def train(args, run_opts, background_process_handler): """ The main function for training. Args: args: a Namespace object with the required parameters obtained from the function process_args() run_opts: RunOpts object obtained from the process_args() """ arg_string = pprint.pformat(vars(args)) logger.info("Arguments for the experiment\n{0}".format(arg_string)) # Set some variables. num_jobs = common_lib.get_number_of_jobs(args.ali_dir) feat_dim = common_lib.get_feat_dim(args.feat_dir) ivector_dim = common_lib.get_ivector_dim(args.online_ivector_dir) # split the training data into parts for individual jobs # we will use the same number of jobs as that used for alignment common_lib.split_data(args.feat_dir, num_jobs) shutil.copy('{0}/tree'.format(args.ali_dir), args.dir) with open('{0}/num_jobs'.format(args.dir), 'w') as f: f.write(str(num_jobs)) config_dir = '{0}/configs'.format(args.dir) var_file = '{0}/vars'.format(config_dir) variables = common_train_lib.parse_generic_config_vars_file(var_file) # Set some variables. try: model_left_context = variables['model_left_context'] model_right_context = variables['model_right_context'] # this is really the number of times we add layers to the network for # discriminative pretraining num_hidden_layers = variables['num_hidden_layers'] except KeyError as e: raise Exception("KeyError {0}: Variables need to be defined in " "{1}".format(str(e), '{0}/configs'.format(args.dir))) left_context = args.chunk_left_context + model_left_context right_context = args.chunk_right_context + model_right_context # Initialize as "raw" nnet, prior to training the LDA-like preconditioning # matrix. This first config just does any initial splicing that we do; # we do this as it's a convenient way to get the stats for the 'lda-like' # transform. if (args.stage <= -5): logger.info("Initializing a basic network for estimating " "preconditioning matrix") common_lib.run_job("""{command} {dir}/log/nnet_init.log \ nnet3-init --srand=-2 {dir}/configs/init.config \ {dir}/init.raw""".format(command=run_opts.command, dir=args.dir)) default_egs_dir = '{0}/egs'.format(args.dir) if (args.stage <= -4) and args.egs_dir is None: logger.info("Generating egs") train_lib.acoustic_model.generate_egs( data=args.feat_dir, alidir=args.ali_dir, egs_dir=default_egs_dir, left_context=left_context, right_context=right_context, valid_left_context=left_context + args.chunk_width, valid_right_context=right_context + args.chunk_width, run_opts=run_opts, frames_per_eg=args.chunk_width, srand=args.srand, egs_opts=args.egs_opts, cmvn_opts=args.cmvn_opts, online_ivector_dir=args.online_ivector_dir, samples_per_iter=args.samples_per_iter, transform_dir=args.transform_dir, stage=args.egs_stage) if args.egs_dir is None: egs_dir = default_egs_dir else: egs_dir = args.egs_dir [egs_left_context, egs_right_context, frames_per_eg, num_archives ] = (common_train_lib.verify_egs_dir(egs_dir, feat_dim, ivector_dim, left_context, right_context)) assert (args.chunk_width == frames_per_eg) if (args.num_jobs_final > num_archives): raise Exception('num_jobs_final cannot exceed the number of archives ' 'in the egs directory') # copy the properties of the egs to dir for # use during decoding common_train_lib.copy_egs_properties_to_exp_dir(egs_dir, args.dir) if (args.stage <= -3): logger.info('Computing the preconditioning matrix for input features') train_lib.common.compute_preconditioning_matrix( args.dir, egs_dir, num_archives, run_opts, max_lda_jobs=args.max_lda_jobs, rand_prune=args.rand_prune) if (args.stage <= -2): logger.info( "Computing initial vector for FixedScaleComponent before" " softmax, using priors^{prior_scale} and rescaling to" " average 1".format(prior_scale=args.presoftmax_prior_scale_power)) common_train_lib.compute_presoftmax_prior_scale( args.dir, args.ali_dir, num_jobs, run_opts, presoftmax_prior_scale_power=args.presoftmax_prior_scale_power) if (args.stage <= -1): logger.info("Preparing the initial acoustic model.") train_lib.acoustic_model.prepare_initial_acoustic_model( args.dir, args.ali_dir, run_opts) # set num_iters so that as close as possible, we process the data # $num_epochs times, i.e. $num_iters*$avg_num_jobs) == # $num_epochs*$num_archives, where # avg_num_jobs=(num_jobs_initial+num_jobs_final)/2. num_archives_to_process = args.num_epochs * num_archives num_archives_processed = 0 num_iters = ((num_archives_to_process * 2) / (args.num_jobs_initial + args.num_jobs_final)) models_to_combine = common_train_lib.verify_iterations( num_iters, args.num_epochs, num_hidden_layers, num_archives, args.max_models_combine, args.add_layers_period, args.num_jobs_final) def learning_rate(iter, current_num_jobs, num_archives_processed): return