def __init__(self, idim, odim, args, ignore_id=-1):
torch.nn.Module.__init__(self)
if args.transformer_attn_dropout_rate is None:
args.transformer_attn_dropout_rate = args.dropout_rate
self.encoder = Encoder(
idim=idim,
center_len=args.transformer_encoder_center_chunk_len,
left_len=args.transformer_encoder_left_chunk_len,
hop_len=args.transformer_encoder_hop_len,
right_len=args.transformer_encoder_right_chunk_len,
abs_pos=args.transformer_encoder_abs_embed,
rel_pos=args.transformer_encoder_rel_embed,
use_mem=args.transformer_encoder_use_memory,
attention_dim=args.adim,
attention_heads=args.aheads,
linear_units=args.eunits,
num_blocks=args.elayers,
input_layer=args.transformer_input_layer,
dropout_rate=args.dropout_rate,
positional_dropout_rate=args.dropout_rate,
attention_dropout_rate=args.transformer_attn_dropout_rate
)
self.decoder = Decoder(
odim=odim,
attention_dim=args.adim,
attention_heads=args.aheads,
linear_units=args.dunits,
num_blocks=args.dlayers,
dropout_rate=args.dropout_rate,
positional_dropout_rate=args.dropout_rate,
self_attention_dropout_rate=args.transformer_attn_dropout_rate,
src_attention_dropout_rate=args.transformer_attn_dropout_rate
)
self.sos = odim - 1
self.eos = odim - 1
self.odim = odim
self.ignore_id = ignore_id
self.subsample = [1]
self.reporter = Reporter()
# self.lsm_weight = a
self.criterion = LabelSmoothingLoss(self.odim, self.ignore_id, args.lsm_weight,
args.transformer_length_normalized_loss)
# self.verbose = args.verbose
self.reset_parameters(args)
self.adim = args.adim
self.mtlalpha = args.mtlalpha
if args.mtlalpha > 0.0:
self.ctc = CTC(odim, args.adim, args.dropout_rate, ctc_type=args.ctc_type, reduce=True)
else:
self.ctc = None
if args.report_cer or args.report_wer or args.mtlalpha > 0.0:
from espnet.nets.e2e_asr_common import ErrorCalculator
self.error_calculator = ErrorCalculator(args.char_list,
args.sym_space, args.sym_blank,
args.report_cer, args.report_wer)
else:
self.error_calculator = None
self.rnnlm = None
def __init__(
self,
vocab_size: int,
token_list: Union[Tuple[str, ...], List[str]],
frontend: Optional[AbsFrontend],
specaug: Optional[AbsSpecAug],
normalize: Optional[AbsNormalize],
preencoder: Optional[AbsPreEncoder],
encoder: AbsEncoder,
decoder: AbsDecoder,
ctc: CTC,
rnnt_decoder: None,
ctc_weight: float = 0.5,
ignore_id: int = -1,
lsm_weight: float = 0.0,
length_normalized_loss: bool = False,
report_cer: bool = True,
report_wer: bool = True,
sym_space: str = "<space>",
sym_blank: str = "<blank>",
):
assert check_argument_types()
assert 0.0 <= ctc_weight <= 1.0, ctc_weight
assert rnnt_decoder is None, "Not implemented"
super().__init__()
# note that eos is the same as sos (equivalent ID)
self.sos = vocab_size - 1
self.eos = vocab_size - 1
self.vocab_size = vocab_size
self.ignore_id = ignore_id
self.ctc_weight = ctc_weight
self.token_list = token_list.copy()
self.frontend = frontend
self.specaug = specaug
self.normalize = normalize
self.adddiontal_utt_mvn = None
self.preencoder = preencoder
self.encoder = encoder
self.decoder = decoder
if ctc_weight == 0.0:
self.ctc = None
else:
self.ctc = ctc
self.rnnt_decoder = rnnt_decoder
self.criterion_att = LabelSmoothingLoss(
size=vocab_size,
padding_idx=ignore_id,
smoothing=lsm_weight,
normalize_length=length_normalized_loss,
)
if report_cer or report_wer:
self.error_calculator = ErrorCalculator(token_list, sym_space,
sym_blank, report_cer,
report_wer)
else:
self.error_calculator = None
def __init__(self, idim, odim, args, ignore_id=-1):
"""Construct an E2E object.
:param int idim: dimension of inputs
:param int odim: dimension of outputs
:param Namespace args: argument Namespace containing options
"""
torch.nn.Module.__init__(self)
if args.transformer_attn_dropout_rate is None:
args.transformer_attn_dropout_rate = args.dropout_rate
self.encoder = Encoder(
idim=idim,
attention_dim=args.adim,
attention_heads=args.aheads,
linear_units=args.eunits,
num_blocks=args.elayers,
input_layer=args.transformer_input_layer,
dropout_rate=args.dropout_rate,
positional_dropout_rate=args.dropout_rate,
attention_dropout_rate=args.transformer_attn_dropout_rate
)
self.decoder = Decoder(
odim=odim,
attention_dim=args.adim,
attention_heads=args.aheads,
linear_units=args.dunits,
num_blocks=args.dlayers,
dropout_rate=args.dropout_rate,
positional_dropout_rate=args.dropout_rate,
self_attention_dropout_rate=args.transformer_attn_dropout_rate,
src_attention_dropout_rate=args.transformer_attn_dropout_rate
)
self.sos = odim - 1
self.eos = odim - 1
self.odim = odim
self.ignore_id = ignore_id
self.subsample = get_subsample(args, mode='asr', arch='transformer')
self.reporter = Reporter()
# self.lsm_weight = a
self.criterion = LabelSmoothingLoss(self.odim, self.ignore_id, args.lsm_weight,
args.transformer_length_normalized_loss)
# self.verbose = args.verbose
self.reset_parameters(args)
self.adim = args.adim
self.mtlalpha = args.mtlalpha
if args.mtlalpha > 0.0:
self.ctc = CTC(odim, args.adim, args.dropout_rate, ctc_type=args.ctc_type, reduce=True)
else:
self.ctc = None
if args.report_cer or args.report_wer:
from espnet.nets.e2e_asr_common import ErrorCalculator
self.error_calculator = ErrorCalculator(args.char_list,
args.sym_space, args.sym_blank,
args.report_cer, args.report_wer)
else:
self.error_calculator = None
self.rnnlm = None
def __init__(self, idim, odim, args, ignore_id=-1, flag_return=True):
chainer.Chain.__init__(self)
self.mtlalpha = args.mtlalpha
assert 0 <= self.mtlalpha <= 1, "mtlalpha must be [0,1]"
if args.transformer_attn_dropout_rate is None:
self.dropout = args.dropout_rate
else:
self.dropout = args.transformer_attn_dropout_rate
