mirror of
https://github.com/modelscope/FunASR
synced 2025-09-15 14:48:36 +08:00
update
This commit is contained in:
speech_asr
parent
43c30967b0
commit
68852c3072
259
funasr/utils/build_asr_model.py
Normal file
259
funasr/utils/build_asr_model.py
Normal file
@ -0,0 +1,259 @@
|
|||||||
|
import logging
|
||||||
|
|
||||||
|
from funasr.layers.global_mvn import GlobalMVN
|
||||||
|
from funasr.layers.utterance_mvn import UtteranceMVN
|
||||||
|
from funasr.models.ctc import CTC
|
||||||
|
from funasr.models.decoder.abs_decoder import AbsDecoder
|
||||||
|
from funasr.models.decoder.contextual_decoder import ContextualParaformerDecoder
|
||||||
|
from funasr.models.decoder.rnn_decoder import RNNDecoder
|
||||||
|
from funasr.models.decoder.sanm_decoder import ParaformerSANMDecoder, FsmnDecoderSCAMAOpt
|
||||||
|
from funasr.models.decoder.transformer_decoder import (
|
||||||
|
DynamicConvolution2DTransformerDecoder, # noqa: H301
|
||||||
|
)
|
||||||
|
from funasr.models.decoder.transformer_decoder import DynamicConvolutionTransformerDecoder
|
||||||
|
from funasr.models.decoder.transformer_decoder import (
|
||||||
|
LightweightConvolution2DTransformerDecoder, # noqa: H301
|
||||||
|
)
|
||||||
|
from funasr.models.decoder.transformer_decoder import (
|
||||||
|
LightweightConvolutionTransformerDecoder, # noqa: H301
|
||||||
|
)
|
||||||
|
from funasr.models.decoder.transformer_decoder import ParaformerDecoderSAN
|
||||||
|
from funasr.models.decoder.transformer_decoder import TransformerDecoder
|
||||||
|
from funasr.models.e2e_asr import ESPnetASRModel
|
||||||
|
from funasr.models.e2e_asr_mfcca import MFCCA
|
||||||
|
from funasr.models.e2e_asr_paraformer import Paraformer, ParaformerBert, BiCifParaformer, ContextualParaformer
|
||||||
|
from funasr.models.e2e_tp import TimestampPredictor
|
||||||
|
from funasr.models.e2e_uni_asr import UniASR
|
||||||
|
from funasr.models.encoder.conformer_encoder import ConformerEncoder
|
||||||
|
from funasr.models.encoder.data2vec_encoder import Data2VecEncoder
|
||||||
|
from funasr.models.encoder.mfcca_encoder import MFCCAEncoder
|
||||||
|
from funasr.models.encoder.rnn_encoder import RNNEncoder
|
||||||
|
from funasr.models.encoder.sanm_encoder import SANMEncoder, SANMEncoderChunkOpt
|
||||||
|
from funasr.models.encoder.transformer_encoder import TransformerEncoder
|
||||||
|
from funasr.models.frontend.default import DefaultFrontend
|
||||||
|
from funasr.models.frontend.default import MultiChannelFrontend
|
||||||
|
from funasr.models.frontend.fused import FusedFrontends
|
||||||
|
from funasr.models.frontend.s3prl import S3prlFrontend
|
||||||
|
from funasr.models.frontend.wav_frontend import WavFrontend
|
||||||
|
from funasr.models.frontend.windowing import SlidingWindow
|
||||||
|
from funasr.models.predictor.cif import CifPredictor, CifPredictorV2, CifPredictorV3
|
||||||
|
from funasr.models.specaug.specaug import SpecAug
|
||||||
|
from funasr.models.specaug.specaug import SpecAugLFR
|
||||||
|
from funasr.modules.subsampling import Conv1dSubsampling
|
||||||
|
