diff --git a/config.json b/config.json
index 9046bfa3..5701d2f4 100644
--- a/config.json
+++ b/config.json
@@ -7,7 +7,7 @@
         // Audio processing parameters
         "num_mels": 80,         // size of the mel spec frame. 
         "num_freq": 1025,       // number of stft frequency levels. Size of the linear spectogram frame.
-        "sample_rate": 22050,   // wav sample-rate. If different than the original data, it is resampled.
+        "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
         "frame_length_ms": 50,  // stft window length in ms.
         "frame_shift_ms": 12.5, // stft window hop-lengh in ms.
         "preemphasis": 0.97,    // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
@@ -25,30 +25,30 @@
         "do_trim_silence": true  // enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
     },
 
-    "embedding_size": 256,
+    "embedding_size": 256,  // Character embedding vector length. You don't need to change it in general.
     "text_cleaner": "english_cleaners",
-    "epochs": 1000,
-    "lr": 0.001,
-    "lr_decay": false,
-    "warmup_steps": 4000,
+    "epochs": 1000,         // total number of epochs to train.
+    "lr": 0.001,            // Initial learning rate. If Noam decay is active, maximum learning rate.
+    "lr_decay": false,      // if true, Noam learning rate decaying is applied through training.
+    "warmup_steps": 4000,   // Noam decay steps to increase the learning rate from 0 to "lr"
 
-    "batch_size": 20,
-    "eval_batch_size":32,
-    "r": 5,
-    "wd": 0.000001,
-    "checkpoint": true,
-    "save_step": 5000,
-    "print_step": 10,
+    "batch_size": 32,       // Batch size for training. Lower values than 32 might cause hard to learn attention.
+    "eval_batch_size":32,   
+    "r": 5,                 // Number of frames to predict for step.
+    "wd": 0.000001,         // Weight decay weight.
+    "checkpoint": true,     // If true, it saves checkpoints per "save_step"
+    "save_step": 5000,      // Number of training steps expected to save traning stats and checkpoints.
+    "print_step": 10,       // Number of steps to log traning on console.
     "tb_model_param_stats": true,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging. 
 
     "run_eval": true,
-    "data_path": "../../Data/LJSpeech-1.1/",  // can overwritten from command argument
-    "meta_file_train": "transcript_train.txt",      // metafile for training dataloader.
-    "meta_file_val": "transcript_val.txt",    // metafile for evaluation dataloader.
-    "dataset": "tweb",      // one of TTS.dataset.preprocessors depending on your target dataset. Use "tts_cache" for pre-computed dataset by extract_features.py
-    "min_seq_len": 0,       // minimum text length to use in training
-    "max_seq_len": 300,     // maximum text length
-    "output_path": "/media/erogol/data_ssd/Data/models/tweb_models/",      // output path for all training outputs.
+    "data_path": "../../Data/LJSpeech-1.1/",  // DATASET-RELATED: can overwritten from command argument
+    "meta_file_train": "transcript_train.txt",      // DATASET-RELATED: metafile for training dataloader.
+    "meta_file_val": "transcript_val.txt",    // DATASET-RELATED: metafile for evaluation dataloader.
+    "dataset": "tweb",      // DATASET-RELATED: one of TTS.dataset.preprocessors depending on your target dataset. Use "tts_cache" for pre-computed dataset by extract_features.py
+    "min_seq_len": 0,       // DATASET-RELATED: minimum text length to use in training
+    "max_seq_len": 300,     // DATASET-RELATED: maximum text length
+    "output_path": "/media/erogol/data_ssd/Data/models/tweb_models/",      // DATASET-RELATED: output path for all training outputs.
     "num_loader_workers": 8,        // number of training data loader processes. Don't set it too big. 4-8 are good values.
     "num_val_loader_workers": 4     // number of evaluation data loader processes.
 }