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#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from fairseq.models import (
FairseqEncoder,
FairseqEncoderModel,
register_model,
register_model_architecture,
)
from fairseq.modules.fairseq_dropout import FairseqDropout
default_conv_enc_config = """[
(400, 13, 170, 0.2),
(440, 14, 0, 0.214),
(484, 15, 0, 0.22898),
(532, 16, 0, 0.2450086),
(584, 17, 0, 0.262159202),
(642, 18, 0, 0.28051034614),
(706, 19, 0, 0.30014607037),
(776, 20, 0, 0.321156295296),
(852, 21, 0, 0.343637235966),
(936, 22, 0, 0.367691842484),
(1028, 23, 0, 0.393430271458),
(1130, 24, 0, 0.42097039046),
(1242, 25, 0, 0.450438317792),
(1366, 26, 0, 0.481969000038),
(1502, 27, 0, 0.51570683004),
(1652, 28, 0, 0.551806308143),
(1816, 29, 0, 0.590432749713),
]"""
@register_model("asr_w2l_conv_glu_encoder")
class W2lConvGluEncoderModel(FairseqEncoderModel):
def __init__(self, encoder):
super().__init__(encoder)
@staticmethod
def add_args(parser):
"""Add model-specific arguments to the parser."""
parser.add_argument(
"--input-feat-per-channel",
type=int,
metavar="N",
help="encoder input dimension per input channel",
)
parser.add_argument(
"--in-channels",
type=int,
metavar="N",
help="number of encoder input channels",
)
parser.add_argument(
"--conv-enc-config",
type=str,
metavar="EXPR",
help="""
an array of tuples each containing the configuration of one conv layer
[(out_channels, kernel_size, padding, dropout), ...]
""",
)
@classmethod
def build_model(cls, args, task):
"""Build a new model instance."""
conv_enc_config = getattr(args, "conv_enc_config", default_conv_enc_config)
encoder = W2lConvGluEncoder(
vocab_size=len(task.target_dictionary),
input_feat_per_channel=args.input_feat_per_channel,
in_channels=args.in_channels,
conv_enc_config=eval(conv_enc_config),
)
return cls(encoder)
def get_normalized_probs(self, net_output, log_probs, sample=None):
lprobs = super().get_normalized_probs(net_output, log_probs, sample)
lprobs.batch_first = False
return lprobs
class W2lConvGluEncoder(FairseqEncoder):
def __init__(
self, vocab_size, input_feat_per_channel, in_channels, conv_enc_config
):
super().__init__(None)
self.input_dim = input_feat_per_channel
if in_channels != 1:
raise ValueError("only 1 input channel is currently supported")
self.conv_layers = nn.ModuleList()
self.linear_layers = nn.ModuleList()
self.dropouts = []
cur_channels = input_feat_per_channel
for out_channels, kernel_size, padding, dropout in conv_enc_config:
layer = nn.Conv1d(cur_channels, out_channels, kernel_size, padding=padding)
layer.weight.data.mul_(math.sqrt(3)) # match wav2letter init
self.conv_layers.append(nn.utils.weight_norm(layer))
self.dropouts.append(
FairseqDropout(dropout, module_name=self.__class__.__name__)
)
if out_channels % 2 != 0:
raise ValueError("odd # of out_channels is incompatible with GLU")
cur_channels = out_channels // 2 # halved by GLU
for out_channels in [2 * cur_channels, vocab_size]:
layer = nn.Linear(cur_channels, out_channels)
layer.weight.data.mul_(math.sqrt(3))
self.linear_layers.append(nn.utils.weight_norm(layer))
cur_channels = out_channels // 2
def forward(self, src_tokens, src_lengths, **kwargs):
"""
src_tokens: padded tensor (B, T, C * feat)
src_lengths: tensor of original lengths of input utterances (B,)
"""
B, T, _ = src_tokens.size()
x = src_tokens.transpose(1, 2).contiguous() # (B, feat, T) assuming C == 1
for layer_idx in range(len(self.conv_layers)):
x = self.conv_layers[layer_idx](x)
x = F.glu(x, dim=1)
x = self.dropouts[layer_idx](x)
x = x.transpose(1, 2).contiguous() # (B, T, 908)
x = self.linear_layers[0](x)
x = F.glu(x, dim=2)
x = self.dropouts[-1](x)
x = self.linear_layers[1](x)
assert x.size(0) == B
assert x.size(1) == T
encoder_out = x.transpose(0, 1) # (T, B, vocab_size)
# need to debug this -- find a simpler/elegant way in pytorch APIs
encoder_padding_mask = (
torch.arange(T).view(1, T).expand(B, -1).to(x.device)
>= src_lengths.view(B, 1).expand(-1, T)
).t() # (B x T) -> (T x B)
return {
"encoder_out": encoder_out, # (T, B, vocab_size)
"encoder_padding_mask": encoder_padding_mask, # (T, B)
}
def reorder_encoder_out(self, encoder_out, new_order):
encoder_out["encoder_out"] = encoder_out["encoder_out"].index_select(
1, new_order
)
encoder_out["encoder_padding_mask"] = encoder_out[
"encoder_padding_mask"
].index_select(1, new_order)
return encoder_out
def max_positions(self):
"""Maximum input length supported by the encoder."""
return (1e6, 1e6) # an arbitrary large number
@register_model_architecture("asr_w2l_conv_glu_encoder", "w2l_conv_glu_enc")
def w2l_conv_glu_enc(args):
args.input_feat_per_channel = getattr(args, "input_feat_per_channel", 80)
args.in_channels = getattr(args, "in_channels", 1)
args.conv_enc_config = getattr(args, "conv_enc_config", default_conv_enc_config)
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