common_train_lib.get_learning_rate(iter, current_num_jobs, num_iters, num_archives_processed, num_archives_to_process, args.initial_effective_lrate, args.final_effective_lrate) min_deriv_time = None max_deriv_time = None if args.deriv_truncate_margin is not None: min_deriv_time = -args.deriv_truncate_margin - model_left_context max_deriv_time = (args.chunk_width - 1 + args.deriv_truncate_margin + model_right_context) logger.info("Training will run for {0} epochs = " "{1} iterations".format(args.num_epochs, num_iters)) for iter in range(num_iters): if (args.exit_stage is not None) and (iter == args.exit_stage): logger.info("Exiting early due to --exit-stage {0}".format(iter)) return current_num_jobs = int(0.5 + args.num_jobs_initial + (args.num_jobs_final - args.num_jobs_initial) * float(iter) / num_iters) if args.stage <= iter: model_file = "{dir}/{iter}.mdl".format(dir=args.dir, iter=iter) shrinkage_value = 1.0 if args.shrink_value != 1.0: shrinkage_value = ( args.shrink_value if common_train_lib.do_shrinkage( iter, model_file, args.shrink_saturation_threshold) else 1) logger.info("On iteration {0}, learning rate is {1} and " "shrink value is {2}.".format( iter, learning_rate(iter, current_num_jobs, num_archives_processed), shrinkage_value)) train_lib.common.train_one_iteration( dir=args.dir, iter=iter, srand=args.srand, egs_dir=egs_dir, num_jobs=current_num_jobs, num_archives_processed=num_archives_processed, num_archives=num_archives, learning_rate=learning_rate(iter, current_num_jobs, num_archives_processed), shrinkage_value=shrinkage_value, minibatch_size=args.num_chunk_per_minibatch, num_hidden_layers=num_hidden_layers, add_layers_period=args.add_layers_period, left_context=left_context, right_context=right_context, min_deriv_time=min_deriv_time, max_deriv_time=max_deriv_time, momentum=args.momentum, max_param_change=args.max_param_change, shuffle_buffer_size=args.shuffle_buffer_size, cv_minibatch_size=args.cv_minibatch_size, run_opts=run_opts, background_process_handler=background_process_handler) if args.cleanup: # do a clean up everythin but the last 2 models, under certain # conditions common_train_lib.remove_model(args.dir, iter - 2, num_iters, models_to_combine, args.preserve_model_interval) if args.email is not None: reporting_iter_interval = num_iters * args.reporting_interval if iter % reporting_iter_interval == 0: # lets do some reporting [report, times, data ] = (nnet3_log_parse.generate_accuracy_report(args.dir)) message = report subject = ("Update : Expt {dir} : " "Iter {iter}".format(dir=args.dir, iter=iter)) common_lib.send_mail(message, subject, args.email) num_archives_processed = num_archives_processed + current_num_jobs if args.stage <= num_iters: logger.info("Doing final combination to produce final.mdl") train_lib.common.combine_models( dir=args.dir, num_iters=num_iters, models_to_combine=models_to_combine, egs_dir=egs_dir, run_opts=run_opts, left_context=left_context, right_context=right_context, background_process_handler=background_process_handler, chunk_width=args.chunk_width) if args.stage <= num_iters + 1: logger.info("Getting average posterior for purposes of " "adjusting the priors.") avg_post_vec_file = train_lib.common.compute_average_posterior( dir=args.dir, iter='combined', egs_dir=egs_dir, num_archives=num_archives, left_context=left_context, right_context=right_context, prior_subset_size=args.prior_subset_size, run_opts=run_opts) logger.info("Re-adjusting priors based on computed posteriors") combined_model = "{dir}/combined.mdl".format(dir=args.dir) final_model = "{dir}/final.mdl".format(dir=args.dir) train_lib.common.adjust_am_priors(args.dir, combined_model, avg_post_vec_file, final_model, run_opts) if args.cleanup: logger.info("Cleaning up the experiment directory " "{0}".format(args.dir)) remove_egs = args.remove_egs if args.egs_dir is not None: # this egs_dir was not created by this experiment so we will not # delete it remove_egs = False common_train_lib.clean_nnet_dir( nnet_dir=args.dir, num_iters=num_iters, egs_dir=egs_dir, preserve_model_interval=args.preserve_model_interval, remove_egs=remove_egs) # do some reporting [report, times, data] = nnet3_log_parse.generate_accuracy_report(args.dir) if args.email is not None: common_lib.send_mail( report, "Update : Expt {0} : " "complete".format(args.dir), args.email) with open("{dir}/accuracy.report".format(dir=args.dir), "w") as f: f.write(report) common_lib.run_job("steps/info/nnet3_dir_info.pl " "{0}".format(args.dir))
