Mass refactoring

tests/test_vocoder_datasets.py

@@ -0,0 +1,95 @@
+import os
+
+import numpy as np
+from tests import get_tests_path, get_tests_input_path, get_tests_output_path
+from torch.utils.data import DataLoader
+
+from TTS.utils.audio import AudioProcessor
+from TTS.utils.io import load_config
+from TTS.vocoder.datasets.gan_dataset import GANDataset
+from TTS.vocoder.datasets.preprocess import load_wav_data
+
+file_path = os.path.dirname(os.path.realpath(__file__))
+OUTPATH = os.path.join(get_tests_output_path(), "loader_tests/")
+os.makedirs(OUTPATH, exist_ok=True)
+
+C = load_config(os.path.join(get_tests_input_path(), 'test_config.json'))
+
+test_data_path = os.path.join(get_tests_path(), "data/ljspeech/")
+ok_ljspeech = os.path.exists(test_data_path)
+
+
+def gan_dataset_case(batch_size, seq_len, hop_len, conv_pad, return_segments, use_noise_augment, use_cache, num_workers):
+    ''' run dataloader with given parameters and check conditions '''
+    ap = AudioProcessor(**C.audio)
+    _, train_items = load_wav_data(test_data_path, 10)
+    dataset = GANDataset(ap,
+                         train_items,
+                         seq_len=seq_len,
+                         hop_len=hop_len,
+                         pad_short=2000,
+                         conv_pad=conv_pad,
+                         return_segments=return_segments,
+                         use_noise_augment=use_noise_augment,
+                         use_cache=use_cache)
+    loader = DataLoader(dataset=dataset,
+                        batch_size=batch_size,
+                        shuffle=True,
+                        num_workers=num_workers,
+                        pin_memory=True,
+                        drop_last=True)
+
+    max_iter = 10
+    count_iter = 0
+
+    # return random segments or return the whole audio
+    if return_segments:
+        for item1, _ in loader:
+            feat1, wav1 = item1
+            # feat2, wav2 = item2
+            expected_feat_shape = (batch_size, ap.num_mels, seq_len // hop_len + conv_pad * 2)
+
+            # check shapes
+            assert np.all(feat1.shape == expected_feat_shape), f" [!] {feat1.shape} vs {expected_feat_shape}"
+            assert (feat1.shape[2] - conv_pad * 2) * hop_len == wav1.shape[2]
+
+            # check feature vs audio match
+            if not use_noise_augment:
+                for idx in range(batch_size):
+                    audio = wav1[idx].squeeze()
+                    feat = feat1[idx]
+                    mel = ap.melspectrogram(audio)
+                    # the first 2 and the last 2 frames are skipped due to the padding
+                    # differences in stft
+                    assert (feat - mel[:, :feat1.shape[-1]])[:, 2:-2].sum() <= 0, f' [!] {(feat - mel[:, :feat1.shape[-1]])[:, 2:-2].sum()}'
+
+            count_iter += 1
+            # if count_iter == max_iter:
+            #     break
+    else:
+        for item in loader:
+            feat, wav = item
+            expected_feat_shape = (batch_size, ap.num_mels, (wav.shape[-1] // hop_len) + (conv_pad * 2))
+            assert np.all(feat.shape == expected_feat_shape), f" [!] {feat.shape} vs {expected_feat_shape}"
+            assert (feat.shape[2] - conv_pad * 2) * hop_len == wav.shape[2]
+            count_iter += 1
+            if count_iter == max_iter:
+                break
+
+
+def test_parametrized_gan_dataset():
+    ''' test dataloader with different parameters '''
+    params = [
+        [32, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, True, False, True, 0],
+        [32, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, True, False, True, 4],
+        [1, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, True, True, True, 0],
+        [1, C.audio['hop_length'], C.audio['hop_length'], 0, True, True, True, 0],
+        [1, C.audio['hop_length'] * 10, C.audio['hop_length'], 2, True, True, True, 0],
+        [1, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, False, True, True, 0],
+        [1, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, True, False, True, 0],
+        [1, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, True, True, False, 0],
+        [1, C.audio['hop_length'] * 10, C.audio['hop_length'], 0, False, False, False, 0],
+    ]
+    for param in params:
+        print(param)
+        gan_dataset_case(*param)