Create modeling_xvector.py

modeling_xvector.py

@@ -0,0 +1,550 @@
+import math
+import torch
+import typing as tp
+import torch.nn as nn
+import torch.nn.functional as F
+from transformers.utils import ModelOutput
+from transformers.modeling_utils import PreTrainedModel
+from transformers.modeling_outputs import SequenceClassifierOutput
+
+from .helpers_xvector import Fbank
+from .configuration_xvector import XvectorConfig
+
+
+class InputNormalization(nn.Module):
+
+    spk_dict_mean: tp.Dict[int, torch.Tensor]
+    spk_dict_std: tp.Dict[int, torch.Tensor]
+    spk_dict_count: tp.Dict[int, int]
+
+    def __init__(
+        self,
+        mean_norm=True,
+        std_norm=True,
+        norm_type="global",
+        avg_factor=None,
+        requires_grad=False,
+        update_until_epoch=3,
+    ):
+        super().__init__()
+        self.mean_norm = mean_norm
+        self.std_norm = std_norm
+        self.norm_type = norm_type
+        self.avg_factor = avg_factor
+        self.requires_grad = requires_grad
+        self.glob_mean = torch.tensor([0])
+        self.glob_std = torch.tensor([0])
+        self.spk_dict_mean = {}
+        self.spk_dict_std = {}
+        self.spk_dict_count = {}
+        self.weight = 1.0
+        self.count = 0
+        self.eps = 1e-10
+        self.update_until_epoch = update_until_epoch
+
+    def forward(self, input_values, lengths=None, spk_ids=torch.tensor([]), epoch=0):
+        """Returns the tensor with the surrounding context.
+        Arguments
+        ---------
+        x : tensor
+            A batch of tensors.
+        lengths : tensor
+            A batch of tensors containing the relative length of each
+            sentence (e.g, [0.7, 0.9, 1.0]). It is used to avoid
+            computing stats on zero-padded steps.
+        spk_ids : tensor containing the ids of each speaker (e.g, [0 10 6]).
+            It is used to perform per-speaker normalization when
+            norm_type='speaker'.
+        """
+        x = input_values
+        N_batches = x.shape[0]
+
+        current_means = []
+        current_stds = []
+
+        for snt_id in range(N_batches):
+            # Avoiding padded time steps
+            # lengths = torch.sum(attention_mask, dim=1)
+            # relative_lengths = lengths / torch.max(lengths)
+            # actual_size = torch.round(relative_lengths[snt_id] * x.shape[1]).int()
+            actual_size = torch.round(lengths[snt_id] * x.shape[1]).int()
+
+            # computing statistics
+            current_mean, current_std = self._compute_current_stats(
+                x[snt_id, 0:actual_size, ...]
+            )
+
+            current_means.append(current_mean)
+            current_stds.append(current_std)
+
+            if self.norm_type == "sentence":
+                x[snt_id] = (x[snt_id] - current_mean.data) / current_std.data
+
+            if self.norm_type == "speaker":
+                spk_id = int(spk_ids[snt_id][0])
+
+                if self.training:
+                    if spk_id not in self.spk_dict_mean:
+                        # Initialization of the dictionary
+                        self.spk_dict_mean[spk_id] = current_mean
+                        self.spk_dict_std[spk_id] = current_std
+                        self.spk_dict_count[spk_id] = 1
+
+                    else:
+                        self.spk_dict_count[spk_id] = (
+                            self.spk_dict_count[spk_id] + 1
+                        )
+
+                        if self.avg_factor is None:
+                            self.weight = 1 / self.spk_dict_count[spk_id]
+                        else:
+                            self.weight = self.avg_factor
+
+                        self.spk_dict_mean[spk_id] = (
+                            (1 - self.weight) * self.spk_dict_mean[spk_id]
+                            + self.weight * current_mean
+                        )
+                        self.spk_dict_std[spk_id] = (
+                            (1 - self.weight) * self.spk_dict_std[spk_id]
+                            + self.weight * current_std
+                        )
+
+                        self.spk_dict_mean[spk_id].detach()
+                        self.spk_dict_std[spk_id].detach()
+
+                    speaker_mean = self.spk_dict_mean[spk_id].data
+                    speaker_std = self.spk_dict_std[spk_id].data
+                else:
+                    if spk_id in self.spk_dict_mean:
+                        speaker_mean = self.spk_dict_mean[spk_id].data
+                        speaker_std = self.spk_dict_std[spk_id].data
+                    else:
+                        speaker_mean = current_mean.data
+                        speaker_std = current_std.data
+
+                x[snt_id] = (x[snt_id] - speaker_mean) / speaker_std
+
+        if self.norm_type == "batch" or self.norm_type == "global":
+            current_mean = torch.mean(torch.stack(current_means), dim=0)
+            current_std = torch.mean(torch.stack(current_stds), dim=0)
+
+            if self.norm_type == "batch":
+                x = (x - current_mean.data) / (current_std.data)
+
+            if self.norm_type == "global":
+                if self.training:
+                    if self.count == 0:
+                        self.glob_mean = current_mean
+                        self.glob_std = current_std
+
+                    elif epoch < self.update_until_epoch:
+                        if self.avg_factor is None:
+                            self.weight = 1 / (self.count + 1)
+                        else:
+                            self.weight = self.avg_factor
+
+                        self.glob_mean = (
+                            1 - self.weight
+                        ) * self.glob_mean + self.weight * current_mean
+
+                        self.glob_std = (
+                            1 - self.weight
+                        ) * self.glob_std + self.weight * current_std
+
+                    self.glob_mean.detach()
+                    self.glob_std.detach()
+
+                    self.count = self.count + 1
+
+                x = (x - self.glob_mean.data) / (self.glob_std.data)
+
+        return x
+
+    def _compute_current_stats(self, x):
+        """Returns the tensor with the surrounding context.
