diff --git a/egs/libritts/TTS/prepare.sh b/egs/libritts/TTS/prepare.sh
index d18f8a677..c0e2918f6 100644
--- a/egs/libritts/TTS/prepare.sh
+++ b/egs/libritts/TTS/prepare.sh
@@ -53,6 +53,9 @@ if [ $stage -le 0 ] && [ $stop_stage -ge 0 ]; then
     log "Downloading x-vector"
 
     git clone https://huggingface.co/datasets/zrjin/xvector_nnet_1a_libritts_clean_460 $dl_dir/xvector_nnet_1a_libritts_clean_460
+
+    mkdir -p exp/xvector_nnet_1a/
+    cp -r $dl_dir/xvector_nnet_1a_libritts_clean_460/* exp/xvector_nnet_1a/
   fi
 
 fi
diff --git a/egs/libritts/TTS/vits/duration_predictor.py b/egs/libritts/TTS/vits/duration_predictor.py
new file mode 120000
index 000000000..9972b476f
--- /dev/null
+++ b/egs/libritts/TTS/vits/duration_predictor.py
@@ -0,0 +1 @@
+../../../ljspeech/TTS/vits/duration_predictor.py
\ No newline at end of file
diff --git a/egs/libritts/TTS/vits/flow.py b/egs/libritts/TTS/vits/flow.py
new file mode 120000
index 000000000..e65d91ea7
--- /dev/null
+++ b/egs/libritts/TTS/vits/flow.py
@@ -0,0 +1 @@
+../../../ljspeech/TTS/vits/flow.py
\ No newline at end of file
diff --git a/egs/libritts/TTS/vits/generator.py b/egs/libritts/TTS/vits/generator.py
new file mode 120000
index 000000000..611679bfa
--- /dev/null
+++ b/egs/libritts/TTS/vits/generator.py
@@ -0,0 +1 @@
+../../../ljspeech/TTS/vits/generator.py
\ No newline at end of file
diff --git a/egs/libritts/TTS/vits/hifigan.py b/egs/libritts/TTS/vits/hifigan.py
new file mode 120000
index 000000000..5ac025de7
--- /dev/null
+++ b/egs/libritts/TTS/vits/hifigan.py
@@ -0,0 +1 @@
+../../../ljspeech/TTS/vits/hifigan.py
\ No newline at end of file
diff --git a/egs/libritts/TTS/vits/infer.py b/egs/libritts/TTS/vits/infer.py
new file mode 100755
index 000000000..3a94a6546
--- /dev/null
+++ b/egs/libritts/TTS/vits/infer.py
@@ -0,0 +1,273 @@
+#!/usr/bin/env python3
+#
+# Copyright      2023 Xiaomi Corporation     (Author: Zengwei Yao,
+#                                                     Zengrui Jin,)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+This script performs model inference on test set.
+
+Usage:
+./vits/infer.py \
+    --epoch 1000 \
+    --exp-dir ./vits/exp \
+    --max-duration 500
+"""
+
+
+import argparse
+import logging
+from concurrent.futures import ThreadPoolExecutor
+from pathlib import Path
+from typing import Dict, List
+
+import k2
+import torch
+import torch.nn as nn
+import torchaudio
+from tokenizer import Tokenizer
+from train import get_model, get_params
+from tts_datamodule import LibrittsTtsDataModule
+
+from icefall.checkpoint import load_checkpoint
+from icefall.utils import AttributeDict, setup_logger
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--epoch",
+        type=int,
+        default=1000,
+        help="""It specifies the checkpoint to use for decoding.
+        Note: Epoch counts from 1.
+        """,
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="vits/exp",
+        help="The experiment dir",
+    )
+
+    parser.add_argument(
+        "--tokens",
+        type=str,
+        default="data/tokens.txt",
+        help="""Path to vocabulary.""",
+    )
+
+    return parser
+
+
+def infer_dataset(
+    dl: torch.utils.data.DataLoader,
+    subset: str,
+    params: AttributeDict,
+    model: nn.Module,
+    tokenizer: Tokenizer,
+    speaker_map: Dict[str, int],
+) -> None:
+    """Decode dataset.
+    The ground-truth and generated audio pairs will be saved to `params.save_wav_dir`.
+
+    Args:
+      dl:
+        PyTorch's dataloader containing the dataset to decode.
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The neural model.
+      tokenizer:
+        Used to convert text to phonemes.
+    """
+
+    #  Background worker save audios to disk.
+    def _save_worker(
+        subset: str,
+        batch_size: int,
+        cut_ids: List[str],
+        audio: torch.Tensor,
+        audio_pred: torch.Tensor,
+        audio_lens: List[int],
+        audio_lens_pred: List[int],
+    ):
+        for i in range(batch_size):
+            torchaudio.save(
+                str(params.save_wav_dir / subset / f"{cut_ids[i]}_gt.wav"),
+                audio[i : i + 1, : audio_lens[i]],
+                sample_rate=params.sampling_rate,
+            )
+            torchaudio.save(
+                str(params.save_wav_dir / subset / f"{cut_ids[i]}_pred.wav"),
+                audio_pred[i : i + 1, : audio_lens_pred[i]],
+                sample_rate=params.sampling_rate,
+            )
+
+    device = next(model.parameters()).device
+    num_cuts = 0
+    log_interval = 5
+
+    try:
+        num_batches = len(dl)
+    except TypeError:
+        num_batches = "?"
+
+    futures = []
+    with ThreadPoolExecutor(max_workers=1) as executor:
+        for batch_idx, batch in enumerate(dl):
+            batch_size = len(batch["tokens"])
+
+            tokens = batch["tokens"]
+            tokens = tokenizer.tokens_to_token_ids(
+                tokens, intersperse_blank=True, add_sos=True, add_eos=True
+            )
+            tokens = k2.RaggedTensor(tokens)
+            row_splits = tokens.shape.row_splits(1)
+            tokens_lens = row_splits[1:] - row_splits[:-1]
+            tokens = tokens.to(device)
+            tokens_lens = tokens_lens.to(device)
+            # tensor of shape (B, T)
+            tokens = tokens.pad(mode="constant", padding_value=tokenizer.pad_id)
+            speakers = (
+                torch.Tensor([speaker_map[sid] for sid in batch["speakers"]])
+                .int()
+                .to(device)
+            )
+
+            audio = batch["audio"]
+            audio_lens = batch["audio_lens"].tolist()
+            cut_ids = [cut.id for cut in batch["cut"]]
+
+            audio_pred, _, durations = model.inference_batch(
+                text=tokens,
+                text_lengths=tokens_lens,
+                sids=speakers,
+            )
+            audio_pred = audio_pred.detach().cpu()
+            # convert to samples
+            audio_lens_pred = (
+                (durations.sum(1) * params.frame_shift).to(dtype=torch.int64).tolist()
+            )
+
+            futures.append(
+                executor.submit(
+                    _save_worker,
+                    subset,
+                    batch_size,
+                    cut_ids,
+                    audio,
+                    audio_pred,
+                    audio_lens,
+                    audio_lens_pred,
+                )
+            )
+
+            num_cuts += batch_size
+
+            if batch_idx % log_interval == 0:
+                batch_str = f"{batch_idx}/{num_batches}"
+
+                logging.info(
+                    f"batch {batch_str}, cuts processed until now is {num_cuts}"
+                )
+        # return results
+        for f in futures:
+            f.result()
+
+
+@torch.no_grad()
+def main():
+    parser = get_parser()
+    LibrittsTtsDataModule.add_arguments(parser)
+    args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    params = get_params()
+    params.update(vars(args))
+
+    params.suffix = f"epoch-{params.epoch}"
+
+    params.res_dir = params.exp_dir / "infer" / params.suffix
+    params.save_wav_dir = params.res_dir / "wav"
+    params.save_wav_dir.mkdir(parents=True, exist_ok=True)
+
+    setup_logger(f"{params.res_dir}/log-infer-{params.suffix}")
+    logging.info("Infer started")
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", 0)
+
+    tokenizer = Tokenizer(params.tokens)
+    params.blank_id = tokenizer.pad_id
+    params.vocab_size = tokenizer.vocab_size
+
+    # we need cut ids to display recognition results.
