Add libritts dataset option (#818)

Co-authored-by: Ji Chen <jimchen90@devfair0160.h2.fair>

examples/pipeline_wavernn/datasets.py

@@ -4,7 +4,7 @@ import random
 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
@@ -48,13 +48,21 @@ class Processed(torch.utils.data.Dataset):
         return item[0].squeeze(0), specgram
 
 
-def split_process_ljspeech(args, transforms):
+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)
 
+    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 torch.utils.data import DataLoader
 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,