def main(args):
deepspeech = DeepSpeech.construct(config_path=CONFIG_PATH, alphabet_path=ALPHABET_PATH)
if args.pretrained_weights:
deepspeech.load(args.pretrained_weights)
train_generator, dev_generator = create_generators(deepspeech, args)
deepspeech.fit(train_generator, dev_generator, epochs=args.epochs, shuffle=False)
deepspeech.save(WEIGHTS_PATH)
def setup_deepspeech(config_path: str,
alphabet_path: str,
pretrained_weights: str = '') -> DeepSpeech:
deepspeech = DeepSpeech.construct(config_path, alphabet_path)
if pretrained_weights:
deepspeech.load(pretrained_weights)
return deepspeech
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 load_extended_model(config_path, alphabet_path, pretrained_weights):
deepspeech = DeepSpeech.construct(config_path=config_path,
alphabet_path=alphabet_path)
freeze(deepspeech.model)
gpus = get_available_gpus()
config = Configuration(config_path)
extended_model = create_extended_model(deepspeech.model,
config,
is_gpu=len(gpus) > 0)
optimizer = DeepSpeech.get_optimizer(**config.optimizer)
loss = DeepSpeech.get_loss()
gpus = get_available_gpus()
deepspeech.model = extended_model
deepspeech.compiled_model = DeepSpeech.compile_model(
extended_model, optimizer, loss, gpus)
deepspeech.load(pretrained_weights)
return deepspeech
def main(args):
deepspeech = DeepSpeech.construct(config_path=CONFIG_PATH, alphabet_path=ALPHABET_PATH)
if args.pretrained_weights:
deepspeech.load(args.pretrained_weights)
freeze(deepspeech.model)
gpus = get_available_gpus()
config = Configuration(CONFIG_PATH)
extended_model = create_extended_model(deepspeech.model, config, is_gpu=len(gpus) > 0)
optimizer = DeepSpeech.get_optimizer(**config.optimizer)
loss = DeepSpeech.get_loss()
gpus = get_available_gpus()
deepspeech.model = extended_model
deepspeech.compiled_model = DeepSpeech.compile_model(extended_model, optimizer, loss, gpus)
train_generator, dev_generator = create_generators(deepspeech, args)
deepspeech.fit(train_generator, dev_generator, epochs=args.epochs, shuffle=False)
deepspeech.save(WEIGHTS_PATH)
def main(args):
deepspeech = DeepSpeech.construct(config_path=config_path,
alphabet_path=alphabet_path)
if args.pretrained_weights:
deepspeech.load(args.pretrained_weights)
train_generator = deepspeech.create_generator(
args.train,
batch_size=args.batch_size,
source=args.source,
shuffle_after_epoch=args.shuffle_after_epoch)
dev_generator = deepspeech.create_generator(args.dev,
batch_size=args.batch_size,
source=args.source)
deepspeech.fit(train_generator,
dev_generator,
epochs=args.epochs,
shuffle=False)
deepspeech.save(weights_path)
def deepspeech(config_path: str, alphabet_path: str) -> DeepSpeech:
return DeepSpeech.construct(config_path, alphabet_path)