def test_dtw_symmetric(self):
x = mfcc(self.tidigits[17]['samples'])
y = mfcc(self.tidigits[3]['samples'])
dist = lambda x, y: np.linalg.norm(x - y, ord=2)
d1 = dtw(x, y, dist=dist, debug=False)
d2 = dtw(y, x, dist=dist, debug=False)
self.assertEqual(d2, d1)
def test_dtw(self):
x = mfcc(self.tidigits[12]['samples'])
y = mfcc(self.tidigits[22]['samples'])
dist = lambda x, y: np.linalg.norm(x - y, ord=2)
d1, cost1, acc_cost1, path1 = dtw_mod.dtw(x, y, dist=dist)
d2 = dtw(x, y, dist=dist, debug=False)
assert_almost_equal(d1, d2)
def extract_feature(path, states):
data = []
for root, dirs, files in os.walk(path):
for file in files:
if file.endswith('.wav'):
filename = os.path.join(root, file)
samples, samplingrate = loadAudio(filename)
print(filename + '... ', end='')
# feature extraction (=> inputs for DNN)
lmfcc = mfcc(samples)
mspec_ = mspec(samples)
# forced alignment (=> targets for DNN)
wordTrans = list(
path2info(filename)
[2]) # word transcription (contained in the filename)
phoneTrans = words2phones(
wordTrans,
prondict) # word transcription => phone transcription
targets = forcedAlignment(lmfcc, phoneHMMs, phoneTrans)
targets = np.array([states.index(t) for t in targets
]) # save targets as indeces
data.append({
'filename': filename,
'lmfcc': lmfcc,
'mspec': mspec_,
'targets': targets
})
print('done')
return np.array(data)
def test_dtw_zero(self):
x = mfcc(self.tidigits[7]['samples'])
dist = lambda x, y: np.linalg.norm(x - y, ord=2)
d1, cost1, acc_cost1, path1 = dtw_mod.dtw(x, x, dist=dist)
d2 = dtw(x, x, dist=dist, debug=False)
assert_almost_equal(d1, d2)
# assert_allclose(d2, 0.0)
self.assertEqual(d2, 0.0)
def q6_speech_segment_GMM():
mfcc_features = []
for i in range(data_dict.shape[0]):
mfcc_features.append(mfcc(data_dict[0]['samples']))
mfcc_features = np.vstack(mfcc_features)
n_components = [4, 8, 16, 32]
idx_seven = [16, 17, 38, 39]
seven_list = data_dict[idx_seven]
test_mfcc_seven = []
for i in range(len(idx_seven)):
test_mfcc_seven.append(mfcc(seven_list[i]['samples']))
for comp in n_components:
#train GMM model
gmm = GaussianMixture(n_components=comp, covariance_type='diag')
gmm.fit(mfcc_features)
for i in range(len(idx_seven)):
test_data = test_mfcc_seven[i]
prob = gmm.predict_proba(test_data) #compute posterior
plot_p_color_mesh(
prob, 'GMM posterior, n_component=%d, seven #%d' % (comp, i))
def q5_feature_correlation():
mfcc_features_list = []
mspec_features_list = []
for sample in data_dict:
mfcc_feature = mfcc(sample["samples"])
mfcc_features_list.append(mfcc_feature)
mspec_feature = mspec(sample["samples"])
mspec_features_list.append(mspec_feature)
mfcc_features_list = np.vstack(mfcc_features_list)
mspec_features_list = np.vstack(mspec_features_list)
mfcc_cor = np.corrcoef(mfcc_features_list, rowvar=False)
mspec_cor = np.corrcoef(mspec_features_list, rowvar=False)
plot_p_color_mesh(mfcc_cor, 'MxM Mfcc correlations')
plot_p_color_mesh(mspec_cor, 'MxM mspec correlations')
def concat_all_features(data, feature="mfcc"):
assert feature in ["mfcc", "mspec"]
all_features = None
for d in data:
sample = d['samples']
sampling_rate = d['samplingrate']
if feature == "mfcc":
features = mfcc(sample, samplingrate=sampling_rate)
elif feature == "mspec":
features = mspec(sample, samplingrate=sampling_rate)
if all_features is None:
all_features = features
else:
all_features = np.concatenate((all_features, features), axis=0)
return all_features
def feature_extraction_and_force_alignment(filepath, nstates, phoneHMMs):
"""
handle one .wav file
"""
samples, samplingrate = loadAudio(filepath)
wordTrans = list(path2info(filepath)[2])
phoneTrans = words2phones(wordTrans, prondict)
stateTrans = [phone + '_' + str(stateid) for phone in phoneTrans
for stateid in range(nstates[phone])]
lmfcc_result = mfcc(samples)
mspec_result = mspec(samples)
targets = []
_, viterbi_path = forcedAlignment(lmfcc_result, phoneHMMs, phoneTrans)
targets = [stateTrans[idx] for idx in viterbi_path.astype(np.int16)]
return lmfcc_result, mspec_result, targets
def q7_global_distance():
global_distances = np.zeros((44, 44))
all_mfcc = []
for i in range(44):
all_mfcc.append(mfcc(data_dict[i]['samples']))
for i in range(44):
for j in range(44):
if i == j:
continue
elif global_distances[j, i] != 0:
global_distances[i, j] = global_distances[j, i]
else:
global_d, _, acc_d, _ = dtw(all_mfcc[i], all_mfcc[j],
euclidean)
global_distances[i, j] = global_d
plot_p_color_mesh(global_distances, 'global distance matrix')
np.save('global distance.npy', global_distances)
