Rename `load_meta_data` as `load_tts_data`

TTS/bin/compute_embeddings.py

@@ -5,7 +5,7 @@ from argparse import RawTextHelpFormatter
 from tqdm import tqdm
 
 from TTS.config import load_config
-from TTS.tts.datasets import load_meta_data
+from TTS.tts.datasets import load_tts_samples
 from TTS.tts.utils.speakers import SpeakerManager
 
 parser = argparse.ArgumentParser(
@@ -36,7 +36,7 @@ args = parser.parse_args()
 
 c_dataset = load_config(args.config_dataset_path)
 
-meta_data_train, meta_data_eval = load_meta_data(c_dataset.datasets, eval_split=args.eval)
+meta_data_train, meta_data_eval = load_tts_samples(c_dataset.datasets, eval_split=args.eval)
 wav_files = meta_data_train + meta_data_eval
 
 speaker_manager = SpeakerManager(

TTS/bin/compute_statistics.py

@@ -10,7 +10,7 @@ from tqdm import tqdm
 
 # from TTS.utils.io import load_config
 from TTS.config import load_config
-from TTS.tts.datasets import load_meta_data
+from TTS.tts.datasets import load_tts_samples
 from TTS.utils.audio import AudioProcessor
 
 
@@ -41,7 +41,7 @@ def main():
     if args.data_path:
         dataset_items = glob.glob(os.path.join(args.data_path, "**", "*.wav"), recursive=True)
     else:
-        dataset_items = load_meta_data(CONFIG.datasets)[0]  # take only train data
+        dataset_items = load_tts_samples(CONFIG.datasets)[0]  # take only train data
     print(f" > There are {len(dataset_items)} files.")
 
     mel_sum = 0

TTS/bin/extract_tts_spectrograms.py

@@ -10,8 +10,7 @@ from torch.utils.data import DataLoader
 from tqdm import tqdm
 
 from TTS.config import load_config
-from TTS.tts.datasets import load_meta_data
+from TTS.tts.datasets import TTSDataset, load_tts_samples
-from TTS.tts.datasets.TTSDataset import TTSDataset
 from TTS.tts.models import setup_model
 from TTS.tts.utils.speakers import get_speaker_manager
 from TTS.utils.audio import AudioProcessor
@@ -230,7 +229,7 @@ def main(args):  # pylint: disable=redefined-outer-name
     ap = AudioProcessor(**c.audio)
 
     # load data instances
-    meta_data_train, meta_data_eval = load_meta_data(c.datasets, eval_split=args.eval)
+    meta_data_train, meta_data_eval = load_tts_samples(c.datasets, eval_split=args.eval)
 
     # use eval and training partitions
     meta_data = meta_data_train + meta_data_eval

TTS/bin/find_unique_chars.py

@@ -3,7 +3,7 @@ import argparse
 from argparse import RawTextHelpFormatter
 
 from TTS.config import load_config
-from TTS.tts.datasets import load_meta_data
+from TTS.tts.datasets import load_tts_samples
 
 
 def main():
@@ -23,7 +23,7 @@ def main():
     c = load_config(args.config_path)
 
     # load all datasets
-    train_items, eval_items = load_meta_data(c.datasets, eval_split=True)
+    train_items, eval_items = load_tts_samples(c.datasets, eval_split=True)
     items = train_items + eval_items
 
     texts = "".join(item[0] for item in items)

TTS/tts/datasets/__init__.py

@@ -1,12 +1,12 @@
 import sys
 from collections import Counter
 from pathlib import Path
-from typing import Dict, List, Tuple
+from typing import Dict, List, Tuple, Union
 
 import numpy as np
 
+from TTS.tts.datasets.dataset import *
 from TTS.tts.datasets.formatters import *
-from TTS.tts.datasets.TTSDataset import TTSDataset
 
 
 def split_dataset(items):
@@ -31,11 +31,12 @@ def split_dataset(items):
     return items[:eval_split_size], items[eval_split_size:]
 
 
-def load_meta_data(datasets: List[Dict], eval_split=True) -> Tuple[List[List], List[List]]:
+def load_tts_samples(datasets: Union[List[Dict], Dict], eval_split=True) -> Tuple[List[List], List[List]]:
     """Parse the dataset, load the samples as a list and load the attention alignments if provided.
 
     Args:
-        datasets (List[Dict]): A list of dataset dictionaries or dataset configs.
+        datasets (List[Dict], Dict): A list of datasets or a single dataset dictionary. If multiple datasets are
+            in the list, they are all merged.
         eval_split (bool, optional): If true, create a evaluation split. If an eval split provided explicitly, generate
             an eval split automatically. Defaults to True.
 
@@ -44,6 +45,8 @@ def load_meta_data(datasets: List[Dict], eval_split=True) -> Tuple[List[List], L
     """
     meta_data_train_all = []
     meta_data_eval_all = [] if eval_split else None
+    if not isinstance(datasets, list):
+        datasets = [datasets]
     for dataset in datasets:
         name = dataset["name"]
         root_path = dataset["path"]

notebooks/dataset_analysis/PhonemeCoverage.ipynb

@@ -50,7 +50,7 @@
    "source": [
     "# import stuff\n",
     "from TTS.utils.io import load_config\n",
-    "from TTS.tts.datasets.formatters import load_meta_data\n",
+    "from TTS.tts.datasets.formatters import load_tts_samples\n",
     "from TTS.tts.utils.text import phoneme_to_sequence, sequence_to_phoneme\n",
     "from tqdm import tqdm\n",
     "from matplotlib import pylab as plt\n",
@@ -75,7 +75,7 @@
     "CONFIG = load_config(CONFIG_FILE)\n",
     "\n",
     "# Load some properties from config.json\n",
-    "CONFIG_METADATA = sorted(load_meta_data(CONFIG.datasets)[0])\n",
+    "CONFIG_METADATA = sorted(load_tts_samples(CONFIG.datasets)[0])\n",
     "CONFIG_METADATA = CONFIG_METADATA\n",
     "CONFIG_DATASET = CONFIG.datasets[0]\n",
     "CONFIG_PHONEME_LANGUAGE = CONFIG.phoneme_language\n",