Documenting pt. 2

audio_methods.py

@@ -2,6 +2,7 @@
 import tensorflow as tf
 import numpy as np
 import pandas as pd
+import VAE from model
 
 # Audio
 import pretty_midi
@@ -57,7 +58,7 @@ def midi_to_notes(midi_file: str) -> pd.DataFrame:
   return notes_df / _SCALING_FACTORS # Scale
 
 
-def display_audio(pm: pretty_midi.PrettyMIDI, seconds=-1):
+def display_audio(pm: pretty_midi.PrettyMIDI, seconds=-1) -> display.Audio:
   """
   Display a song in PrettyMIDI format as a display.Audio object.
   This method specially comes in useful in a jupyter notebook.
@@ -83,21 +84,21 @@ def display_audio(pm: pretty_midi.PrettyMIDI, seconds=-1):
 
 # Define function to convert song map to wav
 
-def map_to_wav(song_map: pd.DataFrame, out_file: str, velocity: int=100):
+def map_to_wav(song_map: pd.DataFrame, out_file: str, velocity: int=100) -> pretty_midi.PrettyMIDI:
   """
-  Convert "song map" to midi file (reverse process with respect to midi_to_notes)
+  Convert "song map" to midi file (reverse process with respect to 
+  midi_to_notes) and (optionally) save it, generating a PrettyMidi object in the process.
 
   Parameters:
-  song_map (pd.DataFrame): Path to the midi file.
-  out_file (str): Name of the [CONTINUE].
+  song_map (pd.DataFrame): 3xN matrix where each column is a note, composed of
+  pitch, duration and step.
+  out_file (str): Path or file to write .mid file to. If null, no saving is done.
+  velocity: Note loudness, i. e. the hardness a piano key is struck with.
 
   Returns:
-  pd.Dataframe: 3xN matrix as a dataframe where each column is a note,
-                composed of pitch, duration and step.
+  pretty_midi.PrettyMIDI: PrettyMIDI object containing the song's representation.
   """
     
-  # 
-    
   contracted_map = tf.squeeze(song_map)
   song_map_T = contracted_map.numpy().T
   notes = pd.DataFrame(song_map_T, columns=["pitch", "step", "duration"]).mul(_SCALING_FACTORS, axis=1)
@@ -122,10 +123,21 @@ def map_to_wav(song_map: pd.DataFrame, out_file: str, velocity: int=100):
     prev_start = start
 
   pm.instruments.append(instrument)
-  pm.write(out_file)
+  if (out_file):
+      pm.write(out_file)
   return pm
 
-def generate_and_display(out_file, model, z_sample=None, velocity=100, seconds=120):
+def generate_and_display(out_file:str, model: VAE, out_file: str=null, z_sample: tf.Tensor=None, velocity: int=100, seconds: int=120) -> display.Audio:
+  """
+  Generate a song, (optionally) save it and display it [CONTINUE]
+
+  Parameters:
+  
+
+  Returns:
+  
+  """
+    
   song_map = model.generate(z_sample)
   wav = map_to_wav(song_map, out_file, velocity)