from funasr.train.class_choices import ClassChoices
|
||||||
|
|
||||||
|
frontend_choices = ClassChoices(
|
||||||
|
name="frontend",
|
||||||
|
classes=dict(
|
||||||
|
default=DefaultFrontend,
|
||||||
|
sliding_window=SlidingWindow,
|
||||||
|
s3prl=S3prlFrontend,
|
||||||
|
fused=FusedFrontends,
|
||||||
|
wav_frontend=WavFrontend,
|
||||||
|
multichannelfrontend=MultiChannelFrontend,
|
||||||
|
),
|
||||||
|
default="default",
|
||||||
|
)
|
||||||
|
specaug_choices = ClassChoices(
|
||||||
|
name="specaug",
|
||||||
|
classes=dict(
|
||||||
|
specaug=SpecAug,
|
||||||
|
specaug_lfr=SpecAugLFR,
|
||||||
|
),
|
||||||
|
default=None,
|
||||||
|
optional=True,
|
||||||
|
)
|
||||||
|
normalize_choices = ClassChoices(
|
||||||
|
"normalize",
|
||||||
|
classes=dict(
|
||||||
|
global_mvn=GlobalMVN,
|
||||||
|
utterance_mvn=UtteranceMVN,
|
||||||
|
),
|
||||||
|
default=None,
|
||||||
|
optional=True,
|
||||||
|
)
|
||||||
|
model_choices = ClassChoices(
|
||||||
|
"model",
|
||||||
|
classes=dict(
|
||||||
|
asr=ESPnetASRModel,
|
||||||
|
uniasr=UniASR,
|
||||||
|
paraformer=Paraformer,
|
||||||
|
paraformer_bert=ParaformerBert,
|
||||||
|
bicif_paraformer=BiCifParaformer,
|
||||||
|
contextual_paraformer=ContextualParaformer,
|
||||||
|
mfcca=MFCCA,
|
||||||
|
timestamp_prediction=TimestampPredictor,
|
||||||
|
),
|
||||||
|
default="asr",
|
||||||
|
)
|
||||||
|
encoder_choices = ClassChoices(
|
||||||
|
"encoder",
|
||||||
|
classes=dict(
|
||||||
|
conformer=ConformerEncoder,
|
||||||
|
transformer=TransformerEncoder,
|
||||||
|
rnn=RNNEncoder,
|
||||||
|
sanm=SANMEncoder,
|
||||||
|
sanm_chunk_opt=SANMEncoderChunkOpt,
|
||||||
|
data2vec_encoder=Data2VecEncoder,
|
||||||
|
mfcca_enc=MFCCAEncoder,
|
||||||
|
),
|
||||||
|
default="rnn",
|
||||||
|
)
|
||||||
|
encoder_choices2 = ClassChoices(
|
||||||
|
"encoder2",
|
||||||
|
classes=dict(
|
||||||
|
conformer=ConformerEncoder,
|
||||||
|
transformer=TransformerEncoder,
|
||||||
|
rnn=RNNEncoder,
|
||||||
|
sanm=SANMEncoder,
|
||||||
|
sanm_chunk_opt=SANMEncoderChunkOpt,
|
||||||
|
),
|
||||||
|
default="rnn",
|
||||||
|
)
|
||||||
|
decoder_choices = ClassChoices(
|
||||||
|
"decoder",
|
||||||
|
classes=dict(
|
||||||
|
transformer=TransformerDecoder,
|
||||||
|
lightweight_conv=LightweightConvolutionTransformerDecoder,
|
||||||
|
lightweight_conv2d=LightweightConvolution2DTransformerDecoder,
|
||||||
|
dynamic_conv=DynamicConvolutionTransformerDecoder,
|
||||||
|
dynamic_conv2d=DynamicConvolution2DTransformerDecoder,
|
||||||
|
rnn=RNNDecoder,
|
||||||
|
fsmn_scama_opt=FsmnDecoderSCAMAOpt,
|
||||||
|
paraformer_decoder_sanm=ParaformerSANMDecoder,
|
||||||
|
paraformer_decoder_san=ParaformerDecoderSAN,
|
||||||
|
contextual_paraformer_decoder=ContextualParaformerDecoder,
|
||||||
|
),
|
||||||
|
default="rnn",
|
||||||
|
)
|
||||||
|
decoder_choices2 = ClassChoices(
|
||||||
|
"decoder2",
|
||||||
|
classes=dict(
|
||||||
|
transformer=TransformerDecoder,
|
||||||
|
lightweight_conv=LightweightConvolutionTransformerDecoder,
|
||||||
|
lightweight_conv2d=LightweightConvolution2DTransformerDecoder,
|
