        super().__init__()
-        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]),
-                    )
-                ),
-            ]
-        )
-
-    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_parametrizations(l, "weight")
-
-
-class HifiganGenerator(torch.nn.Module):
-    def __init__(
-        self,
-        in_channels,
-        out_channels,
-        resblock_type,
-        resblock_dilation_sizes,
-        resblock_kernel_sizes,
-        upsample_kernel_sizes,
-        upsample_initial_channel,
-        upsample_factors,
-        inference_padding=5,
-        cond_channels=0,
-        conv_pre_weight_norm=True,
-        conv_post_weight_norm=True,
-        conv_post_bias=True,
-        cond_in_each_up_layer=False,
-    ):
-        r"""HiFiGAN Generator with Multi-Receptive Field Fusion (MRF)
-
-        Network:
-            x -> lrelu -> upsampling_layer -> resblock1_k1x1 -> z1 -> + -> z_sum / #resblocks -> lrelu -> conv_post_7x1 -> tanh -> o
-                                                 ..          -> zI ---|
-                                              resblockN_kNx1 -> zN ---'
-
-        Args:
-            in_channels (int): number of input tensor channels.
-            out_channels (int): number of output tensor channels.
-            resblock_type (str): type of the `ResBlock`. '1' or '2'.
-            resblock_dilation_sizes (List[List[int]]): list of dilation values in each layer of a `ResBlock`.
-            resblock_kernel_sizes (List[int]): list of kernel sizes for each `ResBlock`.
-            upsample_kernel_sizes (List[int]): list of kernel sizes for each transposed convolution.
-            upsample_initial_channel (int): number of channels for the first upsampling layer. This is divided by 2
-                for each consecutive upsampling layer.
-            upsample_factors (List[int]): upsampling factors (stride) for each upsampling layer.
-            inference_padding (int): constant padding applied to the input at inference time. Defaults to 5.
-        """
-        super().__init__()
-        self.inference_padding = inference_padding
-        self.num_kernels = len(resblock_kernel_sizes)
-        self.num_upsamples = len(upsample_factors)
-        self.cond_in_each_up_layer = cond_in_each_up_layer
-
-        # initial upsampling layers
-        self.conv_pre = weight_norm(Conv1d(in_channels, upsample_initial_channel, 7, 1, padding=3))
-        resblock = ResBlock1 if resblock_type == "1" else ResBlock2
-        # upsampling layers
-        self.ups = nn.ModuleList()
-        for i, (u, k) in enumerate(zip(upsample_factors, upsample_kernel_sizes)):
-            self.ups.append(
-                weight_norm(
-                    ConvTranspose1d(
-                        upsample_initial_channel // (2**i),
-                        upsample_initial_channel // (2 ** (i + 1)),
-                        k,
-                        u,
-                        padding=(k - u) // 2,
-                    )
-                )
-            )
-        # MRF blocks
-        self.resblocks = nn.ModuleList()
-        for i in range(len(self.ups)):
-            ch = upsample_initial_channel // (2 ** (i + 1))
-            for _, (k, d) in enumerate(zip(resblock_kernel_sizes, resblock_dilation_sizes)):
-                self.resblocks.append(resblock(ch, k, d))
-        # post convolution layer
-        self.conv_post = weight_norm(Conv1d(ch, out_channels, 7, 1, padding=3, bias=conv_post_bias))
-        if cond_channels > 0:
-            self.cond_layer = nn.Conv1d(cond_channels, upsample_initial_channel, 1)
-
-        if not conv_pre_weight_norm:
-            remove_parametrizations(self.conv_pre, "weight")
-
-        if not conv_post_weight_norm:
-            remove_parametrizations(self.conv_post, "weight")
-
-        if self.cond_in_each_up_layer:
-            self.conds = nn.ModuleList()
-            for i in range(len(self.ups)):
-                ch = upsample_initial_channel // (2 ** (i + 1))
-                self.conds.append(nn.Conv1d(cond_channels, ch, 1))
-
-    def forward(self, x, g=None):
-        """
-        Args:
-            x (Tensor): feature input tensor.
-            g (Tensor): global conditioning input tensor.
-
-        Returns:
-            Tensor: output waveform.
