# MIT License
#
# Copyright (c) 2021 Soohwan Kim and Sangchun Ha and Soyoung Cho
#
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# of this software and associated documentation files (the "Software"), to deal
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# SOFTWARE.
from omegaconf import DictConfig
from openspeech.models import register_model, OpenspeechEncoderDecoderModel
from openspeech.decoders import LSTMAttentionDecoder
from openspeech.encoders import LSTMEncoder, ConvolutionalLSTMEncoder
from openspeech.tokenizers.tokenizer import Tokenizer
from openspeech.models.listen_attend_spell.configurations import (
ListenAttendSpellConfigs,
JointCTCListenAttendSpellConfigs,
ListenAttendSpellWithLocationAwareConfigs,
ListenAttendSpellWithMultiHeadConfigs,
DeepCNNWithJointCTCListenAttendSpellConfigs,
)
[docs]@register_model('listen_attend_spell', dataclass=ListenAttendSpellConfigs)
class ListenAttendSpellModel(OpenspeechEncoderDecoderModel):
r"""
Listen, Attend and Spell model with configurable encoder and decoder.
Paper: https://arxiv.org/abs/1508.01211
Args:
configs (DictConfig): configuration set.
tokenizer (Tokeizer): tokenizer is in charge of preparing the inputs for a model.
Inputs:
- **inputs** (torch.FloatTensor): A input sequence passed to encoders. Typically for inputs this will be a padded `FloatTensor` of size ``(batch, seq_length, dimension)``.
- **input_lengths** (torch.LongTensor): The length of input tensor. ``(batch)``
Returns:
outputs (dict): Result of model predictions.
"""
def __init__(self, configs: DictConfig, tokenizer: Tokenizer) -> None:
super(ListenAttendSpellModel, self).__init__(configs, tokenizer)
def build_model(self):
self.encoder = LSTMEncoder(
input_dim=self.configs.audio.num_mels,
num_layers=self.configs.model.num_encoder_layers,
num_classes=self.num_classes,
hidden_state_dim=self.configs.model.hidden_state_dim,
dropout_p=self.configs.model.encoder_dropout_p,
bidirectional=self.configs.model.encoder_bidirectional,
rnn_type=self.configs.model.rnn_type,
joint_ctc_attention=self.configs.model.joint_ctc_attention,
)
decoder_hidden_state_dim = self.configs.model.hidden_state_dim << 1 \
if self.configs.model.encoder_bidirectional \
else self.configs.model.hidden_state_dim
self.decoder = LSTMAttentionDecoder(
num_classes=self.num_classes,
max_length=self.configs.model.max_length,
hidden_state_dim=decoder_hidden_state_dim,
pad_id=self.tokenizer.pad_id,
sos_id=self.tokenizer.sos_id,
eos_id=self.tokenizer.eos_id,
num_heads=self.configs.model.num_attention_heads,
dropout_p=self.configs.model.decoder_dropout_p,
num_layers=self.configs.model.num_decoder_layers,
attn_mechanism=self.configs.model.decoder_attn_mechanism,
rnn_type=self.configs.model.rnn_type,
)
[docs] def set_beam_decoder(self, beam_size: int = 3):
""" Setting beam search decoder """
from openspeech.search import BeamSearchLSTM
self.decoder = BeamSearchLSTM(
decoder=self.decoder,
beam_size=beam_size,
)
[docs]@register_model('listen_attend_spell_with_location_aware', dataclass=ListenAttendSpellWithLocationAwareConfigs)
class ListenAttendSpellWithLocationAwareModel(OpenspeechEncoderDecoderModel):
r"""
Listen, Attend and Spell model with configurable encoder and decoder.
Paper: https://arxiv.org/abs/1508.01211
Args:
configs (DictConfig): configuration set.
tokenizer (Tokeizer): tokenizer is in charge of preparing the inputs for a model.
Inputs:
- **inputs** (torch.FloatTensor): A input sequence passed to encoders. Typically for inputs this will be a padded `FloatTensor` of size ``(batch, seq_length, dimension)``.
- **input_lengths** (torch.LongTensor): The length of input tensor. ``(batch)``
Returns:
outputs (dict): Result of model predictions.
