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