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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
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