def eval_plda(iv_file, ndx_file, enroll_file, test_subseg2orig_file,
preproc_file, coh_iv_file, coh_list, coh_nbest,
coh_nbest_discard, model_file, score_file, plda_type, **kwargs):
logging.info('loading data')
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
tdr = TDR(iv_file, ndx_file, enroll_file, None, test_subseg2orig_file,
preproc)
x_e, x_t, enroll, ndx, orig_seg = tdr.read()
logging.info('loading plda model: %s' % (model_file))
model = F.load_plda(plda_type, model_file)
t1 = time.time()
logging.info('computing llr')
scores = model.llr_1vs1(x_e, x_t)
dt = time.time() - t1
num_trials = len(enroll) * x_t.shape[0]
logging.info(
'scoring elapsed time: %.2f s. elapsed time per trial: %.2f ms.' %
(dt, dt / num_trials * 1000))
logging.info('loading cohort data')
vr = VR(coh_iv_file, coh_list, preproc)
x_coh = vr.read()
t2 = time.time()
logging.info('score cohort vs test')
scores_coh_test = model.llr_1vs1(x_coh, x_t)
logging.info('score enroll vs cohort')
scores_enr_coh = model.llr_1vs1(x_e, x_coh)
dt = time.time() - t2
logging.info('cohort-scoring elapsed time: %.2f s.' % (dt))
t2 = time.time()
logging.info('apply s-norm')
snorm = SNorm(nbest=coh_nbest, nbest_discard=coh_nbest_discard)
scores = snorm.predict(scores, scores_coh_test, scores_enr_coh)
dt = time.time() - t2
logging.info('s-norm elapsed time: %.2f s.' % (dt))
dt = time.time() - t1
logging.info(
('total-scoring elapsed time: %.2f s. '
'elapsed time per trial: %.2f ms.') % (dt, dt / num_trials * 1000))
logging.info('combine cluster scores')
scores = combine_diar_scores(ndx, orig_seg, scores)
logging.info('saving scores to %s' % (score_file))
s = TrialScores(enroll, ndx.seg_set, scores)
s = s.align_with_ndx(ndx)
s.save_txt(score_file)
def tracking_plda(iv_file, ndx_file, enroll_file, segments_file, preproc_file,
model_file, rttm_file, plda_type, **kwargs):
logging.info('loading data')
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
tdr = TDR(iv_file, ndx_file, enroll_file, segments_file, preproc)
x_e, x_t, enroll, ndx_seg, ext_segments = tdr.read()
logging.info('loading plda model: %s' % (model_file))
model = F.load_plda(plda_type, model_file)
t1 = time.time()
logging.info('computing llr')
scores = model.llr_1vs1(x_e, x_t)
dt = time.time() - t1
num_trials = len(enroll) * x_t.shape[0]
logging.info(
'scoring elapsed time: %.2f s. elapsed time per trial: %.2f ms.' %
(dt, dt / num_trials * 1000))
scores = TrialScores(enroll, ndx_seg.seg_set, scores)
new_ext_segment_ids, ext_segment_ids, model_ids, scores = flatten_segment_scores(
ndx_seg, scores)
new_ext_segments = prepare_output_ext_segments(ext_segments,
new_ext_segment_ids,
ext_segment_ids, model_ids,
scores)
new_ext_segments.save(rttm_file + '_es')
rttm = RTTM.create_spkdiar_from_ext_segments(new_ext_segments)
rttm.save(rttm_file)