self.use_label_smoothing = False
self.char_list = args.char_list
self.space = args.sym_space
self.blank = args.sym_blank
self.scale_emb = args.adim**0.5
self.sos = odim - 1
self.eos = odim - 1
self.subsample = [0]
self.ignore_id = ignore_id
self.reset_parameters(args)
with self.init_scope():
self.encoder = Encoder(idim,
args,
initialW=self.initialW,
initial_bias=self.initialB)
self.decoder = Decoder(odim,
args,
initialW=self.initialW,
initial_bias=self.initialB)
self.criterion = LabelSmoothingLoss(
args.lsm_weight, len(args.char_list),
args.transformer_length_normalized_loss)
if args.mtlalpha > 0.0:
if args.ctc_type == 'builtin':
logging.info("Using chainer CTC implementation")
self.ctc = ctc.CTC(odim, args.adim, args.dropout_rate)
elif args.ctc_type == 'warpctc':
logging.info("Using warpctc CTC implementation")
self.ctc = ctc.WarpCTC(odim, args.adim, args.dropout_rate)
else:
raise ValueError(
'ctc_type must be "builtin" or "warpctc": {}'.format(
args.ctc_type))
else:
self.ctc = None
self.dims = args.adim
self.odim = odim
self.flag_return = flag_return
if args.report_cer or args.report_wer:
from espnet.nets.e2e_asr_common import ErrorCalculator
self.error_calculator = ErrorCalculator(args.char_list,
args.sym_space,
args.sym_blank,
args.report_cer,
args.report_wer)
else:
self.error_calculator = None
if 'Namespace' in str(type(args)):
self.verbose = 0 if 'verbose' not in args else args.verbose
else:
self.verbose = 0 if args.verbose is None else args.verbose
def __init__(
self,
vocab_size: int,
token_list: Union[Tuple[str, ...], List[str]],
frontend: Optional[AbsFrontend],
specaug: Optional[AbsSpecAug],
normalize: Optional[AbsNormalize],
preencoder: Optional[AbsPreEncoder],
encoder: AbsEncoder,
ignore_id: int = -1,
lsm_weight: float = 0.0,
length_normalized_loss: bool = False,
report_cer: bool = False,
report_wer: bool = False,
sym_space: str = "<space>",
sym_blank: str = "<blank>",
pred_masked_weight: float = 1.0,
pred_nomask_weight: float = 0.0,
loss_weights: float = 0.0,
):
assert check_argument_types()
super().__init__()
# note that eos is the same as sos (equivalent ID)
self.sos = vocab_size - 1
self.eos = vocab_size - 1
self.vocab_size = vocab_size
self.ignore_id = ignore_id
self.token_list = token_list.copy()
self.frontend = frontend
self.specaug = specaug
self.normalize = normalize
self.preencoder = preencoder
self.encoder = encoder
self.criterion_att = HubertPretrainLoss(
pred_masked_weight,
pred_nomask_weight,
loss_weights,
)
self.pred_masked_weight = pred_masked_weight
self.pred_nomask_weight = pred_nomask_weight
self.loss_weights = loss_weights
if report_cer or report_wer:
self.error_calculator = ErrorCalculator(token_list, sym_space,
sym_blank, report_cer,
report_wer)
else:
self.error_calculator = None
def test_error_calculator_nospace(tmpdir):
from espnet.nets.e2e_asr_common import ErrorCalculator
space = "<space>"
blank = "<blank>"
char_list = [blank, 'a', 'e', 'i', 'o', 'u']
ys_pad = [np.random.randint(0, len(char_list), x) for x in range(120, 150, 5)]
ys_hat = [np.random.randint(0, len(char_list), x) for x in range(120, 150, 5)]
cer, wer = True, True
ec = ErrorCalculator(char_list, space, blank,
cer, wer)
cer_ctc_val = ec(ys_pad, ys_hat, is_ctc=True)
_cer, _wer = ec(ys_pad, ys_hat)
assert cer_ctc_val is not None
assert _cer is not None
assert _wer is not None
def test_error_calculator(tmpdir, typ):
from espnet.nets.e2e_asr_common import ErrorCalculator
space = "<space>"
blank = "<blank>"
char_list = [blank, space, "a", "e", "i", "o", "u"]
ys_pad = [
np.random.randint(0, len(char_list), x) for x in range(120, 150, 5)
]
ys_hat = [
np.random.randint(0, len(char_list), x) for x in range(120, 150, 5)
]
if typ == "ctc":
cer, wer = False, False
elif typ == "wer":
cer, wer = False, True
elif typ == "cer":
cer, wer = True, False
else:
cer, wer = True, True
ec = ErrorCalculator(char_list, space, blank, cer, wer)
if typ == "ctc":
cer_ctc_val = ec(ys_pad, ys_hat, is_ctc=True)
_cer, _wer = ec(ys_pad, ys_hat)
assert cer_ctc_val is not None
assert _cer is None
assert _wer is None
elif typ == "wer":
_cer, _wer = ec(ys_pad, ys_hat)
assert _cer is None
assert _wer is not None
elif typ == "cer":
_cer, _wer = ec(ys_pad, ys_hat)
assert _cer is not None
assert _wer is None
else:
cer_ctc_val = ec(ys_pad, ys_hat, is_ctc=True)
_cer, _wer = ec(ys_pad, ys_hat)
assert cer_ctc_val is not None
assert _cer is not None
assert _wer is not None
def test_error_calculator(tmpdir, typ):
from espnet.nets.e2e_asr_common import ErrorCalculator
space = "<space>"
blank = "<blank>"
char_list = [blank, space, 'a', 'e', 'i', 'o', 'u']
ys_pad = [np.random.randint(0, 7, x) for x in range(120, 150, 5)]
ys_hat = [np.random.randint(0, 7, x) for x in range(120, 150, 5)]
if typ == 'ctc':
cer, wer = False, False
elif typ == 'wer':
cer, wer = False, True
elif typ == 'cer':
cer, wer = True, False
else:
cer, wer = True, True
ec = ErrorCalculator(char_list, space, blank,
cer, wer)
if typ == 'ctc':
cer_ctc_val = ec(ys_pad, ys_hat, is_ctc=True)
_cer, _wer = ec(ys_pad, ys_hat)
assert cer_ctc_val is not None
assert _cer is None
assert _wer is None
elif typ == 'wer':
_cer, _wer = ec(ys_pad, ys_hat)
assert _cer is None
assert _wer is not None
elif typ == 'cer':
_cer, _wer = ec(ys_pad, ys_hat)
assert _cer is not None
assert _wer is None
else:
cer_ctc_val = ec(ys_pad, ys_hat, is_ctc=True)
_cer, _wer = ec(ys_pad, ys_hat)
assert cer_ctc_val is not None
assert _cer is not None
assert _wer is not None
def __init__(
self,
vocab_size: int,
token_list: Union[Tuple[str, ...], List[str]],
frontend: Optional[AbsFrontend],
specaug: Optional[AbsSpecAug],
normalize: Optional[AbsNormalize],
