diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..e8261d5d24601fe14df2264dd59dbb4b3e25dc58 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files
+*.wav filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..f135a20c3f6be97897079f02c5edfb36e0203fc2
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,219 @@
+# pretrained models
+models/**/*.pt
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[codz]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#   Usually these files are written by a python script from a template
+#   before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py.cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+# Pipfile.lock
+
+# UV
+#   Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+# uv.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+# poetry.lock
+# poetry.toml
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#   pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python.
+#   https://pdm-project.org/en/latest/usage/project/#working-with-version-control
+# pdm.lock
+# pdm.toml
+.pdm-python
+.pdm-build/
+
+# pixi
+#   Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control.
+# pixi.lock
+#   Pixi creates a virtual environment in the .pixi directory, just like venv module creates one
+#   in the .venv directory. It is recommended not to include this directory in version control.
+.pixi
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# Redis
+*.rdb
+*.aof
+*.pid
+
+# RabbitMQ
+mnesia/
+rabbitmq/
+rabbitmq-data/
+
+# ActiveMQ
+activemq-data/
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.envrc
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#   JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#   be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#   and can be added to the global gitignore or merged into this file.  For a more nuclear
+#   option (not recommended) you can uncomment the following to ignore the entire idea folder.
+# .idea/
+
+# Abstra
+#   Abstra is an AI-powered process automation framework.
+#   Ignore directories containing user credentials, local state, and settings.
+#   Learn more at https://abstra.io/docs
+.abstra/
+
+# Visual Studio Code
+#   Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore
+#   that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore
+#   and can be added to the global gitignore or merged into this file. However, if you prefer,
+#   you could uncomment the following to ignore the entire vscode folder
+# .vscode/
+
+# Ruff stuff:
+.ruff_cache/
+
+# PyPI configuration file
+.pypirc
+
+# Marimo
+marimo/_static/
+marimo/_lsp/
+__marimo__/
+
+# Streamlit
+.streamlit/secrets.toml
diff --git a/Makefile b/Makefile
new file mode 100644
index 0000000000000000000000000000000000000000..f9c8ec4150fbb8a487a0cc9a25aa361c5702609e
--- /dev/null
+++ b/Makefile
@@ -0,0 +1,5 @@
+run:
+	PYTHONPATH=. uv run gradio app.py
+
+export-requirements:
+	uv export --format requirements-txt > requirements.txt
diff --git a/README.md b/README.md
index ce1ce9636f8a68ee32d9ad22dc9480d0981b77f8..5da84506fb363fd7e95cef3fdb8d3c7f8cd44362 100644
--- a/README.md
+++ b/README.md
@@ -4,7 +4,9 @@ emoji: 🐨
 colorFrom: purple
 colorTo: pink
 sdk: gradio
+python_version: 3.13
 sdk_version: 5.49.1
+suggested_hardware: g4
 app_file: app.py
 pinned: false
 ---
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..8bd31edfcc1dcebc8b2588be76b1454d9ced5cda
--- /dev/null
+++ b/app.py
@@ -0,0 +1,18 @@
+import gradio as gr
+
+from apps.audio_cloning.main import main as audio_cloning
+from apps.dev.main import main as dev
+
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    audio_cloning()
+
+
+with demo.route(name="Dev", path="/dev"):
+    dev()
+
+
+if __name__ == "__main__":
+    # demo.queue(max_size=2, concurrency_limit=2, concurrency_id="gpu_queue")
+    # auth = ("charaxim", "chrmx-demo-wordpass")
+    # demo.launch(share=False, auth=auth)
+    demo.launch(share=False)
diff --git a/apps/audio_cloning/main.py b/apps/audio_cloning/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..f02dc72c09d77c480023a7bd1acc1a52707be60e
--- /dev/null
+++ b/apps/audio_cloning/main.py
@@ -0,0 +1,15 @@
+import gradio as gr
+
+from .vallex.main import main as vallex
+
+
+def main():
+    gr.Markdown("# Charamix Audio Cloning Prototype")
+
+    # zero-shot audio cloning
+    with gr.Tab("Zero-shot Audio Cloning with VALL-E-X"):
+        vallex()
+
+    # fine-tuning audio cloning
+    # with gr.Tab("Fine-tuning Audio Cloning"):
+    #     gr.Markdown("TODO")
diff --git a/apps/audio_cloning/vallex/data/__init__.py b/apps/audio_cloning/vallex/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..10b382e660f62e45dd9fc62de9e1503765da875d
--- /dev/null
+++ b/apps/audio_cloning/vallex/data/__init__.py
@@ -0,0 +1,3 @@
+# from .datamodule import *
+# from .tokenizer import *
+from .collation import *
diff --git a/apps/audio_cloning/vallex/data/collation.py b/apps/audio_cloning/vallex/data/collation.py
new file mode 100644
index 0000000000000000000000000000000000000000..a4f7d86e26299be5e0f2c8ebc87e3eda7eecf74a
--- /dev/null
+++ b/apps/audio_cloning/vallex/data/collation.py
@@ -0,0 +1,107 @@
+from typing import List, Tuple
+
+import numpy as np
+import torch
+
+
+class TextTokenCollater:
+    """Collate list of text tokens
+
+    Map sentences to integers. Sentences are padded to equal length.
+    Beginning and end-of-sequence symbols can be added.
+
+    Example:
+        >>> token_collater = TextTokenCollater(text_tokens)
+        >>> tokens_batch, tokens_lens = token_collater(text)
+
+    Returns:
+        tokens_batch: IntTensor of shape (B, L)
+            B: batch dimension, number of input sentences
+            L: length of the longest sentence
+        tokens_lens: IntTensor of shape (B,)
+            Length of each sentence after adding <eos> and <bos>
+            but before padding.
+    """
+
+    def __init__(
+        self,
+        text_tokens: List[str],
+        add_eos: bool = True,
+        add_bos: bool = True,
+        pad_symbol: str = "<pad>",
+        bos_symbol: str = "<bos>",
+        eos_symbol: str = "<eos>",
+    ):
+        self.pad_symbol = pad_symbol
+
+        self.add_eos = add_eos
+        self.add_bos = add_bos
+
+        self.bos_symbol = bos_symbol
+        self.eos_symbol = eos_symbol
+
+        unique_tokens = (
+            [pad_symbol]
+            + ([bos_symbol] if add_bos else [])
+            + ([eos_symbol] if add_eos else [])
+            + sorted(text_tokens)
+        )
+
+        self.token2idx = {token: idx for idx, token in enumerate(unique_tokens)}
+        self.idx2token = [token for token in unique_tokens]
+
+    def index(self, tokens_list: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:
+        seqs, seq_lens = [], []
+        for tokens in tokens_list:
+            assert all([True if s in self.token2idx else False for s in tokens]) is True
+            seq = (
+                ([self.bos_symbol] if self.add_bos else [])
+                + list(tokens)
+                + ([self.eos_symbol] if self.add_eos else [])
+            )
+            seqs.append(seq)
+            seq_lens.append(len(seq))
+
+        max_len = max(seq_lens)
+        for k, (seq, seq_len) in enumerate(zip(seqs, seq_lens)):
+            seq.extend([self.pad_symbol] * (max_len - seq_len))
+
+        tokens = torch.from_numpy(
+            np.array(
+                [[self.token2idx[token] for token in seq] for seq in seqs],
+                dtype=np.int64,
+            )
+        )
+        tokens_lens = torch.IntTensor(seq_lens)
+
+        return tokens, tokens_lens
+
+    def __call__(self, texts: List[str]) -> Tuple[torch.Tensor, torch.Tensor]:
+        tokens_seqs = [[p for p in text] for text in texts]
+        max_len = len(max(tokens_seqs, key=len))
+
+        seqs = [
+            ([self.bos_symbol] if self.add_bos else [])
+            + list(seq)
+            + ([self.eos_symbol] if self.add_eos else [])
+            + [self.pad_symbol] * (max_len - len(seq))
+            for seq in tokens_seqs
+        ]
+
+        tokens_batch = torch.from_numpy(
+            np.array(
+                [seq for seq in seqs],
+                dtype=np.int64,
+            )
+        )
+
+        tokens_lens = torch.IntTensor(
+            [len(seq) + int(self.add_eos) + int(self.add_bos) for seq in tokens_seqs]
+        )
+
+        return tokens_batch, tokens_lens
+
+
+def get_text_token_collater() -> TextTokenCollater:
+    collater = TextTokenCollater(["0"], add_bos=False, add_eos=False)
+    return collater
diff --git a/apps/audio_cloning/vallex/data/datamodule.py b/apps/audio_cloning/vallex/data/datamodule.py
new file mode 100644
index 0000000000000000000000000000000000000000..9df73ad2ce44d166461909ca5096f3101cbd8b37
--- /dev/null
+++ b/apps/audio_cloning/vallex/data/datamodule.py
@@ -0,0 +1,419 @@
+# Copyright      2023                          (authors: Feiteng Li)
+#
+# 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 inspect
+import logging
+from functools import lru_cache
+from pathlib import Path
+from typing import Any, Dict, Optional
+
+import torch
+# from icefall.utils import str2bool
+# from lhotse import CutSet, load_manifest_lazy
+# from lhotse.dataset import (
+#     CutConcatenate,
+#     DynamicBucketingSampler,
+#     PrecomputedFeatures,
+#     SingleCutSampler,
+#     SpecAugment,
+# )
+# from lhotse.dataset.input_strategies import OnTheFlyFeatures
+# from lhotse.utils import fix_random_seed
+from torch.utils.data import DataLoader
+
+from data.collation import get_text_token_collater
+# from data.dataset import SpeechSynthesisDataset
+from data.fbank import get_fbank_extractor
+from data.input_strategies import PromptedPrecomputedFeatures
+
+# PrecomputedFeatures = PrecomputedFeatures
+
+
+class _SeedWorkers:
+    def __init__(self, seed: int):
+        self.seed = seed
+
+    def __call__(self, worker_id: int):
+        fix_random_seed(self.seed + worker_id)
+
+
+def _get_input_strategy(input_strategy, dataset, cuts):
+    if input_strategy == "PromptedPrecomputedFeatures":
+        return PromptedPrecomputedFeatures(dataset, cuts)
+
+    return eval(input_strategy)()
+
+
+class TtsDataModule:
+    """
+    DataModule for VALL-E TTS experiments.
+    It assumes there is always one train and valid dataloader.
+
+    It contains all the common data pipeline modules used in TTS
+    experiments, e.g.:
+    - dynamic batch size,
+    - bucketing samplers,
+    - cut concatenation[not used & tested yet],
+    - augmentation[not used & tested yet],
+    - on-the-fly feature extraction[not used & tested yet]
+
+    This class should be derived for specific corpora used in TTS 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/tokenized"),
+            help="Path to directory with train/valid/test cuts.",
+        )
+        group.add_argument(
+            "--max-duration",
+            type=int,
+            default=40.0,
+            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=10,
+            help="The number of buckets for the DynamicBucketingSampler"
+            "(you might want to increase it for larger datasets).",
+        )
+        group.add_argument(
+            "--concatenate-cuts",
+            type=str2bool,
+            default=False,
+            help="When enabled, utterances (cuts) will be concatenated "
+            "to minimize the amount of padding.",
+        )
+        group.add_argument(
+            "--duration-factor",
+            type=float,
+            default=1.0,
+            help="Determines the maximum duration of a concatenated cut "
+            "relative to the duration of the longest cut in a batch.",
+        )
+        group.add_argument(
+            "--gap",
+            type=float,
+            default=0.1,
+            help="The amount of padding (in seconds) inserted between "
+            "concatenated cuts. This padding is filled with noise when "
+            "noise augmentation is used.",
+        )
+        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=False,
+            help="Whether to drop last batch. Used by sampler.",
+        )
+        group.add_argument(
+            "--return-cuts",
+            type=str2bool,
+            default=True,
+            help="When enabled, each batch will have the "
+            "field: batch['supervisions']['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(
+            "--enable-spec-aug",
+            type=str2bool,
+            default=False,
+            help="When enabled, use SpecAugment for training dataset.",
+        )
+
+        group.add_argument(
+            "--spec-aug-time-warp-factor",
+            type=int,
+            default=80,
+            help="Used only when --enable-spec-aug is True. "
+            "It specifies the factor for time warping in SpecAugment. "
+            "Larger values mean more warping. "
+            "A value less than 1 means to disable time warp.",
+        )
+
+        group.add_argument(
+            "--input-strategy",
+            type=str,
+            default="PrecomputedFeatures",
+            help="AudioSamples or PrecomputedFeatures or PromptedPrecomputedFeatures",
+        )
+
+        group.add_argument(
+            "--dataset",
+            type=str,
+            default="ljspeech",
+            help="--input-strategy PromptedPrecomputedFeatures needs dataset name to prepare prompts.",
+        )
+
+        parser.add_argument(
+            "--text-tokens",
+            type=str,
+            default="data/tokenized/unique_text_tokens.k2symbols",
+            help="Path to the unique text tokens file",
+        )
+
+        parser.add_argument(
+            "--sampling-rate",
+            type=int,
+            default=24000,
+            help="""Audio sampling rate.""",
+        )
+
+    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.
+        """
+        transforms = []
+
+        if self.args.concatenate_cuts:
+            logging.info(
+                f"Using cut concatenation with duration factor "
+                f"{self.args.duration_factor} and gap {self.args.gap}."
+            )
+            # Cut concatenation should be the first transform in the list,
+            # so that if we e.g. mix noise in, it will fill the gaps between
+            # different utterances.
+            transforms = [
+                CutConcatenate(
+                    duration_factor=self.args.duration_factor, gap=self.args.gap
+                )
+            ] + transforms
+
+        input_transforms = []
+        if self.args.enable_spec_aug:
+            logging.info("Enable SpecAugment")
+            logging.info(
+                f"Time warp factor: {self.args.spec_aug_time_warp_factor}"
+            )
+            # Set the value of num_frame_masks according to Lhotse's version.
+            # In different Lhotse's versions, the default of num_frame_masks is
+            # different.
+            num_frame_masks = 10
+            num_frame_masks_parameter = inspect.signature(
+                SpecAugment.__init__
+            ).parameters["num_frame_masks"]
+            if num_frame_masks_parameter.default == 1:
+                num_frame_masks = 2
+            logging.info(f"Num frame mask: {num_frame_masks}")
+            input_transforms.append(
+                SpecAugment(
+                    time_warp_factor=self.args.spec_aug_time_warp_factor,
+                    num_frame_masks=num_frame_masks,
+                    features_mask_size=27,
+                    num_feature_masks=2,
+                    frames_mask_size=100,
+                )
+            )
+        else:
+            logging.info("Disable SpecAugment")
+
+        logging.info("About to create train dataset")
+        if self.args.on_the_fly_feats:
+            # NOTE: the PerturbSpeed transform should be added only if we
+            # remove it from data prep stage.
+            # Add on-the-fly speed perturbation; since originally it would
+            # have increased epoch size by 3, we will apply prob 2/3 and use
+            # 3x more epochs.
+            # Speed perturbation probably should come first before
+            # concatenation, but in principle the transforms order doesn't have
+            # to be strict (e.g. could be randomized)
+            # transforms = [PerturbSpeed(factors=[0.9, 1.1], p=2/3)] + transforms   # noqa
+            # Drop feats to be on the safe side.
+            train = SpeechSynthesisDataset(
+                get_text_token_collater(self.args.text_tokens),
+                cut_transforms=transforms,
+                feature_input_strategy=OnTheFlyFeatures(get_fbank_extractor()),
+                feature_transforms=input_transforms,
+            )
+        else:
+            train = SpeechSynthesisDataset(
+                get_text_token_collater(self.args.text_tokens),
+                feature_input_strategy=_get_input_strategy(
+                    self.args.input_strategy, self.args.dataset, cuts_train
+                ),
+                cut_transforms=transforms,
+                feature_transforms=input_transforms,
+            )
+
+        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,
+                drop_last=self.args.drop_last,
+            )
+        else:
+            logging.info(
+                "Using SingleCutSampler and sort by duraton(ascending=True)."
+            )
+            cuts_train = cuts_train.to_eager().sort_by_duration(ascending=True)
+            train_sampler = SingleCutSampler(
+                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:
+            validate = SpeechSynthesisDataset(
+                get_text_token_collater(self.args.text_tokens),
+                feature_input_strategy=OnTheFlyFeatures(get_fbank_extractor()),
+                cut_transforms=[],
+            )
+        else:
+            validate = SpeechSynthesisDataset(
+                get_text_token_collater(self.args.text_tokens),
+                feature_input_strategy=_get_input_strategy(
+                    self.args.input_strategy, self.args.dataset, cuts_valid
+                ),
+                cut_transforms=[],
+            )
+        valid_sampler = DynamicBucketingSampler(
+            cuts_valid,
+            max_duration=self.args.max_duration,
+            shuffle=False,
+        )
+        logging.info("About to create dev dataloader")
+        valid_dl = DataLoader(
+            validate,
+            sampler=valid_sampler,
+            batch_size=None,
+            num_workers=4,
+            persistent_workers=False,
+        )
+
+        return valid_dl
+
+    def test_dataloaders(self, cuts: CutSet) -> DataLoader:
+        logging.debug("About to create test dataset")
+        test = SpeechSynthesisDataset(
+            get_text_token_collater(self.args.text_tokens),
+            feature_input_strategy=OnTheFlyFeatures(get_fbank_extractor())
+            if self.args.on_the_fly_feats
+            else _get_input_strategy(
+                self.args.input_strategy, self.args.dataset, cuts
+            ),
+            cut_transforms=[],
+        )
+        sampler = DynamicBucketingSampler(
+            cuts,
+            max_duration=self.args.max_duration,
+            shuffle=False,
+        )
+        logging.debug("About to create test dataloader")
+        test_dl = DataLoader(
+            test,
+            batch_size=None,
+            sampler=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 / "cuts_train.jsonl.gz"
+        )
+
+    @lru_cache()
+    def dev_cuts(self) -> CutSet:
+        logging.info("About to get dev cuts")
+        return load_manifest_lazy(self.args.manifest_dir / "cuts_dev.jsonl.gz")
+
+    @lru_cache()
+    def test_cuts(self) -> CutSet:
+        logging.info("About to get test cuts")
+        return load_manifest_lazy(self.args.manifest_dir / "cuts_test.jsonl.gz")
diff --git a/apps/audio_cloning/vallex/data/dataset.py b/apps/audio_cloning/vallex/data/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..196ba1d7759da16b854b2be3a90872d11e93ee59
--- /dev/null
+++ b/apps/audio_cloning/vallex/data/dataset.py
@@ -0,0 +1,242 @@
+# Copyright      2023                           (authors: Feiteng Li)
+#
+# 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.
+
+"""
+modified from lhoste.dataset.speech_synthesis.py
+"""
+
+import torch
+import math
+import h5py
+from tokenizers import Tokenizer
+from typing import Union, List
+import numpy as np
+from tqdm import tqdm
+
+_pad        = '_'
+_punctuation = ',.!?-~…'
+_letters = 'NQabdefghijklmnopstuvwxyzɑæʃʑçɯɪɔɛɹðəɫɥɸʊɾʒθβŋɦ⁼ʰ`^#*=ˈˌ→↓↑ '
+symbols = [_pad] + list(_punctuation) + list(_letters)
+
+language_dict = {
+    'en': 0,
+    'zh': 1,
+    'ja': 2,
+}
+def seq2phone(tokens: Union[List, np.ndarray]):
+    """
+    Convert tokenized phoneme ID sequence back to phoneme string
+    :param tokens: phoneme tokens
+    :return: recovered phoneme sequence
+    """
+    phones = "".join([symbols[i] for i in tokens])
+    return phones
+
+class DynamicBatchSampler(torch.utils.data.Sampler):
+    def __init__(self, sampler, num_tokens_fn, num_buckets=100, min_size=0, max_size=1000,
+                 max_tokens=None, max_sentences=None, drop_last=False):
+        """
+
+        :param sampler:
+        :param num_tokens_fn: 根据idx返回样本的长度的函数
+        :param num_buckets: 利用桶原理将相似长度的样本放在一个batchsize中，桶的数量
+        :param min_size: 最小长度的样本， 小于这个值的样本会被过滤掉。 依据这个值来创建样桶
+        :param max_size: 最大长度的样本
+        :param max_sentences: batch_size, 但是这里可以通过max_sentences 和 max_tokens 共同控制最终的大小
+        """
+        super(DynamicBatchSampler, self).__init__(sampler)
+        self.sampler = sampler
+        self.num_tokens_fn = num_tokens_fn
+        self.num_buckets = num_buckets
+
+        self.min_size = min_size
+        self.max_size = max_size
+
+        assert max_size <= max_tokens, "max_size should be smaller than max tokens"
+        assert max_tokens is not None or max_sentences is not None, \
+            "max_tokens and max_sentences should not be null at the same time, please specify one parameter at least"
+        self.max_tokens = max_tokens if max_tokens is not None else float('Inf')
+        self.max_sentences = max_sentences if max_sentences is not None else float('Inf')
+        self.drop_last = drop_last
+
+    def set_epoch(self, epoch):
+        self.sampler.set_epoch(epoch)
+    def is_batch_full(self, num_tokens, batch):
+        if len(batch) == 0:
+            return False
+        if len(batch) == self.max_sentences:
+            return True
+        if num_tokens > self.max_tokens:
+            return True
+        return False
+
+    def __iter__(self):
+        buckets = [[] for _ in range(self.num_buckets)]
+        sample_len = [0] * self.num_buckets
+
+        for idx in self.sampler:
+            idx_length = self.num_tokens_fn(idx)
+            if not (self.min_size <= idx_length <= self.max_size):
+                print("sentence at index {} of size {} exceeds max_tokens, the sentence is ignored".format(idx, idx_length))
+                continue
+
+            index_buckets = math.floor((idx_length - self.min_size) / (self.max_size - self.min_size + 1)
+                                       * self.num_buckets)
+            sample_len[index_buckets] = max(sample_len[index_buckets], idx_length)
+
+            num_tokens = (len(buckets[index_buckets]) + 1) * sample_len[index_buckets]
+            if self.is_batch_full(num_tokens, buckets[index_buckets]):
+                # yield this batch
+                yield buckets[index_buckets]
+                buckets[index_buckets] = []
+                sample_len[index_buckets] = 0
+
+            buckets[index_buckets].append(idx)
+
+        # process left-over
+        leftover_batch = []
+        leftover_sample_len = 0
+        leftover = [idx for bucket in buckets for idx in bucket]
+        for idx in leftover:
+            idx_length = self.num_tokens_fn(idx)
+            leftover_sample_len = max(leftover_sample_len, idx_length)
+            num_tokens = (len(leftover_batch) + 1) * leftover_sample_len
+            if self.is_batch_full(num_tokens, leftover_batch):
+                yield leftover_batch
+                leftover_batch = []
+                leftover_sample_len = 0
+            leftover_batch.append(idx)
+
+        if len(leftover_batch) > 0 and not self.drop_last:
+            yield leftover_batch
+
+    def __len__(self):
+        # we do not know the exactly batch size, so do not call len(dataloader)
+        pass
+
+
+class AudioDataset(torch.utils.data.Dataset):
+    def __init__(self, h5_path, ann_path, tokenizer_path):
+        self.h5_path = h5_path
+        with open(ann_path, 'r', encoding='utf-8') as f:
+            lines = f.readlines()
+        ls = [l.split("|") for l in lines]
+        ls_T = list(zip(*ls))
+        del ls_T[-1]
+        self.h5_paths, self.durations, self.langs, self.texts = \
+            list(ls_T[0]), list(ls_T[1]), list(ls_T[2]), list(ls_T[3])
+        self.durations = [float(dur) for dur in self.durations]
+        self.tokenizer = Tokenizer.from_file(tokenizer_path)
+
+        self._archive = None
+
+    def __len__(self):
+        return len(self.h5_paths)
+
+    def get_dur(self, idx):
+        return self.durations[idx]
+
+    @property
+    def archive(self):
+        if self._archive is None:  # lazy loading here!
