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#!/usr/bin/env python3
import math
import torch
import torch.nn as nn
from fairseq.data.data_utils import compute_mask_indices
from fairseq.models import FairseqEncoder
from fairseq.models.wav2vec import ConvFeatureExtractionModel
from fairseq.modules import GradMultiply, LayerNorm, SamePad, TransformerEncoderLayer
# Transformer encoder with wave input, it is adopted from wav2vec 2.0 Encoder.
# use wav input
# use trained position embedding so it is easier to match with text input
class SpeechWavTransformerEncoder(FairseqEncoder):
# extra parameters for speech encoder besides those defined in transformermodel
@staticmethod
def add_args(parser):
parser.add_argument(
"--dropout-input",
type=float,
metavar="D",
help="dropout to apply to the input (after feat extr)",
)
parser.add_argument(
"--dropout-features",
type=float,
metavar="D",
help="dropout to apply to the unmasked features (after feat extr)",
)
parser.add_argument(
"--speech-extractor-mode",
type=str,
default="layer_norm",
choices=["default", "layer_norm"],
help="feature extractor norm",
)
parser.add_argument(
"--speech-conv-bias",
action="store_true",
help="include bias in speech conv encoder",
)
parser.add_argument(
"--conv-feature-layers",
default="[(512, 10, 5)] + [(512, 3, 2)] * 4 + [(512,2,2)] + [(512,2,2)]",
help="string describing convolutional feature extraction layers in form of a python list that contains [(dim, kernel_size, stride), ...]",
)
parser.add_argument(
"--speech-mask-length",
type=int,
help="repeat the mask indices multiple times",
)
parser.add_argument(
"--speech-mask-prob",
type=float,
help="probability of replacing a token with mask",
)
parser.add_argument(
"--speech-mask-selection",
type=str,
choices=["static", "uniform", "normal", "poisson"],
help="how to choose masks",
)
parser.add_argument(
"--speech-mask-other",
type=float,
help="stdev of the mask length in case of 'normal' selection strategy",
)
parser.add_argument(
"--speech-no-mask-overlap",
action="store_true",
help="whether to allow masks to overlap",
)
parser.add_argument(
"--speech-mask-min-space",
type=int,
help="min space between spans (if no overlap is enabled)",
)
parser.add_argument(
"--speech-mask-channel-length",
type=int,
help="repeat the mask indices multiple times",
)
parser.add_argument(
"--speech-mask-channel-prob",
type=float,
help="probability of replacing a token with mask",
)
parser.add_argument(
"--speech-mask-channel-selection",
type=str,
choices=["static", "uniform", "normal", "poisson"],
help="how to choose masks",
)
parser.add_argument(
"--speech-mask-channel-other",
type=float,
help="stdev of the mask length in case of 'normal' selection strategy",
)
parser.add_argument(
"--speech-no-mask-channel-overlap",
action="store_true",
help="whether to allow masks to overlap",
)
parser.add_argument(
"--no-scale-feature",
action="store_true",
help="no scale for the calculated features",
)
parser.add_argument(
"--speech-mask-channel-min-space",
type=int,
help="min space between spans (if no overlap is enabled)",
)
parser.add_argument(
"--feature-grad-mult",
type=float,
help="reset feature grad mult in wav2vec 2.0 to this",
)
# positional embeddings
parser.add_argument(
"--conv-pos",
type=int,
default=128,
help="number of filters for convolutional positional embeddings",
)
parser.add_argument(
"--conv-pos-groups",
type=int,
default=16,
help="number of groups for convolutional positional embedding",
)
# model configures
parser.add_argument(
"--speech-encoder-layers",
type=int,
help="number of speech encoder layers",
)
parser.add_argument(
"--text-encoder-layers",
type=int,
help="number of text encoder layers",
)
def __init__(self, args, alway_mask=False):
super().__init__(args)
self.args = args
self.dropout = args.dropout
self.embedding_dim = args.encoder_embed_dim
self.feat_scale = math.sqrt(args.encoder_embed_dim)
if args.no_scale_feature:
self.feat_scale = 1.0
subsample = ConvFeatureExtractionModel(
conv_layers=eval(args.conv_feature_layers),
dropout=0.0,
mode=args.speech_extractor_mode, # default, layer_norm
conv_bias=args.speech_conv_bias,
)
self.feature_enc_layers = eval(args.conv_feature_layers)
self.subsample = subsample
self.feat_proj = (
nn.Linear(self.feature_enc_layers[-1][0], self.embedding_dim)
if self.feature_enc_layers[-1][0] != self.embedding_dim
else None
)
