def __init__(self, args, save_path=None, idx2token=None):
super(ModelBase, self).__init__()
self.save_path = save_path
# for encoder, decoder
self.input_type = args.input_type
self.input_dim = args.input_dim
self.enc_type = args.enc_type
self.enc_n_units = args.enc_n_units
if args.enc_type in ['blstm', 'bgru', 'conv_blstm', 'conv_bgru']:
self.enc_n_units *= 2
self.dec_type = args.dec_type
# for OOV resolution
self.enc_n_layers = args.enc_n_layers
self.enc_n_layers_sub1 = args.enc_n_layers_sub1
self.subsample = [int(s) for s in args.subsample.split('_')]
# for decoder
self.vocab = args.vocab
self.vocab_sub1 = args.vocab_sub1
self.vocab_sub2 = args.vocab_sub2
self.blank = 0
self.unk = 1
self.eos = 2
self.pad = 3
# NOTE: reserved in advance
# for the sub tasks
self.main_weight = 1 - args.sub1_weight - args.sub2_weight
self.sub1_weight = args.sub1_weight
self.sub2_weight = args.sub2_weight
self.mtl_per_batch = args.mtl_per_batch
self.task_specific_layer = args.task_specific_layer
# for CTC
self.ctc_weight = min(args.ctc_weight, self.main_weight)
self.ctc_weight_sub1 = min(args.ctc_weight_sub1, self.sub1_weight)
self.ctc_weight_sub2 = min(args.ctc_weight_sub2, self.sub2_weight)
# for backward decoder
self.bwd_weight = min(args.bwd_weight, self.main_weight)
self.fwd_weight = self.main_weight - self.bwd_weight - self.ctc_weight
self.fwd_weight_sub1 = self.sub1_weight - self.ctc_weight_sub1
self.fwd_weight_sub2 = self.sub2_weight - self.ctc_weight_sub2
# for MBR
self.mbr_training = args.mbr_training
self.recog_params = vars(args)
self.idx2token = idx2token
# Feature extraction
self.gaussian_noise = args.gaussian_noise
self.n_stacks = args.n_stacks
self.n_skips = args.n_skips
self.n_splices = args.n_splices
self.use_specaug = args.n_freq_masks > 0 or args.n_time_masks > 0
self.specaug = None
self.flip_time_prob = args.flip_time_prob
self.flip_freq_prob = args.flip_freq_prob
self.weight_noise = args.weight_noise
if self.use_specaug:
assert args.n_stacks == 1 and args.n_skips == 1
assert args.n_splices == 1
self.specaug = SpecAugment(F=args.freq_width,
T=args.time_width,
n_freq_masks=args.n_freq_masks,
n_time_masks=args.n_time_masks,
p=args.time_width_upper)
# Frontend
self.ssn = None
if args.sequence_summary_network:
assert args.input_type == 'speech'
self.ssn = SequenceSummaryNetwork(args.input_dim,
n_units=512,
n_layers=3,
bottleneck_dim=100,
dropout=0,
param_init=args.param_init)
# Encoder
self.enc = build_encoder(args)
if args.freeze_encoder:
for p in self.enc.parameters():
p.requires_grad = False
# main task
external_lm = None
directions = []
if self.fwd_weight > 0 or (self.bwd_weight == 0
and self.ctc_weight > 0):
directions.append('fwd')
if self.bwd_weight > 0:
directions.append('bwd')
for dir in directions:
# Load the LM for LM fusion and decoder initialization
if args.external_lm and dir == 'fwd':
external_lm = RNNLM(args.lm_conf)
load_checkpoint(external_lm, args.external_lm)
# freeze LM parameters
for n, p in external_lm.named_parameters():
p.requires_grad = False
# Decoder
special_symbols = {
'blank': self.blank,
'unk': self.unk,
'eos': self.eos,
'pad': self.pad,
}
dec = build_decoder(
args, special_symbols, self.enc.output_dim, args.vocab,
self.ctc_weight, args.ctc_fc_list, self.main_weight -
self.bwd_weight if dir == 'fwd' else self.bwd_weight,
external_lm)
setattr(self, 'dec_' + dir, dec)
# sub task
for sub in ['sub1', 'sub2']:
if getattr(self, sub + '_weight') > 0:
dec_sub = build_decoder(args, special_symbols,