+    args.return_cuts = True
+    libritts = LibrittsTtsDataModule(args)
+    speaker_map = libritts.speakers()
+    params.num_spks = len(speaker_map)
+
+    logging.info(f"Device: {device}")
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_model(params)
+
+    load_checkpoint(f"{params.exp_dir}/epoch-{params.epoch}.pt", model)
+
+    model.to(device)
+    model.eval()
+
+    num_param_g = sum([p.numel() for p in model.generator.parameters()])
+    logging.info(f"Number of parameters in generator: {num_param_g}")
+    num_param_d = sum([p.numel() for p in model.discriminator.parameters()])
+    logging.info(f"Number of parameters in discriminator: {num_param_d}")
+    logging.info(f"Total number of parameters: {num_param_g + num_param_d}")
+
+    test_cuts = libritts.test_cuts()
+    test_dl = libritts.test_dataloaders(test_cuts)
+
+    valid_cuts = libritts.valid_cuts()
+    valid_dl = libritts.valid_dataloaders(valid_cuts)
+
+    infer_sets = {"test": test_dl, "valid": valid_dl}
+
+    for subset, dl in infer_sets.items():
+        save_wav_dir = params.res_dir / "wav" / subset
+        save_wav_dir.mkdir(parents=True, exist_ok=True)
+
+        logging.info(f"Processing {subset} set, saving to {save_wav_dir}")
+
+        infer_dataset(
+            dl=dl,
+            subset=subset,
+            params=params,
+            model=model,
+            tokenizer=tokenizer,
+            speaker_map=speaker_map,
+        )
+
+    logging.info(f"Wav files are saved to {params.save_wav_dir}")
+    logging.info("Done!")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/egs/libritts/TTS/vits/loss.py b/egs/libritts/TTS/vits/loss.py
new file mode 120000
index 000000000..672e5ff68
--- /dev/null
+++ b/egs/libritts/TTS/vits/loss.py
@@ -0,0 +1 @@
+../../../ljspeech/TTS/vits/loss.py
\ No newline at end of file
diff --git a/egs/libritts/TTS/vits/monotonic_align b/egs/libritts/TTS/vits/monotonic_align
new file mode 120000
index 000000000..71934e7cc
--- /dev/null
+++ b/egs/libritts/TTS/vits/monotonic_align
@@ -0,0 +1 @@
+../../../ljspeech/TTS/vits/monotonic_align
\ No newline at end of file
diff --git a/egs/libritts/TTS/vits/posterior_encoder.py b/egs/libritts/TTS/vits/posterior_encoder.py
new file mode 120000
index 000000000..41d64a3a6
--- /dev/null
+++ b/egs/libritts/TTS/vits/posterior_encoder.py
@@ -0,0 +1 @@
+../../../ljspeech/TTS/vits/posterior_encoder.py
\ No newline at end of file
diff --git a/egs/libritts/TTS/vits/residual_coupling.py b/egs/libritts/TTS/vits/residual_coupling.py
new file mode 120000
index 000000000..f979adbf0
--- /dev/null
+++ b/egs/libritts/TTS/vits/residual_coupling.py
@@ -0,0 +1 @@
+../../../ljspeech/TTS/vits/residual_coupling.py
\ No newline at end of file
diff --git a/egs/libritts/TTS/vits/test_onnx.py b/egs/libritts/TTS/vits/test_onnx.py
new file mode 100755
index 000000000..ae6587338
--- /dev/null
+++ b/egs/libritts/TTS/vits/test_onnx.py
@@ -0,0 +1,141 @@
+#!/usr/bin/env python3
+#
+# Copyright   2023-2024   Xiaomi Corporation     (Author: Zengwei Yao,
+#                                                         Zengrui Jin,)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+This script is used to test the exported onnx model by vits/export-onnx.py
+
+Use the onnx model to generate a wav:
+./vits/test_onnx.py \
+  --model-filename vits/exp/vits-epoch-1000.onnx \
+  --tokens data/tokens.txt
+"""
+
+
+import argparse
+import logging
+from pathlib import Path
+
+import onnxruntime as ort
+import torch
+import torchaudio
+from tokenizer import Tokenizer
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--model-filename",
+        type=str,
+        required=True,
+        help="Path to the onnx model.",
+    )
+
+    parser.add_argument(
+        "--speakers",
+        type=Path,
+        default=Path("data/speakers.txt"),
+        help="Path to speakers.txt file.",
+    )
+    parser.add_argument(
+        "--tokens",
+        type=str,
+        default="data/tokens.txt",
+        help="""Path to vocabulary.""",
+    )
+
+    return parser
+
+
+class OnnxModel:
+    def __init__(self, model_filename: str):
+        session_opts = ort.SessionOptions()
+        session_opts.inter_op_num_threads = 1
+        session_opts.intra_op_num_threads = 4
+
+        self.session_opts = session_opts
+
+        self.model = ort.InferenceSession(
+            model_filename,
+            sess_options=self.session_opts,
+            providers=["CPUExecutionProvider"],
+        )
+        logging.info(f"{self.model.get_modelmeta().custom_metadata_map}")
+
+    def __call__(
+        self, tokens: torch.Tensor, tokens_lens: torch.Tensor, speaker: torch.Tensor
+    ) -> torch.Tensor:
+        """
+        Args:
+          tokens:
+            A 1-D tensor of shape (1, T)
+        Returns:
+            A tensor of shape (1, T')
+        """
+        noise_scale = torch.tensor([0.667], dtype=torch.float32)
+        noise_scale_dur = torch.tensor([0.8], dtype=torch.float32)
+        alpha = torch.tensor([1.0], dtype=torch.float32)
+
+        out = self.model.run(
+            [
+                self.model.get_outputs()[0].name,
+            ],
+            {
+                self.model.get_inputs()[0].name: tokens.numpy(),
+                self.model.get_inputs()[1].name: tokens_lens.numpy(),
+                self.model.get_inputs()[2].name: noise_scale.numpy(),
+                self.model.get_inputs()[3].name: alpha.numpy(),
+                self.model.get_inputs()[4].name: noise_scale_dur.numpy(),
+                self.model.get_inputs()[5].name: speaker.numpy(),
+            },
+        )[0]
+        return torch.from_numpy(out)
+
+
+def main():
+    args = get_parser().parse_args()
+
+    tokenizer = Tokenizer(args.tokens)
+
+    with open(args.speakers) as f:
+        speaker_map = {line.strip(): i for i, line in enumerate(f)}
+    args.num_spks = len(speaker_map)
+
+    logging.info("About to create onnx model")
+    model = OnnxModel(args.model_filename)
+
+    text = "I went there to see the land, the people and how their system works, end quote."
