import math import torch from torch import nn from torch.nn import TransformerEncoder import torch.nn.functional as F from .layers import MFCC, Attention, LinearNorm, ConvNorm, ConvBlock class ASRCNN(nn.Module): def __init__( self, input_dim=80, hidden_dim=256, n_token=35, n_layers=6, token_embedding_dim=256, ): super().__init__() self.n_token = n_token self.n_down = 1 self.to_mfcc = MFCC() self.init_cnn = ConvNorm( input_dim // 2, hidden_dim, kernel_size=7, padding=3, stride=2 ) self.cnns = nn.Sequential( *[ nn.Sequential( ConvBlock(hidden_dim), nn.GroupNorm(num_groups=1, num_channels=hidden_dim), ) for n in range(n_layers) ] ) self.projection = ConvNorm(hidden_dim, hidden_dim // 2) self.ctc_linear = nn.Sequential( LinearNorm(hidden_dim // 2, hidden_dim), nn.ReLU(), LinearNorm(hidden_dim, n_token), ) self.asr_s2s = ASRS2S( embedding_dim=token_embedding_dim, hidden_dim=hidden_dim // 2, n_token=n_token, ) def forward(self, x, src_key_padding_mask=None, text_input=None): x = self.to_mfcc(x) x = self.init_cnn(x) x = self.cnns(x) x = self.projection(x) x = x.transpose(1, 2) ctc_logit = self.ctc_linear(x) if text_input is not None: _, s2s_logit, s2s_attn = self.asr_s2s(x, src_key_padding_mask, text_input) return ctc_logit, s2s_logit, s2s_attn else: return ctc_logit def get_feature(self, x): x = self.to_mfcc(x.squeeze(1)) x = self.init_cnn(x) x = self.cnns(x) x = self.projection(x) return x def length_to_mask(self, lengths): mask = ( torch.arange(lengths.max()) .unsqueeze(0) .expand(lengths.shape[0], -1) .type_as(lengths) ) mask = torch.gt(mask + 1, lengths.unsqueeze(1)).to(lengths.device) return mask def get_future_mask(self, out_length, unmask_future_steps=0): """ Args: out_length (int): returned mask shape is (out_length, out_length). unmask_futre_steps (int): unmasking future step size. Return: mask (torch.BoolTensor): mask future timesteps mask[i, j] = True if i > j + unmask_future_steps else False """ index_tensor = torch.arange(out_length).unsqueeze(0).expand(out_length, -1) mask = torch.gt(index_tensor, index_tensor.T + unmask_future_steps) return mask class ASRS2S(nn.Module): def __init__( self, embedding_dim=256, hidden_dim=512, n_location_filters=32, location_kernel_size=63, n_token=40, ): super(ASRS2S, self).__init__() self.embedding = nn.Embedding(n_token, embedding_dim) val_range = math.sqrt(6 / hidden_dim) self.embedding.weight.data.uniform_(-val_range, val_range) self.decoder_rnn_dim = hidden_dim self.project_to_n_symbols = nn.Linear(self.decoder_rnn_dim, n_token) self.attention_layer = Attention( self.decoder_rnn_dim, hidden_dim, hidden_dim, n_location_filters, location_kernel_size, ) self.decoder_rnn = nn.LSTMCell( self.decoder_rnn_dim + embedding_dim, self.decoder_rnn_dim ) self.project_to_hidden = nn.Sequential( LinearNorm(self.decoder_rnn_dim * 2, hidden_dim), nn.Tanh() ) self.sos = 1 self.eos = 2 def initialize_decoder_states(self, memory, mask): """ moemory.shape = (B, L, H) = (Batchsize, Maxtimestep, Hiddendim) """ B, L, H = memory.shape self.decoder_hidden = torch.zeros((B, self.decoder_rnn_dim)).type_as(memory) self.decoder_cell = torch.zeros((B, self.decoder_rnn_dim)).type_as(memory) self.attention_weights = torch.zeros((B, L)).type_as(memory) self.attention_weights_cum = torch.zeros((B, L)).type_as(memory) self.attention_context = torch.zeros((B, H)).type_as(memory) self.memory = memory self.processed_memory = self.attention_layer.memory_layer(memory) self.mask = mask self.unk_index = 3 self.random_mask = 0.1 def forward(self, memory, memory_mask, text_input): """ moemory.shape = (B, L, H) = (Batchsize, Maxtimestep, Hiddendim) moemory_mask.shape = (B, L, ) texts_input.shape = (B, T) """ self.initialize_decoder_states(memory, memory_mask) # text random mask random_mask = (torch.rand(text_input.shape) < self.random_mask).to( text_input.device ) _text_input = text_input.clone() _text_input.masked_fill_(random_mask, self.unk_index) decoder_inputs = self.embedding(_text_input).transpose( 0, 1 ) # -> [T, B, channel] start_embedding = self.embedding( torch.LongTensor([self.sos] * decoder_inputs.size(1)).to( decoder_inputs.device ) ) decoder_inputs = torch.cat( (start_embedding.unsqueeze(0), decoder_inputs), dim=0 ) hidden_outputs, logit_outputs, alignments = [], [], [] while len(hidden_outputs) < decoder_inputs.size(0): decoder_input = decoder_inputs[len(hidden_outputs)] hidden, logit, attention_weights = self.decode(decoder_input) hidden_outputs += [hidden] logit_outputs += [logit] alignments += [attention_weights] hidden_outputs, logit_outputs, alignments = self.parse_decoder_outputs( hidden_outputs, logit_outputs, alignments ) return hidden_outputs, logit_outputs, alignments def decode(self, decoder_input): cell_input = torch.cat((decoder_input, self.attention_context), -1) self.decoder_hidden, self.decoder_cell = self.decoder_rnn( cell_input, (self.decoder_hidden, self.decoder_cell) ) attention_weights_cat = torch.cat( ( self.attention_weights.unsqueeze(1), self.attention_weights_cum.unsqueeze(1), ), dim=1, ) self.attention_context, self.attention_weights = self.attention_layer( self.decoder_hidden, self.memory, self.processed_memory, attention_weights_cat, self.mask, ) self.attention_weights_cum += self.attention_weights hidden_and_context = torch.cat( (self.decoder_hidden, self.attention_context), -1 ) hidden = self.project_to_hidden(hidden_and_context) # dropout to increasing g logit = self.project_to_n_symbols(F.dropout(hidden, 0.5, self.training)) return hidden, logit, self.attention_weights def parse_decoder_outputs(self, hidden, logit, alignments): # -> [B, T_out + 1, max_time] alignments = torch.stack(alignments).transpose(0, 1) # [T_out + 1, B, n_symbols] -> [B, T_out + 1, n_symbols] logit = torch.stack(logit).transpose(0, 1).contiguous() hidden = torch.stack(hidden).transpose(0, 1).contiguous() return hidden, logit, alignments