preencoder: Optional[AbsPreEncoder],
encoder: AbsEncoder,
postencoder: Optional[AbsPostEncoder],
decoder: MLMDecoder,
ctc: CTC,
joint_network: Optional[torch.nn.Module] = None,
ctc_weight: float = 0.5,
interctc_weight: float = 0.0,
ignore_id: int = -1,
lsm_weight: float = 0.0,
length_normalized_loss: bool = False,
report_cer: bool = True,
report_wer: bool = True,
sym_space: str = "<space>",
sym_blank: str = "<blank>",
sym_mask: str = "<mask>",
extract_feats_in_collect_stats: bool = True,
):
assert check_argument_types()
super().__init__(
vocab_size=vocab_size,
token_list=token_list,
frontend=frontend,
specaug=specaug,
normalize=normalize,
preencoder=preencoder,
encoder=encoder,
postencoder=postencoder,
decoder=decoder,
ctc=ctc,
joint_network=joint_network,
ctc_weight=ctc_weight,
interctc_weight=interctc_weight,
ignore_id=ignore_id,
lsm_weight=lsm_weight,
length_normalized_loss=length_normalized_loss,
report_cer=report_cer,
report_wer=report_wer,
sym_space=sym_space,
sym_blank=sym_blank,
extract_feats_in_collect_stats=extract_feats_in_collect_stats,
)
# Add <mask> and override inherited fields
token_list.append(sym_mask)
vocab_size += 1
self.vocab_size = vocab_size
self.mask_token = vocab_size - 1
self.token_list = token_list.copy()
# MLM loss
del self.criterion_att
self.criterion_mlm = LabelSmoothingLoss(
size=vocab_size,
padding_idx=ignore_id,
smoothing=lsm_weight,
normalize_length=length_normalized_loss,
)
self.error_calculator = None
if report_cer or report_wer:
self.error_calculator = ErrorCalculator(token_list, sym_space,
sym_blank, report_cer,
report_wer)
def __init__(
self,
vocab_size: int,
token_list: Union[Tuple[str, ...], List[str]],
frontend: Optional[AbsFrontend],
specaug: Optional[AbsSpecAug],
normalize: Optional[AbsNormalize],
preencoder: Optional[AbsPreEncoder],
encoder: AbsEncoder,
postencoder: Optional[AbsPostEncoder],
decoder: AbsDecoder,
ctc: CTC,
rnnt_decoder: None,
ctc_weight: float = 0.5,
ignore_id: int = -1,
lsm_weight: float = 0.0,
length_normalized_loss: bool = False,
report_cer: bool = True,
report_wer: bool = True,
sym_space: str = "<space>",
sym_blank: str = "<blank>",
extract_feats_in_collect_stats: bool = True,
):
assert check_argument_types()
assert 0.0 <= ctc_weight <= 1.0, ctc_weight
assert rnnt_decoder is None, "Not implemented"
super().__init__()
# note that eos is the same as sos (equivalent ID)
self.sos = vocab_size - 1
self.eos = vocab_size - 1
self.vocab_size = vocab_size
self.ignore_id = ignore_id
self.ctc_weight = ctc_weight
self.token_list = token_list.copy()
self.frontend = frontend
self.specaug = specaug
self.normalize = normalize
self.preencoder = preencoder
self.postencoder = postencoder
self.encoder = encoder
# we set self.decoder = None in the CTC mode since
# self.decoder parameters were never used and PyTorch complained
# and threw an Exception in the multi-GPU experiment.
# thanks Jeff Farris for pointing out the issue.
if ctc_weight == 1.0:
self.decoder = None
else:
self.decoder = decoder
if ctc_weight == 0.0:
self.ctc = None
else:
self.ctc = ctc
self.rnnt_decoder = rnnt_decoder
self.criterion_att = LabelSmoothingLoss(
size=vocab_size,
padding_idx=ignore_id,
smoothing=lsm_weight,
normalize_length=length_normalized_loss,
)
if report_cer or report_wer:
self.error_calculator = ErrorCalculator(token_list, sym_space,
sym_blank, report_cer,
report_wer)
else:
self.error_calculator = None
self.extract_feats_in_collect_stats = extract_feats_in_collect_stats
def __init__(self, idim, odim, args, ignore_id=-1, flag_return=True):
"""Initialize the transformer."""
chainer.Chain.__init__(self)
self.mtlalpha = args.mtlalpha
assert 0 <= self.mtlalpha <= 1, "mtlalpha must be [0,1]"
if args.transformer_attn_dropout_rate is None:
args.transformer_attn_dropout_rate = args.dropout_rate
self.use_label_smoothing = False
self.char_list = args.char_list
self.space = args.sym_space
self.blank = args.sym_blank
self.scale_emb = args.adim**0.5
self.sos = odim - 1
self.eos = odim - 1
self.subsample = get_subsample(args, mode="asr", arch="transformer")
self.ignore_id = ignore_id
self.reset_parameters(args)
with self.init_scope():
self.encoder = Encoder(
idim=idim,
attention_dim=args.adim,
attention_heads=args.aheads,
linear_units=args.eunits,
input_layer=args.transformer_input_layer,
dropout_rate=args.dropout_rate,
positional_dropout_rate=args.dropout_rate,
attention_dropout_rate=args.transformer_attn_dropout_rate,
initialW=self.initialW,
initial_bias=self.initialB,
)
self.decoder = Decoder(odim,
args,
initialW=self.initialW,
initial_bias=self.initialB)
self.criterion = LabelSmoothingLoss(
args.lsm_weight,
len(args.char_list),
args.transformer_length_normalized_loss,
)
if args.mtlalpha > 0.0:
if args.ctc_type == "builtin":
logging.info("Using chainer CTC implementation")
self.ctc = ctc.CTC(odim, args.adim, args.dropout_rate)
elif args.ctc_type == "warpctc":
logging.info("Using warpctc CTC implementation")
self.ctc = ctc.WarpCTC(odim, args.adim, args.dropout_rate)
else:
raise ValueError(
'ctc_type must be "builtin" or "warpctc": {}'.format(
args.ctc_type))
else:
self.ctc = None
self.dims = args.adim
self.odim = odim
self.flag_return = flag_return
if args.report_cer or args.report_wer:
self.error_calculator = ErrorCalculator(
args.char_list,
args.sym_space,
args.sym_blank,
args.report_cer,
args.report_wer,
)
else:
self.error_calculator = None
if "Namespace" in str(type(args)):
self.verbose = 0 if "verbose" not in args else args.verbose
else:
self.verbose = 0 if args.verbose is None else args.verbose
def __init__(self, idim, odim, args, ignore_id=-1):
"""Construct an E2E object.
