from typing import Optional
from typing import Sequence
from typing import Tuple
import numpy as np
import torch
from typeguard import check_argument_types
from espnet.nets.pytorch_backend.nets_utils import make_pad_mask
from espnet.nets.pytorch_backend.rnn.encoders import RNN
from espnet.nets.pytorch_backend.rnn.encoders import RNNP
from espnet2.asr.encoder.abs_encoder import AbsEncoder
[docs]class RNNEncoder(AbsEncoder):
"""RNNEncoder class.
Args:
input_size: The number of expected features in the input
output_size: The number of output features
hidden_size: The number of hidden features
bidirectional: If ``True`` becomes a bidirectional LSTM
use_projection: Use projection layer or not
num_layers: Number of recurrent layers
dropout: dropout probability
"""
def __init__(
self,
input_size: int,
rnn_type: str = "lstm",
bidirectional: bool = True,
use_projection: bool = True,
num_layers: int = 4,
hidden_size: int = 320,
output_size: int = 320,
dropout: float = 0.0,
subsample: Optional[Sequence[int]] = (2, 2, 1, 1),
):
assert check_argument_types()
super().__init__()
self._output_size = output_size
self.rnn_type = rnn_type
self.bidirectional = bidirectional
self.use_projection = use_projection
if rnn_type not in {"lstm", "gru"}:
raise ValueError(f"Not supported rnn_type={rnn_type}")
if subsample is None:
subsample = np.ones(num_layers + 1, dtype=np.int)
else:
subsample = subsample[:num_layers]
# Append 1 at the beginning because the second or later is used
subsample = np.pad(
np.array(subsample, dtype=np.int),
[1, num_layers - len(subsample)],
mode="constant",
constant_values=1,
)
rnn_type = ("b" if bidirectional else "") + rnn_type
if use_projection:
self.enc = torch.nn.ModuleList(
[
RNNP(
input_size,
num_layers,
hidden_size,
output_size,
subsample,
dropout,
typ=rnn_type,
)
]
)
else:
self.enc = torch.nn.ModuleList(
[
RNN(
input_size,
num_layers,
hidden_size,
output_size,
dropout,
typ=rnn_type,
)
]
)
[docs] def output_size(self) -> int:
return self._output_size
[docs] def forward(
self,
xs_pad: torch.Tensor,
ilens: torch.Tensor,
prev_states: torch.Tensor = None,
) -> Tuple[torch.Tensor, torch.Tensor, 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)
if self.use_projection:
xs_pad.masked_fill_(make_pad_mask(ilens, xs_pad, 1), 0.0)
else:
xs_pad = xs_pad.masked_fill(make_pad_mask(ilens, xs_pad, 1), 0.0)
return xs_pad, ilens, current_states