Vits2 prototype

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/glow_tts/transformer.py

@@ -430,3 +430,133 @@ def forward(self, x, x_mask):
             x = self.norm_layers_2[i](x + y)
         x = x * x_mask
         return x
+
+class ConditionalRelativePositionTransformer(nn.Module):
+    """Transformer with Relative Potional Encoding and conditioned on external embeddings at cond_layer_idx'th layer.
+
+    https://arxiv.org/abs/2307.16430
+
+    Args:
+        in_channels (int): number of channels of the input tensor.
+        out_chanels (int): number of channels of the output tensor.
+        hidden_channels (int): model hidden channels.
+        hidden_channels_ffn (int): hidden channels of FeedForwardNetwork.
+        num_heads (int): number of attention heads.
+        num_layers (int): number of transformer layers.
+        kernel_size (int, optional): kernel size of feed-forward inner layers. Defaults to 1.
+        dropout_p (float, optional): dropout rate for self-attention and feed-forward inner layers_per_stack. Defaults to 0.
+        rel_attn_window_size (int, optional): relation attention window size.
+            If 4, for each time step next and previous 4 time steps are attended.
+            If default, relative encoding is disabled and it is a regular transformer.
+            Defaults to None.
+        input_length (int, optional): input lenght to limit position encoding. Defaults to None.
+        layer_norm_type (str, optional): type "1" uses torch tensor operations and type "2" uses torch layer_norm
+            primitive. Use type "2", type "1: is for backward compat. Defaults to "1".
+        cond_channels (int): number of channels of the external embeddings.
+        cond_layer_idx (int): layer index to condition at. (using 3rd layer by default as in the paper)
+    """
+
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        hidden_channels: int,
+        hidden_channels_ffn: int,
+        num_heads: int,
+        num_layers: int,
+        kernel_size=1,
+        dropout_p=0.0,
+        rel_attn_window_size: int = None,
+        input_length: int = None,
+        layer_norm_type: str = "1",
+        cond_channels: int = 0,
+        cond_layer_idx: int = 2,
+    ):
+        super().__init__()
+        self.cond_channels = cond_channels
+        if cond_layer_idx < 0 or cond_layer_idx >= num_layers:
+            raise ValueError(" [!] cond_layer_idx should be in [0, num_layers)")
+        self.cond_layer_idx = cond_layer_idx
+        self.cond_proj = None
+        if self.cond_channels:
+            self.cond_proj = nn.Linear(cond_channels, hidden_channels)    
+
+        self.hidden_channels = hidden_channels
+        self.hidden_channels_ffn = hidden_channels_ffn
+        self.num_heads = num_heads
+        self.num_layers = num_layers
+        self.kernel_size = kernel_size
+        self.dropout_p = dropout_p
+        self.rel_attn_window_size = rel_attn_window_size
+
+        self.dropout = nn.Dropout(dropout_p)
+        self.attn_layers = nn.ModuleList()
+        self.norm_layers_1 = nn.ModuleList()
+        self.ffn_layers = nn.ModuleList()
+        self.norm_layers_2 = nn.ModuleList()
+
+        for idx in range(self.num_layers):
+            self.attn_layers.append(
+                RelativePositionMultiHeadAttention(
+                    hidden_channels if idx != 0 else in_channels,
+                    hidden_channels,
+                    num_heads,
+                    rel_attn_window_size=rel_attn_window_size,
+                    dropout_p=dropout_p,
+                    input_length=input_length,
+                )
+            )
+            if layer_norm_type == "1":
+                self.norm_layers_1.append(LayerNorm(hidden_channels))
+            elif layer_norm_type == "2":
+                self.norm_layers_1.append(LayerNorm2(hidden_channels))
+            else:
+                raise ValueError(" [!] Unknown layer norm type")
+
+            if hidden_channels != out_channels and (idx + 1) == self.num_layers:
+                self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
+
+            self.ffn_layers.append(
+                FeedForwardNetwork(
+                    hidden_channels,
+                    hidden_channels if (idx + 1) != self.num_layers else out_channels,
+                    hidden_channels_ffn,
+                    kernel_size,
+                    dropout_p=dropout_p,
+                )
+            )
+
+            if layer_norm_type == "1":
+                self.norm_layers_2.append(LayerNorm(hidden_channels if (idx + 1) != self.num_layers else out_channels))
+            elif layer_norm_type == "2":
+                self.norm_layers_2.append(LayerNorm2(hidden_channels if (idx + 1) != self.num_layers else out_channels))
+            else:
+                raise ValueError(" [!] Unknown layer norm type")
+
+    def forward(self, x, x_mask, g=None):
+        """
+        Shapes:
+            - x: :math:`[B, C, T]`
+            - x_mask: :math:`[B, 1, T]`
+            - g: :math:`[B, C, T]`
+        """
+        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
+        for i in range(self.num_layers):
+            if i == self.cond_layer_idx and self.cond_proj is not None:
+                g = self.cond_proj(g.transpose(1, 2))
+                g = g.transpose(1, 2)
+                x = x + g
+            x = x * x_mask
+            y = self.attn_layers[i](x, x, attn_mask)
+            y = self.dropout(y)
+            x = self.norm_layers_1[i](x + y)
+
+            y = self.ffn_layers[i](x, x_mask)
+            y = self.dropout(y)
+
+            if (i + 1) == self.num_layers and hasattr(self, "proj"):
+                x = self.proj(x)
+
+            x = self.norm_layers_2[i](x + y)
+        x = x * x_mask
+        return x

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):