||||||
|
dynamic_conv=DynamicConvolutionTransformerDecoder,
|
||||||
|
dynamic_conv2d=DynamicConvolution2DTransformerDecoder,
|
||||||
|
rnn=RNNDecoder,
|
||||||
|
fsmn_scama_opt=FsmnDecoderSCAMAOpt,
|
||||||
|
paraformer_decoder_sanm=ParaformerSANMDecoder,
|
||||||
|
),
|
||||||
|
type_check=AbsDecoder,
|
||||||
|
default="rnn",
|
||||||
|
)
|
||||||
|
predictor_choices = ClassChoices(
|
||||||
|
name="predictor",
|
||||||
|
classes=dict(
|
||||||
|
cif_predictor=CifPredictor,
|
||||||
|
ctc_predictor=None,
|
||||||
|
cif_predictor_v2=CifPredictorV2,
|
||||||
|
cif_predictor_v3=CifPredictorV3,
|
||||||
|
),
|
||||||
|
default="cif_predictor",
|
||||||
|
optional=True,
|
||||||
|
)
|
||||||
|
predictor_choices2 = ClassChoices(
|
||||||
|
name="predictor2",
|
||||||
|
classes=dict(
|
||||||
|
cif_predictor=CifPredictor,
|
||||||
|
ctc_predictor=None,
|
||||||
|
cif_predictor_v2=CifPredictorV2,
|
||||||
|
),
|
||||||
|
default="cif_predictor",
|
||||||
|
optional=True,
|
||||||
|
)
|
||||||
|
stride_conv_choices = ClassChoices(
|
||||||
|
name="stride_conv",
|
||||||
|
classes=dict(
|
||||||
|
stride_conv1d=Conv1dSubsampling
|
||||||
|
),
|
||||||
|
default="stride_conv1d",
|
||||||
|
optional=True,
|
||||||
|
)
|
||||||
|
class_choices_list = [
|
||||||
|
# --frontend and --frontend_conf
|
||||||
|
frontend_choices,
|
||||||
|
# --specaug and --specaug_conf
|
||||||
|
specaug_choices,
|
||||||
|
# --normalize and --normalize_conf
|
||||||
|
normalize_choices,
|
||||||
|
# --model and --model_conf
|
||||||
|
model_choices,
|
||||||
|
# --encoder and --encoder_conf
|
||||||
|
encoder_choices,
|
||||||
|
# --decoder and --decoder_conf
|
||||||
|
decoder_choices,
|
||||||
|
# --predictor and --predictor_conf
|
||||||
|
predictor_choices,
|
||||||
|
# --encoder2 and --encoder2_conf
|
||||||
|
encoder_choices2,
|
||||||
|
# --decoder2 and --decoder2_conf
|
||||||
|
decoder_choices2,
|
||||||
|
# --predictor2 and --predictor2_conf
|
||||||
|
predictor_choices2,
|
||||||
|
# --stride_conv and --stride_conv_conf
|
||||||
|
stride_conv_choices,
|
||||||
|
]
|
||||||
|
|
||||||
|
def build_asr_model(args):
|
||||||
|
# token_list
|
||||||
|
if args.token_list is not None:
|
||||||
|
with open(args.token_list) as f:
|
||||||
|
token_list = [line.rstrip() for line in f]
|
||||||
|
args.token_list = list(token_list)
|
||||||
|
vocab_size = len(token_list)
|
||||||
|
logging.info(f"Vocabulary size: {vocab_size}")
|
||||||
|
else:
|
||||||
|
vocab_size = None
|
||||||
|
|
||||||
|
# frontend
|
||||||
|
if args.input_size is None:
|
||||||
|
# Extract features in the model
|
||||||
|
frontend_class = frontend_choices.get_class(args.frontend)
|
||||||
|
if args.frontend == 'wav_frontend':
|
||||||
|
frontend = frontend_class(cmvn_file=args.cmvn_file, **args.frontend_conf)
|
||||||
|
else:
|
||||||
|
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
|
||||||
|
|
||||||
|
# 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
|
||||||
|
|
||||||
|
# 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
|
||||||
|
|
||||||
|
# encoder
|
||||||
|
