def eval_plda(iv_file, ndx_file, enroll_file, test_file,
preproc_file,
model_file, score_file, plda_type, **kwargs):
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
tdr_args = TDR.filter_args(**kwargs)
tdr = TDR(iv_file, ndx_file, enroll_file, test_file, preproc, **tdr_args)
x_e, x_t, enroll, ndx = tdr.read()
model = F.load_plda(plda_type, model_file)
t1 = time.time()
scores = model.llr_1vs1(x_e, x_t)
dt = time.time() - t1
num_trials = x_e.shape[0] * x_t.shape[0]
logging.info('Elapsed time: %.2f s. Elapsed time per trial: %.2f ms.'
% (dt, dt/num_trials*1000))
s = TrialScores(enroll, ndx.seg_set, scores)
s.save(score_file)
def train_mvn(iv_file, train_list, preproc_file, name, save_tlist,
append_tlist, output_path, **kwargs):
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
vr_args = VR.filter_args(**kwargs)
vr = VR(iv_file, train_list, preproc, **vr_args)
x = vr.read()
t1 = time.time()
model = MVN(name=name)
model.fit(x)
logging.info('Elapsed time: %.2f s.' % (time.time() - t1))
if save_tlist:
if append_tlist and preproc is not None:
preproc.append(model)
model = preproc
else:
model = TransformList(model)
model.save(output_path)
def eval_cos(iv_file, ndx_file, enroll_file, test_file,
preproc_file, score_file, **kwargs):
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
tdr_args = TDR.filter_args(**kwargs)
tdr = TDR(iv_file, ndx_file, enroll_file, test_file, preproc, **tdr_args)
x_e, x_t, enroll, ndx = tdr.read()
lnorm = LNorm()
x_e = lnorm.predict(x_e)
x_t = lnorm.predict(x_t)
t1 = time.time()
scores = np.dot(x_e, x_t.T)
dt = time.time() - t1
num_trials = x_e.shape[0] * x_t.shape[0]
logging.info('Elapsed time: %.2f s. Elapsed time per trial: %.2f ms.'
% (dt, dt/num_trials*1000))
s = TrialScores(enroll, ndx.seg_set, scores)
s.save(score_file)
def plot_vector_hist(iv_file, v_list, preproc_file, output_path, num_bins,
normed, **kwargs):
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
vr_args = VR.filter_args(**kwargs)
vr = VR(iv_file, v_list, preproc, **vr_args)
x = vr.read()
t1 = time.time()
if not os.path.exists(output_path):
os.makedirs(ouput_path)
for i in xrange(x.shape[1]):
fig_file = '%s/D%04d.pdf' % (output_path, i)
plt.hist(x[:, i], num_bins, normed=normed)
plt.xlabel('Dim %d' % i)
plt.grid(True)
plt.show()
plt.savefig(fig_file)
plt.clf()
logging.info('Elapsed time: %.2f s.' % (time.time() - t1))
def train_plda(iv_file, train_list, val_list, preproc_file,
epochs, ml_md, md_epochs,
output_path, **kwargs):
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
vcr_args = VCR.filter_args(**kwargs)
vcr_train = VCR(iv_file, train_list, preproc, **vcr_args)
x, class_ids = vcr_train.read()
x_val = None
class_ids_val = None
if val_list is not None:
vcr_val = VCR(iv_file, val_list, preproc, **vcr_args)
x_val, class_ids_val = vcr_val.read()
t1 = time.time()
plda_args = F.filter_train_args(**kwargs)
model = F.create_plda(**plda_args)
elbos = model.fit(x, class_ids, x_val=x_val, class_ids_val=class_ids_val,
epochs=epochs, ml_md=ml_md, md_epochs=md_epochs)
logging.info('Elapsed time: %.2f s.' % (time.time()-t1))
model.save(output_path)
elbo = np.vstack(elbos)
num = np.arange(epochs)
elbo = np.vstack((num, elbo)).T
elbo_path=os.path.splitext(output_path)[0] + '.csv'
np.savetxt(elbo_path, elbo, delimiter=',')
def train_gauss(iv_file, train_list, preproc_file,
save_tlist, append_tlist, input_path, output_path, **kwargs):
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
vr_args = VR.filter_args(**kwargs)
vr = VR(iv_file, train_list, preproc, **vr_args)
x = vr.read()
t1 = time.time()
model_args = Gaussianizer.filter_args(**kwargs)
model = load_model(input_path, **model_args)
model.fit(x)