+            self._archive = h5py.File(self.h5_path, "r")
+        return self._archive
+    def __getitem__(self, idx):
+        archive = self.archive
+        h5_path = self.h5_paths[idx]
+        sub = archive[h5_path]
+        audio_tokens = sub['audio'][()]
+        phone_tokens = sub['text'][()]
+        dur = self.durations[idx]
+        lang = self.langs[idx]
+        text = self.texts[idx]
+        # tokenization should be done within dataloader
+        phones = seq2phone(phone_tokens)
+        phones = phones.replace(" ", "_")
+        if not len(phones):
+            cptpho_tokens = self.tokenizer.encode(text).ids
+        else:
+            cptpho_tokens = self.tokenizer.encode(phones).ids
+        assert len(cptpho_tokens)
+        return {
+            'utt_id': h5_path,
+            'text': text,
+            'audio': None,
+            'audio_lens': None,
+            'audio_features': audio_tokens,
+            'audio_features_lens': len(audio_tokens.T),
+            'text_tokens': np.array(cptpho_tokens),
+            'text_tokens_lens': len(cptpho_tokens),
+            'language': language_dict[lang],
+        }
+
+def collate(batch):
+    utt_id_s = [b['utt_id'] for b in batch]
+    text_s = [b['text'] for b in batch]
+
+    audio_s = [b['audio'] for b in batch]
+    audio_lens_s = [b['audio_lens'] for b in batch]
+
+    audio_features_lens_s = [b['audio_features_lens'] for b in batch]
+    # create an empty tensor with maximum audio feature length
+    audio_features_s = torch.zeros([len(batch), max(audio_features_lens_s), 8], dtype=torch.int64) - 1 # audio pad with -1
+
+    text_tokens_lens_s = [b['text_tokens_lens'] for b in batch]
+    # create an empty tensor with maximum text tokens length
+    text_tokens_s = torch.zeros([len(batch), max(text_tokens_lens_s)], dtype=torch.int64) + 3 # [PAD] token id 3
+
+    language_s = [b['language'] for b in batch]
+
+    for i, b in enumerate(batch):
+        audio_features = b['audio_features']
+        audio_features_lens = b['audio_features_lens']
+        audio_features_s[i, :audio_features_lens, :] = torch.LongTensor(audio_features.T)
+
+        text_tokens = b['text_tokens']
+        text_tokens_lens = b['text_tokens_lens']
+        text_tokens_s[i, :text_tokens_lens] = torch.LongTensor(text_tokens)
+
+    batch = {
+        'utt_id': utt_id_s,
+        'text': text_s,
+        'audio': audio_s,
+        'audio_lens': audio_lens_s,
+        'audio_features': audio_features_s,
+        'audio_features_lens': torch.LongTensor(np.array(audio_features_lens_s)),
+        'text_tokens': text_tokens_s,
+        'text_tokens_lens': torch.LongTensor(np.array(text_tokens_lens_s)),
+        'languages': torch.LongTensor(np.array(language_s)),
+    }
+    return batch
+
+def create_dataloader(data_dir="/root/valle/egs/mix", n_gpus=1, rank=0, num_workers=0, num_buckets=10, max_duration=120):
+    train_dataset = AudioDataset(h5_path=f"{data_dir}/audio_sum.hdf5",
+                                 ann_path=f"{data_dir}/audio_ann_sum.txt",
+                                 tokenizer_path=f"{data_dir}/bpe_69.json")
+    ran_sampler = torch.utils.data.distributed.DistributedSampler(
+            train_dataset,
+            num_replicas=n_gpus,
+            rank=rank,
+            shuffle=True,
+        )
+    dynamic_sampler = DynamicBatchSampler(ran_sampler, train_dataset.get_dur, num_buckets=num_buckets, max_size=20,
+                                          max_tokens=max_duration)
+
+
+    train_loader = torch.utils.data.DataLoader(train_dataset, num_workers=num_workers, collate_fn=collate,
+                                               batch_sampler=dynamic_sampler)
+
+    return train_loader
diff --git a/apps/audio_cloning/vallex/data/fbank.py b/apps/audio_cloning/vallex/data/fbank.py
new file mode 100644
index 0000000000000000000000000000000000000000..4c302baa321cdcb9c345eb3a9aaa0868a4ff13c7
--- /dev/null
+++ b/apps/audio_cloning/vallex/data/fbank.py
@@ -0,0 +1,212 @@
+# Copyright      2023                          (authors: Feiteng Li)
+#
+# 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.
+
+
+from dataclasses import asdict, dataclass
+from typing import Any, Dict, Optional, Union
+
+import numpy as np
+import torch
+# from lhotse.features.base import FeatureExtractor
+# from lhotse.utils import EPSILON, Seconds, compute_num_frames
+from librosa.filters import mel as librosa_mel_fn
+
+
+@dataclass
+class BigVGANFbankConfig:
+    # Spectogram-related part
+    # Note that frame_length and frame_shift will be converted to milliseconds before torchaudio/Kaldi sees them
+    frame_length: Seconds = 1024 / 24000.0
+    frame_shift: Seconds = 256 / 24000.0
+    remove_dc_offset: bool = True
+    round_to_power_of_two: bool = True
+
+    # Fbank-related part
+    low_freq: float = 0.0
+    high_freq: float = 12000.0
+    num_mel_bins: int = 100
+    use_energy: bool = False
+
+    def to_dict(self) -> Dict[str, Any]:
+        return asdict(self)
+
+    @staticmethod
+    def from_dict(data: Dict[str, Any]) -> "BigVGANFbankConfig":
+        return BigVGANFbankConfig(**data)
+
+
+def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
+    return torch.log(torch.clamp(x, min=clip_val) * C)
+
+
+def spectral_normalize_torch(magnitudes):
+    output = dynamic_range_compression_torch(magnitudes)
+    return output
+
+
+# https://github.com/NVIDIA/BigVGAN
+# bigvgan_24khz_100band https://drive.google.com/drive/folders/1EpxX6AsxjCbbk0mmAhE0td6eYiABr8Oz
+class BigVGANFbank(FeatureExtractor):
+    name = "fbank"
+    config_type = BigVGANFbankConfig
+
+    def __init__(self, config: Optional[Any] = None):
+        super(BigVGANFbank, self).__init__(config)
+        sampling_rate = 24000
+        self.mel_basis = torch.from_numpy(
+            librosa_mel_fn(
+                sampling_rate,
+                1024,
+                self.config.num_mel_bins,
+                self.config.low_freq,
+                self.config.high_freq,
+            ).astype(np.float32)
+        )
+        self.hann_window = torch.hann_window(1024)
+
+    def _feature_fn(self, samples, **kwargs):
+        win_length, n_fft = 1024, 1024
+        hop_size = 256
+        if True:
+            sampling_rate = 24000
+            duration = round(samples.shape[-1] / sampling_rate, ndigits=12)
+            expected_num_frames = compute_num_frames(
+                duration=duration,
+                frame_shift=self.frame_shift,
+                sampling_rate=sampling_rate,
+            )
+            pad_size = (
+                (expected_num_frames - 1) * hop_size
+                + win_length
+                - samples.shape[-1]
+            )
+            assert pad_size >= 0
+
+            y = torch.nn.functional.pad(
+                samples,
+                (0, pad_size),
+                mode="constant",
+            )
+        else:
+            y = torch.nn.functional.pad(
+                samples,
+                (int((n_fft - hop_size) / 2), int((n_fft - hop_size) / 2)),
+                mode="reflect",
+            )
+
+        y = y.squeeze(1)
+
+        # complex tensor as default, then use view_as_real for future pytorch compatibility
+        spec = torch.stft(
+            y,
+            n_fft,
+            hop_length=hop_size,
+            win_length=win_length,
+            window=self.hann_window,
+            center=False,
+            pad_mode="reflect",
+            normalized=False,
+            onesided=True,
+            return_complex=True,
+        )
+        spec = torch.view_as_real(spec)
+        spec = torch.sqrt(spec.pow(2).sum(-1) + (1e-9))
+
+        spec = torch.matmul(self.mel_basis, spec)
+        spec = spectral_normalize_torch(spec)
+
+        return spec.transpose(2, 1).squeeze(0)
+
+    def extract(
+        self, samples: Union[np.ndarray, torch.Tensor], sampling_rate: int
+    ) -> np.ndarray:
+        assert sampling_rate == 24000
+        params = asdict(self.config)
+        params.update({"sample_frequency": sampling_rate, "snip_edges": False})
+        params["frame_shift"] *= 1000.0
+        params["frame_length"] *= 1000.0
+        if not isinstance(samples, torch.Tensor):
+            samples = torch.from_numpy(samples)
+        # Torchaudio Kaldi feature extractors expect the channel dimension to be first.
+        if len(samples.shape) == 1:
+            samples = samples.unsqueeze(0)
+        features = self._feature_fn(samples, **params).to(torch.float32)
+        return features.numpy()
+
+    @property
+    def frame_shift(self) -> Seconds:
+        return self.config.frame_shift
+
+    def feature_dim(self, sampling_rate: int) -> int:
+        return self.config.num_mel_bins
+
+    @staticmethod
+    def mix(
+        features_a: np.ndarray,
+        features_b: np.ndarray,
+        energy_scaling_factor_b: float,
+    ) -> np.ndarray:
+        return np.log(
+            np.maximum(
+                # protection against log(0); max with EPSILON is adequate since these are energies (always >= 0)
+                EPSILON,
+                np.exp(features_a)
+                + energy_scaling_factor_b * np.exp(features_b),
+            )
+        )
+
+    @staticmethod
+    def compute_energy(features: np.ndarray) -> float:
+        return float(np.sum(np.exp(features)))
+
+
+def get_fbank_extractor() -> BigVGANFbank:
+    return BigVGANFbank(BigVGANFbankConfig())
+
+
+if __name__ == "__main__":
+    extractor = BigVGANFbank(BigVGANFbankConfig())
+
+    samples = torch.from_numpy(np.random.random([1000]).astype(np.float32))
+    samples = torch.clip(samples, -1.0, 1.0)
+    fbank = extractor.extract(samples, 24000.0)
+    print(f"fbank {fbank.shape}")
+
+    from scipy.io.wavfile import read
+
+    MAX_WAV_VALUE = 32768.0
+
+    sampling_rate, samples = read(
+        "egs/libritts/prompts/5639_40744_000000_000002.wav"
+    )
+    print(f"samples: [{samples.min()}, {samples.max()}]")
+    fbank = extractor.extract(samples.astype(np.float32) / MAX_WAV_VALUE, 24000)
+    print(f"fbank {fbank.shape}")
+
+    import matplotlib.pyplot as plt
+
+    _ = plt.figure(figsize=(18, 10))
+    plt.imshow(
+        X=fbank.transpose(1, 0),
+        cmap=plt.get_cmap("jet"),
+        aspect="auto",
+        interpolation="nearest",
+    )
+    plt.gca().invert_yaxis()
+    plt.savefig("egs/libritts/prompts/5639_40744_000000_000002.png")
+    plt.close()
+
+    print("fbank test PASS!")
diff --git a/apps/audio_cloning/vallex/data/input_strategies.py b/apps/audio_cloning/vallex/data/input_strategies.py
new file mode 100644
index 0000000000000000000000000000000000000000..0b576e1999f631676776fd15bbf902a47266b85f
--- /dev/null
+++ b/apps/audio_cloning/vallex/data/input_strategies.py
@@ -0,0 +1,159 @@
+import random
+from collections import defaultdict
+from concurrent.futures import ThreadPoolExecutor
+from typing import Tuple, Type
+
+# from lhotse import CutSet
+# from lhotse.dataset.collation import collate_features
+# from lhotse.dataset.input_strategies import (
+#     ExecutorType,
+#     PrecomputedFeatures,
+#     _get_executor,
+# )
+# from lhotse.utils import fastcopy
+
+
+class PromptedFeatures:
+    def __init__(self, prompts, features):
+        self.prompts = prompts
+        self.features = features
+
+    def to(self, device):
+        return PromptedFeatures(
+            self.prompts.to(device), self.features.to(device)
+        )
+
+    def sum(self):
+        return self.features.sum()
+
+    @property
+    def ndim(self):
+        return self.features.ndim
+
+    @property
+    def data(self):
+        return (self.prompts, self.features)
+
+
+# class PromptedPrecomputedFeatures(PrecomputedFeatures):
+#     """
+#     :class:`InputStrategy` that reads pre-computed features, whose manifests
+#     are attached to cuts, from disk.
+#
+#     It automatically pads the feature matrices with pre or post feature.
+#
+#     .. automethod:: __call__
+#     """
+#
+#     def __init__(
+#         self,
+#         dataset: str,
+#         cuts: CutSet,
+#         num_workers: int = 0,
+#         executor_type: Type[ExecutorType] = ThreadPoolExecutor,
+#     ) -> None:
+#         super(PromptedPrecomputedFeatures, self).__init__(
+#             num_workers, executor_type
+#         )
+#
+#         self.utt2neighbors = defaultdict(lambda: [])
+#
+#         if dataset.lower() == "libritts":
+#             # 909_131041_000013_000002
+#             # 909_131041_000013_000003
+#             speaker2utts = defaultdict(lambda: [])
+#
+#             utt2cut = {}
+#             for cut in cuts:
+#                 speaker = cut.supervisions[0].speaker
+#                 speaker2utts[speaker].append(cut.id)
+#                 utt2cut[cut.id] = cut
+#
+#             for spk in speaker2utts:
+#                 uttids = sorted(speaker2utts[spk])
+#                 # Using the property of sorted keys to find previous utterance
+#                 # The keys has structure speaker_book_x_y e.g. 1089_134691_000004_000001
+#                 if len(uttids) == 1:
+#                     self.utt2neighbors[uttids[0]].append(utt2cut[uttids[0]])
+#                     continue
+#
+#                 utt2prevutt = dict(zip(uttids, [uttids[1]] + uttids[:-1]))
+#                 utt2postutt = dict(zip(uttids[:-1], uttids[1:]))
+#
+#                 for utt in utt2prevutt:
+#                     self.utt2neighbors[utt].append(utt2cut[utt2prevutt[utt]])
+#
+#                 for utt in utt2postutt:
+#                     self.utt2neighbors[utt].append(utt2cut[utt2postutt[utt]])
+#         elif dataset.lower() == "ljspeech":
+#             utt2cut = {}
+#             uttids = []
+#             for cut in cuts:
+#                 uttids.append(cut.id)
+#                 utt2cut[cut.id] = cut
+#
+#             if len(uttids) == 1:
+#                 self.utt2neighbors[uttids[0]].append(utt2cut[uttids[0]])
+#             else:
+#                 # Using the property of sorted keys to find previous utterance
+#                 # The keys has structure: LJ001-0010
+#                 utt2prevutt = dict(zip(uttids, [uttids[1]] + uttids[:-1]))
+#                 utt2postutt = dict(zip(uttids[:-1], uttids[1:]))
+#
+#                 for utt in utt2postutt:
+#                     postutt = utt2postutt[utt]
+#                     if utt[:5] == postutt[:5]:
+#                         self.utt2neighbors[utt].append(utt2cut[postutt])
+#
+#                 for utt in utt2prevutt:
+#                     prevutt = utt2prevutt[utt]
+#                     if utt[:5] == prevutt[:5] or not self.utt2neighbors[utt]:
+#                         self.utt2neighbors[utt].append(utt2cut[prevutt])
+#         else:
+#             raise ValueError
+#
+#     def __call__(
+#         self, cuts: CutSet
+#     ) -> Tuple[PromptedFeatures, PromptedFeatures]:
+#         """
+#         Reads the pre-computed features from disk/other storage.
+#         The returned shape is``(B, T, F) => (batch_size, num_frames, num_features)``.
+#
+#         :return: a tensor with collated features, and a tensor of ``num_frames`` of each cut before padding.
+#         """
+#         features, features_lens = collate_features(
+#             cuts,
+#             executor=_get_executor(
+#                 self.num_workers, executor_type=self._executor_type
+#             ),
+#         )
+#
+#         prompts_cuts = []
+#         for k, cut in enumerate(cuts):
+#             prompts_cut = random.choice(self.utt2neighbors[cut.id])
+#             prompts_cuts.append(fastcopy(prompts_cut, id=f"{cut.id}-{str(k)}"))
+#
+#         mini_duration = min([cut.duration for cut in prompts_cuts] + [3.0])
+#         # prompts_cuts = CutSet.from_cuts(prompts_cuts).truncate(
+#         #     max_duration=mini_duration,
+#         #     offset_type="random",
+#         #     preserve_id=True,
+#         # )
+#         prompts_cuts = CutSet(
+#             cuts={k: cut for k, cut in enumerate(prompts_cuts)}
+#         ).truncate(
+#             max_duration=mini_duration,
+#             offset_type="random",
+#             preserve_id=False,
+#         )
+#
+#         prompts, prompts_lens = collate_features(
+#             prompts_cuts,
+#             executor=_get_executor(
+#                 self.num_workers, executor_type=self._executor_type
+#             ),
+#         )
+#
+#         return PromptedFeatures(prompts, features), PromptedFeatures(
+#             prompts_lens, features_lens
+#         )
diff --git a/apps/audio_cloning/vallex/data/symbol_table.py b/apps/audio_cloning/vallex/data/symbol_table.py
new file mode 100644
index 0000000000000000000000000000000000000000..33b63b19933ca4a7885af2b6597e3e25afe12314
--- /dev/null
+++ b/apps/audio_cloning/vallex/data/symbol_table.py
@@ -0,0 +1,289 @@
+# Copyright      2020  Mobvoi Inc.        (authors: Fangjun Kuang)
+#
+# 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.
+
+from dataclasses import dataclass, field
+from typing import Dict, Generic, List, Optional, TypeVar, Union
+
+Symbol = TypeVar("Symbol")
+
+
+# Disable __repr__ otherwise it could freeze e.g. Jupyter.
+@dataclass(repr=False)
+class SymbolTable(Generic[Symbol]):
+    """SymbolTable that maps symbol IDs, found on the FSA arcs to
+    actual objects. These objects can be arbitrary Python objects
+    that can serve as keys in a dictionary (i.e. they need to be
+    hashable and immutable).
+
+    The SymbolTable can only be read to/written from disk if the
+    symbols are strings.
+    """
+
+    _id2sym: Dict[int, Symbol] = field(default_factory=dict)
+    """Map an integer to a symbol.
+    """
+
+    _sym2id: Dict[Symbol, int] = field(default_factory=dict)
+    """Map a symbol to an integer.
+    """
+
+    _next_available_id: int = 1
+    """A helper internal field that helps adding new symbols
+    to the table efficiently.
+    """
+
+    eps: Symbol = "<eps>"
+    """Null symbol, always mapped to index 0.
+    """
+
+    def __post_init__(self):
+        for idx, sym in self._id2sym.items():
+            assert self._sym2id[sym] == idx
+            assert idx >= 0
+
+        for sym, idx in self._sym2id.items():
+            assert idx >= 0
+            assert self._id2sym[idx] == sym
+
+        if 0 not in self._id2sym:
+            self._id2sym[0] = self.eps
+            self._sym2id[self.eps] = 0
+        else:
+            assert self._id2sym[0] == self.eps
+            assert self._sym2id[self.eps] == 0
+
+        self._next_available_id = max(self._id2sym) + 1
+
+    @staticmethod
+    def from_str(s: str) -> "SymbolTable":
+        """Build a symbol table from a string.
+
+        The string consists of lines. Every line has two fields separated
+        by space(s), tab(s) or both. The first field is the symbol and the
+        second the integer id of the symbol.
+
+        Args:
+          s:
+            The input string with the format described above.
+        Returns:
+          An instance of :class:`SymbolTable`.
+        """
+        id2sym: Dict[int, str] = dict()
+        sym2id: Dict[str, int] = dict()
+
+        for line in s.split("\n"):
+            fields = line.split()
+            if len(fields) == 0:
+                continue  # skip empty lines
+            assert len(fields) == 2, (
+                f"Expect a line with 2 fields. Given: {len(fields)}"
+            )
+            sym, idx = fields[0], int(fields[1])
+            assert sym not in sym2id, f"Duplicated symbol {sym}"
+            assert idx not in id2sym, f"Duplicated id {idx}"
+            id2sym[idx] = sym
+            sym2id[sym] = idx
+
+        eps = id2sym.get(0, "<eps>")
+
+        return SymbolTable(_id2sym=id2sym, _sym2id=sym2id, eps=eps)
+
+    @staticmethod
+    def from_file(filename: str) -> "SymbolTable":
+        """Build a symbol table from file.
+
+        Every line in the symbol table file has two fields separated by
+        space(s), tab(s) or both. The following is an example file:
+
+        .. code-block::
+
+            <eps> 0
+            a 1
+            b 2
+            c 3
+
+        Args:
+          filename:
+            Name of the symbol table file. Its format is documented above.
+
+        Returns:
+          An instance of :class:`SymbolTable`.
+
+        """
+        with open(filename, "r", encoding="utf-8") as f:
+            return SymbolTable.from_str(f.read().strip())
+
+    def to_str(self) -> str:
+        """
+        Returns:
+          Return a string representation of this object. You can pass
+          it to the method ``from_str`` to recreate an identical object.
+        """
+        s = ""
+        for idx, symbol in sorted(self._id2sym.items()):
+            s += f"{symbol} {idx}\n"
+        return s
+
+    def to_file(self, filename: str):
+        """Serialize the SymbolTable to a file.
+
+        Every line in the symbol table file has two fields separated by
+        space(s), tab(s) or both. The following is an example file:
+
+        .. code-block::
+
+            <eps> 0
+            a 1
+            b 2
+            c 3
+
+        Args:
+          filename:
+            Name of the symbol table file. Its format is documented above.
+        """
+        with open(filename, "w", encoding="utf-8") as f:
+            for idx, symbol in sorted(self._id2sym.items()):
+                print(symbol, idx, file=f)
+
+    def add(self, symbol: Symbol, index: Optional[int] = None) -> int:
+        """Add a new symbol to the SymbolTable.
+
+        Args:
+            symbol:
+                The symbol to be added.
+            index:
+                Optional int id to which the symbol should be assigned.
+                If it is not available, a ValueError will be raised.
+
+        Returns:
+            The int id to which the symbol has been assigned.
+        """
+        # Already in the table? Return its ID.
+        if symbol in self._sym2id:
+            return self._sym2id[symbol]
+        # Specific ID not provided - use next available.
+        if index is None:
+            index = self._next_available_id
+        # Specific ID provided but not available.
+        if index in self._id2sym:
+            raise ValueError(
+                f"Cannot assign id '{index}' to '{symbol}' - "
+                f"already occupied by {self._id2sym[index]}"
+            )
+        self._sym2id[symbol] = index
+        self._id2sym[index] = symbol
+
+        # Update next available ID if needed
+        if self._next_available_id <= index:
+            self._next_available_id = index + 1
+
+        return index
+
+    def get(self, k: Union[int, Symbol]) -> Union[Symbol, int]:
+        """Get a symbol for an id or get an id for a symbol
+
+        Args:
+          k:
+            If it is an id, it tries to find the symbol corresponding
+            to the id; if it is a symbol, it tries to find the id
+            corresponding to the symbol.
+
+        Returns:
+          An id or a symbol depending on the given `k`.
+        """
+        if isinstance(k, int):
+            return self._id2sym[k]
+        else:
+            return self._sym2id[k]
+
+    def merge(self, other: "SymbolTable") -> "SymbolTable":
+        """Create a union of two SymbolTables.
+        Raises an AssertionError if the same IDs are occupied by
+        different symbols.
+
+        Args:
+            other:
+                A symbol table to merge with ``self``.
+
+        Returns:
+            A new symbol table.
+        """
+        self._check_compatible(other)
+
+        id2sym = {**self._id2sym, **other._id2sym}
+        sym2id = {**self._sym2id, **other._sym2id}
+
+        return SymbolTable(_id2sym=id2sym, _sym2id=sym2id, eps=self.eps)
+
+    def _check_compatible(self, other: "SymbolTable") -> None:
+        # Epsilon compatibility
+        assert self.eps == other.eps, (
+            f"Mismatched epsilon symbol: {self.eps} != {other.eps}"
+        )
+        # IDs compatibility
+        common_ids = set(self._id2sym).intersection(other._id2sym)
+        for idx in common_ids:
+            assert self[idx] == other[idx], (
+                f"ID conflict for id: {idx}, "
+                f'self[idx] = "{self[idx]}", '
+                f'other[idx] = "{other[idx]}"'
+            )
+        # Symbols compatibility
+        common_symbols = set(self._sym2id).intersection(other._sym2id)
+        for sym in common_symbols:
+            assert self[sym] == other[sym], (
+                f"ID conflict for id: {sym}, "
+                f'self[sym] = "{self[sym]}", '
+                f'other[sym] = "{other[sym]}"'
+            )
+
+    def __getitem__(self, item: Union[int, Symbol]) -> Union[Symbol, int]:
+        return self.get(item)
+
+    def __contains__(self, item: Union[int, Symbol]) -> bool:
+        if isinstance(item, int):
+            return item in self._id2sym
+        else:
+            return item in self._sym2id
+
+    def __len__(self) -> int:
+        return len(self._id2sym)
+
+    def __eq__(self, other: "SymbolTable") -> bool:
+        if len(self) != len(other):
+            return False
+
+        for s in self.symbols:
+            if self[s] != other[s]:
+                return False
+
+        return True
+
+    @property
+    def ids(self) -> List[int]:
+        """Returns a list of integer IDs corresponding to the symbols."""
