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annotation.txt

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+go to the supermarket
+the house
+go to the pharmacy
+go to bed
+wash the car

app.py

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+import pyttsx3
+import speech_recognition as sr
+from playsound import playsound
+import random
+import datetime
+hour = datetime.datetime.now().strftime('%H:%M')
+#print(hour)
+date = datetime.date.today().strftime('%d/%B/%Y')
+#print(date)
+date = date.split('/')
+#print(date)
+import webbrowser as wb
+import tensorflow as tf
+import numpy as np
+import librosa
+import matplotlib.pyplot as plt
+import seaborn as sns
+sns.set()
+from modules import commands_answers, load_agenda
+commands = commands_answers.commands
+answers = commands_answers.answers
+#print(commands)
+#print(answers)
+
+my_name = 'Bob'
+
+# MacOS
+chrome_path = 'open -a /Applications/Google\ Chrome.app %s'
+# Windows
+#chrome_path = 'C:/Program Files/Google/Chrome/Application/chrome.exe %s'
+# Linux
+# chrome_path = '/usr/bin/google-chrome %s'
+
+def search(sentence):
+    wb.get(chrome_path).open('https://www.google.com/search?q=' + sentence)
+
+#search('python programming language')
+
+MODEL_TYPES = ['EMOTION']
+def load_model_by_name(model_type):
+    if model_type == MODEL_TYPES[0]:
+        model = tf.keras.models.load_model('models/speech_emotion_recognition.hdf5')
+        model_dict = list(['calm', 'happy', 'fear', 'nervous', 'neutral', 'disgust', 'surprise', 'sad'])
+        SAMPLE_RATE = 48000
+    return model, model_dict, SAMPLE_RATE
+
+#print(load_model_by_name('EMOTION'))
+#print(load_model_by_name('EMOTION')[0].summary())
+
+model_type = 'EMOTION'
+loaded_model = load_model_by_name(model_type)
+
+def predict_sound(AUDIO, SAMPLE_RATE, plot = True):
+    results = []
+    wav_data, sample_rate = librosa.load(AUDIO, sr = SAMPLE_RATE)
+    #print(wav_data.shape)
+    #print(sample_rate)
+    #print(wav_data)
+    # ' librosa ' -> 'librosa'
+    # https://librosa.org/doc/main/generated/librosa.effects.trim.html
+    clip, index = librosa.effects.trim(wav_data, top_db=60, frame_length=512, hop_length=64)
+    splitted_audio_data = tf.signal.frame(clip, sample_rate, sample_rate, pad_end = True, pad_value = 0)
+    for i, data in enumerate(splitted_audio_data.numpy()):
+        #print('Audio split: ', i)
+        #print(data.shape)
+        #print(data)
+        # Mel frequency: https://en.wikipedia.org/wiki/Mel-frequency_cepstrum
+        # PCA
+        mfccs_features = librosa.feature.mfcc(y = data, sr = sample_rate, n_mfcc=40)
+        #print(mfccs_features.shape)
+        #print(mfccs_features)
+        mfccs_scaled_features = np.mean(mfccs_features.T, axis = 0)
+        mfccs_scaled_features = mfccs_scaled_features.reshape(1, -1)
+        #print(mfccs_scaled_features.shape)
+        mfccs_scaled_features = mfccs_scaled_features[:, :, np.newaxis]
+        # batch
+        #print(mfccs_scaled_features.shape)
+        predictions = loaded_model[0].predict(mfccs_scaled_features)
+        #print(predictions)
+        #print(predictions.sum())
+        if plot:
+            plt.figure(figsize=(len(splitted_audio_data), 5))
+            plt.barh(loaded_model[1], predictions[0])
+            plt.tight_layout()
+            plt.show()
+
+        predictions = predictions.argmax(axis = 1)
+        #print(predictions)
+        predictions = predictions.astype(int).flatten()
+        predictions = loaded_model[1][predictions[0]]
+        results.append(predictions)
+        #print(results)
+
+        result_str = 'PART ' + str(i) + ': ' + str(predictions).upper()
+        #print(result_str)
+
+    count_results = [[results.count(x), x] for x in set(results)]
+    #print(count_results)
+
+    #print(max(count_results))
+    return max(count_results)
+
+#playsound('sad.wav')
+#predict_sound('sad.wav', loaded_model[2], plot=False)
+
+def play_music_youtube(emotion):
+    play = False
+    if emotion == 'sad' or emotion == 'fear':
+        wb.get(chrome_path).open('https://www.youtube.com/watch?v=k32IPg4dbz0&ab_channel=Amelhorm%C3%BAsicainstrumental')
+        play = True
+    if emotion == 'nervous' or emotion == 'surprise':
+        wb.get(chrome_path).open('https://www.youtube.com/watch?v=pWjmpSD-ph0&ab_channel=CassioToledo')
+        play = True
+    return play
+
+#play_music_youtube('sad')
+#play_music_youtube('surprise')
+#emotion = predict_sound('sad.wav', loaded_model[2], plot=False)
+#print(emotion)
+#play_music_youtube(emotion[1])
+
+def speak(text):
+    engine = pyttsx3.init()
