Add libritts dataset option

examples/pipeline_wavernn/datasets.py

@@ -4,7 +4,7 @@
 import torch
 import torchaudio
 from torch.utils.data.dataset import random_split
-from torchaudio.datasets import LJSPEECH
+from torchaudio.datasets import LJSPEECH, LIBRITTS
 from torchaudio.transforms import MuLawEncoding
 
 from processing import bits_to_normalized_waveform, normalized_waveform_to_bits
@@ -48,12 +48,20 @@ def process_datapoint(self, item):
         return item[0].squeeze(0), specgram
 
 
-def split_process_ljspeech(args, transforms):
-    data = LJSPEECH(root=args.file_path, download=False)
+def split_process_dataset(args, transforms):
+    if args.dataset == 'ljspeech':
+        data = LJSPEECH(root=args.file_path, download=False)
 
-    val_length = int(len(data) * args.val_ratio)
-    lengths = [len(data) - val_length, val_length]
-    train_dataset, val_dataset = random_split(data, lengths)
+        val_length = int(len(data) * args.val_ratio)
+        lengths = [len(data) - val_length, val_length]
+        train_dataset, val_dataset = random_split(data, lengths)
+
+    elif args.dataset == 'libritts':
+        train_dataset = LIBRITTS(root=args.file_path, url='train-clean-100', download=False)
+        val_dataset = LIBRITTS(root=args.file_path, url='dev-clean', download=False)
+
+    else:
+        raise ValueError(f"Expected dataset: `ljspeech` or `libritts`, but found {args.dataset}")
 
     train_dataset = Processed(train_dataset, transforms)
     val_dataset = Processed(val_dataset, transforms)

examples/pipeline_wavernn/main.py

@@ -15,7 +15,7 @@
 from torchaudio.datasets.utils import bg_iterator
 from torchaudio.models._wavernn import _WaveRNN
 
-from datasets import collate_factory, split_process_ljspeech
+from datasets import collate_factory, split_process_dataset
 from losses import LongCrossEntropyLoss, MoLLoss
 from processing import LinearToMel, NormalizeDB
 from utils import MetricLogger, count_parameters, save_checkpoint
@@ -55,6 +55,13 @@ def parse_args():
         metavar="N",
         help="print frequency in epochs",
     )
+    parser.add_argument(
+        "--dataset",
+        default="ljspeech",
+        choices=["ljspeech", "libritts"],
+        type=str,
+        help="select dataset to train with",
+    )
     parser.add_argument(
         "--batch-size", default=256, type=int, metavar="N", help="mini-batch size"
     )
@@ -269,7 +276,7 @@ def main(args):
         NormalizeDB(min_level_db=args.min_level_db),
     )
 
-    train_dataset, val_dataset = split_process_ljspeech(args, transforms)
+    train_dataset, val_dataset = split_process_dataset(args, transforms)
 
     loader_training_params = {
         "num_workers": args.workers,