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make_critic_data.py

@@ -8,6 +8,22 @@
 TRAIN=Path("./critic_data/train")
 
 def make_critic_data(num_to_generate, replace, prefix, model_to_load, training, gen_size, use_test_prompt, generator_bs, tt_split):
+    '''
+    Generates samples of real and fake data. Files are written to:
+        critic_data/test/fake, critic_data/test/real, critic_data/train/real, critic_data/test/fake
+
+    Inputs:
+       num_to_generate - the number of fake pieces to create. These are then split into files bptt long (according to the backprop size of the model)
+       replace - if true, clear the existing files in the critic_data folders and replace with these new files
+       prefix - add prefix to generated files (needed if not replacing the old data)
+       model_to_load - model for generating fake pieces
+       training - whether to use light, med, full, or extra training on those models
+       gen_size - fake piece generation size (pieces are often better in the beginning and sound worse as they progress for a long time)
+       use_test_prompt - generate fake pieces from test prompts (pieces the model hasn't seen before)
+       generator_bs - number of pieces to generate in parallel
+       tt_split - test/train split (fraction of pieces to put into the test folder)
+    '''
+
     PATHS=create_paths()
 
     # Load pretrained model and training/test text
@@ -43,7 +59,7 @@ def make_critic_data(num_to_generate, replace, prefix, model_to_load, training,
                                                     trunc_size=random.randint(1,10), prompts=prompts,
                                                     params=params, TEXT=TEXT)
 
-        # Write to train/real and train/fake, or test/real and test/fake 
+        # Write to train/fake and test/fake 
         # Choose randomly whether train or test, according to the test_train_split (tt_split) frequency
 
         num_samples=0
@@ -58,6 +74,8 @@ def make_critic_data(num_to_generate, replace, prefix, model_to_load, training,
                 f.close()
                 num_samples+=1
 
+        # Pull human-composed samples randomly from the prompts.  Add to train/real and test/real 
+        # Choose randomly whether train or test, according to the test_train_split (tt_split) frequency
         musical_prompts=generate_musical_prompts(prompts, bptt, num_samples)
         for i in range(num_samples):
             dest=TEST if random.random()<tt_split else TRAIN
@@ -69,7 +87,7 @@ def make_critic_data(num_to_generate, replace, prefix, model_to_load, training,
 if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("-model", help="Trained model in ./data/models", required=True)
-    parser.add_argument("-num", help="Number of files to generate (default 1000)", type=int)
+    parser.add_argument("--num", help="Number of files to generate (default 1000)", type=int)
     parser.set_defaults(num=1000)
     parser.add_argument("--replace", dest="replace", action="store_true", help="Overwrite existing test/train critic data")
     parser.set_defaults(replace=False)