check config with a function

config.json

@@ -9,7 +9,7 @@
         "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,   // DATASET-RELATED: wav sample-rate. If different than the original data, it is resampled.
-        "frame_length_ms": 50,  // stft window length in ms.
+        "frame_length_ms": 50.0,  // stft window length in ms.
         "frame_shift_ms": 12.5, // stft window hop-lengh in ms.
         "preemphasis": 0.98,    // pre-emphasis to reduce spec noise and make it more structured. If 0.0, no -pre-emphasis.
         "min_level_db": -100,   // normalization range
@@ -19,7 +19,7 @@
         // Normalization parameters
         "signal_norm": true,    // normalize the spec values in range [0, 1]
         "symmetric_norm": true, // move normalization to range [-1, 1]
-        "max_norm": 4,          // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
+        "max_norm": 4.0,          // scale normalization to range [-max_norm, max_norm] or [0, max_norm]
         "clip_norm": true,      // clip normalized values into the range.
         "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!!
@@ -36,11 +36,12 @@
     "reinit_layers": [],    // give a list of layer names to restore from the given checkpoint. If not defined, it reloads all heuristically matching layers.
 
     // TRAINING
-    "batch_size": 32,       // Batch size for training. Lower values than 32 might cause hard to learn attention. It is overwritten by 'gradual_training'.
+    "batch_size": 2,       // 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.  
     "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.
+    "grad_accum": 2,        // if N > 1, enable gradient accumulation for N iterations. It is useful for low memory GPUs. 
 
     // VALIDATION
     "run_eval": true,
@@ -49,7 +50,7 @@
 
     // OPTIMIZER
     "noam_schedule": false,        // use noam warmup and lr schedule.
-    "grad_clip": 1,                // upper limit for gradients for clipping.
+    "grad_clip": 1.0,                // upper limit for gradients for clipping.
     "epochs": 1000,                // total number of epochs to train.
     "lr": 0.0001,                  // Initial learning rate. If Noam decay is active, maximum learning rate.
     "wd": 0.000001,         // Weight decay weight.

train.py

@@ -20,7 +20,7 @@ from TTS.utils.generic_utils import (
     get_git_branch, load_config, remove_experiment_folder, save_best_model,
     save_checkpoint, adam_weight_decay, set_init_dict, copy_config_file,
     setup_model, gradual_training_scheduler, KeepAverage,
-    set_weight_decay)
+    set_weight_decay, check_config)
 from TTS.utils.logger import Logger
 from TTS.utils.speakers import load_speaker_mapping, save_speaker_mapping, \
     get_speakers
@@ -687,6 +687,7 @@ if __name__ == '__main__':
 
     # setup output paths and read configs
     c = load_config(args.config_path)
+    check_config(c)
     _ = os.path.dirname(os.path.realpath(__file__))
 