+        Arguments
+        ---------
+        x : tensor
+            A batch of tensors.
+        """
+        # Compute current mean
+        if self.mean_norm:
+            current_mean = torch.mean(x, dim=0).detach().data
+        else:
+            current_mean = torch.tensor([0.0], device=x.device)
+
+        # Compute current std
+        if self.std_norm:
+            current_std = torch.std(x, dim=0).detach().data
+        else:
+            current_std = torch.tensor([1.0], device=x.device)
+
+        # Improving numerical stability of std
+        current_std = torch.max(
+            current_std, self.eps * torch.ones_like(current_std)
+        )
+
+        return current_mean, current_std
+
+    def _statistics_dict(self):
+        """Fills the dictionary containing the normalization statistics."""
+        state = {}
+        state["count"] = self.count
+        state["glob_mean"] = self.glob_mean
+        state["glob_std"] = self.glob_std
+        state["spk_dict_mean"] = self.spk_dict_mean
+        state["spk_dict_std"] = self.spk_dict_std
+        state["spk_dict_count"] = self.spk_dict_count
+
+        return state
+
+    def _load_statistics_dict(self, state):
+        """Loads the dictionary containing the statistics.
+        Arguments
+        ---------
+        state : dict
+            A dictionary containing the normalization statistics.
+        """
+        self.count = state["count"]
+        if isinstance(state["glob_mean"], int):
+            self.glob_mean = state["glob_mean"]
+            self.glob_std = state["glob_std"]
+        else:
+            self.glob_mean = state["glob_mean"]  # .to(self.device_inp)
+            self.glob_std = state["glob_std"]  # .to(self.device_inp)
+
+        # Loading the spk_dict_mean in the right device
+        self.spk_dict_mean = {}
+        for spk in state["spk_dict_mean"]:
+            self.spk_dict_mean[spk] = state["spk_dict_mean"][spk].to(
+                self.device_inp
+            )
+
+        # Loading the spk_dict_std in the right device
+        self.spk_dict_std = {}
+        for spk in state["spk_dict_std"]:
+            self.spk_dict_std[spk] = state["spk_dict_std"][spk].to(
+                self.device_inp
+            )
+
+        self.spk_dict_count = state["spk_dict_count"]
+
+        return state
+
+    def to(self, device):
+        """Puts the needed tensors in the right device."""
+        self = super(InputNormalization, self).to(device)
+        self.glob_mean = self.glob_mean.to(device)
+        self.glob_std = self.glob_std.to(device)
+        for spk in self.spk_dict_mean:
+            self.spk_dict_mean[spk] = self.spk_dict_mean[spk].to(device)
+            self.spk_dict_std[spk] = self.spk_dict_std[spk].to(device)
+        return self
+
+
+class TdnnLayer(nn.Module):
+
+    def __init__(
+        self, 
+        in_channels, 
+        out_channels, 
+        kernel_size, 
+        dilation=1, 
+        stride=1, 
+        padding=0, 
+        padding_mode="reflect", 
+        activation=torch.nn.LeakyReLU, 
+    ):
+        super(TdnnLayer, self).__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.kernel_size = kernel_size
+        self.dilation = dilation
+        self.stride = stride
+        self.padding = padding
+        self.padding_mode = padding_mode
+        self.activation = activation
+
+        self.conv = nn.Conv1d(
+            self.in_channels, 
+            self.out_channels, 
+            self.kernel_size, 
+            dilation=self.dilation, 
+            padding=self.padding
+        )
+
+        # Set Affine=false to be compatible with the original kaldi version
+        # self.ln = nn.LayerNorm(out_channels, elementwise_affine=False)
+        self.norm = nn.BatchNorm1d(out_channels, affine=False)
+
+    def forward(self, x):
+        x = self._manage_padding(x, self.kernel_size, self.dilation, self.stride)
+        out = self.conv(x)
+        out = self.activation()(out) 
+        out = self.norm(out)
+        return out
+
+    def _manage_padding(
+        self, x, kernel_size: int, dilation: int, stride: int,
+    ):
+        # Detecting input shape
+        L_in = self.in_channels
+
+        # Time padding
+        padding = get_padding_elem(L_in, stride, kernel_size, dilation)
+
+        # Applying padding
+        x = F.pad(x, padding, mode=self.padding_mode)
+
+        return x
+
+
+def get_padding_elem(L_in: int, stride: int, kernel_size: int, dilation: int):
+    """This function computes the number of elements to add for zero-padding.