if save_tlist:
if append_tlist and preproc is not None:
preproc.append(model)
model = preproc
else:
model = TransformList(model)
model.save(output_path)
def eval_plda(iv_file, ndx_file, enroll_file, test_file, preproc_file,
model_file, score_file, pool_method, **kwargs):
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
tdr_args = TDR.filter_args(**kwargs)
tdr = TDR(iv_file, ndx_file, enroll_file, test_file, preproc, **tdr_args)
x_e, x_t, enroll, ndx = tdr.read()
enroll, ids_e = np.unique(enroll, return_inverse=True)
model = F.load_plda(plda_type, model_file)
t1 = time.time()
scores = model.llr_Nvs1(x_e, x_t, method=pool_method, ids1=ids_e)
dt = time.time() - t1
num_trials = len(enroll) * x_t.shape[0]
logging.info('Elapsed time: %.2f s. Elapsed time per trial: %.2f ms.' %
(dt, dt / num_trials * 1000))
s = TrialScores(enroll, ndx.seg_set, scores)
s.save(score_file)
def eval_svm(iv_file, class2int_file, test_file,
preproc_file,
model_file, score_file, vector_score_file,
eval_type, **kwargs):
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
tdr_args = TDR.filter_args(**kwargs)
tdr = TDR(iv_file, class2int_file, test_file, preproc, **tdr_args)
x, ndx = tdr.read()
model = SVM.load(model_file)
t1 = time.time()
scores = model.predict(x, eval_type)
dt = time.time() - t1
num_trials = scores.shape[0]*scores.shape[1]
logging.info('Elapsed time: %.2f s. Elapsed time per trial: %.2f ms.'
% (dt, dt/num_trials*1000))
s = TrialScores(ndx.model_set, ndx.seg_set, scores.T)
s.save(score_file)
if vector_score_file is not None:
h5 = HDW(vector_score_file)
h5.write(ndx.seg_set, '', scores)
def eval_plda(iv_file, ndx_file, enroll_file, test_subseg2orig_file,
preproc_file,
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('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 train_lda(iv_file, train_list, preproc_file, lda_dim, name, save_tlist,
append_tlist, output_path, **kwargs):
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
vcr_args = VCR.filter_args(**kwargs)
vcr = VCR(iv_file, train_list, preproc, **vcr_args)
x, class_ids = vcr.read()
t1 = time.time()
model = LDA(lda_dim=lda_dim, name=name)
model.fit(x, class_ids)
logging.info('Elapsed time: %.2f s.' % (time.time() - t1))
x = model.predict(x)
s_mat = SbSw()
s_mat.fit(x, class_ids)
logging.debug(s_mat.Sb[:4, :4])
logging.debug(s_mat.Sw[:4, :4])
if save_tlist:
if append_tlist and preproc is not None:
preproc.append(model)
model = preproc
else:
model = TransformList(model)
model.save(output_path)
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 extract_embed(seq_file, model_file, preproc_file, output_path,
max_seq_length, pooling_output, write_format, **kwargs):
set_float_cpu('float32')
sr_args = SDRF.filter_args(**kwargs)
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
sr = SDRF.create(seq_file, transform=preproc, **sr_args)
t1 = time.time()
model = SeqQEmbed.load(model_file)
model.build(max_seq_length)
model.build_embed(pooling_output)
y_dim = model.embed_dim
_, seq_lengths = sr.read_num_rows()
sr.reset()
num_seqs = len(seq_lengths)
p1_y = np.zeros((num_seqs, y_dim), dtype=float_keras())
p2_y = np.zeros((num_seqs, y_dim), dtype=float_keras())
keys = []
for i in xrange(num_seqs):
ti1 = time.time()
key, data = sr.read(1)
ti2 = time.time()
logging.info('Extracting embeddings %d/%d for %s, num_frames: %d' %
(i, num_seqs, key[0], data[0].shape[0]))
keys.append(key[0])
p1_y[i], p2_y[i] = model.predict_embed(data[0])
ti4 = time.time()
logging.info('Elapsed time embeddings %d/%d for %s, total: %.2f read: %.2f, vae: %.2f' %
(i, num_seqs, key, ti4-ti1, ti2-ti1, ti4-ti2))
logging.info('Extract elapsed time: %.2f' % (time.time() - t1))
if write_format == 'p1':