+    tokens = tokenizer.texts_to_token_ids(
+        [text], intersperse_blank=True, add_sos=True, add_eos=True
+    )
+    tokens = torch.tensor(tokens)  # (1, T)
+    tokens_lens = torch.tensor([tokens.shape[1]], dtype=torch.int64)  # (1, T)
+    speaker = torch.tensor([1], dtype=torch.int64)  # (1, )
+    audio = model(tokens, tokens_lens, speaker)  # (1, T')
+
+    torchaudio.save(str("test_onnx.wav"), audio, sample_rate=22050)
+    logging.info("Saved to test_onnx.wav")
+
+
+if __name__ == "__main__":
+    formatter = "%(asctime)s %(levelname)s [%(filename)s:%(lineno)d] %(message)s"
+    logging.basicConfig(format=formatter, level=logging.INFO)
+    main()
diff --git a/egs/libritts/TTS/vits/text_encoder.py b/egs/libritts/TTS/vits/text_encoder.py
new file mode 120000
index 000000000..0efba277e
--- /dev/null
+++ b/egs/libritts/TTS/vits/text_encoder.py
@@ -0,0 +1 @@
+../../../ljspeech/TTS/vits/text_encoder.py
\ No newline at end of file
diff --git a/egs/libritts/TTS/vits/tokenizer.py b/egs/libritts/TTS/vits/tokenizer.py
new file mode 120000
index 000000000..057b0dc4b
--- /dev/null
+++ b/egs/libritts/TTS/vits/tokenizer.py
@@ -0,0 +1 @@
+../../../ljspeech/TTS/vits/tokenizer.py
\ No newline at end of file
diff --git a/egs/libritts/TTS/vits/train.py b/egs/libritts/TTS/vits/train.py
new file mode 100755
index 000000000..7058f3d76
--- /dev/null
+++ b/egs/libritts/TTS/vits/train.py
@@ -0,0 +1,1002 @@
+#!/usr/bin/env python3
+# Copyright   2023-2024  Xiaomi Corporation     (Author: Zengwei Yao,
+#                                                        Zengrui Jin,)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import argparse
+import logging
+from pathlib import Path
+from shutil import copyfile
+from typing import Any, Dict, Optional, Tuple, Union
+
+import k2
+import numpy as np
+import torch
+import torch.multiprocessing as mp
+import torch.nn as nn
+from lhotse.cut import Cut
+from lhotse.utils import fix_random_seed
+from tokenizer import Tokenizer
+from torch.cuda.amp import GradScaler, autocast
+from torch.nn.parallel import DistributedDataParallel as DDP
+from torch.optim import Optimizer
+from torch.utils.tensorboard import SummaryWriter
+from tts_datamodule import LibrittsTtsDataModule
+from utils import MetricsTracker, plot_feature, save_checkpoint
+from vits import VITS
+
+from icefall import diagnostics
+from icefall.checkpoint import load_checkpoint
+from icefall.dist import cleanup_dist, setup_dist
+from icefall.env import get_env_info
+from icefall.hooks import register_inf_check_hooks
+from icefall.utils import AttributeDict, setup_logger, str2bool
+
+LRSchedulerType = torch.optim.lr_scheduler._LRScheduler
+
+
+def get_parser():
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter
+    )
+
+    parser.add_argument(
+        "--world-size",
+        type=int,
+        default=1,
+        help="Number of GPUs for DDP training.",
+    )
+
+    parser.add_argument(
+        "--master-port",
+        type=int,
+        default=12354,
+        help="Master port to use for DDP training.",
+    )
+
+    parser.add_argument(
+        "--tensorboard",
+        type=str2bool,
+        default=True,
+        help="Should various information be logged in tensorboard.",
+    )
+
+    parser.add_argument(
+        "--num-epochs",
+        type=int,
+        default=1000,
+        help="Number of epochs to train.",
+    )
+
+    parser.add_argument(
+        "--start-epoch",
+        type=int,
+        default=1,
+        help="""Resume training from this epoch. It should be positive.
+        If larger than 1, it will load checkpoint from
+        exp-dir/epoch-{start_epoch-1}.pt
+        """,
+    )
+
+    parser.add_argument(
+        "--exp-dir",
+        type=str,
+        default="vits/exp",
+        help="""The experiment dir.
+        It specifies the directory where all training related
+        files, e.g., checkpoints, log, etc, are saved
+        """,
+    )
+
+    parser.add_argument(
+        "--tokens",
+        type=str,
+        default="data/tokens.txt",
+        help="""Path to vocabulary.""",
+    )
+
+    parser.add_argument(
+        "--lr", type=float, default=2.0e-4, help="The base learning rate."
+    )
+
+    parser.add_argument(
+        "--seed",
+        type=int,
+        default=42,
+        help="The seed for random generators intended for reproducibility",
+    )
+
+    parser.add_argument(
+        "--print-diagnostics",
+        type=str2bool,
+        default=False,
+        help="Accumulate stats on activations, print them and exit.",
+    )
+
+    parser.add_argument(
+        "--inf-check",
+        type=str2bool,
+        default=False,
+        help="Add hooks to check for infinite module outputs and gradients.",
+    )
+
+    parser.add_argument(
+        "--save-every-n",
+        type=int,
+        default=20,
+        help="""Save checkpoint after processing this number of epochs"
+        periodically. We save checkpoint to exp-dir/ whenever
+        params.cur_epoch % save_every_n == 0. The checkpoint filename
+        has the form: f'exp-dir/epoch-{params.cur_epoch}.pt'.
+        Since it will take around 1000 epochs, we suggest using a large
+        save_every_n to save disk space.
+        """,
+    )
+
+    parser.add_argument(
+        "--use-fp16",
+        type=str2bool,
+        default=False,
+        help="Whether to use half precision training.",
+    )
+
+    return parser
+
+
+def get_params() -> AttributeDict:
+    """Return a dict containing training parameters.
+
+    All training related parameters that are not passed from the commandline
+    are saved in the variable `params`.
+
+    Commandline options are merged into `params` after they are parsed, so
+    you can also access them via `params`.
+
+    Explanation of options saved in `params`:
+
+        - best_train_loss: Best training loss so far. It is used to select
+                           the model that has the lowest training loss. It is
+                           updated during the training.
+
+        - best_valid_loss: Best validation loss so far. It is used to select
+                           the model that has the lowest validation loss. It is
+                           updated during the training.
+
+        - best_train_epoch: It is the epoch that has the best training loss.
+
+        - best_valid_epoch: It is the epoch that has the best validation loss.
+
+        - batch_idx_train: Used to writing statistics to tensorboard. It
+                           contains number of batches trained so far across
+                           epochs.
+
+        - log_interval:  Print training loss if batch_idx % log_interval` is 0
+
+        - valid_interval:  Run validation if batch_idx % valid_interval is 0
+
+        - feature_dim: The model input dim. It has to match the one used
+                       in computing features.
+
+        - subsampling_factor:  The subsampling factor for the model.
+
+        - encoder_dim: Hidden dim for multi-head attention model.
+
+        - num_decoder_layers: Number of decoder layer of transformer decoder.
+
+        - warm_step: The warmup period that dictates the decay of the
+              scale on "simple" (un-pruned) loss.
+    """
+    params = AttributeDict(
+        {
+            # training params
+            "best_train_loss": float("inf"),
+            "best_valid_loss": float("inf"),
+            "best_train_epoch": -1,
+            "best_valid_epoch": -1,
+            "batch_idx_train": -1,  # 0
+            "log_interval": 50,
+            "valid_interval": 200,
+            "env_info": get_env_info(),
+            "sampling_rate": 22050,
+            "frame_shift": 256,
+            "frame_length": 1024,
+            "feature_dim": 513,  # 1024 // 2 + 1, 1024 is fft_length
+            "n_mels": 80,
+            "lambda_adv": 1.0,  # loss scaling coefficient for adversarial loss
+            "lambda_mel": 45.0,  # loss scaling coefficient for Mel loss
+            "lambda_feat_match": 2.0,  # loss scaling coefficient for feat match loss
+            "lambda_dur": 1.0,  # loss scaling coefficient for duration loss
+            "lambda_kl": 1.0,  # loss scaling coefficient for KL divergence loss
+        }
+    )
+
+    return params
+
+
+def load_checkpoint_if_available(
+    params: AttributeDict, model: nn.Module
+) -> Optional[Dict[str, Any]]:
+    """Load checkpoint from file.
+
+    If params.start_epoch is larger than 1, it will load the checkpoint from
+    `params.start_epoch - 1`.
+
+    Apart from loading state dict for `model` and `optimizer` it also updates
+    `best_train_epoch`, `best_train_loss`, `best_valid_epoch`,
+    and `best_valid_loss` in `params`.
