def __init__(self,config):
self.config=config['learning_config']
self.am = AM(config)
self.am.load_model(training=True)
if self.am.model_type!='MultiTask':
self.dg = AM_DataLoader(config)
else:
self.dg=MultiTask_DataLoader(config)
self.dg.speech_config['reduction_factor']=self.am.model.time_reduction_factor
self.dg.load_state(self.config['running_config']['outdir'])
if self.am.model_type=='CTC':
self.runner = ctc_runners.CTCTrainer(self.dg.speech_featurizer,self.dg.text_featurizer,self.config['running_config'])
elif self.am.model_type=='LAS':
self.runner=las_runners.LASTrainer(self.dg.speech_featurizer,self.dg.text_featurizer,self.config['running_config'])
self.dg.LAS=True
elif self.am.model_type == 'MultiTask':
self.runner = multi_runners.MultiTaskLASTrainer(self.dg.speech_featurizer, self.dg.token4_featurizer,
self.config['running_config'])
else:
self.runner = transducer_runners.TransducerTrainer(self.dg.speech_featurizer,self.dg.text_featurizer,self.config['running_config'] )
self.STT = self.am.model
if self.dg.augment.available():
factor=2
else:
factor=1
self.opt = tf.keras.optimizers.Adamax(**config['optimizer_config'])
self.runner.set_total_train_steps(self.dg.get_per_epoch_steps() * self.config['running_config']['num_epochs']*factor)
self.runner.compile(self.STT,self.opt)
self.dg.batch=self.runner.global_batch_size
def __init__(self, config):
self.config = config['learning_config']
self.config['running_config'].update(
{'streaming': config['speech_config']['streaming']})
self.am = AM(config)
self.am.load_model(training=True)
if self.am.model_type != 'MultiTask':
self.dg = AM_DataLoader(config)
else:
self.dg = MultiTask_DataLoader(config)
self.dg.speech_config[
'reduction_factor'] = self.am.model.time_reduction_factor
self.dg.load_state(self.config['running_config']['outdir'])
if self.am.model_type == 'CTC':
self.runner = ctc_runners.CTCTrainer(self.dg.speech_featurizer,
self.dg.text_featurizer,
self.config['running_config'])
elif self.am.model_type == 'LAS':
self.runner = las_runners.LASTrainer(self.dg.speech_featurizer,
self.dg.text_featurizer,
self.config['running_config'])
self.dg.LAS = True
elif self.am.model_type == 'MultiTask':
self.runner = multi_runners.MultiTaskCTCTrainer(
self.dg.speech_featurizer, self.config['running_config'])
else:
self.runner = transducer_runners.TransducerTrainer(
self.dg.speech_featurizer, self.dg.text_featurizer,
self.config['running_config'])
self.STT = self.am.model
if self.dg.augment.available():
factor = 2
else:
factor = 1
all_train_step = self.dg.get_per_epoch_steps(
) * self.config['running_config']['num_epochs'] * factor
lr = CustomSchedule(config['model_config']['dmodel'],
warmup_steps=int(all_train_step * 0.1))
config['optimizer_config']['learning_rate'] = lr
self.opt = tf.keras.optimizers.Adam(**config['optimizer_config'])
self.runner.set_total_train_steps(all_train_step)
self.runner.compile(self.STT, self.opt)
self.dg.batch = self.runner.global_batch_size
def __init__(self,config):
self.config=config['learning_config']
self.am = AM(config)
self.am.load_model(training=False)
f,c=self.am.speech_feature.compute_feature_dim()
self.am.model.return_pb_function(f,c)
if self.am.model_type!='MultiTask':
self.dg = AM_DataLoader(config,training=False)
self.runner = am_tester.AMTester(self.config['running_config'], self.dg.text_featurizer)
else:
self.dg=MultiTask_DataLoader(config,training=False)
self.runner=multi_task_tester.MultiTaskTester(self.config['running_config'],self.dg.token3_featurizer,self.dg.token4_featurizer)
self.STT = self.am.model
self.runner.set_progbar(self.dg.eval_per_epoch_steps())
self.runner.compile(self.STT)
def __init__(self, config):
self.config = config['learning_config']
self.am = AM(config)
self.am.load_model(training=False)
if self.am.model_type != 'MultiTask':
self.dg = AM_DataLoader(config, training=False)
self.runner = am_tester.AMTester(
self.config['running_config'],
self.dg.text_featurizer,
streaming=config['speech_config']['streaming'])
else:
self.dg = MultiTask_DataLoader(config, training=False)
self.runner = multi_task_tester.MultiTaskTester(
self.config['running_config'], self.dg.token3_featurizer)
self.STT = self.am.model
self.runner.set_progbar(self.dg.eval_per_epoch_steps())
self.runner.set_all_steps(self.dg.eval_per_epoch_steps())
self.runner.compile(self.STT)