+        ans = list(self._id2sym.keys())
+        ans.sort()
+        return ans
+
+    @property
+    def symbols(self) -> List[Symbol]:
+        """Returns a list of symbols (e.g., strings) corresponding to
+        the integer IDs.
+        """
+        ans = list(self._sym2id.keys())
+        ans.sort()
+        return ans
diff --git a/apps/audio_cloning/vallex/data/tokenizer.py b/apps/audio_cloning/vallex/data/tokenizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..a556f1418e2f4b29833e135f3e0380b776c9429b
--- /dev/null
+++ b/apps/audio_cloning/vallex/data/tokenizer.py
@@ -0,0 +1,121 @@
+#!/usr/bin/env python3
+# Copyright    2023                            (authors: Feiteng Li)
+#
+# 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.
+
+from typing import Any
+
+import numpy as np
+import torch
+import torchaudio
+from encodec import EncodecModel
+from encodec.utils import convert_audio
+
+try:
+    pass
+except Exception:
+    pass
+
+
+def remove_encodec_weight_norm(model):
+    from encodec.modules import SConv1d
+    from encodec.modules.seanet import SConvTranspose1d, SEANetResnetBlock
+    from torch.nn.utils import remove_weight_norm
+
+    encoder = model.encoder.model
+    for key in encoder._modules:
+        if isinstance(encoder._modules[key], SEANetResnetBlock):
+            remove_weight_norm(encoder._modules[key].shortcut.conv.conv)
+            block_modules = encoder._modules[key].block._modules
+            for skey in block_modules:
+                if isinstance(block_modules[skey], SConv1d):
+                    remove_weight_norm(block_modules[skey].conv.conv)
+        elif isinstance(encoder._modules[key], SConv1d):
+            remove_weight_norm(encoder._modules[key].conv.conv)
+
+    decoder = model.decoder.model
+    for key in decoder._modules:
+        if isinstance(decoder._modules[key], SEANetResnetBlock):
+            remove_weight_norm(decoder._modules[key].shortcut.conv.conv)
+            block_modules = decoder._modules[key].block._modules
+            for skey in block_modules:
+                if isinstance(block_modules[skey], SConv1d):
+                    remove_weight_norm(block_modules[skey].conv.conv)
+        elif isinstance(decoder._modules[key], SConvTranspose1d):
+            remove_weight_norm(decoder._modules[key].convtr.convtr)
+        elif isinstance(decoder._modules[key], SConv1d):
+            remove_weight_norm(decoder._modules[key].conv.conv)
+
+
+class AudioTokenizer:
+    """EnCodec audio."""
+
+    def __init__(
+        self,
+        device: Any = None,
+    ) -> None:
+        # Instantiate a pretrained EnCodec model
+        model = EncodecModel.encodec_model_24khz()
+        model.set_target_bandwidth(6.0)
+        remove_encodec_weight_norm(model)
+
+        if not device:
+            device = torch.device("cpu")
+            if torch.cuda.is_available():
+                device = torch.device("cuda:0")
+            if torch.backends.mps.is_available():
+                device = torch.device("mps")
+
+        self._device = device
+
+        self.codec = model.to(device)
+        self.sample_rate = model.sample_rate
+        self.channels = model.channels
+
+    @property
+    def device(self):
+        return self._device
+
+    def encode(self, wav: torch.Tensor) -> torch.Tensor:
+        return self.codec.encode(wav.to(self.device))
+
+    def decode(self, frames: torch.Tensor) -> torch.Tensor:
+        return self.codec.decode(frames)
+
+
+def tokenize_audio(tokenizer: AudioTokenizer, audio):
+    # Load and pre-process the audio waveform
+    if isinstance(audio, str):
+        wav, sr = torchaudio.load(audio)
+    else:
+        wav, sr = audio
+    wav = convert_audio(wav, sr, tokenizer.sample_rate, tokenizer.channels)
+    wav = wav.unsqueeze(0)
+
+    # Extract discrete codes from EnCodec
+    with torch.no_grad():
+        encoded_frames = tokenizer.encode(wav)
+    return encoded_frames
+
+
+if __name__ == "__main__":
+    model = EncodecModel.encodec_model_24khz()
+    model.set_target_bandwidth(6.0)
+
+    samples = torch.from_numpy(np.random.random([4, 1, 1600])).type(torch.float32)
+    codes_raw = model.encode(samples)
+
+    remove_encodec_weight_norm(model)
+    codes_norm = model.encode(samples)
+
+    assert torch.allclose(codes_raw[0][0], codes_norm[0][0])
diff --git a/apps/audio_cloning/vallex/descriptions.py b/apps/audio_cloning/vallex/descriptions.py
new file mode 100644
index 0000000000000000000000000000000000000000..bfcb34ab4ecf22b896935554d87209cb1ec67dd4
--- /dev/null
+++ b/apps/audio_cloning/vallex/descriptions.py
@@ -0,0 +1,34 @@
+top_md_org = """
+# VALL-E X
+VALL-E X can synthesize high-quality personalized speech with only a 3-second enrolled recording of
+an unseen speaker as an acoustic prompt, even in another language for a monolingual speaker.<br>
+This implementation supports zero-shot, mono-lingual/cross-lingual text-to-speech functionality of three languages (English, Chinese, Japanese)<br>
+See this [demo](https://plachtaa.github.io/) page for more details.
+"""
+
+top_ja_md = """
+# VALL-E X
+
+VALL-E X は、未学習の話者でも 3 秒間の音声プロンプトだけで高品質なパーソナライズ音声を合成できます。<br>
+単一言語話者であっても別の言語による音声合成が可能です。<br>
+本実装は英語・中国語・日本語のゼロショット単言語/クロス言語テキスト読み上げをサポートしています。
+
+## Reference
+
+- [github.com/Plachtaa/VALL-E-X](https://github.com/Plachtaa/VALL-E-X/tree/master#readme)
+- [github.com/gemelo-ai/vocos](https://github.com/gemelo-ai/vocos)
+"""
+
+infer_from_audio_md_org = """
+Upload a speech of 3~10 seconds as the audio prompt and type in the text you'd like to synthesize.<br>
+The model will synthesize speech of given text with the same voice of your audio prompt.<br>
+The model also tends to preserve the emotion & acoustic environment of your given speech.<br>
+For faster inference, please use **"Make prompt"** to get a `.npz` file as the encoded audio prompt, and use it by **"Infer from prompt"**
+"""
+
+infer_from_audio_ja_md = """
+3〜10 秒程度の音声をプロンプトとしてアップロードし、合成したいテキストを入力してください。<br>
+モデルは、プロンプトと同じ声質でテキストを読み上げる音声を生成します。<br>
+元の音声に含まれる感情や音響環境も比較的保持されます。<br>
+推論を高速化したい場合は **"Make prompt"** で `.npz` ファイルを作成し、 **"Infer from prompt"** で利用してください。
+"""
diff --git a/apps/audio_cloning/vallex/examples.py b/apps/audio_cloning/vallex/examples.py
new file mode 100644
index 0000000000000000000000000000000000000000..e0d538f29bc417fe26b2220661f731d15b676b78
--- /dev/null
+++ b/apps/audio_cloning/vallex/examples.py
@@ -0,0 +1,108 @@
+_prompts_dir = "apps/audio_cloning/vallex/prompts"
+
+infer_from_audio_examples = [
+    [
+        "私のクローンに騙されないでください。",
+        "日本語",
+        "no-accent",
+        f"{_prompts_dir}/ja-okuwaki.wav",
+        None,
+        "こんにちは、私の名前はオクワキヨウスケです。",
+    ],
+    [
+        "ぼくのクローンに騙されないでくれなのだ。",
+        "日本語",
+        "no-accent",
+        f"{_prompts_dir}/ja-zundamon.wav",
+        None,
+        "はじめまして、ずんだもんなのだ",
+    ],
+    [
+        "私のクローンに騙されないでください。",
+        "日本語",
+        "no-accent",
+        f"{_prompts_dir}/ja-okuwaki-long.wav",
+        None,
+        "こんにちは、私の名前はオクワキヨウスケです。これは音声クローニング用のサンプルです。",
+    ],
+    [
+        "私の声を真似するのはそんなに面白いですか？",
+        "日本語",
+        "no-accent",
+        f"{_prompts_dir}/ja-2.ogg",
+        None,
+        "初めまして、朝武よしのです。",
+    ],
+    [
+        "This is how this machine has taken my voice.",
+        "English",
+        "no-accent",
+        f"{_prompts_dir}/en-2.wav",
+        None,
+        "Wow, look at that! That's no ordinary Teddy bear!",
+    ],
+    [
+        "我喜欢抽电子烟，尤其是锐刻五代。",
+        "中文",
+        "no-accent",
+        f"{_prompts_dir}/zh-1.wav",
+        None,
+        "今天我很荣幸，",
+    ],
+    [
+        "你可以听得出来我有多困。",
+        "中文",
+        "no-accent",
+        f"{_prompts_dir}/en-1.wav",
+        None,
+        "",
+    ],
+    [
+        "この文は、クロスリンガル合成の例です。",
+        "日本語",
+        "no-accent",
+        f"{_prompts_dir}/zh-2.wav",
+        None,
+        "",
+    ],
+    [
+        "Actually, I can't speak English, but this machine helped me do it.",
+        "English",
+        "no-accent",
+        f"{_prompts_dir}/ja-1.wav",
+        None,
+        "",
+    ],
+]
+
+make_npz_prompt_examples = [
+    [
+        "Gem-trader",
+        f"{_prompts_dir}/en-2.wav",
+        None,
+        "Wow, look at that! That's no ordinary Teddy bear!",
+    ],
+    ["Ding Zhen", f"{_prompts_dir}/zh-1.wav", None, "今天我很荣幸，"],
+    ["Yoshino", f"{_prompts_dir}/ja-2.ogg", None, "初めまして、朝武よしのです。"],
+    ["Sleepy-woman", f"{_prompts_dir}/en-1.wav", None, ""],
+    ["Yae", f"{_prompts_dir}/zh-2.wav", None, ""],
+    ["Cafe", f"{_prompts_dir}/ja-1.wav", None, ""],
+]
+
+infer_from_prompt_examples = [
+    [
+        "A prompt contains voice, prosody and emotion information of a certain speaker.",
+        "English",
+        "no-accent",
+        f"{_prompts_dir}/vctk_1",
+        None,
+    ],
+    [
+        "This prompt is made with an audio of three seconds.",
+        "English",
+        "no-accent",
+        f"{_prompts_dir}/librispeech_1",
+        None,
+    ],
+    ["This prompt is made with Chinese speech", "English", "no-accent", "seel", None],
+]
diff --git a/apps/audio_cloning/vallex/g2p/__init__.py b/apps/audio_cloning/vallex/g2p/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0e132e5b03a4c77afb325f33c3aa68cc46e15abf
--- /dev/null
+++ b/apps/audio_cloning/vallex/g2p/__init__.py
@@ -0,0 +1,84 @@
+"""from https://github.com/keithito/tacotron"""
+
+# import utils.g2p.cleaners
+from tokenizers import Tokenizer
+
+import apps.audio_cloning.vallex.g2p.cleaners as cleaners
+
+from .symbols import symbols
+
+# Mappings from symbol to numeric ID and vice versa:
+_symbol_to_id = {s: i for i, s in enumerate(symbols)}
+_id_to_symbol = {i: s for i, s in enumerate(symbols)}
+
+TOKENIZER_PATH = "./apps/audio_cloning/vallex/g2p/bpe_1024.json"
+
+
+class PhonemeBpeTokenizer:
+    def __init__(self, tokenizer_path=TOKENIZER_PATH):
+        print(f"Initializing PhonemeBpeTokenizer with tokenizer path: {tokenizer_path}")
+        self.tokenizer = Tokenizer.from_file(tokenizer_path)
+
+    def tokenize(self, text):
+        # 1. convert text to phoneme
+        phonemes, langs = _clean_text(text, ["cje_cleaners"])
+        # 2. replace blank space " " with "_"
+        phonemes = phonemes.replace(" ", "_")
+        # 3. tokenize phonemes
+        phoneme_tokens = self.tokenizer.encode(phonemes).ids
+        assert len(phoneme_tokens) == len(langs)
+        if not len(phoneme_tokens):
+            raise ValueError("Empty text is given")
+        return phoneme_tokens, langs
+
+
+def text_to_sequence(text, cleaner_names):
+    """Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
+    Args:
+      text: string to convert to a sequence
+      cleaner_names: names of the cleaner functions to run the text through
+    Returns:
+      List of integers corresponding to the symbols in the text
+    """
+    sequence = []
+    symbol_to_id = {s: i for i, s in enumerate(symbols)}
+    clean_text = _clean_text(text, cleaner_names)
+    for symbol in clean_text:
+        if symbol not in symbol_to_id.keys():
+            continue
+        symbol_id = symbol_to_id[symbol]
+        sequence += [symbol_id]
+    return sequence
+
+
+def cleaned_text_to_sequence(cleaned_text):
+    """Converts a string of text to a sequence of IDs corresponding to the symbols in the text.
+    Args:
+      text: string to convert to a sequence
+    Returns:
+      List of integers corresponding to the symbols in the text
+    """
+    sequence = [
+        _symbol_to_id[symbol]
+        for symbol in cleaned_text
+        if symbol in _symbol_to_id.keys()
+    ]
+    return sequence
+
+
+def sequence_to_text(sequence):
+    """Converts a sequence of IDs back to a string"""
+    result = ""
+    for symbol_id in sequence:
+        s = _id_to_symbol[symbol_id]
+        result += s
+    return result
+
+
+def _clean_text(text, cleaner_names):
+    for name in cleaner_names:
+        cleaner = getattr(cleaners, name)
+        if not cleaner:
+            raise Exception("Unknown cleaner: %s" % name)
+        text, langs = cleaner(text)
+    return text, langs
diff --git a/apps/audio_cloning/vallex/g2p/bpe_1024.json b/apps/audio_cloning/vallex/g2p/bpe_1024.json
new file mode 100644
index 0000000000000000000000000000000000000000..19331439acfb9d16944399e986b0aee38c95758c
--- /dev/null
+++ b/apps/audio_cloning/vallex/g2p/bpe_1024.json
@@ -0,0 +1,2049 @@
+{
+  "version": "1.0",
+  "truncation": null,
+  "padding": null,
+  "added_tokens": [
+    {
+      "id": 0,
+      "content": "[UNK]",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 1,
+      "content": "[CLS]",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 2,
+      "content": "[SEP]",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 3,
+      "content": "[PAD]",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 4,
+      "content": "[MASK]",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    }
+  ],
+  "normalizer": null,
+  "pre_tokenizer": {
+    "type": "Whitespace"
+  },
+  "post_processor": null,
+  "decoder": null,
+  "model": {
+    "type": "BPE",
+    "dropout": null,
+    "unk_token": "[UNK]",
+    "continuing_subword_prefix": null,
+    "end_of_word_suffix": null,
+    "fuse_unk": false,
+    "byte_fallback": false,
+    "vocab": {
+      "[UNK]": 0,
+      "[CLS]": 1,
+      "[SEP]": 2,
+      "[PAD]": 3,
+      "[MASK]": 4,
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+      "*": 7,
+      ",": 8,
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+      ".": 10,
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+      "?": 12,
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+      "_joʊ": 224,
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+      "ɛz": 226,
+      "--": 227,
+      "vəɹ": 228,
+      "`→": 229,
+      "ʃən": 230,
+      "_ɪz": 231,
+      "_meɪ": 232,
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+      "_ki": 235,
+      "_hɪz": 236,
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+      "_ˈf": 239,
+      "↓↑.": 240,
+      "_wɪθ": 241,
+      "ju": 242,
+      "iɑŋ": 243,
+      "→.": 244,
+      "_so": 245,
+      "_həɹ": 246,
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+      "_mo": 249,
+      "_maɪ": 250,
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+      "`↓↑": 377,
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+      "_ze": 993,
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+      "ɪˌ": 999,
+      "_hɪmˈsɛɫf": 1000,
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+      "wɪn": 1008,
+      "əθɪŋ": 1009,
+      "_pɹəˈ": 1010,
+      "kɯn": 1011,
+      "ˈju": 1012,
+      "_fɔɹ": 1013,
+      "pʰi": 1014,
+      "pʰiŋ": 1015,
+      "o_i": 1016,
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+      "tʰiŋ": 1019,
+      "_ne": 1020,
+      "gəɹ": 1021,
+      "æts": 1022,
+      "_ˈɹi": 1023
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+    "merges": [
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+      "↓ ↑",
+      "_ ˈ",
+      "ə n",
+      "_ s",
+      "a ɪ",
+      "ə ɹ",
+      "e ɪ",
+      "o ʊ",
+      "_ k",
+      "ʃ i",
+      "_ w",
+      "_ ð",
+      "t s",
+      "t ʃ",
+      "_t s",
+      "_ h",
+      "_ ə",
+      "_ m",
+      "a n",
+      "_ n",
+      "_ð ə",
+      "ɛ n",
+      "ɑ ʊ",
+      "ɑ ŋ",
+      "` ⁼",
+      "_ p",
+      "_ i",
+      "_ ɪ",
+      "_t ʃ",
+      "_ l",
+      "j ɛn",
+      "_ d",
+      "_ f",
+      "_ j",
+      "w o",
+      "_ b",
+      "t a",
+      "` ↓",
+      "t e",
+      "ən d",
+      "_ ʃi",
+      "w a",
+      "k a",
+      "ɪ ŋ",
+      "i n",
+      "s t",
+      "l i",
+      "ʊ ŋ",
+      "_t ɪ",
+      "t o",
+      "w eɪ",
+      "_ ənd",
+      "ʰ i",
+      "_ə v",
+      "ə ŋ",
+      "n o",
+      "_ x",
+      "ɾ ɯ",
+      "n a",
+      "_ a",
+      "_ ɹ",
+      "ɪ n",
+      "g a",
+      "d e",
+      "j oʊ",
+      "æ n",
+      "k ɯ",
+      "ɾ e",
+      "m a",
+      "_ðə _ˈ",
+      "ɾ a",
+      "ɛ ɹ",
+      "m o",
+      "ɔ ɹ",
+      "ə ɫ",
+      "_ g",
+      "d a",
+      "* ↑",
+      "ɪ ˈ",
+      "_ o",
+      "_ ʃ",
+      "i ŋ",
+      "j a",
+      "ə m",
+      "_ ˌ",
+      "a ʊ",
+      "_ə ˈ",
+      "` ↑",
+      "ə t",
+      "_ aɪ",
+      "o o",
+      "s ɯ",
+      "↓ .",
+      "_ɪ n",
+      "_h i",
+      "_w ɪ",
+      "ɪ z",
+      "_n a",
+      "w an",
+      "_k o",
+      "_w o",
+      "ɪ d",
+      "ɾ i",
+      "_j u",
+      "m ə",
+      "_l ə",
+      "_h æ",
+      "_ðə t",
+      "ɑ ɹ",
+      "t ʰ",
+      "k i",
+      "… …",
+      "ɑ z",
+      "_ ɔ",
+      "_m i",
+      "_w ɑz",
+      "_ˈ s",
+      "↓ ,",
+      "_t ʰ",
+      "ə ˈ",
+      "d ʑ",
+      "ɪ t",
+      "_k ʰ",
+      "i ɛ",
+      "_m a",
+      "ɪ s",
+      "ts ɯ",
+      "_n i",
+      "_ɪ t",
+      "k e",
+      "i ɑʊ",
+      "_k a",
+      "_ əɹ",
+      "n d",
+      "_ˈ p",
+      "k o",
+      "j o",
+      "ɹ i",
+      "m ən",
+      "ʊ d",
+      "_ˈ m",
+      "_f əɹ",
+      "tʃ ʰi",
+      "s a",
+      "ʰ ɥ",
+      "k ʰ",
+      "ˈ s",
+      "ɑ t",
+      "ɛ d",
+      "s e",
+      "t ʃi",
+      "ɛ ɫ",
+      "_ˈ k",
+      "_j oʊ",
+      "t əɹ",
+      "ɛ z",
+      "- -",
+      "v əɹ",
+      "` →",
+      "ʃ ən",
+      "_ɪ z",
+      "_m eɪ",
+      "_ æ",
+      "d ʒ",
+      "_k i",
+      "_h ɪz",
+      "_b i",
+      "u ɑŋ",
+      "_ˈ f",
+      "↓↑ .",
+      "_wɪ θ",
+      "j u",
+      "i ɑŋ",
+      "→ .",
+      "_s o",
+      "_h əɹ",
+      "↑ .",
+      "n i",
+      "_m o",
+      "_m aɪ",
+      "l aɪ",
+      "ɥ ɛ",
+      "_t a",
+      "ən t",
+      "_tʃ ʰi",
+      "_s ɯ",
+      "_ θ",
+      "_ ɛz",
+      "w ən",