     OUT_PATH = args.continue_path

utils/generic_utils.py

@@ -389,3 +389,131 @@ class KeepAverage():
     def update_values(self, value_dict):
         for key, value in value_dict.items():
             self.update_value(key, value)
+
+
+def _check_argument(name, c, enum_list=None, max_val=None, min_val=None, restricted=False, val_type=None):
+    if restricted:
+        assert name in c.keys(), f' [!] {name} not defined in config.json'
+    if name in c.keys():
+        if max_val:
+            assert c[name] <= max_val, f' [!] {name} is larger than max value {max_val}'
+        if min_val:
+            assert c[name] >= min_val, f' [!] {name} is smaller than min value {min_val}'
+        if enum_list:
+            assert c[name].lower() in enum_list, f' [!] {name} is not a valid value'
+        if val_type:
+            assert type(c[name]) is val_type or c[name] is None, f' [!] {name} has wrong type - {type(c[name])} vs {val_type}'
+    
+
+
+def check_config(c):
+    _check_argument('model', c, enum_list=['tacotron', 'tacotron2'], restricted=True, val_type=str)
+    _check_argument('run_name', c, restricted=True, val_type=str)
+    _check_argument('run_description', c, val_type=str)
+
+    # AUDIO
+    _check_argument('audio', c, restricted=True, val_type=dict)
+
+    # audio processing parameters
+    _check_argument('num_mels', c['audio'], restricted=True, val_type=int, min_val=10, max_val=2056)
+    _check_argument('num_freq', c['audio'], restricted=True, val_type=int, min_val=128, max_val=4058)
+    _check_argument('sample_rate', c['audio'], restricted=True, val_type=int, min_val=512, max_val=100000)
+    _check_argument('frame_length_ms', c['audio'], restricted=True, val_type=float, min_val=10, max_val=1000)
+    _check_argument('frame_shift_ms', c['audio'], restricted=True, val_type=float, min_val=1, max_val=1000)
+    _check_argument('preemphasis', c['audio'], restricted=True, val_type=float, min_val=0, max_val=1)
+    _check_argument('min_level_db', c['audio'], restricted=True, val_type=int, min_val=-1000, max_val=10)
+    _check_argument('ref_level_db', c['audio'], restricted=True, val_type=int, min_val=0, max_val=1000)
+    _check_argument('power', c['audio'], restricted=True, val_type=float, min_val=1, max_val=5)
+    _check_argument('griffin_lim_iters', c['audio'], restricted=True, val_type=int, min_val=10, max_val=1000)
+
+    # normalization parameters
+    _check_argument('signal_norm', c['audio'], restricted=True, val_type=bool)
+    _check_argument('symmetric_norm', c['audio'], restricted=True, val_type=bool)
+    _check_argument('max_norm', c['audio'], restricted=True, val_type=float, min_val=0.1, max_val=1000)
+    _check_argument('clip_norm', c['audio'], restricted=True, val_type=bool)
+    _check_argument('mel_fmin', c['audio'], restricted=True, val_type=float, min_val=0.0, max_val=1000)
+    _check_argument('mel_fmax', c['audio'], restricted=True, val_type=float, min_val=500.0)
+    _check_argument('do_trim_silence', c['audio'], restricted=True, val_type=bool)
+    _check_argument('trim_db', c['audio'], restricted=True, val_type=int)
+
+    # training parameters
+    _check_argument('batch_size', c, restricted=True, val_type=int, min_val=1)
+    _check_argument('eval_batch_size', c, restricted=True, val_type=int, min_val=1)
+    _check_argument('r', c, restricted=True, val_type=int, min_val=1)
+    _check_argument('gradual_training', c, restricted=False, val_type=list)
+    _check_argument('loss_masking', c, restricted=True, val_type=bool)
+    _check_argument('grad_accum', c, restricted=True, val_type=int, min_val=1, max_val=100)
+
+    # validation parameters
+    _check_argument('run_eval', c, restricted=True, val_type=bool)
+    _check_argument('test_delay_epochs', c, restricted=True, val_type=int, min_val=0)
+    _check_argument('test_sentences_file', c, restricted=False, val_type=str)
+
+    # optimizer
+    _check_argument('noam_schedule', c, restricted=False, val_type=bool)
+    _check_argument('grad_clip', c, restricted=True, val_type=float, min_val=0.0)
+    _check_argument('epochs', c, restricted=True, val_type=int, min_val=1)
+    _check_argument('lr', c, restricted=True, val_type=float, min_val=0)
+    _check_argument('wd', c, restricted=True, val_type=float, min_val=0)
+    _check_argument('warmup_steps', c, restricted=True, val_type=int, min_val=0)
+    _check_argument('seq_len_norm', c, restricted=True, val_type=bool)
+
+    # tacotron prenet
+    _check_argument('memory_size', c, restricted=True, val_type=int, min_val=-1)
+    _check_argument('prenet_type', c, restricted=True, val_type=str, enum_list=['original', 'bn'])
+    _check_argument('prenet_dropout', c, restricted=True, val_type=bool)
+
+    # attention
+    _check_argument('attention_type', c, restricted=True, val_type=str, enum_list=['graves', 'original'])
+    _check_argument('attention_heads', c, restricted=True, val_type=int)
+    _check_argument('attention_norm', c, restricted=True, val_type=str, enum_list=['sigmoid', 'softmax'])
+    _check_argument('windowing', c, restricted=True, val_type=bool)
+    _check_argument('use_forward_attn', c, restricted=True, val_type=bool)
+    _check_argument('forward_attn_mask', c, restricted=True, val_type=bool)
+    _check_argument('transition_agent', c, restricted=True, val_type=bool)
+    _check_argument('transition_agent', c, restricted=True, val_type=bool)
+    _check_argument('location_attn', c, restricted=True, val_type=bool)
+    _check_argument('bidirectional_decoder', c, restricted=True, val_type=bool)
+
+    # stopnet
+    _check_argument('stopnet', c, restricted=True, val_type=bool)
+    _check_argument('separate_stopnet', c, restricted=True, val_type=bool)
+
+    # tensorboard
+    _check_argument('print_step', c, restricted=True, val_type=int, min_val=1)
+    _check_argument('save_step', c, restricted=True, val_type=int, min_val=1)
+    _check_argument('checkpoint', c, restricted=True, val_type=bool)
+    _check_argument('tb_model_param_stats', c, restricted=True, val_type=bool)
+
+    # dataloading
+    _check_argument('text_cleaner', c, restricted=True, val_type=str, enum_list=['english_cleaners', 'phoneme_cleaners', 'transliteration_cleaners', 'basic_cleaners'])
+    _check_argument('enable_eos_bos_chars', c, restricted=True, val_type=bool)
+    _check_argument('num_loader_workers', c, restricted=True, val_type=int, min_val=0)
+    _check_argument('num_val_loader_workers', c, restricted=True, val_type=int, min_val=0)
+    _check_argument('batch_group_size', c, restricted=True, val_type=int, min_val=0)
+    _check_argument('min_seq_len', c, restricted=True, val_type=int, min_val=0)
+    _check_argument('max_seq_len', c, restricted=True, val_type=int, min_val=10)
+
+    # paths
+    _check_argument('output_path', c, restricted=True, val_type=str)
+
+    # multi-speaker gst
+    _check_argument('use_speaker_embedding', c, restricted=True, val_type=bool)
+    _check_argument('style_wav_for_test', c, restricted=True, val_type=str)
+    _check_argument('use_gst', c, restricted=True, val_type=bool)
+
+    # datasets - checking only the first entry
+    _check_argument('datasets', c, restricted=True, val_type=list)
+    for dataset_entry in c['datasets']:
+        _check_argument('name', dataset_entry, restricted=True, val_type=str)
+        _check_argument('path', dataset_entry, restricted=True, val_type=str)
+        _check_argument('meta_file_train', dataset_entry, restricted=True, val_type=str)
+        _check_argument('meta_file_val', dataset_entry, restricted=True, val_type=str)
+
+
+
+
+    
+
+
+