self.enc.output_dim,
getattr(self, 'vocab_' + sub),
getattr(self, 'ctc_weight_' + sub),
getattr(args, 'ctc_fc_list_' + sub),
getattr(self,
sub + '_weight'), external_lm)
setattr(self, 'dec_fwd_' + sub, dec_sub)
if args.input_type == 'text':
if args.vocab == args.vocab_sub1:
# Share the embedding layer between input and output
self.embed = dec.embed
else:
self.embed = nn.Embedding(args.vocab_sub1,
args.emb_dim,
padding_idx=self.pad)
self.dropout_emb = nn.Dropout(p=args.dropout_emb)
# Recurrent weights are orthogonalized
if args.rec_weight_orthogonal:
self.reset_parameters(args.param_init,
dist='orthogonal',
keys=['rnn', 'weight'])
# Initialize bias in forget gate with 1
# self.init_forget_gate_bias_with_one()
# Fix all parameters except for the gating parts in deep fusion
if args.lm_fusion == 'deep' and external_lm is not None:
for n, p in self.named_parameters():
if 'output' in n or 'output_bn' in n or 'linear' in n:
p.requires_grad = True
else:
p.requires_grad = False
def __init__(self, args, save_path=None, idx2token=None):
super(ModelBase, self).__init__()
self.save_path = save_path
# for encoder, decoder
self.input_type = args.input_type
self.input_dim = args.input_dim
self.enc_type = args.enc_type
self.dec_type = args.dec_type
# for OOV resolution
self.enc_n_layers = args.enc_n_layers
self.enc_n_layers_sub1 = args.enc_n_layers_sub1
self.subsample = [int(s) for s in args.subsample.split('_')]
# for decoder
self.vocab = args.vocab
self.vocab_sub1 = args.vocab_sub1
self.vocab_sub2 = args.vocab_sub2
self.blank = 0
self.unk = 1
self.eos = 2
self.pad = 3
# NOTE: reserved in advance
# for the sub tasks
self.main_weight = args.total_weight - args.sub1_weight - args.sub2_weight
self.sub1_weight = args.sub1_weight
self.sub2_weight = args.sub2_weight
self.mtl_per_batch = args.mtl_per_batch
self.task_specific_layer = args.task_specific_layer
# for CTC
self.ctc_weight = min(args.ctc_weight, self.main_weight)
self.ctc_weight_sub1 = min(args.ctc_weight_sub1, self.sub1_weight)
self.ctc_weight_sub2 = min(args.ctc_weight_sub2, self.sub2_weight)
# for backward decoder
self.bwd_weight = min(args.bwd_weight, self.main_weight)
self.fwd_weight = self.main_weight - self.bwd_weight - self.ctc_weight
self.fwd_weight_sub1 = self.sub1_weight - self.ctc_weight_sub1
self.fwd_weight_sub2 = self.sub2_weight - self.ctc_weight_sub2
# for MBR
self.mbr_training = args.mbr_training
self.recog_params = vars(args)
self.idx2token = idx2token
# for discourse-aware model
self.utt_id_prev = None
# Feature extraction
self.input_noise_std = args.input_noise_std
self.n_stacks = args.n_stacks
self.n_skips = args.n_skips
self.n_splices = args.n_splices
self.weight_noise_std = args.weight_noise_std
self.specaug = None
if args.n_freq_masks > 0 or args.n_time_masks > 0:
assert args.n_stacks == 1 and args.n_skips == 1
assert args.n_splices == 1
self.specaug = SpecAugment(
F=args.freq_width,
T=args.time_width,
n_freq_masks=args.n_freq_masks,
n_time_masks=args.n_time_masks,
p=args.time_width_upper,
adaptive_number_ratio=args.adaptive_number_ratio,
adaptive_size_ratio=args.adaptive_size_ratio,
max_n_time_masks=args.max_n_time_masks)
# Frontend
self.ssn = None
if args.sequence_summary_network:
assert args.input_type == 'speech'
self.ssn = SequenceSummaryNetwork(args.input_dim,
n_units=512,
n_layers=3,
bottleneck_dim=100,
dropout=0,
param_init=args.param_init)
# Encoder
self.enc = build_encoder(args)
if args.freeze_encoder:
for n, p in self.enc.named_parameters():
