model.py

# MIT License
# 
# Copyright (c) 2022 Tada Makepeace
# 
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
# 
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
# 
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

import torch.nn as nn
import torch.nn.functional as F


class CNNLayerNorm(nn.Module):
    """Layer normalization built for cnns input"""
    def __init__(self, n_feats):
        super(CNNLayerNorm, self).__init__()
        self.layer_norm = nn.LayerNorm(n_feats)

    def forward(self, x):
        # x (batch, channel, feature, time)
        x = x.transpose(2, 3).contiguous() # (batch, channel, time, feature)
        x = self.layer_norm(x)
        return x.transpose(2, 3).contiguous() # (batch, channel, feature, time) 


class ResidualCNN(nn.Module):
    """Residual CNN inspired by https://arxiv.org/pdf/1603.05027.pdf
        except with layer norm instead of batch norm
    """
    def __init__(self, in_channels, out_channels, kernel, stride, dropout, n_feats):
        super(ResidualCNN, self).__init__()

        self.cnn1 = nn.Conv2d(in_channels, out_channels, kernel, stride, padding=kernel//2)
        self.cnn2 = nn.Conv2d(out_channels, out_channels, kernel, stride, padding=kernel//2)
        self.dropout1 = nn.Dropout(dropout)
        self.dropout2 = nn.Dropout(dropout)
        self.layer_norm1 = CNNLayerNorm(n_feats)
        self.layer_norm2 = CNNLayerNorm(n_feats)

    def forward(self, x):
        residual = x  # (batch, channel, feature, time)
        x = self.layer_norm1(x)
        x = F.gelu(x)
        x = self.dropout1(x)
        x = self.cnn1(x)
        x = self.layer_norm2(x)
        x = F.gelu(x)
        x = self.dropout2(x)
        x = self.cnn2(x)
        x += residual
        return x # (batch, channel, feature, time)


class BidirectionalGRU(nn.Module):
    def __init__(self, rnn_dim, hidden_size, dropout, batch_first):
        super(BidirectionalGRU, self).__init__()

        self.BiGRU = nn.GRU(
            input_size=rnn_dim, hidden_size=hidden_size,
            num_layers=1, batch_first=batch_first, bidirectional=True)
        self.layer_norm = nn.LayerNorm(rnn_dim)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        x = self.layer_norm(x)
        x = F.gelu(x)
        x, _ = self.BiGRU(x)
        x = self.dropout(x)
        return x


class SpeechRecognitionModel(nn.Module):
    """Modern version of the DeepSpeech2 from Michael Nguyen, Machine
    Learning Research Engineer at AssemblyAI and Niko Laskaris at Comet.ml"""
    def __init__(self, n_cnn_layers, n_rnn_layers, rnn_dim, n_class, n_feats, stride=2, dropout=0.1):
        super(SpeechRecognitionModel, self).__init__()
        n_feats = n_feats//2
        self.cnn = nn.Conv2d(1, 32, 3, stride=stride, padding=3//2)  # cnn for extracting heirachal features

        # n residual cnn layers with filter size of 32
        self.rescnn_layers = nn.Sequential(*[
            ResidualCNN(32, 32, kernel=3, stride=1, dropout=dropout, n_feats=n_feats) 
            for _ in range(n_cnn_layers)
        ])
        self.fully_connected = nn.Linear(n_feats*32, rnn_dim)
        self.birnn_layers = nn.Sequential(*[
            BidirectionalGRU(rnn_dim=rnn_dim if i==0 else rnn_dim*2,
                             hidden_size=rnn_dim, dropout=dropout, batch_first=i==0)
            for i in range(n_rnn_layers)
        ])
        self.classifier = nn.Sequential(
            nn.Linear(rnn_dim*2, rnn_dim),  # birnn returns rnn_dim*2
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(rnn_dim, n_class)
        )

    def forward(self, x):
        # print("PRE MODEL INPUT SHAPE:", x.shape)
        x = self.cnn(x)
        x = self.rescnn_layers(x)
        # print("POST MODEL INPUT SHAPE:", x.shape)
        sizes = x.size()
        x = x.view(sizes[0], sizes[1] * sizes[2], sizes[3])  # (batch, feature, time)
        # print("VIEWED MODEL INPUT SHAPE:", x.shape)
        x = x.transpose(1, 2) # (batch, time, feature)
        x = self.fully_connected(x)
        x = self.birnn_layers(x)
        x = self.classifier(x)
        # print("POST-SEQ SHAPE:", x.shape)
        return x