:param int idim: dimension of inputs
:param int odim: dimension of outputs
:param Namespace args: argument Namespace containing options
"""
torch.nn.Module.__init__(self)
if args.transformer_attn_dropout_rate is None:
args.transformer_attn_dropout_rate = args.dropout_rate
self.encoder_type = getattr(args, 'encoder_type', 'all_add')
self.vbs = getattr(args, 'vbs', False)
self.noise = getattr(args, 'noise_type', 'none')
if self.encoder_type == 'all_add':
from espnet.nets.pytorch_backend.transformer.multimodal_encoder_all_add import MultimodalEncoder
elif self.encoder_type == 'proportion_add':
from espnet.nets.pytorch_backend.transformer.multimodal_encoder_proportion_add import MultimodalEncoder
elif self.encoder_type == 'vat':
from espnet.nets.pytorch_backend.transformer.multimodal_encoder_vat import MultimodalEncoder
self.encoder = MultimodalEncoder(
idim=idim,
attention_dim=args.adim,
attention_heads=args.aheads,
linear_units=args.eunits,
num_blocks=args.elayers,
visual_dim=args.visual_dim,
input_layer=args.transformer_input_layer,
dropout_rate=args.dropout_rate,
positional_dropout_rate=args.dropout_rate,
attention_dropout_rate=args.transformer_attn_dropout_rate,
vbs=self.vbs)
self.decoder = MultimodalDecoder(
odim=odim,
attention_dim=args.adim,
attention_heads=args.aheads,
linear_units=args.dunits,
num_blocks=args.dlayers,
dropout_rate=args.dropout_rate,
positional_dropout_rate=args.dropout_rate,
self_attention_dropout_rate=args.transformer_attn_dropout_rate,
src_attention_dropout_rate=args.transformer_attn_dropout_rate)
self.pad = 0
self.sos = odim - 1
self.eos = odim - 1
self.odim = odim
self.ignore_id = ignore_id
self.subsample = get_subsample(args, mode='st', arch='transformer')
self.reporter = Reporter()
# self.lsm_weight = a
self.criterion = LabelSmoothingLoss(
self.odim, self.ignore_id, args.lsm_weight,
args.transformer_length_normalized_loss)
# self.verbose = args.verbose
self.adim = args.adim
# submodule for ASR task
self.mtlalpha = args.mtlalpha
self.asr_weight = getattr(args, "asr_weight", 0.0)
if self.asr_weight > 0 and args.mtlalpha < 1:
self.decoder_asr = Decoder(
odim=odim,
attention_dim=args.adim,
attention_heads=args.aheads,
linear_units=args.dunits,
num_blocks=args.dlayers,
dropout_rate=args.dropout_rate,
positional_dropout_rate=args.dropout_rate,
self_attention_dropout_rate=args.transformer_attn_dropout_rate,
src_attention_dropout_rate=args.transformer_attn_dropout_rate,
)
# submodule for MT task
self.mt_weight = getattr(args, "mt_weight", 0.0)
if self.mt_weight > 0:
self.encoder_mt = Encoder(
idim=odim,
attention_dim=args.adim,
attention_heads=args.aheads,
linear_units=args.dunits,
num_blocks=args.dlayers,
input_layer='embed',
dropout_rate=args.dropout_rate,
positional_dropout_rate=args.dropout_rate,
attention_dropout_rate=args.transformer_attn_dropout_rate,
padding_idx=0)
self.reset_parameters(args) # place after the submodule initialization
if args.mtlalpha > 0.0:
self.ctc = CTC(odim,
args.adim,
args.dropout_rate,
ctc_type=args.ctc_type,
reduce=True)
else:
self.ctc = None
if self.asr_weight > 0 and (args.report_cer or args.report_wer):
from espnet.nets.e2e_asr_common import ErrorCalculator
self.error_calculator = ErrorCalculator(args.char_list,
args.sym_space,
args.sym_blank,
args.report_cer,
args.report_wer)
else:
self.error_calculator = None
self.rnnlm = None
# multilingual E2E-ST related
self.multilingual = getattr(args, "multilingual", False)
self.replace_sos = getattr(args, "replace_sos", False)
if self.multilingual:
assert self.replace_sos
self.device = "cuda:0" if torch.cuda.is_available() else "cpu"
def __init__(self, idim, odim, args, ignore_id=-1):
"""Construct an E2E object.
:param int idim: dimension of inputs
:param int odim: dimension of outputs
:param Namespace args: argument Namespace containing options
"""
torch.nn.Module.__init__(self)
if args.transformer_attn_dropout_rate is None:
args.transformer_attn_dropout_rate = args.dropout_rate
self.encoder = Encoder(
idim=idim,
attention_dim=args.adim,
attention_heads=args.aheads,
linear_units=args.eunits,
num_blocks=args.elayers,
input_layer=args.transformer_input_layer,
dropout_rate=args.dropout_rate,
positional_dropout_rate=args.dropout_rate,
attention_dropout_rate=args.transformer_attn_dropout_rate
)
# submodule for ASR task
self.mtlalpha = args.mtlalpha
self.asr_weight = getattr(args, "asr_weight", 0.0)
self.do_asr = self.asr_weight > 0 and args.mtlalpha < 1
# cross-attention parameters
self.cross_weight = getattr(args, "cross_weight", 0.0)
self.cross_self = getattr(args, "cross_self", False)
self.cross_src = getattr(args, "cross_src", False)
self.cross_operator = getattr(args, "cross_operator", None)
self.cross_to_asr = getattr(args, "cross_to_asr", False)
self.cross_to_st = getattr(args, "cross_to_st", False)
self.num_decoders = getattr(args, "num_decoders", 1)
self.wait_k_asr = getattr(args, "wait_k_asr", 0)
self.wait_k_st = getattr(args, "wait_k_st", 0)
self.cross_src_from = getattr(args, "cross_src_from", "embedding")
self.cross_self_from = getattr(args, "cross_self_from", "embedding")
self.cross_weight_learnable = getattr(args, "cross_weight_learnable", False)
# one-to-many ST experiments
self.one_to_many = getattr(args, "one_to_many", False)
self.langs_dict = getattr(args, "langs_dict", None)
self.lang_tok = getattr(args, "lang_tok", None)
self.normalize_before = getattr(args, "normalize_before", True)
logging.info(f'self.normalize_before = {self.normalize_before}')
# Check parameters
if self.cross_operator == 'sum' and self.cross_weight <= 0:
assert (not self.cross_to_asr) and (not self.cross_to_st)
if self.cross_to_asr or self.cross_to_st:
assert self.do_asr
assert self.cross_self or self.cross_src
assert bool(self.cross_operator) == (self.do_asr and (self.cross_to_asr or self.cross_to_st))
if self.cross_src_from != "embedding" or self.cross_self_from != "embedding":
assert self.normalize_before
if self.wait_k_asr > 0:
assert self.wait_k_st == 0
elif self.wait_k_st > 0:
assert self.wait_k_asr == 0
else:
assert self.wait_k_asr == 0
assert self.wait_k_st == 0
logging.info("*** Cross attention parameters ***")
if self.cross_to_asr:
logging.info("| Cross to ASR")
if self.cross_to_st:
logging.info("| Cross to ST")
if self.cross_self:
logging.info("| Cross at Self")
if self.cross_src:
logging.info("| Cross at Source")
if self.cross_to_asr or self.cross_to_st:
logging.info(f'| Cross operator: {self.cross_operator}')
logging.info(f'| Cross sum weight: {self.cross_weight}')
if self.cross_src:
logging.info(f'| Cross source from: {self.cross_src_from}')
if self.cross_self:
logging.info(f'| Cross self from: {self.cross_self_from}')
logging.info(f'| wait_k_asr = {self.wait_k_asr}')
logging.info(f'| wait_k_st = {self.wait_k_st}')
if (self.cross_src_from != "embedding" and self.cross_src) and (not self.normalize_before):
logging.warning(f'WARNING: Resort to using self.cross_src_from == embedding for cross at source attention.')
if (self.cross_self_from != "embedding" and self.cross_self) and (not self.normalize_before):
logging.warning(f'WARNING: Resort to using self.cross_self_from == embedding for cross at self attention.')