+
+    Args:
+      params:
+        The return value of :func:`get_params`.
+      model:
+        The training model.
+    Returns:
+      Return a dict containing previously saved training info.
+    """
+    if params.start_epoch > 1:
+        filename = params.exp_dir / f"epoch-{params.start_epoch-1}.pt"
+    else:
+        return None
+
+    assert filename.is_file(), f"{filename} does not exist!"
+
+    saved_params = load_checkpoint(filename, model=model)
+
+    keys = [
+        "best_train_epoch",
+        "best_valid_epoch",
+        "batch_idx_train",
+        "best_train_loss",
+        "best_valid_loss",
+    ]
+    for k in keys:
+        params[k] = saved_params[k]
+
+    return saved_params
+
+
+def get_model(params: AttributeDict) -> nn.Module:
+    mel_loss_params = {
+        "n_mels": params.n_mels,
+        "frame_length": params.frame_length,
+        "frame_shift": params.frame_shift,
+    }
+    generator_params = {
+        "hidden_channels": 192,
+        "spks": None,
+        "langs": None,
+        "spk_embed_dim": 512,
+        "global_channels": 256,
+        "segment_size": 32,
+        "text_encoder_attention_heads": 2,
+        "text_encoder_ffn_expand": 4,
+        "text_encoder_cnn_module_kernel": 5,
+        "text_encoder_blocks": 6,
+        "text_encoder_dropout_rate": 0.1,
+        "decoder_kernel_size": 7,
+        "decoder_channels": 512,
+        "decoder_upsample_scales": [8, 8, 2, 2],
+        "decoder_upsample_kernel_sizes": [16, 16, 4, 4],
+        "decoder_resblock_kernel_sizes": [3, 7, 11],
+        "decoder_resblock_dilations": [[1, 3, 5], [1, 3, 5], [1, 3, 5]],
+        "use_weight_norm_in_decoder": True,
+        "posterior_encoder_kernel_size": 5,
+        "posterior_encoder_layers": 16,
+        "posterior_encoder_stacks": 1,
+        "posterior_encoder_base_dilation": 1,
+        "posterior_encoder_dropout_rate": 0.0,
+        "use_weight_norm_in_posterior_encoder": True,
+        "flow_flows": 4,
+        "flow_kernel_size": 5,
+        "flow_base_dilation": 1,
+        "flow_layers": 4,
+        "flow_dropout_rate": 0.0,
+        "use_weight_norm_in_flow": True,
+        "use_only_mean_in_flow": True,
+        "stochastic_duration_predictor_kernel_size": 3,
+        "stochastic_duration_predictor_dropout_rate": 0.5,
+        "stochastic_duration_predictor_flows": 4,
+        "stochastic_duration_predictor_dds_conv_layers": 3,
+    }
+    model = VITS(
+        vocab_size=params.vocab_size,
+        feature_dim=params.feature_dim,
+        sampling_rate=params.sampling_rate,
+        generator_params=generator_params,
+        mel_loss_params=mel_loss_params,
+        lambda_adv=params.lambda_adv,
+        lambda_mel=params.lambda_mel,
+        lambda_feat_match=params.lambda_feat_match,
+        lambda_dur=params.lambda_dur,
+        lambda_kl=params.lambda_kl,
+    )
+    return model
+
+
+def prepare_input(
+    batch: dict,
+    tokenizer: Tokenizer,
+    device: torch.device,
+    speaker_map: Dict[str, int],
+):
+    """Parse batch data"""
+    audio = batch["audio"].to(device)
+    features = batch["features"].to(device)
+    audio_lens = batch["audio_lens"].to(device)
+    features_lens = batch["features_lens"].to(device)
+    tokens = batch["tokens"]
+    speakers = (
+        torch.Tensor([speaker_map[sid] for sid in batch["speakers"]]).int().to(device)
+    )
+
+    tokens = tokenizer.tokens_to_token_ids(
+        tokens, intersperse_blank=True, add_sos=True, add_eos=True
+    )
+    tokens = k2.RaggedTensor(tokens)
+    row_splits = tokens.shape.row_splits(1)
+    tokens_lens = row_splits[1:] - row_splits[:-1]
+    tokens = tokens.to(device)
+    tokens_lens = tokens_lens.to(device)
+    # a tensor of shape (B, T)
+    tokens = tokens.pad(mode="constant", padding_value=tokenizer.pad_id)
+
+    return audio, audio_lens, features, features_lens, tokens, tokens_lens, speakers
+
+
+def train_one_epoch(
+    params: AttributeDict,
+    model: Union[nn.Module, DDP],
+    tokenizer: Tokenizer,
+    optimizer_g: Optimizer,
+    optimizer_d: Optimizer,
+    scheduler_g: LRSchedulerType,
+    scheduler_d: LRSchedulerType,
+    train_dl: torch.utils.data.DataLoader,
+    valid_dl: torch.utils.data.DataLoader,
+    speaker_map: Dict[str, int],
+    scaler: GradScaler,
+    tb_writer: Optional[SummaryWriter] = None,
+    world_size: int = 1,
+    rank: int = 0,
+) -> None:
+    """Train the model for one epoch.
+
+    The training loss from the mean of all frames is saved in
+    `params.train_loss`. It runs the validation process every
+    `params.valid_interval` batches.
+
+    Args:
+      params:
+        It is returned by :func:`get_params`.
+      model:
+        The model for training.
+      tokenizer:
+        Used to convert text to phonemes.
+      optimizer_g:
+        The optimizer for generator.
+      optimizer_d:
+        The optimizer for discriminator.
+      scheduler_g:
+        The learning rate scheduler for generator, we call step() every epoch.
+      scheduler_d:
+        The learning rate scheduler for discriminator, we call step() every epoch.
+      train_dl:
+        Dataloader for the training dataset.
+      valid_dl:
+        Dataloader for the validation dataset.
+      scaler:
+        The scaler used for mix precision training.
+      tb_writer:
+        Writer to write log messages to tensorboard.
+      world_size:
+        Number of nodes in DDP training. If it is 1, DDP is disabled.
+      rank:
+        The rank of the node in DDP training. If no DDP is used, it should
+        be set to 0.
+    """
+    model.train()
+    device = model.device if isinstance(model, DDP) else next(model.parameters()).device
+
+    # used to summary the stats over iterations in one epoch
+    tot_loss = MetricsTracker()
+
+    saved_bad_model = False
+
+    def save_bad_model(suffix: str = ""):
+        save_checkpoint(
+            filename=params.exp_dir / f"bad-model{suffix}-{rank}.pt",
+            model=model,
+            params=params,
+            optimizer_g=optimizer_g,
+            optimizer_d=optimizer_d,
+            scheduler_g=scheduler_g,
+            scheduler_d=scheduler_d,
+            sampler=train_dl.sampler,
+            scaler=scaler,
+            rank=0,
+        )
+
+    for batch_idx, batch in enumerate(train_dl):
+        params.batch_idx_train += 1
+
+        batch_size = len(batch["tokens"])
+        (
+            audio,
+            audio_lens,
+            features,
+            features_lens,
+            tokens,
+            tokens_lens,
+            speakers,
+        ) = prepare_input(batch, tokenizer, device, speaker_map)
+
+        loss_info = MetricsTracker()
+        loss_info["samples"] = batch_size
+
+        try:
+            with autocast(enabled=params.use_fp16):
+                # forward discriminator
+                loss_d, stats_d = model(
+                    text=tokens,
+                    text_lengths=tokens_lens,
+                    feats=features,
+                    feats_lengths=features_lens,
+                    speech=audio,
+                    speech_lengths=audio_lens,
+                    sids=speakers,
+                    forward_generator=False,
+                )
+            for k, v in stats_d.items():
+                loss_info[k] = v * batch_size
+            # update discriminator
+            optimizer_d.zero_grad()
+            scaler.scale(loss_d).backward()
+            scaler.step(optimizer_d)
+
+            with autocast(enabled=params.use_fp16):
+                # forward generator
+                loss_g, stats_g = model(
+                    text=tokens,
+                    text_lengths=tokens_lens,
+                    feats=features,
+                    feats_lengths=features_lens,
+                    speech=audio,
+                    speech_lengths=audio_lens,
+                    sids=speakers,
+                    forward_generator=True,
+                    return_sample=params.batch_idx_train % params.log_interval == 0,
+                )
+            for k, v in stats_g.items():
+                if "returned_sample" not in k:
+                    loss_info[k] = v * batch_size
+            # update generator
+            optimizer_g.zero_grad()
+            scaler.scale(loss_g).backward()
+            scaler.step(optimizer_g)
+            scaler.update()
+
+            # summary stats
+            tot_loss = tot_loss + loss_info
+        except:  # noqa
+            save_bad_model()
+            raise
+
+        if params.print_diagnostics and batch_idx == 5:
+            return
+
+        if params.batch_idx_train % 100 == 0 and params.use_fp16:
+            # If the grad scale was less than 1, try increasing it.    The _growth_interval
+            # of the grad scaler is configurable, but we can't configure it to have different
+            # behavior depending on the current grad scale.