if 'bridge' in n or 'sub1' in n:
continue
p.requires_grad = False
logger.info('freeze %s' % n)
special_symbols = {
'blank': self.blank,
'unk': self.unk,
'eos': self.eos,
'pad': self.pad,
}
# main task
external_lm = None
directions = []
if self.fwd_weight > 0 or (self.bwd_weight == 0
and self.ctc_weight > 0):
directions.append('fwd')
if self.bwd_weight > 0:
directions.append('bwd')
for dir in directions:
# Load the LM for LM fusion and decoder initialization
if args.external_lm and dir == 'fwd':
external_lm = RNNLM(args.lm_conf)
load_checkpoint(args.external_lm, external_lm)
# freeze LM parameters
for n, p in external_lm.named_parameters():
p.requires_grad = False
# Decoder
dec = build_decoder(
args, special_symbols, self.enc.output_dim, args.vocab,
self.ctc_weight, self.main_weight -
self.bwd_weight if dir == 'fwd' else self.bwd_weight,
external_lm)
setattr(self, 'dec_' + dir, dec)
# sub task
for sub in ['sub1', 'sub2']:
if getattr(self, sub + '_weight') > 0:
args_sub = copy.deepcopy(args)
if hasattr(args, 'dec_config_' + sub):
for k, v in getattr(args, 'dec_config_' + sub).items():
setattr(args_sub, k, v)
# NOTE: Other parameters are the same as the main decoder
dec_sub = build_decoder(args_sub, special_symbols,
getattr(self.enc, 'output_dim_' + sub),
getattr(self, 'vocab_' + sub),
getattr(self, 'ctc_weight_' + sub),
getattr(self, sub + '_weight'),
external_lm)
setattr(self, 'dec_fwd_' + sub, dec_sub)
if args.input_type == 'text':
if args.vocab == args.vocab_sub1:
# Share the embedding layer between input and output
self.embed = dec.embed
else:
self.embed = nn.Embedding(args.vocab_sub1,
args.emb_dim,
padding_idx=self.pad)
self.dropout_emb = nn.Dropout(p=args.dropout_emb)
# Initialize bias in forget gate with 1
# self.init_forget_gate_bias_with_one()
# Fix all parameters except for the gating parts in deep fusion
if args.lm_fusion == 'deep' and external_lm is not None:
for n, p in self.named_parameters():
if 'output' in n or 'output_bn' in n or 'linear' in n:
p.requires_grad = True
else:
p.requires_grad = False
def __init__(self, args, save_path=None):
super(ModelBase, self).__init__()
self.save_path = save_path
# for encoder, decoder
self.input_type = args.input_type
self.input_dim = args.input_dim
self.enc_type = args.enc_type
self.enc_n_units = args.enc_n_units
if args.enc_type in ['blstm', 'bgru', 'conv_blstm', 'conv_bgru']:
self.enc_n_units *= 2
self.dec_type = args.dec_type
# for OOV resolution
self.enc_n_layers = args.enc_n_layers
self.enc_n_layers_sub1 = args.enc_n_layers_sub1
self.subsample = [int(s) for s in args.subsample.split('_')]
# for decoder
self.vocab = args.vocab
self.vocab_sub1 = args.vocab_sub1
self.vocab_sub2 = args.vocab_sub2
self.blank = 0
self.unk = 1
self.eos = 2
self.pad = 3
# NOTE: reserved in advance
# for the sub tasks
self.main_weight = 1 - args.sub1_weight - args.sub2_weight
self.sub1_weight = args.sub1_weight
self.sub2_weight = args.sub2_weight
self.mtl_per_batch = args.mtl_per_batch
self.task_specific_layer = args.task_specific_layer
# for CTC
self.ctc_weight = min(args.ctc_weight, self.main_weight)
self.ctc_weight_sub1 = min(args.ctc_weight_sub1, self.sub1_weight)
self.ctc_weight_sub2 = min(args.ctc_weight_sub2, self.sub2_weight)
# for backward decoder
self.bwd_weight = min(args.bwd_weight, self.main_weight)
self.fwd_weight = self.main_weight - self.bwd_weight - self.ctc_weight
self.fwd_weight_sub1 = self.sub1_weight - self.ctc_weight_sub1
self.fwd_weight_sub2 = self.sub2_weight - self.ctc_weight_sub2