nstates = {phone: phoneHMMs[phone]['means'].shape[0] for phone in phones}
stateList = [p + '_' + str(i) for p in phones for i in range(nstates[p])]
########################
# 4.2 Forced alignment #
########################
if DEBUG:
# load correct example
example = np.load('data/lab3_example.npz',
allow_pickle=True)['example'].item()
# feature extraction
filename = 'data/tidigits/disc_4.1.1/tidigits/train/man/nw/z43a.wav'
samples, samplingrate = loadAudio(filename)
lmfcc = mfcc(samples)
# transcription
wordTrans = list(path2info(filename)
[2]) # word transcription (contained in the filename)
phoneTrans = words2phones(
wordTrans, prondict) # word transcription => phone transcription
stateTrans = [
p + '_' + str(i) for p in phoneTrans for i in range(nstates[p])
] # phone transcription => state transcription
# combined HMM for utterance
utteranceHMM = concatHMMs(phoneHMMs, phoneTrans)
# Viterbi decoder
obsloglik = log_multivariate_normal_density_diag(lmfcc,
# E.g. # of states of ah = 3; ah -> ['ah_0', 'ah_1', 'ah_2']
stateList = list()
for ph in phoneHMMs.keys():
for i in range(nstates[ph]):
stateList.append('%s_%d' % (ph, i))
# --------------------------------------------------------------
data = list()
for root, dirs, files in walk(folder_to_extract):
for f in tqdm(files):
if not f.endswith('.wav'):
continue
# do our work
filename = os.path.join(root, f)
sample, srate = loadAudio(filename)
mspec_x = mspec(sample, samplingrate=srate)
lmfcc_x = mfcc(sample, samplingrate=srate)
wordTrans = list(path2info(filename)[2])
phoneTrans = words2phones(wordTrans, prondict)
targets = forcedAlignment(lmfcc_x, phoneHMMs, phoneTrans)
# convert the targets from str to int
idx_targets = [stateList.index(t) for t in targets]
data.append({
'filename': filename,
'lmfcc': lmfcc_x,
'mspec': mspec_x,
'targets': idx_targets
})
kwargs = {data_type: data}
np.savez(dump_file_name, **kwargs)
Calculate a global distance matrix and save the gdist file
"""
import numpy as np
import config
from lab1_proto import dtw
from lab1_proto import mfcc
from scipy.cluster.hierarchy import dendrogram, linkage
from matplotlib import pyplot as plt
if __name__ == "__main__":
data = np.load('data/lab1_data.npz')['data']
# calcualte the global distance matrix
ndata = len(data)
# global_dist matrix is symmetric
# the diagonal terms are zeros
global_dist = np.zeros((ndata, ndata))
cnt = 0
for i, j in zip(*np.triu_indices(ndata, k=1)):
feature_i = mfcc(data[i]['samples'])
feature_j = mfcc(data[j]['samples'])
d = dtw(feature_i, feature_j)
global_dist[i, j] = d
global_dist[j, i] = d
cnt += 1
if cnt % 100 == 0:
print("Calculated %d global distances" % cnt, flush=True)
np.save(config.gdist_npy_file, global_dist)
customPlot(fft_, 'spec:abs(FFT)^2', True)
logMel = logMelSpectrum(fft_, sampling_rate, 512)
if (np.allclose(example['mspec'], logMel, atol=1e-08)):
customPlot(logMel, 'mspec:Mel Filterbank', True)
mfcc_ = cepstrum(logMel, 13)
if (np.allclose(example['mfcc'], mfcc_, atol=1e-08)):
customPlot(mfcc_, 'mfcc:MFCCs', True)
lmfcc_ = lifter(mfcc_)
if (np.allclose(example['lmfcc'], lmfcc_, atol=1e-08)):
customPlot(lmfcc_, 'lmfcc:Liftered MFCCs', True)
from lab1_proto import mfcc, mspec
data = np.load('lab1_data.npz', allow_pickle=True)['data']
for i in range(data.shape[0]):
samples = data[i]['samples']
s = mfcc(samples)
t = mspec(samples)
if (i == 0):
data_mfcc = s
data_mspec = t
else:
data_mfcc = np.append(data_mfcc, s, axis=0)
data_mspec = np.append(data_mspec, t, axis=0)
plt.pcolormesh(np.corrcoef(data_mfcc.T)) ## how corrcoef works ?
plt.pcolormesh(np.corrcoef(data_mspec.T))
from sklearn.mixture import GaussianMixture
gmm = GaussianMixture(n_components=4).fit(data_mfcc)
labels = gmm.predict(data_mfcc)
verbose=1) # fix the initialization for repetition
# train the GMM with all data
clf.fit(all_features)
for digit in ['1', '4', '7']:
test_data = pick_data_by_digit(data, digit=digit)
fig, axes = plt.subplots(nrows=len(test_data),
ncols=1,
sharex=True,
sharey=True,
figsize=(12, 8))
# prediction and plot the posterior matrix
for i, d in enumerate(test_data):
features = mfcc(d['samples'], samplingrate=d['samplingrate'])
posterior_prob = clf.predict_proba(features)
ax = axes[i]
title = 'Posterior for digit {digit} by {speaker} ({gender})'.format(
**d)
ax.set_title(title)
im = ax.matshow(posterior_prob.T)
ax.xaxis.tick_bottom()
ax.set_aspect('auto')
for ax in axes.flat:
ax.set(xlabel='frame', ylabel='classes')
# Hide x labels and tick labels for top plots and y ticks for right plots.
for ax in axes.flat:
ax.label_outer()