encoder_class = encoder_choices.get_class(args.encoder)
|
||||||
|
encoder = encoder_class(input_size=input_size, **args.encoder_conf)
|
||||||
|
|
||||||
|
# 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,
|
||||||
|
)
|
||||||
|
|
||||||
|
# ctc
|
||||||
|
ctc = CTC(
|
||||||
|
odim=vocab_size, encoder_output_size=encoder.output_size(), **args.ctc_conf
|
||||||
|
)
|
||||||
|
|
||||||
|
if args.model == "asr":
|
||||||
|
model
|
||||||
|
|
||||||
|
|
||||||
@ -1,233 +1,10 @@
|
|||||||
import logging
|
from funasr.utils.build_asr_model import build_asr_model
|
||||||
|
|
||||||
from funasr.layers.global_mvn import GlobalMVN
|
|
||||||
from funasr.layers.utterance_mvn import UtteranceMVN
|
|
||||||
from funasr.models.ctc import CTC
|
|
||||||
from funasr.models.decoder.abs_decoder import AbsDecoder
|
|
||||||
from funasr.models.decoder.contextual_decoder import ContextualParaformerDecoder
|
|
||||||
from funasr.models.decoder.rnn_decoder import RNNDecoder
|
|
||||||
from funasr.models.decoder.sanm_decoder import ParaformerSANMDecoder, FsmnDecoderSCAMAOpt
|
|
||||||
from funasr.models.decoder.transformer_decoder import (
|
|
||||||
DynamicConvolution2DTransformerDecoder, # noqa: H301
|
|
||||||
)
|
|
||||||
from funasr.models.decoder.transformer_decoder import DynamicConvolutionTransformerDecoder
|
|
||||||
from funasr.models.decoder.transformer_decoder import (
|
|
||||||
LightweightConvolution2DTransformerDecoder, # noqa: H301
|
|
||||||
)
|
|
||||||
from funasr.models.decoder.transformer_decoder import (
|
|
||||||
LightweightConvolutionTransformerDecoder, # noqa: H301
|
|
||||||
)
|
|
||||||
from funasr.models.decoder.transformer_decoder import ParaformerDecoderSAN
|
|
||||||
from funasr.models.decoder.transformer_decoder import TransformerDecoder
|
|
||||||
from funasr.models.e2e_asr import ESPnetASRModel
|
|
||||||
from funasr.models.e2e_asr_mfcca import MFCCA
|
|
||||||
from funasr.models.e2e_asr_paraformer import Paraformer, ParaformerBert, BiCifParaformer, ContextualParaformer
|
|
||||||
from funasr.models.e2e_tp import TimestampPredictor
|
|
||||||
from funasr.models.e2e_uni_asr import UniASR
|
|
||||||
from funasr.models.encoder.conformer_encoder import ConformerEncoder
|
|
||||||
from funasr.models.encoder.data2vec_encoder import Data2VecEncoder
|
|
||||||
from funasr.models.encoder.mfcca_encoder import MFCCAEncoder
|
|
||||||
from funasr.models.encoder.rnn_encoder import RNNEncoder
|
|
||||||
from funasr.models.encoder.sanm_encoder import SANMEncoder, SANMEncoderChunkOpt
|
|
||||||
from funasr.models.encoder.transformer_encoder import TransformerEncoder
|
|
||||||
from funasr.models.frontend.default import DefaultFrontend
|
|
||||||
from funasr.models.frontend.default import MultiChannelFrontend
|
|
||||||
from funasr.models.frontend.fused import FusedFrontends
|
|
||||||
from funasr.models.frontend.s3prl import S3prlFrontend
|
|
||||||
from funasr.models.frontend.wav_frontend import WavFrontend
|
|
||||||
from funasr.models.frontend.windowing import SlidingWindow
|
|
||||||
from funasr.models.predictor.cif import CifPredictor, CifPredictorV2, CifPredictorV3
