athena.solver

high-level abstraction of different stages in speech processing

Module Contents

Classes

BaseSolver	Base Solver.
HorovodSolver	A multi-processer solver based on Horovod
DecoderSolver	DecoderSolver
SynthesisSolver	SynthesisSolver

class athena.solver.BaseSolver(model, optimizer, sample_signature, eval_sample_signature=None, config=None, **kwargs)

Bases: tensorflow.keras.Model

Base Solver.

default_config

static initialize_devices(visible_gpu_idx=None)

initialize hvd devices, should be called firstly

static clip_by_norm(grads, norm)

clip norm using tf.clip_by_norm

train_step(self, samples)

train the model 1 step

train(self, dataset, total_batches=-1)

Update the model in 1 epoch

evaluate_step(self, samples)

evaluate the model 1 step

evaluate(self, dataset, epoch)

evaluate the model

class athena.solver.HorovodSolver(model, optimizer, sample_signature, eval_sample_signature=None, config=None, **kwargs)

Bases: athena.solver.BaseSolver

A multi-processer solver based on Horovod

static initialize_devices(visible_gpu_idx=None)

initialize hvd devices, should be called firstly

train_step(self, samples)

train the model 1 step

train(self, dataset, total_batches=-1)

Update the model in 1 epoch

evaluate(self, dataset, epoch=0)

evaluate the model

class athena.solver.DecoderSolver(model, config=None, lm_model=None)

Bases: athena.solver.BaseSolver

DecoderSolver

default_config

decode(self, dataset, rank_size=1)

decode the model

class athena.solver.SynthesisSolver(model, data_descriptions=None, config=None)

Bases: athena.solver.BaseSolver

SynthesisSolver

default_config

synthesize(self, dataset)

synthesize using vocoder on dataset