Source code for espnet2.tasks.st

import argparse
import logging
from typing import Callable, Collection, Dict, List, Optional, Tuple

import numpy as np
import torch
from typeguard import check_argument_types, check_return_type

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.encoder.abs_encoder import AbsEncoder
from espnet2.asr.encoder.conformer_encoder import ConformerEncoder
from espnet2.asr.encoder.contextual_block_transformer_encoder import (
    ContextualBlockTransformerEncoder,
)
from espnet2.asr.encoder.hubert_encoder import (
    FairseqHubertEncoder,
    FairseqHubertPretrainEncoder,
)
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.encoder.wav2vec2_encoder import FairSeqWav2Vec2Encoder
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.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.layers.abs_normalize import AbsNormalize
from espnet2.layers.global_mvn import GlobalMVN
from espnet2.layers.utterance_mvn import UtteranceMVN
from espnet2.st.espnet_model import ESPnetSTModel
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.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 float_or_none, int_or_none, str2bool, 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,
)
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,
        wav2vec2=FairSeqWav2Vec2Encoder,
        hubert=FairseqHubertEncoder,
        hubert_pretrain=FairseqHubertPretrainEncoder,
    ),
    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",
)
extra_asr_decoder_choices = ClassChoices(
    "extra_asr_decoder",
    classes=dict(
        transformer=TransformerDecoder,
        lightweight_conv=LightweightConvolutionTransformerDecoder,
        lightweight_conv2d=LightweightConvolution2DTransformerDecoder,
        dynamic_conv=DynamicConvolutionTransformerDecoder,
        dynamic_conv2d=DynamicConvolution2DTransformerDecoder,
        rnn=RNNDecoder,
    ),
    type_check=AbsDecoder,
    default="rnn",
)
extra_mt_decoder_choices = ClassChoices(
    "extra_mt_decoder",
    classes=dict(
        transformer=TransformerDecoder,
        lightweight_conv=LightweightConvolutionTransformerDecoder,
        lightweight_conv2d=LightweightConvolution2DTransformerDecoder,
        dynamic_conv=DynamicConvolutionTransformerDecoder,
        dynamic_conv2d=DynamicConvolution2DTransformerDecoder,
        rnn=RNNDecoder,
    ),
    type_check=AbsDecoder,
    default="rnn",
)


[docs]class STTask(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, # --normalize and --normalize_conf normalize_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, # --extra_asr_decoder and --extra_asr_decoder_conf extra_asr_decoder_choices, # --extra_mt_decoder and --extra_mt_decoder_conf extra_mt_decoder_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 += ["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.add_argument( "--model_conf", action=NestedDictAction, default=get_default_kwargs(ESPnetSTModel), 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", ) 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", "none"], 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)", ) group.add_argument( "--non_linguistic_symbols", type=str_or_none, help="non_linguistic_symbols file path", ) group.add_argument( "--cleaner", type=str_or_none, choices=[None, "tacotron", "jaconv", "vietnamese"], default=None, help="Apply text cleaning", ) group.add_argument( "--g2p", type=str_or_none, choices=g2p_choices, default=None, help="Specify g2p method if --token_type=phn", ) group.add_argument( "--speech_volume_normalize", type=float_or_none, default=None, help="Scale the maximum amplitude to the given value.", ) group.add_argument( "--rir_scp", type=str_or_none, default=None, help="The file path of rir scp file.", ) group.add_argument( "--rir_apply_prob", type=float, default=1.0, help="THe probability for applying RIR convolution.", ) group.add_argument( "--noise_scp", type=str_or_none, default=None, help="The file path of noise scp file.", ) group.add_argument( "--noise_apply_prob", type=float, default=1.0, help="The probability applying Noise adding.", ) group.add_argument( "--noise_db_range", type=str, default="13_15", help="The range of noise decibel level.", ) group.add_argument( "--short_noise_thres", type=float, default=0.5, help="If len(noise) / len(speech) is smaller than this threshold during " "dynamic mixing, a warning will be displayed.", ) 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.src_token_type == "none": args.src_token_type = None 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, # NOTE(kamo): Check attribute existence for backward compatibility 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", short_noise_thres=args.short_noise_thres if hasattr(args, "short_noise_thres") else 0.5, speech_volume_normalize=args.speech_volume_normalize if hasattr(args, "speech_volume_normalize") else None, speech_name="speech", text_name=["text", "src_text"], ) 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", "text") else: # Recognition mode retval = ("speech",) return retval
[docs] @classmethod def optional_data_names( cls, train: bool = True, inference: bool = False ) -> Tuple[str, ...]: if not inference: retval = ("src_text",) else: retval = () assert check_return_type(retval) return retval
[docs] @classmethod def build_model(cls, args: argparse.Namespace) -> ESPnetSTModel: assert check_argument_types() 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(**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. 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 if src_token_list is not None: ctc = CTC( odim=src_vocab_size, encoder_output_size=encoder_output_size, **args.ctc_conf, ) else: ctc = None # 7. ASR extra decoder if ( getattr(args, "extra_asr_decoder", None) is not None and src_token_list is not None ): extra_asr_decoder_class = extra_asr_decoder_choices.get_class( args.extra_asr_decoder ) extra_asr_decoder = extra_asr_decoder_class( vocab_size=src_vocab_size, encoder_output_size=encoder_output_size, **args.extra_asr_decoder_conf, ) else: extra_asr_decoder = None # 8. MT extra decoder if getattr(args, "extra_mt_decoder", None) is not None: extra_mt_decoder_class = extra_mt_decoder_choices.get_class( args.extra_mt_decoder ) extra_mt_decoder = extra_mt_decoder_class( vocab_size=vocab_size, encoder_output_size=encoder_output_size, **args.extra_mt_decoder_conf, ) else: extra_asr_decoder = None # 8. Build model model = ESPnetSTModel( vocab_size=vocab_size, src_vocab_size=src_vocab_size, frontend=frontend, specaug=specaug, normalize=normalize, preencoder=preencoder, encoder=encoder, postencoder=postencoder, decoder=decoder, ctc=ctc, extra_asr_decoder=extra_asr_decoder, extra_mt_decoder=extra_mt_decoder, token_list=token_list, src_token_list=src_token_list, **args.model_conf, ) # FIXME(kamo): Should be done in model? # 9. Initialize if args.init is not None: initialize(model, args.init) assert check_return_type(model) return model