+      "m e",
+      "m i",
+      "_hæ d",
+      "_h a",
+      "ə s",
+      "_ˈ l",
+      "_s t",
+      "ð əɹ",
+      "oʊ n",
+      "_w a",
+      "ʰ əŋ",
+      "_n ɑt",
+      "* .",
+      "k t",
+      "_ˈ h",
+      "d o",
+      "ɥ æn",
+      "n e",
+      "_t o",
+      "_w ən",
+      "_n o",
+      "_l aɪ",
+      "_w əɹ",
+      "↑ ,",
+      "→ ,",
+      "ɛ s",
+      "↓↑ ,",
+      "_ɔ n",
+      "ʰ u",
+      "s o",
+      "_ˈ b",
+      "ɫ d",
+      "ɪ k",
+      "ɪ st",
+      "_f ɹ",
+      "_ð ɛɹ",
+      "_w eɪ",
+      "ka ɾa",
+      "_ˈ d",
+      "_hæ v",
+      "ts ʰ",
+      "w aɪ",
+      "ɾ o",
+      "ɛ m",
+      "_æ t",
+      "ʊ ɹ",
+      "_ˈ w",
+      "b a",
+      "_n oʊ",
+      "ʰ jɛn",
+      "ɹ eɪ",
+      "_j o",
+      "ɸ ɯ",
+      "_s a",
+      "_ɹ ɪˈ",
+      "_ˈ n",
+      "a i",
+      "_b ət",
+      "ɪ ɹ",
+      "tʃ ʰɥ",
+      "_d ʑ",
+      "ə ˌ",
+      "_ð ɪs",
+      ". .",
+      "x wa",
+      "_ɪ m",
+      "_d ɪˈ",
+      "_k ən",
+      "dʑ i",
+      "* ,",
+      "ɑ n",
+      "_ʃi ɑŋ",
+      "_k ɯ",
+      "ʃi n",
+      "_s oʊ",
+      "b i",
+      "tʰ jɛn",
+      "te _i",
+      "_ts ʰ",
+      "_ ɯ",
+      "aɪ t",
+      "ʰi ŋ",
+      "ð ə",
+      "_ɔ ɫ",
+      "_ˈ ɹ",
+      "na i",
+      "əɹ d",
+      "_ˈ t",
+      "_ ən",
+      "_tʃ ʰɥ",
+      "_i ɛ",
+      "l eɪ",
+      "ɛɹ i",
+      "ˈ t",
+      "h a",
+      "ʃi ŋ",
+      "ɛ vəɹ",
+      "z ɯ",
+      "_w i",
+      "_j a",
+      "ɛ k",
+      "ʰ ɑŋ",
+      "_ts ɯ",
+      "_əv _ðə",
+      "ta ʃi",
+      "_s ɛd",
+      "_x ə",
+      "_l i",
+      "_s i",
+      "de sɯ",
+      "_ˌ ɪn",
+      "ʃ jɛn",
+      "_b aɪ",
+      "o n",
+      "_x ɑʊ",
+      "_ð eɪ",
+      "_x aɪ",
+      "` ↓↑",
+      "x weɪ",
+      "h i",
+      "_s e",
+      "ə _s",
+      "_fɹ əm",
+      "ʊ t",
+      "d i",
+      "aʊ t",
+      "ə b",
+      "s ɹ",
+      "ə z",
+      "_x weɪ",
+      "_kʰ ə",
+      "ɹ u",
+      "_ u",
+      "_d e",
+      "aɪ d",
+      "ɪ v",
+      "b ɯ",
+      "_h o",
+      "əɹ z",
+      "j oo",
+      "_b ɪˈ",
+      "_tʰ a",
+      "ɛ t",
+      "e n",
+      "ɛn i",
+      "ə st",
+      "æ k",
+      "ə _ts",
+      "_ˈ ɪn",
+      "t i",
+      "ɥ n",
+      "_d ʒ",
+      "x ɑʊ",
+      "_ˈ v",
+      "ʃi ɑŋ",
+      "p ʰ",
+      "_wɪ tʃ",
+      "eɪ m",
+      "oʊ z",
+      "ə ðəɹ",
+      "f ɑŋ",
+      "_ˈ g",
+      "_d o",
+      "_ʃi ɑʊ",
+      "_ˈ æ",
+      "_j ʊɹ",
+      "_ð ɛm",
+      "ɪ m",
+      "ɛ st",
+      "æn d",
+      "_d u",
+      "ɯ ɯ",
+      "k an",
+      "_d a",
+      "in o",
+      "_ e",
+      "_w ʊd",
+      "ɛn d",
+      "m eɪ",
+      "θ ɪŋ",
+      "_ʃ jɛn",
+      "i z",
+      "aɪ m",
+      "_h u",
+      "_əˈ b",
+      "ən s",
+      "_wɪ ɫ",
+      "t ʰi",
+      "g o",
+      "ɛn t",
+      "f u",
+      "æ p",
+      "x oʊ",
+      "eɪ k",
+      "ʊ k",
+      "əɹ ˈ",
+      "_θ ɪŋ",
+      "ə l",
+      "p ɹ",
+      "ə tʃ",
+      "n t",
+      "_ ɸɯ",
+      "l u",
+      "_ˈ ɔ",
+      "_i ɑʊ",
+      "l ə",
+      "t u",
+      "_dʑ i",
+      "eɪ t",
+      "_ʃi n",
+      "n na",
+      "_ˈp ɹ",
+      "f ən",
+      "_ə p",
+      "n jɛn",
+      "_a ʊt",
+      "f ɔɹ",
+      "_t u",
+      "eɪ ʃən",
+      "ɪ ɫ",
+      "_w ət",
+      "_ɪ f",
+      "_ ɥ",
+      "_f a",
+      "ˈ w",
+      "tʃ ʰjɛn",
+      "_w ɪn",
+      "oʊ ɫd",
+      "_əˈ p",
+      "aʊ nd",
+      "s an",
+      "h e",
+      "_b ɪn",
+      "f a",
+      "ɪ f",
+      "ɔ ŋ",
+      "g e",
+      "_ɪn _ðə",
+      "m iŋ",
+      "_p ɹ",
+      "in a",
+      "an o",
+      "əb əɫ",
+      "k ˈs",
+      "_ˈ ɛni",
+      "n əŋ",
+      "ə d",
+      "_əv _ðə_ˈ",
+      "_w aɪ",
+      "_t aɪm",
+      "ˈs ɛɫ",
+      "ʃi ɛ",
+      "_k əm",
+      "æ st",
+      "_g oʊ",
+      "m ɯ",
+      "ˈ p",
+      "_ˈ st",
+      "ə _t",
+      "p t",
+      "_p ʰ",
+      "ʰ ɹ",
+      "ʃ ja",
+      "i wa",
+      "ɪ l",
+      "b ət",
+      "_f ɑŋ",
+      "h o",
+      "i v",
+      "l oʊ",
+      "b e",
+      "_laɪ k",
+      "ɪ ʃ",
+      "_f u",
+      "z e",
+      "ə _tʃ",
+      "ɑɹ t",
+      "ɔɹ d",
+      "tʃʰi ŋ",
+      "m p",
+      "_ðə _s",
+      "_əˈb aʊt",
+      "_ˈ oʊ",
+      "kʰ ə",
+      "d _tɪ",
+      "ŋ ga",
+      "ə li",
+      "_kʰ an",
+      "ç i",
+      "_ˈ ju",
+      "_k ʊd",
+      "ɔ ɫ",
+      "ɔ t",
+      "_ɪ ts",
+      "_s an",
+      "tʃ a",
+      "i _na",
+      "x ə",
+      "ɛ kt",
+      "_m ɔɹ",
+      "te _kɯ",
+      "ɪd ʒ",
+      "j ʊŋ",
+      "_w an",
+      "æ t",
+      "ka t",
+      "ˈsɛɫ f",
+      "_k e",
+      "aɪ nd",
+      "i t",
+      "_ ɑɹ",
+      "s p",
+      "oʊn t",
+      "_t ʃi",
+      "tsʰ ɹ",
+      "_x ən",
+      "_əˈ g",
+      "ə _k",
+      "to _i",
+      "_t ʰi",
+      "_i ŋ",
+      "aʊ n",
+      "g ɯ",
+      "_ɪ kˈs",
+      "ɛ v",
+      "g i",
+      "k s",
+      "_s əm",
+      "an a",
+      "ɪt əɫ",
+      "n an",
+      "_ˈɪn tu",
+      "_hi ɹ",
+      "_t e",
+      "_n aʊ",
+      "ʃi ɑʊ",
+      "ʃ o",
+      "ɹ e",
+      "x aɪ",
+      "_tʃʰi ŋ",
+      "_s ɹ",
+      "_h aʊ",
+      "? .",
+      "_f eɪ",
+      "li ŋ",
+      "_ʃ ja",
+      "_ˈ dʒ",
+      "_s eɪ",
+      "ˈ n",
+      "s oʊ",
+      "tʰ ʊŋ",
+      "_l joʊ",
+      "m aɪ",
+      "_b ɹ",
+      "ɹeɪ t",
+      "_n əŋ",
+      "ʰ ə",
+      "æn s",
+      "_ˈɔ l",
+      "ta tʃi",
+      "n to",
+      "_ˌɪn ˈ",
+      "l e",
+      "n de",
+      "_ˈv ɛɹi",
+      "mən t",
+      "ɾi ma",
+      "_ð ɛn",
+      "_h əz",
+      "_ɹ i",
+      "f təɹ",
+      "_s p",
+      "ɾe wa",
+      "ga _a",
+      "z _əv",
+      "_m iŋ",
+      "_tɪ _ðə",
+      "ɹ aɪ",
+      "ɛ l",
+      "ɹ æ",
+      "_h oʊ",
+      "x u",
+      "oʊn li",
+      "ŋ k",
+      "i _i",
+      "_d ɪd",
+      "_dʒ ɪst",
+      "in g",
+      "ka i",
+      "_m æn",
+      "_i n",
+      "z o",
+      "ə f",
+      "da ke",
+      "_ˈs əm",
+      "ɾɯ _no",
+      "_g o",
+      "tʃ əɹ",
+      "i te",
+      "`↓ .",
+      "_kʰ aɪ",
+      "s k",
+      "ɔɹ s",
+      "_t ʰiŋ",
+      "_n ə",
+      "p əɫ",
+      "_tɪ _bi",
+      "ˈ fɔɹ",
+      "m u",
+      "s u",
+      "a a",
+      "ɪst əɹ",
+      "ʰ an",
+      "p əɹ",
+      "ə _p",
+      "li ɑŋ",
+      "_ v",
+      "oʊ st",
+      "_əˈg ɛn",
+      "ən z",
+      "N o",
+      "ɔɹ t",
+      "_s əˈ",
+      "_m ɯ",
+      "tʃ ʰ",
+      "_ˈl ɪtəɫ",
+      "_x wo",
+      "_ˌ bi",
+      "_ˈoʊ vəɹ",
+      "_ çi",
+      "_d eɪ",
+      "aɪ n",
+      "_ʃi ŋ",
+      "i _ʃi",
+      "_tsʰ aɪ",
+      "ʃ oo",
+      "ɾ oo",
+      "b əɹ",
+      "ʰ a",
+      "ˈ ɛs",
+      "_ɪn _ðə_ˈ",
+      "N wa",
+      "_ð ən",
+      "s aɪ",
+      "_ˈju ˈɛs",
+      "n da",
+      "_p leɪ",
+      "ɪŋ _tɪ",
+      "ɪt i",
+      "_m e",
+      "_ʃ ʊd",
+      "_n u",
+      "_ðə _k",
+      "z a",
+      "_ˈ ɛvəɹ",
+      "əɹ n",
+      "æ d",
+      "ˈ m",
+      "_d oʊnt",
+      "_m əst",
+      "j ɯɯ",
+      "ɑɹ d",
+      "_ jɛn",
+      "ʃ ɥ",
+      "_ˈ oʊnli",
+      "_ʃ o",
+      "_l iŋ",
+      "s s",
+      "ɑ l",
+      "de a",
+      "ɾe ta",
+      "m jɛn",
+      "_g ʊd",
+      "_w ɔ",
+      "i mo",
+      "no _ko",
+      "_ ɥæn",
+      "nd ʒ",
+      "ɪ ʃən",
+      "o _ʃi",
+      "_θɪŋ k",
+      "_n an",
+      "to _o",
+      "_tʰ ʊŋ",
+      "l joʊ",
+      "ta i",
+      "mə _s",
+      "_j ɯ",
+      "_ uɑŋ",
+      "_ˌbi ˈfɔɹ",
+      "æ s",
+      "_tʃ ʰjɛn",
+      "i k",
+      "_b æk",
+      "_ˈ iv",
+      "eɪ n",
+      "u n",
+      "l a",
+      "ˈ k",
+      "_d aʊn",
+      "an ai",
+      "_l ɛ",
+      "əɹ t",
+      "ð ɛɹ",
+      "_ˈæ ftəɹ",
+      "da t",
+      "f an",
+      "b əɫ",
+      "te mo",
+      "tʰ a",
+      "ɾɯ _ko",
+      "ˈ v",
+      "f eɪ",
+      "_m ətʃ",
+      "x wo",
+      "ɹ oʊ",
+      "_b a",
+      "_ˈn ɛvəɹ",
+      "_meɪ d",
+      "_j ʊŋ",
+      "_əˈp ɑn",
+      "! ?",
+      "_ˈ ʃ",
+      "_ðə_ˈ k",
+      "f t",
+      "_b o",
+      "_ɪn _ə",
+      "tʃʰɥ æn",
+      "ˈ z",
+      "`↓ ,",
+      "_bɪˈ k",
+      "ɪ g",
+      "k in",
+      "_k l",
+      "ɾɯ _n",
+      "_l ɑʊ",
+      "-- --",
+      "i ka",
+      "_ɹ aɪt",
+      "z d",
+      "z _ənd",
+      "_k jo",
+      "x wan",
+      "to o",
+      "_g ɪt",
+      "_l iɑŋ",
+      "ta _n",
+      "_k eɪm",
+      "_ˈ əðəɹ",
+      "_w ɛɫ",
+      "te ki",
+      "se e",
+      "j ɯ",
+      "i _o",
+      "to _ʃi",
+      "f əɫ",
+      "b o",
+      "ˌ t",
+      "ɪ p",
+      "an e",
+      "_tʰ jɛn",
+      "_tʃ o",
+      "ɾ jo",
+      "ɪn s",
+      "_h e",
+      "ŋ ka",
+      "ʃ ɥɛ",
+      "dʑ a",
+      "v d",
+      "ʰ wan",
+      "_g ɹeɪt",
+      "_əv _ə",
+      "ənd əɹ",
+      "ke do",
+      "_ðə _b",
+      "ə k",
+      "_t eɪk",
+      "kʰ an",
+      "_ˈɔl ˌ",
+      "s wo",
+      "_ɪt _wɑz",
+      "_ʃ ɥ",
+      "_si m",
+      "_ˈf ɑ",
+      "m in",
+      "i _a",
+      "s oo",
+      "ɛn s",
+      "_s ətʃ",
+      "tʰ aɪ",
+      "_ ga",
+      "i _ka",
+      "k oo",
+      "_fəɹ st",
+      "_ˈ tʃ",
+      "n no",
+      "ə _ɹ",
+      "ta ɾa",
+      "tʃʰ joʊ",
+      "_æ m",
+      "_m u",
+      "_meɪ k",
+      "↓ …",
+      "ɪˈ θ",
+      "ɑ b",
+      "ɹ a",
+      "_w ɛɹ",
+      "_ðə_ˈ s",
+      "_əˈ l",
+      "_ oʊɫd",
+      "æ l",
+      "_ˈp i",
+      "_l ɔŋ",
+      "dʑ o",
+      "_tʰ aɪ",
+      "ɔɹ n",
+      "əɫ z",
+      "_t əˈ",
+      "_əˈ weɪ",
+      "p a",
+      "_ð iz",
+      "_ˈs p",
+      "n n",
+      "ma e",
+      "to wa",
+      "ta _no",
+      "_ an",
+      "kʰ aɪ",
+      "ɾa ɾe",
+      "eɪ s",
+      "ɑ d",
+      "_w ɪˈθ",
+      "_ˈiv ɪn",
+      "_l u",
+      "ɔ ɪ",
+      "l ɪŋ",
+      "ət i",
+      "_ðə _f",
+      "o ʃi",
+      "_l a",
+      "s i",
+      "t ɪd",
+      "h aʊ",
+      "pʰ in",
+      "ˈ st",
+      "_ˈp əɹ",
+      "e ɹ",
+      "* !",
+      "_ˈm ɪstəɹ",
+      "ʃ a",
+      "_ˌ ɪm",
+      "ˌ θɪŋ",
+      "_n eɪ",
+      "_n ɥ",
+      "ɑ k",
+      "_ɹ u",
+      "_ʃ ɯ",
+      "_ðə_ˈ m",
+      "de mo",
+      "_d ɹ",
+      "dʑ oo",
+      "_st ɪɫ",
+      "_p ʰiŋ",
+      "ə _i",
+      "_ɪkˈs p",
+      "_w ɛnt",
+      "ɪ ɹi",
+      "əˈ m",
+      "o _ka",
+      "_əˈ k",
+      "ɔ k",
+      "_ ɥɛ",
+      "_l ʊk",
+      "ˈ d",
+      "ka ʃi",
+      "_wɪθ _ə",
+      "l jɛn",
+      "ɔ n",
+      "_l jɛn",
+      "_h ɛɫ",
+      "u ɹ",
+      "_tʰ oʊ",
+      "_tʃʰɥ æn",
+      "_s k",
+      "tsʰ aɪ",
+      "ɛ təɹ",
+      "_m in",
+      "n oʊ",
+      "ʃ ɯ",
+      "_θ ɹu",
+      "_θ ɔt",
+      "da jo",
+      "w i",
+      "i _ko",
+      "_t ɹ",
+      "_f an",
+      "ɹ ɛ",
+      "sa N",
+      "_hi _wɑz",
+      "_ ɾe",
+      "_ə m",
+      "te _ki",
+      "_x oʊ",
+      "ˈ l",
+      "ˈ g",
+      "ga _i",
+      "_ɔn _ðə",
+      "_x wa",
+      "v ɪŋ",
+      "m an",
+      "f əɹ",
+      "_ oʊn",
+      "ˈ ɹ",
+      "_k ɹ",
+      "te _o",
+      "ɪ li",
+      "_ʃ ɥɛ",
+      "_f əŋ",
+      "æ ɫ",
+      "ɑ p",
+      "_ˈ ɛv",
+      "eɪ ndʒ",
+      "i ɫ",
+      "w ət",
+      "ɛ ðəɹ",
+      "_f ən",
+      "ɾe e",
+      "_hi _hæd",
+      "_maɪ t",
+      "_g e",
+      "æ kt",
+      "ɪ ts",
+      "_h ɪm",
+      "_ ze",
+      "i i",
+      "_ N",
+      "_əv _hɪz",
+      "_g ɹ",
+      "æn t",
+      "ɪ ˌ",
+      "_hɪm ˈsɛɫf",
+      "wa _na",
+      "aɪ əɹ",
+      "dʑ anai",
+      "kan a",
+      "aɪ z",
+      "_ɪt _ɪz",
+      "ma se",
+      "w ɪn",
+      "ə θɪŋ",
+      "_pɹ əˈ",
+      "kɯ n",
+      "ˈ ju",
+      "_f ɔɹ",
+      "p ʰi",
+      "p ʰiŋ",
+      "o _i",
+      "v z",
+      "ɔ ɪn",
+      "t ʰiŋ",
+      "_n e",
+      "g əɹ",
+      "æ ts",
+      "_ˈ ɹi"
+    ]
+  }
+}
\ No newline at end of file
diff --git a/apps/audio_cloning/vallex/g2p/bpe_69.json b/apps/audio_cloning/vallex/g2p/bpe_69.json
new file mode 100644
index 0000000000000000000000000000000000000000..45eba9955fdcf9a7c764027b43930bf28722492b
--- /dev/null
+++ b/apps/audio_cloning/vallex/g2p/bpe_69.json
@@ -0,0 +1,141 @@
+{
+  "version": "1.0",
+  "truncation": null,
+  "padding": null,
+  "added_tokens": [
+    {
+      "id": 0,
+      "content": "[UNK]",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 1,
+      "content": "[CLS]",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 2,
+      "content": "[SEP]",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 3,
+      "content": "[PAD]",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 4,
+      "content": "[MASK]",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    }
+  ],
+  "normalizer": null,
+  "pre_tokenizer": {
+    "type": "Whitespace"
+  },
+  "post_processor": null,
+  "decoder": null,
+  "model": {
+    "type": "BPE",
+    "dropout": null,
+    "unk_token": "[UNK]",
+    "continuing_subword_prefix": null,
+    "end_of_word_suffix": null,
+    "fuse_unk": false,
+    "byte_fallback": false,
+    "vocab": {
+      "[UNK]": 0,
+      "[CLS]": 1,
+      "[SEP]": 2,
+      "[PAD]": 3,
+      "[MASK]": 4,
+      "!": 5,
+      "#": 6,
+      "*": 7,
+      ",": 8,
+      "-": 9,
+      ".": 10,
+      "=": 11,
+      "?": 12,
+      "N": 13,
+      "Q": 14,
+      "^": 15,
+      "_": 16,
+      "`": 17,
+      "a": 18,
+      "b": 19,
+      "d": 20,
+      "e": 21,
+      "f": 22,
+      "g": 23,
+      "h": 24,
+      "i": 25,
+      "j": 26,
+      "k": 27,
+      "l": 28,
+      "m": 29,
+      "n": 30,
+      "o": 31,
+      "p": 32,
+      "s": 33,
+      "t": 34,
+      "u": 35,
+      "v": 36,
+      "w": 37,
+      "x": 38,
+      "y": 39,
+      "z": 40,
+      "~": 41,
+      "æ": 42,
+      "ç": 43,
+      "ð": 44,
+      "ŋ": 45,
+      "ɑ": 46,
+      "ɔ": 47,
+      "ə": 48,
+      "ɛ": 49,
+      "ɥ": 50,
+      "ɪ": 51,
+      "ɫ": 52,
+      "ɯ": 53,
+      "ɸ": 54,
+      "ɹ": 55,
+      "ɾ": 56,
+      "ʃ": 57,
+      "ʊ": 58,
+      "ʑ": 59,
+      "ʒ": 60,
+      "ʰ": 61,
+      "ˈ": 62,
+      "ˌ": 63,
+      "θ": 64,
+      "…": 65,
+      "⁼": 66,
+      "↑": 67,
+      "→": 68,
+      "↓": 69
+    },
+    "merges": [
+    ]
+  }
+}
\ No newline at end of file
diff --git a/apps/audio_cloning/vallex/g2p/cleaners.py b/apps/audio_cloning/vallex/g2p/cleaners.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec567392641c45485f7d27105121e5dc23314533
--- /dev/null
+++ b/apps/audio_cloning/vallex/g2p/cleaners.py
@@ -0,0 +1,76 @@
+import re
+
+from .english import english_to_ipa2
+from .japanese import japanese_to_ipa2, japanese_to_romaji_with_accent
+from .mandarin import (
+    chinese_to_bopomofo,
+    chinese_to_ipa,
+    latin_to_bopomofo,
+    number_to_chinese,
+)
+
+patterns = [r"\[EN\](.*?)\[EN\]", r"\[ZH\](.*?)\[ZH\]", r"\[JA\](.*?)\[JA\]"]
+
+
+def japanese_cleaners(text):
+    text = japanese_to_romaji_with_accent(text)
+    text = re.sub(r"([A-Za-z])$", r"\1.", text)
+    return text
+
+
+def japanese_cleaners2(text):
+    return japanese_cleaners(text).replace("ts", "ʦ").replace("...", "…")
+
+
+def chinese_cleaners(text):
+    """Pipeline for Chinese text"""
+    text = number_to_chinese(text)
+    text = chinese_to_bopomofo(text)
+    text = latin_to_bopomofo(text)
+    text = re.sub(r"([ˉˊˇˋ˙])$", r"\1。", text)
+    return text
+
+
+def cje_cleaners(text):
+    matches = []
+    for pattern in patterns:
+        matches.extend(re.finditer(pattern, text))
+
+    matches.sort(key=lambda x: x.start())  # Sort matches by their start positions
+
+    outputs = ""
+    output_langs = []
+
+    for match in matches:
+        text_segment = text[match.start() : match.end()]
+        phon = clean_one(text_segment)
+        if "[EN]" in text_segment:
+            lang = "en"
+        elif "[ZH]" in text_segment:
+            lang = "zh"
+        elif "[JA]" in text_segment:
+            lang = "ja"
+        else:
+            raise ValueError("If you see this error, please report this bug to issues.")
+        outputs += phon
+        output_langs += [lang] * len(phon)
+    assert len(outputs) == len(output_langs)
+    return outputs, output_langs
+
+
+def clean_one(text):
+    if text.find("[ZH]") != -1:
+        text = re.sub(
+            r"\[ZH\](.*?)\[ZH\]", lambda x: chinese_to_ipa(x.group(1)) + " ", text
+        )
+    if text.find("[JA]") != -1:
+        text = re.sub(
+            r"\[JA\](.*?)\[JA\]", lambda x: japanese_to_ipa2(x.group(1)) + " ", text
+        )
+    if text.find("[EN]") != -1:
+        text = re.sub(
+            r"\[EN\](.*?)\[EN\]", lambda x: english_to_ipa2(x.group(1)) + " ", text
+        )
+    text = re.sub(r"\s+$", "", text)
+    text = re.sub(r"([^\.,!\?\-…~])$", r"\1.", text)
+    return text
diff --git a/apps/audio_cloning/vallex/g2p/english.py b/apps/audio_cloning/vallex/g2p/english.py
new file mode 100644
index 0000000000000000000000000000000000000000..96d31358a891055ae6dac101d04fa5fdc816fe11
--- /dev/null
+++ b/apps/audio_cloning/vallex/g2p/english.py
@@ -0,0 +1,197 @@
+import re
+
+import inflect
+from unidecode import unidecode
+
+"""from https://github.com/keithito/tacotron"""
+
+"""
+Cleaners are transformations that run over the input text at both training and eval time.
+
+Cleaners can be selected by passing a comma-delimited list of cleaner names as the "cleaners"
+hyperparameter. Some cleaners are English-specific. You'll typically want to use:
+  1. "english_cleaners" for English text
+  2. "transliteration_cleaners" for non-English text that can be transliterated to ASCII using
+     the Unidecode library (https://pypi.python.org/pypi/Unidecode)
+  3. "basic_cleaners" if you do not want to transliterate (in this case, you should also update
+     the symbols in symbols.py to match your data).