|
|
||||||
from funasr.models.specaug.specaug import SpecAug
|
|
||||||
from funasr.models.specaug.specaug import SpecAugLFR
|
|
||||||
from funasr.modules.subsampling import Conv1dSubsampling
|
|
||||||
from funasr.train.class_choices import ClassChoices
|
|
||||||
|
|
||||||
frontend_choices = ClassChoices(
|
|
||||||
name="frontend",
|
|
||||||
classes=dict(
|
|
||||||
default=DefaultFrontend,
|
|
||||||
sliding_window=SlidingWindow,
|
|
||||||
s3prl=S3prlFrontend,
|
|
||||||
fused=FusedFrontends,
|
|
||||||
wav_frontend=WavFrontend,
|
|
||||||
multichannelfrontend=MultiChannelFrontend,
|
|
||||||
),
|
|
||||||
default="default",
|
|
||||||
)
|
|
||||||
specaug_choices = ClassChoices(
|
|
||||||
name="specaug",
|
|
||||||
classes=dict(
|
|
||||||
specaug=SpecAug,
|
|
||||||
specaug_lfr=SpecAugLFR,
|
|
||||||
),
|
|
||||||
default=None,
|
|
||||||
optional=True,
|
|
||||||
)
|
|
||||||
normalize_choices = ClassChoices(
|
|
||||||
"normalize",
|
|
||||||
classes=dict(
|
|
||||||
global_mvn=GlobalMVN,
|
|
||||||
utterance_mvn=UtteranceMVN,
|
|
||||||
),
|
|
||||||
default=None,
|
|
||||||
optional=True,
|
|
||||||
)
|
|
||||||
model_choices = ClassChoices(
|
|
||||||
"model",
|
|
||||||
classes=dict(
|
|
||||||
asr=ESPnetASRModel,
|
|
||||||
uniasr=UniASR,
|
|
||||||
paraformer=Paraformer,
|
|
||||||
paraformer_bert=ParaformerBert,
|
|
||||||
bicif_paraformer=BiCifParaformer,
|
|
||||||
contextual_paraformer=ContextualParaformer,
|
|
||||||
mfcca=MFCCA,
|
|
||||||
timestamp_prediction=TimestampPredictor,
|
|
||||||
),
|
|
||||||
default="asr",
|
|
||||||
)
|
|
||||||
encoder_choices = ClassChoices(
|
|
||||||
"encoder",
|
|
||||||
classes=dict(
|
|
||||||
conformer=ConformerEncoder,
|
|
||||||
transformer=TransformerEncoder,
|
|
||||||
rnn=RNNEncoder,
|
|
||||||
sanm=SANMEncoder,
|
|
||||||
sanm_chunk_opt=SANMEncoderChunkOpt,
|
|
||||||
data2vec_encoder=Data2VecEncoder,
|
|
||||||
mfcca_enc=MFCCAEncoder,
|
|
||||||
),
|
|
||||||
default="rnn",
|
|
||||||
)
|
|
||||||
encoder_choices2 = ClassChoices(
|
|
||||||
"encoder2",
|
|
||||||
classes=dict(
|
|
||||||
conformer=ConformerEncoder,
|
|
||||||
transformer=TransformerEncoder,
|
|
||||||
rnn=RNNEncoder,
|
|
||||||
sanm=SANMEncoder,
|
|
||||||
sanm_chunk_opt=SANMEncoderChunkOpt,
|
|
||||||
),
|
|
||||||
default="rnn",
|
|
||||||
)
|
|
||||||
decoder_choices = ClassChoices(
|
|
||||||
"decoder",
|
|
||||||
classes=dict(
|
|
||||||
transformer=TransformerDecoder,
|
|
||||||
lightweight_conv=LightweightConvolutionTransformerDecoder,
|
|
||||||
lightweight_conv2d=LightweightConvolution2DTransformerDecoder,
|
|
||||||
dynamic_conv=DynamicConvolutionTransformerDecoder,
|
|
||||||
dynamic_conv2d=DynamicConvolution2DTransformerDecoder,
|
|
||||||
rnn=RNNDecoder,
|
|
||||||
fsmn_scama_opt=FsmnDecoderSCAMAOpt,
|
|
||||||
paraformer_decoder_sanm=ParaformerSANMDecoder,
|
|
||||||
paraformer_decoder_san=ParaformerDecoderSAN,
|
|
||||||
contextual_paraformer_decoder=ContextualParaformerDecoder,
|
|
||||||
),
|
|
||||||
default="rnn",
|
|
||||||
)
|
|
||||||
decoder_choices2 = ClassChoices(
