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| 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 | |