"""
def __init__(self, configs: DictConfig, tokenizer: Tokenizer) -> None:
super(ListenAttendSpellWithLocationAwareModel, self).__init__(configs, tokenizer)
def build_model(self):
self.encoder = LSTMEncoder(
input_dim=self.configs.audio.num_mels,
num_layers=self.configs.model.num_encoder_layers,
num_classes=self.num_classes,
hidden_state_dim=self.configs.model.hidden_state_dim,
dropout_p=self.configs.model.encoder_dropout_p,
bidirectional=self.configs.model.encoder_bidirectional,
rnn_type=self.configs.model.rnn_type,
joint_ctc_attention=self.configs.model.joint_ctc_attention,
)
decoder_hidden_state_dim = self.configs.model.hidden_state_dim << 1 \
if self.configs.model.encoder_bidirectional \
else self.configs.model.hidden_state_dim
self.decoder = LSTMAttentionDecoder(
num_classes=self.num_classes,
max_length=self.configs.model.max_length,
hidden_state_dim=decoder_hidden_state_dim,
pad_id=self.tokenizer.pad_id,
sos_id=self.tokenizer.sos_id,
eos_id=self.tokenizer.eos_id,
num_heads=self.configs.model.num_attention_heads,
dropout_p=self.configs.model.decoder_dropout_p,
num_layers=self.configs.model.num_decoder_layers,
attn_mechanism=self.configs.model.decoder_attn_mechanism,
rnn_type=self.configs.model.rnn_type,
)
[docs] def set_beam_decoder(self, beam_size: int = 3):
""" Setting beam search decoder """
from openspeech.search import BeamSearchLSTM
self.decoder = BeamSearchLSTM(
decoder=self.decoder,
beam_size=beam_size,
)
[docs]@register_model('listen_attend_spell_with_multi_head', dataclass=ListenAttendSpellWithMultiHeadConfigs)
class ListenAttendSpellWithMultiHeadModel(OpenspeechEncoderDecoderModel):
r"""
Listen, Attend and Spell model with configurable encoder and decoder.
Paper: https://arxiv.org/abs/1508.01211
Args:
configs (DictConfig): configuration set.
tokenizer (Tokeizer): tokenizer is in charge of preparing the inputs for a model.
Inputs:
- **inputs** (torch.FloatTensor): A input sequence passed to encoders. Typically for inputs this will be a padded `FloatTensor` of size ``(batch, seq_length, dimension)``.
- **input_lengths** (torch.LongTensor): The length of input tensor. ``(batch)``
Returns:
outputs (dict): Result of model predictions.
"""
def __init__(self, configs: DictConfig, tokenizer: Tokenizer) -> None:
super(ListenAttendSpellWithMultiHeadModel, self).__init__(configs, tokenizer)
def build_model(self):
self.encoder = LSTMEncoder(
input_dim=self.configs.audio.num_mels,
num_layers=self.configs.model.num_encoder_layers,
num_classes=self.num_classes,
hidden_state_dim=self.configs.model.hidden_state_dim,
dropout_p=self.configs.model.encoder_dropout_p,
bidirectional=self.configs.model.encoder_bidirectional,
rnn_type=self.configs.model.rnn_type,
joint_ctc_attention=self.configs.model.joint_ctc_attention,
)
decoder_hidden_state_dim = self.configs.model.hidden_state_dim << 1 \
if self.configs.model.encoder_bidirectional \
else self.configs.model.hidden_state_dim
self.decoder = LSTMAttentionDecoder(
num_classes=self.num_classes,
max_length=self.configs.model.max_length,
hidden_state_dim=decoder_hidden_state_dim,
pad_id=self.tokenizer.pad_id,
sos_id=self.tokenizer.sos_id,
eos_id=self.tokenizer.eos_id,
num_heads=self.configs.model.num_attention_heads,
dropout_p=self.configs.model.decoder_dropout_p,
num_layers=self.configs.model.num_decoder_layers,
attn_mechanism=self.configs.model.decoder_attn_mechanism,
rnn_type=self.configs.model.rnn_type,
)
[docs] def set_beam_decoder(self, beam_size: int = 3):
""" Setting beam search decoder """
from openspeech.search import BeamSearchLSTM
self.decoder = BeamSearchLSTM(
decoder=self.decoder,
beam_size=beam_size,
)
[docs]@register_model('joint_ctc_listen_attend_spell', dataclass=JointCTCListenAttendSpellConfigs)
class JointCTCListenAttendSpellModel(OpenspeechEncoderDecoderModel):
r"""
Joint CTC-Attention Listen, Attend and Spell model with configurable encoder and decoder.
Paper: https://arxiv.org/abs/1609.06773
Args:
configs (DictConfig): configuration set.
tokenizer (Tokeizer): tokenizer is in charge of preparing the inputs for a model.
Inputs:
- **inputs** (torch.FloatTensor): A input sequence passed to encoders. Typically for inputs this will be a padded `FloatTensor` of size ``(batch, seq_length, dimension)``.
