File size: 6,149 Bytes
c14c4fe | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 | from typing import List
from typing import Optional
from typing import Sequence
from typing import Tuple
from typing import Union
import logging
import torch
import torch.nn as nn
from torch.nn import functional as F
import numpy as np
from funasr.models.transformer.utils.nets_utils import make_pad_mask
from funasr.models.transformer.layer_norm import LayerNorm
from funasr.models.encoder.abs_encoder import AbsEncoder
import math
from funasr.models.transformer.utils.repeat import repeat
from funasr.models.transformer.utils.multi_layer_conv import FsmnFeedForward
class FsmnBlock(torch.nn.Module):
def __init__(
self,
n_feat,
dropout_rate,
kernel_size,
fsmn_shift=0,
):
super().__init__()
self.dropout = nn.Dropout(p=dropout_rate)
self.fsmn_block = nn.Conv1d(
n_feat, n_feat, kernel_size, stride=1, padding=0, groups=n_feat, bias=False
)
# padding
left_padding = (kernel_size - 1) // 2
if fsmn_shift > 0:
left_padding = left_padding + fsmn_shift
right_padding = kernel_size - 1 - left_padding
self.pad_fn = nn.ConstantPad1d((left_padding, right_padding), 0.0)
def forward(self, inputs, mask, mask_shfit_chunk=None):
b, t, d = inputs.size()
if mask is not None:
mask = torch.reshape(mask, (b, -1, 1))
if mask_shfit_chunk is not None:
mask = mask * mask_shfit_chunk
inputs = inputs * mask
x = inputs.transpose(1, 2)
x = self.pad_fn(x)
x = self.fsmn_block(x)
x = x.transpose(1, 2)
x = x + inputs
x = self.dropout(x)
return x * mask
class EncoderLayer(torch.nn.Module):
def __init__(self, in_size, size, feed_forward, fsmn_block, dropout_rate=0.0):
super().__init__()
self.in_size = in_size
self.size = size
self.ffn = feed_forward
self.memory = fsmn_block
self.dropout = nn.Dropout(dropout_rate)
def forward(
self, xs_pad: torch.Tensor, mask: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:
# xs_pad in Batch, Time, Dim
context = self.ffn(xs_pad)[0]
memory = self.memory(context, mask)
memory = self.dropout(memory)
if self.in_size == self.size:
return memory + xs_pad, mask
return memory, mask
class FsmnEncoder(AbsEncoder):
"""Encoder using Fsmn"""
def __init__(
self,
in_units,
filter_size,
fsmn_num_layers,
dnn_num_layers,
num_memory_units=512,
ffn_inner_dim=2048,
dropout_rate=0.0,
shift=0,
position_encoder=None,
sample_rate=1,
out_units=None,
tf2torch_tensor_name_prefix_torch="post_net",
tf2torch_tensor_name_prefix_tf="EAND/post_net",
):
"""Initializes the parameters of the encoder.
Args:
filter_size: the total order of memory block
fsmn_num_layers: The number of fsmn layers.
dnn_num_layers: The number of dnn layers
num_units: The number of memory units.
ffn_inner_dim: The number of units of the inner linear transformation
in the feed forward layer.
dropout_rate: The probability to drop units from the outputs.
shift: left padding, to control delay
position_encoder: The :class:`opennmt.layers.position.PositionEncoder` to
apply on inputs or ``None``.
"""
super(FsmnEncoder, self).__init__()
self.in_units = in_units
self.filter_size = filter_size
self.fsmn_num_layers = fsmn_num_layers
self.dnn_num_layers = dnn_num_layers
self.num_memory_units = num_memory_units
self.ffn_inner_dim = ffn_inner_dim
self.dropout_rate = dropout_rate
self.shift = shift
if not isinstance(shift, list):
self.shift = [shift for _ in range(self.fsmn_num_layers)]
self.sample_rate = sample_rate
if not isinstance(sample_rate, list):
self.sample_rate = [sample_rate for _ in range(self.fsmn_num_layers)]
self.position_encoder = position_encoder
self.dropout = nn.Dropout(dropout_rate)
self.out_units = out_units
self.tf2torch_tensor_name_prefix_torch = tf2torch_tensor_name_prefix_torch
self.tf2torch_tensor_name_prefix_tf = tf2torch_tensor_name_prefix_tf
self.fsmn_layers = repeat(
self.fsmn_num_layers,
lambda lnum: EncoderLayer(
in_units if lnum == 0 else num_memory_units,
num_memory_units,
FsmnFeedForward(
in_units if lnum == 0 else num_memory_units,
ffn_inner_dim,
num_memory_units,
1,
dropout_rate,
),
FsmnBlock(num_memory_units, dropout_rate, filter_size, self.shift[lnum]),
),
)
self.dnn_layers = repeat(
dnn_num_layers,
lambda lnum: FsmnFeedForward(
num_memory_units,
ffn_inner_dim,
num_memory_units,
1,
dropout_rate,
),
)
if out_units is not None:
self.conv1d = nn.Conv1d(num_memory_units, out_units, 1, 1)
def output_size(self) -> int:
return self.num_memory_units
def forward(
self, xs_pad: torch.Tensor, ilens: torch.Tensor, prev_states: torch.Tensor = None
) -> Tuple[torch.Tensor, torch.Tensor, Optional[torch.Tensor]]:
inputs = xs_pad
if self.position_encoder is not None:
inputs = self.position_encoder(inputs)
inputs = self.dropout(inputs)
masks = (~make_pad_mask(ilens)[:, None, :]).to(xs_pad.device)
inputs = self.fsmn_layers(inputs, masks)[0]
inputs = self.dnn_layers(inputs)[0]
if self.out_units is not None:
inputs = self.conv1d(inputs.transpose(1, 2)).transpose(1, 2)
return inputs, ilens, None
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