import logging
import numpy as np
import torch
import torch.nn.functional as F
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from espnet.nets.e2e_asr_common import get_vgg2l_odim
from espnet.nets.pytorch_backend.nets_utils import make_pad_mask, to_device
[docs]class RNNP(torch.nn.Module):
"""RNN with projection layer module
:param int idim: dimension of inputs
:param int elayers: number of encoder layers
:param int cdim: number of rnn units (resulted in cdim * 2 if bidirectional)
:param int hdim: number of projection units
:param np.ndarray subsample: list of subsampling numbers
:param float dropout: dropout rate
:param str typ: The RNN type
"""
def __init__(self, idim, elayers, cdim, hdim, subsample, dropout, typ="blstm"):
super(RNNP, self).__init__()
bidir = typ[0] == "b"
for i in range(elayers):
if i == 0:
inputdim = idim
else:
inputdim = hdim
RNN = torch.nn.LSTM if "lstm" in typ else torch.nn.GRU
rnn = RNN(
inputdim, cdim, num_layers=1, bidirectional=bidir, batch_first=True
)
setattr(self, "%s%d" % ("birnn" if bidir else "rnn", i), rnn)
# bottleneck layer to merge
if bidir:
setattr(self, "bt%d" % i, torch.nn.Linear(2 * cdim, hdim))
else:
setattr(self, "bt%d" % i, torch.nn.Linear(cdim, hdim))
self.elayers = elayers
self.cdim = cdim
self.subsample = subsample
self.typ = typ
self.bidir = bidir
self.dropout = dropout
[docs] def forward(self, xs_pad, ilens, prev_state=None):
"""RNNP forward
:param torch.Tensor xs_pad: batch of padded input sequences (B, Tmax, idim)
:param torch.Tensor ilens: batch of lengths of input sequences (B)
:param torch.Tensor prev_state: batch of previous RNN states
:return: batch of hidden state sequences (B, Tmax, hdim)
:rtype: torch.Tensor
"""
logging.debug(self.__class__.__name__ + " input lengths: " + str(ilens))
elayer_states = []
for layer in range(self.elayers):
if not isinstance(ilens, torch.Tensor):
ilens = torch.tensor(ilens)
xs_pack = pack_padded_sequence(xs_pad, ilens.cpu(), batch_first=True)
rnn = getattr(self, ("birnn" if self.bidir else "rnn") + str(layer))
if self.training:
rnn.flatten_parameters()
if prev_state is not None and rnn.bidirectional:
prev_state = reset_backward_rnn_state(prev_state)
ys, states = rnn(
xs_pack, hx=None if prev_state is None else prev_state[layer]
)
elayer_states.append(states)
# ys: utt list of frame x cdim x 2 (2: means bidirectional)
ys_pad, ilens = pad_packed_sequence(ys, batch_first=True)
sub = self.subsample[layer + 1]
if sub > 1:
ys_pad = ys_pad[:, ::sub]
ilens = torch.tensor([int(i + 1) // sub for i in ilens])
# (sum _utt frame_utt) x dim
projection_layer = getattr(self, "bt%d" % layer)
projected = projection_layer(ys_pad.contiguous().view(-1, ys_pad.size(2)))
xs_pad = projected.view(ys_pad.size(0), ys_pad.size(1), -1)
if layer < self.elayers - 1:
xs_pad = torch.tanh(F.dropout(xs_pad, p=self.dropout))
return xs_pad, ilens, elayer_states # x: utt list of frame x dim
[docs]class RNN(torch.nn.Module):
"""RNN module
:param int idim: dimension of inputs
:param int elayers: number of encoder layers
:param int cdim: number of rnn units (resulted in cdim * 2 if bidirectional)
:param int hdim: number of final projection units
:param float dropout: dropout rate
