class GMMRec(object):
def __init__(self):
self.features = []
self.gmmset = GMMSet()
self.classes = []
self.models = []
def delete_speaker(self, name):
if name in self.classes:
ind = self.classes.index(name)
del self.classes[ind]
del self.models[ind]
self.classes.remove(name)
ind = self.gmmset.y.index(name)
del self.gmmset.gmms[ind]
self.gmmset.y.remove(name)
else:
print name, "not in the list!"
def enroll_model(self, name, model):
if name not in self.classes:
self.classes.append(name)
self.models.append(model)
self.features.append(None)
gmm = self.load(model)
self.gmmset.add_new(gmm, name)
def enroll(self, name, mfcc_vecs, model=None):
if name not in self.classes:
feature = mfcc_vecs.astype(np.float32)
self.features.append(feature)
self.classes.append(name)
self.models.append(model)
else:
print name + " already enrolled, please delete the old one first!"
def get_mfcc(self, audio_path):
(sr, sig) = wav.read(audio_path)
if len(sig.shape) > 1:
sig = sig[:, 0]
cleansig = remove_silence(sr, sig)
mfcc_vecs = mfcc(cleansig, sr, numcep=19)
mfcc_delta = librosa.feature.delta(mfcc_vecs.T)
mfcc_delta2 = librosa.feature.delta(mfcc_vecs.T, order=2)
feats = np.vstack([mfcc_vecs.T, mfcc_delta, mfcc_delta2])
return feats.T
def enroll_file(self, name, fn, model=None):
if name not in self.classes:
fn_mfcc = np.array(self.get_mfcc(fn))
self.enroll(name, fn_mfcc, model=model)
else:
print name + " already enrolled, please delete the old one first!"
def _get_gmm_set(self):
return GMMSet()
def train(self, gmm_order=None):
for name, feats, model in zip(self.classes, self.features,
self.models):
if (name not in self.gmmset.y) and (name is not None):
gmm = self.gmmset.fit_new(feats, name, gmm_order)
if model is not None:
self.dump(model, part=gmm)
else:
print name + " already trained, skip!"
def predict(self, mfcc_vecs):
feature = mfcc_vecs.astype(np.float32)
return self.gmmset.predict_one(feature)
def dump(self, fname, part=None):
with open(fname, 'w') as f:
if part is None:
pickle.dump(self, f, -1)
else:
pickle.dump(part, f, -1)
@staticmethod
def load(fname):
with open(fname, 'r') as f:
R = pickle.load(f)
return R
