Update `vocoder` datasets and `setup_dataset`

TTS/vocoder/datasets/__init__.py

@@ -0,0 +1,57 @@
+from typing import List
+
+from coqpit import Coqpit
+from torch.utils.data import Dataset
+
+from TTS.utils.audio import AudioProcessor
+from TTS.vocoder.datasets.gan_dataset import GANDataset
+from TTS.vocoder.datasets.wavegrad_dataset import WaveGradDataset
+from TTS.vocoder.datasets.wavernn_dataset import WaveRNNDataset
+
+
+def setup_dataset(config: Coqpit, ap: AudioProcessor, is_eval: bool, data_items: List, verbose: bool) -> Dataset:
+    if config.model.lower() in "gan":
+        dataset = GANDataset(
+            ap=ap,
+            items=data_items,
+            seq_len=config.seq_len,
+            hop_len=ap.hop_length,
+            pad_short=config.pad_short,
+            conv_pad=config.conv_pad,
+            return_pairs=config.diff_samples_for_G_and_D if "diff_samples_for_G_and_D" in config else False,
+            is_training=not is_eval,
+            return_segments=not is_eval,
+            use_noise_augment=config.use_noise_augment,
+            use_cache=config.use_cache,
+            verbose=verbose,
+        )
+        dataset.shuffle_mapping()
+    elif config.model.lower() == "wavegrad":
+        dataset = WaveGradDataset(
+            ap=ap,
+            items=data_items,
+            seq_len=config.seq_len,
+            hop_len=ap.hop_length,
+            pad_short=config.pad_short,
+            conv_pad=config.conv_pad,
+            is_training=not is_eval,
+            return_segments=True,
+            use_noise_augment=False,
+            use_cache=config.use_cache,
+            verbose=verbose,
+        )
+    elif config.model.lower() == "wavernn":
+        dataset = WaveRNNDataset(
+            ap=ap,
+            items=data_items,
+            seq_len=config.seq_len,
+            hop_len=ap.hop_length,
+            pad=config.model_params.pad,
+            mode=config.model_params.mode,
+            mulaw=config.model_params.mulaw,
+            is_training=not is_eval,
+            verbose=verbose,
+        )
+    else:
+        raise ValueError(f" [!] Dataset for model {config.model.lower()} cannot be found.")
+    return dataset

TTS/vocoder/datasets/preprocess.py

@@ -3,10 +3,21 @@ import os
 from pathlib import Path
 
 import numpy as np
+from coqpit import Coqpit
 from tqdm import tqdm
 
+from TTS.utils.audio import AudioProcessor
 
-def preprocess_wav_files(out_path, config, ap):
+
+def preprocess_wav_files(out_path: str, config: Coqpit, ap: AudioProcessor):
+    """Process wav and compute mel and quantized wave signal.
+    It is mainly used by WaveRNN dataloader.
+
+    Args:
+        out_path (str): Parent folder path to save the files.
+        config (Coqpit): Model config.
+        ap (AudioProcessor): Audio processor.
+    """
     os.makedirs(os.path.join(out_path, "quant"), exist_ok=True)
     os.makedirs(os.path.join(out_path, "mel"), exist_ok=True)
     wav_files = find_wav_files(config.data_path)
@@ -18,7 +29,9 @@ def preprocess_wav_files(out_path, config, ap):
         mel = ap.melspectrogram(y)
         np.save(mel_path, mel)
         if isinstance(config.mode, int):
-            quant = ap.mulaw_encode(y, qc=config.mode) if config.mulaw else ap.quantize(y, bits=config.mode)
+            quant = (
+                ap.mulaw_encode(y, qc=config.mode) if config.model_params.mulaw else ap.quantize(y, bits=config.mode)
+            )
             np.save(quant_path, quant)
 
 

TTS/vocoder/datasets/wavegrad_dataset.py

@@ -136,4 +136,4 @@ class WaveGradDataset(Dataset):
             mels[idx, :, : mel.shape[1]] = mel
             audios[idx, : audio.shape[0]] = audio
 
-        return mels, audios
+        return audios, mels

TTS/vocoder/datasets/wavernn_dataset.py

@@ -10,16 +10,7 @@ class WaveRNNDataset(Dataset):
     """
 
     def __init__(
-        self,
-        ap,
-        items,
-        seq_len,
-        hop_len,
-        pad,
-        mode,
-        mulaw,
-        is_training=True,
-        verbose=False,
+        self, ap, items, seq_len, hop_len, pad, mode, mulaw, is_training=True, verbose=False, return_segments=True
     ):
 
         super().__init__()
@@ -34,6 +25,7 @@ class WaveRNNDataset(Dataset):
         self.mulaw = mulaw
         self.is_training = is_training
         self.verbose = verbose
+        self.return_segments = return_segments
 
         assert self.seq_len % self.hop_len == 0
 
@@ -44,6 +36,16 @@ class WaveRNNDataset(Dataset):
         item = self.load_item(index)
         return item
 
+    def load_test_samples(self, num_samples):
+        samples = []
+        return_segments = self.return_segments
+        self.return_segments = False
+        for idx in range(num_samples):
+            mel, audio, _ = self.load_item(idx)
+            samples.append([mel, audio])
+        self.return_segments = return_segments
+        return samples
+
     def load_item(self, index):
         """
         load (audio, feat) couple if feature_path is set
@@ -53,7 +55,10 @@ class WaveRNNDataset(Dataset):
 
             wavpath = self.item_list[index]
             audio = self.ap.load_wav(wavpath)
-            min_audio_len = 2 * self.seq_len + (2 * self.pad * self.hop_len)
+            if self.return_segments:
+                min_audio_len = 2 * self.seq_len + (2 * self.pad * self.hop_len)
+            else:
+                min_audio_len = audio.shape[0] + (2 * self.pad * self.hop_len)
             if audio.shape[0] < min_audio_len:
                 print(" [!] Instance is too short! : {}".format(wavpath))
                 audio = np.pad(audio, [0, min_audio_len - audio.shape[0] + self.hop_len])