+"""
+
+
+_inflect = inflect.engine()
+_comma_number_re = re.compile(r"([0-9][0-9\,]+[0-9])")
+_decimal_number_re = re.compile(r"([0-9]+\.[0-9]+)")
+_pounds_re = re.compile(r"£([0-9\,]*[0-9]+)")
+_dollars_re = re.compile(r"\$([0-9\.\,]*[0-9]+)")
+_ordinal_re = re.compile(r"[0-9]+(st|nd|rd|th)")
+_number_re = re.compile(r"[0-9]+")
+
+# List of (regular expression, replacement) pairs for abbreviations:
+_abbreviations = [
+    (re.compile("\\b%s\\." % x[0], re.IGNORECASE), x[1])
+    for x in [
+        ("mrs", "misess"),
+        ("mr", "mister"),
+        ("dr", "doctor"),
+        ("st", "saint"),
+        ("co", "company"),
+        ("jr", "junior"),
+        ("maj", "major"),
+        ("gen", "general"),
+        ("drs", "doctors"),
+        ("rev", "reverend"),
+        ("lt", "lieutenant"),
+        ("hon", "honorable"),
+        ("sgt", "sergeant"),
+        ("capt", "captain"),
+        ("esq", "esquire"),
+        ("ltd", "limited"),
+        ("col", "colonel"),
+        ("ft", "fort"),
+    ]
+]
+
+
+# List of (ipa, lazy ipa) pairs:
+_lazy_ipa = [
+    (re.compile("%s" % x[0]), x[1])
+    for x in [
+        ("r", "ɹ"),
+        ("æ", "e"),
+        ("ɑ", "a"),
+        ("ɔ", "o"),
+        ("ð", "z"),
+        ("θ", "s"),
+        ("ɛ", "e"),
+        ("ɪ", "i"),
+        ("ʊ", "u"),
+        ("ʒ", "ʥ"),
+        ("ʤ", "ʥ"),
+        ("ˈ", "↓"),
+    ]
+]
+
+# List of (ipa, lazy ipa2) pairs:
+_lazy_ipa2 = [
+    (re.compile("%s" % x[0]), x[1])
+    for x in [
+        ("r", "ɹ"),
+        ("ð", "z"),
+        ("θ", "s"),
+        ("ʒ", "ʑ"),
+        ("ʤ", "dʑ"),
+        ("ˈ", "↓"),
+    ]
+]
+
+# List of (ipa, ipa2) pairs
+_ipa_to_ipa2 = [
+    (re.compile("%s" % x[0]), x[1]) for x in [("r", "ɹ"), ("ʤ", "dʒ"), ("ʧ", "tʃ")]
+]
+
+
+def expand_abbreviations(text):
+    for regex, replacement in _abbreviations:
+        text = re.sub(regex, replacement, text)
+    return text
+
+
+def collapse_whitespace(text):
+    return re.sub(r"\s+", " ", text)
+
+
+def _remove_commas(m):
+    return m.group(1).replace(",", "")
+
+
+def _expand_decimal_point(m):
+    return m.group(1).replace(".", " point ")
+
+
+def _expand_dollars(m):
+    match = m.group(1)
+    parts = match.split(".")
+    if len(parts) > 2:
+        return match + " dollars"  # Unexpected format
+    dollars = int(parts[0]) if parts[0] else 0
+    cents = int(parts[1]) if len(parts) > 1 and parts[1] else 0
+    if dollars and cents:
+        dollar_unit = "dollar" if dollars == 1 else "dollars"
+        cent_unit = "cent" if cents == 1 else "cents"
+        return "%s %s, %s %s" % (dollars, dollar_unit, cents, cent_unit)
+    elif dollars:
+        dollar_unit = "dollar" if dollars == 1 else "dollars"
+        return "%s %s" % (dollars, dollar_unit)
+    elif cents:
+        cent_unit = "cent" if cents == 1 else "cents"
+        return "%s %s" % (cents, cent_unit)
+    else:
+        return "zero dollars"
+
+
+def _expand_ordinal(m):
+    return _inflect.number_to_words(m.group(0))
+
+
+def _expand_number(m):
+    num = int(m.group(0))
+    if num > 1000 and num < 3000:
+        if num == 2000:
+            return "two thousand"
+        elif num > 2000 and num < 2010:
+            return "two thousand " + _inflect.number_to_words(num % 100)
+        elif num % 100 == 0:
+            return _inflect.number_to_words(num // 100) + " hundred"
+        else:
+            return _inflect.number_to_words(
+                num, andword="", zero="oh", group=2
+            ).replace(", ", " ")
+    else:
+        return _inflect.number_to_words(num, andword="")
+
+
+def normalize_numbers(text):
+    text = re.sub(_comma_number_re, _remove_commas, text)
+    text = re.sub(_pounds_re, r"\1 pounds", text)
+    text = re.sub(_dollars_re, _expand_dollars, text)
+    text = re.sub(_decimal_number_re, _expand_decimal_point, text)
+    text = re.sub(_ordinal_re, _expand_ordinal, text)
+    text = re.sub(_number_re, _expand_number, text)
+    return text
+
+
+def mark_dark_l(text):
+    return re.sub(r"l([^aeiouæɑɔəɛɪʊ ]*(?: |$))", lambda x: "ɫ" + x.group(1), text)
+
+
+def english_to_ipa(text):
+    import eng_to_ipa as ipa
+
+    text = unidecode(text).lower()
+    text = expand_abbreviations(text)
+    text = normalize_numbers(text)
+    phonemes = ipa.convert(text)
+    phonemes = collapse_whitespace(phonemes)
+    return phonemes
+
+
+def english_to_lazy_ipa(text):
+    text = english_to_ipa(text)
+    for regex, replacement in _lazy_ipa:
+        text = re.sub(regex, replacement, text)
+    return text
+
+
+def english_to_ipa2(text):
+    text = english_to_ipa(text)
+    text = mark_dark_l(text)
+    for regex, replacement in _ipa_to_ipa2:
+        text = re.sub(regex, replacement, text)
+    return text.replace("...", "…")
+
+
+def english_to_lazy_ipa2(text):
+    text = english_to_ipa(text)
+    for regex, replacement in _lazy_ipa2:
+        text = re.sub(regex, replacement, text)
+    return text
diff --git a/apps/audio_cloning/vallex/g2p/japanese.py b/apps/audio_cloning/vallex/g2p/japanese.py
new file mode 100644
index 0000000000000000000000000000000000000000..6f403df51736e52047ae414f2f77790a1d06cdac
--- /dev/null
+++ b/apps/audio_cloning/vallex/g2p/japanese.py
@@ -0,0 +1,173 @@
+import re
+
+from unidecode import unidecode
+
+# Regular expression matching Japanese without punctuation marks:
+_japanese_characters = re.compile(
+    r"[A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]"
+)
+
+# Regular expression matching non-Japanese characters or punctuation marks:
+_japanese_marks = re.compile(
+    r"[^A-Za-z\d\u3005\u3040-\u30ff\u4e00-\u9fff\uff11-\uff19\uff21-\uff3a\uff41-\uff5a\uff66-\uff9d]"
+)
+
+# List of (symbol, Japanese) pairs for marks:
+_symbols_to_japanese = [(re.compile("%s" % x[0]), x[1]) for x in [("％", "パーセント")]]
+
+# List of (romaji, ipa) pairs for marks:
+_romaji_to_ipa = [
+    (re.compile("%s" % x[0]), x[1])
+    for x in [
+        ("ts", "ʦ"),
+        ("u", "ɯ"),
+        ("j", "ʥ"),
+        ("y", "j"),
+        ("ni", "n^i"),
+        ("nj", "n^"),
+        ("hi", "çi"),
+        ("hj", "ç"),
+        ("f", "ɸ"),
+        ("I", "i*"),
+        ("U", "ɯ*"),
+        ("r", "ɾ"),
+    ]
+]
+
+# List of (romaji, ipa2) pairs for marks:
+_romaji_to_ipa2 = [
+    (re.compile("%s" % x[0]), x[1])
+    for x in [
+        ("u", "ɯ"),
+        ("ʧ", "tʃ"),
+        ("j", "dʑ"),
+        ("y", "j"),
+        ("ni", "n^i"),
+        ("nj", "n^"),
+        ("hi", "çi"),
+        ("hj", "ç"),
+        ("f", "ɸ"),
+        ("I", "i*"),
+        ("U", "ɯ*"),
+        ("r", "ɾ"),
+    ]
+]
+
+# List of (consonant, sokuon) pairs:
+_real_sokuon = [
+    (re.compile("%s" % x[0]), x[1])
+    for x in [
+        (r"Q([↑↓]*[kg])", r"k#\1"),
+        (r"Q([↑↓]*[tdjʧ])", r"t#\1"),
+        (r"Q([↑↓]*[sʃ])", r"s\1"),
+        (r"Q([↑↓]*[pb])", r"p#\1"),
+    ]
+]
+
+# List of (consonant, hatsuon) pairs:
+_real_hatsuon = [
+    (re.compile("%s" % x[0]), x[1])
+    for x in [
+        (r"N([↑↓]*[pbm])", r"m\1"),
+        (r"N([↑↓]*[ʧʥj])", r"n^\1"),
+        (r"N([↑↓]*[tdn])", r"n\1"),
+        (r"N([↑↓]*[kg])", r"ŋ\1"),
+    ]
+]
+
+
+def symbols_to_japanese(text):
+    for regex, replacement in _symbols_to_japanese:
+        text = re.sub(regex, replacement, text)
+    return text
+
+
+def japanese_to_romaji_with_accent(text):
+    """Reference https://r9y9.github.io/ttslearn/latest/notebooks/ch10_Recipe-Tacotron.html"""
+    import pyopenjtalk
+
+    text = symbols_to_japanese(text)
+    sentences = re.split(_japanese_marks, text)
+    marks = re.findall(_japanese_marks, text)
+    text = ""
+    for i, sentence in enumerate(sentences):
+        if re.match(_japanese_characters, sentence):
+            if text != "":
+                text += " "
+            labels = pyopenjtalk.extract_fullcontext(sentence)
+            for n, label in enumerate(labels):
+                phoneme = re.search(r"\-([^\+]*)\+", label).group(1)
+                if phoneme not in ["sil", "pau"]:
+                    text += (
+                        phoneme.replace("ch", "ʧ").replace("sh", "ʃ").replace("cl", "Q")
+                    )
+                else:
+                    continue
+                # n_moras = int(re.search(r'/F:(\d+)_', label).group(1))
+                a1 = int(re.search(r"/A:(\-?[0-9]+)\+", label).group(1))
+                a2 = int(re.search(r"\+(\d+)\+", label).group(1))
+                a3 = int(re.search(r"\+(\d+)/", label).group(1))
+                if re.search(r"\-([^\+]*)\+", labels[n + 1]).group(1) in ["sil", "pau"]:
+                    a2_next = -1
+                else:
+                    a2_next = int(re.search(r"\+(\d+)\+", labels[n + 1]).group(1))
+                # Accent phrase boundary
+                if a3 == 1 and a2_next == 1:
+                    text += " "
+                # Falling
+                elif a1 == 0 and a2_next == a2 + 1:
+                    text += "↓"
+                # Rising
+                elif a2 == 1 and a2_next == 2:
+                    text += "↑"
+        if i < len(marks):
+            text += unidecode(marks[i]).replace(" ", "")
+    return text
+
+
+def get_real_sokuon(text):
+    for regex, replacement in _real_sokuon:
+        text = re.sub(regex, replacement, text)
+    return text
+
+
+def get_real_hatsuon(text):
+    for regex, replacement in _real_hatsuon:
+        text = re.sub(regex, replacement, text)
+    return text
+
+
+def japanese_to_ipa(text):
+    text = japanese_to_romaji_with_accent(text).replace("...", "…")
+    text = re.sub(
+        r"([aiueo])\1+", lambda x: x.group(0)[0] + "ː" * (len(x.group(0)) - 1), text
+    )
+    text = get_real_sokuon(text)
+    text = get_real_hatsuon(text)
+    for regex, replacement in _romaji_to_ipa:
+        text = re.sub(regex, replacement, text)
+    return text
+
+
+def japanese_to_ipa2(text):
+    text = japanese_to_romaji_with_accent(text).replace("...", "…")
+    text = get_real_sokuon(text)
+    text = get_real_hatsuon(text)
+    for regex, replacement in _romaji_to_ipa2:
+        text = re.sub(regex, replacement, text)
+    return text
+
+
+def japanese_to_ipa3(text):
+    text = (
+        japanese_to_ipa2(text)
+        .replace("n^", "ȵ")
+        .replace("ʃ", "ɕ")
+        .replace("*", "\u0325")
+        .replace("#", "\u031a")
+    )
+    text = re.sub(
+        r"([aiɯeo])\1+", lambda x: x.group(0)[0] + "ː" * (len(x.group(0)) - 1), text
+    )
+    text = re.sub(r"((?:^|\s)(?:ts|tɕ|[kpt]))", r"\1ʰ", text)
+    return text
diff --git a/apps/audio_cloning/vallex/g2p/mandarin.py b/apps/audio_cloning/vallex/g2p/mandarin.py
new file mode 100644
index 0000000000000000000000000000000000000000..456b0587ee702f48cf6e183dfad6ed4b47fc0cf5
--- /dev/null
+++ b/apps/audio_cloning/vallex/g2p/mandarin.py
@@ -0,0 +1,337 @@
+import re
+
+import cn2an
+import jieba
+
+# List of (Latin alphabet, bopomofo) pairs:
+_latin_to_bopomofo = [
+    (re.compile("%s" % x[0], re.IGNORECASE), x[1])
+    for x in [
+        ("a", "ㄟˉ"),
+        ("b", "ㄅㄧˋ"),
+        ("c", "ㄙㄧˉ"),
+        ("d", "ㄉㄧˋ"),
+        ("e", "ㄧˋ"),
+        ("f", "ㄝˊㄈㄨˋ"),
+        ("g", "ㄐㄧˋ"),
+        ("h", "ㄝˇㄑㄩˋ"),
+        ("i", "ㄞˋ"),
+        ("j", "ㄐㄟˋ"),
+        ("k", "ㄎㄟˋ"),
+        ("l", "ㄝˊㄛˋ"),
+        ("m", "ㄝˊㄇㄨˋ"),
+        ("n", "ㄣˉ"),
+        ("o", "ㄡˉ"),
+        ("p", "ㄆㄧˉ"),
+        ("q", "ㄎㄧㄡˉ"),
+        ("r", "ㄚˋ"),
+        ("s", "ㄝˊㄙˋ"),
+        ("t", "ㄊㄧˋ"),
+        ("u", "ㄧㄡˉ"),
+        ("v", "ㄨㄧˉ"),
+        ("w", "ㄉㄚˋㄅㄨˋㄌㄧㄡˋ"),
+        ("x", "ㄝˉㄎㄨˋㄙˋ"),
+        ("y", "ㄨㄞˋ"),
+        ("z", "ㄗㄟˋ"),
+    ]
+]
+
+# List of (bopomofo, romaji) pairs:
+_bopomofo_to_romaji = [
+    (re.compile("%s" % x[0]), x[1])
+    for x in [
+        ("ㄅㄛ", "p⁼wo"),
+        ("ㄆㄛ", "pʰwo"),
+        ("ㄇㄛ", "mwo"),
+        ("ㄈㄛ", "fwo"),
+        ("ㄅ", "p⁼"),
+        ("ㄆ", "pʰ"),
+        ("ㄇ", "m"),
+        ("ㄈ", "f"),
+        ("ㄉ", "t⁼"),
+        ("ㄊ", "tʰ"),
+        ("ㄋ", "n"),
+        ("ㄌ", "l"),
+        ("ㄍ", "k⁼"),
+        ("ㄎ", "kʰ"),
+        ("ㄏ", "h"),
+        ("ㄐ", "ʧ⁼"),
+        ("ㄑ", "ʧʰ"),
+        ("ㄒ", "ʃ"),
+        ("ㄓ", "ʦ`⁼"),
+        ("ㄔ", "ʦ`ʰ"),
+        ("ㄕ", "s`"),
+        ("ㄖ", "ɹ`"),
+        ("ㄗ", "ʦ⁼"),
+        ("ㄘ", "ʦʰ"),
+        ("ㄙ", "s"),
+        ("ㄚ", "a"),
+        ("ㄛ", "o"),
+        ("ㄜ", "ə"),
+        ("ㄝ", "e"),
+        ("ㄞ", "ai"),
+        ("ㄟ", "ei"),
+        ("ㄠ", "au"),
+        ("ㄡ", "ou"),
+        ("ㄧㄢ", "yeNN"),
+        ("ㄢ", "aNN"),
+        ("ㄧㄣ", "iNN"),
+        ("ㄣ", "əNN"),
+        ("ㄤ", "aNg"),
+        ("ㄧㄥ", "iNg"),
+        ("ㄨㄥ", "uNg"),
+        ("ㄩㄥ", "yuNg"),
+        ("ㄥ", "əNg"),
+        ("ㄦ", "əɻ"),
+        ("ㄧ", "i"),
+        ("ㄨ", "u"),
+        ("ㄩ", "ɥ"),
+        ("ˉ", "→"),
+        ("ˊ", "↑"),
+        ("ˇ", "↓↑"),
+        ("ˋ", "↓"),
+        ("˙", ""),
+        ("，", ","),
+        ("。", "."),
+        ("！", "!"),
+        ("？", "?"),
+        ("—", "-"),
+    ]
+]
+
+# List of (romaji, ipa) pairs:
+_romaji_to_ipa = [
+    (re.compile("%s" % x[0], re.IGNORECASE), x[1])
+    for x in [
+        ("ʃy", "ʃ"),
+        ("ʧʰy", "ʧʰ"),
+        ("ʧ⁼y", "ʧ⁼"),
+        ("NN", "n"),
+        ("Ng", "ŋ"),
+        ("y", "j"),
+        ("h", "x"),
+    ]
+]
+
+# List of (bopomofo, ipa) pairs:
+_bopomofo_to_ipa = [
+    (re.compile("%s" % x[0]), x[1])
+    for x in [
+        ("ㄅㄛ", "p⁼wo"),
+        ("ㄆㄛ", "pʰwo"),
+        ("ㄇㄛ", "mwo"),
+        ("ㄈㄛ", "fwo"),
+        ("ㄅ", "p⁼"),
+        ("ㄆ", "pʰ"),
+        ("ㄇ", "m"),
+        ("ㄈ", "f"),
+        ("ㄉ", "t⁼"),
+        ("ㄊ", "tʰ"),
+        ("ㄋ", "n"),
+        ("ㄌ", "l"),
+        ("ㄍ", "k⁼"),
+        ("ㄎ", "kʰ"),
+        ("ㄏ", "x"),
+        ("ㄐ", "tʃ⁼"),
+        ("ㄑ", "tʃʰ"),
+        ("ㄒ", "ʃ"),
+        ("ㄓ", "ts`⁼"),
+        ("ㄔ", "ts`ʰ"),
+        ("ㄕ", "s`"),
+        ("ㄖ", "ɹ`"),
+        ("ㄗ", "ts⁼"),
+        ("ㄘ", "tsʰ"),
+        ("ㄙ", "s"),
+        ("ㄚ", "a"),
+        ("ㄛ", "o"),
+        ("ㄜ", "ə"),
+        ("ㄝ", "ɛ"),
+        ("ㄞ", "aɪ"),
+        ("ㄟ", "eɪ"),
+        ("ㄠ", "ɑʊ"),
+        ("ㄡ", "oʊ"),
+        ("ㄧㄢ", "jɛn"),
+        ("ㄩㄢ", "ɥæn"),
+        ("ㄢ", "an"),
+        ("ㄧㄣ", "in"),
+        ("ㄩㄣ", "ɥn"),
+        ("ㄣ", "ən"),
+        ("ㄤ", "ɑŋ"),
+        ("ㄧㄥ", "iŋ"),
+        ("ㄨㄥ", "ʊŋ"),
+        ("ㄩㄥ", "jʊŋ"),
+        ("ㄥ", "əŋ"),
+        ("ㄦ", "əɻ"),
+        ("ㄧ", "i"),
+        ("ㄨ", "u"),
+        ("ㄩ", "ɥ"),
+        ("ˉ", "→"),
+        ("ˊ", "↑"),
+        ("ˇ", "↓↑"),
+        ("ˋ", "↓"),
+        ("˙", ""),
+        ("，", ","),
+        ("。", "."),
+        ("！", "!"),
+        ("？", "?"),
+        ("—", "-"),
+    ]
+]
+
+# List of (bopomofo, ipa2) pairs:
+_bopomofo_to_ipa2 = [
+    (re.compile("%s" % x[0]), x[1])
+    for x in [
+        ("ㄅㄛ", "pwo"),
+        ("ㄆㄛ", "pʰwo"),
+        ("ㄇㄛ", "mwo"),
+        ("ㄈㄛ", "fwo"),
+        ("ㄅ", "p"),
+        ("ㄆ", "pʰ"),
+        ("ㄇ", "m"),
+        ("ㄈ", "f"),
+        ("ㄉ", "t"),
+        ("ㄊ", "tʰ"),
+        ("ㄋ", "n"),
+        ("ㄌ", "l"),
+        ("ㄍ", "k"),
+        ("ㄎ", "kʰ"),
+        ("ㄏ", "h"),
+        ("ㄐ", "tɕ"),
+        ("ㄑ", "tɕʰ"),
+        ("ㄒ", "ɕ"),
+        ("ㄓ", "tʂ"),
+        ("ㄔ", "tʂʰ"),
+        ("ㄕ", "ʂ"),
+        ("ㄖ", "ɻ"),
+        ("ㄗ", "ts"),
+        ("ㄘ", "tsʰ"),
+        ("ㄙ", "s"),
+        ("ㄚ", "a"),
+        ("ㄛ", "o"),
+        ("ㄜ", "ɤ"),
+        ("ㄝ", "ɛ"),
+        ("ㄞ", "aɪ"),
+        ("ㄟ", "eɪ"),
+        ("ㄠ", "ɑʊ"),
+        ("ㄡ", "oʊ"),
+        ("ㄧㄢ", "jɛn"),
+        ("ㄩㄢ", "yæn"),
+        ("ㄢ", "an"),
+        ("ㄧㄣ", "in"),
+        ("ㄩㄣ", "yn"),
+        ("ㄣ", "ən"),
+        ("ㄤ", "ɑŋ"),
+        ("ㄧㄥ", "iŋ"),
+        ("ㄨㄥ", "ʊŋ"),
+        ("ㄩㄥ", "jʊŋ"),
+        ("ㄥ", "ɤŋ"),
+        ("ㄦ", "əɻ"),
+        ("ㄧ", "i"),
+        ("ㄨ", "u"),
+        ("ㄩ", "y"),
+        ("ˉ", "˥"),
+        ("ˊ", "˧˥"),
+        ("ˇ", "˨˩˦"),
+        ("ˋ", "˥˩"),
+        ("˙", ""),
+        ("，", ","),
+        ("。", "."),
+        ("！", "!"),
+        ("？", "?"),
+        ("—", "-"),
+    ]
+]
+
+
+def number_to_chinese(text):
+    numbers = re.findall(r"\d+(?:\.?\d+)?", text)
+    for number in numbers:
+        text = text.replace(number, cn2an.an2cn(number), 1)
+    return text
+
+
+def chinese_to_bopomofo(text):
+    from pypinyin import BOPOMOFO, lazy_pinyin
+
+    text = text.replace("、", "，").replace("；", "，").replace("：", "，")
+    words = jieba.lcut(text, cut_all=False)
+    text = ""
+    for word in words:
+        bopomofos = lazy_pinyin(word, BOPOMOFO)
+        if not re.search("[\u4e00-\u9fff]", word):
+            text += word
+            continue
+        for i in range(len(bopomofos)):
+            bopomofos[i] = re.sub(r"([\u3105-\u3129])$", r"\1ˉ", bopomofos[i])
+        if text != "":
+            text += " "
+        text += "".join(bopomofos)
+    return text
+
+
+def latin_to_bopomofo(text):
+    for regex, replacement in _latin_to_bopomofo:
+        text = re.sub(regex, replacement, text)
+    return text
+
+
+def bopomofo_to_romaji(text):
+    for regex, replacement in _bopomofo_to_romaji:
+        text = re.sub(regex, replacement, text)
+    return text
+
+
+def bopomofo_to_ipa(text):
+    for regex, replacement in _bopomofo_to_ipa:
+        text = re.sub(regex, replacement, text)
+    return text
+
+
+def bopomofo_to_ipa2(text):
+    for regex, replacement in _bopomofo_to_ipa2:
+        text = re.sub(regex, replacement, text)
+    return text
+
+
+def chinese_to_romaji(text):
+    text = number_to_chinese(text)
+    text = chinese_to_bopomofo(text)
+    text = latin_to_bopomofo(text)
+    text = bopomofo_to_romaji(text)
+    text = re.sub("i([aoe])", r"y\1", text)
+    text = re.sub("u([aoəe])", r"w\1", text)
+    text = re.sub("([ʦsɹ]`[⁼ʰ]?)([→↓↑ ]+|$)", r"\1ɹ`\2", text).replace("ɻ", "ɹ`")
+    text = re.sub("([ʦs][⁼ʰ]?)([→↓↑ ]+|$)", r"\1ɹ\2", text)
+    return text
+
+
+def chinese_to_lazy_ipa(text):
+    text = chinese_to_romaji(text)
+    for regex, replacement in _romaji_to_ipa:
+        text = re.sub(regex, replacement, text)
+    return text
+
+
+def chinese_to_ipa(text):
+    text = number_to_chinese(text)
+    text = chinese_to_bopomofo(text)
+    text = latin_to_bopomofo(text)
+    text = bopomofo_to_ipa(text)
+    text = re.sub("i([aoe])", r"j\1", text)
+    text = re.sub("u([aoəe])", r"w\1", text)
+    text = re.sub("([sɹ]`[⁼ʰ]?)([→↓↑ ]+|$)", r"\1ɹ`\2", text).replace("ɻ", "ɹ`")
+    text = re.sub("([s][⁼ʰ]?)([→↓↑ ]+|$)", r"\1ɹ\2", text)
+    return text
+
+
+def chinese_to_ipa2(text):
+    text = number_to_chinese(text)
+    text = chinese_to_bopomofo(text)
+    text = latin_to_bopomofo(text)
+    text = bopomofo_to_ipa2(text)
+    text = re.sub(r"i([aoe])", r"j\1", text)
+    text = re.sub(r"u([aoəe])", r"w\1", text)
+    text = re.sub(r"([ʂɹ]ʰ?)([˩˨˧˦˥ ]+|$)", r"\1ʅ\2", text)
+    text = re.sub(r"(sʰ?)([˩˨˧˦˥ ]+|$)", r"\1ɿ\2", text)
+    return text
diff --git a/apps/audio_cloning/vallex/g2p/symbols.py b/apps/audio_cloning/vallex/g2p/symbols.py
new file mode 100644
index 0000000000000000000000000000000000000000..789e9df25d3d93d1976ef22d15d77f51d170ed00
--- /dev/null
+++ b/apps/audio_cloning/vallex/g2p/symbols.py
@@ -0,0 +1,76 @@
+'''
+Defines the set of symbols used in text input to the model.
