Create app.py

app.py

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+import librosa as lb
+import soundfile as sf
+import numpy as np
+import os, glob, pickle        
+import sounddevice as sd
+import time
+import requests
+import webbrowser
+import random
+from sklearn.model_selection import train_test_split
+from sklearn.neural_network import MLPClassifier
+from sklearn.metrics import accuracy_score
+from scipy.io.wavfile import write
+
+emotion_labels = {
+  '01':'neutral',
+  '02':'calm',
+  '03':'happy',
+  '04':'sad',
+  '05':'angry',
+  '06':'fearful',
+  '07':'disgust',
+  '08':'surprised'
+}
+
+focused_emotion_labels = ['happy', 'sad', 'angry']
+
+def audio_features(file_title, mfcc, chroma, mel):
+    with sf.SoundFile(file_title) as audio_recording:
+        audio = audio_recording.read(dtype="float32")
+        sample_rate = audio_recording.samplerate
+
+        if chroma:
+            stft=np.abs(lb.stft(audio))
+            result=np.array([])
+        if mfcc:
+            mfccs=np.mean(lb.feature.mfcc(y=audio, sr=sample_rate, n_mfcc=40).T, axis=0)
+            result=np.hstack((result, mfccs))
+        if chroma:
+            chroma=np.mean(lb.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0)
+            result=np.hstack((result, chroma))
+        if mel:
+            mel=np.mean(lb.feature.melspectrogram(audio, sr=sample_rate).T,axis=0)
+            result=np.hstack((result, mel))
+        return result
+
+def get_emotion_form_file_name(file_name):
+    return emotion_labels[file_name.split("-")[2]]
+
+def loading_audio_data():
+    x = [] # Input - features
+    y = [] # Output - labels emotions
+
+    # Going through all sound files
+    for file in glob.glob("data//Actor_*//*.wav"):
+        file_name = os.path.basename(file)
+        emotion = get_emotion_form_file_name(file_name)
+
+        # Work only with sounds that are part of focused_emotion_labels 
+        if emotion in focused_emotion_labels:
+            try:
+                feature = audio_features(file, mfcc=True, chroma=True, mel=True)
+                x.append(feature)
+                y.append(emotion)
+            except:
+                print("This file wasn't process because of an error: " + file)
+                
+    # Split the dateset, most for training it, and some for testing it accuracy  
+    final_dataset = train_test_split(np.array(x), y, test_size=0.1, random_state=9)
+    return final_dataset
+
+def record_sound():
+    fs = 44100  # Sample rate
+    seconds = 3  # Duration of recording
+
+    print("Recoreding in 3")
+    time.sleep(1) 
+    print("Recoreding in 2")
+    time.sleep(1) 
+    print("Recoreding in 1")
+    time.sleep(1) 
+    
+    # Record and save
+    my_recording = sd.rec(int(seconds * fs), samplerate=fs, channels=1)
+    print("Recoreding: Started")
+    sd.wait()
+    print("Recoreding: Stopped")
+    write('output.wav', fs, my_recording)
+
+    return glob.glob("output.wav")[0]
+
+def get_playlist(mood):
+    # Sign up to rapidAPI, subscribe to this end point, and obtain your headers (x-rapidapi-key)
+    # https://rapidapi.com/shekhar1000.sc/api/unsa-unofficial-spotify-api/
+
+    url = "https://unsa-unofficial-spotify-api.p.rapidapi.com/search"
+
+    querystring = {"query": mood,"count":"10","type":"playlists"}
+
+    headers = {
+        'x-rapidapi-key': "06b6013060msh678afa5c6a5cf22p116a90jsn8b2b444ad800",
+        'x-rapidapi-host': "unsa-unofficial-spotify-api.p.rapidapi.com"
+        }
+
+    response = requests.request("GET", url, headers=headers, params=querystring)
+
+    playlist_id = response.json()["Results"][random.randint(0,9)]["id"]
+
+    return playlist_id
+
+def open_playlist_in_browser(playlist_id):
+    webbrowser.open('https://open.spotify.com/playlist/' + str(playlist_id))
+
+def train_model():
+    # Prep
+    X_train, X_test, y_train, y_test = loading_audio_data()
+
+    # Create and train modal
+    model = MLPClassifier(hidden_layer_sizes = (200,), learning_rate = "adaptive", max_iter = 400)
+    model.fit(X_train,y_train)
+
+    # Predict
+    y_pred = model.predict(X_test)
+
+    # Model Prediction Accuracy Score
+    accuracy = accuracy_score(y_true = y_test, y_pred = y_pred) * 100
+    print ("Accuracy of Recognizer is: %.2f" % accuracy)
+
+    return model, accuracy
+
+def recognize_your_mood(model):
+    while True:
+        my_sound_file = record_sound()
+
+        feature = audio_features(my_sound_file, mfcc=True, chroma=True, mel=True)
+        mood_prediction = model.predict([feature])[0]
+
+        print("Are you " + mood_prediction + "? type yes/no")
+        if (input() == "yes"):
+            return mood_prediction
+
+def main():
+    # Training modal (TODO: should probably save the result and not run every time)
+    model, accuracy = train_model()
+
+    if accuracy > 60:
+        mood = recognize_your_mood(model)
+        playlist_id = get_playlist(mood)
+        open_playlist_in_browser(playlist_id)
+
+if __name__ == "__main__":
+    main()
+
+# ----- Some Info -----
+# Dataset: RAVDESS - 60 audio clips of each actor X 24 actors = 1440 audio clips