def train(args, run_opts, background_process_handler): """ The main function for training. Args: args: a Namespace object with the required parameters obtained from the function process_args() run_opts: RunOpts object obtained from the process_args() """ arg_string = pprint.pformat(vars(args)) logger.info("Arguments for the experiment\n{0}".format(arg_string)) # Check files chain_lib.check_for_required_files(args.feat_dir, args.tree_dir, args.lat_dir) # Set some variables. num_jobs = common_lib.get_number_of_jobs(args.tree_dir) feat_dim = common_lib.get_feat_dim(args.feat_dir) ivector_dim = common_lib.get_ivector_dim(args.online_ivector_dir) # split the training data into parts for individual jobs # we will use the same number of jobs as that used for alignment common_lib.split_data(args.feat_dir, num_jobs) shutil.copy('{0}/tree'.format(args.tree_dir), args.dir) with open('{0}/num_jobs'.format(args.dir), 'w') as f: f.write(str(num_jobs)) config_dir = '{0}/configs'.format(args.dir) var_file = '{0}/vars'.format(config_dir) variables = common_train_lib.parse_generic_config_vars_file(var_file) # Set some variables. try: model_left_context = variables['model_left_context'] model_right_context = variables['model_right_context'] # this is really the number of times we add layers to the network for # discriminative pretraining num_hidden_layers = variables['num_hidden_layers'] except KeyError as e: raise Exception("KeyError {0}: Variables need to be defined in " "{1}".format(str(e), '{0}/configs'.format(args.dir))) left_context = args.chunk_left_context + model_left_context right_context = args.chunk_right_context + model_right_context # Initialize as "raw" nnet, prior to training the LDA-like preconditioning # matrix. This first config just does any initial splicing that we do; # we do this as it's a convenient way to get the stats for the 'lda-like' # transform. if (args.stage <= -6): logger.info("Creating phone language-model") chain_lib.create_phone_lm(args.dir, args.tree_dir, run_opts, lm_opts=args.lm_opts) if (args.stage <= -5): logger.info("Creating denominator FST") chain_lib.create_denominator_fst(args.dir, args.tree_dir, run_opts) if (args.stage <= -4): logger.info("Initializing a basic network for estimating " "preconditioning matrix") common_lib.run_kaldi_command( """{command} {dir}/log/nnet_init.log \ nnet3-init --srand=-2 {dir}/configs/init.config \ {dir}/init.raw""".format(command=run_opts.command, dir=args.dir)) egs_left_context = left_context + args.frame_subsampling_factor/2 egs_right_context = right_context + args.frame_subsampling_factor/2 default_egs_dir = '{0}/egs'.format(args.dir) if (args.stage <= -3) and args.egs_dir is None: logger.info("Generating egs") # this is where get_egs.sh is called. chain_lib.generate_chain_egs( dir=args.dir, data=args.feat_dir, lat_dir=args.lat_dir, egs_dir=default_egs_dir, left_context=egs_left_context, right_context=egs_right_context, run_opts=run_opts, left_tolerance=args.left_tolerance, right_tolerance=args.right_tolerance, frame_subsampling_factor=args.frame_subsampling_factor, alignment_subsampling_factor=args.alignment_subsampling_factor, frames_per_eg=args.chunk_width, srand=args.srand, egs_opts=args.egs_opts, cmvn_opts=args.cmvn_opts, online_ivector_dir=args.online_ivector_dir, frames_per_iter=args.frames_per_iter, transform_dir=args.transform_dir, stage=args.egs_stage) if args.egs_dir is None: egs_dir = default_egs_dir else: egs_dir = args.egs_dir [egs_left_context, egs_right_context, frames_per_eg, num_archives] = ( common_train_lib.verify_egs_dir(egs_dir, feat_dim, ivector_dim, egs_left_context, egs_right_context)) assert(args.chunk_width == frames_per_eg) num_archives_expanded = num_archives * args.frame_subsampling_factor if (args.num_jobs_final > num_archives_expanded): raise Exception('num_jobs_final cannot exceed the ' 'expanded number of archives') # copy the properties of the egs to dir for # use during decoding common_train_lib.copy_egs_properties_to_exp_dir(egs_dir, args.dir) if (args.stage <= -2): logger.info('Computing the preconditioning matrix for input features') chain_lib.compute_preconditioning_matrix( args.dir, egs_dir, num_archives, run_opts, max_lda_jobs=args.max_lda_jobs, rand_prune=args.rand_prune) if (args.stage <= -1): logger.info("Preparing the initial acoustic model.") chain_lib.prepare_initial_acoustic_model(args.dir, run_opts) with open("{0}/frame_subsampling_factor".format(args.dir), "w") as f: f.write(str(args.frame_subsampling_factor)) # set num_iters so that as close as possible, we process the data # $num_epochs times, i.e. $num_iters*$avg_num_jobs) == # $num_epochs*$num_archives, where # avg_num_jobs=(num_jobs_initial+num_jobs_final)/2. num_archives_to_process = args.num_epochs * num_archives_expanded num_archives_processed = 0 num_iters = ((num_archives_to_process * 2) / (args.num_jobs_initial + args.num_jobs_final)) models_to_combine = common_train_lib.verify_iterations( num_iters, args.num_epochs, num_hidden_layers, num_archives_expanded, args.max_models_combine, args.add_layers_period, args.num_jobs_final) def learning_rate(iter, current_num_jobs, num_archives_processed): return