def main(unused_argv):
if args.config is None:
raise Exception("Config file must be provided")
json_file = open('model_configs/%s.json' % args.config).read()
json_dict = json.loads(json_file)
input_cls = utils.get_batched_input_class(json_dict.get("dataset", "default"))
model_cls = utils.get_model_class(json_dict.get("model"))
print(input_cls)
hparams = utils.create_hparams(args, model_cls)
hparams.hcopy_path = configs.HCOPY_PATH
hparams.hcopy_config = configs.HCOPY_CONFIG_PATH
hparams.input_path = os.path.join("tmp", "input.tmp")
tf.reset_default_graph()
graph = tf.Graph()
mode = tf.estimator.ModeKeys.PREDICT
with graph.as_default():
trainer = Trainer(hparams, model_cls, input_cls, mode)
trainer.build_model()
sess = tf.Session(graph=graph)
load_model(sess, model_cls, hparams)
trainer.init(sess)
exit()
os.system('cls') # clear screen
#infer("test.wav", sess, trainer)
#return
while True:
if input("Start recording? [Y/n]: ") != 'n':
print("Recording...")
record("test.wav")
# infer(hparams)
print("Inferring...")
infer("test.wav", sess, trainer)
def eval(hparams, args, Model, BatchedInput):
tf.reset_default_graph()
graph = tf.Graph()
mode = tf.estimator.ModeKeys.EVAL
hparams.batch_size = hparams.eval_batch_size
with graph.as_default():
trainer = Trainer(hparams, Model, BatchedInput, mode)
trainer.build_model()
sess = tf.Session(graph=graph)
load_model(sess, Model, hparams)
trainer.init(sess)
dlgids = []
lers = []
pbar = tqdm(total=trainer.data_size, ncols=100)
pbar.set_description("Eval")
fo = open(os.path.join(hparams.summaries_dir, "eval_ret.txt"), "w")
utils.prepare_output_path(hparams)
errs = {}
ref_lens = {}
while True:
try:
ids, ground_truth_labels, predicted_labels, ground_truth_len, predicted_len = trainer.eval(
sess)
utils.write_log(hparams, [str(ground_truth_labels)])
decode_fns = trainer.test_model.get_decode_fns()
# dlgids += list([str(id).split('/')[-2] for id in ids])
metrics = (args.metrics or hparams.metrics).split(',')
for acc_id, (gt_labels, p_labels, gt_len, p_len) in \
enumerate(zip(ground_truth_labels, predicted_labels,
ground_truth_len, predicted_len)):
if acc_id not in lers: lers[acc_id] = []
for i in range(len(gt_labels)):
if acc_id == 1 and (hparams.model
== "da_attention_seg"):
ler, str_original, str_decoded = ops_utils.joint_evaluate(
hparams,
ground_truth_labels[0][i],
predicted_labels[0][i],
ground_truth_labels[1][i],
predicted_labels[1][i],
decode_fns[acc_id],
)
else:
err, ref_len, str_original, str_decoded = ops_utils.evaluate(
gt_labels[i],
# gt_labels[i][:gt_len[i]],
p_labels[i],
# p_labels[i][:p_len[i]],
decode_fns[acc_id],
metrics[acc_id],
acc_id)
if err is not None:
errs[acc_id].append(err)
ref_lens[acc_id].append(ref_len)
if hparams.input_unit == "word":
str_original = ' '.join(str_original)
str_decoded = ' '.join(str_decoded)
elif hparams.input_unit == "char":
str_original = ''.join(str_original).replace(
'_', ' ')
str_decoded = ''.join(str_decoded).replace(
'_', ' ')
tqdm.write(
"\nGT: %s\nPR: %s\nLER: %.3f\n" %
(str_original, str_decoded, err / ref_len))
#tqdm.write(str(p_labels[i]))
#tqdm.write("%d %d" % (gt_len[i], p_len[i]))
meta = tf.SummaryMetadata()
meta.plugin_data.plugin_name = "text"
# update pbar progress and postfix
pbar.update(trainer.batch_size)
bar_pf = {}
for acc_id in range(len(ground_truth_labels)):
bar_pf["er" + str(acc_id)] = "%2.2f" % (
sum(errs[acc_id]) / sum(ref_lens[acc_id]) * 100)
pbar.set_postfix(bar_pf)
except tf.errors.OutOfRangeError:
break
# acc_by_ids = {}
# for i, id in enumerate(dlgids):
# if id not in acc_by_ids: acc_by_ids[id] = []
# acc_by_ids[id].append(lers[0][i])
# print("\n\n----- Statistics -----")
# for id, ls in acc_by_ids.items():
# print("%s\t%2.2f" % (id, sum(ls) / len(ls)))
# fo.write("LER: %2.2f" % (sum(lers) / len(lers) * 100))
# print(len(lers[0]))
fo.close()