+            cur_grad_scale = scaler._scale.item()
+
+            if cur_grad_scale < 8.0 or (
+                cur_grad_scale < 32.0 and params.batch_idx_train % 400 == 0
+            ):
+                scaler.update(cur_grad_scale * 2.0)
+            if cur_grad_scale < 0.01:
+                if not saved_bad_model:
+                    save_bad_model(suffix="-first-warning")
+                    saved_bad_model = True
+                logging.warning(f"Grad scale is small: {cur_grad_scale}")
+            if cur_grad_scale < 1.0e-05:
+                save_bad_model()
+                raise RuntimeError(
+                    f"grad_scale is too small, exiting: {cur_grad_scale}"
+                )
+
+        if params.batch_idx_train % params.log_interval == 0:
+            cur_lr_g = max(scheduler_g.get_last_lr())
+            cur_lr_d = max(scheduler_d.get_last_lr())
+            cur_grad_scale = scaler._scale.item() if params.use_fp16 else 1.0
+
+            logging.info(
+                f"Epoch {params.cur_epoch}, batch {batch_idx}, "
+                f"global_batch_idx: {params.batch_idx_train}, batch size: {batch_size}, "
+                f"loss[{loss_info}], tot_loss[{tot_loss}], "
+                f"cur_lr_g: {cur_lr_g:.2e}, cur_lr_d: {cur_lr_d:.2e}, "
+                + (f"grad_scale: {scaler._scale.item()}" if params.use_fp16 else "")
+            )
+
+            if tb_writer is not None:
+                tb_writer.add_scalar(
+                    "train/learning_rate_g", cur_lr_g, params.batch_idx_train
+                )
+                tb_writer.add_scalar(
+                    "train/learning_rate_d", cur_lr_d, params.batch_idx_train
+                )
+                loss_info.write_summary(
+                    tb_writer, "train/current_", params.batch_idx_train
+                )
+                tot_loss.write_summary(tb_writer, "train/tot_", params.batch_idx_train)
+                if params.use_fp16:
+                    tb_writer.add_scalar(
+                        "train/grad_scale", cur_grad_scale, params.batch_idx_train
+                    )
+                if "returned_sample" in stats_g:
+                    speech_hat_, speech_, mel_hat_, mel_ = stats_g["returned_sample"]
+                    tb_writer.add_audio(
+                        "train/speech_hat_",
+                        speech_hat_,
+                        params.batch_idx_train,
+                        params.sampling_rate,
+                    )
+                    tb_writer.add_audio(
+                        "train/speech_",
+                        speech_,
+                        params.batch_idx_train,
+                        params.sampling_rate,
+                    )
+                    tb_writer.add_image(
+                        "train/mel_hat_",
+                        plot_feature(mel_hat_),
+                        params.batch_idx_train,
+                        dataformats="HWC",
+                    )
+                    tb_writer.add_image(
+                        "train/mel_",
+                        plot_feature(mel_),
+                        params.batch_idx_train,
+                        dataformats="HWC",
+                    )
+
+        if (
+            params.batch_idx_train % params.valid_interval == 0
+            and not params.print_diagnostics
+        ):
+            logging.info("Computing validation loss")
+            valid_info, (speech_hat, speech) = compute_validation_loss(
+                params=params,
+                model=model,
+                tokenizer=tokenizer,
+                valid_dl=valid_dl,
+                speaker_map=speaker_map,
+                world_size=world_size,
+            )
+            model.train()
+            logging.info(f"Epoch {params.cur_epoch}, validation: {valid_info}")
+            logging.info(
+                f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB"
+            )
+            if tb_writer is not None:
+                valid_info.write_summary(
+                    tb_writer, "train/valid_", params.batch_idx_train
+                )
+                tb_writer.add_audio(
+                    "train/valdi_speech_hat",
+                    speech_hat,
+                    params.batch_idx_train,
+                    params.sampling_rate,
+                )
+                tb_writer.add_audio(
+                    "train/valdi_speech",
+                    speech,
+                    params.batch_idx_train,
+                    params.sampling_rate,
+                )
+
+    loss_value = tot_loss["generator_loss"] / tot_loss["samples"]
+    params.train_loss = loss_value
+    if params.train_loss < params.best_train_loss:
+        params.best_train_epoch = params.cur_epoch
+        params.best_train_loss = params.train_loss
+
+
+def compute_validation_loss(
+    params: AttributeDict,
+    model: Union[nn.Module, DDP],
+    tokenizer: Tokenizer,
+    valid_dl: torch.utils.data.DataLoader,
+    speaker_map: Dict[str, int],
+    world_size: int = 1,
+    rank: int = 0,
+) -> Tuple[MetricsTracker, Tuple[np.ndarray, np.ndarray]]:
+    """Run the validation process."""
+    model.eval()
+    device = model.device if isinstance(model, DDP) else next(model.parameters()).device
+
+    # used to summary the stats over iterations
+    tot_loss = MetricsTracker()
+    returned_sample = None
+
+    with torch.no_grad():
+        for batch_idx, batch in enumerate(valid_dl):
+            batch_size = len(batch["tokens"])
+            (
+                audio,
+                audio_lens,
+                features,
+                features_lens,
+                tokens,
+                tokens_lens,
+                speakers,
+            ) = prepare_input(batch, tokenizer, device, speaker_map)
+
+            loss_info = MetricsTracker()
+            loss_info["samples"] = batch_size
+
+            # forward discriminator
+            loss_d, stats_d = model(
+                text=tokens,
+                text_lengths=tokens_lens,
+                feats=features,
+                feats_lengths=features_lens,
+                speech=audio,
+                speech_lengths=audio_lens,
+                sids=speakers,
+                forward_generator=False,
+            )
+            assert loss_d.requires_grad is False
+            for k, v in stats_d.items():
+                loss_info[k] = v * batch_size
+
+            # forward generator
+            loss_g, stats_g = model(
+                text=tokens,
+                text_lengths=tokens_lens,
+                feats=features,
+                feats_lengths=features_lens,
+                speech=audio,
+                speech_lengths=audio_lens,
+                sids=speakers,
+                forward_generator=True,
+            )
+            assert loss_g.requires_grad is False
+            for k, v in stats_g.items():
+                loss_info[k] = v * batch_size
+
+            # summary stats
+            tot_loss = tot_loss + loss_info
+
+            # infer for first batch:
+            if batch_idx == 0 and rank == 0:
+                inner_model = model.module if isinstance(model, DDP) else model
+                audio_pred, _, duration = inner_model.inference(
+                    text=tokens[0, : tokens_lens[0].item()],
+                    sids=speakers[0],
+                )
+                audio_pred = audio_pred.data.cpu().numpy()
+                audio_len_pred = (
+                    (duration.sum(0) * params.frame_shift).to(dtype=torch.int64).item()
+                )
+                assert audio_len_pred == len(audio_pred), (
+                    audio_len_pred,
+                    len(audio_pred),
+                )
+                audio_gt = audio[0, : audio_lens[0].item()].data.cpu().numpy()
+                returned_sample = (audio_pred, audio_gt)
+
+    if world_size > 1:
+        tot_loss.reduce(device)
+
+    loss_value = tot_loss["generator_loss"] / tot_loss["samples"]
+    if loss_value < params.best_valid_loss:
+        params.best_valid_epoch = params.cur_epoch
+        params.best_valid_loss = loss_value
+
+    return tot_loss, returned_sample
+
+
+def scan_pessimistic_batches_for_oom(
+    model: Union[nn.Module, DDP],
+    train_dl: torch.utils.data.DataLoader,
+    tokenizer: Tokenizer,
+    optimizer_g: torch.optim.Optimizer,
+    optimizer_d: torch.optim.Optimizer,
+    speaker_map: Dict[str, int],
+    params: AttributeDict,
+):
+    from lhotse.dataset import find_pessimistic_batches
+
+    logging.info(
+        "Sanity check -- see if any of the batches in epoch 1 would cause OOM."