+    engine.setProperty('rate', 90) # number of words per second
+    engine.setProperty('volume', 1) # min: 0, max: 1
+    engine.say(text)
+    engine.runAndWait()
+
+#speak("Testing the Assistant's Speech Synthesizer")
+
+def listen_microphone():
+    microphone = sr.Recognizer()
+    with sr.Microphone() as source:
+        microphone.adjust_for_ambient_noise(source, duration=0.8)
+        print('Listening: ')
+        audio = microphone.listen(source)
+        with open('recordings/speech.wav', 'wb') as f:
+            f.write(audio.get_wav_data())
+    try:
+        # https://pypi.org/project/SpeechRecognition/
+        sentence = microphone.recognize_google(audio, language='en-US')
+        print('You said: ' + sentence)
+    except sr.UnknownValueError:
+        sentence = ''
+        print('Not understood')
+    return sentence
+
+#playsound('recordings/speech.wav')
+#listen_microphone()
+
+def test_models():
+    audio_source = '/Users/jonesgranatyr/Documents/Ensino/IA Expert/Cursos/Virtual assistent/virtual_assistant/recordings/speech.wav'
+    prediction = predict_sound(audio_source, loaded_model[2], plot = False)
+    return prediction
+
+#print(test_models())
+
+playing = False
+mode_control = False
+print('[INFO] Ready to start!')
+playsound('n1.mp3')
+
+while (1):
+    result = listen_microphone()
+
+    if my_name in result:
+        result = str(result.split(my_name + ' ')[1])
+        result = result.lower()
+        #print('The assistant has been activacted!')
+        #print('After processing: ', result)
+
+        if result in commands[0]:
+            playsound('n2.mp3')
+            speak('I will read my list of functionalities: ' + answers[0])
+
+        if result in commands[3]:
+            playsound('n2.mp3')
+            speak('It is now ' + datetime.datetime.now().strftime('%H:%M'))
+
+        if result in commands[4]:
+            playsound('n2.mp3')
+            speak('Today is ' + date[0] + ' of ' + date[1])
+
+        if result in commands[1]:
+            playsound('n2.mp3')
+            speak('Please, tell me the activity!')
+            result = listen_microphone()
+            annotation = open('annotation.txt', mode='a+', encoding='utf-8')
+            annotation.write(result + '\n')
+            annotation.close()
+            speak(''.join(random.sample(answers[1], k = 1)))
+            speak('Want me to read the notes?')
+            result = listen_microphone()
+            if result == 'yes' or result == 'sure':
+                with open('annotation.txt') as file_source:
+                    lines = file_source.readlines()
+                    for line in lines:
+                        speak(line)
+            else:
+                speak('Ok!')
+
+        if result in commands[2]:
+            playsound('n2.mp3')
+            speak(''.join(random.sample(answers[2], k = 1)))
+            result = listen_microphone()
+            search(result)
+
+        if result in commands[6]:
+            playsound('n2.mp3')
+            if load_agenda.load_agenda():
+                speak('These are the events for today:')
+                for i in range(len(load_agenda.load_agenda()[1])):
+                    speak(load_agenda.load_agenda()[1][i] + ' ' + load_agenda.load_agenda()[0][i] + ' schedule for ' + str(load_agenda.load_agenda()[2][i]))
+            else:
+                speak('There are not events for today considering the current time!')
+
+        if result in commands[5]:
+            mode_control = True
+            playsound('n1.mp3')
+            speak('Emotion analysis mode has been activacted!')
+
+        if mode_control:
+            analyse = test_models()
+            print(f'I heard {analyse} in your voice!')
+            if not playing:
+                playing = play_music_youtube(analyse[1])
+
+        if result == 'turn off':
+            playsound('n2.mp3')
+            speak(''.join(random.sample(answers[4], k = 1)))
+            break
+    else:
+        playsound('n3.mp3')
+
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testing.py

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+#from playsound import playsound
+#playsound('n3.mp3')
+
+#import speech_recognition
+#print('Speech Recognition:', speech_recognition.__version__)
+
+#import pyttsx3
+#pyttsx3.speak('We are now testing the library')
+
+import tensorflow
+print('TensorFlow: ', tensorflow.__version__)
+import librosa
+print('Librosa: ', librosa.__version__)
+import matplotlib
+print('Matplotlib:', matplotlib._get_version())
+import seaborn
+print('Seaborn: ', seaborn.__version__)