y = p1_y
elif write_format == 'p1+p2':
y = np.hstack((p1_y, p2_y))
else:
y = p2_y
hw = DWF.create(output_path)
hw.write(keys, y)
def load_model(input_path, **kwargs):
if input_path is None:
return Gaussianizer(**kwargs)
try:
return Gaussianizer.load(input_path)
except:
tfl = TransformList.load(input_path)
for tf in tfl.transforms:
if tf.name == name:
return tf
def load_model(input_path, name, **kwargs):
if input_path is None:
return PCA(name=name, **kwargs)
try:
return PCA.load(input_path)
except:
tfl = TransformList.load(input_path)
for tf in tfl.transforms:
if tf.name == name:
return tf
def extract_ivector(seq_file, file_list, gmm_file, model_file, preproc_file, output_path,
qy_only, **kwargs):
set_float_cpu('float32')
sr_args = SR.filter_eval_args(**kwargs)
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
gmm = DiagGMM.load_from_kaldi(gmm_file)
sr = SR(seq_file, file_list, batch_size=1,
shuffle_seqs=False,
preproc=preproc, **sr_args)
t1 = time.time()
# if qy_only:
# model = TVAEY.load(model_file)
# else:
model = TVAEYZ.load(model_file)
model.build(max_seq_length=sr.max_batch_seq_length)
y = np.zeros((sr.num_seqs, model.y_dim), dtype=float_keras())
xx = np.zeros((1, sr.max_batch_seq_length, model.x_dim), dtype=float_keras())
rr = np.zeros((1, sr.max_batch_seq_length, model.r_dim), dtype=float_keras())
keys = []
for i in xrange(sr.num_seqs):
ti1 = time.time()
x, key = sr.read_next_seq()
ti2 = time.time()
r = gmm.compute_z(x)
ti3 = time.time()
logging.info('Extracting i-vector %d/%d for %s, num_frames: %d' % (i, sr.num_seqs, key, x.shape[0]))
keys.append(key)
xx[:,:,:] = 0
rr[:,:,:] = 0
xx[0,:x.shape[0]] = x
rr[0,:x.shape[0]] = r
y[i] = model.compute_qy_x([xx, rr], batch_size=1)[0]
ti4 = time.time()
logging.info('Elapsed time i-vector %d/%d for %s, total: %.2f read: %.2f, gmm: %.2f, vae: %.2f' %
(i, sr.num_seqs, key, ti4-ti1, ti2-ti1, ti3-ti2, ti4-ti3))
logging.info('Extract elapsed time: %.2f' % (time.time() - t1))
hw = HypDataWriter(output_path)
hw.write(keys, '', y)
def extract_ivector(seq_file, file_list, model_file, preproc_file, output_path,
qy_only, **kwargs):
set_float_cpu('float32')
sr_args = SR.filter_eval_args(**kwargs)
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
sr = SR(seq_file,
file_list,
batch_size=1,
shuffle_seqs=False,
preproc=preproc,
**sr_args)
t1 = time.time()
if qy_only:
model = TVAEY.load(model_file)
else:
model = TVAEYZ.load(model_file)
model.build(max_seq_length=sr.max_batch_seq_length)
logging.info(time.time() - t1)
logging.info(model.y_dim)
y = np.zeros((sr.num_seqs, model.y_dim), dtype=float_keras())
xx = np.zeros((1, sr.max_batch_seq_length, model.x_dim),
dtype=float_keras())
keys = []
for i in xrange(sr.num_seqs):
x, key = sr.read_next_seq()
logging.info('Extracting i-vector %d/%d for %s\n' %
(i, sr.num_seqs, key))
keys.append(key)
xx[:, :, :] = 0
xx[0, :x.shape[0]] = x
y[i] = model.compute_qy_x(xx, batch_size=1)[0]
logging.info('Extract elapsed time: %.2f' % (time.time() - t1))
hw = HypDataWriter(output_path)
hw.write(keys, '', y)
def load_model(input_path, with_lnorm, name, **kwargs):
if input_path is None:
if with_lnorm:
return LNorm(name=name, **kwargs)
else:
return CentWhiten(name=name, **kwargs)
try:
if with_lnorm:
return LNorm.load(input_path)
else:
return CentWhiten(input_path)
except:
tfl = TransformList.load(input_path)
for tf in tfl.transforms:
if tf.name == name:
return tf
def train_linear_gbe(iv_file, train_list, preproc_file, output_path, **kwargs):
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
vcr_args = VCR.filter_args(**kwargs)
vcr_train = VCR(iv_file, train_list, preproc, **vcr_args)
x, class_ids = vcr_train.read()
t1 = time.time()