# Feature extraction
self.gaussian_noise = args.gaussian_noise
self.n_stacks = args.n_stacks
self.n_skips = args.n_skips
self.n_splices = args.n_splices
self.is_specaug = args.n_freq_masks > 0 or args.n_time_masks > 0
self.specaug = None
if self.is_specaug:
assert args.n_stacks == 1 and args.n_skips == 1
assert args.n_splices == 1
self.specaug = SpecAugment(F=args.freq_width,
T=args.time_width,
n_freq_masks=args.n_freq_masks,
n_time_masks=args.n_time_masks,
p=args.time_width_upper)
# Frontend
self.ssn = None
if args.sequence_summary_network:
assert args.input_type == 'speech'
self.ssn = SequenceSummaryNetwork(args.input_dim,
n_units=512,
n_layers=3,
bottleneck_dim=100,
dropout=0,
param_init=args.param_init)
# Encoder
self.enc = select_encoder(args)
if args.freeze_encoder:
for p in self.enc.parameters():
p.requires_grad = False
# main task
directions = []
if self.fwd_weight > 0 or self.ctc_weight > 0:
directions.append('fwd')
if self.bwd_weight > 0:
directions.append('bwd')
for dir in directions:
# Load the LM for LM fusion
if args.lm_fusion and dir == 'fwd':
lm_fusion = RNNLM(args.lm_conf)
lm_fusion, _ = load_checkpoint(lm_fusion, args.lm_fusion)
else:
lm_fusion = None
# TODO(hirofumi): for backward RNNLM
# Load the LM for LM initialization
if args.lm_init and dir == 'fwd':
lm_init = RNNLM(args.lm_conf)
lm_init, _ = load_checkpoint(lm_init, args.lm_init)
else:
lm_init = None
# TODO(hirofumi): for backward RNNLM
# Decoder
if args.dec_type == 'transformer':
dec = TransformerDecoder(
eos=self.eos,
unk=self.unk,
pad=self.pad,
blank=self.blank,
enc_n_units=self.enc.output_dim,
attn_type=args.transformer_attn_type,
attn_n_heads=args.transformer_attn_n_heads,
n_layers=args.dec_n_layers,
d_model=args.d_model,
d_ff=args.d_ff,
vocab=self.vocab,
tie_embedding=args.tie_embedding,
pe_type=args.pe_type,
layer_norm_eps=args.layer_norm_eps,
dropout=args.dropout_dec,
dropout_emb=args.dropout_emb,
dropout_att=args.dropout_att,
lsm_prob=args.lsm_prob,
focal_loss_weight=args.focal_loss_weight,
focal_loss_gamma=args.focal_loss_gamma,
ctc_weight=self.ctc_weight if dir == 'fwd' else 0,
ctc_lsm_prob=args.ctc_lsm_prob,
ctc_fc_list=[
int(fc) for fc in args.ctc_fc_list.split('_')
] if args.ctc_fc_list is not None
and len(args.ctc_fc_list) > 0 else [],
backward=(dir == 'bwd'),
global_weight=self.main_weight -
self.bwd_weight if dir == 'fwd' else self.bwd_weight,
mtl_per_batch=args.mtl_per_batch)
elif 'transducer' in args.dec_type:
dec = RNNTransducer(
eos=self.eos,
unk=self.unk,
pad=self.pad,
blank=self.blank,
enc_n_units=self.enc.output_dim,
rnn_type=args.dec_type,
n_units=args.dec_n_units,
n_projs=args.dec_n_projs,
n_layers=args.dec_n_layers,
residual=args.dec_residual,
bottleneck_dim=args.dec_bottleneck_dim,
emb_dim=args.emb_dim,
vocab=self.vocab,
dropout=args.dropout_dec,
dropout_emb=args.dropout_emb,
lsm_prob=args.lsm_prob,
ctc_weight=self.ctc_weight if dir == 'fwd' else 0,
ctc_lsm_prob=args.ctc_lsm_prob,
ctc_fc_list=[
int(fc) for fc in args.ctc_fc_list.split('_')
] if args.ctc_fc_list is not None
and len(args.ctc_fc_list) > 0 else [],
lm_init=lm_init,
lmobj_weight=args.lmobj_weight,
share_lm_softmax=args.share_lm_softmax,
global_weight=self.main_weight -
self.bwd_weight if dir == 'fwd' else self.bwd_weight,
mtl_per_batch=args.mtl_per_batch,
param_init=args.param_init)
else:
dec = RNNDecoder(
eos=self.eos,
unk=self.unk,
pad=self.pad,
blank=self.blank,
enc_n_units=self.enc.output_dim,
attn_type=args.attn_type,
