Alignment Layers

Forced Alignment Layer

class returnn.tf.layers.basic.ForcedAlignmentLayer(align_target, topology, input_type, blank_idx=-1, blank_included=False, **kwargs)[source]

Calculates a forced alignment, via Viterbi algorithm.

Parameters:

  • align_target (LayerBase)

  • topology (str) – e.g. “ctc” or “rna” (RNA is CTC without label loop)

  • input_type (str) – “log_prob” or “prob”

  • blank_idx (int) – vocab index of the blank symbol

  • blank_included (bool) – whether blank token of the align target is included in the vocabulary

layer_class: Optional[str] = 'forced_align'[source]
classmethod get_sub_layer_out_data_from_opts(layer_name, parent_layer_kwargs)[source]
Parameters:

  • layer_name (str) – sub layer name

  • parent_layer_kwargs (dict[str])

Returns:

Data template, class type of sub-layer, layer opts (transformed)

Return type:

(Data, type, dict[str])|None

get_sub_layer(layer_name)[source]
Parameters:

layer_name (str)

Return type:

LayerBase|None

classmethod get_available_sub_layer_names(parent_layer_kwargs)[source]
Parameters:

parent_layer_kwargs (dict[str])

Return type:

list[str]

get_dep_layers()[source]
Return type:

list[LayerBase]

classmethod transform_config_dict(d, network, get_layer)[source]
Parameters:
classmethod get_out_data_from_opts(name, sources, **kwargs)[source]
Parameters:
Return type:

Data

input_data: Optional[Data][source]
kwargs: Optional[Dict[str]][source]
output_before_activation: Optional[OutputWithActivation][source]
output_loss: Optional[tf.Tensor][source]
rec_vars_outputs: Dict[str, tf.Tensor][source]
search_choices: Optional[SearchChoices][source]
params: Dict[str, tf.Variable][source]
saveable_param_replace: Dict[tf.Variable, Union['tensorflow.python.training.saver.BaseSaverBuilder.SaveableObject', None]][source]
stats: Dict[str, tf.Tensor][source]

Fast Baum-Welch Layer

class returnn.tf.layers.basic.FastBaumWelchLayer(align_target, align_target_key=None, ctc_opts=None, sprint_opts=None, input_type='log_prob', tdp_scale=1.0, am_scale=1.0, min_prob=0.0, staircase_seq_len_source=None, **kwargs)[source]

Calls fast_baum_welch() or fast_baum_welch_by_sprint_automata(). We expect that our input are +log scores, e.g. use log-softmax.

Parameters:

  • align_target (str) – e.g. “sprint”, “ctc” or “staircase”

  • align_target_key (str|None) – e.g. “classes”, used for e.g. align_target “ctc”

  • ctc_opts (dict[str]) – used for align_target “ctc”

  • sprint_opts (dict[str]) – used for Sprint (RASR) for align_target “sprint”

  • input_type (str) – “log_prob” or “prob”

  • tdp_scale (float)

  • am_scale (float)

  • min_prob (float) – clips the minimum prob (value in [0,1])

  • staircase_seq_len_source (LayerBase|None)

layer_class: Optional[str] = 'fast_bw'[source]
recurrent = True[source]
output_loss: Optional[tf.Tensor][source]
classmethod transform_config_dict(d, network, get_layer)[source]
Parameters:
classmethod get_out_data_from_opts(name, sources, **kwargs)[source]
Parameters:
Return type:

Data

input_data: Optional[Data][source]
kwargs: Optional[Dict[str]][source]
output_before_activation: Optional[OutputWithActivation][source]
rec_vars_outputs: Dict[str, tf.Tensor][source]
search_choices: Optional[SearchChoices][source]
params: Dict[str, tf.Variable][source]
saveable_param_replace: Dict[tf.Variable, Union['tensorflow.python.training.saver.BaseSaverBuilder.SaveableObject', None]][source]
stats: Dict[str, tf.Tensor][source]