def Speech():
estimators = get_best_estimators(True)
estimators_str, estimator_dict = get_estimators_name(estimators)
import argparse
parser = argparse.ArgumentParser(description="""
Testing emotion recognition system using your voice,
please consider changing the model and/or parameters as you wish.
""")
parser.add_argument(
"-e",
"--emotions",
help=
"""Emotions to recognize separated by a comma ',', available emotions are
"neutral", "calm", "happy" "sad", "angry", "fear", "disgust", "ps" (pleasant surprise)
and "boredom", default is "sad,neutral,happy"
""",
default="sad,neutral,happy,angry,fear,disgust,ps")
parser.add_argument("-m",
"--model",
help="""
The model to use, 8 models available are: {},
default is "BaggingClassifier"
""".format(estimators_str),
default="BaggingClassifier")
# Parse the arguments passed
args = parser.parse_args()
features = ["mfcc", "chroma", "mel"]
detector = EmotionRecognizer(estimator_dict[args.model],
emotions=args.emotions.split(","),
features=features,
verbose=0)
detector.train()
print("Please talk")
filename = "test.wav"
#This bad above this line, give you confusion matrix, I am writing this comment cause I hate you!
record_to_file(filename)
result = detector.predict(filename)
#detector.draw_confusion_matrix()
return result, (detector.test_score() / 2) * 100
def plot_histograms(classifiers=True, beta=0.5, n_classes=3, verbose=1):
"""
Loads different estimators from `grid` folder and calculate some statistics to plot histograms.
Params:
classifiers (bool): if `True`, this will plot classifiers, regressors otherwise.
beta (float): beta value for calculating fbeta score for various estimators.
n_classes (int): number of classes
"""
# get the estimators from the performed grid search result
estimators = get_best_estimators(classifiers)
final_result = {}
for estimator, params, cv_score in estimators:
final_result[estimator.__class__.__name__] = []
for i in range(3):
result = {}
# initialize the class
detector = EmotionRecognizer(estimator, verbose=0)
# load the data
detector.load_data()
if i == 0:
# first get 1% of sample data
sample_size = 0.01
elif i == 1:
# second get 10% of sample data
sample_size = 0.1
elif i == 2:
# last get all the data
sample_size = 1
# calculate number of training and testing samples
n_train_samples = int(len(detector.X_train) * sample_size)
n_test_samples = int(len(detector.X_test) * sample_size)
# set the data
detector.X_train = detector.X_train[:n_train_samples]
detector.X_test = detector.X_test[:n_test_samples]
detector.y_train = detector.y_train[:n_train_samples]
detector.y_test = detector.y_test[:n_test_samples]
# calculate train time
t_train = time()
detector.train()
t_train = time() - t_train
# calculate test time
t_test = time()
test_accuracy = detector.test_score()
t_test = time() - t_test
# set the result to the dictionary
result['train_time'] = t_train
result['pred_time'] = t_test
result['acc_train'] = cv_score
result['acc_test'] = test_accuracy
result['f_train'] = detector.train_fbeta_score(beta)
result['f_test'] = detector.test_fbeta_score(beta)
if verbose:
print(
f"[+] {estimator.__class__.__name__} with {sample_size*100}% ({n_train_samples}) data samples achieved {cv_score*100:.3f}% Validation Score in {t_train:.3f}s & {test_accuracy*100:.3f}% Test Score in {t_test:.3f}s"
)
# append the dictionary to the list of results
final_result[estimator.__class__.__name__].append(result)
if verbose:
print()
visualize(final_result, n_classes=n_classes)
def get_best_estimators(self):
"""Loads estimators from grid files and returns them"""
return get_best_estimators(self.classification)
#####################################################################################################
# Emotion Analyzer #
#####################################################################################################
def get_estimators_name(estimators):
result = [
'"{}"'.format(estimator.__class__.__name__)
for estimator, _, _ in estimators
]
return ','.join(result), {
estimator_name.strip('"'): estimator
for estimator_name, (estimator, _, _) in zip(result, estimators)
}
estimators = get_best_estimators(True)
estimators_str, estimator_dict = get_estimators_name(estimators)
features = ["mfcc", "chroma", "mel"]
detector = EmotionRecognizer(estimator_dict['BaggingClassifier'],
emotions='angry,happy,neutral'.split(","),
features=features,
verbose=0)
modelObj = open('model_angry.pkl', 'rb')
model_emotion = pickle.load(modelObj)
#####################################################################################################
def sentiment_scores(sentence):
# Create a SentimentIntensityAnalyzer object.
sid_obj = SentimentIntensityAnalyzer()