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)