hidden_size = 1024
dropout = 0.0
feature_fname = 'mfcc_delta_features.pt'
logging.basicConfig(level=logging.INFO)
logging.info('Loading data')
data = dict(train=D.flickr8k_loader(split='train',
batch_size=batch_size,
shuffle=True,
feature_fname=feature_fname),
val=D.flickr8k_loader(split='val',
batch_size=batch_size,
shuffle=False,
feature_fname=feature_fname))
fd = D.Flickr8KData
fd.init_vocabulary(data['train'].dataset)
# Saving config
pickle.dump(
dict(feature_fname=feature_fname,
label_encoder=fd.get_label_encoder(),
language='en'), open('config.pkl', 'wb'))
logging.info('Building model')
net = M.TextImage(M.get_default_config())
run_config = dict(max_lr=2 * 1e-4, epochs=32)
logging.info('Training')
M.experiment(net, data, run_config)
# Replacing original transcriptions with ASR's output
for i in range(len(hyp_asr)):
item = ds.split_data[i]
if item[2] == ref_asr[i]:
ds.split_data[i] = (item[0], item[1], hyp_asr[i])
else:
msg = 'Extracted reference #{} ({}) doesn\'t match dataset\'s \
one ({}) for {} set.'
msg = msg.format(i, ref_asr[i], ds.split_data[i][3], set_name)
logging.warning(msg)
if args.asr_model_dir:
# Saving config for text-image model
pickle.dump(
dict(feature_fname=feature_fname,
label_encoder=fd.get_label_encoder(),
language='en'), open('config.pkl', 'wb'))
logging.info('Building model text-image')
net = M2.TextImage(M2.get_default_config())
run_config = dict(max_lr=2 * 1e-4, epochs=32)
logging.info('Training text-image')
M2.experiment(net, data, run_config)
suffix = str(ds_factor).zfill(lz)
res_fname = 'result_text_image_{}.json'.format(suffix)
copyfile('result.json', res_fname)
net_fname = 'ti_{}.best.pt'.format(ds_factor)
copy_best(res_fname, net_fname)