- **input_lengths** (torch.LongTensor): The length of input tensor. ``(batch)``
Returns:
outputs (dict): Result of model predictions.
"""
def __init__(self, configs: DictConfig, tokenizer: Tokenizer) -> None:
super(JointCTCListenAttendSpellModel, self).__init__(configs, tokenizer)
def build_model(self):
self.encoder = LSTMEncoder(
input_dim=self.configs.audio.num_mels,
num_layers=self.configs.model.num_encoder_layers,
num_classes=self.num_classes,
hidden_state_dim=self.configs.model.hidden_state_dim,
dropout_p=self.configs.model.encoder_dropout_p,
bidirectional=self.configs.model.encoder_bidirectional,
rnn_type=self.configs.model.rnn_type,
joint_ctc_attention=self.configs.model.joint_ctc_attention,
)
decoder_hidden_state_dim = self.configs.model.hidden_state_dim << 1 \
if self.configs.model.encoder_bidirectional \
else self.configs.model.hidden_state_dim
self.decoder = LSTMAttentionDecoder(
num_classes=self.num_classes,
max_length=self.configs.model.max_length,
hidden_state_dim=decoder_hidden_state_dim,
pad_id=self.tokenizer.pad_id,
sos_id=self.tokenizer.sos_id,
eos_id=self.tokenizer.eos_id,
num_heads=self.configs.model.num_attention_heads,
dropout_p=self.configs.model.decoder_dropout_p,
num_layers=self.configs.model.num_decoder_layers,
attn_mechanism=self.configs.model.decoder_attn_mechanism,
rnn_type=self.configs.model.rnn_type,
)
[docs] def set_beam_decoder(self, beam_size: int = 3):
""" Setting beam search decoder """
from openspeech.search.beam_search_lstm import BeamSearchLSTM
self.decoder = BeamSearchLSTM(
decoder=self.decoder,
beam_size=beam_size,
)
[docs]@register_model('deep_cnn_with_joint_ctc_listen_attend_spell', dataclass=DeepCNNWithJointCTCListenAttendSpellConfigs)
class DeepCNNWithJointCTCListenAttendSpellModel(OpenspeechEncoderDecoderModel):
r"""
Listen, Attend and Spell model with configurable encoder and decoder.
Paper: https://arxiv.org/abs/1508.01211
Args:
configs (DictConfig): configuration set.
tokenizer (Tokenizer): tokenizer is in charge of preparing the inputs for a model.
Inputs:
- **inputs** (torch.FloatTensor): A input sequence passed to encoders. Typically for inputs this will be a padded `FloatTensor` of size ``(batch, seq_length, dimension)``.
- **input_lengths** (torch.LongTensor): The length of input tensor. ``(batch)``
Returns:
outputs (dict): Result of model predictions.
"""
def __init__(self, configs: DictConfig, tokenizer: Tokenizer) -> None:
super(DeepCNNWithJointCTCListenAttendSpellModel, self).__init__(configs, tokenizer)
def build_model(self):
self.encoder = ConvolutionalLSTMEncoder(
input_dim=self.configs.audio.num_mels,
num_layers=self.configs.model.num_encoder_layers,
num_classes=self.num_classes,
hidden_state_dim=self.configs.model.hidden_state_dim,
dropout_p=self.configs.model.encoder_dropout_p,
bidirectional=self.configs.model.encoder_bidirectional,
rnn_type=self.configs.model.rnn_type,
joint_ctc_attention=self.configs.model.joint_ctc_attention,
)
decoder_hidden_state_dim = self.configs.model.hidden_state_dim << 1 \
if self.configs.model.encoder_bidirectional \
else self.configs.model.hidden_state_dim
self.decoder = LSTMAttentionDecoder(
num_classes=self.num_classes,
max_length=self.configs.model.max_length,
hidden_state_dim=decoder_hidden_state_dim,
pad_id=self.tokenizer.pad_id,
sos_id=self.tokenizer.sos_id,
eos_id=self.tokenizer.eos_id,
num_heads=self.configs.model.num_attention_heads,
dropout_p=self.configs.model.decoder_dropout_p,
num_layers=self.configs.model.num_decoder_layers,
attn_mechanism=self.configs.model.decoder_attn_mechanism,
rnn_type=self.configs.model.rnn_type,
)
[docs] def set_beam_decoder(self, beam_size: int = 3):
""" Setting beam search decoder """
from openspeech.search import BeamSearchLSTM
self.decoder = BeamSearchLSTM(
decoder=self.decoder,
beam_size=beam_size,
)