self.feat_layer_norm = LayerNorm(self.feature_enc_layers[-1][0])
self.embed_positions = nn.Conv1d(
self.embedding_dim,
self.embedding_dim,
kernel_size=args.conv_pos,
padding=args.conv_pos // 2,
groups=args.conv_pos_groups,
)
std = math.sqrt(4 / (args.conv_pos * self.embedding_dim))
nn.init.normal_(self.embed_positions.weight, mean=0, std=std)
nn.init.constant_(self.embed_positions.bias, 0)
self.embed_positions = nn.utils.weight_norm(
self.embed_positions, name="weight", dim=2
)
self.embed_positions = nn.Sequential(
self.embed_positions, SamePad(args.conv_pos), nn.GELU()
)
self.mask_prob = args.speech_mask_prob
self.mask_selection = args.speech_mask_selection
self.mask_other = args.speech_mask_other
self.mask_length = args.speech_mask_length
self.no_mask_overlap = args.speech_no_mask_overlap
self.mask_min_space = args.speech_mask_min_space
self.mask_channel_prob = args.speech_mask_channel_prob
self.mask_channel_selection = args.speech_mask_channel_selection
self.mask_channel_other = args.speech_mask_channel_other
self.mask_channel_length = args.speech_mask_channel_length
self.no_mask_channel_overlap = args.speech_no_mask_channel_overlap
self.mask_channel_min_space = args.speech_mask_channel_min_space
self.dropout_input = nn.Dropout(args.dropout_input)
self.dropout_features = nn.Dropout(args.dropout_features)
self.feature_grad_mult = args.feature_grad_mult
self.mask_emb = nn.Parameter(
torch.FloatTensor(args.encoder_embed_dim).uniform_()
)
self.layers = nn.ModuleList(
[TransformerEncoderLayer(args) for _ in range(args.encoder_layers)]
)
self.layer_norm = LayerNorm(args.encoder_embed_dim)
self.normalize_before = args.encoder_normalize_before
self.alway_mask = alway_mask
def _get_feat_extract_output_lengths(self, input_lengths: torch.LongTensor):
"""
Computes the output length of the convolutional layers
"""
def _conv_out_length(input_length, kernel_size, stride):
return torch.floor((input_length - kernel_size) / stride + 1)
for i in range(len(self.feature_enc_layers)):
input_lengths = _conv_out_length(
input_lengths,
self.feature_enc_layers[i][1],
self.feature_enc_layers[i][2],
)
return input_lengths.to(torch.long)
def apply_mask(self, x, padding_mask):
B, T, C = x.shape
if self.mask_prob > 0:
mask_indices = compute_mask_indices(
(B, T),
padding_mask,
self.mask_prob,
self.mask_length,
self.mask_selection,
self.mask_other,
min_masks=2,
no_overlap=self.no_mask_overlap,
min_space=self.mask_min_space,
)
mask_indices = torch.from_numpy(mask_indices).to(x.device)
x[mask_indices] = self.mask_emb
else:
mask_indices = None
if self.mask_channel_prob > 0:
mask_channel_indices = compute_mask_indices(
(B, C),
None,
self.mask_channel_prob,
self.mask_channel_length,
self.mask_channel_selection,
self.mask_channel_other,
no_overlap=self.no_mask_channel_overlap,
min_space=self.mask_channel_min_space,
)
mask_channel_indices = (
torch.from_numpy(mask_channel_indices)
.to(x.device)
.unsqueeze(1)
.expand(-1, T, -1)
)
x[mask_channel_indices] = 0
return x, mask_indices
def forward(
self,
src_tokens,
src_lengths,
return_all_hiddens=False,
padding_mask=None,
features_only=True,
):
mask = self.training or self.alway_mask
if self.feature_grad_mult > 0 and self.training:
features = self.subsample(src_tokens)
if self.feature_grad_mult != 1.0:
features = GradMultiply.apply(features, self.feature_grad_mult)
else:
with torch.no_grad():
features = self.subsample(src_tokens)
features = features.transpose(1, 2)
features = self.feat_layer_norm(features)
if self.feat_proj is not None:
features = self.feat_proj(features)
if padding_mask is not None:
input_lengths = (1 - padding_mask.long()).sum(-1)
else:
input_lengths = src_lengths
# apply conv formula to get real output_lengths
output_lengths = self._get_feat_extract_output_lengths(input_lengths)
padding_mask = torch.zeros(
features.shape[:2], dtype=features.dtype, device=features.device
)
# these two operations makes sure that all values
# before the output lengths indices are attended to
padding_mask[
(
torch.arange(padding_mask.shape[0], device=padding_mask.device),
output_lengths - 1,
)
] = 1
padding_mask = (1 - padding_mask.flip([-1]).cumsum(-1).flip([-1])).bool()
features = self.feat_scale * features if self.feat_scale != 1.0 else features
unmasked_features = features.clone()
features = self.dropout_input(features)
unmasked_features = self.dropout_features(unmasked_features)
if mask:
