added some modules, etc

TTS/tts/configs/vits2_config.py

@@ -0,0 +1,181 @@
+from dataclasses import dataclass, field
+from typing import List
+
+from TTS.tts.configs.shared_configs import BaseTTSConfig
+from TTS.tts.models.vits2 import Vits2Args, Vits2AudioConfig
+
+
+@dataclass
+class Vits2Config(BaseTTSConfig):
+    """Defines parameters for VITS2 End2End TTS model.
+
+    Args:
+        model (str):
+            Model name. Do not change unless you know what you are doing.
+
+        model_args (Vits2Args):
+            Model architecture arguments. Defaults to `Vits2Args()`.
+
+        audio (Vits2AudioConfig):
+            Audio processing configuration. Defaults to `Vits2AudioConfig()`.
+
+        grad_clip (List):
+            Gradient clipping thresholds for each optimizer. Defaults to `[1000.0, 1000.0]`.
+
+        lr_gen (float):
+            Initial learning rate for the generator. Defaults to 0.0002.
+
+        lr_disc (float):
+            Initial learning rate for the discriminator. Defaults to 0.0002.
+
+        lr_scheduler_gen (str):
+            Name of the learning rate scheduler for the generator. One of the `torch.optim.lr_scheduler.*`. Defaults to
+            `ExponentialLR`.
+
+        lr_scheduler_gen_params (dict):
+            Parameters for the learning rate scheduler of the generator. Defaults to `{'gamma': 0.999875, "last_epoch":-1}`.
+
+        lr_scheduler_disc (str):
+            Name of the learning rate scheduler for the discriminator. One of the `torch.optim.lr_scheduler.*`. Defaults to
+            `ExponentialLR`.
+
+        lr_scheduler_disc_params (dict):
+            Parameters for the learning rate scheduler of the discriminator. Defaults to `{'gamma': 0.999875, "last_epoch":-1}`.
+
+        scheduler_after_epoch (bool):
+            If true, step the schedulers after each epoch else after each step. Defaults to `False`.
+
+        optimizer (str):
+            Name of the optimizer to use with both the generator and the discriminator networks. One of the
+            `torch.optim.*`. Defaults to `AdamW`.
+
+        kl_loss_alpha (float):
+            Loss weight for KL loss. Defaults to 1.0.
+
+        disc_loss_alpha (float):
+            Loss weight for the discriminator loss. Defaults to 1.0.
+
+        gen_loss_alpha (float):
+            Loss weight for the generator loss. Defaults to 1.0.
+
+        feat_loss_alpha (float):
+            Loss weight for the feature matching loss. Defaults to 1.0.
+
+        mel_loss_alpha (float):
+            Loss weight for the mel loss. Defaults to 45.0.
+
+        return_wav (bool):
+            If true, data loader returns the waveform as well as the other outputs. Do not change. Defaults to `True`.
+
+        compute_linear_spec (bool):
+            If true, the linear spectrogram is computed and returned alongside the mel output. Do not change. Defaults to `True`.
+
+        use_weighted_sampler (bool):
+            If true, use weighted sampler with bucketing for balancing samples between datasets used in training. Defaults to `False`.
+
+        weighted_sampler_attrs (dict):
+            Key retuned by the formatter to be used for weighted sampler. For example `{"root_path": 2.0, "speaker_name": 1.0}` sets sample probabilities
+            by overweighting `root_path` by 2.0. Defaults to `{}`.
+
+        weighted_sampler_multipliers (dict):
+            Weight each unique value of a key returned by the formatter for weighted sampling.
+            For example `{"root_path":{"/raid/datasets/libritts-clean-16khz-bwe-coqui_44khz/LibriTTS/train-clean-100/":1.0, "/raid/datasets/libritts-clean-16khz-bwe-coqui_44khz/LibriTTS/train-clean-360/": 0.5}`.