common_train_lib.get_learning_rate(iter, current_num_jobs, num_iters, num_archives_processed, num_archives_to_process, args.initial_effective_lrate, args.final_effective_lrate) min_deriv_time = None max_deriv_time = None if args.deriv_truncate_margin is not None: min_deriv_time = -args.deriv_truncate_margin - model_left_context max_deriv_time = (args.chunk_width - 1 + args.deriv_truncate_margin + model_right_context) logger.info("Training will run for {0} epochs = " "{1} iterations".format(args.num_epochs, num_iters)) for iter in range(num_iters): if (args.exit_stage is not None) and (iter == args.exit_stage): logger.info("Exiting early due to --exit-stage {0}".format(iter)) return current_num_jobs = int(0.5 + args.num_jobs_initial + (args.num_jobs_final - args.num_jobs_initial) * float(iter) / num_iters) if args.stage <= iter: model_file = "{dir}/{iter}.mdl".format(dir=args.dir, iter=iter) shrinkage_value = 1.0 if args.shrink_value != 1.0: shrinkage_value = (args.shrink_value if common_train_lib.do_shrinkage( iter, model_file, args.shrink_saturation_threshold) else 1 ) logger.info("On iteration {0}, learning rate is {1} and " "shrink value is {2}.".format( iter, learning_rate(iter, current_num_jobs, num_archives_processed), shrinkage_value)) chain_lib.train_one_iteration( dir=args.dir, iter=iter, srand=args.srand, egs_dir=egs_dir, num_jobs=current_num_jobs, num_archives_processed=num_archives_processed, num_archives=num_archives, learning_rate=learning_rate(iter, current_num_jobs, num_archives_processed), shrinkage_value=shrinkage_value, num_chunk_per_minibatch=args.num_chunk_per_minibatch, num_hidden_layers=num_hidden_layers, add_layers_period=args.add_layers_period, left_context=left_context, right_context=right_context, apply_deriv_weights=args.apply_deriv_weights, min_deriv_time=min_deriv_time, max_deriv_time=max_deriv_time, l2_regularize=args.l2_regularize, xent_regularize=args.xent_regularize, leaky_hmm_coefficient=args.leaky_hmm_coefficient, momentum=args.momentum, max_param_change=args.max_param_change, shuffle_buffer_size=args.shuffle_buffer_size, frame_subsampling_factor=args.frame_subsampling_factor, truncate_deriv_weights=args.truncate_deriv_weights, run_opts=run_opts, background_process_handler=background_process_handler) if args.cleanup: # do a clean up everythin but the last 2 models, under certain # conditions common_train_lib.remove_model( args.dir, iter-2, num_iters, models_to_combine, args.preserve_model_interval) if args.email is not None: reporting_iter_interval = num_iters * args.reporting_interval if iter % reporting_iter_interval == 0: # lets do some reporting [report, times, data] = ( nnet3_log_parse.generate_accuracy_report( args.dir, "log-probability")) message = report subject = ("Update : Expt {dir} : " "Iter {iter}".format(dir=args.dir, iter=iter)) common_lib.send_mail(message, subject, args.email) num_archives_processed = num_archives_processed + current_num_jobs if args.stage <= num_iters: logger.info("Doing final combination to produce final.mdl") chain_lib.combine_models( dir=args.dir, num_iters=num_iters, models_to_combine=models_to_combine, num_chunk_per_minibatch=args.num_chunk_per_minibatch, egs_dir=egs_dir, left_context=left_context, right_context=right_context, leaky_hmm_coefficient=args.leaky_hmm_coefficient, l2_regularize=args.l2_regularize, xent_regularize=args.xent_regularize, run_opts=run_opts, background_process_handler=background_process_handler) if args.cleanup: logger.info("Cleaning up the experiment directory " "{0}".format(args.dir)) remove_egs = args.remove_egs if args.egs_dir is not None: # this egs_dir was not created by this experiment so we will not # delete it remove_egs = False common_train_lib.clean_nnet_dir( args.dir, num_iters, egs_dir, preserve_model_interval=args.preserve_model_interval, remove_egs=remove_egs) # do some reporting [report, times, data] = nnet3_log_parse.generate_accuracy_report( args.dir, "log-probability") if args.email is not None: common_lib.send_mail(report, "Update : Expt {0} : " "complete".format(args.dir), args.email) with open("{dir}/accuracy.report".format(dir=args.dir), "w") as f: f.write(report) common_lib.run_kaldi_command("steps/info/nnet3_dir_info.pl " "{0}".format(args.dir))
