Source code for espnet2.tasks.asr_transducer

"""ASR Transducer Task."""

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.frontend.abs_frontend import AbsFrontend
from espnet2.asr.frontend.default import DefaultFrontend
from espnet2.asr.frontend.windowing import SlidingWindow
from espnet2.asr.specaug.abs_specaug import AbsSpecAug
from espnet2.asr.specaug.specaug import SpecAug
from espnet2.asr_transducer.decoder.abs_decoder import AbsDecoder
from espnet2.asr_transducer.decoder.mega_decoder import MEGADecoder
from espnet2.asr_transducer.decoder.rnn_decoder import RNNDecoder
from espnet2.asr_transducer.decoder.rwkv_decoder import RWKVDecoder
from espnet2.asr_transducer.decoder.stateless_decoder import StatelessDecoder
from espnet2.asr_transducer.encoder.encoder import Encoder
from espnet2.asr_transducer.espnet_transducer_model import ESPnetASRTransducerModel
from espnet2.asr_transducer.joint_network import JointNetwork
from espnet2.layers.abs_normalize import AbsNormalize
from espnet2.layers.global_mvn import GlobalMVN
from espnet2.layers.utterance_mvn import UtteranceMVN
from espnet2.tasks.abs_task import AbsTask
from espnet2.text.phoneme_tokenizer import g2p_choices
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 float_or_none, int_or_none, str2bool, str_or_none

frontend_choices = ClassChoices(
specaug_choices = ClassChoices(
normalize_choices = ClassChoices(
decoder_choices = ClassChoices(

[docs]class ASRTransducerTask(AbsTask): """ASR Transducer Task definition.""" num_optimizers: int = 1 class_choices_list = [ frontend_choices, specaug_choices, normalize_choices, decoder_choices, ] trainer = Trainer
[docs] @classmethod def add_task_arguments(cls, parser: argparse.ArgumentParser): """Add Transducer task arguments. Args: cls: ASRTransducerTask object. parser: Transducer arguments parser. """ group = parser.add_argument_group(description="Task related.") required = parser.get_default("required") required += ["token_list"] group.add_argument( "--token_list", type=str_or_none, default=None, help="Integer-string mapper for tokens.", ) group.add_argument( "--input_size", type=int_or_none, default=None, help="The number of dimensions for input features.", ) group.add_argument( "--init", type=str_or_none, default=None, help="Type of model initialization to use.", ) group.add_argument( "--model_conf", action=NestedDictAction, default=get_default_kwargs(ESPnetASRTransducerModel), help="The keyword arguments for the model class.", ) group.add_argument( "--encoder_conf", action=NestedDictAction, default={}, help="The keyword arguments for the encoder class.", ) group.add_argument( "--joint_network_conf", action=NestedDictAction, default={}, help="The keyword arguments for the joint network class.", ) group = parser.add_argument_group(description="Preprocess related.") group.add_argument( "--use_preprocessor", type=str2bool, default=True, help="Whether to apply preprocessing to input data.", ) group.add_argument( "--token_type", type=str, default="bpe", choices=["bpe", "char", "word", "phn"], help="The type of tokens to use during tokenization.", ) group.add_argument( "--bpemodel", type=str_or_none, default=None, help="The path of the sentencepiece model.", ) group.add_argument( "--non_linguistic_symbols", type=str_or_none, help="The 'non_linguistic_symbols' file path.", ) group.add_argument( "--cleaner", type=str_or_none, choices=[None, "tacotron", "jaconv", "vietnamese"], default=None, help="Text cleaner to use.", ) group.add_argument( "--g2p", type=str_or_none, choices=g2p_choices, default=None, help="g2p method to use if --token_type=phn.", ) group.add_argument( "--speech_volume_normalize", type=float_or_none, default=None, help="Normalization value for maximum amplitude scaling.", ) group.add_argument( "--rir_scp", type=str_or_none, default=None, help="The RIR SCP file path.", ) group.add_argument( "--rir_apply_prob", type=float, default=1.0, help="The probability of the applied RIR convolution.", ) group.add_argument( "--noise_scp", type=str_or_none, default=None, help="The path of noise SCP file.", ) group.add_argument( "--noise_apply_prob", type=float, default=1.0, help="The probability of the applied noise addition.", ) group.add_argument( "--noise_db_range", type=str, default="13_15", help="The range of the noise decibel level.", ) for class_choices in cls.class_choices_list: # Append --<name> and --<name>_conf. # e.g. --decoder and --decoder_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]], ]: """Build collate function. Args: cls: ASRTransducerTask object. args: Task arguments. train: Training mode. Return: : Callable collate function. """ 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]]]: """Build pre-processing function. Args: cls: ASRTransducerTask object. args: Task arguments. train: Training mode. Return: : Callable pre-processing function. """ if args.use_preprocessor: retval = CommonPreprocessor( train=train, token_type=args.token_type, token_list=args.token_list, bpemodel=args.bpemodel, non_linguistic_symbols=args.non_linguistic_symbols, text_cleaner=args.cleaner, g2p_type=args.g2p, rir_scp=args.rir_scp if hasattr(args, "rir_scp") else None, rir_apply_prob=( args.rir_apply_prob if hasattr(args, "rir_apply_prob") else 1.0 ), noise_scp=args.noise_scp if hasattr(args, "noise_scp") else None, noise_apply_prob=( args.noise_apply_prob if hasattr(args, "noise_apply_prob") else 1.0 ), noise_db_range=( args.noise_db_range if hasattr(args, "noise_db_range") else "13_15" ), speech_volume_normalize=( args.speech_volume_normalize if hasattr(args, "rir_scp") else None ), ) else: retval = None return retval
[docs] @classmethod def required_data_names( cls, train: bool = True, inference: bool = False ) -> Tuple[str, ...]: """Required data depending on task mode. Args: cls: ASRTransducerTask object. train: Training mode. inference: Inference mode. Return: retval: Required task data. """ if not inference: retval = ("speech", "text") else: retval = ("speech",) return retval
[docs] @classmethod def optional_data_names( cls, train: bool = True, inference: bool = False ) -> Tuple[str, ...]: """Optional data depending on task mode. Args: cls: ASRTransducerTask object. train: Training mode. inference: Inference mode. Return: retval: Optional task data. """ retval = () return retval
[docs] @classmethod @typechecked def build_model(cls, args: argparse.Namespace) -> ESPnetASRTransducerModel: """Required data depending on task mode. Args: cls: ASRTransducerTask object. args: Task arguments. Return: model: ASR Transducer model. """ 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) if hasattr(args, "scheduler_conf"): args.model_conf["warmup_steps"] = args.scheduler_conf.get( "warmup_steps", 25000 )"Vocabulary size: {vocab_size }") # 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 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. Encoder encoder = Encoder(input_size, **args.encoder_conf) encoder_output_size = encoder.output_size # 5. Decoder decoder_class = decoder_choices.get_class(args.decoder) decoder = decoder_class( vocab_size, **args.decoder_conf, ) decoder_output_size = decoder.output_size # 6. Joint Network joint_network = JointNetwork( vocab_size, encoder_output_size, decoder_output_size, **args.joint_network_conf, ) # 7. Build model model = ESPnetASRTransducerModel( vocab_size=vocab_size, token_list=token_list, frontend=frontend, specaug=specaug, normalize=normalize, encoder=encoder, decoder=decoder, joint_network=joint_network, **args.model_conf, ) # 8. Initialize model if args.init is not None: raise NotImplementedError( "Currently not supported.", "Initialization part will be reworked in a short future.", ) return model