+            It will sample instances from `train-clean-100` 2 times more than `train-clean-360`. Defaults to `{}`.
+
+        r (int):
+            Number of spectrogram frames to be generated at a time. Do not change. Defaults to `1`.
+
+        add_blank (bool):
+            If true, a blank token is added in between every character. Defaults to `True`.
+
+        test_sentences (List[List]):
+            List of sentences with speaker and language information to be used for testing.
+
+        language_ids_file (str):
+            Path to the language ids file.
+
+        use_language_embedding (bool):
+            If true, language embedding is used. Defaults to `False`.
+
+    Note:
+        Check :class:`TTS.tts.configs.shared_configs.BaseTTSConfig` for the inherited parameters.
+
+    Example:
+
+        >>> from TTS.tts.configs.vits2_config import Vits2Config
+        >>> config = Vits2Config()
+    """
+
+    model: str = "vits2"
+    # model specific params
+    model_args: Vits2Args = field(default_factory=Vits2Args)
+    audio: Vits2AudioConfig = field(default_factory=Vits2AudioConfig)
+
+    # optimizer
+    grad_clip: List[float] = field(default_factory=lambda: [1000, 1000])
+    lr_gen: float = 0.0002
+    lr_disc: float = 0.0002
+    lr_dur: float = 0.0002
+
+    lr_scheduler_gen: str = "ExponentialLR"
+    lr_scheduler_gen_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1})
+    lr_scheduler_disc: str = "ExponentialLR"
+    lr_scheduler_disc_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1})
+    lr_scheduler_dur: str = "ExponentialLR"
+    lr_scheduler_dur_params: dict = field(default_factory=lambda: {"gamma": 0.999875, "last_epoch": -1})
+
+    scheduler_after_epoch: bool = True
+    optimizer: str = "AdamW"
+    optimizer_params: dict = field(default_factory=lambda: {"betas": [0.8, 0.99], "eps": 1e-9, "weight_decay": 0.01})
+
+    # loss params
+    kl_loss_alpha: float = 1.0
+    disc_loss_alpha: float = 1.0
+    gen_loss_alpha: float = 1.0
+    feat_loss_alpha: float = 1.0
+    mel_loss_alpha: float = 45.0
+    dur_loss_alpha: float = 1.0
+    speaker_encoder_loss_alpha: float = 1.0
+
+    # data loader params
+    return_wav: bool = True
+    compute_linear_spec: bool = True
+
+    # sampler params
+    use_weighted_sampler: bool = False  # TODO: move it to the base config
+    weighted_sampler_attrs: dict = field(default_factory=lambda: {})
+    weighted_sampler_multipliers: dict = field(default_factory=lambda: {})
+
+    # overrides
+    r: int = 1  # DO NOT CHANGE
+    add_blank: bool = True
+
+    # testing
+    test_sentences: List[List] = field(
+        default_factory=lambda: [
+            ["It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent."],
+            ["Be a voice, not an echo."],
+            ["I'm sorry Dave. I'm afraid I can't do that."],
+            ["This cake is great. It's so delicious and moist."],
+            ["Prior to November 22, 1963."],
+        ]
+    )
+
+    # multi-speaker settings
+    # use speaker embedding layer
+    num_speakers: int = 0
+    use_speaker_embedding: bool = False
+    speakers_file: str = None
+    speaker_embedding_channels: int = 256
+    language_ids_file: str = None
+    use_language_embedding: bool = False
+
+    # use d-vectors
+    use_d_vector_file: bool = False
+    d_vector_file: List[str] = None
+    d_vector_dim: int = None
+
+    def __post_init__(self):
+        for key, val in self.model_args.items():
+            if hasattr(self, key):
+                self[key] = val

