def test_fit(deepspeech: DeepSpeech, generator: DataGenerator,
config_path: str, alphabet_path: str, test_dir: str):
# Test save best weights (overwrite the best result)
weights_path = os.path.join(test_dir, 'weights_copy.hdf5')
deepspeech.save(weights_path)
distributed_weights = deepspeech.compiled_model.get_weights()
model_checkpoint = deepspeech.callbacks[1]
model_checkpoint.best_result = 0
model_checkpoint.best_weights_path = weights_path
history = deepspeech.fit(train_generator=generator,
dev_generator=generator,
epochs=1,
shuffle=False)
assert type(history) == History
# Test the returned model has `test_weights`
deepspeech_weights = deepspeech.model.get_weights()
new_deepspeech = DeepSpeech.construct(config_path, alphabet_path)
new_deepspeech.load(model_checkpoint.best_weights_path)
new_deepspeech_weights = new_deepspeech.model.get_weights()
assert is_same(deepspeech_weights, new_deepspeech_weights)
# Test that distributed model appropriate update weights
new_distributed_weights = deepspeech.compiled_model.get_weights()
assert is_same(distributed_weights, new_distributed_weights)
shutil.rmtree('tests/checkpoints')
os.remove('tests/weights_copy.hdf5')
def __run_program(configuration_line):
""" Run DeepSpeech - save log file. """
configuration_file_path, *parameters = configuration_line.split('|')
configuration = Configuration(configuration_file_path)
__update_parameters(configuration, parameters)
__create_experiment_dir(configuration)
deepspeech_output = os.path.join(configuration.exp_dir, 'program.out')
with open(deepspeech_output, 'w') as f:
with redirect_stdout(f):
ds = DeepSpeech(configuration)
ds.train()
ds.save()
import argparse
import os
from source.deepspeech import DeepSpeech
from source.configuration import Configuration
abspath = os.path.abspath(__file__)
ROOT_DIR = os.path.dirname(abspath)
os.chdir(ROOT_DIR)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--configuration', help='Experiment configuration')
args = parser.parse_args()
# Read configuration file
config = Configuration(file_path=args.configuration)
# Set up DeepSpeech object
ds = DeepSpeech(config)
# Model optimization
ds.train()
# Save whole deepspeech model
ds.save()