def eval(self):
common.CHK_GT(len(self.total), 0)
sum = 0
for c in self.total:
sum += self.correct[c] / self.total[c]
uar = sum / len(self.total)
return 100 * (1.0 - uar)
def main(args):
# Check and set variables
common.CHK_GT(args.frames_to_stack, 0)
if args.frames_to_skip is None:
args.frames_to_skip = args.frames_to_stack
common.CHK_GT(args.frames_to_skip, 0)
ds = TemporalData.from_kaldi(args.scp)
io.log('Loaded dataset containing {} utts'.format(len(ds.get_utt_names())))
io.log('Outputting stacked features (stack: {}, skip: {}) to stdout...'.
format(args.frames_to_stack, args.frames_to_skip))
for utt_name in ds.get_utt_names():
data = ds.get_data_by_utt_name(utt_name)
stacked = stack_data(data, args.frames_to_stack, args.frames_to_skip)
print_matrix(utt_name, stacked)
def parse_per_utt(fname):
"""
:type fname: str
:param fname: Kaldi per-utt eval results file (generated by score.sh)
Return a dict of utt-level records.
"""
results = OrderedDict()
with open(fname, 'r') as f:
for line in f:
ary = line.strip().split()
common.CHK_GT(len(ary), 2)
if ary[0] not in results:
results[ary[0]] = {}
if ary[1] == '#csid':
results[ary[0]][ary[1]] = [int(x) for x in ary[2:]]
else:
results[ary[0]][ary[1]] = ary[2:]
return results
def eval(self):
common.CHK_GT(self.total, 0)
return self.error / self.total
def eval(self):
common.CHK_GT(self.total, 0)
acc = self.correct / self.total
return 100 * (1.0 - acc)
def eval(self):
common.CHK_GT(len(self.stats), 0)
return numpy.mean(self.stats)