+    )
+    device = model.device if isinstance(model, DDP) else next(model.parameters()).device
+    batches, crit_values = find_pessimistic_batches(train_dl.sampler)
+    for criterion, cuts in batches.items():
+        batch = train_dl.dataset[cuts]
+        (
+            audio,
+            audio_lens,
+            features,
+            features_lens,
+            tokens,
+            tokens_lens,
+            speakers,
+        ) = prepare_input(batch, tokenizer, device, speaker_map)
+        try:
+            # for discriminator
+            with autocast(enabled=params.use_fp16):
+                loss_d, stats_d = model(
+                    text=tokens,
+                    text_lengths=tokens_lens,
+                    feats=features,
+                    feats_lengths=features_lens,
+                    speech=audio,
+                    speech_lengths=audio_lens,
+                    sids=speakers,
+                    forward_generator=False,
+                )
+            optimizer_d.zero_grad()
+            loss_d.backward()
+            # for generator
+            with autocast(enabled=params.use_fp16):
+                loss_g, stats_g = model(
+                    text=tokens,
+                    text_lengths=tokens_lens,
+                    feats=features,
+                    feats_lengths=features_lens,
+                    speech=audio,
+                    speech_lengths=audio_lens,
+                    sids=speakers,
+                    forward_generator=True,
+                )
+            optimizer_g.zero_grad()
+            loss_g.backward()
+        except Exception as e:
+            if "CUDA out of memory" in str(e):
+                logging.error(
+                    "Your GPU ran out of memory with the current "
+                    "max_duration setting. We recommend decreasing "
+                    "max_duration and trying again.\n"
+                    f"Failing criterion: {criterion} "
+                    f"(={crit_values[criterion]}) ..."
+                )
+            raise
+        logging.info(
+            f"Maximum memory allocated so far is {torch.cuda.max_memory_allocated()//1000000}MB"
+        )
+
+
+def run(rank, world_size, args):
+    """
+    Args:
+      rank:
+        It is a value between 0 and `world_size-1`, which is
+        passed automatically by `mp.spawn()` in :func:`main`.
+        The node with rank 0 is responsible for saving checkpoint.
+      world_size:
+        Number of GPUs for DDP training.
+      args:
+        The return value of get_parser().parse_args()
+    """
+    params = get_params()
+    params.update(vars(args))
+
+    fix_random_seed(params.seed)
+    if world_size > 1:
+        setup_dist(rank, world_size, params.master_port)
+
+    setup_logger(f"{params.exp_dir}/log/log-train")
+    logging.info("Training started")
+
+    if args.tensorboard and rank == 0:
+        tb_writer = SummaryWriter(log_dir=f"{params.exp_dir}/tensorboard")
+    else:
+        tb_writer = None
+
+    device = torch.device("cpu")
+    if torch.cuda.is_available():
+        device = torch.device("cuda", rank)
+    logging.info(f"Device: {device}")
+
+    tokenizer = Tokenizer(params.tokens)
+    params.blank_id = tokenizer.pad_id
+    params.vocab_size = tokenizer.vocab_size
+
+    libritts = LibrittsTtsDataModule(args)
+
+    train_cuts = libritts.train_cuts()
+    speaker_map = libritts.speakers()
+    params.num_spks = len(speaker_map)
+
+    logging.info(params)
+
+    logging.info("About to create model")
+    model = get_model(params)
+    generator = model.generator
+    discriminator = model.discriminator
+
+    num_param_g = sum([p.numel() for p in generator.parameters()])
+    logging.info(f"Number of parameters in generator: {num_param_g}")
+    num_param_d = sum([p.numel() for p in discriminator.parameters()])
+    logging.info(f"Number of parameters in discriminator: {num_param_d}")
+    logging.info(f"Total number of parameters: {num_param_g + num_param_d}")
+
+    assert params.start_epoch > 0, params.start_epoch
+    checkpoints = load_checkpoint_if_available(params=params, model=model)
+
+    model.to(device)
+    if world_size > 1:
+        logging.info("Using DDP")
+        model = DDP(model, device_ids=[rank], find_unused_parameters=True)
+
+    optimizer_g = torch.optim.AdamW(
+        generator.parameters(), lr=params.lr, betas=(0.8, 0.99), eps=1e-9
+    )
+    optimizer_d = torch.optim.AdamW(
+        discriminator.parameters(), lr=params.lr, betas=(0.8, 0.99), eps=1e-9
+    )
+
+    scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optimizer_g, gamma=0.999875)
+    scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optimizer_d, gamma=0.999875)
+
+    if checkpoints is not None:
+        # load state_dict for optimizers
+        if "optimizer_g" in checkpoints:
+            logging.info("Loading optimizer_g state dict")
+            optimizer_g.load_state_dict(checkpoints["optimizer_g"])
+        if "optimizer_d" in checkpoints:
+            logging.info("Loading optimizer_d state dict")
+            optimizer_d.load_state_dict(checkpoints["optimizer_d"])
+
+        # load state_dict for schedulers
+        if "scheduler_g" in checkpoints:
+            logging.info("Loading scheduler_g state dict")
+            scheduler_g.load_state_dict(checkpoints["scheduler_g"])
+        if "scheduler_d" in checkpoints:
+            logging.info("Loading scheduler_d state dict")
+            scheduler_d.load_state_dict(checkpoints["scheduler_d"])
+
+    if params.print_diagnostics:
+        opts = diagnostics.TensorDiagnosticOptions(
+            512
+        )  # allow 4 megabytes per sub-module
+        diagnostic = diagnostics.attach_diagnostics(model, opts)
+
+    if params.inf_check:
+        register_inf_check_hooks(model)
+
+    def remove_short_and_long_utt(c: Cut):
+        # Keep only utterances with duration between 1 second and 20 seconds
+        # You should use ../local/display_manifest_statistics.py to get
+        # an utterance duration distribution for your dataset to select
+        # the threshold
+        if c.duration < 1.0 or c.duration > 20.0:
+            # logging.warning(
+            #     f"Exclude cut with ID {c.id} from training. Duration: {c.duration}"
+            # )
+            return False
+        return True
+
+    train_cuts = train_cuts.filter(remove_short_and_long_utt)
+    train_dl = libritts.train_dataloaders(train_cuts)
+
+    valid_cuts = libritts.valid_cuts()
+    valid_dl = libritts.valid_dataloaders(valid_cuts)
+
+    if not params.print_diagnostics:
+        scan_pessimistic_batches_for_oom(
+            model=model,
+            train_dl=train_dl,
+            tokenizer=tokenizer,
+            optimizer_g=optimizer_g,
+            optimizer_d=optimizer_d,
+            speaker_map=speaker_map,
+            params=params,
+        )
+
+    scaler = GradScaler(enabled=params.use_fp16, init_scale=1.0)
+    if checkpoints and "grad_scaler" in checkpoints:
+        logging.info("Loading grad scaler state dict")
+        scaler.load_state_dict(checkpoints["grad_scaler"])
+
+    for epoch in range(params.start_epoch, params.num_epochs + 1):
+        logging.info(f"Start epoch {epoch}")
+
+        fix_random_seed(params.seed + epoch - 1)
+        train_dl.sampler.set_epoch(epoch - 1)
+
+        params.cur_epoch = epoch
+
+        if tb_writer is not None:
+            tb_writer.add_scalar("train/epoch", epoch, params.batch_idx_train)
+
+        train_one_epoch(
+            params=params,
+            model=model,
+            tokenizer=tokenizer,
+            optimizer_g=optimizer_g,
+            optimizer_d=optimizer_d,
+            scheduler_g=scheduler_g,
+            scheduler_d=scheduler_d,