model_args = GBE.filter_train_args(**kwargs)
model = GBE(**model_args)
model.fit(x, class_ids)
logging.info('Elapsed time: %.2f s.' % (time.time() - t1))
model.save(output_path)
def eval_elbo(seq_file, file_list, model_file, preproc_file, output_file,
ubm_type, **kwargs):
sr_args = SR.filter_eval_args(**kwargs)
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
sr = SR(seq_file,
file_list,
batch_size=1,
shuffle_seqs=False,
preproc=preproc,
**sr_args)
t1 = time.time()
if ubm_type == 'diag-gmm':
model = DiagGMM.load(model_file)
else:
model = DiagGMM.load_from_kaldi(model_file)
model.initialize()
elbo = np.zeros((sr.num_seqs, ), dtype=float_cpu())
num_frames = np.zeros((sr.num_seqs, ), dtype=int)
keys = []
for i in xrange(sr.num_seqs):
x, key = sr.read_next_seq()
keys.append(key)
elbo[i] = model.elbo(x)
num_frames[i] = x.shape[0]
num_total_frames = np.sum(num_frames)
total_elbo = np.sum(elbo)
total_elbo_norm = total_elbo / num_total_frames
logging.info('Extract elapsed time: %.2f' % (time.time() - t1))
s = 'Total ELBO: %f\nELBO_NORM %f' % (total_elbo, total_elbo_norm)
logging.info(s)
with open(output_file, 'w') as f:
f.write(s)
def compute_gmm_post(seq_file, file_list, model_file, preproc_file,
output_path, num_comp, **kwargs):
sr_args = SR.filter_eval_args(**kwargs)
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
gmm = DiagGMM.load_from_kaldi(model_file)
sr = SR(seq_file,
file_list,
batch_size=1,
shuffle_seqs=False,
preproc=preproc,
**sr_args)
t1 = time.time()
logging.info(time.time() - t1)
index = np.zeros((sr.num_seqs, num_comp), dtype=int)
hw = HypDataWriter(output_path)
for i in xrange(sr.num_seqs):
x, key = sr.read_next_seq()
logging.info('Extracting i-vector %d/%d for %s, num_frames: %d' %
(i, sr.num_seqs, key, x.shape[0]))
r = gmm.compute_z(x)
r_s, index = to_sparse(r, num_comp)
if i == 0:
r2 = to_dense(r_s, index, r.shape[1])
logging.degug(np.sort(r[0, :])[-12:])
logging.degug(np.sort(r2[0, :])[-12:])
logging.degug(np.argsort(r[0, :])[-12:])
logging.degug(np.argsort(r2[0, :])[-12:])
hw.write([key], '.r', [r_s])
hw.write([key], '.index', [index])
logging.info('Extract elapsed time: %.2f' % (time.time() - t1))
def eval_pdda(iv_file, ndx_file, enroll_file, test_file,
preproc_file,
model_file, score_file,
pool_method, eval_method,
num_samples_y, num_samples_z, num_samples_elbo, qy_only,
**kwargs):
set_float_cpu('float32')
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
tdr_args = TDR.filter_args(**kwargs)
tdr = TDR(iv_file, ndx_file, enroll_file, test_file, preproc, **tdt_args)
x_e, x_t, enroll, ndx = tdr.read()
enroll, ids_e = np.unique(enroll, return_inverse=True)
if qy_only:
model = TVAEY.load(model_file)
model.build(max_seq_length=2, num_samples=num_samples_y)
else:
model = TVAEYZ.load(model_file)
model.build(max_seq_length=2,
num_samples_y=num_samples_y, num_samples_z=num_samples_z)
t1 = time.time()
scores = model.eval_llr_Nvs1(x_e, ids_e, x_t,
pool_method=pool_method,
eval_method=eval_method,
num_samples=num_samples_elbo)
dt = time.time() - t1
num_trials = len(enroll) * x_t.shape[0]
logging.info('Elapsed time: %.2f s. Elapsed time per trial: %.2f ms.' %
(dt, dt/num_trials*1000))
s = TrialScores(enroll, ndx.seg_set, scores)
s.save(score_file)
def train_cw(iv_file, train_list, preproc_file, with_lnorm,
save_tlist, append_tlist, input_path, output_path, **kwargs):
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
vr_args = VR.filter_args(**kwargs)
vr = VR(iv_file, train_list, preproc, **vr_args)
x = vr.read()
t1 = time.time()
model_args = CentWhiten.filter_args(**kwargs)
model = load_model(input_path, with_lnorm, **model_args)
model.fit(x)
logging.info('Elapsed time: %.2f s.' % (time.time()-t1))