+'''
+
+# japanese_cleaners
+# _pad        = '_'
+# _punctuation = ',.!?-'
+# _letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧ↓↑ '
+
+
+'''# japanese_cleaners2
+_pad        = '_'
+_punctuation = ',.!?-~…'
+_letters = 'AEINOQUabdefghijkmnoprstuvwyzʃʧʦ↓↑ '
+'''
+
+
+'''# korean_cleaners
+_pad        = '_'
+_punctuation = ',.!?…~'
+_letters = 'ㄱㄴㄷㄹㅁㅂㅅㅇㅈㅊㅋㅌㅍㅎㄲㄸㅃㅆㅉㅏㅓㅗㅜㅡㅣㅐㅔ '
+'''
+
+'''# chinese_cleaners
+_pad        = '_'
+_punctuation = '，。！？—…'
+_letters = 'ㄅㄆㄇㄈㄉㄊㄋㄌㄍㄎㄏㄐㄑㄒㄓㄔㄕㄖㄗㄘㄙㄚㄛㄜㄝㄞㄟㄠㄡㄢㄣㄤㄥㄦㄧㄨㄩˉˊˇˋ˙ '
+'''
+
+# # zh_ja_mixture_cleaners
+# _pad        = '_'
+# _punctuation = ',.!?-~…'
+# _letters = 'AEINOQUabdefghijklmnoprstuvwyzʃʧʦɯɹəɥ⁼ʰ`→↓↑ '
+
+
+'''# sanskrit_cleaners
+_pad        = '_'
+_punctuation = '।'
+_letters = 'ँंःअआइईउऊऋएऐओऔकखगघङचछजझञटठडढणतथदधनपफबभमयरलळवशषसहऽािीुूृॄेैोौ्ॠॢ '
+'''
+
+'''# cjks_cleaners
+_pad        = '_'
+_punctuation = ',.!?-~…'
+_letters = 'NQabdefghijklmnopstuvwxyzʃʧʥʦɯɹəɥçɸɾβŋɦː⁼ʰ`^#*=→↓↑ '
+'''
+
+'''# thai_cleaners
+_pad        = '_'
+_punctuation = '.!? '
+_letters = 'กขฃคฆงจฉชซฌญฎฏฐฑฒณดตถทธนบปผฝพฟภมยรฤลวศษสหฬอฮฯะัาำิีึืุูเแโใไๅๆ็่้๊๋์'
+'''
+
+# # cjke_cleaners2
+_pad        = '_'
+_punctuation = ',.!?-~…'
+_letters = 'NQabdefghijklmnopstuvwxyzɑæʃʑçɯɪɔɛɹðəɫɥɸʊɾʒθβŋɦ⁼ʰ`^#*=ˈˌ→↓↑ '
+
+
+'''# shanghainese_cleaners
+_pad        = '_'
+_punctuation = ',.!?…'
+_letters = 'abdfghiklmnopstuvyzøŋȵɑɔɕəɤɦɪɿʑʔʰ̩̃ᴀᴇ15678 '
+'''
+
+'''# chinese_dialect_cleaners
+_pad        = '_'
+_punctuation = ',.!?~…─'
+_letters = '#Nabdefghijklmnoprstuvwxyzæçøŋœȵɐɑɒɓɔɕɗɘəɚɛɜɣɤɦɪɭɯɵɷɸɻɾɿʂʅʊʋʌʏʑʔʦʮʰʷˀː˥˦˧˨˩̥̩̃̚ᴀᴇ↑↓∅ⱼ '
+'''
+
+# Export all symbols:
+symbols = [_pad] + list(_punctuation) + list(_letters)
+
+# Special symbol ids
+SPACE_ID = symbols.index(" ")
diff --git a/apps/audio_cloning/vallex/macros.py b/apps/audio_cloning/vallex/macros.py
new file mode 100644
index 0000000000000000000000000000000000000000..b25bc52b82a8ad4afc6a775066254be32c48cd76
--- /dev/null
+++ b/apps/audio_cloning/vallex/macros.py
@@ -0,0 +1,34 @@
+NUM_LAYERS = 12
+NUM_HEAD = 16
+N_DIM = 1024
+PREFIX_MODE = 1
+NUM_QUANTIZERS = 8
+SAMPLE_RATE = 24000
+
+lang2token = {
+    "zh": "[ZH]",
+    "ja": "[JA]",
+    "en": "[EN]",
+    "mix": "",
+}
+
+lang2code = {
+    "zh": 0,
+    "ja": 1,
+    "en": 2,
+}
+
+token2lang = {"[ZH]": "zh", "[JA]": "ja", "[EN]": "en", "": "mix"}
+
+code2lang = {
+    0: "zh",
+    1: "ja",
+    2: "en",
+}
+
+langdropdown2token = {
+    "English": "[EN]",
+    "中文": "[ZH]",
+    "日本語": "[JA]",
+    "Mix": "",
+}
diff --git a/apps/audio_cloning/vallex/main.py b/apps/audio_cloning/vallex/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..a38423b8aa89b9bd9b0cddc60ae37818dd2cfd49
--- /dev/null
+++ b/apps/audio_cloning/vallex/main.py
@@ -0,0 +1,461 @@
+import logging
+import multiprocessing
+import os
+import pathlib
+import platform
+import sys
+import tempfile
+import time
+
+import gradio as gr
+import langid
+import nltk
+import numpy as np
+import torch
+import torchaudio
+import whisper
+from vocos import Vocos
+
+from .data.collation import get_text_token_collater
+from .data.tokenizer import (
+    AudioTokenizer,
+    tokenize_audio,
+)
+from .descriptions import infer_from_audio_ja_md, top_ja_md
+from .examples import infer_from_audio_examples
+from .g2p import PhonemeBpeTokenizer
+from .macros import (
+    N_DIM,
+    NUM_HEAD,
+    NUM_LAYERS,
+    NUM_QUANTIZERS,
+    PREFIX_MODE,
+    lang2code,
+    lang2token,
+    langdropdown2token,
+    token2lang,
+)
+from .models.vallex import VALLE
+
+logger = logging.getLogger(__name__)
+
+# set languages
+langid.set_languages(["en", "zh", "ja"])
+
+# set nltk data path
+nltk.data.path = nltk.data.path + [os.path.join(os.getcwd(), "nltk_data")]
+logger.info("nltk_data path: %s", nltk.data.path)
+
+# get encoding
+logger.info(
+    "default encoding is %s,file system encoding is %s",
+    sys.getdefaultencoding(),
+    sys.getfilesystemencoding(),
+)
+
+# check python version
+logger.info("You are using Python version %s", platform.python_version())
+if sys.version_info[0] < 3 or sys.version_info[1] < 7:
+    logger.warning("The Python version is too low and may cause problems")
+if platform.system().lower() == "windows":
+    temp = pathlib.PosixPath
+    pathlib.PosixPath = pathlib.WindowsPath
+else:
+    temp = pathlib.WindowsPath
+    pathlib.WindowsPath = pathlib.PosixPath
+os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"] = "python"
+
+# set torch threads (guarded for hot-reload)
+thread_count = multiprocessing.cpu_count()
+logger.info("Use %d cpu cores for computing", thread_count)
+if not getattr(torch, "_vallex_threads_configured", False):
+    torch.set_num_threads(thread_count)
+    try:
+        torch.set_num_interop_threads(thread_count)
+    except RuntimeError as err:
+        logger.warning("Skipping set_num_interop_threads: %s", err)
+    torch._C._jit_set_profiling_executor(False)
+    torch._C._jit_set_profiling_mode(False)
+    torch._C._set_graph_executor_optimize(False)
+
+    # gradio のリロード時に torch.set_num_iterop_threads を実行するとエラーになるので、設定済みのフラグをセット
+    setattr(torch, "_vallex_threads_configured", True)
+else:
+    logger.info("Torch threads already configured; skipping reconfiguration")
+
+# set text tokenizer and collater
+logger.info("Setting text tokenizer and collater...")
+tokenizer_path = "./apps/audio_cloning/vallex/g2p/bpe_69.json"
+text_tokenizer = PhonemeBpeTokenizer(tokenizer_path=tokenizer_path)
+text_collater = get_text_token_collater()
+
+# set device
+logger.info("Setting device...")
+device = torch.device("cpu")
+if torch.cuda.is_available():
+    device = torch.device("cuda", 0)
+# if torch.backends.mps.is_available():
+#     device = torch.device("mps")
+logger.info("Device set to %s", device)
+
+# Download VALL-E-X model weights if not exists
+OUTPUT_DIR_CHECKPOINTS = "./models/checkpoints"
+if platform.system().lower() == "linux":
+    # docker（linux）環境では /app/models/checkpoints にする
+    OUTPUT_DIR_CHECKPOINTS = "/app/models/checkpoints"
+
+OUTPUT_FILENAME_CHECKPOINTS = "vallex-checkpoint.pt"
+OUTPUT_PATH_CHECKPOINTS = os.path.join(
+    OUTPUT_DIR_CHECKPOINTS, OUTPUT_FILENAME_CHECKPOINTS
+)
+if not os.path.exists(OUTPUT_DIR_CHECKPOINTS):
+    os.makedirs(OUTPUT_DIR_CHECKPOINTS, exist_ok=True)
+if not os.path.exists(OUTPUT_PATH_CHECKPOINTS):
+    import wget
+
+    logging.info(
+        "Downloading model from https://huggingface.co/Plachta/VALL-E-X/resolve/main/vallex-checkpoint.pt ..."
+    )
+    wget.download(
+        "https://huggingface.co/Plachta/VALL-E-X/resolve/main/vallex-checkpoint.pt",
+        out=OUTPUT_PATH_CHECKPOINTS,
+        bar=wget.bar_adaptive,
+    )
+    raise Exception(
+        "\n Model weights download failed, please go to 'https://huggingface.co/Plachta/VALL-E-X/resolve/main/vallex-checkpoint.pt'"
+        "\n manually download model weights and put it to {} .".format(
+            os.getcwd() + f"{OUTPUT_DIR_CHECKPOINTS}"
+        )
+    )
+
+# initialize VALL-E-X model
+model = VALLE(
+    N_DIM,
+    NUM_HEAD,
+    NUM_LAYERS,
+    norm_first=True,
+    add_prenet=False,
+    prefix_mode=PREFIX_MODE,
+    share_embedding=True,
+    nar_scale_factor=1.0,
+    prepend_bos=True,
+    num_quantizers=NUM_QUANTIZERS,
+)
+checkpoint = torch.load(OUTPUT_PATH_CHECKPOINTS, map_location="cpu", weights_only=False)
+missing_keys, unexpected_keys = model.load_state_dict(checkpoint["model"], strict=True)
+assert not missing_keys
+model.eval()
+
+# Encodec-based tokenizer: converts reference audio into discrete conditioning tokens for VALLE
+logger.info("Initializing Encodec-based tokenizer...")
+audio_tokenizer = AudioTokenizer(device)
+
+# Vocos vocoder: decodes VALLE's discrete acoustic codes back into a 24 kHz waveform
+vocos = Vocos.from_pretrained("charactr/vocos-encodec-24khz").to(device)
+
+# initialize ASR model
+OUTPUT_DIR_WHISPER = "./models/whisper"
+if platform.system().lower() == "linux":
+    OUTPUT_DIR_WHISPER = "/app/models/whisper"
+
+if not os.path.exists(OUTPUT_DIR_WHISPER):
+    os.makedirs(OUTPUT_DIR_WHISPER, exist_ok=True)
+try:
+    logger.info("Loading Whisper model...")
+    model_name = "tiny"
+    whisper_model = whisper.load_model(
+        model_name, download_root=OUTPUT_DIR_WHISPER
+    ).cpu()
+    logger.info("Whisper model loaded successfully")
+except Exception as e:
+    logging.info(e)
+    raise Exception(
+        "\n Whisper download failed or damaged, please go to "
+        "'https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt'"
+        "\n manually download model and put it to {} .".format(os.getcwd() + "/whisper")
+    )
+
+# Initialize Voice Presets
+logger.info("Initializing Voice Presets...")
+PRESETS_DIR = "apps/audio_cloning/vallex/presets"
+preset_list = os.walk(PRESETS_DIR).__next__()[2]
+preset_list = [preset[:-4] for preset in preset_list if preset.endswith(".npz")]
+
+
+def clear_prompts():
+    try:
+        path = tempfile.gettempdir()
+        for eachfile in os.listdir(path):
+            filename = os.path.join(path, eachfile)
+            if os.path.isfile(filename) and filename.endswith(".npz"):
+                lastmodifytime = os.stat(filename).st_mtime
+                endfiletime = time.time() - 60
+                if endfiletime > lastmodifytime:
+                    os.remove(filename)
+    except Exception as e:
+        logger.error("Error clearing prompts: %s", e)
+        return
+
+
+def transcribe_one(model, audio_path):
+    # load audio and pad/trim it to fit 30 seconds
+    audio = whisper.load_audio(audio_path)
+    audio = whisper.pad_or_trim(audio)
+
+    # make log-Mel spectrogram and move to the same device as the model
+    mel = whisper.log_mel_spectrogram(audio).to(model.device)
+
+    # detect the spoken language
+    _, probs = model.detect_language(mel)
+    print(f"Detected language: {max(probs, key=probs.get)}")
+    lang = max(probs, key=probs.get)
+    # decode the audio
+    options = whisper.DecodingOptions(
+        temperature=1.0,
+        best_of=5,
+        fp16=False if device == torch.device("cpu") else True,
+        sample_len=150,
+    )
+    result = whisper.decode(model, mel, options)
+
+    # print the recognized text
+    print(result.text)
+
+    text_pr = result.text
+    if text_pr.strip(" ")[-1] not in "?!.,。，？！。、":
+        text_pr += "."
+    return lang, text_pr
+
+
+def make_npz_prompt(name, uploaded_audio, recorded_audio, transcript_content):
+    global model, text_collater, text_tokenizer, audio_tokenizer
+    clear_prompts()
+    audio_prompt = uploaded_audio if uploaded_audio is not None else recorded_audio
+    sr, wav_pr = audio_prompt
+    if not isinstance(wav_pr, torch.FloatTensor):
+        wav_pr = torch.FloatTensor(wav_pr)
+    if wav_pr.abs().max() > 1:
+        wav_pr /= wav_pr.abs().max()
+    if wav_pr.size(-1) == 2:
+        wav_pr = wav_pr[:, 0]
+    if wav_pr.ndim == 1:
+        wav_pr = wav_pr.unsqueeze(0)
+    assert wav_pr.ndim and wav_pr.size(0) == 1
+
+    if transcript_content == "":
+        text_pr, lang_pr = make_prompt(name, wav_pr, sr, save=False)
+    else:
+        lang_pr = langid.classify(str(transcript_content))[0]
+        lang_token = lang2token[lang_pr]
+        text_pr = f"{lang_token}{str(transcript_content)}{lang_token}"
+    # tokenize audio
+    encoded_frames = tokenize_audio(audio_tokenizer, (wav_pr, sr))
+    audio_tokens = encoded_frames[0][0].transpose(2, 1).cpu().numpy()
+
+    # tokenize text
+    phonemes, _ = text_tokenizer.tokenize(text=f"{text_pr}".strip())
+    text_tokens, enroll_x_lens = text_collater([phonemes])
+
+    message = f"Detected language: {lang_pr}\n Detected text {text_pr}\n"
+
+    # save as npz file
+    np.savez(
+        os.path.join(tempfile.gettempdir(), f"{name}.npz"),
+        audio_tokens=audio_tokens,
+        text_tokens=text_tokens,
+        lang_code=lang2code[lang_pr],
+    )
+    return message, os.path.join(tempfile.gettempdir(), f"{name}.npz")
+
+
+def make_prompt(name, wav, sr, save=True):
+    global whisper_model
+    whisper_model.to(device)
+    if not isinstance(wav, torch.FloatTensor):
+        wav = torch.tensor(wav)
+    if wav.abs().max() > 1:
+        wav /= wav.abs().max()
+    if wav.size(-1) == 2:
+        wav = wav.mean(-1, keepdim=False)
+    if wav.ndim == 1:
+        wav = wav.unsqueeze(0)
+    assert wav.ndim and wav.size(0) == 1
+    torchaudio.save(f"./prompts/{name}.wav", wav, sr)
+    lang, text = transcribe_one(whisper_model, f"./prompts/{name}.wav")
+    lang_token = lang2token[lang]
+    text = lang_token + text + lang_token
+    with open(f"./prompts/{name}.txt", "w", encoding="utf-8") as f:
+        f.write(text)
+    if not save:
+        os.remove(f"./prompts/{name}.wav")
+        os.remove(f"./prompts/{name}.txt")
+
+    whisper_model.cpu()
+    torch.cuda.empty_cache()
+    return text, lang
+
+
+@torch.no_grad()
+def infer_from_audio(
+    text, language, accent, audio_prompt, record_audio_prompt, transcript_content
+):
+    global model, text_collater, text_tokenizer, audio_tokenizer
+    audio_prompt = audio_prompt if audio_prompt is not None else record_audio_prompt
+    sr, wav_pr = audio_prompt
+    if not isinstance(wav_pr, torch.FloatTensor):
+        wav_pr = torch.FloatTensor(wav_pr)
+    if wav_pr.abs().max() > 1:
+        wav_pr /= wav_pr.abs().max()
+    if wav_pr.size(-1) == 2:
+        wav_pr = wav_pr[:, 0]
+    if wav_pr.ndim == 1:
+        wav_pr = wav_pr.unsqueeze(0)
+    assert wav_pr.ndim and wav_pr.size(0) == 1
+
+    if transcript_content == "":
+        text_pr, lang_pr = make_prompt("dummy", wav_pr, sr, save=False)
+    else:
+        lang_pr = langid.classify(str(transcript_content))[0]
+        lang_token = lang2token[lang_pr]
+        text_pr = f"{lang_token}{str(transcript_content)}{lang_token}"
+
+    if language == "auto-detect":
+        lang_token = lang2token[langid.classify(text)[0]]
+    else:
+        lang_token = langdropdown2token[language]
+    lang = token2lang[lang_token]
+    text = lang_token + text + lang_token
+
+    # onload model
+    model.to(device)
+
+    # tokenize audio
+    encoded_frames = tokenize_audio(audio_tokenizer, (wav_pr, sr))
+    audio_prompts = encoded_frames[0][0].transpose(2, 1).to(device)
+
+    # tokenize text
+    logging.info(f"synthesize text: {text}")
+    phone_tokens, langs = text_tokenizer.tokenize(text=f"_{text}".strip())
+    text_tokens, text_tokens_lens = text_collater([phone_tokens])
+
+    enroll_x_lens = None
+    if text_pr:
+        text_prompts, _ = text_tokenizer.tokenize(text=f"{text_pr}".strip())
+        text_prompts, enroll_x_lens = text_collater([text_prompts])
+    text_tokens = torch.cat([text_prompts, text_tokens], dim=-1)
+    text_tokens_lens += enroll_x_lens
+    lang = lang if accent == "no-accent" else token2lang[langdropdown2token[accent]]
+    encoded_frames = model.inference(
+        text_tokens.to(device),
+        text_tokens_lens.to(device),
+        audio_prompts,
+        enroll_x_lens=enroll_x_lens,
+        top_k=-100,
+        temperature=1,
+        prompt_language=lang_pr,
+        text_language=langs if accent == "no-accent" else lang,
+        best_of=5,
+    )
+    # Decode with Vocos
+    frames = encoded_frames.permute(2, 0, 1)
+    features = vocos.codes_to_features(frames)
+    samples = vocos.decode(features, bandwidth_id=torch.tensor([2], device=device))
+
+    # offload model
+    model.to("cpu")
+    torch.cuda.empty_cache()
+
+    message = f"text prompt: {text_pr}\nsythesized text: {text}"
+    return message, (24000, samples.squeeze(0).cpu().numpy())
+
+
+def main():
+    app = gr.Blocks(title="VALL-E X")
+    with app:
+        gr.Markdown(top_ja_md)
+        with gr.Tab("Infer from audio"):
+            gr.Markdown(infer_from_audio_ja_md)
+            with gr.Row():
+                with gr.Column():
+                    textbox = gr.TextArea(
+                        label="音声合成で喋らせたいテキスト",
+                        # placeholder="Type your sentence here",
+                        placeholder="ここに音声合成で喋らせたいテキストを入力してください。",
+                        value="Welcome back, Master. What can I do for you today?",
+                        elem_id="tts-input",
+                    )
+                    language_dropdown = gr.Dropdown(
+                        choices=["auto-detect", "English", "中文", "日本語"],
+                        value="auto-detect",
+                        label="language",
+                    )
+                    accent_dropdown = gr.Dropdown(
+                        choices=["no-accent", "English", "中文", "日本語"],
+                        value="no-accent",
+                        label="accent",
+                    )
+                    textbox_transcript = gr.TextArea(
+                        label="Transcript",
+                        # placeholder="Write transcript here. (leave empty to use whisper)",
+                        placeholder="アップロードした音声、または録音した音声のテキストを入力してください。(whisper を使用する場合は空のままにしてください。)",
+                        value="",
+                        elem_id="prompt-name",
+                    )
+                    upload_audio_prompt = gr.Audio(
+                        label="音声アップロード",
+                        sources=["upload"],
+                        interactive=True,
+                    )
+                    record_audio_prompt = gr.Audio(
+                        label="音声を録音する",
+                        sources=["microphone"],
+                        interactive=True,
+                    )
+                with gr.Column():
+                    text_output = gr.Textbox(label="Message")
+                    audio_output = gr.Audio(label="Output Audio", elem_id="tts-audio")
+                    btn = gr.Button("音声合成を開始する")
+                    btn.click(
+                        infer_from_audio,
+                        inputs=[
+                            textbox,
+                            language_dropdown,
+                            accent_dropdown,
+                            upload_audio_prompt,
+                            record_audio_prompt,
+                            textbox_transcript,
+                        ],
+                        outputs=[text_output, audio_output],
+                    )
+                    textbox_mp = gr.TextArea(
+                        label="Prompt name",
+                        placeholder="Name your prompt here",
+                        value="prompt_1",
+                        elem_id="prompt-name",
+                    )
+                    btn_mp = gr.Button("Make prompt!")