attn_dim=args.attn_dim,
attn_sharpening_factor=args.attn_sharpening,
attn_sigmoid_smoothing=args.attn_sigmoid,
attn_conv_out_channels=args.attn_conv_n_channels,
attn_conv_kernel_size=args.attn_conv_width,
attn_n_heads=args.attn_n_heads,
rnn_type=args.dec_type,
n_units=args.dec_n_units,
n_projs=args.dec_n_projs,
n_layers=args.dec_n_layers,
loop_type=args.dec_loop_type,
residual=args.dec_residual,
bottleneck_dim=args.dec_bottleneck_dim,
emb_dim=args.emb_dim,
vocab=self.vocab,
tie_embedding=args.tie_embedding,
dropout=args.dropout_dec,
dropout_emb=args.dropout_emb,
dropout_att=args.dropout_att,
zoneout=args.zoneout,
ss_prob=args.ss_prob,
ss_type=args.ss_type,
lsm_prob=args.lsm_prob,
focal_loss_weight=args.focal_loss_weight,
focal_loss_gamma=args.focal_loss_gamma,
ctc_weight=self.ctc_weight if dir == 'fwd' else 0,
ctc_lsm_prob=args.ctc_lsm_prob,
ctc_fc_list=[
int(fc) for fc in args.ctc_fc_list.split('_')
] if args.ctc_fc_list is not None
and len(args.ctc_fc_list) > 0 else [],
input_feeding=args.input_feeding,
backward=(dir == 'bwd'),
lm_fusion=lm_fusion,
lm_fusion_type=args.lm_fusion_type,
discourse_aware=args.discourse_aware,
lm_init=lm_init,
lmobj_weight=args.lmobj_weight,
share_lm_softmax=args.share_lm_softmax,
global_weight=self.main_weight -
self.bwd_weight if dir == 'fwd' else self.bwd_weight,
mtl_per_batch=args.mtl_per_batch,
adaptive_softmax=args.adaptive_softmax,
param_init=args.param_init,
replace_sos=args.replace_sos)
setattr(self, 'dec_' + dir, dec)
# sub task
for sub in ['sub1', 'sub2']:
if getattr(self, sub + '_weight') > 0:
if args.dec_type == 'transformer':
raise NotImplementedError
else:
dec_sub = RNNDecoder(
eos=self.eos,
unk=self.unk,
pad=self.pad,
blank=self.blank,
enc_n_units=self.enc_n_units,
attn_type=args.attn_type,
attn_dim=args.attn_dim,
attn_sharpening_factor=args.attn_sharpening,
attn_sigmoid_smoothing=args.attn_sigmoid,
attn_conv_out_channels=args.attn_conv_n_channels,
attn_conv_kernel_size=args.attn_conv_width,
attn_n_heads=1,
rnn_type=args.dec_type,
n_units=args.dec_n_units,
n_projs=args.dec_n_projs,
n_layers=args.dec_n_layers,
loop_type=args.dec_loop_type,
residual=args.dec_residual,
bottleneck_dim=args.dec_bottleneck_dim,
emb_dim=args.emb_dim,
tie_embedding=args.tie_embedding,
vocab=getattr(self, 'vocab_' + sub),
dropout=args.dropout_dec,
dropout_emb=args.dropout_emb,
dropout_att=args.dropout_att,
ss_prob=args.ss_prob,
ss_type=args.ss_type,
lsm_prob=args.lsm_prob,
focal_loss_weight=args.focal_loss_weight,
focal_loss_gamma=args.focal_loss_gamma,
ctc_weight=getattr(self, 'ctc_weight_' + sub),
ctc_lsm_prob=args.ctc_lsm_prob,
ctc_fc_list=[
int(fc) for fc in getattr(args, 'ctc_fc_list_' +
sub).split('_')
] if getattr(args, 'ctc_fc_list_' + sub) is not None
and len(getattr(args, 'ctc_fc_list_' + sub)) > 0 else
[],
input_feeding=args.input_feeding,
global_weight=getattr(self, sub + '_weight'),
mtl_per_batch=args.mtl_per_batch,
param_init=args.param_init)
setattr(self, 'dec_fwd_' + sub, dec_sub)
if args.input_type == 'text':
if args.vocab == args.vocab_sub1:
# Share the embedding layer between input and output
self.embed = dec.embed
else:
self.embed = Embedding(vocab=args.vocab_sub1,
emb_dim=args.emb_dim,
dropout=args.dropout_emb,
ignore_index=self.pad)
# Recurrent weights are orthogonalized
if args.rec_weight_orthogonal:
self.reset_parameters(args.param_init,
dist='orthogonal',
keys=['rnn', 'weight'])
# Initialize bias in forget gate with 1
# self.init_forget_gate_bias_with_one()
# Fix all parameters except for the gating parts in deep fusion
if args.lm_fusion_type == 'deep' and args.lm_fusion:
