Create FX Functions

backend/app/analysis/sentiment_analysis.py

@@ -5,44 +5,37 @@
 
 def text_to_sound(user_text):
     """
-    Takes text, runs NLTK's sentiment analyzer, and then returns a sound object
+    Takes text, runs NLTK's sentiment analyzer, and then returns a sound object.
+    The formula used to generate a note frequency is based off of this article:
+    <https://towardsdatascience.com/music-in-python-2f054deb41f4>.
 
     :param user_text: A str representing some input text
-    :return: A dictionary of audio metadata with the following keys:
-        audio_samples: An int16 Numpy array
-        sample_rate: An int, recorded in Hz
-        notes: A list of 2-element tuples (freq, duration) for each note's frequency (Hz) and duration (s) in the audio
-
+    :return: A tuple of audio data containing an int16 NumPy array of samples and a sample rate in Hz (int).
     """
     A4 = 440
     sample_rate = 44100
-    T = 1
-    t = np.linspace(0, T, T * sample_rate, False)
+    duration = 1
+    t = np.linspace(0, duration, duration * sample_rate, False)
 
     nltk_analyzer = SentimentIntensityAnalyzer()
     all_sentences = nltk.sent_tokenize(user_text)
 
     sin_waves = []
-    all_notes = []
 
     for each_sentence in all_sentences:
         # the compound score ranges from -1 to 1, this shifts the score so that there is no negative score
         # multiplying by 44 and rounding the score gives us a range of keys from 0 to 88
         score = round((nltk_analyzer.polarity_scores(each_sentence)['compound'] + 1) * 44)
+
         # formula calculates the frequency of a musical note using A4 as the base note
-        # the score is the nth key starting from key 0 being A0
+        # the score is the nth key starting from key 0 being A0 (hence the extra +1)
+        # the exact middle of the piano is between key 43 (E4) and 44 (F4) if A0 is the zeroth note
+        # thus, a neutral score should generate an F4 note
         frequency = A4 * (2 ** ((score + 1 - 49) / 12))
         sin_waves.append(np.sin(2 * np.pi * frequency * t))
-        all_notes.append((frequency, T, score))
 
     audio = np.hstack(sin_waves)
     audio *= 32767 / np.max(np.abs(audio))
     audio = audio.astype(np.int16)
 
-    audio_metadata = {
-        'audio_samples': audio,
-        'sample_rate': sample_rate,
-        'notes': all_notes
-    }
-
-    return audio_metadata
+    return audio, sample_rate

backend/app/common.py

@@ -6,6 +6,117 @@
 from scipy.io import wavfile
 from textwrap import dedent
 
+# Dictionary copied from the table in this web page: <https://pages.mtu.edu/~suits/notefreqs.html>
+NOTE_FREQS = {
+    'C0': 16.35,
+    'C#0/Db0': 17.32,
+    'D0': 18.35,
+    'D#0/Eb0': 19.45,
+    'E0': 20.60,
+    'F0': 21.83,
+    'F#0/Gb0': 23.12,
+    'G0': 24.50,
+    'G#0/Ab0': 25.96,
+    'A0': 27.50,
+    'A#0/Bb0': 29.14,
+    'B0': 30.87,
+    'C1': 32.70,
+    'C#1/Db1': 34.65,
+    'D1': 36.71,
+    'D#1/Eb1': 38.89,
+    'E1': 41.20,
+    'F1': 43.65,
+    'F#1/Gb1': 46.25,
+    'G1': 49.00,
+    'G#1/Ab1': 51.91,
+    'A1': 55.00,
+    'A#1/Bb1': 58.27,
+    'B1': 61.74,
+    'C2': 65.41,
+    'C#2/Db2': 69.30,
+    'D2': 73.42,
+    'D#2/Eb2': 77.78,
+    'E2': 82.41,
+    'F2': 87.31,
+    'F#2/Gb2': 92.50,
+    'G2': 98.00,
+    'G#2/Ab2': 103.83,
+    'A2': 110.00,
+    'A#2/Bb2': 116.54,
+    'B2': 123.47,
+    'C3': 130.81,
+    'C#3/Db3': 138.59,
+    'D3': 146.83,
+    'D#3/Eb3': 155.56,
+    'E3': 164.81,
+    'F3': 174.61,
+    'F#3/Gb3': 185.00,
+    'G3': 196.00,
+    'G#3/Ab3': 207.65,
+    'A3': 220.00,
+    'A#3/Bb3': 233.08,
+    'B3': 246.94,
+    'C4': 261.63,
+    'C#4/Db4': 277.18,
+    'D4': 293.66,
+    'D#4/Eb4': 311.13,
+    'E4': 329.63,
+    'F4': 349.23,
+    'F#4/Gb4': 369.99,
+    'G4': 392.00,
+    'G#4/Ab4': 415.30,
+    'A4': 440.00,
+    'A#4/Bb4': 466.16,
+    'B4': 493.88,
+    'C5': 523.25,
+    'C#5/Db5': 554.37,
+    'D5': 587.33,
+    'D#5/Eb5': 622.25,
+    'E5': 659.25,
+    'F5': 698.46,
+    'F#5/Gb5': 739.99,
+    'G5': 783.99,
+    'G#5/Ab5': 830.61,
+    'A5': 880.00,
+    'A#5/Bb5': 932.33,
+    'B5': 987.77,
+    'C6': 1046.50,
+    'C#6/Db6': 1108.73,
+    'D6': 1174.66,
+    'D#6/Eb6': 1244.51,
+    'E6': 1318.51,
+    'F6': 1396.91,
+    'F#6/Gb6': 1479.98,
+    'G6': 1567.98,
+    'G#6/Ab6': 1661.22,
+    'A6': 1760.00,
+    'B6': 1975.53,
+    'C7': 2093.00,
+    'C#7/Db7': 2217.46,
+    'D7': 2349.32,
+    'D#7/Eb7': 2489.02,
+    'E7': 2637.02,
+    'F7': 2793.83,
+    'F#7/Gb7': 2959.96,
+    'G7': 3135.96,
+    'G#7/Ab7': 3322.44,
+    'A7': 3520.00,
+    'A#7/Bb7': 3729.31,
+    'B7': 3951.07,
+    'C8': 4186.01,
+    'C#8/Db8': 4434.92,
+    'D8': 4698.63,
+    'D#8/Eb8': 4978.03,
+    'E8': 5724.04,
+    'F8': 5587.65,
+    'F#8/Gb8': 5919.91,
+    'G8': 6271.93,
+    'G#8/Ab8': 6644.88,
+    'A8': 7040.00,
+    'A#8/Bb8': 7458.62,
+    'B8': 7902.13
+}
+
 
 def wav_to_base64(byte_array, sample_rate):
     """