self.dual_decoder = DualDecoder(
odim=odim,
attention_dim=args.adim,
attention_heads=args.aheads,
linear_units=args.dunits,
num_blocks=args.dlayers,
dropout_rate=args.dropout_rate,
positional_dropout_rate=args.dropout_rate,
self_attention_dropout_rate=args.transformer_attn_dropout_rate,
src_attention_dropout_rate=args.transformer_attn_dropout_rate,
normalize_before=self.normalize_before,
cross_operator=self.cross_operator,
cross_weight_learnable=self.cross_weight_learnable,
cross_weight=self.cross_weight,
cross_self=self.cross_self,
cross_src=self.cross_src,
cross_to_asr=self.cross_to_asr,
cross_to_st=self.cross_to_st
)
self.pad = 0
self.sos = odim - 1
self.eos = odim - 1
self.odim = odim
self.idim = idim
self.ignore_id = ignore_id
self.subsample = get_subsample(args, mode='st', arch='transformer')
self.reporter = Reporter()
# self.lsm_weight = a
self.criterion = LabelSmoothingLoss(self.odim, self.ignore_id, args.lsm_weight,
args.transformer_length_normalized_loss)
# self.verbose = args.verbose
self.adim = args.adim
# submodule for MT task
self.mt_weight = getattr(args, "mt_weight", 0.0)
if self.mt_weight > 0:
self.encoder_mt = Encoder(
idim=odim,
attention_dim=args.adim,
attention_heads=args.aheads,
linear_units=args.dunits,
num_blocks=args.dlayers,
input_layer='embed',
dropout_rate=args.dropout_rate,
positional_dropout_rate=args.dropout_rate,
attention_dropout_rate=args.transformer_attn_dropout_rate,
padding_idx=0
)
self.reset_parameters(args) # place after the submodule initialization
if args.mtlalpha > 0.0:
self.ctc = CTC(odim, args.adim, args.dropout_rate, ctc_type=args.ctc_type, reduce=True)
else:
self.ctc = None
if self.asr_weight > 0 and (args.report_cer or args.report_wer):
from espnet.nets.e2e_asr_common import ErrorCalculator
self.error_calculator = ErrorCalculator(args.char_list,
args.sym_space, args.sym_blank,
args.report_cer, args.report_wer)
else:
self.error_calculator = None
self.rnnlm = None
# multilingual E2E-ST related
self.multilingual = getattr(args, "multilingual", False)
self.replace_sos = getattr(args, "replace_sos", False)
if self.multilingual:
assert self.replace_sos
if self.lang_tok == "encoder-pre-sum":
self.language_embeddings = build_embedding(self.langs_dict, self.idim, padding_idx=self.pad)
print(f'language_embeddings: {self.language_embeddings}')
def __init__(
self,
vocab_size: int,
token_list: Union[Tuple[str, ...], List[str]],
enh: Optional[AbsEnhancement],
frontend: Optional[AbsFrontend],
specaug: Optional[AbsSpecAug],
normalize: Optional[AbsNormalize],
encoder: AbsEncoder,
decoder: AbsDecoder,
ctc: CTC,
rnnt_decoder: None,
ctc_weight: float = 0.5,
ignore_id: int = -1,
lsm_weight: float = 0.0,
enh_weight: float = 0.5,
length_normalized_loss: bool = False,
report_cer: bool = True,
report_wer: bool = True,
sym_space: str = "<space>",
sym_blank: str = "<blank>",
):
assert check_argument_types()
assert 0.0 <= ctc_weight <= 1.0, ctc_weight
assert 0.0 <= enh_weight <= 1.0, ctc_weight
assert rnnt_decoder is None, "Not implemented"
super().__init__()
# note that eos is the same as sos (equivalent ID)
self.sos = vocab_size - 1
self.eos = vocab_size - 1
self.vocab_size = vocab_size
self.ignore_id = ignore_id
self.ctc_weight = ctc_weight
self.enh_weight = enh_weight
self.token_list = token_list.copy()
self.enh_model = enh
self.num_spk = enh.num_spk
self.mask_type = getattr(self.enh_model, "mask_type", None)
# get loss type for model training
self.loss_type = getattr(self.enh_model, "loss_type", None)
assert self.loss_type in (
# mse_loss(predicted_mask, target_label)
"mask_mse",
# mse_loss(enhanced_magnitude_spectrum, target_magnitude_spectrum)
"magnitude",
# mse_loss(enhanced_complex_spectrum, target_complex_spectrum)
"spectrum",
# si_snr(enhanced_waveform, target_waveform)
"si_snr",
), self.loss_type
self.frontend = frontend
self.specaug = specaug
self.normalize = normalize
self.encoder = encoder
self.decoder = decoder
if ctc_weight == 0.0:
self.ctc = None
else:
self.ctc = ctc
self.rnnt_decoder = rnnt_decoder
self.criterion_att = LabelSmoothingLoss(
size=vocab_size,
padding_idx=ignore_id,
smoothing=lsm_weight,
normalize_length=length_normalized_loss,
)
if report_cer or report_wer:
self.error_calculator = ErrorCalculator(token_list, sym_space,
sym_blank, report_cer,
report_wer)
else:
self.error_calculator = None
# TODO(Jing): find out the -1 or 0 here
# self.idx_blank = token_list.index(sym_blank) # 0
self.idx_blank = -1
def __init__(
self,
vocab_size: int,
token_list: Union[Tuple[str, ...], List[str]],
frontend: Optional[AbsFrontend],
specaug: Optional[AbsSpecAug],
normalize: Optional[AbsNormalize],
preencoder: Optional[AbsPreEncoder],
encoder: AbsEncoder,
postencoder: Optional[AbsPostEncoder],
decoder: AbsDecoder,
ctc: CTC,
joint_network: Optional[torch.nn.Module],
ctc_weight: float = 0.5,
interctc_weight: float = 0.0,
ignore_id: int = -1,
lsm_weight: float = 0.0,
length_normalized_loss: bool = False,
report_cer: bool = True,
report_wer: bool = True,
sym_space: str = "<space>",
sym_blank: str = "<blank>",
extract_feats_in_collect_stats: bool = True,
):
assert check_argument_types()
assert 0.0 <= ctc_weight <= 1.0, ctc_weight
assert 0.0 <= interctc_weight < 1.0, interctc_weight
super().__init__()
# note that eos is the same as sos (equivalent ID)
self.blank_id = 0
self.sos = vocab_size - 1
self.eos = vocab_size - 1
self.vocab_size = vocab_size
self.ignore_id = ignore_id
self.ctc_weight = ctc_weight
self.interctc_weight = interctc_weight
self.token_list = token_list.copy()
self.frontend = frontend
self.specaug = specaug
self.normalize = normalize
self.preencoder = preencoder
self.postencoder = postencoder
self.encoder = encoder
if not hasattr(self.encoder, "interctc_use_conditioning"):
self.encoder.interctc_use_conditioning = False
if self.encoder.interctc_use_conditioning:
self.encoder.conditioning_layer = torch.nn.Linear(
vocab_size, self.encoder.output_size()
)
self.use_transducer_decoder = joint_network is not None
self.error_calculator = None
if self.use_transducer_decoder:
from warprnnt_pytorch import RNNTLoss
self.decoder = decoder
self.joint_network = joint_network
self.criterion_transducer = RNNTLoss(
blank=self.blank_id,
fastemit_lambda=0.0,
)
if report_cer or report_wer:
self.error_calculator_trans = ErrorCalculatorTransducer(
decoder,
joint_network,
token_list,
sym_space,
sym_blank,
report_cer=report_cer,
report_wer=report_wer,
)
else:
self.error_calculator_trans = None
if self.ctc_weight != 0:
self.error_calculator = ErrorCalculator(
token_list, sym_space, sym_blank, report_cer, report_wer
)
else:
