def __init__(self, input_stream, output_stream, flags_stream, phone_map_fname,
tmp_dir, batch_size):
self.phone_map = read_phone_map(phone_map_fname)
input_it = kio.read_mat_ark(input_stream)
output_dict = {k: v for k, v in kio.read_ali_ark(output_stream)}
self.flags_dict = {k: v for k, v in kio.read_vec_int_ark(flags_stream)}
self.flags = None
gi.DataLoader.__init__(self, input_it, output_dict, tmp_dir, batch_size)
parser.add_argument('feats')
parser.add_argument('phones')
parser.add_argument('flags')
parser.add_argument('exp_dir')
parser.add_argument('eval_dir')
args = parser.parse_args()
os.makedirs(args.eval_dir, exist_ok=True)
model_fname = os.path.join(args.exp_dir, 'final.h5')
phn_map_fname = os.path.join(args.exp_dir, 'phn_sil_to_idx.int')
score_fname = os.path.join(args.eval_dir, 'score.ark')
score_txt_fname = os.path.join(args.eval_dir, 'score.txt')
score_txt = open(score_txt_fname, 'w')
# Load phone map
phn_map = read_phone_map(phn_map_fname)
# Load kaldi files
flags_dict = {k: v for k, v in kio.read_vec_int_ark(args.flags)}
ali_dict = {k: v for k, v in kio.read_ali_ark(args.phones)}
feats_it = kio.read_mat_ark(args.feats)
# Load classifier model
model = MispModel.load(model_fname)
with open(score_fname, 'wb') as f:
for utt, feats in feats_it:
# Workaround to deal with missing alignments
if utt not in ali_dict:
continue
def __init__(self, phone_map_fname, pfeats_lang):
self.__dict__ = globals()[pfeats_lang]
self.phone_map = read_phone_map(phone_map_fname)
self.pfeats_tbl = np.array(self.table, dtype=np.int)