rename MyDataset -> TTSDataset

TTS/bin/compute_attention_masks.py

@@ -8,7 +8,7 @@ import torch
 from torch.utils.data import DataLoader
 from tqdm import tqdm
 
-from TTS.tts.datasets.TTSDataset import MyDataset
+from TTS.tts.datasets.TTSDataset import TTSDataset
 from TTS.tts.utils.generic_utils import setup_model
 from TTS.tts.utils.io import load_checkpoint
 from TTS.tts.utils.text.symbols import make_symbols, phonemes, symbols
@@ -83,7 +83,7 @@ Example run:
     preprocessor = importlib.import_module("TTS.tts.datasets.preprocess")
     preprocessor = getattr(preprocessor, args.dataset)
     meta_data = preprocessor(args.data_path, args.dataset_metafile)
-    dataset = MyDataset(
+    dataset = TTSDataset(
         model.decoder.r,
         C.text_cleaner,
         compute_linear_spec=False,

TTS/bin/extract_tts_spectrograms.py

@@ -11,7 +11,7 @@ from tqdm import tqdm
 
 from TTS.config import load_config
 from TTS.tts.datasets.preprocess import load_meta_data
-from TTS.tts.datasets.TTSDataset import MyDataset
+from TTS.tts.datasets.TTSDataset import TTSDataset
 from TTS.tts.utils.generic_utils import setup_model
 from TTS.tts.utils.speakers import parse_speakers
 from TTS.tts.utils.text.symbols import make_symbols, phonemes, symbols
@@ -22,7 +22,7 @@ use_cuda = torch.cuda.is_available()
 
 
 def setup_loader(ap, r, verbose=False):
-    dataset = MyDataset(
+    dataset = TTSDataset(
         r,
         c.text_cleaner,
         compute_linear_spec=False,

TTS/bin/train_align_tts.py

@@ -14,7 +14,7 @@ from torch.utils.data import DataLoader
 from torch.utils.data.distributed import DistributedSampler
 
 from TTS.tts.datasets.preprocess import load_meta_data
-from TTS.tts.datasets.TTSDataset import MyDataset
+from TTS.tts.datasets.TTSDataset import TTSDataset
 from TTS.tts.layers.losses import AlignTTSLoss
 from TTS.tts.utils.generic_utils import setup_model
 from TTS.tts.utils.io import save_best_model, save_checkpoint
@@ -38,7 +38,7 @@ def setup_loader(ap, r, is_val=False, verbose=False):
     if is_val and not config.run_eval:
         loader = None
     else:
-        dataset = MyDataset(
+        dataset = TTSDataset(
             r,
             config.text_cleaner,
             compute_linear_spec=False,

TTS/bin/train_glow_tts.py

@@ -15,7 +15,7 @@ from torch.utils.data import DataLoader
 from torch.utils.data.distributed import DistributedSampler
 
 from TTS.tts.datasets.preprocess import load_meta_data
-from TTS.tts.datasets.TTSDataset import MyDataset
+from TTS.tts.datasets.TTSDataset import TTSDataset
 from TTS.tts.layers.losses import GlowTTSLoss
 from TTS.tts.utils.generic_utils import setup_model
 from TTS.tts.utils.io import save_best_model, save_checkpoint
@@ -38,7 +38,7 @@ def setup_loader(ap, r, is_val=False, verbose=False):
     if is_val and not config.run_eval:
         loader = None
     else:
-        dataset = MyDataset(
+        dataset = TTSDataset(
             r,
             config.text_cleaner,
             compute_linear_spec=False,

TTS/bin/train_speedy_speech.py

@@ -16,7 +16,7 @@ from torch.utils.data import DataLoader
 from torch.utils.data.distributed import DistributedSampler
 
 from TTS.tts.datasets.preprocess import load_meta_data
-from TTS.tts.datasets.TTSDataset import MyDataset
+from TTS.tts.datasets.TTSDataset import TTSDataset
 from TTS.tts.layers.losses import SpeedySpeechLoss
 from TTS.tts.utils.generic_utils import setup_model
 from TTS.tts.utils.io import save_best_model, save_checkpoint
@@ -39,7 +39,7 @@ def setup_loader(ap, r, is_val=False, verbose=False):
     if is_val and not config.run_eval:
         loader = None
     else:
-        dataset = MyDataset(
+        dataset = TTSDataset(
             r,
             config.text_cleaner,
             compute_linear_spec=False,

