import argparse
from typing import Callable
from typing import Collection
from typing import Dict
from typing import List
from typing import Optional
from typing import Tuple
import numpy as np
import torch
from typeguard import check_argument_types
from typeguard import check_return_type
from espnet2.asr.encoder.abs_encoder import AbsEncoder
from espnet2.asr.encoder.conformer_encoder import ConformerEncoder
from espnet2.asr.encoder.rnn_encoder import RNNEncoder
from espnet2.asr.encoder.transformer_encoder import TransformerEncoder
from espnet2.asr.frontend.abs_frontend import AbsFrontend
from espnet2.asr.frontend.default import DefaultFrontend
from espnet2.asr.frontend.s3prl import S3prlFrontend
from espnet2.asr.frontend.windowing import SlidingWindow
from espnet2.asr.specaug.abs_specaug import AbsSpecAug
from espnet2.asr.specaug.specaug import SpecAug
from espnet2.diar.attractor.abs_attractor import AbsAttractor
from espnet2.diar.attractor.rnn_attractor import RnnAttractor
from espnet2.diar.decoder.abs_decoder import AbsDecoder
from espnet2.diar.decoder.linear_decoder import LinearDecoder
from espnet2.diar.espnet_model import ESPnetDiarizationModel
from espnet2.layers.abs_normalize import AbsNormalize
from espnet2.layers.global_mvn import GlobalMVN
from espnet2.layers.label_aggregation import LabelAggregate
from espnet2.layers.utterance_mvn import UtteranceMVN
from espnet2.tasks.abs_task import AbsTask
from espnet2.torch_utils.initialize import initialize
from espnet2.train.class_choices import ClassChoices
from espnet2.train.collate_fn import CommonCollateFn
from espnet2.train.preprocessor import CommonPreprocessor
from espnet2.train.trainer import Trainer
from espnet2.utils.get_default_kwargs import get_default_kwargs
from espnet2.utils.nested_dict_action import NestedDictAction
from espnet2.utils.types import int_or_none
from espnet2.utils.types import str2bool
from espnet2.utils.types import str_or_none
frontend_choices = ClassChoices(
name="frontend",
classes=dict(
default=DefaultFrontend,
sliding_window=SlidingWindow,
s3prl=S3prlFrontend,
),
type_check=AbsFrontend,
default="default",
)
specaug_choices = ClassChoices(
name="specaug",
classes=dict(specaug=SpecAug),
type_check=AbsSpecAug,
default=None,
optional=True,
)
normalize_choices = ClassChoices(
"normalize",
classes=dict(
global_mvn=GlobalMVN,
utterance_mvn=UtteranceMVN,
),
type_check=AbsNormalize,
default="utterance_mvn",
optional=True,
)
label_aggregator_choices = ClassChoices(
"label_aggregator",
classes=dict(label_aggregator=LabelAggregate),
default="label_aggregator",
)
encoder_choices = ClassChoices(
"encoder",
classes=dict(
conformer=ConformerEncoder,
transformer=TransformerEncoder,
rnn=RNNEncoder,
),
type_check=AbsEncoder,
default="rnn",
)
decoder_choices = ClassChoices(
"decoder",
classes=dict(linear=LinearDecoder),
type_check=AbsDecoder,
default="linear",
)
attractor_choices = ClassChoices(
"attractor",
classes=dict(
rnn=RnnAttractor,
),
type_check=AbsAttractor,
default=None,
optional=True,
)
[docs]class DiarizationTask(AbsTask):
# If you need more than one optimizer, change this value
num_optimizers: int = 1
# Add variable objects configurations
class_choices_list = [
# --frontend and --frontend_conf
frontend_choices,
# --specaug and --specaug_conf
specaug_choices,
# --normalize and --normalize_conf
normalize_choices,
# --encoder and --encoder_conf
encoder_choices,
# --decoder and --decoder_conf
decoder_choices,
# --label_aggregator and --label_aggregator_conf
label_aggregator_choices,
# --attractor and --attractor_conf
attractor_choices,
]
# If you need to modify train() or eval() procedures, change Trainer class here
trainer = Trainer
[docs] @classmethod
def add_task_arguments(cls, parser: argparse.ArgumentParser):
group = parser.add_argument_group(description="Task related")
group.add_argument(
"--num_spk",
type=int_or_none,
default=None,
help="The number fo speakers (for each recording) used in system training",
)
group.add_argument(
"--init",
type=lambda x: str_or_none(x.lower()),
default=None,
help="The initialization method",
choices=[
"chainer",
"xavier_uniform",
"xavier_normal",
"kaiming_uniform",
"kaiming_normal",
None,
],
)
group.add_argument(
"--input_size",
type=int_or_none,
default=None,
help="The number of input dimension of the feature",
)
group.add_argument(
"--model_conf",
action=NestedDictAction,
default=get_default_kwargs(ESPnetDiarizationModel),
help="The keyword arguments for model class.",
)
group = parser.add_argument_group(description="Preprocess related")
group.add_argument(
"--use_preprocessor",
type=str2bool,
default=True,
help="Apply preprocessing to data or not",
)
for class_choices in cls.class_choices_list:
