mirror of
https://github.com/HumanAIGC/lite-avatar.git
synced 2026-02-05 18:09:20 +08:00
add files
This commit is contained in:
烨玮
376
funasr_local/tasks/data2vec.py
Normal file
376
funasr_local/tasks/data2vec.py
Normal file
@@ -0,0 +1,376 @@
|
||||
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 funasr_local.datasets.collate_fn import CommonCollateFn
|
||||
from funasr_local.datasets.preprocessor import CommonPreprocessor
|
||||
from funasr_local.layers.abs_normalize import AbsNormalize
|
||||
from funasr_local.layers.global_mvn import GlobalMVN
|
||||
from funasr_local.layers.utterance_mvn import UtteranceMVN
|
||||
from funasr_local.models.data2vec import Data2VecPretrainModel
|
||||
from funasr_local.models.encoder.abs_encoder import AbsEncoder
|
||||
from funasr_local.models.encoder.data2vec_encoder import Data2VecEncoder
|
||||
from funasr_local.models.frontend.abs_frontend import AbsFrontend
|
||||
from funasr_local.models.frontend.default import DefaultFrontend
|
||||
from funasr_local.models.frontend.windowing import SlidingWindow
|
||||
from funasr_local.models.preencoder.abs_preencoder import AbsPreEncoder
|
||||
from funasr_local.models.preencoder.sinc import LightweightSincConvs
|
||||
from funasr_local.models.specaug.abs_specaug import AbsSpecAug
|
||||
from funasr_local.models.specaug.specaug import SpecAug
|
||||
from funasr_local.tasks.abs_task import AbsTask
|
||||
from funasr_local.text.phoneme_tokenizer import g2p_choices
|
||||
from funasr_local.torch_utils.initialize import initialize
|
||||
from funasr_local.train.class_choices import ClassChoices
|
||||
from funasr_local.train.trainer import Trainer
|
||||
from funasr_local.utils.types import float_or_none
|
||||
from funasr_local.utils.types import int_or_none
|
||||
from funasr_local.utils.types import str2bool
|
||||
from funasr_local.utils.types import 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=None,
|
||||
optional=True,
|
||||
)
|
||||
preencoder_choices = ClassChoices(
|
||||
name="preencoder",
|
||||
classes=dict(
|
||||
sinc=LightweightSincConvs,
|
||||
),
|
||||
type_check=AbsPreEncoder,
|
||||
default=None,
|
||||
optional=True,
|
||||
)
|
||||
encoder_choices = ClassChoices(
|
||||
"encoder",
|
||||
classes=dict(
|
||||
data2vec_encoder=Data2VecEncoder,
|
||||
),
|
||||
type_check=AbsEncoder,
|
||||
default="data2vec_encoder",
|
||||
)
|
||||
model_choices = ClassChoices(
|
||||
"model",
|
||||
classes=dict(
|
||||
data2vec=Data2VecPretrainModel,
|
||||
),
|
||||
default="data2vec",
|
||||
)
|
||||
|
||||
|
||||
class Data2VecTask(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
|
||||
|
||||
@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
|
||||
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(
|
||||
"--input_size",
|
||||
type=int_or_none,
|
||||
default=None,
|
||||
help="The number of input dimension of the feature",
|
||||
)
|
||||
|
||||
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=None,
|
||||
choices=["bpe", "char", "word", "phn"],
|
||||
help="The text will be tokenized " "in the specified level token",
|
||||
)
|
||||
|
||||
group.add_argument(
|
||||
"--feats_type",
|
||||
type=str,
|
||||
default='fbank',
|
||||
help="feats type, e.g. fbank, wav, ark_wav(needed to be scale normalization)",
|
||||
)
|
||||
|
||||
group.add_argument(
|
||||
"--bpemodel",
|
||||
type=str_or_none,
|
||||
default=None,
|
||||
help="The model file of sentencepiece",
|
||||
)
|
||||
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(
|
||||
"--speech_volume_normalize",
|
||||
type=float_or_none,
|
||||
default=None,
|
||||
help="Scale the maximum amplitude to the given value.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--rir_scp",
|
||||
type=str_or_none,
|
||||
default=None,
|
||||
help="The file path of rir scp file.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--rir_apply_prob",
|
||||
type=float,
|
||||
default=1.0,
|
||||
help="THe probability for applying RIR convolution.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--noise_scp",
|
||||
type=str_or_none,
|
||||
default=None,
|
||||
help="The file path of noise scp file.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--noise_apply_prob",
|
||||
type=float,
|
||||
default=1.0,
|
||||
help="The probability applying Noise adding.",
|
||||
)
|
||||
parser.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)
|
||||
|
||||
@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()
|
||||
return CommonCollateFn(clipping=True)
|
||||
|
||||
@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:
|
||||
retval = CommonPreprocessor(
|
||||
train=train,
|
||||
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=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
|
||||
assert check_return_type(retval)
|
||||
return retval
|
||||
|
||||
@classmethod
|
||||
def required_data_names(
|
||||
cls, train: bool = True, inference: bool = False
|
||||
) -> Tuple[str, ...]:
|
||||
# for pre-training
|
||||
retval = ("speech",)
|
||||
return retval
|
||||
|
||||
@classmethod
|
||||
def optional_data_names(
|
||||
cls, train: bool = True, inference: bool = False
|
||||
) -> Tuple[str, ...]:
|
||||
retval = ()
|
||||
assert check_return_type(retval)
|
||||
return retval
|
||||
|
||||
@classmethod
|
||||
def build_model(cls, args: argparse.Namespace):
|
||||
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. 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
|
||||
|
||||
# 5. Encoder
|
||||
encoder_class = encoder_choices.get_class(args.encoder)
|
||||
encoder = encoder_class(
|
||||
input_size=input_size,
|
||||
**args.encoder_conf,
|
||||
)
|
||||
|
||||
# 6. Build model
|
||||
try:
|
||||
model_class = model_choices.get_class(args.model)
|
||||
except AttributeError:
|
||||
model_class = model_choices.get_class("data2vec")
|
||||
model = model_class(
|
||||
frontend=frontend,
|
||||
specaug=specaug,
|
||||
normalize=normalize,
|
||||
preencoder=preencoder,
|
||||
encoder=encoder,
|
||||
)
|
||||
|
||||
# 7. Initialize
|
||||
if args.init is not None:
|
||||
initialize(model, args.init)
|
||||
|
||||
assert check_return_type(model)
|
||||
return model
|
||||
Reference in New Issue
Block a user