# MIT License
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# Copyright (c) 2021 Soohwan Kim
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from omegaconf import DictConfig
from torch import Tensor
from typing import Dict
from collections import OrderedDict
from openspeech.models import register_model
from openspeech.models import OpenspeechCTCModel
from openspeech.encoders.quartznet import QuartzNet
from openspeech.models.quartznet5x5.configurations import QuartzNet5x5Configs
from openspeech.vocabs.vocab import Vocabulary
[docs]@register_model('quartznet5x5', dataclass=QuartzNet5x5Configs)
class QuartzNet5x5Model(OpenspeechCTCModel):
r"""
QUARTZNET: DEEP AUTOMATIC SPEECH RECOGNITION WITH 1D TIME-CHANNEL SEPARABLE CONVOLUTIONS
Paper: https://arxiv.org/abs/1910.10261.pdf
Args:
configs (DictConfig): configuration set.
vocab (Vocabulary): the class of vocabulary
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:
* dict (dict): Result of model predictions that contains `y_hats`, `logits`, `output_lengths`
"""
def __init__(self, configs: DictConfig, vocab: Vocabulary, ) -> None:
super(QuartzNet5x5Model, self).__init__(configs, vocab)
def build_model(self):
self.encoder = QuartzNet(
configs=self.configs,
input_dim=self.configs.audio.num_mels,
num_classes=self.num_classes,
)
[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:
* dict (dict): Result of model predictions that contains `y_hats`, `logits`, `output_lengths`
"""
return super(QuartzNet5x5Model, 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
"""
return super(QuartzNet5x5Model, self).training_step(batch, batch_idx)
[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
"""
return super(QuartzNet5x5Model, self).validation_step(batch, batch_idx)
[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
"""
return super(QuartzNet5x5Model, self).test_step(batch, batch_idx)