# 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.models import register_model
from openspeech.models import OpenspeechTransducerModel
from openspeech.decoders import RNNTransducerDecoder
from openspeech.encoders import RNNTransducerEncoder
from openspeech.models.rnn_transducer.configurations import RNNTransducerConfigs
from openspeech.tokenizers.tokenizer import Tokenizer
[docs]@register_model('rnn_transducer', dataclass=RNNTransducerConfigs)
class RNNTransducerModel(OpenspeechTransducerModel):
r"""
RNN-Transducer are a form of sequence-to-sequence models that do not employ attention mechanisms.
Unlike most sequence-to-sequence models, which typically need to process the entire input sequence
(the waveform in our case) to produce an output (the sentence), the RNN-T continuously processes input samples and
streams output symbols, a property that is welcome for speech dictation. In our implementation,
the output symbols are the characters of the alphabet.
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(RNNTransducerModel, self).__init__(configs, tokenizer)
def build_model(self):
self.encoder = RNNTransducerEncoder(
input_dim=self.configs.audio.num_mels,
hidden_state_dim=self.configs.model.encoder_hidden_state_dim,
output_dim=self.configs.model.output_dim,
num_layers=self.configs.model.num_encoder_layers,
rnn_type=self.configs.model.rnn_type,
dropout_p=self.configs.model.encoder_dropout_p,
)
self.decoder = RNNTransducerDecoder(
num_classes=self.num_classes,
hidden_state_dim=self.configs.model.decoder_hidden_state_dim,
output_dim=self.configs.model.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,
)