x = model.predict(x)
gauss=Normal(x_dim=x.shape[1])
gauss.fit(x=x)
logging.debug(gauss.mu[:4])
logging.debug(gauss.Sigma[:4,:4])
if save_tlist:
if append_tlist and preproc is not None:
preproc.append(model)
model = preproc
else:
model = TransformList(model)
model.save(output_path)
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)
def extract_ivector(seq_file, file_list, gmm_file, model_file, preproc_file,
output_path, qy_only, **kwargs):
set_float_cpu('float32')
sr_args = SR.filter_eval_args(**kwargs)
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
gmm = DiagGMM.load_from_kaldi(gmm_file)
sr = SR(seq_file,
file_list,
batch_size=1,
shuffle_seqs=False,
preproc=preproc,
**sr_args)
t1 = time.time()
# if qy_only:
# model = TVAEY.load(model_file)
# else:
model = TVAEYZ.load(model_file)
#model.build(max_seq_length=sr.max_batch_seq_length)
#model.build(max_seq_length=1)
model.x_dim = 60
model.r_dim = 2048
model.y_dim = 400
y = np.zeros((sr.num_seqs, model.y_dim), dtype=float_keras())
xx = np.zeros((1, sr.max_batch_seq_length, model.x_dim),
dtype=float_keras())
rr = np.zeros((1, sr.max_batch_seq_length, model.r_dim),
dtype=float_keras())
keys = []
xp = Input(shape=(
sr.max_batch_seq_length,
model.x_dim,
))
rp = Input(shape=(
sr.max_batch_seq_length,
model.r_dim,
))
qy_param = model.qy_net([xp, rp])
qy_net = Model([xp, rp], qy_param)
for i in xrange(sr.num_seqs):
ti1 = time.time()
x, key = sr.read_next_seq()
ti2 = time.time()
r = gmm.compute_z(x)
ti3 = time.time()
logging.info('Extracting i-vector %d/%d for %s, num_frames: %d' %
(i, sr.num_seqs, key, x.shape[0]))
keys.append(key)
# xp = Input(shape=(x.shape[0], model.x_dim,))
# rp = Input(shape=(x.shape[0], model.r_dim,))
# qy_param = model.qy_net([xp, rp])
ti5 = time.time()
xx[:, :, :] = 0
rr[:, :, :] = 0
xx[0, :x.shape[0]] = x
rr[0, :x.shape[0]] = r
# x = np.expand_dims(x, axis=0)
# r = np.expand_dims(r, axis=0)
# qy_net = Model([xp, rp], qy_param)
y[i] = qy_net.predict([xx, rr], batch_size=1)[0]
# del qy_net
# y[i] = model.compute_qy_x2([x, r], batch_size=1)[0]
#for i in xrange(10):
#gc.collect()
ti4 = time.time()
logging.info(
'Elapsed time i-vector %d/%d for %s, total: %.2f read: %.2f, gmm: %.2f, vae: %.2f qy: %.2f'
% (i, sr.num_seqs, key, ti4 - ti1, ti2 - ti1, ti3 - ti2, ti4 - ti5,
ti5 - ti3))
# print('Elapsed time i-vector %d/%d for %s, total: %.2f read: %.2f, gmm: %.2f, vae: %.2f' %
# (i, sr.num_seqs, key, ti4-ti1, ti2-ti1, ti3-ti2, ti4-ti3))
logging.info('Extract elapsed time: %.2f' % (time.time() - t1))
hw = HypDataWriter(output_path)
hw.write(keys, '', y)
def extract_ivector(seq_file, file_list, post_file, model_file, preproc_file,
output_path, qy_only, max_length, layer_name, **kwargs):
set_float_cpu('float32')
sr_args = SR.filter_eval_args(**kwargs)
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
sr = SR(seq_file,
file_list,
post_file,
batch_size=1,
shuffle_seqs=False,
preproc=preproc,
**sr_args)
t1 = time.time()
# if qy_only:
# model = TVAEY.load(model_file)
# else:
model = TVAEYZ.load(model_file)
pt_input = model.pt_net.input
pt_output = model.pt_net.get_layer(layer_name).output
pt_dim = model.pt_net.get_layer(layer_name).output_shape[-1]
#model.build(max_seq_length=sr.max_batch_seq_length)
model.build(max_seq_length=1)
max_length = np.minimum(sr.max_batch_seq_length, max_length)
y = np.zeros((sr.num_seqs, pt_dim), dtype=float_keras())
xx = np.zeros((1, max_length, model.x_dim), dtype=float_keras())
rr = np.zeros((1, max_length, model.r_dim), dtype=float_keras())
keys = []
xp = Input(shape=(
max_length,
model.x_dim,
))
rp = Input(shape=(
max_length,
model.r_dim,
))
qy_param = model.qy_net([xp, rp])
pt_net = Model(pt_input, pt_output)
