Source code for openspeech.models.quartznet15x5.model

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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.quartznet15x5.configurations import QuartzNet15x5Configs
from openspeech.vocabs.vocab import Vocabulary


[docs]@register_model('quartznet15x5', dataclass=QuartzNet15x5Configs) class QuartzNet15x5Model(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(QuartzNet15x5Model, 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(QuartzNet15x5Model, 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(QuartzNet15x5Model, 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(QuartzNet15x5Model, 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(QuartzNet15x5Model, self).test_step(batch, batch_idx)