move split_dataset

TTS/tts/datasets/preprocess.py

@@ -2,19 +2,41 @@ import os
 import re
 import sys
 import xml.etree.ElementTree as ET
+from collections import Counter
 from glob import glob
 from pathlib import Path
 from typing import List
 
+import numpy as np
 from tqdm import tqdm
 
-from TTS.tts.utils.generic_utils import split_dataset
-
 ####################
 # UTILITIES
 ####################
 
 
+def split_dataset(items):
+    speakers = [item[-1] for item in items]
+    is_multi_speaker = len(set(speakers)) > 1
+    eval_split_size = min(500, int(len(items) * 0.01))
+    assert eval_split_size > 0, " [!] You do not have enough samples to train. You need at least 100 samples."
+    np.random.seed(0)
+    np.random.shuffle(items)
+    if is_multi_speaker:
+        items_eval = []
+        speakers = [item[-1] for item in items]
+        speaker_counter = Counter(speakers)
+        while len(items_eval) < eval_split_size:
+            item_idx = np.random.randint(0, len(items))
+            speaker_to_be_removed = items[item_idx][-1]
+            if speaker_counter[speaker_to_be_removed] > 1:
+                items_eval.append(items[item_idx])
+                speaker_counter[speaker_to_be_removed] -= 1
+                del items[item_idx]
+        return items_eval, items
+    return items[:eval_split_size], items[eval_split_size:]
+
+
 def load_meta_data(datasets, eval_split=True):
     meta_data_train_all = []
     meta_data_eval_all = [] if eval_split else None
@@ -38,7 +60,7 @@ def load_meta_data(datasets, eval_split=True):
         meta_data_train_all += meta_data_train
         # load attention masks for duration predictor training
         if dataset.meta_file_attn_mask is not None:
-            meta_data = dict(load_attention_mask_meta_data(dataset['meta_file_attn_mask']))
+            meta_data = dict(load_attention_mask_meta_data(dataset["meta_file_attn_mask"]))
             for idx, ins in enumerate(meta_data_train_all):
                 attn_file = meta_data[ins[1]].strip()
                 meta_data_train_all[idx].append(attn_file)

TTS/tts/utils/generic_utils.py

@@ -1,32 +1,8 @@
-import importlib
-import re
-from collections import Counter
+import torch
 
 from TTS.utils.generic_utils import find_module
 
 
-def split_dataset(items):
-    speakers = [item[-1] for item in items]
-    is_multi_speaker = len(set(speakers)) > 1
-    eval_split_size = min(500, int(len(items) * 0.01))
-    assert eval_split_size > 0, " [!] You do not have enough samples to train. You need at least 100 samples."
-    np.random.seed(0)
-    np.random.shuffle(items)
-    if is_multi_speaker:
-        items_eval = []
-        speakers = [item[-1] for item in items]
-        speaker_counter = Counter(speakers)
-        while len(items_eval) < eval_split_size:
-            item_idx = np.random.randint(0, len(items))
-            speaker_to_be_removed = items[item_idx][-1]
-            if speaker_counter[speaker_to_be_removed] > 1:
-                items_eval.append(items[item_idx])
-                speaker_counter[speaker_to_be_removed] -= 1
-                del items[item_idx]
-        return items_eval, items
-    return items[:eval_split_size], items[eval_split_size:]
-
-
 # from https://gist.github.com/jihunchoi/f1434a77df9db1bb337417854b398df1
 def sequence_mask(sequence_length, max_len=None):
     if max_len is None: