config remove empty chars

config.json

@@ -1,5 +1,5 @@
 {
-    "model": "Tacotron2",   
+    "model": "Tacotron2",
     "run_name": "ljspeech",
     "run_description": "tacotron2",
 
@@ -11,12 +11,12 @@
         "hop_length": 256,       // stft window hop-lengh in ms.
         "frame_length_ms": null, // stft window length in ms.If null, 'win_length' is used.
         "frame_shift_ms": null,  // stft window hop-lengh in ms. If null, 'hop_length' is used.
-        
+
         // Audio processing parameters
         "sample_rate": 22050,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
         "preemphasis": 0.0,     // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
         "ref_level_db": 20,     // reference level db, theoretically 20db is the sound of air.
-        
+
         // Silence trimming
         "do_trim_silence": true,// enable trimming of slience of audio as you load it. LJspeech (false), TWEB (false), Nancy (true)
         "trim_db": 60,          // threshold for timming silence. Set this according to your dataset.
@@ -26,7 +26,7 @@
         "griffin_lim_iters": 60,// #griffin-lim iterations. 30-60 is a good range. Larger the value, slower the generation.
 
         // MelSpectrogram parameters
-        "num_mels": 80,         // size of the mel spec frame. 
+        "num_mels": 80,         // size of the mel spec frame.
         "mel_fmin": 0.0,        // minimum freq level for mel-spec. ~50 for male and ~95 for female voices. Tune for dataset!!
         "mel_fmax": 8000.0,     // maximum freq level for mel-spec. Tune for dataset!!
 
@@ -50,7 +50,7 @@
     //     "punctuations":"!'(),-.:;? ",
     //     "phonemes":"iyɨʉɯuɪʏʊeøɘəɵɤoɛœɜɞʌɔæɐaɶɑɒᵻʘɓǀɗǃʄǂɠǁʛpbtdʈɖcɟkɡqɢʔɴŋɲɳnɱmʙrʀⱱɾɽɸβfvθðszʃʒʂʐçʝxɣχʁħʕhɦɬɮʋɹɻjɰlɭʎʟˈˌːˑʍwɥʜʢʡɕʑɺɧɚ˞ɫ"
     // },
-    
+
     // DISTRIBUTED TRAINING
     "distributed":{
         "backend": "nccl",
@@ -61,8 +61,8 @@
 
     // TRAINING
     "batch_size": 32,       // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
-    "eval_batch_size":16,   
-    "r": 7,                 // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled.  
+    "eval_batch_size":16,
+    "r": 7,                 // Number of decoder frames to predict per iteration. Set the initial values if gradual training is enabled.
     "gradual_training": [[0, 7, 64], [1, 5, 64], [50000, 3, 32], [130000, 2, 32], [290000, 1, 32]], //set gradual training steps [first_step, r, batch_size]. If it is null, gradual training is disabled. For Tacotron, you might need to reduce the 'batch_size' as you proceeed.
     "loss_masking": true,         // enable / disable loss masking against the sequence padding.
     "ga_alpha": 10.0,        // weight for guided attention loss. If > 0, guided attention is enabled.
@@ -80,11 +80,11 @@
     "wd": 0.000001,         // Weight decay weight.
     "warmup_steps": 4000,          // Noam decay steps to increase the learning rate from 0 to "lr"
     "seq_len_norm": false,	   // Normalize eash sample loss with its length to alleviate imbalanced datasets. Use it if your dataset is small or has skewed distribution of sequence lengths.
-    
+
     // TACOTRON PRENET
-    "memory_size": -1,              // ONLY TACOTRON - size of the memory queue used fro storing last decoder predictions for auto-regression. If < 0, memory queue is disabled and decoder only uses the last prediction frame. 
+    "memory_size": -1,              // ONLY TACOTRON - size of the memory queue used fro storing last decoder predictions for auto-regression. If < 0, memory queue is disabled and decoder only uses the last prediction frame.
     "prenet_type": "original",     // "original" or "bn".
-    "prenet_dropout": true,        // enable/disable dropout at prenet. 
+    "prenet_dropout": true,        // enable/disable dropout at prenet.
 
     // ATTENTION
     "attention_type": "original",  // 'original' or 'graves'
@@ -98,16 +98,16 @@
     "bidirectional_decoder": false,  // use https://arxiv.org/abs/1907.09006. Use it, if attention does not work well with your dataset.
 
     // STOPNET
-    "stopnet": true,               // Train stopnet predicting the end of synthesis. 
+    "stopnet": true,               // Train stopnet predicting the end of synthesis.
     "separate_stopnet": true,     // Train stopnet seperately if 'stopnet==true'. It prevents stopnet loss to influence the rest of the model. It causes a better model, but it trains SLOWER.
 
     // TENSORBOARD and LOGGING
     "print_step": 25,       // Number of steps to log traning on console.
-    "print_eval": false,     // If True, it prints loss values in evalulation. 
+    "print_eval": false,     // If True, it prints loss values in evalulation.
     "save_step": 10000,      // Number of training steps expected to save traninpg stats and checkpoints.
     "checkpoint": true,     // If true, it saves checkpoints per "save_step"
-    "tb_model_param_stats": false,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging. 
-    
+    "tb_model_param_stats": false,     // true, plots param stats per layer on tensorboard. Might be memory consuming, but good for debugging.
+
     // DATA LOADING
     "text_cleaner": "phoneme_cleaners",
     "enable_eos_bos_chars": false, // enable/disable beginning of sentence and end of sentence chars.
@@ -119,7 +119,7 @@
 
     // PATHS
     "output_path": "/home/erogol/Models/LJSpeech/",
- 
+
     // PHONEMES
     "phoneme_cache_path": "mozilla_us_phonemes_3",  // phoneme computation is slow, therefore, it caches results in the given folder.
     "use_phonemes": true,           // use phonemes instead of raw characters. It is suggested for better pronounciation.