def extract_false_positives(num_mix_params,first_pass_fns):
for num_mix in num_mix_params:
affinities = np.load('data/%d_affinities.npy' % (num_mix))
for fnr in first_pass_fnrs:
out = open('data/false_pos_times_%d_%d.pkl' % (num_mix,fnr),'rb')
false_pos_times=pickle.load(out)
out.close()
false_positives = rf.get_false_positives(false_pos_times,
S_config=sp,
E_config=ep,
offset=0,
waveform_offset=7)
example_mat = gtrd.recover_example_map(false_positives)
false_pos_assigned_phns,false_pos_phn_contexts,utt_paths,file_idx,start_ends = gtrd.recover_assigned_phns(false_positives,example_mat)
np.savez('data/false_pos_phns_assigned_contexts_%d_%d.npy' % (num_mix,fnr),
example_mat=example_mat,
assigned_phns=false_pos_assigned_phns,
phn_contexts=false_pos_phn_contexts,
utt_paths=utt_paths,
file_idx=file_idx,
start_ends=start_ends)
np.save('data/false_positives_example_mat_%d_%d.npy' % (num_mix,fnr),example_mat)
lengths,waveforms = gtrd.recover_waveforms(false_positives,example_mat)
np.savez('data/false_positives_waveforms_lengths_%d_%d.npz' % (num_mix,fnr),
lengths=lengths,
waveforms=waveforms)
Slengths,Ss = gtrd.recover_specs(false_positives,example_mat)
np.savez('data/false_positives_Ss_lengths_%d_%d.npz' % (num_mix,fnr),
lengths=Slengths,
Ss=Ss)
Elengths,Es = gtrd.recover_edgemaps(false_positives,example_mat)
np.savez('data/false_positives_Es_lengths_%d_%d.npz' % (num_mix,fnr),
lengths=Elengths,
Es=Es)
templates = et.recover_different_length_templates(affinities,
Es,
Elengths)
spec_templates = et.recover_different_length_templates(affinities,
Ss,
Slengths)
cluster_counts = np.zeros(num_mix)
for mix_id in xrange(num_mix):
os.system("mkdir -p data/%d_%d_%d" % (num_mix,fnr,mix_id))
for example_id in xrange(waveforms.shape[0]):
if affinities[example_id].max() > .999:
cluster_id = np.argmax(affinities[example_id])
wavfile.write('data/%d_%d_%d/%d.wav' % (num_mix,fnr,cluster_id,cluster_counts[cluster_id]),16000,((2**15-1)*waveforms[example_id]).astype(np.int16))
cluster_counts[cluster_id] += 1
np.save('data/aar1_padded_examplesE.npy',Es)
num_mix = 2
import matplotlib.pyplot as plt
num_mix_params = [2,3,4,5,6,7,8,9]
for num_mix in num_mix_params:
print num_mix
bem = bm.BernoulliMixture(num_mix,Es)
bem.run_EM(.000001)
templates = et.recover_different_length_templates(bem.affinities,
Es,
lengths)
spec_templates = et.recover_different_length_templates(bem.affinities,
Ss,
Slengths)
num_plots = len(spec_templates)
num_rows = 2
num_cols = num_plots/num_rows+1
for i in xrange(len(spec_templates)):
plt.subplot(num_cols,num_rows,i+1)
plt.imshow(spec_templates[i].T[::-1],interpolation='nearest')
plt.savefig('aar1_spec_templates_%d.png' % num_mix)
np.savez('aar1_templates_%d.npz' % num_mix, templates)
np.save('aar1_affinities_%d.npy' % num_mix, bem.affinities)
#
# we are now going to do the clustering procedure on these
#
aar_examples = np.load(tmp_data_path+'aar_examples.npy')
aar_lengths = np.load(tmp_data_path + 'aar_lengths.npy')
clipped_bgd = np.load(tmp_data_path+'clipped_train_bgd.npy')
aar_bgd_padded = et.pad_examples_bgd_samples(aar_examples,aar_lengths,clipped_bgd)
bem2 = bm.BernoulliMixture(2,aar_bgd_padded)
bem2.run_EM(.000001)
aar2 = et.recover_different_length_templates(bem2.affinities,aar_examples,aar_lengths)
for i in xrange(2):
np.save(tmp_data_path + 'aar2_%d.npy' % i,aar2[i])
aar_mixture = tuple(
np.load(tmp_data_path+'aar2_%d.npy' % i)
for i in xrange(2))
# now we will get the detection array associated with these two
test_example_lengths = np.load(tmp_data_path+'test_example_lengths.npy')
detection_array = np.zeros((test_example_lengths.shape[0],
int(test_example_lengths.max()/float(log_part_blocks.shape[1]) + .5) + 2),dtype=np.float32)
def perform_phn_template_estimation(phn,utterances_path,
file_indices,sp,ep,
num_mix_params,
phn_mapping=None,
waveform_offset=15,
chunk_length=1000):
phn_tuple = (phn,)
print phn
phn_features,avg_bgd=gtrd.get_syllable_features_directory(utterances_path,file_indices,phn_tuple,
S_config=sp,E_config=ep,offset=0,
E_verbose=False,return_avg_bgd=True,
waveform_offset=15,
phn_mapping=phn_mapping)
bgd = np.clip(avg_bgd.E,.01,.99)
np.save('data/bgd.npy',bgd)
example_mat = gtrd.recover_example_map(phn_features)
lengths,waveforms = gtrd.recover_waveforms(phn_features,example_mat)
np.savez('data/waveforms_lengths.npz',waveforms=waveforms,
lengths=lengths,
example_mat=example_mat)
Slengths,Ss = gtrd.recover_specs(phn_features,example_mat)