def train(args, run_opts, background_process_handler): """ The main function for training. Args: args: a Namespace object with the required parameters obtained from the function process_args() run_opts: RunOpts object obtained from the process_args() """ arg_string = pprint.pformat(vars(args)) logger.info("Arguments for the experiment\n{0}".format(arg_string)) # Set some variables. feat_dim = common_lib.get_feat_dim(args.feat_dir) ivector_dim = common_lib.get_ivector_dim(args.online_ivector_dir) ivector_id = common_lib.get_ivector_extractor_id(args.online_ivector_dir) config_dir = '{0}/configs'.format(args.dir) var_file = '{0}/vars'.format(config_dir) variables = common_train_lib.parse_generic_config_vars_file(var_file) # Set some variables. try: model_left_context = variables['model_left_context'] model_right_context = variables['model_right_context'] # this is really the number of times we add layers to the network for # discriminative pretraining num_hidden_layers = variables['num_hidden_layers'] add_lda = common_lib.str_to_bool(variables['add_lda']) include_log_softmax = common_lib.str_to_bool( variables['include_log_softmax']) except KeyError as e: raise Exception("KeyError {0}: Variables need to be defined in " "{1}".format(str(e), '{0}/configs'.format(args.dir))) left_context = args.chunk_left_context + model_left_context right_context = args.chunk_right_context + model_right_context left_context_initial = (args.chunk_left_context_initial + model_left_context if args.chunk_left_context_initial >= 0 else -1) right_context_final = (args.chunk_right_context_final + model_right_context if args.chunk_right_context_final >= 0 else -1) # Initialize as "raw" nnet, prior to training the LDA-like preconditioning # matrix. This first config just does any initial splicing that we do; # we do this as it's a convenient way to get the stats for the 'lda-like' # transform. if (args.stage <= -4): logger.info("Initializing a basic network") common_lib.run_job( """{command} {dir}/log/nnet_init.log \ nnet3-init --srand=-2 {dir}/configs/init.config \ {dir}/init.raw""".format(command=run_opts.command, dir=args.dir)) default_egs_dir = '{0}/egs'.format(args.dir) if (args.stage <= -3) and args.egs_dir is None: logger.info("Generating egs") if args.use_dense_targets: target_type = "dense" try: num_targets = int(variables['num_targets']) if (common_lib.get_feat_dim_from_scp(args.targets_scp) != num_targets): raise Exception("Mismatch between num-targets provided to " "script vs configs") except KeyError as e: num_targets = -1 else: target_type = "sparse" try: num_targets = int(variables['num_targets']) except KeyError as e: raise Exception("KeyError {0}: Variables need to be defined " "in {1}".format( str(e), '{0}/configs'.format(args.dir))) train_lib.raw_model.generate_egs_using_targets( data=args.feat_dir, targets_scp=args.targets_scp, egs_dir=default_egs_dir, left_context=left_context, right_context=right_context, left_context_initial=left_context_initial, right_context_final=right_context_final, run_opts=run_opts, frames_per_eg_str=args.chunk_width, srand=args.srand, egs_opts=args.egs_opts, cmvn_opts=args.cmvn_opts, online_ivector_dir=args.online_ivector_dir, samples_per_iter=args.samples_per_iter, transform_dir=args.transform_dir, stage=args.egs_stage, target_type=target_type, num_targets=num_targets) if args.egs_dir is None: egs_dir = default_egs_dir else: egs_dir = args.egs_dir [egs_left_context, egs_right_context, frames_per_eg_str, num_archives] = ( common_train_lib.verify_egs_dir(egs_dir, feat_dim, ivector_dim, ivector_id, left_context, right_context)) if args.chunk_width != frames_per_eg_str: raise Exception("mismatch between --egs.chunk-width and the frames_per_eg " "in the egs dir {0} vs {1}".format(args.chunk_width, frames_per_eg_str)) if (args.num_jobs_final > num_archives): raise Exception('num_jobs_final cannot exceed the number of archives ' 'in the egs directory') # copy the properties of the egs to dir for # use during decoding common_train_lib.copy_egs_properties_to_exp_dir(egs_dir, args.dir) if (add_lda and args.stage <= -2): logger.info('Computing the preconditioning matrix for input features') train_lib.common.compute_preconditioning_matrix( args.dir, egs_dir, num_archives, run_opts, max_lda_jobs=args.max_lda_jobs, rand_prune=args.rand_prune) if (args.stage <= -1): logger.info("Preparing the initial network.") common_train_lib.prepare_initial_network(args.dir, run_opts) # set num_iters so that as close as possible, we process the data # $num_epochs times, i.e. $num_iters*$avg_num_jobs) == # $num_epochs*$num_archives, where # avg_num_jobs=(num_jobs_initial+num_jobs_final)/2. num_archives_to_process = int(args.num_epochs * num_archives) num_archives_processed = 0 num_iters = ((num_archives_to_process * 2) / (args.num_jobs_initial + args.num_jobs_final)) models_to_combine = common_train_lib.verify_iterations( num_iters, args.num_epochs, num_hidden_layers, num_archives, args.max_models_combine, args.add_layers_period, args.num_jobs_final) def learning_rate(iter, current_num_jobs, num_archives_processed): return common_train_lib.get_learning_rate(iter, current_num_jobs, num_iters, num_archives_processed, num_archives_to_process, args.initial_effective_lrate, args.final_effective_lrate) min_deriv_time = None max_deriv_time_relative = None if args.deriv_truncate_margin is not None: min_deriv_time = -args.deriv_truncate_margin - model_left_context max_deriv_time_relative = \ args.deriv_truncate_margin + model_right_context logger.info("Training will run for {0} epochs = " "{1} iterations".format(args.num_epochs, num_iters)) for iter in range(num_iters): if (args.exit_stage is not None) and (iter == args.exit_stage): logger.info("Exiting early due to --exit-stage {0}".format(iter)) return current_num_jobs = int(0.5 + args.num_jobs_initial + (args.num_jobs_final - args.num_jobs_initial) * float(iter) / num_iters) if args.stage <= iter: model_file = "{dir}/{iter}.raw".format(dir=args.dir, iter=iter) shrinkage_value = 1.0 if args.shrink_value != 1.0: shrinkage_value = (args.shrink_value if common_train_lib.do_shrinkage( iter, model_file, args.shrink_saturation_threshold, get_raw_nnet_from_am=False) else 1 ) train_lib.common.train_one_iteration( dir=args.dir, iter=iter, srand=args.srand, egs_dir=egs_dir, num_jobs=current_num_jobs, num_archives_processed=num_archives_processed, num_archives=num_archives, learning_rate=learning_rate(iter, current_num_jobs, num_archives_processed), dropout_edit_string=common_train_lib.get_dropout_edit_string( args.dropout_schedule, float(num_archives_processed) / num_archives_to_process, iter), shrinkage_value=shrinkage_value, minibatch_size_str=args.num_chunk_per_minibatch, num_hidden_layers=num_hidden_layers, add_layers_period=args.add_layers_period, left_context=left_context, right_context=right_context, min_deriv_time=min_deriv_time, max_deriv_time_relative=max_deriv_time_relative, momentum=args.momentum, max_param_change=args.max_param_change, shuffle_buffer_size=args.shuffle_buffer_size, run_opts=run_opts, get_raw_nnet_from_am=False, background_process_handler=background_process_handler) if args.cleanup: # do a clean up everythin but the last 2 models, under certain # conditions common_train_lib.remove_model( args.dir, iter-2, num_iters, models_to_combine, args.preserve_model_interval, get_raw_nnet_from_am=False) if args.email is not None: reporting_iter_interval = num_iters * args.reporting_interval if iter % reporting_iter_interval == 0: # lets do some reporting [report, times, data] = ( nnet3_log_parse.generate_acc_logprob_report(args.dir)) message = report subject = ("Update : Expt {dir} : " "Iter {iter}".format(dir=args.dir, iter=iter)) common_lib.send_mail(message, subject, args.email) num_archives_processed = num_archives_processed + current_num_jobs if args.stage <= num_iters: logger.info("Doing final combination to produce final.raw") train_lib.common.combine_models( dir=args.dir, num_iters=num_iters, models_to_combine=models_to_combine, egs_dir=egs_dir, left_context=left_context, right_context=right_context, minibatch_size_str=args.num_chunk_per_minibatch, run_opts=run_opts, chunk_width=args.chunk_width, background_process_handler=background_process_handler, get_raw_nnet_from_am=False, sum_to_one_penalty=args.combine_sum_to_one_penalty) if include_log_softmax and args.stage <= num_iters + 1: logger.info("Getting average posterior for purposes of " "adjusting the priors.") train_lib.common.compute_average_posterior( dir=args.dir, iter='final', egs_dir=egs_dir, num_archives=num_archives, left_context=left_context, right_context=right_context, prior_subset_size=args.prior_subset_size, run_opts=run_opts, get_raw_nnet_from_am=False) if args.cleanup: logger.info("Cleaning up the experiment directory " "{0}".format(args.dir)) remove_egs = args.remove_egs if args.egs_dir is not None: # this egs_dir was not created by this experiment so we will not # delete it remove_egs = False common_train_lib.clean_nnet_dir( nnet_dir=args.dir, num_iters=num_iters, egs_dir=egs_dir, preserve_model_interval=args.preserve_model_interval, remove_egs=remove_egs, get_raw_nnet_from_am=False) # do some reporting [report, times, data] = nnet3_log_parse.generate_acc_logprob_report(args.dir) if args.email is not None: common_lib.send_mail(report, "Update : Expt {0} : " "complete".format(args.dir), args.email) with open("{dir}/accuracy.report".format(dir=args.dir), "w") as f: f.write(report) common_lib.run_job("steps/info/nnet3_dir_info.pl " "{0}".format(args.dir))
