# MIT License
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# Copyright (c) 2021 Soohwan Kim and Sangchun Ha and Soyoung Cho
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from omegaconf import DictConfig
from openspeech.lm.lstm_lm import LSTMForLanguageModel
from openspeech.models import register_model
from openspeech.models.lstm_lm.configurations import LSTMLanguageModelConfigs
from openspeech.models.openspeech_language_model import OpenspeechLanguageModel
from openspeech.tokenizers.tokenizer import Tokenizer
[docs]@register_model('lstm_lm', dataclass=LSTMLanguageModelConfigs)
class LSTMLanguageModel(OpenspeechLanguageModel):
r"""
LSTM language model.
Paper: http://www-i6.informatik.rwth-aachen.de/publications/download/820/Sundermeyer-2012.pdf
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)``.
Returns:
outputs (dict): Result of model predictions.
"""
def __init__(self, configs: DictConfig, tokenizer: Tokenizer) -> None:
super(LSTMLanguageModel, self).__init__(configs, tokenizer)
def build_model(self):
self.lm = LSTMForLanguageModel(
num_classes=self.num_classes,
max_length=self.configs.model.max_length,
hidden_state_dim=self.configs.model.hidden_state_dim,
pad_id=self.tokenizer.pad_id,
sos_id=self.tokenizer.sos_id,
eos_id=self.tokenizer.eos_id,
dropout_p=self.configs.model.dropout_p,
num_layers=self.configs.model.num_layers,
rnn_type=self.configs.model.rnn_type,
)