def evaluate_deepspeech():
setup_logging(LOGGER)
path_to_model_checkpoint = '/Users/florianlay/roboy/sonosco/pretrained/deepspeech_final.pth'
config_path = "bootstrap_deepspeech.yaml"
config = parse_yaml(config_path)
experiment = Experiment.create(config, LOGGER)
model = DeepSpeech2.load_model(path_to_model_checkpoint)
model.eval()
processor = AudioDataProcessor(**config)
test_dataset = AudioDataset(processor, manifest_filepath=config["test_manifest"])
sampler = RandomSampler(data_source=test_dataset, replacement=True,
num_samples=2)
test_loader = AudioDataLoader(dataset=test_dataset, num_workers=config["num_data_workers"], sampler=sampler)
device = torch.device("cuda" if CUDA_ENABLED else "cpu")
metrics = [word_error_rate, character_error_rate]
evaluator = ModelEvaluator(model,
test_loader,
config['bootstrap_size'],
config['num_bootstraps'],
decoder=GreedyDecoder(config['labels']),
device=device,
metrics=metrics)
evaluator.start_evaluation(log_path=experiment.logs)
model.to(device)
# Create data loaders
train_loader, val_loader, test_loader = create_data_loaders(**config)
# Create model trainer
trainer = ModelTrainer(model, loss=cross_entropy_loss, epochs=config["max_epochs"],
train_data_loader=train_loader, val_data_loader=val_loader,
test_data_loader=test_loader,
lr=config["learning_rate"], custom_model_eval=True,
metrics=[word_error_rate, character_error_rate],
decoder=GreedyDecoder(config["decoder"]['labels']),
device=device, test_step=config["test_step"])
trainer.add_callback(TbTextComparisonCallback(log_dir=experiment.plots_path))
trainer.add_callback(TbTeacherForcingTextComparisonCallback(log_dir=experiment.plots_path))
trainer.add_callback(DisableSoftWindowAttention())
# Setup experiment with a model trainer
experiment.setup_model_trainer(trainer, checkpoints=True, tensorboard=True)
try:
experiment.start()
except KeyboardInterrupt:
experiment.stop()
if __name__ == '__main__':
setup_logging(LOGGER)
main()
def main(**kwargs):
global LOGGER
LOGGER = logging.getLogger(SONOSCO)
setup_logging(LOGGER)
try_download_common_voice(**kwargs)
def main(*args, **kwargs):
setup_logging(LOGGER)
create_manifest(*args, **kwargs)