+            train_dl=train_dl,
+            valid_dl=valid_dl,
+            speaker_map=speaker_map,
+            scaler=scaler,
+            tb_writer=tb_writer,
+            world_size=world_size,
+            rank=rank,
+        )
+
+        if params.print_diagnostics:
+            diagnostic.print_diagnostics()
+            break
+
+        if epoch % params.save_every_n == 0 or epoch == params.num_epochs:
+            filename = params.exp_dir / f"epoch-{params.cur_epoch}.pt"
+            save_checkpoint(
+                filename=filename,
+                params=params,
+                model=model,
+                optimizer_g=optimizer_g,
+                optimizer_d=optimizer_d,
+                scheduler_g=scheduler_g,
+                scheduler_d=scheduler_d,
+                sampler=train_dl.sampler,
+                scaler=scaler,
+                rank=rank,
+            )
+            if rank == 0:
+                if params.best_train_epoch == params.cur_epoch:
+                    best_train_filename = params.exp_dir / "best-train-loss.pt"
+                    copyfile(src=filename, dst=best_train_filename)
+
+                if params.best_valid_epoch == params.cur_epoch:
+                    best_valid_filename = params.exp_dir / "best-valid-loss.pt"
+                    copyfile(src=filename, dst=best_valid_filename)
+
+        # step per epoch
+        scheduler_g.step()
+        scheduler_d.step()
+
+    logging.info("Done!")
+
+    if world_size > 1:
+        torch.distributed.barrier()
+        cleanup_dist()
+
+
+def main():
+    parser = get_parser()
+    LibrittsTtsDataModule.add_arguments(parser)
+    args = parser.parse_args()
+    args.exp_dir = Path(args.exp_dir)
+
+    world_size = args.world_size
+    assert world_size >= 1
+    if world_size > 1:
+        mp.spawn(run, args=(world_size, args), nprocs=world_size, join=True)
+    else:
+        run(rank=0, world_size=1, args=args)
+
+
+torch.set_num_threads(1)
+torch.set_num_interop_threads(1)
+
+if __name__ == "__main__":
+    main()
diff --git a/egs/libritts/TTS/vits/transform.py b/egs/libritts/TTS/vits/transform.py
new file mode 120000
index 000000000..962647408
--- /dev/null
+++ b/egs/libritts/TTS/vits/transform.py
@@ -0,0 +1 @@
+../../../ljspeech/TTS/vits/transform.py
\ No newline at end of file
diff --git a/egs/libritts/TTS/vits/tts_datamodule.py b/egs/libritts/TTS/vits/tts_datamodule.py
new file mode 100644
index 000000000..05350603b
--- /dev/null
+++ b/egs/libritts/TTS/vits/tts_datamodule.py
@@ -0,0 +1,341 @@
+# Copyright      2021  Piotr Żelasko
+# Copyright      2022-2024  Xiaomi Corporation     (Authors: Mingshuang Luo,
+#                                                            Zengwei Yao,
+#                                                            Zengrui Jin,)
+#
+# See ../../../../LICENSE for clarification regarding multiple authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+
+import argparse
+import logging
+from functools import lru_cache
+from pathlib import Path
+from typing import Any, Dict, Optional
+
+import torch
+from lhotse import CutSet, Spectrogram, SpectrogramConfig, load_manifest_lazy
+from lhotse.dataset import (  # noqa F401 for PrecomputedFeatures
+    CutConcatenate,
+    CutMix,
+    DynamicBucketingSampler,
+    PrecomputedFeatures,
+    SimpleCutSampler,
+    SpeechSynthesisDataset,
+)
+from lhotse.dataset.input_strategies import (  # noqa F401 For AudioSamples
+    AudioSamples,
+    OnTheFlyFeatures,
+)
+from lhotse.utils import fix_random_seed
+from torch.utils.data import DataLoader
+
+from icefall.utils import str2bool
+
+
+class _SeedWorkers:
+    def __init__(self, seed: int):
+        self.seed = seed
+
+    def __call__(self, worker_id: int):
+        fix_random_seed(self.seed + worker_id)
+
+LIBRITTS_SAMPLING_RATE = 24000
+
+class LibrittsTtsDataModule:
+    """
+    DataModule for tts experiments.
+    It assumes there is always one train and valid dataloader,
+    but there can be multiple test dataloaders (e.g. LibriSpeech test-clean
+    and test-other).
+
+    It contains all the common data pipeline modules used in ASR
+    experiments, e.g.:
+    - dynamic batch size,
+    - bucketing samplers,
+    - cut concatenation,
+    - on-the-fly feature extraction
+
+    This class should be derived for specific corpora used in ASR tasks.
+    """
+
+    def __init__(self, args: argparse.Namespace):
+        self.args = args
+
+    @classmethod
+    def add_arguments(cls, parser: argparse.ArgumentParser):
+        group = parser.add_argument_group(
+            title="TTS data related options",
+            description="These options are used for the preparation of "
+            "PyTorch DataLoaders from Lhotse CutSet's -- they control the "
+            "effective batch sizes, sampling strategies, applied data "
+            "augmentations, etc.",
+        )
+
+        group.add_argument(
+            "--manifest-dir",
+            type=Path,
+            default=Path("data/spectrogram"),
+            help="Path to directory with train/valid/test cuts.",
+        )
+        group.add_argument(
+            "--speakers",
+            type=Path,
+            default=Path("data/speakers.txt"),
+            help="Path to speakers.txt file.",
+        )
+        group.add_argument(
+            "--max-duration",
+            type=int,
+            default=200.0,
+            help="Maximum pooled recordings duration (seconds) in a "
+            "single batch. You can reduce it if it causes CUDA OOM.",
+        )
+        group.add_argument(
+            "--bucketing-sampler",
+            type=str2bool,
+            default=True,
+            help="When enabled, the batches will come from buckets of "
+            "similar duration (saves padding frames).",
+        )
+        group.add_argument(
+            "--num-buckets",
+            type=int,
+            default=30,
+            help="The number of buckets for the DynamicBucketingSampler"
+            "(you might want to increase it for larger datasets).",
+        )
+
+        group.add_argument(
+            "--on-the-fly-feats",
+            type=str2bool,
+            default=False,
+            help="When enabled, use on-the-fly cut mixing and feature "
+            "extraction. Will drop existing precomputed feature manifests "
+            "if available.",
+        )
+        group.add_argument(
+            "--shuffle",
+            type=str2bool,
+            default=True,
+            help="When enabled (=default), the examples will be "
+            "shuffled for each epoch.",
+        )
+        group.add_argument(
+            "--drop-last",
+            type=str2bool,
+            default=True,
+            help="Whether to drop last batch. Used by sampler.",
+        )
+        group.add_argument(
+            "--return-cuts",
+            type=str2bool,
+            default=False,
+            help="When enabled, each batch will have the "
+            "field: batch['cut'] with the cuts that "
+            "were used to construct it.",
+        )
+        group.add_argument(
+            "--num-workers",
+            type=int,
+            default=8,
+            help="The number of training dataloader workers that "
+            "collect the batches.",
+        )
+
+        group.add_argument(
+            "--input-strategy",
+            type=str,
+            default="PrecomputedFeatures",
+            help="AudioSamples or PrecomputedFeatures",
+        )
+
+    def train_dataloaders(
+        self,
+        cuts_train: CutSet,
+        sampler_state_dict: Optional[Dict[str, Any]] = None,
+    ) -> DataLoader:
+        """
+        Args:
+          cuts_train:
+            CutSet for training.