for n, p in self.named_parameters():
if 'output' in n or 'output_bn' in n or 'linear' in n:
p.requires_grad = True
else:
p.requires_grad = False
def __init__(self, args):
super(ModelBase, self).__init__()
# for encoder
self.input_type = args.input_type
self.input_dim = args.input_dim
self.n_stacks = args.n_stacks
self.n_skips = args.n_skips
self.n_splices = args.n_splices
self.enc_type = args.enc_type
self.enc_n_units = args.enc_n_units
if args.enc_type in ['blstm', 'bgru']:
self.enc_n_units *= 2
self.bridge_layer = args.bridge_layer
# for OOV resolution
self.enc_n_layers = args.enc_n_layers
self.enc_n_layers_sub1 = args.enc_n_layers_sub1
self.subsample = [int(s) for s in args.subsample.split('_')]
# for attention layer
self.attn_n_heads = args.attn_n_heads
# for decoder
self.vocab = args.vocab
self.vocab_sub1 = args.vocab_sub1
self.vocab_sub2 = args.vocab_sub2
self.blank = 0
self.unk = 1
self.eos = 2
self.pad = 3
# NOTE: reserved in advance
# for the sub tasks
self.main_weight = 1 - args.sub1_weight - args.sub2_weight
self.sub1_weight = args.sub1_weight
self.sub2_weight = args.sub2_weight
self.mtl_per_batch = args.mtl_per_batch
self.task_specific_layer = args.task_specific_layer
# for CTC
self.ctc_weight = min(args.ctc_weight, self.main_weight)
self.ctc_weight_sub1 = min(args.ctc_weight_sub1, self.sub1_weight)
self.ctc_weight_sub2 = min(args.ctc_weight_sub2, self.sub2_weight)
# for backward decoder
self.bwd_weight = min(args.bwd_weight, self.main_weight)
self.fwd_weight = self.main_weight - self.bwd_weight - self.ctc_weight
self.fwd_weight_sub1 = self.sub1_weight - self.ctc_weight_sub1
self.fwd_weight_sub2 = self.sub2_weight - self.ctc_weight_sub2
# Feature extraction
self.ssn = None
if args.sequence_summary_network:
assert args.input_type == 'speech'
self.ssn = SequenceSummaryNetwork(args.input_dim,
n_units=512,
n_layers=3,
bottleneck_dim=100,
dropout=0)
# Encoder
if args.enc_type == 'transformer':
self.enc = TransformerEncoder(
input_dim=args.input_dim
if args.input_type == 'speech' else args.emb_dim,
attn_type=args.transformer_attn_type,
attn_n_heads=args.transformer_attn_n_heads,
n_layers=args.transformer_enc_n_layers,
d_model=args.d_model,
d_ff=args.d_ff,
# pe_type=args.pe_type,
pe_type=False,
dropout_in=args.dropout_in,
dropout=args.dropout_enc,
dropout_att=args.dropout_att,
layer_norm_eps=args.layer_norm_eps,
n_stacks=args.n_stacks,
n_splices=args.n_splices,
conv_in_channel=args.conv_in_channel,
conv_channels=args.conv_channels,
conv_kernel_sizes=args.conv_kernel_sizes,
conv_strides=args.conv_strides,
conv_poolings=args.conv_poolings,
conv_batch_norm=args.conv_batch_norm,
conv_residual=args.conv_residual,
conv_bottleneck_dim=args.conv_bottleneck_dim)
else:
self.enc = RNNEncoder(
input_dim=args.input_dim
if args.input_type == 'speech' else args.emb_dim,
rnn_type=args.enc_type,
n_units=args.enc_n_units,
n_projs=args.enc_n_projs,
n_layers=args.enc_n_layers,
n_layers_sub1=args.enc_n_layers_sub1,
n_layers_sub2=args.enc_n_layers_sub2,
dropout_in=args.dropout_in,
dropout=args.dropout_enc,
subsample=list(map(int, args.subsample.split('_'))) + [1] *
(args.enc_n_layers - len(args.subsample.split('_'))),
subsample_type=args.subsample_type,
n_stacks=args.n_stacks,
n_splices=args.n_splices,
conv_in_channel=args.conv_in_channel,
conv_channels=args.conv_channels,
conv_kernel_sizes=args.conv_kernel_sizes,
conv_strides=args.conv_strides,
