Using the model for prediction

[DinaAlBassam]

Hello,

I am trying to use the already trained model directly for predicting the emotions.

I wrote this put this code in a python file and run it:
def predict():
lb = LabelEncoder()
Model_filename = 'saved_models/Emotion_Voice_Detection_Model.h5'
Model = load_model(Model_filename)
X, sample_rate = librosa.load('filename.wav', res_type='kaiser_fast',duration=2.5,sr=22050*2,offset=0.5)
sample_rate = np.array(sample_rate)
mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=13),axis=0)
featurelive = mfccs
livedf2 = featurelive
livedf2= pd.DataFrame(data=livedf2)
livedf2 = livedf2.stack().to_frame().T
twodim= np.expand_dims(livedf2, axis=2)
livepreds = Model.predict(twodim,batch_size=32,verbose=1)
livepreds1=livepreds.argmax(axis=1)
liveabc = livepreds1.astype(int).flatten()
livepredictions = (lb.inverse_transform((liveabc)))
livepredictions

But, it displays an error in the (lb.inverse_transform), it says that the (lb) need to be trained first .. Is there a method where I can use it which returns the emotion's name, without a need for using the dataset and training the model again?

Also I have another question, Is this model a language-independent model?
Thanks,

[dlpazs]

Try lb.fit() first, have a look at the sklearn label encoder docs

[dlpazs]

I found this worked for a simple test but I think I have the labels incorrect:

pred_to_class = {
	0: "female_angry",
	1: "female_calm",
	2: "female_fearful",
	3: "female_happy",
	4: "female_sad",
	5: "male_angry",
	6: "male_calm",
	7: "male_fearful",
	8: "male_happy",
	9: "male_sad"
}


def predict():
	lb = LabelEncoder()
	Model_filename = 'saved_models/Emotion_Voice_Detection_Model.h5'
	# Model = load_model(Model_filename)

	json_file = open('model.json', 'r')
	loaded_model_json = json_file.read()
	json_file.close()
	loaded_model = model_from_json(loaded_model_json)
	loaded_model.load_weights("saved_models/Emotion_Voice_Detection_Model.h5")
	print("Loaded model from disk")


	X, sample_rate = librosa.load('output10.wav', 
		res_type='kaiser_fast',
		duration=2.5,
		sr=22050*2,
		offset=0.5)
	sample_rate = np.array(sample_rate)
	mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=13),axis=0)
	featurelive = mfccs
	livedf2 = featurelive
	livedf2= pd.DataFrame(data=livedf2)
	livedf2 = livedf2.stack().to_frame().T
	twodim= np.expand_dims(livedf2, axis=2)

	preds = loaded_model.predict(twodim, 
                         batch_size=32, 
                         verbose=1)
	preds1 = preds.argmax(axis=1)
	abc = preds1.astype(int).flatten()
	print(pred_to_class[abc.item()])

[Kai-Karren]

@dlpazs version is working fine for me.