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| import os
import warnings
import numpy as np
import scipy.io.wavfile as wf
import python_speech_features as sf
import hmmlearn.hmm as hl
warnings.filterwarnings('ignore',
category=DeprecationWarning)
np.seterr(all='ignore')
def search_speeches(directory, speeches):
directory = os.path.normpath(directory)
if not os.path.isdir(directory):
raise IOError("The directory '" +
directory + "' doesn't exist!")
for entry in os.listdir(directory):
label = directory[directory.rfind(
os.path.sep) + 1:]
path = os.path.join(directory, entry)
if os.path.isdir(path):
search_speeches(path, speeches)
elif os.path.isfile(path) and \
path.endswith('.wav'):
if label not in speeches:
speeches[label] = []
speeches[label].append(path)
train_speeches = {}
search_speeches('../data/speeches/training',
train_speeches)
train_x, train_y = [], []
for label, filenames in train_speeches.items():
mfccs = np.array([])
for filename in filenames:
sample_rate, sigs = wf.read(filename)
mfcc = sf.mfcc(sigs, sample_rate)
if len(mfccs) == 0:
mfccs = mfcc
else:
mfccs = np.append(
mfccs, mfcc, axis=0)
train_x.append(mfccs)
train_y.append(label)
modles = {}
for mfccs, label in zip(train_x, train_y):
model = hl.GaussianHMM(
n_components=4, covariance_type='diag',
n_iter=1000)
modles[label] = model.fit(mfccs)
test_speeches = {}
search_speeches('../data/speeches/testing',
test_speeches)
test_x, test_y = [], []
for label, filenames in test_speeches.items():
mfccs = np.array([])
for filename in filenames:
sample_rate, sigs = wf.read(filename)
mfcc = sf.mfcc(sigs, sample_rate)
if len(mfccs) == 0:
mfccs = mfcc
else:
mfccs = np.append(mfccs, mfcc, axis=0)
test_x.append(mfccs)
test_y.append(label)
pred_test_y = []
for mfccs in test_x:
best_score, best_label = None, None
for label, model in modles.items():
score = model.score(mfccs)
if (best_score is None) or \
(best_score < score):
best_score, best_label = \
score, label
pred_test_y.append(best_label)
print(test_y)
print(pred_test_y)
|