athena.models.mtl_seq2seq

a implementation of deep speech 2 model can be used as a sample for ctc model

Module Contents

Classes

MtlTransformerCtc

In speech recognition, adding CTC loss to Attention-based seq-to-seq model is known to

class athena.models.mtl_seq2seq.MtlTransformerCtc(data_descriptions, config=None)

Bases: athena.models.base.BaseModel

In speech recognition, adding CTC loss to Attention-based seq-to-seq model is known to help convergence. It usually gives better results than using attention alone.

SUPPORTED_MODEL

default_config

call(self, samples, training=None)

call function in keras layers

get_loss(self, outputs, samples, training=None)

get loss used for training

compute_logit_length(self, samples)

compute the logit length

reset_metrics(self)

reset the metrics

restore_from_pretrained_model(self, pretrained_model, model_type='')

A more general-purpose interface for pretrained model restoration

Parameters:

• pretrained_model – checkpoint path of mpc model
• model_type – the type of pretrained model to restore

decode(self, samples, hparams, decoder)

Initialization of the model for decoding, decoder is called here to create predictions :param samples: the data source to be decoded :param hparams: decoding configs are included here :param decoder: it contains the main decoding operations

Returns:the corresponding decoding results Return type:predictions

deploy(self)

deployment function