def testDataSet(datasetpath, succes_rate):
for model in os.listdir('GMMModels/'):
models_name.append(model)
tmp = Models.retrieveModels('GMMModels/' + model)
models.append(tmp)
total_files = 0
for folder in os.listdir(datasetpath):
for audio in os.listdir(datasetpath + folder):
total_files += 1
samplerate, audiofile = rd(datasetpath + folder + '/' + audio)
mfcc_features = extract_features(audiofile, samplerate)
max_score = -999999
max_model = models[0]
i = 0
for model in models:
score = model.score(mfcc_features)
if score > max_score:
max_score = score
max_model = models_name[i]
i = i + 1
if max_model[:len(max_model) - 4] == folder:
succes_rate += 1
succes_rate = (succes_rate * 1.00) / total_files
print(str((succes_rate) * 100) + "%")
def testSingleaudio(testpath):
samplerate, audiofile = rd(testpath)
mfcc_features = extract_features(audiofile, samplerate)
maxscore = -999999
speaker = ""
for model in os.listdir('GMMModels/'):
tmp = Models.retrieveModels('GMMModels/' + model)
score = tmp.score(mfcc_features)
if score > maxscore:
maxscore = score
speaker = model.split(".gmm")[0]
return speaker
def singleModelTraining(audiopath,modeldest) : mfcc_features = np.asarray(()) for folder in os.listdir(audiopath) : samplerate , audiofile = rd(audiopath + '/'+folder ) tmp = extract_features(audiofile,samplerate) if mfcc_features.size == 0 : mfcc_features = tmp else : mfcc_features = np.vstack((mfcc_features , tmp)) gmm = GMM(n_components = 16, max_iter = 200, covariance_type='diag',n_init = 3) gmm.fit(mfcc_features) Models.saveModels(modeldest , gmm , audiopath[12:])
def to_array(name):
a = np.asarray(((rd(name))[1])[:, 0], dtype = np.float32)
return a