emb = pt_net(qy_param[0])
emb_net = Model([xp, rp], emb)
model.pt_net.summary()
pt_net.summary()
emb_net.summary()
logging.info(layer_name)
#emb_net = Model([xp, rp], pt_net.get_layer('pt').get_layer(layer_name).output)
#pt_net = Model(model.pt_net.input, model.pt_net.get_layer(layer_name).output)
# emb = pt_net(qy_param[0])
#emb_net = Model([xp, rp], emb)
for i in xrange(sr.num_seqs):
ti1 = time.time()
x, r, key = sr.read_next_seq()
ti2 = time.time()
logging.info('Extracting i-vector %d/%d for %s, num_frames: %d' %
(i, sr.num_seqs, key, x.shape[0]))
keys.append(key)
xx[:, :, :] = 0
rr[:, :, :] = 0
if x.shape[0] <= max_length:
xx[0, :x.shape[0]] = x
rr[0, :x.shape[0]] = r
y[i] = emb_net.predict([xx, rr], batch_size=1)
else:
num_batches = int(np.ceil(x.shape[0] / max_length))
for j in xrange(num_batches - 1):
start = j * max_length
xx[0] = x[start:start + max_length]
rr[0] = r[start:start + max_length]
y[i] += emb_net.predict([xx, rr], batch_size=1).ravel()
xx[0] = x[-max_length:]
rr[0] = r[-max_length:]
y[i] += emb_net.predict([xx, rr], batch_size=1).ravel()
y[i] /= num_batches
ti4 = time.time()
logging.info(
'Elapsed time i-vector %d/%d for %s, total: %.2f read: %.2f, vae: %.2f'
% (i, sr.num_seqs, key, ti4 - ti1, ti2 - ti1, ti4 - ti2))
logging.info('Extract elapsed time: %.2f' % (time.time() - t1))
hw = HypDataWriter(output_path)
hw.write(keys, '', y)
def train_embed(data_path, train_list, val_list, px_net_path, pt_net_path,
qy_net_path, qz_net_path, init_path, epochs, preproc_file,
output_path, freeze_embed, **kwargs):
g = reserve_gpu()
set_float_cpu(float_keras())
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
sg_args = G.filter_args(**kwargs)
sg = G(data_path,
train_list,
shuffle_seqs=True,
reset_rng=False,
transform=preproc,
**sg_args)
max_length = sg.max_seq_length
gen_val = None
if val_list is not None:
sg_val = G(data_path,
val_list,
transform=preproc,
shuffle_seqs=False,
reset_rng=True,
**sg_args)
max_length = max(max_length, sg_val.max_seq_length)
gen_val = data_generator(sg_val, max_length)
gen_train = data_generator(sg, max_length)
if init_path is None:
model, init_epoch = KML.load_checkpoint(output_path, epochs)
if model is None:
embed_args = VAE.filter_args(**kwargs)
logging.debug(embed_args)
px_net = load_model_arch(px_net_path)
qy_net = load_model_arch(qy_net_path)
qz_net = load_model_arch(qz_net_path)
pt_net = load_model_arch(pt_net_path)
model = VAE(px_net, qy_net, qz_net, pt_net, **embed_args)
else:
sg.cur_epoch = init_epoch
sg.reset()
else:
logging.info('loading init model: %s' % init_path)
model = KML.load(init_path)
model.px_weight = kwargs['px_weight']
model.pt_weight = kwargs['pt_weight']
model.kl_qy_weight = kwargs['kl_qy_weight']
model.kl_qz_weight = kwargs['kl_qz_weight']
opt_args = KOF.filter_args(**kwargs)
cb_args = KCF.filter_args(**kwargs)
logging.debug(sg_args)
logging.debug(opt_args)
logging.debug(cb_args)
logging.info('max length: %d' % max_length)
t1 = time.time()
if freeze_embed:
model.prepool_net.trainable = False
model.build(max_length)
logging.info(time.time() - t1)
cb = KCF.create_callbacks(model, output_path, **cb_args)
opt = KOF.create_optimizer(**opt_args)
model.compile(optimizer=opt)
h = model.fit_generator(gen_train,
validation_data=gen_val,
steps_per_epoch=sg.steps_per_epoch,
validation_steps=sg_val.steps_per_epoch,
initial_epoch=sg.cur_epoch,
epochs=epochs,
callbacks=cb,
max_queue_size=10)
logging.info('Train elapsed time: %.2f' % (time.time() - t1))
model.save(output_path + '/model')
def extract_embed(seq_file, file_list, model_file, preproc_file, output_path,
max_length, layer_names, **kwargs):
set_float_cpu('float32')
sr_args = SR.filter_eval_args(**kwargs)
