#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Thanks to Abdelrahman Mohamed and Wei-Ning Hsu's help in this implementation,
# Their origial Hubert work is in:
# Paper: https://arxiv.org/pdf/2106.07447.pdf
# Code in Fairseq: https://github.com/pytorch/fairseq/tree/master/examples/hubert
import argparse
import logging
from typing import Callable, Collection, Dict, List, Optional, Tuple, Union
import humanfriendly
import numpy as np
import torch
from typeguard import typechecked
from espnet2.asr.encoder.abs_encoder import AbsEncoder
from espnet2.asr.encoder.hubert_encoder import ( # noqa: H301
FairseqHubertPretrainEncoder,
TorchAudioHuBERTPretrainEncoder,
)
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.preencoder.abs_preencoder import AbsPreEncoder
from espnet2.asr.preencoder.sinc import LightweightSincConvs
from espnet2.asr.specaug.abs_specaug import AbsSpecAug
from espnet2.asr.specaug.specaug import SpecAug
from espnet2.hubert.espnet_model import (
HubertPretrainModel,
TorchAudioHubertPretrainModel,
)
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.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 HuBERTCollateFn
from espnet2.train.preprocessor import CommonPreprocessor
from espnet2.train.trainer import Trainer
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),
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,
),
type_check=AbsPreEncoder,
default=None,
optional=True,
)
encoder_choices = ClassChoices(
"encoder",
classes=dict(
hubert_pretrain=FairseqHubertPretrainEncoder,
torchaudio_hubert=TorchAudioHuBERTPretrainEncoder,
),
type_check=AbsEncoder,
default="hubert_pretrain",
)
model_choices = ClassChoices(
"model",
classes=dict(
fairseq=HubertPretrainModel,
torchaudio=TorchAudioHubertPretrainModel,
),
type_check=AbsESPnetModel,
default="fairseq",
)
[docs]class HubertTask(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,
# --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 += ["token_list"]
group.add_argument(
"--token_list",
type=str_or_none,
default=None,
help="A text mapping int-id to token",
)
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(
"--collate_fn_conf",
action=NestedDictAction,
default=dict(),
help="The keyword arguments for collate_fn class.",
)
group.add_argument(
"--input_size",
type=int_or_none,
default=None,
help="The number of input dimension of the feature",
)
group.add_argument(
"--num_classes",
type=int,
default=None,
help="The number of classes in hubert",
)
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 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",
)
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.",
)
parser.add_argument(
"--pred_masked_weight",
type=float,
default=1.0,
help="weight for predictive loss for masked frames",
)
parser.add_argument(
"--pred_nomask_weight",
type=float,
default=0.0,
help="weight for predictive loss for unmasked frames",
)
parser.add_argument(
"--loss_weights",
type=float,
default=0.0,
help="weights for additional loss terms (not first one)",
)
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]],
]:
# default sampling rate is 16000
fs = args.frontend_conf.get("fs", 16000)
if isinstance(fs, str):
fs = humanfriendly.parse_size(fs)
sample_rate = fs / 1000
if args.encoder_conf.get("extractor_conv_layer_config", None) is None:
# corresponding to default conv extractor
# refer to espnet2/asr/encoder/hubert_encoder.py
reception_field = 400
stride_field = 320
else:
stride_field, reception_field = 1, 1
for conv_config in args.encoder_conf["extractor_conv_layer_config"][::-1]:
_, kernel, stride = conv_config
stride_field *= stride
reception_field = stride * (reception_field - 1) + kernel
window_size = reception_field / sample_rate
window_shift = stride_field / sample_rate
return HuBERTCollateFn(
float_pad_value=0.0,
int_pad_value=-1,
label_downsampling=args.collate_fn_conf.get("label_downsampling", 1),
pad=args.collate_fn_conf.get("pad", False),
rand_crop=args.collate_fn_conf.get("rand_crop", True),
crop_audio=not args.collect_stats,
window_size=window_size,
window_shift=window_shift,
sample_rate=sample_rate,
)
[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 = 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,
# NOTE(kamo): Check attribute existence for backward compatibility
rir_scp=getattr(args, "rir_scp", None),
rir_apply_prob=getattr(args, "rir_apply_prob", 1.0),
noise_scp=getattr(args, "noise_scp", None),
noise_apply_prob=getattr(args, "noise_apply_prob", 1.0),
noise_db_range=getattr(args, "noise_db_range", "13_15"),
short_noise_thres=getattr(args, "short_noise_thres", 0.5),
speech_volume_normalize=getattr(args, "rir_scp", None),
**getattr(args, "preprocessor_conf", {}),
)
else:
retval = None
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, ...]:
retval = ()
return retval
[docs] @classmethod
@typechecked
def build_model(
cls, args: argparse.Namespace
) -> Union[HubertPretrainModel, TorchAudioHubertPretrainModel]:
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 }")
# 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 = {**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,
num_classes=args.num_classes,
**args.encoder_conf,
)
# 8. Build model
try:
model_class = model_choices.get_class(args.model)
except AttributeError:
model_class = model_choices.get_class("fairseq")
model = model_class(
vocab_size=vocab_size,
frontend=frontend,
specaug=specaug,
normalize=normalize,
preencoder=preencoder,
encoder=encoder,
token_list=token_list,
**args.model_conf,
)
# 9. Initialize
if args.init is not None:
initialize(model, args.init)
return model