Ss = Ss.astype(np.float32)
np.savez('data/Ss_lengths.npz' ,Ss=Ss,Slengths=Slengths,example_mat=example_mat)
Elengths,Es = gtrd.recover_edgemaps(phn_features,example_mat,bgd=bgd)
Es = Es.astype(np.uint8)
np.savez('data/Es_lengths.npz' ,Es=Es,Elengths=Elengths,example_mat=example_mat)
# the Es are padded from recover_edgemaps
f = open('data/mixture_estimation_stats_%s.data' % phn,'w')
for num_mix in num_mix_params:
print num_mix
if num_mix == 1:
affinities = np.ones((Es.shape[0],1),dtype=np.float64)
mean_length = int(np.mean(Elengths) + .5)
templates = (np.mean(Es,0)[:mean_length],)
spec_templates = (np.mean(Ss,0)[:mean_length],)
np.save('data/%d_affinities.npy' % (num_mix),
affinities)
np.save('data/%d_templates.npy' % (num_mix),
templates)
np.save('data/%d_spec_templates.npy' % (num_mix),
spec_templates)
np.save('data/%d_templates_%s.npy' % (num_mix,phn),
templates)
np.save('data/%d_spec_templates_%s.npy' % (num_mix,phn),
spec_templates)
#
# write the data to the mixture file for checking purposes
# format is:
# num_components total c0 c1 c2 ... ck
f.write('%d %d %g\n' % (num_mix,
len(affinities),np.sum(affinities[:,0])))
else:
if len(Es) > chunk_length:
bem = bm.BernoulliMixture(num_mix,Es[:chunk_length])
bem.run_EM(.000001)
for i in xrange(1,len(Es)/chunk_length):
start_idx = i*chunk_length
block_length = min(chunk_length,len(Es)-start_idx)
if block_length < chunk_length:
end_idx = len(Es)
start_idx = len(Es)-chunk_length
block_length = chunk_length
else:
end_idx = start_idx + block_length
bem.data_mat = Es[start_idx:end_idx].reshape(
block_length,bem.data_length)
bem.run_EM(.000001)
else:
bem = bm.BernoulliMixture(num_mix,Es)
bem.run_EM(.000001)
templates = et.recover_different_length_templates(bem.affinities,
Es[start_idx:end_idx],
Elengths[start_idx:end_idx])
spec_templates = et.recover_different_length_templates(bem.affinities,
Ss[start_idx:end_idx],
Slengths[start_idx:end_idx])
np.save('data/%d_affinities.npy' % (num_mix),
bem.affinities)
np.savez('data/%d_templates.npz' % (num_mix),
*templates)
np.savez('data/%d_spec_templates.npz' % (num_mix),
*spec_templates)
np.savez('data/%d_templates_%s.npz' % (num_mix,phn),
*templates)
np.savez('data/%d_spec_templates_%s.npz' % (num_mix,phn),
*spec_templates)
f.write('%d %d ' % (num_mix,
len(affinities))
+ ' '.join(str(np.sum(affinities[:,i]))
for i in xrange(affinities.shape[1]))
+'\n')
f.close()
if end_idx < len(Es):
end_idx = len(Es)
start_idx = len(Es)-chunk_length
bem.data_mat = Es[start_idx:end_idx].reshape(
block_length,bem.data_length)
gc.collect()
bem.run_EM(.000001)
else:
print "len(Es)=%d > chunk_length=%d" % (len(Es),chunk_length)
bem = bm.BernoulliMixture(num_mix,Es)
bem.run_EM(.000001)
start_idx = 0
end_idx = len(Es)
print "start_idx=%d\tend_idx=%d" % (start_idx,end_idx)
templates = et.recover_different_length_templates(bem.affinities,
Es[start_idx:end_idx],
Elengths[start_idx:end_idx])
spec_templates = et.recover_different_length_templates(bem.affinities,
Ss[start_idx:end_idx],
Slengths[start_idx:end_idx])
np.save('data/%d_affinities.npy' % (num_mix),
bem.affinities)
np.savez('data/%d_templates.npz' % (num_mix),
*templates)
np.savez('data/%d_spec_templates.npz' % (num_mix),
*spec_templates)
np.savez('data/%d_templates_%s.npz' % (num_mix,phn),
*templates)
np.savez('data/%d_spec_templates_%s.npz' % (num_mix,phn),
*spec_templates)
f.write('%d %d ' % (num_mix,
del backgrounds
# estimate mixture models
#
import template_speech_rec.bernoulli_mixture as bm
mixture_models_syllable = {}
for syllable, examples in padded_examples_syllable_dict.items():
mixture_models_syllable[syllable] = bm.BernoulliMixture(2,examples)
mixture_models_syllable[syllable].run_EM(.000001)
print syllable
template_tuples_syllable = {}
for syllable, mm in mixture_models_syllable.items():
template_tuples_syllable[syllable] = et.recover_different_length_templates(mm.affinities,padded_examples_syllable_dict[syllable],
np.array([e.shape[0] for e in syllable_examples[syllable]]))
for syllable, template_tuples in template_tuples_syllable.items():
for i, template in enumerate(template_tuples):
np.save(tmp_data_path+'template_%s_%s__%d_%d.npy' % (syllable[0],
syllable[1],
len(template_tuples),
i),
template)
test_example_lengths = gtrd.get_detect_lengths(data_path+'Test/')
detection_array = np.zeros((test_example_lengths.shape[0],
int(test_example_lengths.max()/float(log_part_blocks.shape[1]) + .5) + 2),dtype=np.float32)