x, mask_indices = self.apply_mask(features, padding_mask)
else:
x = features
mask_indices = None
def cal_transformer_layers(x, encoder_padding_mask, return_all_hiddens=False):
# x: B x T x C
positions = self.embed_positions(x.transpose(1, 2)).transpose(1, 2)
x = x + positions
if not self.normalize_before:
x = self.layer_norm(x)
# B x T x C -> T x B x C
x = x.transpose(0, 1)
encoder_states = []
for layer in self.layers:
x = layer(x, encoder_padding_mask)
if return_all_hiddens:
encoder_states.append(x)
if self.normalize_before:
x = self.layer_norm(x)
return x, encoder_states
x, encoder_states = cal_transformer_layers(x, padding_mask, return_all_hiddens)
if features_only:
return {
"encoder_out": [x], # [T x B x C]
"encoder_padding_mask": [padding_mask]
if padding_mask is not None
else [], # B x T
"encoder_embedding": [], #
"encoder_states": encoder_states, # List[T x B x C]
"src_tokens": [],
"src_lengths": [],
"mask_indices": [mask_indices],
}
x_unmasked = x
if self.mask_prob > 0 or self.mask_channel_prob > 0:
x_unmasked, _ = cal_transformer_layers(unmasked_features, padding_mask)
return {
"encoder_out": [x], # [T x B x C]
"encoder_unmasked_out": [x_unmasked], # [T x B x C]
"encoder_padding_mask": [padding_mask]
if padding_mask is not None
else [], # B x T
"encoder_embedding": [], #
"encoder_states": encoder_states, # List[T x B x C]
"src_tokens": [],
"src_lengths": [],
"mask_indices": [mask_indices] if mask_indices is not None else [], # B X T
}
def reorder_encoder_out(self, encoder_out, new_order):
new_encoder_out = (
[]
if len(encoder_out["encoder_out"]) == 0
else [x.index_select(1, new_order) for x in encoder_out["encoder_out"]]
)
new_encoder_padding_mask = (
[]
if len(encoder_out["encoder_padding_mask"]) == 0
else [
x.index_select(0, new_order)
for x in encoder_out["encoder_padding_mask"]
]
)
new_encoder_embedding = (
[]
if len(encoder_out["encoder_embedding"]) == 0
else [
x.index_select(0, new_order) for x in encoder_out["encoder_embedding"]
]
)
encoder_states = encoder_out["encoder_states"]
if len(encoder_states) > 0:
for idx, state in enumerate(encoder_states):
encoder_states[idx] = state.index_select(1, new_order)
return {
"encoder_out": new_encoder_out, # T x B x C
"encoder_padding_mask": new_encoder_padding_mask, # B x T
"encoder_embedding": new_encoder_embedding, # B x T x C
"encoder_states": encoder_states, # List[T x B x C]
"src_tokens": [], # B x T
"src_lengths": [], # B x 1
}
class StackedSpeechWavTransformerEncoder(FairseqEncoder):
def __init__(self, speech_enc, text_enc_layers, text_layer_norm):
super().__init__(None)
self.speech_encoder = speech_enc
self.text_encoder_layers = text_enc_layers
self.final_layer_norm = text_layer_norm
def forward(
self,
src_tokens,
src_lengths=None,
return_all_hiddens=False,
padding_mask=None,
features_only=True,
):
out = self.speech_encoder.forward(
src_tokens,
src_lengths,
return_all_hiddens,
padding_mask=padding_mask,
features_only=features_only,
)
x = out["encoder_out"][0]
encoder_padding_mask = None
if len(out["encoder_padding_mask"]) > 0:
encoder_padding_mask = out["encoder_padding_mask"][0]
def cal_text_layers(x, padding_mask, return_all_hiddens=False):
encoder_states = []
for layer in self.text_encoder_layers:
x = layer(x, padding_mask)
if return_all_hiddens:
encoder_states.append(x)
if self.final_layer_norm is not None:
x = self.final_layer_norm(x)
return x, encoder_states
x, encoder_states = cal_text_layers(x, encoder_padding_mask, return_all_hiddens)
if features_only:
return {
"encoder_out": [x], # T x B x C
"encoder_padding_mask": [encoder_padding_mask]
if encoder_padding_mask is not None
else [], # B x T
"encoder_embedding": [], # B x T x C
"encoder_states": encoder_states, # List[T x B x C]
"src_tokens": [],
"src_lengths": [],
}
x_u = out["encoder_unmasked_out"][0]
x_u, _ = cal_text_layers(x_u, encoder_padding_mask)
return {
"encoder_out": [x], # [T x B x C]
"encoder_unmasked_out": [x_u], # [T x B x C]
"encoder_padding_mask": [encoder_padding_mask]
if encoder_padding_mask is not None
else [], # B x T
"encoder_embedding": [], #
"encoder_states": encoder_states, # List[T x B x C]
"src_tokens": [],
"src_lengths": [],
"mask_indices": out["mask_indices"], # B X T
}
def reorder_encoder_out(self, encoder_out, new_order):
return self.speech_encoder.reorder_encoder_out(encoder_out, new_order)
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