i6_core.cart.estimate

class i6_core.cart.estimate.AccumulateCartStatisticsJob(*args, **kwargs)

Goes over all training data and for each triphone state accumulates the values and squared values of the given feature flow

Parameters:
accumulate(task_id)
cleanup_before_run(cmd, retry, task_id, *args)
classmethod create_accumulate_config(crp, alignment_flow, extra_config_accumulate, extra_post_config_accumulate, **kwargs)
Parameters:
Returns:

Return type:

(rasr.config.RasrConfig, rasr.config.RasrConfig)

create_files()
classmethod create_merge_config(crp, extra_config_merge, extra_post_config_merge, **kwargs)
Parameters:
Returns:

Return type:

(rasr.config.RasrConfig, rasr.config.RasrConfig)

classmethod hash(kwargs)
Parameters:

parsed_args (dict[str]) –

Returns:

hash for job given the arguments

Return type:

str

merge()
tasks()
Returns:

yields Task’s

Return type:

list[sisyphus.task.Task]

class i6_core.cart.estimate.EstimateCartJob(*args, **kwargs)

This job estimates a phonetic decision tree. Given a set of accumulated (squared) feature values a single gaussian model is estimated per triphone state. Then iteratively states are merged according to the provided questions such that the log-likelihood of the resulting models is minimized. Finally states which have a low number of occurrences are merged into the closest cluster.

Parameters:
cleanup_before_run(*args)
classmethod create_config(crp, questions, cart_examples, variance_clipping, generate_cluster_file, extra_config, extra_post_config, **kwargs)
Parameters:
create_files()
classmethod hash(kwargs)
Parameters:

parsed_args (dict[str]) –

Returns:

hash for job given the arguments

Return type:

str

run()
tasks()
Returns:

yields Task’s

Return type:

list[sisyphus.task.Task]