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
sr = SR(seq_file,
file_list,
batch_size=1,
shuffle_seqs=False,
preproc=preproc,
**sr_args)
t1 = time.time()
model = SeqEmbed.load(model_file)
model.build()
print(layer_names)
model.build_embed(layer_names)
y_dim = model.embed_dim
max_length = np.minimum(sr.max_batch_seq_length, max_length)
y = np.zeros((sr.num_seqs, y_dim), dtype=float_keras())
xx = np.zeros((1, max_length, model.x_dim), dtype=float_keras())
keys = []
for i in xrange(sr.num_seqs):
ti1 = time.time()
x, key = sr.read_next_seq()
ti2 = time.time()
print('Extracting embeddings %d/%d for %s, num_frames: %d' %
(i, sr.num_seqs, key, x.shape[0]))
keys.append(key)
xx[:, :, :] = 0
if x.shape[0] <= max_length:
xx[0, :x.shape[0]] = x
y[i] = model.predict_embed(xx, batch_size=1)
else:
num_chunks = int(np.ceil(float(x.shape[0]) / max_length))
chunk_size = int(np.ceil(float(x.shape[0]) / num_chunks))
for j in xrange(num_chunks - 1):
start = j * chunk_size
xx[0, :chunk_size] = x[start:start + chunk_size]
y[i] += model.predict_embed(xx, batch_size=1).ravel()
xx[0, :chunk_size] = x[-chunk_size:]
y[i] += model.predict_embed(xx, batch_size=1).ravel()
y[i] /= num_chunks
ti4 = time.time()
print(
'Elapsed time embeddings %d/%d for %s, total: %.2f read: %.2f, vae: %.2f'
% (i, sr.num_seqs, key, ti4 - ti1, ti2 - ti1, ti4 - ti2))
print('Extract elapsed time: %.2f' % (time.time() - t1))
hw = HypDataWriter(output_path)
hw.write(keys, '', y)
def train_embed(seq_file, train_list, val_list, class_list, embed_file,
init_path, epochs, batch_size, preproc_file, output_path,
post_pdf, pooling_input, pooling_output, min_var, **kwargs):
set_float_cpu(float_keras())
sr_args = SR.filter_args(**kwargs)
sr_val_args = SR.filter_val_args(**kwargs)
opt_args = KOF.filter_args(**kwargs)
cb_args = KCF.filter_args(**kwargs)
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
sr = SR(seq_file,
train_list,
class_list,
batch_size=batch_size,
preproc=preproc,
**sr_args)
max_length = sr.max_batch_seq_length
gen_val = None
if val_list is not None:
sr_val = SR(seq_file,
val_list,
class_list,
batch_size=batch_size,
preproc=preproc,
shuffle_seqs=False,
seq_split_mode='sequential',
seq_split_overlap=0,
reset_rng=True,
**sr_val_args)
max_length = max(max_length, sr_val.max_batch_seq_length)
gen_val = data_generator(sr_val, max_length)
gen_train = data_generator(sr, max_length)
t1 = time.time()
if init_path is None:
embed_net = load_model_arch(embed_file)
model = SeqMetaEmbed(embed_net,
num_classes=sr.num_classes,
post_pdf=post_pdf,
pooling_input=pooling_input,
pooling_output=pooling_output,
min_var=min_var)
else:
logging.info('loading init model: %s' % init_path)
model = SeqMetaEmbed.load(init_path)
logging.info('max length: %d' % max_length)
model.build(max_length)
logging.info(time.time() - t1)
cb = KCF.create_callbacks(model, output_path, **cb_args)
opt = KOF.create_optimizer(**opt_args)
model.compile(optimizer=opt)
h = model.fit_generator(gen_train,
validation_data=gen_val,
steps_per_epoch=sr.num_batches,
validation_steps=sr_val.num_batches,
epochs=epochs,
callbacks=cb,
max_queue_size=10)
logging.info('Train elapsed time: %.2f' % (time.time() - t1))
model.save(output_path + '/model')
def train_embed(data_path, train_list, val_list,
train_list_adapt, val_list_adapt,
prepool_net_path, postpool_net_path,
init_path,
epochs,
preproc_file, output_path,
freeze_prepool, freeze_postpool_layers,
**kwargs):
set_float_cpu(float_keras())
if init_path is None:
model, init_epoch = KML.load_checkpoint(output_path, epochs)
if model is None:
emb_args = SeqEmbed.filter_args(**kwargs)
prepool_net = load_model_arch(prepool_net_path)
