from keras.utils import np_utils from sklearn.preprocessing import LabelEncoder

X_train = np.array(trainfeatures) y_train = np.array(trainlabel) X_test = np.array(testfeatures) y_test = np.array(testlabel)

lb = LabelEncoder()

y_train = np_utils.to_categorical(lb.fit_transform(y_train)) y_test = np_utils.to_categorical(lb.fit_transform(y_test))

ERROR AT THIS LINE... please help

Hi, thanks for the work, I have a question about the samples number. After I filter out all the data (ravdess and savee), I just have 1200 samples, 960 train data, and the final acc is around 0.5. I found your train samples is 1378 (X_train.shape is 1378*216), I wonder what I did wrong.

Hey, thanks a lot for the release. I've tried training the model from scratch using the datasets, but I can't reach the same validation loss. I noticed that the pre-trained network in the repo has two more convolutional layers compared to the code in the notebook, but adding them back doesn't help either.

Did you se any additional tricks for training?

For reference, above is what I see, below is what you have in the dataset:

mylist= os.listdir('RawData/')

getting the error as FileNotFoundError. please let me know if anyone know how to solve this error and also guide me if where i need to place the dataset

Hi Mitesh, I’m trying to obtain the 70% accuracy you got but I’m only getting a 35% could you please tell me the database and send me the exact code you used to get the 70%?? Thank you very much. My email is [email protected]

The model was imported perfectly. But the LabelEncoder did not work as: #added in cell 496 from sklearn.preprocessing import LabelEncoder lb = LabelEncoder() livepredictions = (lb.inverse_transform((liveabc))) livepredictions

throws the error:

This LabelEncoder instance is not fitted yet. Call 'fit' with appropriate arguments....

If you could help with this, it will be of great help.

PS: I started by importing all files in cell 1,2,3. Then added 'opt = keras.optimizers.rmsprop(lr=0.00001, decay=1e-6)' to cell 137, since opt was not defined then executed all the blocks in Demo section.

having doubt regarding these lines, what is the data in that and which format .

mylist= os.listdir('RawData/')

data, sampling_rate = librosa.load('RawData/f11 (2).wav')

Hello! im getting this error when im trying to run the live demo. can please help me with this. Thank you.

NameError Traceback (most recent call last) in ----> 1 livepreds = loaded_model.predict(twodim, 2 batch_size=32, 3 verbose=1)

NameError: name 'loaded_model' is not defined

Hello,

I am trying to use your model to test the live audio recorded 'output10.wav'. I get the error for livepredictions = (lb.inverse_transform((liveabc))) as

This LabelEncoder instance is not fitted yet. Call 'fit' with appropriate arguments before using this method.

Do I need to do lb.firTransform(y_train) and y_test before I just run your model? Is there any other possibility to test the model before getting all the features from dataset?

Traceback (most recent call last): File "C:\Users\BhargaviiNadendla\Documents\GitHub\Speech-Emotion-Analyzer\load.py", line 73, in livepredictions = (lb.inverse_transform((liveabc))) File "D:\Anaconda3\envs\Speech-Emotion-Analyzer\lib\site-packages\sklearn\preprocessing\label.py", line 272, in inverse_transform check_is_fitted(self, 'classes_') File "D:\Anaconda3\envs\Speech-Emotion-Analyzer\lib\site-packages\sklearn\utils\validation.py", line 951, in check_is_fitted raise NotFittedError(msg % {'name': type(estimator).name}) sklearn.exceptions.NotFittedError: This LabelEncoder instance is not fitted yet. Call 'fit' with appropriate arguments before using this method.

(Speech-Emotion-Analyzer) C:\Users\BhargaviiNadendla>sklearn.exceptions.NotFittedError: This LabelEncoder instance is not fitted yet. Call 'fit' with appropriate arguments before using this method. 'sklearn.exceptions.NotFittedError:' is not recognized as an internal or external command, operable program or batch file.

i cant run code error: C:\Speech-Emotion-Analyzer>python train.py Using TensorFlow backend. Traceback (most recent call last): File "train.py", line 102, in X,sample_rate = librosa.load('data/'+y) File "C:\Python35\lib\site-packages\librosa\core\audio.py", line 112, in load with audioread.audio_open(os.path.realpath(path)) as input_file: File "C:\Python35\lib\site-packages\audioread_init_.py", line 80, in audio_open return rawread.RawAudioFile(path) File "C:\Python35\lib\site-packages\audioread\rawread.py", line 61, in init self._fh = open(filename, 'rb') PermissionError: [Errno 13] Permission denied: 'C:\Speech-Emotion-Analyzer\data\Actor_01'

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,

This paper doesn't make any sence because you are taking average of 13mfcc features which is quite absurd as you it is ridiculous we actually have to mean for all the frames so there should .T at np.mean at feature extraction and from there everything should change your model, accuracy every thing as your function is fundamentally wrong , Hope you change it as this repo is most stared one ,so this lead to miss information for many

When I tried Loading the model with final_result_gender_test, I got AttributeError:'list' object has no attribute'items'. Please tell me how to resolve.

OS: MacOS Big Sur Environment: VSCode Docker Ubuntu 18.0.4

librosa==0.8.0 numpy==1.18.5 matplotlib==3.1.0 tensorflow==2.2.0 Keras==2.4.3 sklearn==0.0

Hi, Thanks for the nice work. I was trying to just use your model for inference. I looked at the notebook and copied the necessary parts for inference, but get error This LabelEncoder instance is not fitted yet. Can you help what is missing in this code?

import os

from keras import regularizers
import keras
from keras.callbacks import ModelCheckpoint
from keras.layers import Conv1D, MaxPooling1D, AveragePooling1D, Dense, Embedding, Input, Flatten, Dropout, Activation, LSTM
from keras.models import Model, Sequential, model_from_json
from keras.preprocessing import sequence
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.utils import to_categorical
import librosa
import librosa.display
from matplotlib.pyplot import specgram
from sklearn.metrics import confusion_matrix
from sklearn.preprocessing import LabelEncoder

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import tensorflow as tf


opt = keras.optimizers.rmsprop(lr=0.00001, decay=1e-6)
lb = LabelEncoder()


json_file = open('model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
# load weights into new model
loaded_model.load_weights("saved_models/Emotion_Voice_Detection_Model.h5")
print("Loaded model from disk")
 
X, sample_rate = librosa.load('h04.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 = loaded_model.predict(twodim, batch_size=32, verbose=1)

livepreds1=livepreds.argmax(axis=1)
liveabc = livepreds1.astype(int).flatten()
livepredictions = (lb.inverse_transform((liveabc)))
print(livepredictions)