import argparse
import logging
from typing import Callable, Collection, Dict, List, Optional, Tuple
import numpy as np
import torch
from typeguard import typechecked
from espnet2.asr.ctc import CTC
from espnet2.asr.decoder.abs_decoder import AbsDecoder
from espnet2.asr.decoder.rnn_decoder import RNNDecoder
from espnet2.asr.decoder.transformer_decoder import (
DynamicConvolution2DTransformerDecoder,
DynamicConvolutionTransformerDecoder,
LightweightConvolution2DTransformerDecoder,
LightweightConvolutionTransformerDecoder,
TransformerDecoder,
)
from espnet2.asr.discrete_asr_espnet_model import ESPnetDiscreteASRModel
from espnet2.asr.encoder.abs_encoder import AbsEncoder
from espnet2.asr.encoder.branchformer_encoder import BranchformerEncoder
from espnet2.asr.encoder.conformer_encoder import ConformerEncoder
from espnet2.asr.encoder.contextual_block_transformer_encoder import (
ContextualBlockTransformerEncoder,
)
from espnet2.asr.encoder.e_branchformer_encoder import EBranchformerEncoder
from espnet2.asr.encoder.rnn_encoder import RNNEncoder
from espnet2.asr.encoder.transformer_encoder import TransformerEncoder
from espnet2.asr.encoder.vgg_rnn_encoder import VGGRNNEncoder
from espnet2.asr.frontend.abs_frontend import AbsFrontend
from espnet2.asr.postencoder.abs_postencoder import AbsPostEncoder
from espnet2.asr.postencoder.hugging_face_transformers_postencoder import (
HuggingFaceTransformersPostEncoder,
)
from espnet2.asr.preencoder.abs_preencoder import AbsPreEncoder
from espnet2.asr.preencoder.linear import LinearProjection
from espnet2.asr.preencoder.sinc import LightweightSincConvs
from espnet2.asr.specaug.abs_specaug import AbsSpecAug
from espnet2.asr.specaug.specaug import SpecAug
from espnet2.mt.espnet_model import ESPnetMTModel
from espnet2.mt.frontend.embedding import Embedding
from espnet2.tasks.abs_task import AbsTask
from espnet2.text.phoneme_tokenizer import g2p_choices
from espnet2.torch_utils.initialize import initialize
from espnet2.train.abs_espnet_model import AbsESPnetModel
from espnet2.train.class_choices import ClassChoices
from espnet2.train.collate_fn import CommonCollateFn
from espnet2.train.preprocessor import MutliTokenizerCommonPreprocessor
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, str2bool, str_or_none
frontend_choices = ClassChoices(
name="frontend",
classes=dict(
embed=Embedding,
),
type_check=AbsFrontend,
default="embed",
)
specaug_choices = ClassChoices(
name="specaug",
classes=dict(
specaug=SpecAug,
),
type_check=AbsSpecAug,
default=None,
optional=True,
)
preencoder_choices = ClassChoices(
name="preencoder",
classes=dict(
sinc=LightweightSincConvs,
linear=LinearProjection,
),
type_check=AbsPreEncoder,
default=None,
optional=True,
)
encoder_choices = ClassChoices(
"encoder",
classes=dict(
conformer=ConformerEncoder,
transformer=TransformerEncoder,
contextual_block_transformer=ContextualBlockTransformerEncoder,
vgg_rnn=VGGRNNEncoder,
rnn=RNNEncoder,
branchformer=BranchformerEncoder,
e_branchformer=EBranchformerEncoder,
),
type_check=AbsEncoder,
default="rnn",
)
postencoder_choices = ClassChoices(
name="postencoder",
classes=dict(
hugging_face_transformers=HuggingFaceTransformersPostEncoder,
),
type_check=AbsPostEncoder,
default=None,
optional=True,
)
decoder_choices = ClassChoices(
"decoder",
classes=dict(
transformer=TransformerDecoder,
lightweight_conv=LightweightConvolutionTransformerDecoder,
lightweight_conv2d=LightweightConvolution2DTransformerDecoder,
dynamic_conv=DynamicConvolutionTransformerDecoder,
dynamic_conv2d=DynamicConvolution2DTransformerDecoder,
rnn=RNNDecoder,
),
type_check=AbsDecoder,
default="rnn",
)
model_choices = ClassChoices(
"model",
classes=dict(
mt=ESPnetMTModel,
discrete_asr=ESPnetDiscreteASRModel,
),
type_check=AbsESPnetModel,
default="mt",
)
[docs]class MTTask(AbsTask):
# If you need more than one optimizers, 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,
# --preencoder and --preencoder_conf
preencoder_choices,