+                    prompt_output = gr.File(interactive=False)
+                    btn_mp.click(
+                        make_npz_prompt,
+                        inputs=[
+                            textbox_mp,
+                            upload_audio_prompt,
+                            record_audio_prompt,
+                            textbox_transcript,
+                        ],
+                        outputs=[text_output, prompt_output],
+                    )
+            gr.Examples(
+                examples=infer_from_audio_examples,
+                inputs=[
+                    textbox,
+                    language_dropdown,
+                    accent_dropdown,
+                    upload_audio_prompt,
+                    record_audio_prompt,
+                    textbox_transcript,
+                ],
+                outputs=[text_output, audio_output],
+                fn=infer_from_audio,
+                cache_examples=False,
+            )
diff --git a/apps/audio_cloning/vallex/models/__init__.py b/apps/audio_cloning/vallex/models/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..afba460ee316d22162c93a3a47c89fd2233827e9
--- /dev/null
+++ b/apps/audio_cloning/vallex/models/__init__.py
@@ -0,0 +1,127 @@
+import argparse
+
+import torch.nn as nn
+
+from .transformer import Transformer
+from .vallex import VALLE, VALLF
+
+
+def add_model_arguments(parser: argparse.ArgumentParser):
+    parser.add_argument(
+        "--model-name",
+        type=str,
+        default="VALL-E",
+        help="VALL-E, VALL-F, Transformer.",
+    )
+    parser.add_argument(
+        "--decoder-dim",
+        type=int,
+        default=1024,
+        help="Embedding dimension in the decoder model.",
+    )
+    parser.add_argument(
+        "--nhead",
+        type=int,
+        default=16,
+        help="Number of attention heads in the Decoder layers.",
+    )
+    parser.add_argument(
+        "--num-decoder-layers",
+        type=int,
+        default=12,
+        help="Number of Decoder layers.",
+    )
+    parser.add_argument(
+        "--scale-factor",
+        type=float,
+        default=1.0,
+        help="Model scale factor which will be assigned different meanings in different models.",
+    )
+    parser.add_argument(
+        "--norm-first",
+        type=bool,
+        default=True,
+        help="Pre or Post Normalization.",
+    )
+    parser.add_argument(
+        "--add-prenet",
+        type=bool,
+        default=False,
+        help="Whether add PreNet after Inputs.",
+    )
+
+    # VALL-E & F
+    parser.add_argument(
+        "--prefix-mode",
+        type=int,
+        default=1,
+        help="The mode for how to prefix VALL-E NAR Decoder, "
+        "0: no prefix, 1: 0 to random, 2: random to random, 4: chunk of pre or post utterance.",
+    )
+    parser.add_argument(
+        "--share-embedding",
+        type=bool,
+        default=True,
+        help="Share the parameters of the output projection layer with the parameters of the acoustic embedding.",
+    )
+    parser.add_argument(
+        "--prepend-bos",
+        type=bool,
+        default=False,
+        help="Whether prepend <BOS> to the acoustic tokens -> AR Decoder inputs.",
+    )
+    parser.add_argument(
+        "--num-quantizers",
+        type=int,
+        default=8,
+        help="Number of Audio/Semantic quantization layers.",
+    )
+
+    # Transformer
+    parser.add_argument(
+        "--scaling-xformers",
+        type=bool,
+        default=False,
+        help="Apply Reworked Conformer scaling on Transformers.",
+    )
+
+
+def get_model(params) -> nn.Module:
+    if params.model_name.lower() in ["vall-f", "vallf"]:
+        model = VALLF(
+            params.decoder_dim,
+            params.nhead,
+            params.num_decoder_layers,
+            norm_first=params.norm_first,
+            add_prenet=params.add_prenet,
+            prefix_mode=params.prefix_mode,
+            share_embedding=params.share_embedding,
+            nar_scale_factor=params.scale_factor,
+            prepend_bos=params.prepend_bos,
+            num_quantizers=params.num_quantizers,
+        )
+    elif params.model_name.lower() in ["vall-e", "valle"]:
+        model = VALLE(
+            params.decoder_dim,
+            params.nhead,
+            params.num_decoder_layers,
+            norm_first=params.norm_first,
+            add_prenet=params.add_prenet,
+            prefix_mode=params.prefix_mode,
+            share_embedding=params.share_embedding,
+            nar_scale_factor=params.scale_factor,
+            prepend_bos=params.prepend_bos,
+            num_quantizers=params.num_quantizers,
+        )
+    else:
+        assert params.model_name in ["Transformer"]
+        model = Transformer(
+            params.decoder_dim,
+            params.nhead,
+            params.num_decoder_layers,
+            norm_first=params.norm_first,
+            add_prenet=params.add_prenet,
+            scaling_xformers=params.scaling_xformers,
+        )
+
+    return model
diff --git a/apps/audio_cloning/vallex/models/macros.py b/apps/audio_cloning/vallex/models/macros.py
new file mode 100644
index 0000000000000000000000000000000000000000..cbc54966f43b2ef27d87c3b4bc69cb866d2b8fd0
--- /dev/null
+++ b/apps/audio_cloning/vallex/models/macros.py
@@ -0,0 +1,11 @@
+# Text
+NUM_TEXT_TOKENS = 2048
+
+# Audio
+NUM_AUDIO_TOKENS = 1024  # EnCodec RVQ bins
+NUM_MEL_BINS = 100  # BigVGAN bigvgan_24khz_100band
+
+
+# Speaker
+NUM_SPEAKER_CLASSES = 4096
+SPEAKER_EMBEDDING_DIM = 64
diff --git a/apps/audio_cloning/vallex/models/transformer.py b/apps/audio_cloning/vallex/models/transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..2ab50ac9337dd8732866044a91ab51f636fd2752
--- /dev/null
+++ b/apps/audio_cloning/vallex/models/transformer.py
@@ -0,0 +1,386 @@
+# Copyright    2023                             (authors: Feiteng Li)
+#
+# 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.
+
+from functools import partial
+from typing import Any, Dict, List, Tuple, Union
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..modules.embedding import SinePositionalEmbedding, TokenEmbedding
+from ..modules.scaling import BalancedDoubleSwish, ScaledLinear
+from ..modules.transformer import (
+    BalancedBasicNorm,
+    IdentityNorm,
+    TransformerDecoderLayer,
+    TransformerEncoder,
+    TransformerEncoderLayer,
+)
+from .macros import NUM_MEL_BINS, NUM_TEXT_TOKENS
+
+# from icefall.utils import make_pad_mask
+# from torchmetrics.classification import BinaryAccuracy
+from .vallex import Transpose
+from .visualizer import visualize
+
+IdentityNorm = IdentityNorm
+
+
+class Transformer(nn.Module):
+    """It implements seq2seq Transformer TTS for debug(No StopPredictor and SpeakerEmbeding)
+    Neural Speech Synthesis with Transformer Network
+    https://arxiv.org/abs/1809.08895
+    """
+
+    def __init__(
+        self,
+        d_model: int,
+        nhead: int,
+        num_layers: int,
+        norm_first: bool = True,
+        add_prenet: bool = False,
+        scaling_xformers: bool = False,
+    ):
+        """
+        Args:
+          d_model:
+            The number of expected features in the input (required).
+          nhead:
+            The number of heads in the multiheadattention models (required).
+          num_layers:
+            The number of sub-decoder-layers in the decoder (required).
+        """
+        super().__init__()
+        self.text_embedding = TokenEmbedding(d_model, NUM_TEXT_TOKENS)  # W_x
+
+        if add_prenet:
+            self.encoder_prenet = nn.Sequential(
+                Transpose(),
+                nn.Conv1d(d_model, d_model, kernel_size=5, padding="same"),
+                nn.BatchNorm1d(d_model),
+                nn.ReLU(),
+                nn.Dropout(0.5),
+                nn.Conv1d(d_model, d_model, kernel_size=5, padding="same"),
+                nn.BatchNorm1d(d_model),
+                nn.ReLU(),
+                nn.Dropout(0.5),
+                nn.Conv1d(d_model, d_model, kernel_size=5, padding="same"),
+                nn.BatchNorm1d(d_model),
+                nn.ReLU(),
+                nn.Dropout(0.5),
+                Transpose(),
+                nn.Linear(d_model, d_model),
+            )
+
+            self.decoder_prenet = nn.Sequential(
+                nn.Linear(NUM_MEL_BINS, 256),
+                nn.ReLU(),
+                nn.Dropout(0.5),
+                nn.Linear(256, 256),
+                nn.ReLU(),
+                nn.Dropout(0.5),
+                nn.Linear(256, d_model),
+            )
+
+            assert scaling_xformers is False  # TODO: update this block
+        else:
+            self.encoder_prenet = nn.Identity()
+            if scaling_xformers:
+                self.decoder_prenet = ScaledLinear(NUM_MEL_BINS, d_model)
+            else:
+                self.decoder_prenet = nn.Linear(NUM_MEL_BINS, d_model)
+
+        self.encoder_position = SinePositionalEmbedding(
+            d_model,
+            dropout=0.1,
+            scale=False,
+        )
+        self.decoder_position = SinePositionalEmbedding(
+            d_model, dropout=0.1, scale=False
+        )
+
+        if scaling_xformers:
+            self.encoder = TransformerEncoder(
+                TransformerEncoderLayer(
+                    d_model,
+                    nhead,
+                    dim_feedforward=d_model * 4,
+                    dropout=0.1,
+                    batch_first=True,
+                    norm_first=norm_first,
+                    linear1_self_attention_cls=ScaledLinear,
+                    linear2_self_attention_cls=partial(
+                        ScaledLinear, initial_scale=0.01
+                    ),
+                    linear1_feedforward_cls=ScaledLinear,
+                    linear2_feedforward_cls=partial(ScaledLinear, initial_scale=0.01),
+                    activation=partial(
+                        BalancedDoubleSwish,
+                        channel_dim=-1,
+                        max_abs=10.0,
+                        min_prob=0.25,
+                    ),
+                    layer_norm_cls=IdentityNorm,
+                ),
+                num_layers=num_layers,
+                norm=BalancedBasicNorm(d_model) if norm_first else None,
+            )
+
+            self.decoder = nn.TransformerDecoder(
+                TransformerDecoderLayer(
+                    d_model,
+                    nhead,
+                    dim_feedforward=d_model * 4,
+                    dropout=0.1,
+                    batch_first=True,
+                    norm_first=norm_first,
+                    linear1_self_attention_cls=ScaledLinear,
+                    linear2_self_attention_cls=partial(
+                        ScaledLinear, initial_scale=0.01
+                    ),
+                    linear1_feedforward_cls=ScaledLinear,
+                    linear2_feedforward_cls=partial(ScaledLinear, initial_scale=0.01),
+                    activation=partial(
+                        BalancedDoubleSwish,
+                        channel_dim=-1,
+                        max_abs=10.0,
+                        min_prob=0.25,
+                    ),
+                    layer_norm_cls=IdentityNorm,
+                ),
+                num_layers=num_layers,
+                norm=BalancedBasicNorm(d_model) if norm_first else None,
+            )
+
+            self.predict_layer = ScaledLinear(d_model, NUM_MEL_BINS)
+            self.stop_layer = nn.Linear(d_model, 1)
+        else:
+            self.encoder = nn.TransformerEncoder(
+                nn.TransformerEncoderLayer(
+                    d_model,
+                    nhead,
+                    dim_feedforward=d_model * 4,
+                    activation=F.relu,
+                    dropout=0.1,
+                    batch_first=True,
+                    norm_first=norm_first,
+                ),
+                num_layers=num_layers,
+                norm=nn.LayerNorm(d_model) if norm_first else None,
+            )
+
+            self.decoder = nn.TransformerDecoder(
+                nn.TransformerDecoderLayer(
+                    d_model,
+                    nhead,
+                    dim_feedforward=d_model * 4,
+                    activation=F.relu,
+                    dropout=0.1,
+                    batch_first=True,
+                    norm_first=norm_first,
+                ),
+                num_layers=num_layers,
+                norm=nn.LayerNorm(d_model) if norm_first else None,
+            )
+
+            self.predict_layer = nn.Linear(d_model, NUM_MEL_BINS)
+            self.stop_layer = nn.Linear(d_model, 1)
+
+        self.stop_accuracy_metric = BinaryAccuracy(
+            threshold=0.5, multidim_average="global"
+        )
+
+    #     self.apply(self._init_weights)
+
+    # def _init_weights(self, module):
+    #     if isinstance(module, (nn.Linear)):
+    #         module.weight.data.normal_(mean=0.0, std=0.02)
+    #         if isinstance(module, nn.Linear) and module.bias is not None:
+    #             module.bias.data.zero_()
+    #     elif isinstance(module, nn.LayerNorm):
+    #         module.bias.data.zero_()
+    #         module.weight.data.fill_(1.0)
+    #     elif isinstance(module, nn.Embedding):
+    #         module.weight.data.normal_(mean=0.0, std=0.02)
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_lens: torch.Tensor,
+        y: torch.Tensor,
+        y_lens: torch.Tensor,
+        reduction: str = "sum",
+        train_stage: int = 0,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Union[torch.Tensor, None]]:
+        """
+        Args:
+          x:
+            A 2-D tensor of shape (N, S).
+          x_lens:
+            A 1-D tensor of shape (N,). It contains the number of tokens in `x`
+            before padding.
+          y:
+            A 3-D tensor of shape (N, T, 8).
+          y_lens:
+            A 1-D tensor of shape (N,). It contains the number of tokens in `x`
+            before padding.
+          train_stage:
+            Not used in this model.
+        Returns:
+          Return the predicted audio code matrix, cross-entropy loss and Top-10 accuracy.
+        """
+        del train_stage
+
+        assert x.ndim == 2, x.shape
+        assert x_lens.ndim == 1, x_lens.shape
+        assert y.ndim == 3, y.shape
+        assert y_lens.ndim == 1, y_lens.shape
+
+        assert torch.all(x_lens > 0)
+
+        # NOTE: x has been padded in TextTokenCollater
+        x_mask = make_pad_mask(x_lens).to(x.device)
+
+        x = self.text_embedding(x)
+        x = self.encoder_prenet(x)
+        x = self.encoder_position(x)
+        x = self.encoder(x, src_key_padding_mask=x_mask)
+
+        total_loss, metrics = 0.0, {}
+
+        y_mask = make_pad_mask(y_lens).to(y.device)
+        y_mask_float = y_mask.type(torch.float32)
+        data_mask = 1.0 - y_mask_float.unsqueeze(-1)
+
+        # Training
+        # AR Decoder
+        def pad_y(y):
+            y = F.pad(y, (0, 0, 1, 0, 0, 0), value=0).detach()
+            # inputs, targets
+            return y[:, :-1], y[:, 1:]
+
+        y, targets = pad_y(y * data_mask)  # mask padding as zeros
+
+        y_emb = self.decoder_prenet(y)
+        y_pos = self.decoder_position(y_emb)
+
+        y_len = y_lens.max()
+        tgt_mask = torch.triu(
+            torch.ones(y_len, y_len, device=y.device, dtype=torch.bool),
+            diagonal=1,
+        )
+        y_dec = self.decoder(
+            y_pos,
+            x,
+            tgt_mask=tgt_mask,
+            memory_key_padding_mask=x_mask,
+        )
+
+        predict = self.predict_layer(y_dec)
+        # loss
+        total_loss = F.mse_loss(predict, targets, reduction=reduction)
+
+        logits = self.stop_layer(y_dec).squeeze(-1)
+        stop_loss = F.binary_cross_entropy_with_logits(
+            logits,
+            y_mask_float.detach(),
+            weight=1.0 + y_mask_float.detach() * 4.0,
+            reduction=reduction,
+        )
+        metrics["stop_loss"] = stop_loss.detach()
+
+        stop_accuracy = self.stop_accuracy_metric(
+            (torch.sigmoid(logits) >= 0.5).type(torch.int64),
+            y_mask.type(torch.int64),
+        )
+        # icefall MetricsTracker.norm_items()
+        metrics["stop_accuracy"] = stop_accuracy.item() * y_lens.sum().type(
+            torch.float32
+        )
+
+        return ((x, predict), total_loss + 100.0 * stop_loss, metrics)
+
+    def inference(
+        self,
+        x: torch.Tensor,
+        x_lens: torch.Tensor,
+        y: Any = None,
+        **kwargs,
+    ) -> torch.Tensor:
+        """
+        Args:
+          x:
+            A 2-D tensor of shape (1, S).
+          x_lens:
+            A 1-D tensor of shape (1,). It contains the number of tokens in `x`
+            before padding.
+        Returns:
+          Return the predicted audio code matrix and cross-entropy loss.
+        """
+        assert x.ndim == 2, x.shape
+        assert x_lens.ndim == 1, x_lens.shape
+
+        assert torch.all(x_lens > 0)
+
+        x_mask = make_pad_mask(x_lens).to(x.device)
+
+        x = self.text_embedding(x)
+        x = self.encoder_prenet(x)
+        x = self.encoder_position(x)
+        x = self.encoder(x, src_key_padding_mask=x_mask)
+
+        x_mask = make_pad_mask(x_lens).to(x.device)
+
+        # AR Decoder
+        # TODO: Managing decoder steps avoid repetitive computation
+        y = torch.zeros(
+            [x.shape[0], 1, NUM_MEL_BINS], dtype=torch.float32, device=x.device
+        )
+        while True:
+            y_emb = self.decoder_prenet(y)
+            y_pos = self.decoder_position(y_emb)
+
+            tgt_mask = torch.triu(
+                torch.ones(y.shape[1], y.shape[1], device=y.device, dtype=torch.bool),
+                diagonal=1,
+            )
+
+            y_dec = self.decoder(
+                y_pos,
+                x,
+                tgt_mask=tgt_mask,
+                memory_mask=None,
+                memory_key_padding_mask=x_mask,
+            )
+            predict = self.predict_layer(y_dec[:, -1:])
+
+            logits = self.stop_layer(y_dec[:, -1:]) > 0  # sigmoid(0.0) = 0.5
+            if y.shape[1] > x_lens.max() * 10 or all(logits.cpu().numpy()):
+                print(f"TransformerTTS EOS [Text {x_lens[0]} -> Audio {y.shape[1]}]")
+                break
+
+            y = torch.concat([y, predict], dim=1)
+
+        return y[:, 1:]
+
+    def visualize(
+        self,
+        predicts: Tuple[torch.Tensor],
+        batch: Dict[str, Union[List, torch.Tensor]],
+        output_dir: str,
+        limit: int = 4,
+    ) -> None:
+        visualize(predicts, batch, output_dir, limit=limit)
diff --git a/apps/audio_cloning/vallex/models/vallex.py b/apps/audio_cloning/vallex/models/vallex.py
new file mode 100644
index 0000000000000000000000000000000000000000..053db79460c2ec190d8ce0e6504cd1a464b6a7d0
--- /dev/null
+++ b/apps/audio_cloning/vallex/models/vallex.py
@@ -0,0 +1,823 @@
+# Copyright    2023                             (authors: Feiteng Li)
+#
+# 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 random
+from typing import Dict, Iterator, List, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+# from icefall.utils import make_pad_mask
+# from torchmetrics.classification import MulticlassAccuracy
+from ..data.input_strategies import PromptedFeatures
+from ..modules.embedding import SinePositionalEmbedding, TokenEmbedding
+from ..modules.transformer import (
+    AdaptiveLayerNorm,
+    LayerNorm,
+    TransformerDecoderLayer,
+    TransformerEncoder,
+    TransformerEncoderLayer,
+)
+from .macros import NUM_AUDIO_TOKENS, NUM_TEXT_TOKENS
+
+
+class Transpose(nn.Identity):
+    """(N, T, D) -> (N, D, T)"""
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        return input.transpose(1, 2)
+
+
+# NOTE: There are two ways to implement the model
+#       1) [VALL-F] standard TransformerDecoder, use x as memory
+#       2) [VALL-E] modified TransformerDecoder like GPT-x(e.g. causal TransformerEncoder),
+#          use x as the prefix of decoder inputs
+class VALLF(nn.Module):
+    """It implements https://arxiv.org/abs/2301.02111
+    "Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers"
+    """
+
+    def __init__(
+        self,
+        d_model: int,
+        nhead: int,
+        num_layers: int,
+        norm_first: bool = True,
+        add_prenet: bool = False,
+        decoder_cls: Union[
+            nn.TransformerDecoder, nn.TransformerEncoder
+        ] = nn.TransformerDecoder,
+        decoder_layer_cls: Union[
+            TransformerDecoderLayer, TransformerEncoderLayer
+        ] = TransformerDecoderLayer,
+        prefix_mode: int = 0,
+        share_embedding: bool = True,
+        nar_scale_factor: float = 1.0,
+        prepend_bos: bool = True,
+        num_quantizers: int = 8,
+    ):
+        """
+        Args:
+          d_model:
+            The number of expected features in the input (required).
+          nhead:
+            The number of heads in the multiheadattention models (required).
+          num_layers:
+            The number of sub-decoder-layers in the decoder (required).
+        """
+        super().__init__()
+        nar_d_model = int(d_model * nar_scale_factor)
+
+        self.ar_text_embedding = TokenEmbedding(d_model, NUM_TEXT_TOKENS)  # W_x
+        self.nar_text_embedding = TokenEmbedding(nar_d_model, NUM_TEXT_TOKENS)
+
+        # ID NUM_AUDIO_TOKENS     -> PAD
+        # ID NUM_AUDIO_TOKENS + 1 -> BOS
+        self.ar_audio_prepend_bos = prepend_bos
+        self.ar_audio_embedding = TokenEmbedding(
+            d_model, NUM_AUDIO_TOKENS + 1 + int(prepend_bos)
+        )
+
+        # PreNet
+        if add_prenet:
+            self.ar_text_prenet = nn.Sequential(
+                Transpose(),
+                nn.Conv1d(d_model, d_model, kernel_size=5, padding="same"),
+                nn.BatchNorm1d(d_model),
+                nn.ReLU(),
+                nn.Dropout(0.5),
+                nn.Conv1d(d_model, d_model, kernel_size=5, padding="same"),
+                nn.BatchNorm1d(d_model),
+                nn.ReLU(),
+                nn.Dropout(0.5),
+                nn.Conv1d(d_model, d_model, kernel_size=5, padding="same"),
+                nn.BatchNorm1d(d_model),
+                nn.ReLU(),
+                nn.Dropout(0.5),
+                Transpose(),
+                nn.Linear(d_model, d_model),
+            )
+
+            self.ar_audio_prenet = nn.Sequential(
+                nn.Linear(d_model, 256),
+                nn.ReLU(),
+                nn.Dropout(0.25),
+                nn.Linear(256, 256),
+                nn.ReLU(),
+                nn.Dropout(0.25),
+                nn.Linear(256, d_model),
+            )
+        else:
+            self.ar_text_prenet = nn.Identity()
+            self.ar_audio_prenet = nn.Identity()
+
+        self.ar_text_position = SinePositionalEmbedding(
+            d_model,
+            dropout=0.1,
+            scale=False,
+            alpha=True,
+        )
+        self.ar_audio_position = SinePositionalEmbedding(
+            d_model,
+            dropout=0.1,
+            scale=False,
+            alpha=True,
+        )
+
+        self.ar_decoder = decoder_cls(
+            decoder_layer_cls(
+                d_model,
+                nhead,
+                dim_feedforward=d_model * 4,
+                dropout=0.1,
+                batch_first=True,
+                norm_first=norm_first,
+            ),
+            num_layers=num_layers,
+            norm=LayerNorm(d_model) if norm_first else None,
+        )
+        self.ar_predict_layer = nn.Linear(d_model, NUM_AUDIO_TOKENS + 1, bias=False)
+
+        self.rng = random.Random(0)
+        self.num_heads = nhead
+        self.prefix_mode = prefix_mode
+        self.num_quantizers = num_quantizers
+
+        assert num_quantizers >= 1
+        if num_quantizers > 1:
+            self.nar_audio_embeddings = nn.ModuleList(
+                [TokenEmbedding(nar_d_model, NUM_AUDIO_TOKENS + 1)]
+                + [
+                    TokenEmbedding(nar_d_model, NUM_AUDIO_TOKENS)
+                    for i in range(num_quantizers - 1)
+                ]
+            )  # W_a
+
+            # PreNet
+            if add_prenet:
+                self.nar_text_prenet = nn.Sequential(
+                    Transpose(),
+                    nn.Conv1d(nar_d_model, nar_d_model, kernel_size=5, padding="same"),
+                    nn.BatchNorm1d(nar_d_model),
+                    nn.ReLU(),
+                    nn.Dropout(0.5),
+                    nn.Conv1d(nar_d_model, nar_d_model, kernel_size=5, padding="same"),
+                    nn.BatchNorm1d(nar_d_model),
+                    nn.ReLU(),
+                    nn.Dropout(0.5),
+                    nn.Conv1d(nar_d_model, nar_d_model, kernel_size=5, padding="same"),
+                    nn.BatchNorm1d(nar_d_model),
+                    nn.ReLU(),
+                    nn.Dropout(0.5),
+                    Transpose(),
+                    nn.Linear(nar_d_model, nar_d_model),
+                )
+                self.nar_audio_prenet = nn.Sequential(
+                    nn.Linear(nar_d_model, 256),
+                    nn.ReLU(),
+                    nn.Dropout(0.25),
+                    nn.Linear(256, 256),
+                    nn.ReLU(),
+                    nn.Dropout(0.25),
+                    nn.Linear(256, nar_d_model),
+                )
+            else:
+                self.nar_text_prenet = nn.Identity()
+                self.nar_audio_prenet = nn.Identity()
+
+            self.nar_text_position = SinePositionalEmbedding(
+                nar_d_model,
+                dropout=0.0,
+                scale=False,
+                alpha=False,
+            )
+            self.nar_audio_position = SinePositionalEmbedding(
+                nar_d_model,
+                dropout=0.1,
+                scale=False,
+                alpha=False,
+            )
+
+            self.nar_decoder = decoder_cls(
+                decoder_layer_cls(
+                    nar_d_model,
+                    int(nhead * nar_scale_factor),
+                    dim_feedforward=nar_d_model * 4,
+                    dropout=0.1,
+                    batch_first=True,
+                    norm_first=norm_first,
+                    adaptive_layer_norm=True,
+                ),
+                num_layers=int(num_layers * nar_scale_factor),
+                norm=AdaptiveLayerNorm(nar_d_model, norm=nn.LayerNorm(nar_d_model))
+                if norm_first
+                else None,
+            )
+            self.nar_predict_layers = nn.ModuleList(
+                [
+                    nn.Linear(nar_d_model, NUM_AUDIO_TOKENS, bias=False)
+                    for i in range(num_quantizers - 1)
+                ]
+            )
+            self.nar_stage_embeddings = nn.ModuleList(
+                [TokenEmbedding(nar_d_model, 1) for i in range(num_quantizers - 1)]
+            )
+
+            if share_embedding:
+                # We share the parameters of the output projection layer with the parameters of the acoustic embedding Wa
+                # NOTE(Feiteng): In the experiment, this undermines accuracy
+                # self.ar_predict_layer.weight = self.ar_audio_embedding.weight
+
+                # We also share the parameters of the acoustic embedding layer and the output prediction layer,
+                # which means the weights of the j-th prediction layer are the same as the (j + 1)-th acoustic embedding layer.
