i6_core.rasr.feature_scorer

class i6_core.rasr.feature_scorer.DiagonalMaximumScorer(*args, **kwargs)
class i6_core.rasr.feature_scorer.FeatureScorer
apply_config(path, config, post_config)
html()
class i6_core.rasr.feature_scorer.GMMFeatureScorer(mixtures, scale=1.0)
class i6_core.rasr.feature_scorer.InvAlignmentPassThroughFeatureScorer(prior_mixtures, max_segment_length, mapping, priori_scale=0.0)
class i6_core.rasr.feature_scorer.OnnxFeatureScorer(*, mixtures: Path, model: Path, io_map: Dict[str, str], label_log_posterior_scale: float = 1.0, label_prior_scale: float, label_log_prior_file: Optional[Path] = None, apply_log_on_output: bool = False, negate_output: bool = True, intra_op_threads: int = 1, inter_op_threads: int = 1, **kwargs)
Parameters:

  • mixtures – path to a *.mix file e.g. output of either EstimateMixturesJob or CreateDummyMixturesJob

  • model – path of a model e.g. output of ExportPyTorchModelToOnnxJob

  • io_map – mapping between internal rasr identifiers and the model related input/output. Default key values are “features” and “output”, and optionally “features-size”, e.g. io_map = {“features”: “data”, “output”: “classes”}

  • label_log_posterior_scale – scales for the log probability of a label e.g. 1.0 is recommended

  • label_prior_scale – scale for the prior log probability of a label reasonable e.g. values in [0.1, 0.7] interval

  • label_log_prior_file – xml file containing log prior probabilities e.g. estimated from the model via povey method

  • apply_log_on_output – whether to apply the log-function on the output, usefull if the model outputs softmax instead of log-softmax

  • negate_output – whether negate output (because the model outputs log softmax and not negative log softmax

  • intra_op_threads – Onnxruntime session’s number of parallel threads within each operator

  • inter_op_threads – Onnxruntime session’s number of parallel threads between operators used only for parallel execution mode

class i6_core.rasr.feature_scorer.PrecomputedHybridFeatureScorer(prior_mixtures, scale=1.0, priori_scale=0.0, prior_file=None)
class i6_core.rasr.feature_scorer.PreselectionBatchIntScorer(*args, **kwargs)
class i6_core.rasr.feature_scorer.ReturnnScorer(feature_dimension, output_dimension, prior_mixtures, model, mixture_scale=1.0, prior_scale=1.0, prior_file=None, returnn_root=None)
class i6_core.rasr.feature_scorer.SimdDiagonalMaximumScorer(*args, **kwargs)