# --encoder and --encoder_conf
encoder_choices,
# --postencoder and --postencoder_conf
postencoder_choices,
# --decoder and --decoder_conf
decoder_choices,
# --model and --model_conf
model_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")
# NOTE(kamo): add_arguments(..., required=True) can't be used
# to provide --print_config mode. Instead of it, do as
required = parser.get_default("required")
required += ["src_token_list", "token_list"]
group.add_argument(
"--token_list",
type=str_or_none,
default=None,
help="A text mapping int-id to token (for target language)",
)
group.add_argument(
"--src_token_list",
type=str_or_none,
default=None,
help="A text mapping int-id to token (for source language)",
)
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(
"--ctc_conf",
action=NestedDictAction,
default=get_default_kwargs(CTC),
help="The keyword arguments for CTC 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",
)
group.add_argument(
"--token_type",
type=str,
default="bpe",
choices=["bpe", "char", "word", "phn"],
help="The target text will be tokenized " "in the specified level token",
)
group.add_argument(
"--src_token_type",
type=str,
default="bpe",
choices=["bpe", "char", "word", "phn"],
help="The source text will be tokenized " "in the specified level token",
)
group.add_argument(
"--bpemodel",
type=str_or_none,
default=None,
help="The model file of sentencepiece (for target language)",
)
group.add_argument(
"--src_bpemodel",
type=str_or_none,
default=None,
help="The model file of sentencepiece (for source language)",
)
parser.add_argument(
"--non_linguistic_symbols",
type=str_or_none,
help="non_linguistic_symbols file path",
)
parser.add_argument(
"--cleaner",
type=str_or_none,
choices=[None, "tacotron", "jaconv", "vietnamese"],
default=None,
help="Apply text cleaning",
)
parser.add_argument(
"--g2p",
type=str_or_none,
choices=g2p_choices,
default=None,
help="Specify g2p method if --token_type=phn",
)
parser.add_argument(
"--tokenizer_encode_conf",
type=dict,
default=None,
help="Tokenization encoder conf, "
"e.g. BPE dropout: enable_sampling=True, alpha=0.1, nbest_size=-1",
)
parser.add_argument(
"--src_tokenizer_encode_conf",
type=dict,
default=None,
help="Src tokenization encoder conf, "
"e.g. BPE dropout: enable_sampling=True, alpha=0.1, nbest_size=-1",
)
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
@typechecked
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]],
]:
# NOTE(kamo): int value = 0 is reserved by CTC-blank symbol
return CommonCollateFn(float_pad_value=0.0, int_pad_value=-1)
[docs] @classmethod
@typechecked
def build_preprocess_fn(
cls, args: argparse.Namespace, train: bool
) -> Optional[Callable[[str, Dict[str, np.array]], Dict[str, np.ndarray]]]:
if args.use_preprocessor:
retval = MutliTokenizerCommonPreprocessor(
train=train,
token_type=[args.token_type, args.src_token_type],
token_list=[args.token_list, args.src_token_list],
bpemodel=[args.bpemodel, args.src_bpemodel],
non_linguistic_symbols=args.non_linguistic_symbols,
text_cleaner=args.cleaner,
g2p_type=args.g2p,
text_name=["text", "src_text"],
tokenizer_encode_conf=(
[
args.tokenizer_encode_conf,
args.src_tokenizer_encode_conf,
]
if train
else [dict(), dict()]
),
)
else:
retval = None
return retval
[docs] @classmethod
def required_data_names(
cls, train: bool = True, inference: bool = False
) -> Tuple[str, ...]:
if not inference:
retval = ("src_text", "text")
else:
# Recognition mode
retval = ("src_text",)
return retval
[docs] @classmethod
def optional_data_names(
cls, train: bool = True, inference: bool = False
) -> Tuple[str, ...]:
if not inference:
retval = ()
else:
retval = ()
return retval
[docs] @classmethod
@typechecked
def build_model(cls, args: argparse.Namespace) -> ESPnetMTModel:
if isinstance(args.token_list, str):
with open(args.token_list, encoding="utf-8") as f:
token_list = [line.rstrip() for line in f]