-
-        Shapes:
-            x: [B, C, T]
-            Tensor: [B, 1, T]
-        """
-        o = self.conv_pre(x)
-        if hasattr(self, "cond_layer"):
-            o = o + self.cond_layer(g)
-        for i in range(self.num_upsamples):
-            o = F.leaky_relu(o, LRELU_SLOPE)
-            o = self.ups[i](o)
-
-            if self.cond_in_each_up_layer:
-                o = o + self.conds[i](g)
-
-            z_sum = None
-            for j in range(self.num_kernels):
-                if z_sum is None:
-                    z_sum = self.resblocks[i * self.num_kernels + j](o)
-                else:
-                    z_sum += self.resblocks[i * self.num_kernels + j](o)
-            o = z_sum / self.num_kernels
-        o = F.leaky_relu(o)
-        o = self.conv_post(o)
-        o = torch.tanh(o)
-        return o
-
-    @torch.no_grad()
-    def inference(self, c):
-        """
-        Args:
-            x (Tensor): conditioning input tensor.
-
-        Returns:
-            Tensor: output waveform.
-
-        Shapes:
-            x: [B, C, T]
-            Tensor: [B, 1, T]
-        """
-        c = c.to(self.conv_pre.weight.device)
-        c = torch.nn.functional.pad(c, (self.inference_padding, self.inference_padding), "replicate")
-        return self.forward(c)
-
-    def remove_weight_norm(self):
-        logger.info("Removing weight norm...")
-        for l in self.ups:
-            remove_parametrizations(l, "weight")
-        for l in self.resblocks:
-            l.remove_weight_norm()
-        remove_parametrizations(self.conv_pre, "weight")
-        remove_parametrizations(self.conv_post, "weight")
-
-    def load_checkpoint(
-        self, config, checkpoint_path, eval=False, cache=False
-    ):  # pylint: disable=unused-argument, redefined-builtin
-        state = torch.load(checkpoint_path, map_location=torch.device("cpu"), weights_only=is_pytorch_at_least_2_4())
-        self.load_state_dict(state["model"])
-        if eval:
-            self.eval()
-            assert not self.training
-            self.remove_weight_norm()
-
-
-class SELayer(nn.Module):
-    def __init__(self, channel, reduction=8):
-        super(SELayer, self).__init__()
-        self.avg_pool = nn.AdaptiveAvgPool2d(1)
-        self.fc = nn.Sequential(
-            nn.Linear(channel, channel // reduction),
-            nn.ReLU(inplace=True),
-            nn.Linear(channel // reduction, channel),
-            nn.Sigmoid(),
-        )
-
-    def forward(self, x):
-        b, c, _, _ = x.size()
-        y = self.avg_pool(x).view(b, c)
-        y = self.fc(y).view(b, c, 1, 1)
-        return x * y
-
-
-class SEBasicBlock(nn.Module):
-    expansion = 1
-
-    def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=8):
-        super(SEBasicBlock, self).__init__()
-        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(planes)
-        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(planes)
-        self.relu = nn.ReLU(inplace=True)
-        self.se = SELayer(planes, reduction)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        residual = x
-
-        out = self.conv1(x)
-        out = self.relu(out)
-        out = self.bn1(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        out = self.se(out)
-
-        if self.downsample is not None:
-            residual = self.downsample(x)
-
-        out += residual
-        out = self.relu(out)
-        return out
-
-
-def set_init_dict(model_dict, checkpoint_state, c):
-    # Partial initialization: if there is a mismatch with new and old layer, it is skipped.
-    for k, v in checkpoint_state.items():
-        if k not in model_dict:
-            logger.warning("Layer missing in the model definition: %s", k)
-    # 1. filter out unnecessary keys
-    pretrained_dict = {k: v for k, v in checkpoint_state.items() if k in model_dict}
-    # 2. filter out different size layers
-    pretrained_dict = {k: v for k, v in pretrained_dict.items() if v.numel() == model_dict[k].numel()}
-    # 3. skip reinit layers
-    if c.has("reinit_layers") and c.reinit_layers is not None:
-        for reinit_layer_name in c.reinit_layers:
-            pretrained_dict = {k: v for k, v in pretrained_dict.items() if reinit_layer_name not in k}
-    # 4. overwrite entries in the existing state dict
-    model_dict.update(pretrained_dict)
-    logger.info("%d / %d layers are restored.", len(pretrained_dict), len(model_dict))
-    return model_dict
-
-
-class PreEmphasis(nn.Module):
-    def __init__(self, coefficient=0.97):
-        super().__init__()
-        self.coefficient = coefficient
-        self.register_buffer("filter", torch.FloatTensor([-self.coefficient, 1.0]).unsqueeze(0).unsqueeze(0))
-
-    def forward(self, x):
-        assert len(x.size()) == 2
-
-        x = torch.nn.functional.pad(x.unsqueeze(1), (1, 0), "reflect")
-        return torch.nn.functional.conv1d(x, self.filter).squeeze(1)
-
-
-class ResNetSpeakerEncoder(nn.Module):
-    """This is copied from 🐸TTS to remove it from the dependencies."""