TTS/tts/layers/losses.py

@@ -740,6 +740,39 @@ def forward(
         return_dict["loss"] = loss
         return return_dict
 
+class Vits2DurationLoss(nn.Module):
+    def __init__(self, c: Coqpit):
+        super().__init__()
+        self.disc_loss_alpha = c.disc_loss_alpha
+
+    @staticmethod
+    def discriminator_loss(scores_real, scores_fake):
+        loss = 0
+        real_losses = []
+        fake_losses = []
+        for dr, dg in zip(scores_real, scores_fake):
+            dr = dr.float()
+            dg = dg.float()
+            real_loss = torch.mean((1 - dr) ** 2)
+            fake_loss = torch.mean(dg**2)
+            loss += real_loss + fake_loss
+            real_losses.append(real_loss.item())
+            fake_losses.append(fake_loss.item())
+        return loss, real_losses, fake_losses
+
+    def forward(self, scores_disc_real, scores_disc_fake):
+        loss = 0.0
+        return_dict = {}
+        loss_disc, loss_disc_real, _ = self.discriminator_loss(
+            scores_real=scores_disc_real, scores_fake=scores_disc_fake
+        )
+        return_dict["loss_dur_disc"] = loss_disc * self.disc_loss_alpha
+        loss = loss + return_dict["loss_dur_disc"]
+        return_dict["loss"] = loss
+
+        for i, ldr in enumerate(loss_disc_real):
+            return_dict[f"loss_dur_disc_real_{i}"] = ldr
+        return return_dict
 
 class VitsDiscriminatorLoss(nn.Module):
     def __init__(self, c: Coqpit):

TTS/tts/layers/vits2/duration_discriminator.py

@@ -0,0 +1,84 @@
+import torch
+from torch import nn
+from TTS.tts.layers.generic.normalization import LayerNorm2
+
+
+class DurationDiscriminator(nn.Module): #vits2
+    """VITS-2 Duration Discriminator.
+    
+    ::
+      dur_r, dur_hat -> DurationDiscriminator() -> output_probs  
+
+    Args:
+        in_channels (int): number of input channels.
+        filter_channels (int): number of filter channels.
+        kernel_size (int): kernel size of the convolutional layers.
+        p_dropout (float): dropout probability.
+        gin_channels (int): number of global conditioning channels. 
+                            Unused for now. 
+    
+    Returns:
+        List[Tensor]: list of discriminator scores. Real, Predicted/Generated.
+    """
+  # TODO : not using "spk conditioning" for now according to the paper.
+  # Can be a better discriminator if we use it.
+    def __init__(
+            self, 
+            in_channels, 
+            filter_channels, 
+            kernel_size, 
+            p_dropout, 
+            gin_channels=0
+            ):
+        super().__init__()
+
+        self.in_channels = in_channels
+        self.filter_channels = filter_channels
+        self.kernel_size = kernel_size
+        self.p_dropout = p_dropout
+        self.gin_channels = gin_channels
+
+        self.drop = nn.Dropout(p_dropout)
+        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size, padding=kernel_size//2)
+        self.conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
+        self.dur_proj = nn.Conv1d(1, filter_channels, 1)
+
+        self.pre_out_conv_1 = nn.Conv1d(2*filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
+        self.pre_out_norm_1 = LayerNorm2(filter_channels)
+        self.pre_out_conv_2 = nn.Conv1d(filter_channels, filter_channels, kernel_size, padding=kernel_size//2)
+        self.pre_out_norm_2 = LayerNorm2(filter_channels)
+
+        # if gin_channels != 0:
+        #   self.cond = nn.Conv1d(gin_channels, in_channels, 1)
+
+        self.output_layer = nn.Sequential(
+            nn.Linear(filter_channels, 1), 
+            nn.Sigmoid() 
+        )
+
+    def forward_probability(self, x, x_mask, dur, g=None):
+        dur = self.dur_proj(dur)
+        x = torch.cat([x, dur], dim=1)
+        x = self.pre_out_conv_1(x * x_mask)
+        x = self.pre_out_conv_2(x * x_mask)
+        x = x * x_mask
+        x = x.transpose(1, 2)
+        output_prob = self.output_layer(x)
+        return output_prob
+
+    def forward(self, x, x_mask, dur_r, dur_hat, g=None):
+        x = torch.detach(x)
+        # if g is not None:
+        #   g = torch.detach(g)
+        #   x = x + self.cond(g)
+        x = self.conv_1(x * x_mask)
+        # x = self.drop(x)
+        x = self.conv_2(x * x_mask)
+        # x = self.drop(x)
+
+        output_probs = []
+        for dur in [dur_r, dur_hat]:
+            output_prob = self.forward_probability(x, x_mask, dur, g)
+            output_probs.append(output_prob)
+
+        return output_probs