# Overwriting token_list to keep it as "portable".
args.token_list = list(token_list)
elif isinstance(args.token_list, (tuple, list)):
token_list = list(args.token_list)
else:
raise RuntimeError("token_list must be str or list")
vocab_size = len(token_list)
logging.info(f"Vocabulary size: {vocab_size }")
if args.src_token_list is not None:
if isinstance(args.src_token_list, str):
with open(args.src_token_list, encoding="utf-8") as f:
src_token_list = [line.rstrip() for line in f]
# Overwriting src_token_list to keep it as "portable".
args.src_token_list = list(src_token_list)
elif isinstance(args.src_token_list, (tuple, list)):
src_token_list = list(args.src_token_list)
else:
raise RuntimeError("token_list must be str or list")
src_vocab_size = len(src_token_list)
logging.info(f"Source vocabulary size: {src_vocab_size }")
else:
src_token_list, src_vocab_size = None, None
# 1. frontend
if args.input_size is None:
# Extract features in the model
frontend_class = frontend_choices.get_class(args.frontend)
frontend = frontend_class(input_size=src_vocab_size, **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 getattr(args, "specaug", None) is not None:
specaug_class = specaug_choices.get_class(args.specaug)
specaug = specaug_class(**args.specaug_conf)
else:
specaug = None
# 3. Pre-encoder input block
# NOTE(kan-bayashi): Use getattr to keep the compatibility
if getattr(args, "preencoder", None) is not None:
preencoder_class = preencoder_choices.get_class(args.preencoder)
preencoder = preencoder_class(**args.preencoder_conf)
input_size = preencoder.output_size()
else:
preencoder = None
# 4. Encoder
encoder_class = encoder_choices.get_class(args.encoder)
encoder = encoder_class(input_size=input_size, **args.encoder_conf)
# 5. Post-encoder block
# NOTE(kan-bayashi): Use getattr to keep the compatibility
encoder_output_size = encoder.output_size()
if getattr(args, "postencoder", None) is not None:
postencoder_class = postencoder_choices.get_class(args.postencoder)
postencoder = postencoder_class(
input_size=encoder_output_size, **args.postencoder_conf
)
encoder_output_size = postencoder.output_size()
else:
postencoder = None
# 5. Decoder
decoder_class = decoder_choices.get_class(args.decoder)
decoder = decoder_class(
vocab_size=vocab_size,
encoder_output_size=encoder_output_size,
**args.decoder_conf,
)
# 6. CTC
ctc = CTC(
odim=vocab_size, encoder_output_size=encoder_output_size, **args.ctc_conf
)
# 8. Build model
try:
model_class = model_choices.get_class(args.model)
if args.model == "discrete_asr":
extra_model_conf = dict(ctc=ctc, specaug=specaug)
else:
extra_model_conf = dict()
except AttributeError:
model_class = model_choices.get_class("mt")
extra_model_conf = dict()
model = model_class(
vocab_size=vocab_size,
src_vocab_size=src_vocab_size,
frontend=frontend,
preencoder=preencoder,
encoder=encoder,
postencoder=postencoder,
decoder=decoder,
token_list=token_list,
src_token_list=src_token_list,
**args.model_conf,
**extra_model_conf,
)
# FIXME(kamo): Should be done in model?
# 9. Initialize
if args.init is not None:
initialize(model, args.init)
return model