|
|
||||||
"decoder2",
|
|
||||||
classes=dict(
|
|
||||||
transformer=TransformerDecoder,
|
|
||||||
lightweight_conv=LightweightConvolutionTransformerDecoder,
|
|
||||||
lightweight_conv2d=LightweightConvolution2DTransformerDecoder,
|
|
||||||
dynamic_conv=DynamicConvolutionTransformerDecoder,
|
|
||||||
dynamic_conv2d=DynamicConvolution2DTransformerDecoder,
|
|
||||||
rnn=RNNDecoder,
|
|
||||||
fsmn_scama_opt=FsmnDecoderSCAMAOpt,
|
|
||||||
paraformer_decoder_sanm=ParaformerSANMDecoder,
|
|
||||||
),
|
|
||||||
type_check=AbsDecoder,
|
|
||||||
default="rnn",
|
|
||||||
)
|
|
||||||
predictor_choices = ClassChoices(
|
|
||||||
name="predictor",
|
|
||||||
classes=dict(
|
|
||||||
cif_predictor=CifPredictor,
|
|
||||||
ctc_predictor=None,
|
|
||||||
cif_predictor_v2=CifPredictorV2,
|
|
||||||
cif_predictor_v3=CifPredictorV3,
|
|
||||||
),
|
|
||||||
default="cif_predictor",
|
|
||||||
optional=True,
|
|
||||||
)
|
|
||||||
predictor_choices2 = ClassChoices(
|
|
||||||
name="predictor2",
|
|
||||||
classes=dict(
|
|
||||||
cif_predictor=CifPredictor,
|
|
||||||
ctc_predictor=None,
|
|
||||||
cif_predictor_v2=CifPredictorV2,
|
|
||||||
),
|
|
||||||
default="cif_predictor",
|
|
||||||
optional=True,
|
|
||||||
)
|
|
||||||
stride_conv_choices = ClassChoices(
|
|
||||||
name="stride_conv",
|
|
||||||
classes=dict(
|
|
||||||
stride_conv1d=Conv1dSubsampling
|
|
||||||
),
|
|
||||||
default="stride_conv1d",
|
|
||||||
optional=True,
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
def build_model(args):
|
def build_model(args):
|
||||||
# token_list
|
if args.task_name == "asr":
|
||||||
if args.token_list is not None:
|
model = build_asr_model(args)
|
||||||
with open(args.token_list) as f:
|
|
||||||
token_list = [line.rstrip() for line in f]
|
|
||||||
args.token_list = list(token_list)
|
|
||||||
vocab_size = len(token_list)
|
|
||||||
logging.info(f"Vocabulary size: {vocab_size}")
|
|
||||||
else:
|
else:
|
||||||
vocab_size = None
|
raise NotImplementedError("Not supported task: {}".format(args.task_name))
|
||||||
|
|
||||||
# frontend
|
return model
|
||||||
if args.input_size is None:
|
|
||||||
# Extract features in the model
|
|
||||||
frontend_class = frontend_choices.get_class(args.frontend)
|
|
||||||
if args.frontend == 'wav_frontend':
|
|
||||||
frontend = frontend_class(cmvn_file=args.cmvn_file, **args.frontend_conf)
|
|
||||||
else:
|
|
||||||
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
|
|
||||||
|
|
||||||
# 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
|
|
||||||
|
|
||||||
# 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
|
|
||||||
|
|
||||||
# encoder
|
|
||||||
encoder_class = encoder_choices.get_class(args.encoder)
|
|
||||||
encoder = encoder_class(input_size=input_size, **args.encoder_conf)
|
|
||||||
|
|
||||||
# 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,
|
|
||||||
)
|
|
||||||
|
|
||||||
# ctc
|
|
||||||
ctc = CTC(
|
|
||||||
odim=vocab_size, encoder_output_size=encoder.output_size(), **args.ctc_conf
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user