enable loading precomputed vocoder dataset

vocoder/datasets/gan_dataset.py

@@ -28,6 +28,7 @@ class GANDataset(Dataset):
 
         self.ap = ap
         self.item_list = items
+        self.compute_feat = not isinstance(items[0], (tuple, list))
         self.seq_len = seq_len
         self.hop_len = hop_len
         self.pad_short = pad_short
@@ -77,14 +78,26 @@ class GANDataset(Dataset):
 
     def load_item(self, idx):
         """ load (audio, feat) couple """
-        wavpath = self.item_list[idx]
-        # print(wavpath)
+        if self.compute_feat:
+            # compute features from wav
+            wavpath = self.item_list[idx]
+            # print(wavpath)
 
-        if self.use_cache and self.cache[idx] is not None:
-            audio, mel = self.cache[idx]
+            if self.use_cache and self.cache[idx] is not None:
+                audio, mel = self.cache[idx]
+            else:
+                audio = self.ap.load_wav(wavpath)
+                mel = self.ap.melspectrogram(audio)
         else:
-            audio = self.ap.load_wav(wavpath)
-            mel = self.ap.melspectrogram(audio)
+
+            # load precomputed features
+            wavpath, feat_path = self.item_list[idx]
+
+            if self.use_cache and self.cache[idx] is not None:
+                audio, mel = self.cache[idx]
+            else:
+                audio = self.ap.load_wav(wavpath)
+                mel = np.load(feat_path)
 
         if len(audio) < self.seq_len + self.pad_short:
             audio = np.pad(audio, (0, self.seq_len + self.pad_short - len(audio)), \

vocoder/datasets/preprocess.py

@@ -1,5 +1,6 @@
 import glob
 import os
+from pathlib import Path
 
 import numpy as np
 
@@ -9,8 +10,28 @@ def find_wav_files(data_path):
     return wav_paths
 
 
+def find_feat_files(data_path):
+    feat_paths = glob.glob(os.path.join(data_path, '**', '*.npy'), recursive=True)
+    return feat_paths
+
+
 def load_wav_data(data_path, eval_split_size):
     wav_paths = find_wav_files(data_path)
     np.random.seed(0)
     np.random.shuffle(wav_paths)
     return wav_paths[:eval_split_size], wav_paths[eval_split_size:]
+
+
+def load_wav_feat_data(data_path, feat_path, eval_split_size):
+    wav_paths = sorted(find_wav_files(data_path))
+    feat_paths = sorted(find_feat_files(feat_path))
+    assert len(wav_paths) == len(feat_paths)
+    for wav, feat in zip(wav_paths, feat_paths):
+        wav_name = Path(wav).stem
+        feat_name = Path(feat).stem
+        assert wav_name == feat_name
+
+    items = list(zip(wav_paths, feat_paths))
+    np.random.seed(0)
+    np.random.shuffle(items)
+    return items[:eval_split_size], items[eval_split_size:]

vocoder/train.py

@@ -19,7 +19,7 @@ from TTS.utils.radam import RAdam
 from TTS.utils.tensorboard_logger import TensorboardLogger
 from TTS.utils.training import setup_torch_training_env
 from TTS.vocoder.datasets.gan_dataset import GANDataset
-from TTS.vocoder.datasets.preprocess import load_wav_data
+from TTS.vocoder.datasets.preprocess import load_wav_data, load_wav_feat_data
 # from distribute import (DistributedSampler, apply_gradient_allreduce,
 #                         init_distributed, reduce_tensor)
 from TTS.vocoder.layers.losses import DiscriminatorLoss, GeneratorLoss
@@ -543,8 +543,8 @@ def main(args):  # pylint: disable=redefined-outer-name
         best_loss = save_best_model(target_loss,
                                     best_loss,
                                     model_gen,
-                                    scheduler_gen,
                                     optimizer_gen,
+                                    scheduler_gen,
                                     model_disc,
                                     optimizer_disc,
                                     scheduler_disc,