# we set self.decoder = None in the CTC mode since
# self.decoder parameters were never used and PyTorch complained
# and threw an Exception in the multi-GPU experiment.
# thanks Jeff Farris for pointing out the issue.
if ctc_weight == 1.0:
self.decoder = None
else:
self.decoder = decoder
self.criterion_att = LabelSmoothingLoss(
size=vocab_size,
padding_idx=ignore_id,
smoothing=lsm_weight,
normalize_length=length_normalized_loss,
)
if report_cer or report_wer:
self.error_calculator = ErrorCalculator(
token_list, sym_space, sym_blank, report_cer, report_wer
)
if ctc_weight == 0.0:
self.ctc = None
else:
self.ctc = ctc
self.extract_feats_in_collect_stats = extract_feats_in_collect_stats
def __init__(self,
num_time_mask=2,
num_freq_mask=2,
freq_mask_length=15,
time_mask_length=15,
feature_dim=320,
model_size=512,
feed_forward_size=1024,
hidden_size=64,
dropout=0.1,
num_head=8,
num_encoder_layer=6,
num_decoder_layer=6,
vocab_path='testing_vocab.model',
max_feature_length=1024,
max_token_length=50,
enable_spec_augment=True,
share_weight=True,
smoothing=0.1,
restrict_left_length=20,
restrict_right_length=20,
mtlalpha=0.2,
report_wer=True):
super(Transformer, self).__init__()
self.enable_spec_augment = enable_spec_augment
self.max_token_length = max_token_length
self.restrict_left_length = restrict_left_length
self.restrict_right_length = restrict_right_length
self.vocab = Vocab(vocab_path)
self.sos = self.vocab.bos_id
self.eos = self.vocab.eos_id
self.adim = model_size
self.odim = self.vocab.vocab_size
self.ignore_id = self.vocab.pad_id
if enable_spec_augment:
self.spec_augment = SpecAugment(
num_time_mask=num_time_mask,
num_freq_mask=num_freq_mask,
freq_mask_length=freq_mask_length,
time_mask_length=time_mask_length,
max_sequence_length=max_feature_length)
self.encoder = Encoder(idim=feature_dim,
attention_dim=model_size,
attention_heads=num_head,
linear_units=feed_forward_size,
num_blocks=num_encoder_layer,
dropout_rate=dropout,
positional_dropout_rate=dropout,
attention_dropout_rate=dropout,
input_layer='linear',
padding_idx=self.vocab.pad_id)
self.decoder = Decoder(odim=self.vocab.vocab_size,
attention_dim=model_size,
attention_heads=num_head,
linear_units=feed_forward_size,
num_blocks=num_decoder_layer,
dropout_rate=dropout,
positional_dropout_rate=dropout,
self_attention_dropout_rate=dropout,
src_attention_dropout_rate=0,
input_layer='embed',
use_output_layer=False)
self.decoder_linear = t.nn.Linear(model_size,
self.vocab.vocab_size,
bias=True)
self.decoder_switch_linear = t.nn.Linear(model_size, 4, bias=True)
self.criterion = LabelSmoothingLoss(size=self.odim,
smoothing=smoothing,
padding_idx=self.vocab.pad_id,
normalize_length=True)
self.switch_criterion = LabelSmoothingLoss(
size=4,
smoothing=0,
padding_idx=self.vocab.pad_id,
normalize_length=True)
self.mtlalpha = mtlalpha
if mtlalpha > 0.0:
self.ctc = CTC(self.odim,
eprojs=self.adim,
dropout_rate=dropout,
ctc_type='builtin',
reduce=False)
else:
self.ctc = None
if report_wer:
from espnet.nets.e2e_asr_common import ErrorCalculator
def load_token_list(path=vocab_path.replace('.model', '.vocab')):
with open(path) as reader:
data = reader.readlines()
data = [i.split('\t')[0] for i in data]
return data
self.char_list = load_token_list()
self.error_calculator = ErrorCalculator(
char_list=self.char_list,
sym_space=' ',
sym_blank=self.vocab.blank_token,
report_wer=True)
else:
self.error_calculator = None
self.rnnlm = None
self.reporter = Reporter()
self.switch_loss = LabelSmoothingLoss(size=4,
smoothing=0,
padding_idx=0)
print('initing')
initialize(self, init_type='xavier_normal')
print('inited')
def __init__(self, idim, odim, args, ignore_id=-1):
"""Construct an E2E object.
:param int idim: dimension of inputs
:param int odim: dimension of outputs
:param Namespace args: argument Namespace containing options
"""
torch.nn.Module.__init__(self)
if args.transformer_attn_dropout_rate is None:
args.transformer_attn_dropout_rate = args.dropout_rate
self.cn_encoder = Encoder(
idim=idim,
attention_dim=args.adim,
attention_heads=args.aheads,
linear_units=args.eunits,
num_blocks=args.elayers,
input_layer=args.transformer_input_layer,
dropout_rate=args.dropout_rate,
positional_dropout_rate=args.dropout_rate,
attention_dropout_rate=args.transformer_attn_dropout_rate)
self.en_encoder = Encoder(
idim=idim,
attention_dim=args.adim,
attention_heads=args.aheads,
linear_units=args.eunits,
num_blocks=args.elayers,
input_layer=args.transformer_input_layer,
dropout_rate=args.dropout_rate,
positional_dropout_rate=args.dropout_rate,
attention_dropout_rate=args.transformer_attn_dropout_rate)
self.decoder = Decoder(
odim=odim,
attention_dim=args.adim,
attention_heads=args.aheads,
linear_units=args.dunits,
num_blocks=args.dlayers,
dropout_rate=args.dropout_rate,
positional_dropout_rate=args.dropout_rate,
self_attention_dropout_rate=args.transformer_attn_dropout_rate,
src_attention_dropout_rate=args.transformer_attn_dropout_rate)
self.sos = odim - 1
self.eos = odim - 1
self.odim = odim
self.ignore_id = ignore_id
self.subsample = [1]
self.reporter = Reporter()
# self.lsm_weight = a
self.criterion = LabelSmoothingLoss(
self.odim, self.ignore_id, args.lsm_weight,
args.transformer_length_normalized_loss)
# self.verbose = args.verbose
self.adim = args.adim
self.mtlalpha = args.mtlalpha
if args.mtlalpha > 0.0:
self.ctc = CTC(odim,
args.adim,
args.dropout_rate,
ctc_type=args.ctc_type,
reduce=True)
else:
self.ctc = None
if args.report_cer or args.report_wer:
from espnet.nets.e2e_asr_common import ErrorCalculator
self.error_calculator = ErrorCalculator(args.char_list,
args.sym_space,
args.sym_blank,
args.report_cer,
args.report_wer)
else:
self.error_calculator = None
self.rnnlm = None
# yzl23 config
self.remove_blank_in_ctc_mode = True
self.reset_parameters(args) # reset params at the last
self.enc_lambda = args.enc_lambda
logging.warning("Using fixed encoder lambda: {}".format(
self.enc_lambda))
logging.warning(
"Model total size: {}M, requires_grad size: {}M".format(
self.count_parameters(),
self.count_parameters(requires_grad=True)))
def __init__(self, idim, odim, args, ignore_id=-1):
"""Construct an E2E object.
