# MIT License
#
# Copyright (c) 2021 Soohwan Kim and Sangchun Ha and Soyoung Cho
#
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from omegaconf import DictConfig
from torch import Tensor
from typing import Dict
from collections import OrderedDict
from openspeech.decoders import RNNTransducerDecoder, LSTMAttentionDecoder
from openspeech.models import register_model, OpenspeechTransducerModel, OpenspeechEncoderDecoderModel
from openspeech.models import OpenspeechCTCModel
from openspeech.encoders import ContextNetEncoder
from openspeech.modules.wrapper import Linear
from openspeech.tokenizers.tokenizer import Tokenizer
from openspeech.models.contextnet.configurations import (
ContextNetConfigs,
ContextNetTransducerConfigs,
ContextNetLSTMConfigs,
)
[docs]@register_model('contextnet', dataclass=ContextNetConfigs)
class ContextNetModel(OpenspeechCTCModel):
r"""
Conformer Encoder Only Model.
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 that contains `y_hats`, `logits`, `output_lengths`
"""
def __init__(self, configs: DictConfig, tokenizer: Tokenizer) -> None:
super(ContextNetModel, self).__init__(configs, tokenizer)
self.fc = Linear(self.configs.model.encoder_dim, self.num_classes, bias=False)
def build_model(self):
self.encoder = ContextNetEncoder(
num_classes=self.num_classes,
model_size=self.configs.model.model_size,
input_dim=self.configs.audio.num_mels,
num_layers=self.configs.model.num_encoder_layers,
kernel_size=self.configs.model.kernel_size,
num_channels=self.configs.model.num_channels,
output_dim=self.configs.model.encoder_dim,
joint_ctc_attention=False,
)
[docs] def forward(self, inputs: Tensor, input_lengths: Tensor) -> Dict[str, Tensor]:
r"""
Forward propagate a `inputs` and `targets` pair for inference.
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 that contains `y_hats`, `logits`, `output_lengths`
"""
return super(ContextNetModel, self).forward(inputs, input_lengths)
[docs] def training_step(self, batch: tuple, batch_idx: int) -> OrderedDict:
r"""
Forward propagate a `inputs` and `targets` pair for training.
Inputs:
batch (tuple): A train batch contains `inputs`, `targets`, `input_lengths`, `target_lengths`
batch_idx (int): The index of batch
Returns:
loss (torch.Tensor): loss for training
"""
inputs, targets, input_lengths, target_lengths = batch
encoder_outputs, encoder_logits, output_lengths = self.encoder(inputs, input_lengths)
logits = self.fc(encoder_outputs).log_softmax(dim=-1)
return self.collect_outputs(
stage='train',
logits=logits,
output_lengths=output_lengths,
targets=targets,
target_lengths=target_lengths,
)
[docs] def validation_step(self, batch: tuple, batch_idx: int) -> OrderedDict:
r"""
Forward propagate a `inputs` and `targets` pair for validation.
Inputs:
batch (tuple): A train batch contains `inputs`, `targets`, `input_lengths`, `target_lengths`
batch_idx (int): The index of batch
Returns:
loss (torch.Tensor): loss for training
"""
inputs, targets, input_lengths, target_lengths = batch
encoder_outputs, encoder_logits, output_lengths = self.encoder(inputs, input_lengths)
logits = self.fc(encoder_outputs).log_softmax(dim=-1)
return self.collect_outputs(
stage='valid',
logits=logits,
output_lengths=output_lengths,
targets=targets,
target_lengths=target_lengths,
)
[docs] def test_step(self, batch: tuple, batch_idx: int) -> OrderedDict:
r"""
Forward propagate a `inputs` and `targets` pair for test.
Inputs:
batch (tuple): A train batch contains `inputs`, `targets`, `input_lengths`, `target_lengths`
batch_idx (int): The index of batch
Returns:
loss (torch.Tensor): loss for training
"""
inputs, targets, input_lengths, target_lengths = batch
encoder_outputs, encoder_logits, output_lengths = self.encoder(inputs, input_lengths)
logits = self.fc(encoder_outputs).log_softmax(dim=-1)
return self.collect_outputs(
stage='test',
logits=logits,
output_lengths=output_lengths,
targets=targets,
target_lengths=target_lengths,
)
[docs]@register_model('contextnet_lstm', dataclass=ContextNetLSTMConfigs)
class ContextNetLSTMModel(OpenspeechEncoderDecoderModel):
r"""
ContextNet encoder + LSTM decoder.
Args:
configs (DictConfig): configuraion 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 that contains `y_hats`, `logits`,
`encoder_outputs`, `encoder_logits`, `encoder_output_lengths`.
"""
def __init__(self, configs: DictConfig, tokenizer: Tokenizer, ) -> None:
super(ContextNetLSTMModel, self).__init__(configs, tokenizer)
def build_model(self):
self.encoder = ContextNetEncoder(
num_classes=self.num_classes,
model_size=self.configs.model.model_size,
input_dim=self.configs.audio.num_mels,
num_layers=self.configs.model.num_encoder_layers,
kernel_size=self.configs.model.kernel_size,
num_channels=self.configs.model.num_channels,
output_dim=self.configs.model.encoder_dim,
joint_ctc_attention=False,
)
self.decoder = LSTMAttentionDecoder(
num_classes=self.num_classes,
max_length=self.configs.model.max_length,
hidden_state_dim=self.configs.model.encoder_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('contextnet_transducer', dataclass=ContextNetTransducerConfigs)
class ContextNetTransducerModel(OpenspeechTransducerModel):
r"""
ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context
Paper: https://arxiv.org/abs/2005.03191
Args:
configs (DictConfig): configuraion 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(ContextNetTransducerModel, self).__init__(configs, tokenizer)
def build_model(self):
self.encoder = ContextNetEncoder(
num_classes=self.num_classes,
model_size=self.configs.model.model_size,
input_dim=self.configs.audio.num_mels,
num_layers=self.configs.model.num_encoder_layers,
kernel_size=self.configs.model.kernel_size,
num_channels=self.configs.model.num_channels,
output_dim=self.configs.model.encoder_dim,
joint_ctc_attention=False,
)
self.decoder = RNNTransducerDecoder(
num_classes=self.num_classes,
hidden_state_dim=self.configs.model.decoder_hidden_state_dim,
output_dim=self.configs.model.decoder_output_dim,
num_layers=self.configs.model.num_decoder_layers,
rnn_type=self.configs.model.rnn_type,
pad_id=self.tokenizer.pad_id,
sos_id=self.tokenizer.sos_id,
eos_id=self.tokenizer.eos_id,
dropout_p=self.configs.model.decoder_dropout_p,
)