lab1 audio files added

Author: aamini

lab1/__init__.py

@@ -1 +1,2 @@
 import mitdeeplearning.lab1
+import mitdeeplearning.util

lab1/util.py

@@ -1,2 +1,57 @@
-def func():
-    print("test package")
+import os
+import regex as re
+import subprocess
+import urllib
+
+from IPython.display import Audio
+
+
+DATA_URL = 'https://raw.githubusercontent.com/aamini/introtodeeplearning_labs/2019/lab1/data/irish.abc'
+
+def load_training_data():
+    stream = urllib.request.urlopen(DATA_URL)
+    text = stream.read().decode("utf-8")
+    songs = extract_song_snippet(text)
+    return songs
+
+def extract_song_snippet(text):
+    pattern = '\n\n(.*?)\n\n'
+    search_results = re.findall(pattern, text, overlapped=True, flags=re.DOTALL)
+    songs = [song for song in search_results]
+    print("Found {} songs in text".format(len(songs)))
+    return songs
+
+def save_song_to_abc(song, filename="tmp"):
+    save_name = "{}.abc".format(filename)
+    with open(save_name, "w") as f:
+        f.write(song)
+    return filename
+
+def abc2wav(abc_file):
+    # path_to_tool = './introtodeeplearning_labs/lab1/abc2wav'
+    path_to_tool = os.path.join(os.path.dirname(__file__), 'bin', 'abc2wav')
+
+    cmd = "{} {}".format(path_to_tool, abc_file)
+    return os.system(cmd)
+
+def play_wav(wav_file):
+    return Audio(wav_file)
+
+def play_song(song):
+    basename = save_song_to_abc(song)
+    ret = abc2wav(basename+'.abc')
+    if ret == 0: #did not suceed
+        return play_wav(basename+'.wav')
+    return None
+
+def play_generated_song(generated_text):
+    songs = extract_song_snippet(generated_text)
+    if len(songs) == 0:
+        print("No valid songs found in generated text. Try training the \
+            model longer or increasing the amount of generated music to \
+            ensure complete songs are generated!")
+
+    for song in songs:
+        play_song(song)
+    print("None of the songs were valid, try training longer to improve \
+        syntax.")

mitdeeplearning/__init__.py

@@ -1,19 +1,16 @@
-from .lab1 import *
-from .lab2 import *
-# from lab3 import *
-
-
 import matplotlib.pyplot as plt
-import numpy as np
 import tensorflow as tf
 import time
+import progressbar
+
 from IPython import display as ipythondisplay
+from string import Formatter
+
+
 
 
 #####################################
 def custom_progress_text(message):
-  import progressbar
-  from string import Formatter
 
   message_ = message.replace('(', '{')
   message_ = message_.replace(')', '}')
@@ -29,7 +26,6 @@ def custom_progress_text(message):
   return msg
 
 def create_progress_bar(text=None):
-  import progressbar
   if text is None:
     text = progressbar.FormatCustomText('')
   bar = progressbar.ProgressBar(widgets=[
@@ -44,23 +40,7 @@ def display_model(model):
   tf.keras.utils.plot_model(model,
              to_file='tmp.png',
              show_shapes=True)
-  from IPython.display import Image
-  return Image('tmp.png')
-
-
-def plot_sample(x,y,vae):
-    plt.figure(figsize=(2,1))
-    plt.subplot(1, 2, 1)
-
-    idx = np.where(y.numpy()==1)[0][0]
-    plt.imshow(x[idx])
-    plt.grid(False)
-
-    plt.subplot(1, 2, 2)
-    plt.imshow(vae(x)[idx])
-    plt.grid(False)
-
-    plt.show()
+  return ipythondisplay.Image('tmp.png')
 
 
 class LossHistory:
@@ -74,9 +54,6 @@ def get(self):
 
 class PeriodicPlotter:
   def __init__(self, sec, xlabel='', ylabel='', scale=None):
-    from IPython import display as ipythondisplay
-    import matplotlib.pyplot as plt
-    import time
 
     self.xlabel = xlabel
     self.ylabel = ylabel

lab1/abc2wav renamed to mitdeeplearning/bin/abc2wav

@@ -11,6 +11,9 @@ def get_dist(pkgname):
 install_deps = [
     'numpy',
     'regex',
+    'progressbar',
+    'tqdm',
+
 ]
 tf_ver = '2.0.0a'
 if get_dist('tensorflow>='+tf_ver) is None and get_dist('tensorflow_gpu>='+tf_ver) is None:

mitdeeplearning/lab1.py

@@ -0,0 +1,8 @@
+import mitdeeplearning as mdl
+
+songs = mdl.lab1.load_training_data()
+
+basename = mdl.lab1.save_song_to_abc(songs[0])
+ret = mdl.lab1.abc2wav(basename+'.abc')
+
+import pdb; pdb.set_trace()