+          sampler_state_dict:
+            The state dict for the training sampler.
+        """
+        logging.info("About to create train dataset")
+        train = SpeechSynthesisDataset(
+            return_text=False,
+            return_tokens=True,
+            return_spk_ids=True,
+            feature_input_strategy=eval(self.args.input_strategy)(),
+            return_cuts=self.args.return_cuts,
+        )
+
+        if self.args.on_the_fly_feats:
+            sampling_rate = LIBRITTS_SAMPLING_RATE
+            config = SpectrogramConfig(
+                sampling_rate=sampling_rate,
+                frame_length=1024 / sampling_rate,  # (in second),
+                frame_shift=256 / sampling_rate,  # (in second)
+                use_fft_mag=True,
+            )
+            train = SpeechSynthesisDataset(
+                return_text=False,
+                return_tokens=True,
+                return_spk_ids=True,
+                feature_input_strategy=OnTheFlyFeatures(Spectrogram(config)),
+                return_cuts=self.args.return_cuts,
+            )
+
+        if self.args.bucketing_sampler:
+            logging.info("Using DynamicBucketingSampler.")
+            train_sampler = DynamicBucketingSampler(
+                cuts_train,
+                max_duration=self.args.max_duration,
+                shuffle=self.args.shuffle,
+                num_buckets=self.args.num_buckets,
+                buffer_size=self.args.num_buckets * 2000,
+                shuffle_buffer_size=self.args.num_buckets * 5000,
+                drop_last=self.args.drop_last,
+            )
+        else:
+            logging.info("Using SimpleCutSampler.")
+            train_sampler = SimpleCutSampler(
+                cuts_train,
+                max_duration=self.args.max_duration,
+                shuffle=self.args.shuffle,
+            )
+        logging.info("About to create train dataloader")
+
+        if sampler_state_dict is not None:
+            logging.info("Loading sampler state dict")
+            train_sampler.load_state_dict(sampler_state_dict)
+
+        # 'seed' is derived from the current random state, which will have
+        # previously been set in the main process.
+        seed = torch.randint(0, 100000, ()).item()
+        worker_init_fn = _SeedWorkers(seed)
+
+        train_dl = DataLoader(
+            train,
+            sampler=train_sampler,
+            batch_size=None,
+            num_workers=self.args.num_workers,
+            persistent_workers=False,
+            worker_init_fn=worker_init_fn,
+        )
+
+        return train_dl
+
+    def valid_dataloaders(self, cuts_valid: CutSet) -> DataLoader:
+        logging.info("About to create dev dataset")
+        if self.args.on_the_fly_feats:
+            sampling_rate = LIBRITTS_SAMPLING_RATE
+            config = SpectrogramConfig(
+                sampling_rate=sampling_rate,
+                frame_length=1024 / sampling_rate,  # (in second),
+                frame_shift=256 / sampling_rate,  # (in second)
+                use_fft_mag=True,
+            )
+            validate = SpeechSynthesisDataset(
+                return_text=False,
+                return_tokens=True,
+                return_spk_ids=True,
+                feature_input_strategy=OnTheFlyFeatures(Spectrogram(config)),
+                return_cuts=self.args.return_cuts,
+            )
+        else:
+            validate = SpeechSynthesisDataset(
+                return_text=False,
+                return_tokens=True,
+                return_spk_ids=True,
+                feature_input_strategy=eval(self.args.input_strategy)(),
+                return_cuts=self.args.return_cuts,
+            )
+        valid_sampler = DynamicBucketingSampler(
+            cuts_valid,
+            max_duration=self.args.max_duration,
+            shuffle=False,
+        )
+        logging.info("About to create valid dataloader")
+        valid_dl = DataLoader(
+            validate,
+            sampler=valid_sampler,
+            batch_size=None,
+            num_workers=2,
+            persistent_workers=False,
+        )
+
+        return valid_dl
+
+    def test_dataloaders(self, cuts: CutSet) -> DataLoader:
+        logging.info("About to create test dataset")
+        if self.args.on_the_fly_feats:
+            sampling_rate = LIBRITTS_SAMPLING_RATE
+            config = SpectrogramConfig(
+                sampling_rate=sampling_rate,
+                frame_length=1024 / sampling_rate,  # (in second),
+                frame_shift=256 / sampling_rate,  # (in second)
+                use_fft_mag=True,
+            )
+            test = SpeechSynthesisDataset(
+                return_text=False,
+                return_tokens=True,
+                return_spk_ids=True,
+                feature_input_strategy=OnTheFlyFeatures(Spectrogram(config)),
+                return_cuts=self.args.return_cuts,
+            )
+        else:
+            test = SpeechSynthesisDataset(
+                return_text=False,
+                return_tokens=True,
+                return_spk_ids=True,
+                feature_input_strategy=eval(self.args.input_strategy)(),
+                return_cuts=self.args.return_cuts,
+            )
+        test_sampler = DynamicBucketingSampler(
+            cuts,
+            max_duration=self.args.max_duration,
+            shuffle=False,
+        )
+        logging.info("About to create test dataloader")
+        test_dl = DataLoader(
+            test,
+            batch_size=None,
+            sampler=test_sampler,
+            num_workers=self.args.num_workers,
+        )
+        return test_dl
+
+    @lru_cache()
+    def train_cuts(self) -> CutSet:
+        logging.info("About to get train cuts")
+        return load_manifest_lazy(self.args.manifest_dir / "vctk_cuts_train.jsonl.gz")
+
+    @lru_cache()
+    def valid_cuts(self) -> CutSet:
+        logging.info("About to get validation cuts")
+        return load_manifest_lazy(self.args.manifest_dir / "vctk_cuts_valid.jsonl.gz")
+
+    @lru_cache()
+    def test_cuts(self) -> CutSet:
+        logging.info("About to get test cuts")
+        return load_manifest_lazy(self.args.manifest_dir / "vctk_cuts_test.jsonl.gz")
+
+    @lru_cache()
+    def speakers(self) -> Dict[str, int]:
+        logging.info("About to get speakers")
+        with open(self.args.speakers) as f:
+            speakers = {line.strip(): i for i, line in enumerate(f)}
+        return speakers
diff --git a/egs/libritts/TTS/vits/utils.py b/egs/libritts/TTS/vits/utils.py
new file mode 120000
index 000000000..085e764b4
--- /dev/null
+++ b/egs/libritts/TTS/vits/utils.py
@@ -0,0 +1 @@
+../../../ljspeech/TTS/vits/utils.py
\ No newline at end of file
diff --git a/egs/libritts/TTS/vits/vits.py b/egs/libritts/TTS/vits/vits.py
new file mode 120000
index 000000000..1f58cf6fe
--- /dev/null
+++ b/egs/libritts/TTS/vits/vits.py
@@ -0,0 +1 @@
+../../../ljspeech/TTS/vits/vits.py
\ No newline at end of file
diff --git a/egs/libritts/TTS/vits/wavenet.py b/egs/libritts/TTS/vits/wavenet.py
new file mode 120000
index 000000000..28f0a78ee
--- /dev/null
+++ b/egs/libritts/TTS/vits/wavenet.py
@@ -0,0 +1 @@
+../../../ljspeech/TTS/vits/wavenet.py
\ No newline at end of file