-
-    # pylint: disable=W0102
-    def __init__(
-        self,
-        input_dim=64,
-        proj_dim=512,
-        layers=[3, 4, 6, 3],
-        num_filters=[32, 64, 128, 256],
-        encoder_type="ASP",
-        log_input=False,
-        use_torch_spec=False,
-        audio_config=None,
-    ):
-        super(ResNetSpeakerEncoder, self).__init__()
-
-        self.encoder_type = encoder_type
-        self.input_dim = input_dim
-        self.log_input = log_input
-        self.use_torch_spec = use_torch_spec
-        self.audio_config = audio_config
-        self.proj_dim = proj_dim
-
-        self.conv1 = nn.Conv2d(1, num_filters[0], kernel_size=3, stride=1, padding=1)
-        self.relu = nn.ReLU(inplace=True)
-        self.bn1 = nn.BatchNorm2d(num_filters[0])
-
-        self.inplanes = num_filters[0]
-        self.layer1 = self.create_layer(SEBasicBlock, num_filters[0], layers[0])
-        self.layer2 = self.create_layer(SEBasicBlock, num_filters[1], layers[1], stride=(2, 2))
-        self.layer3 = self.create_layer(SEBasicBlock, num_filters[2], layers[2], stride=(2, 2))
-        self.layer4 = self.create_layer(SEBasicBlock, num_filters[3], layers[3], stride=(2, 2))
-
-        self.instancenorm = nn.InstanceNorm1d(input_dim)
-
-        if self.use_torch_spec:
-            self.torch_spec = torch.nn.Sequential(
-                PreEmphasis(audio_config["preemphasis"]),
-                torchaudio.transforms.MelSpectrogram(
-                    sample_rate=audio_config["sample_rate"],
-                    n_fft=audio_config["fft_size"],
-                    win_length=audio_config["win_length"],
-                    hop_length=audio_config["hop_length"],
-                    window_fn=torch.hamming_window,
-                    n_mels=audio_config["num_mels"],
-                ),
-            )
-
-        else:
-            self.torch_spec = None
-
-        outmap_size = int(self.input_dim / 8)
-
-        self.attention = nn.Sequential(
-            nn.Conv1d(num_filters[3] * outmap_size, 128, kernel_size=1),
-            nn.ReLU(),
-            nn.BatchNorm1d(128),
-            nn.Conv1d(128, num_filters[3] * outmap_size, kernel_size=1),
-            nn.Softmax(dim=2),
-        )
-
-        if self.encoder_type == "SAP":
-            out_dim = num_filters[3] * outmap_size
-        elif self.encoder_type == "ASP":
-            out_dim = num_filters[3] * outmap_size * 2
-        else:
-            raise ValueError("Undefined encoder")
-
-        self.fc = nn.Linear(out_dim, proj_dim)
-
-        self._init_layers()
-
-    def _init_layers(self):
-        for m in self.modules():
-            if isinstance(m, nn.Conv2d):
-                nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
-            elif isinstance(m, nn.BatchNorm2d):
-                nn.init.constant_(m.weight, 1)
-                nn.init.constant_(m.bias, 0)
-
-    def create_layer(self, block, planes, blocks, stride=1):
-        downsample = None
-        if stride != 1 or self.inplanes != planes * block.expansion:
-            downsample = nn.Sequential(
-                nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
-                nn.BatchNorm2d(planes * block.expansion),
-            )
-
-        layers = []
-        layers.append(block(self.inplanes, planes, stride, downsample))
-        self.inplanes = planes * block.expansion
-        for _ in range(1, blocks):
-            layers.append(block(self.inplanes, planes))
-
-        return nn.Sequential(*layers)
-
-    # pylint: disable=R0201
-    def new_parameter(self, *size):
-        out = nn.Parameter(torch.FloatTensor(*size))
-        nn.init.xavier_normal_(out)
-        return out
-
-    def forward(self, x, l2_norm=False):
-        """Forward pass of the model.
-
-        Args:
-            x (Tensor): Raw waveform signal or spectrogram frames. If input is a waveform, `torch_spec` must be `True`
-                to compute the spectrogram on-the-fly.
-            l2_norm (bool): Whether to L2-normalize the outputs.