:param int idim: dimension of inputs
:param int odim: dimension of outputs
:param Namespace args: argument Namespace containing options
"""
torch.nn.Module.__init__(self)
if args.transformer_attn_dropout_rate is None:
args.transformer_attn_dropout_rate = args.dropout_rate
self.cn_encoder = Encoder(
idim=idim,
attention_dim=args.adim,
attention_heads=args.aheads,
linear_units=args.eunits,
num_blocks=args.elayers,
input_layer=args.transformer_input_layer,
dropout_rate=args.dropout_rate,
positional_dropout_rate=args.dropout_rate,
attention_dropout_rate=args.transformer_attn_dropout_rate)
self.en_encoder = Encoder(
idim=idim,
attention_dim=args.adim,
attention_heads=args.aheads,
linear_units=args.eunits,
num_blocks=args.elayers,
input_layer=args.transformer_input_layer,
dropout_rate=args.dropout_rate,
positional_dropout_rate=args.dropout_rate,
attention_dropout_rate=args.transformer_attn_dropout_rate)
# gated add module
self.vectorize_lambda = args.vectorize_lambda
lambda_dim = args.adim if self.vectorize_lambda else 1
self.aggregation_module = torch.nn.Sequential(
torch.nn.Linear(2 * args.adim, lambda_dim), torch.nn.Sigmoid())
self.decoder = Decoder(
odim=odim,
attention_dim=args.adim,
attention_heads=args.aheads,
linear_units=args.dunits,
num_blocks=args.dlayers,
dropout_rate=args.dropout_rate,
positional_dropout_rate=args.dropout_rate,
self_attention_dropout_rate=args.transformer_attn_dropout_rate,
src_attention_dropout_rate=args.transformer_attn_dropout_rate)
self.sos = odim - 1
self.eos = odim - 1
self.odim = odim
self.ignore_id = ignore_id
self.subsample = [1]
self.reporter = Reporter()
# self.lsm_weight = a
self.criterion = LabelSmoothingLoss(
self.odim, self.ignore_id, args.lsm_weight,
args.transformer_length_normalized_loss)
# self.verbose = args.verbose
self.adim = args.adim
self.mtlalpha = args.mtlalpha
if args.mtlalpha > 0.0:
self.ctc = CTC(odim,
args.adim,
args.dropout_rate,
ctc_type=args.ctc_type,
reduce=True)
else:
self.ctc = None
if args.report_cer or args.report_wer:
from espnet.nets.e2e_asr_common import ErrorCalculator
self.error_calculator = ErrorCalculator(args.char_list,
args.sym_space,
args.sym_blank,
args.report_cer,
args.report_wer)
else:
self.error_calculator = None
self.rnnlm = None
# yzl23 config
self.remove_blank_in_ctc_mode = True
self.reset_parameters(args) # reset params at the last
# we frozen params here
if args.activated_keys:
activated_keys = args.activated_keys.split(',')
for name, params in self.named_parameters():
requires_grad = False # by default, we'd like to frozen all params
for key in activated_keys:
if key in name:
requires_grad = True # hit the key, activate this param
params.requires_grad = requires_grad
else:
logging.warning("Not frozen anything.")
logging.warning(
"Model total size: {}M, requires_grad size: {}M".format(
self.count_parameters(),
self.count_parameters(requires_grad=True)))
def __init__(self, idim, odim, args, ignore_id=-1):
"""Construct an E2E object.
:param int idim: dimension of inputs
:param int odim: dimension of outputs
:param Namespace args: argument Namespace containing options
"""
torch.nn.Module.__init__(self)
if args.transformer_attn_dropout_rate is None:
args.transformer_attn_dropout_rate = args.dropout_rate
self.encoder = Encoder(
idim=idim,
attention_dim=args.adim,
attention_heads=args.aheads,
linear_units=args.eunits,
num_blocks=args.elayers,
input_layer=args.transformer_input_layer,
dropout_rate=args.dropout_rate,
positional_dropout_rate=args.dropout_rate,
attention_dropout_rate=args.transformer_attn_dropout_rate)
self.decoder = Decoder(
odim=odim,
attention_dim=args.adim,
attention_heads=args.aheads,
linear_units=args.dunits,
num_blocks=args.dlayers,
dropout_rate=args.dropout_rate,
positional_dropout_rate=args.dropout_rate,
self_attention_dropout_rate=args.transformer_attn_dropout_rate,
src_attention_dropout_rate=args.transformer_attn_dropout_rate)
self.sos = odim - 1
self.eos = odim - 1
self.odim = odim
self.ignore_id = ignore_id
self.subsample = [1]
self.reporter = Reporter()
# self.lsm_weight = a
self.criterion = LabelSmoothingLoss(
self.odim, self.ignore_id, args.lsm_weight,
args.transformer_length_normalized_loss)
# self.verbose = args.verbose
self.adim = args.adim
self.mtlalpha = args.mtlalpha
if args.mtlalpha > 0.0:
self.ctc = CTC(odim,
args.adim,
args.dropout_rate,
ctc_type=args.ctc_type,
reduce=True)
else:
self.ctc = None
if args.report_cer or args.report_wer:
from espnet.nets.e2e_asr_common import ErrorCalculator
self.error_calculator = ErrorCalculator(args.char_list,
args.sym_space,
args.sym_blank,
args.report_cer,
args.report_wer)
else:
self.error_calculator = None
self.rnnlm = None
# yzl23 config
self.remove_blank_in_ctc_mode = True
# lid multitask related
adim = args.adim
self.lid_odim = 2 # cn and en
# src attention
self.lid_src_att = MultiHeadedAttention(
args.aheads, args.adim, args.transformer_attn_dropout_rate)
# self.lid_output_layer = torch.nn.Sequential(torch.nn.Linear(adim, adim),
# torch.nn.Tanh(),
# torch.nn.Linear(adim, self.lid_odim))
self.lid_output_layer = torch.nn.Linear(adim, self.lid_odim)
# here we hack to use lsm loss, but with lsm_weight ZERO
self.lid_criterion = LanguageIDMultitakLoss(self.ignore_id, \
normalize_length=args.transformer_length_normalized_loss)
self.lid_mtl_alpha = args.lid_mtl_alpha
logging.warning("language id multitask training alpha %f" %
(self.lid_mtl_alpha))
self.log_lid_mtl_acc = args.log_lid_mtl_acc
# reset parameters
self.reset_parameters(args)
def __init__(self, idim, odim, args, ignore_id=-1):
"""Construct an E2E object.
:param int idim: dimension of inputs
:param int odim: dimension of outputs
:param Namespace args: argument Namespace containing options
"""
torch.nn.Module.__init__(self)
if args.transformer_attn_dropout_rate is None:
args.transformer_attn_dropout_rate = args.dropout_rate
self.encoder = Encoder(
idim=idim,
attention_dim=args.adim,
attention_heads=args.aheads,
linear_units=args.eunits,
num_blocks=args.elayers,
input_layer=args.transformer_input_layer,
dropout_rate=args.dropout_rate,
positional_dropout_rate=args.dropout_rate,
attention_dropout_rate=args.transformer_attn_dropout_rate,
)
# target matching system organization
self.oversampling = args.oversampling
self.residual = args.residual
self.outer = args.outer
self.poster = torch.nn.Linear(args.adim, odim * self.oversampling)
if self.outer:
if self.residual:
self.matcher_res = torch.nn.Linear(idim, odim)
self.matcher = torch.nn.Linear(odim, odim)
else:
self.matcher = torch.nn.Linear(odim + idim, odim)
self.sos = odim - 1
self.eos = odim - 1
self.odim = odim
self.ignore_id = ignore_id
self.subsample = get_subsample(args, mode="asr", arch="transformer")
self.reporter = Reporter()
# self.lsm_weight = a
self.criterion = LabelSmoothingLoss(
self.odim,
self.ignore_id,
args.lsm_weight,
args.transformer_length_normalized_loss,
)
# self.verbose = args.verbose
self.reset_parameters(args)
self.adim = args.adim
self.mtlalpha = args.mtlalpha
if args.mtlalpha > 0.0:
self.ctc = CTC(
odim, args.adim, args.dropout_rate, ctc_type=args.ctc_type, reduce=True
)
else:
self.ctc = None
if args.report_cer or args.report_wer:
self.error_calculator = ErrorCalculator(
args.char_list,
args.sym_space,
args.sym_blank,
args.report_cer,
args.report_wer,
)
else:
self.error_calculator = None
self.rnnlm = None
def __init__(self, idim, odim, args, ignore_id=-1):
"""Construct an E2E object.