def train(args, run_opts, background_process_handler): """ The main function for training. Args: args: a Namespace object with the required parameters obtained from the function process_args() run_opts: RunOpts object obtained from the process_args() """ arg_string = pprint.pformat(vars(args)) logger.info("Arguments for the experiment\n{0}".format(arg_string)) # Check files chain_lib.check_for_required_files(args.feat_dir, args.tree_dir, args.lat_dir) # Set some variables. num_jobs = common_lib.get_number_of_jobs(args.tree_dir) feat_dim = common_lib.get_feat_dim(args.feat_dir) ivector_dim = common_lib.get_ivector_dim(args.online_ivector_dir) ivector_id = common_lib.get_ivector_extractor_id(args.online_ivector_dir) # split the training data into parts for individual jobs # we will use the same number of jobs as that used for alignment common_lib.split_data(args.feat_dir, num_jobs) shutil.copy('{0}/tree'.format(args.tree_dir), args.dir) with open('{0}/num_jobs'.format(args.dir), 'w') as f: f.write(str(num_jobs)) config_dir = '{0}/configs'.format(args.dir) var_file = '{0}/vars'.format(config_dir) variables = common_train_lib.parse_generic_config_vars_file(var_file) # Set some variables. try: model_left_context = variables['model_left_context'] model_right_context = variables['model_right_context'] # this is really the number of times we add layers to the network for # discriminative pretraining num_hidden_layers = variables['num_hidden_layers'] except KeyError as e: raise Exception("KeyError {0}: Variables need to be defined in " "{1}".format(str(e), '{0}/configs'.format(args.dir))) left_context = args.chunk_left_context + model_left_context right_context = args.chunk_right_context + model_right_context left_context_initial = (args.chunk_left_context_initial + model_left_context if args.chunk_left_context_initial >= 0 else -1) right_context_final = (args.chunk_right_context_final + model_right_context if args.chunk_right_context_final >= 0 else -1) # Initialize as "raw" nnet, prior to training the LDA-like preconditioning # matrix. This first config just does any initial splicing that we do; # we do this as it's a convenient way to get the stats for the 'lda-like' # transform. if (args.stage <= -6): logger.info("Creating phone language-model") chain_lib.create_phone_lm(args.dir, args.tree_dir, run_opts, lm_opts=args.lm_opts) if (args.stage <= -5): logger.info("Creating denominator FST") chain_lib.create_denominator_fst(args.dir, args.tree_dir, run_opts) if (args.stage <= -4): logger.info("Initializing a basic network for estimating " "preconditioning matrix") common_lib.run_kaldi_command("""{command} {dir}/log/nnet_init.log \ nnet3-init --srand=-2 {dir}/configs/init.config \ {dir}/init.raw""".format(command=run_opts.command, dir=args.dir)) egs_left_context = left_context + args.frame_subsampling_factor / 2 egs_right_context = right_context + args.frame_subsampling_factor / 2 egs_left_context_initial = (left_context_initial + args.frame_subsampling_factor / 2 if left_context_initial >= 0 else -1) egs_right_context_final = (right_context_final + args.frame_subsampling_factor / 2 if right_context_final >= 0 else -1) default_egs_dir = '{0}/egs'.format(args.dir) if (args.stage <= -3) and args.egs_dir is None: logger.info("Generating egs") # this is where get_egs.sh is called. chain_lib.generate_chain_egs( dir=args.dir, data=args.feat_dir, lat_dir=args.lat_dir, egs_dir=default_egs_dir, left_context=egs_left_context, right_context=egs_right_context, left_context_initial=egs_left_context_initial, right_context_final=egs_right_context_final, run_opts=run_opts, left_tolerance=args.left_tolerance, right_tolerance=args.right_tolerance, frame_subsampling_factor=args.frame_subsampling_factor, alignment_subsampling_factor=args.alignment_subsampling_factor, frames_per_eg_str=args.chunk_width, srand=args.srand, egs_opts=args.egs_opts, cmvn_opts=args.cmvn_opts, online_ivector_dir=args.online_ivector_dir, frames_per_iter=args.frames_per_iter, transform_dir=args.transform_dir, stage=args.egs_stage) if args.egs_dir is None: egs_dir = default_egs_dir else: egs_dir = args.egs_dir [egs_left_context, egs_right_context, frames_per_eg_str, num_archives] = (common_train_lib.verify_egs_dir( egs_dir, feat_dim, ivector_dim, ivector_id, egs_left_context, egs_right_context, egs_left_context_initial, egs_right_context_final)) assert (args.chunk_width == frames_per_eg_str) num_archives_expanded = num_archives * args.frame_subsampling_factor if (args.num_jobs_final > num_archives_expanded): raise Exception('num_jobs_final cannot exceed the ' 'expanded number of archives') # copy the properties of the egs to dir for # use during decoding logger.info("Copying the properties from {0} to {1}".format( egs_dir, args.dir)) common_train_lib.copy_egs_properties_to_exp_dir(egs_dir, args.dir) if (args.stage <= -2): logger.info('Computing the preconditioning matrix for input features') chain_lib.compute_preconditioning_matrix( args.dir, egs_dir, num_archives, run_opts, max_lda_jobs=args.max_lda_jobs, rand_prune=args.rand_prune) if (args.stage <= -1): logger.info("Preparing the initial acoustic model.") chain_lib.prepare_initial_acoustic_model(args.dir, run_opts) with open("{0}/frame_subsampling_factor".format(args.dir), "w") as f: f.write(str(args.frame_subsampling_factor)) # set num_iters so that as close as possible, we process the data # $num_epochs