conv_poolings=args.conv_poolings,
conv_batch_norm=args.conv_batch_norm,
conv_residual=args.conv_residual,
conv_bottleneck_dim=args.conv_bottleneck_dim,
residual=args.enc_residual,
nin=args.enc_nin,
task_specific_layer=args.task_specific_layer)
# NOTE: pure CNN/TDS encoders are also included
if args.freeze_encoder:
for p in self.enc.parameters():
p.requires_grad = False
# Bridge layer between the encoder and decoder
self.is_bridge = False
if (args.enc_type in ['conv', 'tds', 'gated_conv', 'transformer']
and args.ctc_weight < 1
) or args.dec_type == 'transformer' or args.bridge_layer:
self.bridge = LinearND(self.enc.output_dim,
args.d_model if args.dec_type
== 'transformer' else args.dec_n_units,
dropout=args.dropout_enc)
self.is_bridge = True
if self.sub1_weight > 0:
self.bridge_sub1 = LinearND(self.enc.output_dim,
args.dec_n_units,
dropout=args.dropout_enc)
if self.sub2_weight > 0:
self.bridge_sub2 = LinearND(self.enc.output_dim,
args.dec_n_units,
dropout=args.dropout_enc)
self.enc_n_units = args.dec_n_units
# main task
directions = []
if self.fwd_weight > 0 or self.ctc_weight > 0:
directions.append('fwd')
if self.bwd_weight > 0:
directions.append('bwd')
for dir in directions:
# Cold fusion
if args.lm_fusion and dir == 'fwd':
lm = RNNLM(args.lm_conf)
lm, _ = load_checkpoint(lm, args.lm_fusion)
else:
args.lm_conf = False
lm = None
# TODO(hirofumi): cold fusion for backward RNNLM
# Decoder
if args.dec_type == 'transformer':
dec = TransformerDecoder(
eos=self.eos,
unk=self.unk,
pad=self.pad,
blank=self.blank,
enc_n_units=self.enc.output_dim,
attn_type=args.transformer_attn_type,
attn_n_heads=args.transformer_attn_n_heads,
n_layers=args.transformer_dec_n_layers,
d_model=args.d_model,
d_ff=args.d_ff,
pe_type=args.pe_type,
tie_embedding=args.tie_embedding,
vocab=self.vocab,
dropout=args.dropout_dec,
dropout_emb=args.dropout_emb,
dropout_att=args.dropout_att,
lsm_prob=args.lsm_prob,
layer_norm_eps=args.layer_norm_eps,
ctc_weight=self.ctc_weight if dir == 'fwd' else 0,
ctc_fc_list=[
int(fc) for fc in args.ctc_fc_list.split('_')
] if args.ctc_fc_list is not None
and len(args.ctc_fc_list) > 0 else [],
backward=(dir == 'bwd'),
global_weight=self.main_weight -
self.bwd_weight if dir == 'fwd' else self.bwd_weight,
mtl_per_batch=args.mtl_per_batch)
else:
dec = RNNDecoder(
eos=self.eos,
unk=self.unk,
pad=self.pad,
blank=self.blank,
enc_n_units=self.enc.output_dim,
attn_type=args.attn_type,
attn_dim=args.attn_dim,
attn_sharpening_factor=args.attn_sharpening,
attn_sigmoid_smoothing=args.attn_sigmoid,
attn_conv_out_channels=args.attn_conv_n_channels,
attn_conv_kernel_size=args.attn_conv_width,
attn_n_heads=args.attn_n_heads,
rnn_type=args.dec_type,
n_units=args.dec_n_units,
n_projs=args.dec_n_projs,
n_layers=args.dec_n_layers,
loop_type=args.dec_loop_type,
residual=args.dec_residual,
bottleneck_dim=args.dec_bottleneck_dim,
emb_dim=args.emb_dim,
tie_embedding=args.tie_embedding,
vocab=self.vocab,
dropout=args.dropout_dec,
dropout_emb=args.dropout_emb,
dropout_att=args.dropout_att,
ss_prob=args.ss_prob,
ss_type=args.ss_type,
lsm_prob=args.lsm_prob,
fl_weight=args.focal_loss_weight,
fl_gamma=args.focal_loss_gamma,
ctc_weight=self.ctc_weight if dir == 'fwd' else 0,
ctc_fc_list=[
int(fc) for fc in args.ctc_fc_list.split('_')
] if args.ctc_fc_list is not None
and len(args.ctc_fc_list) > 0 else [],
input_feeding=args.input_feeding,
backward=(dir == 'bwd'),
# lm=args.lm_conf,
lm=lm, # TODO(hirofumi): load RNNLM in the model init.