postpool_net = load_model_arch(postpool_net_path)
model = SeqEmbed(prepool_net, postpool_net,
loss='categorical_crossentropy',
**emb_args)
else:
kwargs['init_epoch'] = init_epoch
else:
logging.info('loading init model: %s' % init_path)
model = KML.load(init_path)
sg_args = G.filter_args(**kwargs)
opt_args = KOF.filter_args(**kwargs)
cb_args = KCF.filter_args(**kwargs)
logging.debug(sg_args)
logging.debug(opt_args)
logging.debug(cb_args)
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
sg = G(data_path, train_list, train_list_adapt,
shuffle_seqs=True, reset_rng=False,
transform=preproc, **sg_args)
max_length = sg.max_seq_length
gen_val = None
if val_list is not None:
sg_val = G(data_path, val_list, val_list_adapt,
transform=preproc,
shuffle_seqs=False, reset_rng=True,
**sg_args)
max_length = max(max_length, sg_val.max_seq_length)
gen_val = data_generator(sg_val, max_length)
gen_train = data_generator(sg, max_length)
logging.info('max length: %d' % max_length)
t1 = time.time()
if freeze_prepool:
model.freeze_prepool_net()
if freeze_postpool_layers is not None:
model.freeze_postpool_net_layers(freeze_postpool_layers)
model.build(max_length)
cb = KCF.create_callbacks(model, output_path, **cb_args)
opt = KOF.create_optimizer(**opt_args)
model.compile(optimizer=opt)
h = model.fit_generator(gen_train, validation_data=gen_val,
steps_per_epoch=sg.steps_per_epoch,
validation_steps=sg_val.steps_per_epoch,
initial_epoch=sg.cur_epoch,
epochs=epochs, callbacks=cb, max_queue_size=10)
logging.info('Train elapsed time: %.2f' % (time.time() - t1))
model.save(output_path + '/model')
def extract_ivector(seq_file, file_list, gmm_file, model_file, preproc_file,
output_path, qy_only, max_length, **kwargs):
set_float_cpu('float32')
sr_args = SR.filter_eval_args(**kwargs)
if preproc_file is not None:
preproc = TransformList.load(preproc_file)
else:
preproc = None
gmm = DiagGMM.load_from_kaldi(gmm_file)
sr = SR(seq_file,
file_list,
batch_size=1,
shuffle_seqs=False,
preproc=preproc,
**sr_args)
t1 = time.time()
# if qy_only:
# model = TVAEY.load(model_file)
# else:
model = TVAEYZ.load(model_file)
#model.build(max_seq_length=sr.max_batch_seq_length)
model.build(max_seq_length=1)
max_length = np.minimum(sr.max_batch_seq_length, max_length)
y = np.zeros((sr.num_seqs, model.y_dim), dtype=float_keras())
xx = np.zeros((1, max_length, model.x_dim), dtype=float_keras())
rr = np.zeros((1, max_length, model.r_dim), dtype=float_keras())
keys = []
xp = Input(shape=(
max_length,
model.x_dim,
))
rp = Input(shape=(
max_length,
model.r_dim,
))
qy_param = model.qy_net([xp, rp])
qy_net = Model([xp, rp], qy_param)
for i in xrange(sr.num_seqs):
ti1 = time.time()
x, key = sr.read_next_seq()
ti2 = time.time()
r = gmm.compute_z(x)
ti3 = time.time()
logging.info('Extracting i-vector %d/%d for %s, num_frames: %d' %
(i, sr.num_seqs, key, x.shape[0]))
keys.append(key)
xx[:, :, :] = 0
rr[:, :, :] = 0
if x.shape[0] <= max_length:
xx[0, :x.shape[0]] = x
rr[0, :x.shape[0]] = r
y[i] = qy_net.predict([xx, rr], batch_size=1)[0]
else:
num_batches = int(np.ceil(x.shape[0] / max_length))
for j in xrange(num_batches - 1):
start = j * max_length
xx[0] = x[start:start + max_length]
rr[0] = r[start:start + max_length]
y[i] += qy_net.predict([xx, rr], batch_size=1)[0].ravel()
xx[0] = x[-max_length:]
rr[0] = r[-max_length:]
y[i] += qy_net.predict([xx, rr], batch_size=1)[0].ravel()
y[i] /= num_batches
ti4 = time.time()
logging.info(
'Elapsed time i-vector %d/%d for %s, total: %.2f read: %.2f, gmm: %.2f, vae: %.2f'
%
(i, sr.num_seqs, key, ti4 - ti1, ti2 - ti1, ti3 - ti2, ti4 - ti3))
logging.info('Extract elapsed time: %.2f' % (time.time() - t1))
hw = HypDataWriter(output_path)
hw.write(keys, '', y)