+    Arguments
+    ---------
+    L_in : int
+    stride: int
+    kernel_size : int
+    dilation : int
+    """
+    if stride > 1:
+        padding = [math.floor(kernel_size / 2), math.floor(kernel_size / 2)]
+
+    else:
+        L_out = (
+            math.floor((L_in - dilation * (kernel_size - 1) - 1) / stride) + 1
+        )
+        padding = [
+            math.floor((L_in - L_out) / 2),
+            math.floor((L_in - L_out) / 2),
+        ]
+    return padding
+
+
+class StatisticsPooling(nn.Module):
+
+    def __init__(self, return_mean=True, return_std=True):
+        super().__init__()
+
+        # Small value for GaussNoise
+        self.eps = 1e-5
+        self.return_mean = return_mean
+        self.return_std = return_std
+        if not (self.return_mean or self.return_std):
+            raise ValueError(
+                "both of statistics are equal to False \n"
+                "consider enabling mean and/or std statistic pooling"
+            )
+
+    def forward(self, input_values, lengths=None):
+        """Calculates mean and std for a batch (input tensor).
+        Arguments
+        ---------
+        x : torch.Tensor
+            It represents a tensor for a mini-batch.
+        """
+        x = input_values
+        if lengths is None:
+            if self.return_mean:
+                mean = x.mean(dim=1)
+            if self.return_std:
+                std = x.std(dim=1)
+        else:
+            mean = []
+            std = []
+            for snt_id in range(x.shape[0]):
+                # Avoiding padded time steps
+                # lengths = torch.sum(attention_mask, dim=1)
+                # relative_lengths = lengths / torch.max(lengths)
+                # actual_size = torch.round(relative_lengths[snt_id] * x.shape[1]).int()
+                actual_size = int(torch.round(lengths[snt_id] * x.shape[1]))
+
+                # computing statistics
+                if self.return_mean:
+                    mean.append(
+                        torch.mean(x[snt_id, 0:actual_size, ...], dim=0)
+                    )
+                if self.return_std:
+                    std.append(torch.std(x[snt_id, 0:actual_size, ...], dim=0))
+            if self.return_mean:
+                mean = torch.stack(mean)
+            if self.return_std:
+                std = torch.stack(std)
+
+        if self.return_mean:
+            gnoise = self._get_gauss_noise(mean.size(), device=mean.device)
+            gnoise = gnoise
+            mean += gnoise
+        if self.return_std:
+            std = std + self.eps
+
+        # Append mean and std of the batch
+        if self.return_mean and self.return_std:
+            pooled_stats = torch.cat((mean, std), dim=1)
+            pooled_stats = pooled_stats.unsqueeze(1)
+        elif self.return_mean:
+            pooled_stats = mean.unsqueeze(1)
+        elif self.return_std:
+            pooled_stats = std.unsqueeze(1)
+
+        return pooled_stats
+
+    def _get_gauss_noise(self, shape_of_tensor, device="cpu"):
+        """Returns a tensor of epsilon Gaussian noise.
+        Arguments
+        ---------
+        shape_of_tensor : tensor
+            It represents the size of tensor for generating Gaussian noise.
+        """
+        gnoise = torch.randn(shape_of_tensor, device=device)
+        gnoise -= torch.min(gnoise)
+        gnoise /= torch.max(gnoise)
+        gnoise = self.eps * ((1 - 9) * gnoise + 9)
+
+        return gnoise
+
+
+class XvectorEmbedder(nn.Module):
+
+    def __init__(
+        self, 
+        in_channels=40, 
+        activation=torch.nn.LeakyReLU, 
+        tdnn_blocks=5, 
+        tdnn_channels=[512, 512, 512, 512, 1500], 
+        tdnn_kernel_sizes=[5, 3, 3, 1, 1], 
+        tdnn_dilations=[1, 2, 3, 1, 1], 
+        hidden_size=512, 
+    ) -> None:
+        super(XvectorEmbedder, self).__init__()
+        self.activation = activation
+        self.blocks = nn.ModuleList()
+        for block_index in range(tdnn_blocks):
+            out_channels = tdnn_channels[block_index]
+            tdnn = TdnnLayer(
+                in_channels, 
+                out_channels, 
+                kernel_size=tdnn_kernel_sizes[block_index],
+                dilation=tdnn_dilations[block_index], 
+                activation=activation, 
+            )
+            self.blocks.append(tdnn)
+            in_channels = tdnn_channels[block_index]
+        self.pooler = StatisticsPooling()
+        self.fc = nn.Linear(2 * out_channels, hidden_size)
+
+    def forward(self, input_values, lengths=None):
+        x = input_values
+        x = x.permute(0, 2, 1) # (B, T, F) -> (B, F, T)
+        for block in self.blocks:
+            x = block(x)
+        last_hidden_state = x.permute(0, 2, 1) # (B, F, T) -> (B, T, F)
+        pooler_output = self.pooler(last_hidden_state, lengths)
+        pooler_output = self.fc(pooler_output.squeeze(1))
+        return ModelOutput(
+            last_hidden_state=last_hidden_state, 
+            pooler_output=pooler_output
+        )
+
+
+class CosineSimilarityHead(torch.nn.Module):
+    """
+    This class implements the cosine similarity on the top of features.
+    """
+    def __init__(
+        self,
+        in_channels, 
+        lin_blocks=0,
+        hidden_size=192,
+        num_classes=1211,
+    ):
+        super().__init__()
+        self.blocks = nn.ModuleList()
+
+        for block_index in range(lin_blocks):
+            self.blocks.extend(
+                [
+                    nn.BatchNorm1d(num_features=in_channels),
+                    nn.Linear(in_features=in_channels, out_features=hidden_size),
+                ]
+            )
+            in_channels = hidden_size
+
+        # Final Layer
+        self.weight = nn.Parameter(
+            torch.FloatTensor(num_classes, in_channels)
+        )
+        nn.init.xavier_uniform_(self.weight)
+
+    def forward(self, x):
+        """Returns the output probabilities over speakers.
+        Arguments
+        ---------
+        x : torch.Tensor
+            Torch tensor.
+        """
+        for layer in self.blocks:
+            x = layer(x)
+
+        # Need to be normalized
+        x = F.linear(F.normalize(x), F.normalize(self.weight))
+        return x
+
+
+class XvectorPreTrainedModel(PreTrainedModel):
+
+    config_class = XvectorConfig
+    base_model_prefix = "xvector"
+    main_input_name = "input_values"
+    supports_gradient_checkpointing = True
+
+    def _init_weights(self, module):
+        """Initialize the weights"""
+        if isinstance(module, nn.Linear):
+            # Slightly different from the TF version which uses truncated_normal for initialization
+            # cf https://github.com/pytorch/pytorch/pull/5617
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+        elif isinstance(module, (nn.LayerNorm, nn.GroupNorm)):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+        elif isinstance(module, nn.Conv1d):
+            nn.init.kaiming_normal_(module.weight.data)
+
+        if isinstance(module, (nn.Linear, nn.Conv1d)) and module.bias is not None:
+            module.bias.data.zero_()
+
+
+class XvectorModel(XvectorPreTrainedModel):
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.compute_features = Fbank(
+            n_mels=config.n_mels, 
+            sample_rate=config.sample_rate, 
+            win_length=config.win_length, 
+            hop_length=config.hop_length, 
+        )
+        self.mean_var_norm = InputNormalization(
+            mean_norm=config.mean_norm, 
+            std_norm=config.std_norm, 
+            norm_type=config.norm_type
+        )
+        self.embedding_model = XvectorEmbedder(
+            in_channels=config.n_mels, 
+            activation=nn.LeakyReLU, 
+            tdnn_blocks=config.tdnn_blocks, 
+            tdnn_channels=config.tdnn_channels, 
+            tdnn_kernel_sizes=config.tdnn_kernel_sizes, 
+            tdnn_dilations=config.tdnn_dilations, 
+            hidden_size=config.hidden_size, 
+        )
+
+    def forward(self, input_values, lengths=None):
+        x = input_values
+        # if attention_mask is None:
+        #     attention_mask = torch.ones_like(input_values, device=x.device)
+        x = self.compute_features(x)
+        x = self.mean_var_norm(x, lengths)
+        output = self.embedding_model(x, lengths)
+        return output