:param str typ: The RNN type
"""
def __init__(self, idim, elayers, cdim, hdim, dropout, typ="blstm"):
super(RNN, self).__init__()
bidir = typ[0] == "b"
self.nbrnn = (
torch.nn.LSTM(
idim,
cdim,
elayers,
batch_first=True,
dropout=dropout,
bidirectional=bidir,
)
if "lstm" in typ
else torch.nn.GRU(
idim,
cdim,
elayers,
batch_first=True,
dropout=dropout,
bidirectional=bidir,
)
)
if bidir:
self.l_last = torch.nn.Linear(cdim * 2, hdim)
else:
self.l_last = torch.nn.Linear(cdim, hdim)
self.typ = typ
[docs] def forward(self, xs_pad, ilens, prev_state=None):
"""RNN forward
:param torch.Tensor xs_pad: batch of padded input sequences (B, Tmax, D)
:param torch.Tensor ilens: batch of lengths of input sequences (B)
:param torch.Tensor prev_state: batch of previous RNN states
:return: batch of hidden state sequences (B, Tmax, eprojs)
:rtype: torch.Tensor
"""
logging.debug(self.__class__.__name__ + " input lengths: " + str(ilens))
if not isinstance(ilens, torch.Tensor):
ilens = torch.tensor(ilens)
xs_pack = pack_padded_sequence(xs_pad, ilens.cpu(), batch_first=True)
if self.training:
self.nbrnn.flatten_parameters()
if prev_state is not None and self.nbrnn.bidirectional:
# We assume that when previous state is passed,
# it means that we're streaming the input
# and therefore cannot propagate backward BRNN state
# (otherwise it goes in the wrong direction)
prev_state = reset_backward_rnn_state(prev_state)
ys, states = self.nbrnn(xs_pack, hx=prev_state)
# ys: utt list of frame x cdim x 2 (2: means bidirectional)
ys_pad, ilens = pad_packed_sequence(ys, batch_first=True)
# (sum _utt frame_utt) x dim
projected = torch.tanh(
self.l_last(ys_pad.contiguous().view(-1, ys_pad.size(2)))
)
xs_pad = projected.view(ys_pad.size(0), ys_pad.size(1), -1)
return xs_pad, ilens, states # x: utt list of frame x dim
[docs]def reset_backward_rnn_state(states):
"""Sets backward BRNN states to zeroes
Useful in processing of sliding windows over the inputs
"""
if isinstance(states, (list, tuple)):
for state in states:
state[1::2] = 0.0
else:
states[1::2] = 0.0
return states
[docs]class VGG2L(torch.nn.Module):
"""VGG-like module
:param int in_channel: number of input channels
"""
def __init__(self, in_channel=1):
super(VGG2L, self).__init__()
# CNN layer (VGG motivated)
self.conv1_1 = torch.nn.Conv2d(in_channel, 64, 3, stride=1, padding=1)
self.conv1_2 = torch.nn.Conv2d(64, 64, 3, stride=1, padding=1)
self.conv2_1 = torch.nn.Conv2d(64, 128, 3, stride=1, padding=1)
self.conv2_2 = torch.nn.Conv2d(128, 128, 3, stride=1, padding=1)
self.in_channel = in_channel
[docs] def forward(self, xs_pad, ilens, **kwargs):
"""VGG2L forward
:param torch.Tensor xs_pad: batch of padded input sequences (B, Tmax, D)
:param torch.Tensor ilens: batch of lengths of input sequences (B)
:return: batch of padded hidden state sequences (B, Tmax // 4, 128 * D // 4)
:rtype: torch.Tensor
"""
logging.debug(self.__class__.__name__ + " input lengths: " + str(ilens))
# x: utt x frame x dim
# xs_pad = F.pad_sequence(xs_pad)
# x: utt x 1 (input channel num) x frame x dim
xs_pad = xs_pad.view(
xs_pad.size(0),
xs_pad.size(1),
self.in_channel,
xs_pad.size(2) // self.in_channel,
).transpose(1, 2)