-
-        Shapes:
-            - x: :math:`(N, 1, T_{in})` or :math:`(N, D_{spec}, T_{in})`
-        """
-        x.squeeze_(1)
-        # if you torch spec compute it otherwise use the mel spec computed by the AP
-        if self.use_torch_spec:
-            x = self.torch_spec(x)
-
-        if self.log_input:
-            x = (x + 1e-6).log()
-        x = self.instancenorm(x).unsqueeze(1)
-
-        x = self.conv1(x)
-        x = self.relu(x)
-        x = self.bn1(x)
-
-        x = self.layer1(x)
-        x = self.layer2(x)
-        x = self.layer3(x)
-        x = self.layer4(x)
-
-        x = x.reshape(x.size()[0], -1, x.size()[-1])
-
-        w = self.attention(x)
-
-        if self.encoder_type == "SAP":
-            x = torch.sum(x * w, dim=2)
-        elif self.encoder_type == "ASP":
-            mu = torch.sum(x * w, dim=2)
-            sg = torch.sqrt((torch.sum((x**2) * w, dim=2) - mu**2).clamp(min=1e-5))
-            x = torch.cat((mu, sg), 1)
-
-        x = x.view(x.size()[0], -1)
-        x = self.fc(x)
-
-        if l2_norm:
-            x = torch.nn.functional.normalize(x, p=2, dim=1)
-        return x
-
-    def load_checkpoint(
-        self,
-        checkpoint_path: str,
-        eval: bool = False,
-        use_cuda: bool = False,
-        criterion=None,
-        cache=False,
-    ):
-        state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"), cache=cache)
-        try:
-            self.load_state_dict(state["model"])
-            logger.info("Model fully restored.")
-        except (KeyError, RuntimeError) as error:
-            # If eval raise the error
-            if eval:
-                raise error
-
-            logger.info("Partial model initialization.")
-            model_dict = self.state_dict()
-            model_dict = set_init_dict(model_dict, state["model"])
-            self.load_state_dict(model_dict)
-            del model_dict
-
-        # load the criterion for restore_path
-        if criterion is not None and "criterion" in state:
-            try:
-                criterion.load_state_dict(state["criterion"])
-            except (KeyError, RuntimeError) as error:
-                logger.exception("Criterion load ignored because of: %s", error)
-
-        if use_cuda:
-            self.cuda()
-            if criterion is not None:
-                criterion = criterion.cuda()
-
-        if eval:
-            self.eval()
-            assert not self.training
-
-        if not eval:
-            return criterion, state["step"]
-        return criterion
-
 
 class HifiDecoder(torch.nn.Module):
     def __init__(

TTS/vocoder/models/hifigan_generator.py

@@ -178,6 +178,7 @@ class HifiganGenerator(torch.nn.Module):
         conv_pre_weight_norm=True,
         conv_post_weight_norm=True,
         conv_post_bias=True,
+        cond_in_each_up_layer=False,
     ):
         r"""HiFiGAN Generator with Multi-Receptive Field Fusion (MRF)
 
@@ -202,6 +203,8 @@ class HifiganGenerator(torch.nn.Module):
         self.inference_padding = inference_padding
         self.num_kernels = len(resblock_kernel_sizes)
         self.num_upsamples = len(upsample_factors)
+        self.cond_in_each_up_layer = cond_in_each_up_layer
+
         # initial upsampling layers
         self.conv_pre = weight_norm(Conv1d(in_channels, upsample_initial_channel, 7, 1, padding=3))
         resblock = ResBlock1 if resblock_type == "1" else ResBlock2
@@ -236,6 +239,12 @@ class HifiganGenerator(torch.nn.Module):
         if not conv_post_weight_norm:
             remove_parametrizations(self.conv_post, "weight")
 
+        if self.cond_in_each_up_layer:
+            self.conds = nn.ModuleList()
+            for i in range(len(self.ups)):
+                ch = upsample_initial_channel // (2 ** (i + 1))
+                self.conds.append(nn.Conv1d(cond_channels, ch, 1))
+
     def forward(self, x, g=None):
         """
         Args:
@@ -255,6 +264,10 @@ class HifiganGenerator(torch.nn.Module):
         for i in range(self.num_upsamples):
             o = F.leaky_relu(o, LRELU_SLOPE)
             o = self.ups[i](o)
+
+            if self.cond_in_each_up_layer:
+                o = o + self.conds[i](g)
+
             z_sum = None
             for j in range(self.num_kernels):
                 if z_sum is None: