Layers/Conformer.py

"""
Taken from ESPNet
"""

import torch
import torch.nn.functional as F

from Layers.Attention import RelPositionMultiHeadedAttention
from Layers.Convolution import ConvolutionModule
from Layers.EncoderLayer import EncoderLayer
from Layers.LayerNorm import LayerNorm
from Layers.MultiLayeredConv1d import MultiLayeredConv1d
from Layers.MultiSequential import repeat
from Layers.PositionalEncoding import RelPositionalEncoding
from Layers.Swish import Swish


class Conformer(torch.nn.Module):
    """
    Conformer encoder module.

    Args:
        idim (int): Input dimension.
        attention_dim (int): Dimension of attention.
        attention_heads (int): The number of heads of multi head attention.
        linear_units (int): The number of units of position-wise feed forward.
        num_blocks (int): The number of decoder blocks.
        dropout_rate (float): Dropout rate.
        positional_dropout_rate (float): Dropout rate after adding positional encoding.
        attention_dropout_rate (float): Dropout rate in attention.
        input_layer (Union[str, torch.nn.Module]): Input layer type.
        normalize_before (bool): Whether to use layer_norm before the first block.
        concat_after (bool): Whether to concat attention layer's input and output.
            if True, additional linear will be applied.
            i.e. x -> x + linear(concat(x, att(x)))
            if False, no additional linear will be applied. i.e. x -> x + att(x)
        positionwise_layer_type (str): "linear", "conv1d", or "conv1d-linear".
        positionwise_conv_kernel_size (int): Kernel size of positionwise conv1d layer.
        macaron_style (bool): Whether to use macaron style for positionwise layer.
        pos_enc_layer_type (str): Conformer positional encoding layer type.
        selfattention_layer_type (str): Conformer attention layer type.
        activation_type (str): Conformer activation function type.
        use_cnn_module (bool): Whether to use convolution module.
        cnn_module_kernel (int): Kernerl size of convolution module.
        padding_idx (int): Padding idx for input_layer=embed.

    """

    def __init__(self, idim, attention_dim=256, attention_heads=4, linear_units=2048, num_blocks=6, dropout_rate=0.1, positional_dropout_rate=0.1,
                 attention_dropout_rate=0.0, input_layer="conv2d", normalize_before=True, concat_after=False, positionwise_conv_kernel_size=1,
                 macaron_style=False, use_cnn_module=False, cnn_module_kernel=31, zero_triu=False, utt_embed=None, connect_utt_emb_at_encoder_out=True,
                 spk_emb_bottleneck_size=128, lang_embs=None):
        super(Conformer, self).__init__()

        activation = Swish()
        self.conv_subsampling_factor = 1

        if isinstance(input_layer, torch.nn.Module):
            self.embed = input_layer
            self.pos_enc = RelPositionalEncoding(attention_dim, positional_dropout_rate)
        elif input_layer is None:
            self.embed = None
            self.pos_enc = torch.nn.Sequential(RelPositionalEncoding(attention_dim, positional_dropout_rate))
        else:
            raise ValueError("unknown input_layer: " + input_layer)

        self.normalize_before = normalize_before

        self.connect_utt_emb_at_encoder_out = connect_utt_emb_at_encoder_out
        if utt_embed is not None:
            self.hs_emb_projection = torch.nn.Linear(attention_dim + spk_emb_bottleneck_size, attention_dim)
            # embedding projection derived from https://arxiv.org/pdf/1705.08947.pdf
            self.embedding_projection = torch.nn.Sequential(torch.nn.Linear(utt_embed, spk_emb_bottleneck_size),
                                                            torch.nn.Softsign())
        if lang_embs is not None:
            self.language_embedding = torch.nn.Embedding(num_embeddings=lang_embs, embedding_dim=attention_dim)

        # self-attention module definition
        encoder_selfattn_layer = RelPositionMultiHeadedAttention
        encoder_selfattn_layer_args = (attention_heads, attention_dim, attention_dropout_rate, zero_triu)

        # feed-forward module definition
        positionwise_layer = MultiLayeredConv1d
        positionwise_layer_args = (attention_dim, linear_units, positionwise_conv_kernel_size, dropout_rate,)

        # convolution module definition
        convolution_layer = ConvolutionModule
        convolution_layer_args = (attention_dim, cnn_module_kernel, activation)

        self.encoders = repeat(num_blocks, lambda lnum: EncoderLayer(attention_dim, encoder_selfattn_layer(*encoder_selfattn_layer_args),
                                                                     positionwise_layer(*positionwise_layer_args),
                                                                     positionwise_layer(*positionwise_layer_args) if macaron_style else None,
                                                                     convolution_layer(*convolution_layer_args) if use_cnn_module else None, dropout_rate,
                                                                     normalize_before, concat_after))
        if self.normalize_before:
            self.after_norm = LayerNorm(attention_dim)

    def forward(self, xs, masks, utterance_embedding=None, lang_ids=None):
        """
        Encode input sequence.

        Args:
            utterance_embedding: embedding containing lots of conditioning signals
            step: indicator for when to start updating the embedding function
            xs (torch.Tensor): Input tensor (#batch, time, idim).
            masks (torch.Tensor): Mask tensor (#batch, time).

        Returns:
            torch.Tensor: Output tensor (#batch, time, attention_dim).
            torch.Tensor: Mask tensor (#batch, time).

        """

        if self.embed is not None:
            xs = self.embed(xs)

        if lang_ids is not None:
            lang_embs = self.language_embedding(lang_ids)
            xs = xs + lang_embs  # offset the phoneme distribution of a language

        if utterance_embedding is not None and not self.connect_utt_emb_at_encoder_out:
            xs = self._integrate_with_utt_embed(xs, utterance_embedding)

        xs = self.pos_enc(xs)

        xs, masks = self.encoders(xs, masks)
        if isinstance(xs, tuple):
            xs = xs[0]

        if self.normalize_before:
            xs = self.after_norm(xs)

        if utterance_embedding is not None and self.connect_utt_emb_at_encoder_out:
            xs = self._integrate_with_utt_embed(xs, utterance_embedding)

        return xs, masks

    def _integrate_with_utt_embed(self, hs, utt_embeddings):
        # project embedding into smaller space
        speaker_embeddings_projected = self.embedding_projection(utt_embeddings)
        # concat hidden states with spk embeds and then apply projection
        speaker_embeddings_expanded = F.normalize(speaker_embeddings_projected).unsqueeze(1).expand(-1, hs.size(1), -1)
        hs = self.hs_emb_projection(torch.cat([hs, speaker_embeddings_expanded], dim=-1))
        return hs