# NOTE: max_pool1d ?
xs_pad = F.relu(self.conv1_1(xs_pad))
xs_pad = F.relu(self.conv1_2(xs_pad))
xs_pad = F.max_pool2d(xs_pad, 2, stride=2, ceil_mode=True)
xs_pad = F.relu(self.conv2_1(xs_pad))
xs_pad = F.relu(self.conv2_2(xs_pad))
xs_pad = F.max_pool2d(xs_pad, 2, stride=2, ceil_mode=True)
if torch.is_tensor(ilens):
ilens = ilens.cpu().numpy()
else:
ilens = np.array(ilens, dtype=np.float32)
ilens = np.array(np.ceil(ilens / 2), dtype=np.int64)
ilens = np.array(
np.ceil(np.array(ilens, dtype=np.float32) / 2), dtype=np.int64
).tolist()
# x: utt_list of frame (remove zeropaded frames) x (input channel num x dim)
xs_pad = xs_pad.transpose(1, 2)
xs_pad = xs_pad.contiguous().view(
xs_pad.size(0), xs_pad.size(1), xs_pad.size(2) * xs_pad.size(3)
)
return xs_pad, ilens, None # no state in this layer
[docs]class Encoder(torch.nn.Module):
"""Encoder module
:param str etype: type of encoder network
:param int idim: number of dimensions of encoder network
:param int elayers: number of layers of encoder network
:param int eunits: number of lstm units of encoder network
:param int eprojs: number of projection units of encoder network
:param np.ndarray subsample: list of subsampling numbers
:param float dropout: dropout rate
:param int in_channel: number of input channels
"""
def __init__(
self, etype, idim, elayers, eunits, eprojs, subsample, dropout, in_channel=1
):
super(Encoder, self).__init__()
typ = etype.lstrip("vgg").rstrip("p")
if typ not in ["lstm", "gru", "blstm", "bgru"]:
logging.error("Error: need to specify an appropriate encoder architecture")
if etype.startswith("vgg"):
if etype[-1] == "p":
self.enc = torch.nn.ModuleList(
[
VGG2L(in_channel),
RNNP(
get_vgg2l_odim(idim, in_channel=in_channel),
elayers,
eunits,
eprojs,
subsample,
dropout,
typ=typ,
),
]
)
logging.info("Use CNN-VGG + " + typ.upper() + "P for encoder")
else:
self.enc = torch.nn.ModuleList(
[
VGG2L(in_channel),
RNN(
get_vgg2l_odim(idim, in_channel=in_channel),
elayers,
eunits,
eprojs,
dropout,
typ=typ,
),
]
)
logging.info("Use CNN-VGG + " + typ.upper() + " for encoder")
self.conv_subsampling_factor = 4
else:
if etype[-1] == "p":
self.enc = torch.nn.ModuleList(
[RNNP(idim, elayers, eunits, eprojs, subsample, dropout, typ=typ)]
)
logging.info(typ.upper() + " with every-layer projection for encoder")
else:
self.enc = torch.nn.ModuleList(
[RNN(idim, elayers, eunits, eprojs, dropout, typ=typ)]
)
logging.info(typ.upper() + " without projection for encoder")
self.conv_subsampling_factor = 1
[docs] def forward(self, xs_pad, ilens, prev_states=None):
"""Encoder forward
:param torch.Tensor xs_pad: batch of padded input sequences (B, Tmax, D)
:param torch.Tensor ilens: batch of lengths of input sequences (B)
:param torch.Tensor prev_state: batch of previous encoder hidden states (?, ...)
:return: batch of hidden state sequences (B, Tmax, eprojs)
:rtype: torch.Tensor
"""
if prev_states is None:
prev_states = [None] * len(self.enc)
assert len(prev_states) == len(self.enc)
current_states = []
for module, prev_state in zip(self.enc, prev_states):
xs_pad, ilens, states = module(xs_pad, ilens, prev_state=prev_state)
current_states.append(states)
# make mask to remove bias value in padded part
mask = to_device(xs_pad, make_pad_mask(ilens).unsqueeze(-1))
return xs_pad.masked_fill(mask, 0.0), ilens, current_states
[docs]def encoder_for(args, idim, subsample):
"""Instantiates an encoder module given the program arguments
:param Namespace args: The arguments
:param int or List of integer idim: dimension of input, e.g. 83, or
List of dimensions of inputs, e.g. [83,83]
:param List or List of List subsample: subsample factors, e.g. [1,2,2,1,1], or
List of subsample factors of each encoder.
e.g. [[1,2,2,1,1], [1,2,2,1,1]]
:rtype torch.nn.Module
:return: The encoder module
"""
num_encs = getattr(args, "num_encs", 1) # use getattr to keep compatibility
if num_encs == 1:
# compatible with single encoder asr mode
return Encoder(
args.etype,
idim,
args.elayers,
args.eunits,
args.eprojs,
subsample,
args.dropout_rate,
)
elif num_encs >= 1:
enc_list = torch.nn.ModuleList()
for idx in range(num_encs):
enc = Encoder(
args.etype[idx],
idim[idx],
args.elayers[idx],
args.eunits[idx],
args.eprojs,
subsample[idx],
args.dropout_rate[idx],
)
enc_list.append(enc)
return enc_list
else:
raise ValueError(
"Number of encoders needs to be more than one. {}".format(num_encs)
)