lm_fusion_type=args.lm_fusion_type,
contextualize=args.contextualize,
lm_init=args.lm_init,
lmobj_weight=args.lmobj_weight,
share_lm_softmax=args.share_lm_softmax,
global_weight=self.main_weight -
self.bwd_weight if dir == 'fwd' else self.bwd_weight,
mtl_per_batch=args.mtl_per_batch,
adaptive_softmax=args.adaptive_softmax)
setattr(self, 'dec_' + dir, dec)
# sub task
for sub in ['sub1', 'sub2']:
if getattr(self, sub + '_weight') > 0:
if args.dec_type == 'transformer':
raise NotImplementedError
else:
dec_sub = RNNDecoder(
eos=self.eos,
unk=self.unk,
pad=self.pad,
blank=self.blank,
enc_n_units=self.enc_n_units,
attn_type=args.attn_type,
attn_dim=args.attn_dim,
attn_sharpening_factor=args.attn_sharpening,
attn_sigmoid_smoothing=args.attn_sigmoid,
attn_conv_out_channels=args.attn_conv_n_channels,
attn_conv_kernel_size=args.attn_conv_width,
attn_n_heads=1,
rnn_type=args.dec_type,
n_units=args.dec_n_units,
n_projs=args.dec_n_projs,
n_layers=args.dec_n_layers,
loop_type=args.dec_loop_type,
residual=args.dec_residual,
bottleneck_dim=args.dec_bottleneck_dim,
emb_dim=args.emb_dim,
tie_embedding=args.tie_embedding,
vocab=getattr(self, 'vocab_' + sub),
dropout=args.dropout_dec,
dropout_emb=args.dropout_emb,
dropout_att=args.dropout_att,
ss_prob=args.ss_prob,
ss_type=args.ss_type,
lsm_prob=args.lsm_prob,
fl_weight=args.focal_loss_weight,
fl_gamma=args.focal_loss_gamma,
ctc_weight=getattr(self, 'ctc_weight_' + sub),
ctc_fc_list=[
int(fc) for fc in getattr(args, 'ctc_fc_list_' +
sub).split('_')
] if getattr(args, 'ctc_fc_list_' + sub) is not None
and len(getattr(args, 'ctc_fc_list_' + sub)) > 0 else
[],
input_feeding=args.input_feeding,
global_weight=getattr(self, sub + '_weight'),
mtl_per_batch=args.mtl_per_batch)
setattr(self, 'dec_fwd_' + sub, dec_sub)
if args.input_type == 'text':
if args.vocab == args.vocab_sub1:
# Share the embedding layer between input and output
self.embed_in = dec.embed
else:
self.embed_in = Embedding(vocab=args.vocab_sub1,
emb_dim=args.emb_dim,
dropout=args.dropout_emb,
ignore_index=self.pad)
# Initialize parameters in CNN layers
self.reset_parameters(
args.param_init,
# dist='xavier_uniform',
# dist='kaiming_uniform',
dist='lecun',
keys=['conv'],
ignore_keys=['score'])
# Initialize parameters in the encoder
if args.enc_type == 'transformer':
self.reset_parameters(args.param_init,
dist='xavier_uniform',
keys=['enc'],
ignore_keys=['embed_in'])
self.reset_parameters(args.d_model**-0.5,
dist='normal',
keys=['embed_in'])
else:
self.reset_parameters(args.param_init,
dist=args.param_init_dist,
keys=['enc'],
ignore_keys=['conv'])
# Initialize parameters in the decoder
if args.dec_type == 'transformer':
self.reset_parameters(args.param_init,
dist='xavier_uniform',
keys=['dec'],
ignore_keys=['embed'])
self.reset_parameters(args.d_model**-0.5,
dist='normal',
keys=['embed'])
else:
self.reset_parameters(args.param_init,
dist=args.param_init_dist,
keys=['dec'])
# Initialize bias vectors with zero
self.reset_parameters(0, dist='constant', keys=['bias'])
# Recurrent weights are orthogonalized
if args.rec_weight_orthogonal:
self.reset_parameters(args.param_init,
dist='orthogonal',
keys=['rnn', 'weight'])
# Initialize bias in forget gate with 1
# self.init_forget_gate_bias_with_one()
# Initialize bias in gating with -1 for cold fusion
if args.lm_fusion:
self.reset_parameters(-1,
dist='constant',
keys=['linear_lm_gate.fc.bias'])
if args.lm_fusion_type == 'deep' and args.lm_fusion:
for n, p in self.named_parameters():
if 'output' in n or 'output_bn' in n or 'linear' in n:
p.requires_grad = True
else:
p.requires_grad = False