:param int idim: dimension of inputs
:param int odim: dimension of outputs
:param Namespace args: argument Namespace containing options
"""
torch.nn.Module.__init__(self)
# fill missing arguments for compatibility
args = fill_missing_args(args, self.add_arguments)
if args.transformer_attn_dropout_rate is None:
args.transformer_attn_dropout_rate = args.dropout_rate
self.encoder = Encoder(
idim=idim,
selfattention_layer_type=args.transformer_encoder_selfattn_layer_type,
attention_dim=args.adim,
attention_heads=args.aheads,
conv_wshare=args.wshare,
conv_kernel_length=args.ldconv_encoder_kernel_length,
conv_usebias=args.ldconv_usebias,
linear_units=args.eunits,
num_blocks=args.elayers,
input_layer=args.transformer_input_layer,
dropout_rate=args.dropout_rate,
positional_dropout_rate=args.dropout_rate,
attention_dropout_rate=args.transformer_attn_dropout_rate,
)
if args.mtlalpha < 1:
self.decoder = Decoder(
odim=odim,
selfattention_layer_type=args.transformer_decoder_selfattn_layer_type,
attention_dim=args.adim,
attention_heads=args.aheads,
conv_wshare=args.wshare,
conv_kernel_length=args.ldconv_decoder_kernel_length,
conv_usebias=args.ldconv_usebias,
linear_units=args.dunits,
num_blocks=args.dlayers,
dropout_rate=args.dropout_rate,
positional_dropout_rate=args.dropout_rate,
self_attention_dropout_rate=args.transformer_attn_dropout_rate,
src_attention_dropout_rate=args.transformer_attn_dropout_rate,
)
self.criterion = LabelSmoothingLoss(
odim,
ignore_id,
args.lsm_weight,
args.transformer_length_normalized_loss,
)
else:
self.decoder = None
self.criterion = None
self.blank = 0
self.decoder_mode = args.decoder_mode
if self.decoder_mode == "maskctc":
self.mask_token = odim - 1
self.sos = odim - 2
self.eos = odim - 2
else:
self.sos = odim - 1
self.eos = odim - 1
self.odim = odim
self.ignore_id = ignore_id
self.subsample = get_subsample(args, mode="asr", arch="transformer")
self.reporter = Reporter()
self.reset_parameters(args)
self.adim = args.adim # used for CTC (equal to d_model)
self.mtlalpha = args.mtlalpha
if args.mtlalpha > 0.0:
self.ctc = CTC(
odim, args.adim, args.dropout_rate, ctc_type=args.ctc_type, reduce=True
)
else:
self.ctc = None
if args.report_cer or args.report_wer:
self.error_calculator = ErrorCalculator(
args.char_list,
args.sym_space,
args.sym_blank,
args.report_cer,
args.report_wer,
)
else:
self.error_calculator = None
self.rnnlm = None
def __init__(self, idim, odim, args, ignore_id=-1):
"""Construct an E2E object.
:param int idim: dimension of inputs
:param int odim: dimension of outputs
:param Namespace args: argument Namespace containing options
"""
torch.nn.Module.__init__(self)
if args.transformer_attn_dropout_rate is None:
args.transformer_attn_dropout_rate = args.dropout_rate
self.encoder = Encoder(
idim=idim,
selfattention_layer_type=args.
transformer_encoder_selfattn_layer_type,
attention_dim=args.adim,
attention_heads=args.aheads,
conv_wshare=args.wshare,
conv_kernel_length=args.ldconv_encoder_kernel_length,
conv_usebias=args.ldconv_usebias,
linear_units=args.eunits,
num_blocks=args.elayers,
input_layer=args.transformer_input_layer,
dropout_rate=args.dropout_rate,
positional_dropout_rate=args.dropout_rate,
attention_dropout_rate=args.transformer_attn_dropout_rate,
attention_type=getattr(args, 'transformer_enc_attn_type',
'self_attn'),
max_attn_span=getattr(args, 'enc_max_attn_span', [None]),
span_init=getattr(args, 'span_init', None),
span_ratio=getattr(args, 'span_ratio', None),
ratio_adaptive=getattr(args, 'ratio_adaptive', None))
self.decoder = Decoder(
odim=odim,
selfattention_layer_type=args.
transformer_decoder_selfattn_layer_type,
attention_dim=args.adim,
attention_heads=args.aheads,
conv_wshare=args.wshare,
conv_kernel_length=args.ldconv_decoder_kernel_length,
conv_usebias=args.ldconv_usebias,
linear_units=args.dunits,
num_blocks=args.dlayers,
dropout_rate=args.dropout_rate,
positional_dropout_rate=args.dropout_rate,
self_attention_dropout_rate=args.transformer_attn_dropout_rate,
src_attention_dropout_rate=args.transformer_attn_dropout_rate,
attention_type=getattr(args, 'transformer_dec_attn_type',
'self_attn'),
max_attn_span=getattr(args, 'dec_max_attn_span', [None]),
span_init=getattr(args, 'span_init', None),
span_ratio=getattr(args, 'span_ratio', None),
ratio_adaptive=getattr(args, 'ratio_adaptive', None))
self.sos = odim - 1
self.eos = odim - 1
self.odim = odim
self.ignore_id = ignore_id
self.subsample = get_subsample(args, mode="asr", arch="transformer")
self.reporter = Reporter()
# self.lsm_weight = a
self.criterion = LabelSmoothingLoss(
self.odim,
self.ignore_id,
args.lsm_weight,
args.transformer_length_normalized_loss,
)
# self.verbose = args.verbose
self.reset_parameters(args)
self.adim = args.adim
self.mtlalpha = args.mtlalpha
if args.mtlalpha > 0.0:
self.ctc = CTC(odim,
args.adim,
args.dropout_rate,
ctc_type=args.ctc_type,
reduce=True)
else:
self.ctc = None
if args.report_cer or args.report_wer:
self.error_calculator = ErrorCalculator(
args.char_list,
args.sym_space,
args.sym_blank,
args.report_cer,
args.report_wer,
)
else:
self.error_calculator = None
self.rnnlm = None
self.attention_enc_type = getattr(args, 'transformer_enc_attn_type',
'self_attn')
self.attention_dec_type = getattr(args, 'transformer_dec_attn_type',
'self_attn')
self.span_loss_coef = getattr(args, 'span_loss_coef', None)
self.ratio_adaptive = getattr(args, 'ratio_adaptive', None)
self.sym_blank = args.sym_blank