times, i.e. $num_iters*$avg_num_jobs) == # $num_epochs*$num_archives, where # avg_num_jobs=(num_jobs_initial+num_jobs_final)/2. num_archives_to_process = int(args.num_epochs * num_archives_expanded) num_archives_processed = 0 num_iters = ((num_archives_to_process * 2) / (args.num_jobs_initial + args.num_jobs_final)) models_to_combine = common_train_lib.verify_iterations( num_iters, args.num_epochs, num_hidden_layers, num_archives_expanded, args.max_models_combine, args.add_layers_period, args.num_jobs_final) def learning_rate(iter, current_num_jobs, num_archives_processed): return common_train_lib.get_learning_rate(iter, current_num_jobs, num_iters, num_archives_processed, num_archives_to_process, args.initial_effective_lrate, args.final_effective_lrate) min_deriv_time = None max_deriv_time_relative = None if args.deriv_truncate_margin is not None: min_deriv_time = -args.deriv_truncate_margin - model_left_context max_deriv_time_relative = \ args.deriv_truncate_margin + model_right_context logger.info("Training will run for {0} epochs = " "{1} iterations".format(args.num_epochs, num_iters)) for iter in range(num_iters): if (args.exit_stage is not None) and (iter == args.exit_stage): logger.info("Exiting early due to --exit-stage {0}".format(iter)) return current_num_jobs = int(0.5 + args.num_jobs_initial + (args.num_jobs_final - args.num_jobs_initial) * float(iter) / num_iters) if args.stage <= iter: model_file = "{dir}/{iter}.mdl".format(dir=args.dir, iter=iter) shrinkage_value = 1.0 if args.shrink_value != 1.0: shrinkage_value = ( args.shrink_value if common_train_lib.do_shrinkage( iter, model_file, args.shrink_saturation_threshold) else 1) chain_lib.train_one_iteration( dir=args.dir, iter=iter, srand=args.srand, egs_dir=egs_dir, num_jobs=current_num_jobs, num_archives_processed=num_archives_processed, num_archives=num_archives, learning_rate=learning_rate(iter, current_num_jobs, num_archives_processed), dropout_edit_string=common_train_lib.get_dropout_edit_string( args.dropout_schedule, float(num_archives_processed) / num_archives_to_process, iter), shrinkage_value=shrinkage_value, num_chunk_per_minibatch_str=args.num_chunk_per_minibatch, num_hidden_layers=num_hidden_layers, add_layers_period=args.add_layers_period, left_context=left_context, right_context=right_context, apply_deriv_weights=args.apply_deriv_weights, min_deriv_time=min_deriv_time, max_deriv_time_relative=max_deriv_time_relative, l2_regularize=args.l2_regularize, xent_regularize=args.xent_regularize, leaky_hmm_coefficient=args.leaky_hmm_coefficient, momentum=args.momentum, max_param_change=args.max_param_change, shuffle_buffer_size=args.shuffle_buffer_size, frame_subsampling_factor=args.frame_subsampling_factor, run_opts=run_opts, background_process_handler=background_process_handler) if args.cleanup: # do a clean up everythin but the last 2 models, under certain # conditions common_train_lib.remove_model(args.dir, iter - 2, num_iters, models_to_combine, args.preserve_model_interval) if args.email is not None: reporting_iter_interval = num_iters * args.reporting_interval if iter % reporting_iter_interval == 0: # lets do some reporting [report, times, data] = (nnet3_log_parse.generate_acc_logprob_report( args.dir, "log-probability")) message = report subject = ("Update : Expt {dir} : " "Iter {iter}".format(dir=args.dir, iter=iter)) common_lib.send_mail(message, subject, args.email) num_archives_processed = num_archives_processed + current_num_jobs if args.stage <= num_iters: logger.info("Doing final combination to produce final.mdl") chain_lib.combine_models( dir=args.dir, num_iters=num_iters, models_to_combine=models_to_combine, num_chunk_per_minibatch_str=args.num_chunk_per_minibatch, egs_dir=egs_dir, left_context=left_context, right_context=right_context, leaky_hmm_coefficient=args.leaky_hmm_coefficient, l2_regularize=args.l2_regularize, xent_regularize=args.xent_regularize, run_opts=run_opts, background_process_handler=background_process_handler, sum_to_one_penalty=args.combine_sum_to_one_penalty) if args.cleanup: logger.info("Cleaning up the experiment directory " "{0}".format(args.dir)) remove_egs = args.remove_egs if args.egs_dir is not None: # this egs_dir was not created by this experiment so we will not # delete it remove_egs = False common_train_lib.clean_nnet_dir( args.dir, num_iters, egs_dir, preserve_model_interval=args.preserve_model_interval, remove_egs=remove_egs) # do some reporting [report, times, data ] = nnet3_log_parse.generate_acc_logprob_report(args.dir, "log-probability") if args.email is not None: common_lib.send_mail( report, "Update : Expt {0} : " "complete".format(args.dir), args.email) with open("{dir}/accuracy.report".format(dir=args.dir), "w") as f: f.write(report) common_lib.run_kaldi_command("steps/info/nnet3_dir_info.pl " "{0}".format(args.dir))