# Append --<name> and --<name>_conf.
# e.g. --encoder and --encoder_conf
class_choices.add_arguments(group)
[docs] @classmethod
def build_collate_fn(
cls, args: argparse.Namespace, train: bool
) -> Callable[
[Collection[Tuple[str, Dict[str, np.ndarray]]]],
Tuple[List[str], Dict[str, torch.Tensor]],
]:
assert check_argument_types()
# NOTE(kamo): int value = 0 is reserved by CTC-blank symbol
return CommonCollateFn(float_pad_value=0.0, int_pad_value=-1)
[docs] @classmethod
def build_preprocess_fn(
cls, args: argparse.Namespace, train: bool
) -> Optional[Callable[[str, Dict[str, np.array]], Dict[str, np.ndarray]]]:
assert check_argument_types()
if args.use_preprocessor:
# FIXME (jiatong): add more argument here
retval = CommonPreprocessor(train=train)
else:
retval = None
assert check_return_type(retval)
return retval
[docs] @classmethod
def required_data_names(
cls, train: bool = True, inference: bool = False
) -> Tuple[str, ...]:
if not inference:
retval = ("speech", "spk_labels")
else:
# Recognition mode
retval = ("speech",)
return retval
[docs] @classmethod
def optional_data_names(
cls, train: bool = True, inference: bool = False
) -> Tuple[str, ...]:
# (Note: jiatong): no optional data names for now
retval = ()
assert check_return_type(retval)
return retval
[docs] @classmethod
def build_model(cls, args: argparse.Namespace) -> ESPnetDiarizationModel:
assert check_argument_types()
# 1. frontend
if args.input_size is None:
# Extract features in the model
frontend_class = frontend_choices.get_class(args.frontend)
frontend = frontend_class(**args.frontend_conf)
input_size = frontend.output_size()
else:
# Give features from data-loader
args.frontend = None
args.frontend_conf = {}
frontend = None
input_size = args.input_size
# 2. Data augmentation for spectrogram
if args.specaug is not None:
specaug_class = specaug_choices.get_class(args.specaug)
specaug = specaug_class(**args.specaug_conf)
else:
specaug = None
# 3. Normalization layer
if args.normalize is not None:
normalize_class = normalize_choices.get_class(args.normalize)
normalize = normalize_class(**args.normalize_conf)
else:
normalize = None
# 4. Label Aggregator layer
label_aggregator_class = label_aggregator_choices.get_class(
args.label_aggregator
)
label_aggregator = label_aggregator_class(**args.label_aggregator_conf)
# 5. Encoder
encoder_class = encoder_choices.get_class(args.encoder)
# Note(jiatong): Diarization may not use subsampling when processing
encoder = encoder_class(input_size=input_size, **args.encoder_conf)
# 6a. Decoder
decoder_class = decoder_choices.get_class(args.decoder)
decoder = decoder_class(
num_spk=args.num_spk,
encoder_output_size=encoder.output_size(),
**args.decoder_conf,
)
# 6b. Attractor
if getattr(args, "attractor", None) is not None:
attractor_class = attractor_choices.get_class(args.attractor)
attractor = attractor_class(
encoder_output_size=encoder.output_size(),
**args.attractor_conf,
)
else:
attractor = None
# 7. Build model
model = ESPnetDiarizationModel(
frontend=frontend,
specaug=specaug,
normalize=normalize,
label_aggregator=label_aggregator,
encoder=encoder,
decoder=decoder,
attractor=attractor,
**args.model_conf,
)
# FIXME(kamo): Should be done in model?
# 8. Initialize
if args.init is not None:
initialize(model, args.init)
assert check_return_type(model)
return model