DubmTrainer#
- class montreal_forced_aligner.ivector.trainer.DubmTrainer(num_iterations=4, num_gselect=30, subsample=5, num_frames=500000, num_gaussians=256, num_iterations_init=20, initial_gaussian_proportion=0.5, min_gaussian_weight=0.0001, remove_low_count_gaussians=True, **kwargs)[source]#
Bases:
IvectorModelTrainingMixin
Trainer for diagonal universal background models
- Parameters:
num_iterations (int) – Number of training iterations to perform, defaults to 4
num_gselect (int) – Number of Gaussian-selection indices to use while training
subsample (int) – Subsample factor for feature frames, defaults to 5
num_frames (int) – Number of frames to keep in memory for initialization, defaults to 500000
num_gaussians (int) – Number of gaussians to use for DUBM training, defaults to 256
num_iterations_init (int) – Number of iteration to use when initializing UBM, defaults to 20
initial_gaussian_proportion (float) – Proportion of total gaussians to use initially, defaults to 0.5
min_gaussian_weight (float) – Defaults to 0.0001
remove_low_count_gaussians (bool) – Flag for removing low count gaussians in the final round of training, defaults to True
See also
IvectorModelTrainingMixin
For base ivector training parameters
- acc_global_stats()[source]#
Multiprocessing function that accumulates global GMM stats
See also
AccGlobalStatsFunction
Multiprocessing helper function for each job
DubmTrainer.acc_global_stats_arguments
Job method for generating arguments for the helper function
- gmmbin/gmm-global-sum-accs.cc
Relevant Kaldi binary
- train_diag_ubm.sh
Reference Kaldi script
- acc_global_stats_arguments()[source]#
Generate Job arguments for
AccGlobalStatsFunction
- Returns:
Arguments for processing
- Return type:
list[
AccGlobalStatsArguments
]
- property dubm_options#
Options for DUBM training
- property exported_model_path#
Temporary model path to save intermediate model
- gmm_gselect()[source]#
Multiprocessing function that stores Gaussian selection indices on disk
See also
GmmGselectFunction
Multiprocessing helper function for each job
DubmTrainer.gmm_gselect_arguments
Job method for generating arguments for the helper function
- train_diag_ubm.sh
Reference Kaldi script
- gmm_gselect_arguments()[source]#
Generate Job arguments for
GmmGselectFunction
- Returns:
Arguments for processing
- Return type:
list[
GmmGselectArguments
]
- property model_path#
Current iteration’s DUBM model path
- property next_model_path#
Next iteration’s DUBM model path
- property train_type#
Training identifier