TTS/bin/train_tacotron.py

@@ -12,7 +12,7 @@ import torch
 from torch.utils.data import DataLoader
 
 from TTS.tts.datasets.preprocess import load_meta_data
-from TTS.tts.datasets.TTSDataset import MyDataset
+from TTS.tts.datasets.TTSDataset import TTSDataset
 from TTS.tts.layers.losses import TacotronLoss
 from TTS.tts.utils.generic_utils import setup_model
 from TTS.tts.utils.io import save_best_model, save_checkpoint
@@ -43,7 +43,7 @@ def setup_loader(ap, r, is_val=False, verbose=False, dataset=None):
         loader = None
     else:
         if dataset is None:
-            dataset = MyDataset(
+            dataset = TTSDataset(
                 r,
                 config.text_cleaner,
                 compute_linear_spec=config.model.lower() == "tacotron",

TTS/tts/datasets/TTSDataset.py

@@ -12,7 +12,7 @@ from TTS.tts.utils.data import prepare_data, prepare_stop_target, prepare_tensor
 from TTS.tts.utils.text import pad_with_eos_bos, phoneme_to_sequence, text_to_sequence
 
 
-class MyDataset(Dataset):
+class TTSDataset(Dataset):
     def __init__(
         self,
         outputs_per_step,
@@ -117,12 +117,12 @@ class MyDataset(Dataset):
         try:
             phonemes = np.load(cache_path)
         except FileNotFoundError:
-            phonemes = MyDataset._generate_and_cache_phoneme_sequence(
+            phonemes = TTSDataset._generate_and_cache_phoneme_sequence(
                 text, cache_path, cleaners, language, tp, add_blank
             )
         except (ValueError, IOError):
             print(" [!] failed loading phonemes for {}. " "Recomputing.".format(wav_file))
-            phonemes = MyDataset._generate_and_cache_phoneme_sequence(
+            phonemes = TTSDataset._generate_and_cache_phoneme_sequence(
                 text, cache_path, cleaners, language, tp, add_blank
             )
         if enable_eos_bos:
@@ -190,7 +190,7 @@ class MyDataset(Dataset):
         item = args[0]
         func_args = args[1]
         text, wav_file, *_ = item
-        phonemes = MyDataset._load_or_generate_phoneme_sequence(wav_file, text, *func_args)
+        phonemes = TTSDataset._load_or_generate_phoneme_sequence(wav_file, text, *func_args)
         return phonemes
 
     def compute_input_seq(self, num_workers=0):
@@ -225,7 +225,7 @@ class MyDataset(Dataset):
                 with Pool(num_workers) as p:
                     phonemes = list(
                         tqdm.tqdm(
-                            p.imap(MyDataset._phoneme_worker, [[item, func_args] for item in self.items]),
+                            p.imap(TTSDataset._phoneme_worker, [[item, func_args] for item in self.items]),
                             total=len(self.items),
                         )
                     )

notebooks/ExtractTTSpectrogram.ipynb

@@ -22,7 +22,7 @@
     "import numpy as np\n",
     "from tqdm import tqdm as tqdm\n",
     "from torch.utils.data import DataLoader\n",
-    "from TTS.tts.datasets.TTSDataset import MyDataset\n",
+    "from TTS.tts.datasets.TTSDataset import TTSDataset\n",
     "from TTS.tts.layers.losses import L1LossMasked\n",
     "from TTS.utils.audio import AudioProcessor\n",
     "from TTS.utils.io import load_config\n",
@@ -112,7 +112,7 @@
     "preprocessor = importlib.import_module('TTS.tts.datasets.preprocess')\n",
     "preprocessor = getattr(preprocessor, DATASET.lower())\n",
     "meta_data = preprocessor(DATA_PATH,METADATA_FILE)\n",
-    "dataset = MyDataset(checkpoint['r'], C.text_cleaner, False, ap, meta_data,tp=C.characters if 'characters' in C.keys() else None, use_phonemes=C.use_phonemes,  phoneme_cache_path=C.phoneme_cache_path, enable_eos_bos=C.enable_eos_bos_chars)\n",
+    "dataset = TTSDataset(checkpoint['r'], C.text_cleaner, False, ap, meta_data,tp=C.characters if 'characters' in C.keys() else None, use_phonemes=C.use_phonemes,  phoneme_cache_path=C.phoneme_cache_path, enable_eos_bos=C.enable_eos_bos_chars)\n",
     "loader = DataLoader(dataset, batch_size=BATCH_SIZE, num_workers=4, collate_fn=dataset.collate_fn, shuffle=False, drop_last=False)"
    ]
   },

tests/data_tests/test_loader.py

@@ -38,7 +38,7 @@ class TestTTSDataset(unittest.TestCase):
 
     def _create_dataloader(self, batch_size, r, bgs):
         items = ljspeech(c.data_path, "metadata.csv")
-        dataset = TTSDataset.MyDataset(
+        dataset = TTSDataset.TTSDataset(
             r,
             c.text_cleaner,
             compute_linear_spec=True,