+                for j in range(0, num_quantizers - 2):
+                    self.nar_predict_layers[j].weight = self.nar_audio_embeddings[
+                        j + 2
+                    ].weight
+
+    def stage_parameters(self, stage: int = 1) -> Iterator[nn.Parameter]:
+        assert stage > 0
+        if stage == 1:
+            for name, param in self.named_parameters():
+                if name.startswith("ar_"):
+                    print(f" AR parameter: {name}")
+                    yield param
+
+        if stage == 2:
+            for name, param in self.named_parameters():
+                if name.startswith("nar_"):
+                    print(f"NAR parameter: {name}")
+                    yield param
+
+    def stage_named_parameters(
+        self, stage: int = 1
+    ) -> Iterator[Tuple[str, nn.Parameter]]:
+        assert stage > 0
+        if stage == 1:
+            for pair in self.named_parameters():
+                if pair[0].startswith("ar_"):
+                    yield pair
+
+        if stage == 2:
+            for pair in self.named_parameters():
+                if pair[0].startswith("nar_"):
+                    yield pair
+
+    def pad_y_eos(self, y, y_mask_int, eos_id):
+        targets = F.pad(y, (0, 1), value=0) + eos_id * F.pad(
+            y_mask_int, (0, 1), value=1
+        )
+        # inputs, targets
+        if self.ar_audio_prepend_bos:
+            return (
+                F.pad(targets[:, :-1], (1, 0), value=NUM_AUDIO_TOKENS + 1),
+                targets,
+            )
+
+        return targets[:, :-1], targets[:, 1:]
+
+    def _prepare_prompts(
+        self, y, y_lens, codes, nar_stage, y_prompts_codes, prefix_mode
+    ):
+        # 5.1 For the NAR acoustic prompt tokens, we select a random segment waveform of 3 seconds
+        # from the same utterance.
+        # We implement this differently.
+        if prefix_mode == 0:
+            # no prefix
+            prefix_len = 0
+            y_emb = self.nar_audio_embeddings[0](y)
+            for j in range(1, nar_stage):
+                # Formula (4) (5)
+                y_emb = y_emb + self.nar_audio_embeddings[j](codes[..., j])
+        elif prefix_mode == 1:
+            # prefix at begining
+            int_low = (0.25 * y_lens.min()).type(torch.int64).item()
+            prefix_len = torch.randint(0, int_low * 2, size=()).item()
+            prefix_len = min(prefix_len, 225)  # 24000/320 * 3s = 225 frames
+
+            y_prompts = self.nar_audio_embeddings[0](y[:, :prefix_len])
+            y_emb = self.nar_audio_embeddings[0](y[:, prefix_len:])
+            for j in range(1, self.num_quantizers):
+                y_prompts += self.nar_audio_embeddings[j](codes[:, :prefix_len, j])
+                if j < nar_stage:
+                    y_emb += self.nar_audio_embeddings[j](codes[:, prefix_len:, j])
+            y_emb = torch.concat([y_prompts, y_emb], axis=1)
+        elif prefix_mode in [2, 4]:
+            if prefix_mode == 2:
+                # random prefix
+                prefix_len = min(225, int(0.25 * y_lens.min().item()))
+
+                y_prompts_codes = []
+                for b in range(codes.shape[0]):
+                    start = self.rng.randint(0, y_lens[b].item() - prefix_len)
+                    y_prompts_codes.append(
+                        torch.clone(codes[b, start : start + prefix_len])
+                    )
+                    codes[b, start : start + prefix_len, nar_stage] = NUM_AUDIO_TOKENS
+                y_prompts_codes = torch.stack(y_prompts_codes, dim=0)
+            else:
+                prefix_len = y_prompts_codes.shape[1]
+
+            y_prompts = self.nar_audio_embeddings[0](y_prompts_codes[..., 0])
+            y_emb = self.nar_audio_embeddings[0](y)
+            for j in range(1, self.num_quantizers):
+                y_prompts += self.nar_audio_embeddings[j](y_prompts_codes[..., j])
+                if j < nar_stage:
+                    y_emb += self.nar_audio_embeddings[j](codes[..., j])
+            y_emb = torch.concat([y_prompts, y_emb], axis=1)
+        else:
+            raise ValueError
+
+        return y_emb, prefix_len
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_lens: torch.Tensor,
+        y: Union[torch.Tensor, PromptedFeatures],
+        y_lens: Union[torch.Tensor, PromptedFeatures],
+        reduction: str = "sum",
+        train_stage: int = 0,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Union[torch.Tensor, None]]:
+        raise NotImplementedError
+
+    def inference(
+        self,
+        x: torch.Tensor,
+        x_lens: torch.Tensor,
+        y: torch.Tensor,
+        enroll_x_lens: Union[torch.Tensor, None] = None,
+        top_k: int = -100,
+        temperature: float = 1.0,
+    ) -> torch.Tensor:
+        raise NotImplementedError
+
+    def visualize(
+        self,
+        predicts: Tuple[torch.Tensor],
+        batch: Dict[str, Union[List, torch.Tensor]],
+        output_dir: str,
+        limit: int = 4,
+    ) -> None:
+        raise NotImplementedError
+
+
+class VALLE(VALLF):
+    """It implements https://arxiv.org/abs/2301.02111
+    "Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers"
+    """
+
+    def __init__(
+        self,
+        d_model: int,
+        nhead: int,
+        num_layers: int,
+        norm_first: bool = True,
+        add_prenet: bool = False,
+        prefix_mode: int = 0,
+        share_embedding: bool = True,
+        nar_scale_factor: float = 1.0,
+        **kwargs,
+    ):
+        """
+        Args:
+          d_model:
+            The number of expected features in the input (required).
+          nhead:
+            The number of heads in the multiheadattention models (required).
+          num_layers:
+            The number of sub-decoder-layers in the decoder (required).
+        """
+        super(VALLE, self).__init__(
+            d_model,
+            nhead,
+            num_layers,
+            norm_first=norm_first,
+            add_prenet=add_prenet,
+            decoder_cls=TransformerEncoder,
+            decoder_layer_cls=TransformerEncoderLayer,
+            prefix_mode=prefix_mode,
+            share_embedding=share_embedding,
+            nar_scale_factor=nar_scale_factor,
+            **kwargs,
+        )
+        self.language_ID = {
+            "en": 0,
+            "zh": 1,
+            "ja": 2,
+        }
+        self.ar_language_embedding = TokenEmbedding(d_model, len(self.language_ID))
+        self.nar_language_embedding = TokenEmbedding(d_model, len(self.language_ID))
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        x_lens: torch.Tensor,
+        y: Union[torch.Tensor, PromptedFeatures],
+        y_lens: Union[torch.Tensor, PromptedFeatures],
+        reduction: str = "sum",
+        train_stage: int = 0,
+        **kwargs,
+    ):
+        raise NotImplementedError
+
+    def inference(
+        self,
+        x: torch.Tensor,
+        x_lens: torch.Tensor,
+        y: torch.Tensor,
+        enroll_x_lens: torch.Tensor,
+        top_k: int = -100,
+        temperature: float = 1.0,
+        prompt_language: str = None,
+        text_language: str = None,
+        best_of: int = 1,
+        length_penalty: float = 1.0,
+        return_worst: bool = False,
+    ) -> torch.Tensor:
+        """
+        Args:
+          x:
+            A 2-D tensor of shape (1, S).
+          x_lens:
+            A 1-D tensor of shape (1,). It contains the number of tokens in `x`
+            before padding.
+          y:
+            A 3-D tensor of shape (1, T, 8).
+          top_k: (`optional`) int
+            The number of highest probability tokens to keep for top-k-filtering. Default to -100.
+          temperature: (`optional`) float
+            The value used to module the next token probabilities. Must be strictly positive. Default to 1.0.
+        Returns:
+          Return the predicted audio code matrix.
+        """
+        assert x.ndim == 2, x.shape
+        assert x_lens.ndim == 1, x_lens.shape
+        assert y.ndim == 3, y.shape
+        assert y.shape[0] == 1, y.shape
+
+        assert torch.all(x_lens > 0)
+
+        # NOTE: x has been padded in TextTokenCollater
+        text = x
+        x = self.ar_text_embedding(text)
+        # Add language embedding
+        prompt_language_id = torch.LongTensor(
+            np.array([self.language_ID[prompt_language]])
+        ).to(x.device)
+        if isinstance(text_language, str):
+            text_language_id = torch.LongTensor(
+                np.array([self.language_ID[text_language]])
+            ).to(x.device)
+        elif isinstance(text_language, List):
+            text_language_id = torch.LongTensor(
+                np.array([self.language_ID[tl] for tl in text_language])
+            ).to(x.device)
+        x[:, :enroll_x_lens, :] += self.ar_language_embedding(prompt_language_id)
+        x[:, enroll_x_lens:, :] += self.ar_language_embedding(text_language_id)
+        x = self.ar_text_prenet(x)
+        x = self.ar_text_position(x)
+
+        text_len = x_lens.max()
+        prompts = y
+        prefix_len = y.shape[1]
+
+        # AR Decoder
+        # TODO: Managing decoder steps avoid repetitive computation
+        y = prompts[..., 0]
+        if self.ar_audio_prepend_bos:
+            y = F.pad(y, (1, 0), value=NUM_AUDIO_TOKENS + 1)
+
+        x_len = x_lens.max()
+        x_attn_mask = torch.zeros((x_len, x_len), dtype=torch.bool)
+
+        kv_cache = None
+        use_kv_caching = True
+
+        sum_logprobs = torch.zeros(
+            best_of, device=y.device
+        )  # implement batch decoding here
+        x = x.repeat(best_of, 1, 1)
+        y = y.repeat(best_of, 1)
+        while True:
+            y_emb = self.ar_audio_embedding(y)
+            y_emb = self.ar_audio_prenet(y_emb)
+            y_pos = self.ar_audio_position(y_emb)
+            xy_pos = torch.concat([x, y_pos], dim=1)
+
+            y_len = y.shape[1]
+            x_attn_mask_pad = F.pad(
+                x_attn_mask,
+                (0, y_len),
+                value=True,
+            )
+            y_attn_mask = F.pad(
+                torch.triu(torch.ones(y_len, y_len, dtype=torch.bool), diagonal=1),
+                (x_len, 0),
+                value=False,
+            )
+            xy_attn_mask = torch.concat([x_attn_mask_pad, y_attn_mask], dim=0).to(
+                y.device
+            )
+
+            if use_kv_caching and kv_cache is not None:
+                xy_pos = xy_pos[:, [-1]]
+            else:
+                pass
+
+            xy_dec, kv_cache = self.ar_decoder.infer(
+                xy_pos,
+                mask=xy_attn_mask,
+                past_kv=kv_cache,
+                use_cache=use_kv_caching,
+            )
+            # xy_dec, _ = self.ar_decoder(
+            #     (xy_pos, None),
+            #     mask=xy_attn_mask,
+            # )
+
+            logits = self.ar_predict_layer(xy_dec[:, -1])
+            samples, current_logprobs = topk_sampling(
+                logits, top_k=top_k, top_p=1, temperature=temperature
+            )
+            sum_logprobs += current_logprobs * (y[:, -1] != NUM_AUDIO_TOKENS)
+            samples[y[:, -1] == NUM_AUDIO_TOKENS] = NUM_AUDIO_TOKENS
+            completed = (samples[:, -1] == NUM_AUDIO_TOKENS).all()
+            if completed or (y.shape[1] - prompts.shape[1]) > x_lens.max() * 16:
+                if prompts.shape[1] == y.shape[1]:
+                    raise SyntaxError("well trained model shouldn't reach here.")
+                lengths = torch.sum(y != NUM_AUDIO_TOKENS, dim=1)
+                avg_logprobs = sum_logprobs / lengths**length_penalty
+                # choose the best beam according to sum_logprobs
+                best_beam = y[torch.argmax(avg_logprobs), :]
+                worst_beam = y[torch.argmin(avg_logprobs), :]
+                # strip all eos tokens
+                best_beam = best_beam[best_beam != NUM_AUDIO_TOKENS]
+                worst_beam = worst_beam[worst_beam != NUM_AUDIO_TOKENS]
+                if return_worst:
+                    y = worst_beam.unsqueeze(0)
+                else:
+                    y = best_beam.unsqueeze(0)
+                print(f"VALL-E EOS [{prompts.shape[1]} -> {y.shape[1]}]")
+                break
+
+            y = torch.concat([y, samples], dim=1)
+
+        codes = [y[:, prefix_len + int(self.ar_audio_prepend_bos) :]]
+        if self.num_quantizers == 1:
+            return torch.stack(codes, dim=-1)
+
+        # Non-AR Decoders
+        y_emb = self.nar_audio_embeddings[0](y[:, int(self.ar_audio_prepend_bos) :])
+
+        if self.prefix_mode in [2, 4]:  # Exclude enrolled_phonemes
+            enrolled_len = enroll_x_lens.max().item()
+            # SOS + Synthesis Text + EOS
+            text = torch.concat(
+                [
+                    text[:, :1],
+                    text[:, enrolled_len - 1 :],
+                ],
+                dim=1,
+            )
+            text_len = text_len - (enrolled_len - 2)
+            assert text.shape[0] == 1
+
+        x = self.nar_text_embedding(text)
+        # Add language embedding
+        prompt_language_id = torch.LongTensor(
+            np.array([self.language_ID[prompt_language]])
+        ).to(x.device)
+        if isinstance(text_language, str):
+            text_language_id = torch.LongTensor(
+                np.array([self.language_ID[text_language]])
+            ).to(x.device)
+        elif isinstance(text_language, List):
+            text_language_id = torch.LongTensor(
+                np.array([self.language_ID[tl] for tl in text_language])
+            ).to(x.device)
+        x[:, :enroll_x_lens, :] += self.nar_language_embedding(prompt_language_id)
+        x[:, enroll_x_lens:, :] += self.nar_language_embedding(text_language_id)
+        x = self.nar_text_prenet(x)
+        x = self.nar_text_position(x)
+
+        if self.prefix_mode == 0:
+            for i, (predict_layer, embedding_layer) in enumerate(
+                zip(
+                    self.nar_predict_layers,
+                    self.nar_audio_embeddings[1:],
+                )
+            ):
+                y_pos = self.nar_audio_prenet(y_emb)
+                y_pos = self.nar_audio_position(y_pos)
+                xy_pos = torch.concat([x, y_pos], dim=1)
+
+                xy_dec, _ = self.nar_decoder(
+                    (xy_pos, self.nar_stage_embeddings[i].weight)
+                )
+                logits = predict_layer(xy_dec[:, text_len + prefix_len :])
+
+                samples = torch.argmax(logits, dim=-1)
+                codes.append(samples)
+
+                if i < self.num_quantizers - 2:
+                    y_emb[:, :prefix_len] += embedding_layer(prompts[..., i + 1])
+                    y_emb[:, prefix_len:] += embedding_layer(samples)
+        else:
+            for j in range(1, self.num_quantizers):
+                y_emb[:, :prefix_len] += self.nar_audio_embeddings[j](prompts[..., j])
+
+            for i, (predict_layer, embedding_layer) in enumerate(
+                zip(
+                    self.nar_predict_layers,
+                    self.nar_audio_embeddings[1:],
+                )
+            ):
+                y_pos = self.nar_audio_prenet(y_emb)
+                y_pos = self.nar_audio_position(y_pos)
+                xy_pos = torch.concat([x, y_pos], dim=1)
+
+                xy_dec, _ = self.nar_decoder(
+                    (xy_pos, self.nar_stage_embeddings[i].weight)
+                )
+                logits = predict_layer(xy_dec[:, text_len + prefix_len :])
+
+                samples = torch.argmax(logits, dim=-1)
+                codes.append(samples)
+
+                if i < self.num_quantizers - 2:
+                    y_emb[:, prefix_len:] += embedding_layer(samples)
+
+        assert len(codes) == self.num_quantizers
+        return torch.stack(codes, dim=-1)
+
+    def continual(
+        self,
+        x: torch.Tensor,
+        x_lens: torch.Tensor,
+        y: torch.Tensor,
+    ) -> torch.Tensor:
+        """
+        Args:
+          x:
+            A 2-D tensor of shape (1, S).
+          x_lens:
+            A 1-D tensor of shape (1,). It contains the number of tokens in `x`
+            before padding.
+          y:
+            A 3-D tensor of shape (1, T, 8).
+        Returns:
+          Return the predicted audio code matrix.
+        """
+        assert x.ndim == 2, x.shape
+        assert x_lens.ndim == 1, x_lens.shape
+        assert y.ndim == 3, y.shape
+        assert y.shape[0] == 1, y.shape
+
+        assert torch.all(x_lens > 0)
+        assert self.num_quantizers == 8
+
+        # NOTE: x has been padded in TextTokenCollater
+        text = x
+        x = self.ar_text_embedding(text)
+        x = self.ar_text_prenet(x)
+        x = self.ar_text_position(x)
+
+        text_len = x_lens.max()
+
+        prefix_len = min(int(y.shape[1] * 0.5), 3 * 75)
+
+        # AR Decoder
+        prompts = y[:, :prefix_len]
+
+        codes = [y[:, prefix_len:, 0]]
+        # Non-AR Decoders
+        x = self.nar_text_embedding(text)
+        x = self.nar_text_prenet(x)
+        x = self.nar_text_position(x)
+
+        y_emb = self.nar_audio_embeddings[0](y[..., 0])
+
+        if self.prefix_mode == 0:
+            for i, (predict_layer, embedding_layer) in enumerate(
+                zip(
+                    self.nar_predict_layers,
+                    self.nar_audio_embeddings[1:],
+                )
+            ):
+                y_pos = self.nar_audio_position(y_emb)
+                y_pos = self.nar_audio_prenet(y_pos)
+                xy_pos = torch.concat([x, y_pos], dim=1)
+
+                xy_dec, _ = self.nar_decoder(
+                    (xy_pos, self.nar_stage_embeddings[i].weight)
+                )
+                logits = predict_layer(xy_dec[:, text_len + prefix_len :])
+
+                samples = torch.argmax(logits, dim=-1)
+                codes.append(samples)
+
+                if i < 6:
+                    y_emb[:, :prefix_len] += embedding_layer(prompts[..., i + 1])
+                    y_emb[:, prefix_len:] += embedding_layer(samples)
+        else:
+            for j in range(1, 8):
+                y_emb[:, :prefix_len] += self.nar_audio_embeddings[j](prompts[..., j])
+
+            for i, (predict_layer, embedding_layer) in enumerate(
+                zip(
+                    self.nar_predict_layers,
+                    self.nar_audio_embeddings[1:],
+                )
+            ):
+                y_pos = self.nar_audio_prenet(y_emb)
+                y_pos = self.nar_audio_position(y_pos)
+                xy_pos = torch.concat([x, y_pos], dim=1)
+
+                xy_dec, _ = self.nar_decoder(
+                    (xy_pos, self.nar_stage_embeddings[i].weight)
+                )
+                logits = predict_layer(xy_dec[:, text_len + prefix_len :])
+
+                samples = torch.argmax(logits, dim=-1)
+                codes.append(samples)
+
+                if i < 6:
+                    y_emb[:, prefix_len:] += embedding_layer(samples)
+
+        assert len(codes) == 8
+        return torch.stack(codes, dim=-1)
+
+
+# https://github.com/microsoft/unilm/blob/master/xtune/src/transformers/modeling_utils.py
+def top_k_top_p_filtering(
+    logits, top_k=0, top_p=1.0, filter_value=-float("Inf"), min_tokens_to_keep=1
+):
+    """Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
+    Args:
+        logits: logits distribution shape (batch size, vocabulary size)
+        if top_k > 0: keep only top k tokens with highest probability (top-k filtering).
+        if top_p < 1.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
+            Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
+        Make sure we keep at least min_tokens_to_keep per batch example in the output
+    From: https://gist.github.com/thomwolf/1a5a29f6962089e871b94cbd09daf317
+    """
+    if top_k > 0:
+        top_k = min(max(top_k, min_tokens_to_keep), logits.size(-1))  # Safety check
+        # Remove all tokens with a probability less than the last token of the top-k
+        indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
+        logits[indices_to_remove] = filter_value
+
+    if top_p < 1.0:
+        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
+        cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)
+
+        # Remove tokens with cumulative probability above the threshold (token with 0 are kept)
+        sorted_indices_to_remove = cumulative_probs > top_p
+        if min_tokens_to_keep > 1:
+            # Keep at least min_tokens_to_keep (set to min_tokens_to_keep-1 because we add the first one below)
+            sorted_indices_to_remove[..., :min_tokens_to_keep] = 0
+        # Shift the indices to the right to keep also the first token above the threshold
+        sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
+        sorted_indices_to_remove[..., 0] = 0
+
+        # scatter sorted tensors to original indexing
+        indices_to_remove = sorted_indices_to_remove.scatter(
+            1, sorted_indices, sorted_indices_to_remove
+        )
+        logits[indices_to_remove] = filter_value
+    return logits
+
+
+def topk_sampling(logits, top_k=10, top_p=1.0, temperature=1.0):
+    # temperature: (`optional`) float
+    #     The value used to module the next token probabilities. Must be strictly positive. Default to 1.0.
+    # top_k: (`optional`) int
+    #     The number of highest probability vocabulary tokens to keep for top-k-filtering. Between 1 and infinity. Default to 50.
+    # top_p: (`optional`) float
+    #     The cumulative probability of parameter highest probability vocabulary tokens to keep for nucleus sampling. Must be between 0 and 1. Default to 1.
+
+    # Temperature (higher temperature => more likely to sample low probability tokens)
+    if temperature != 1.0:
+        logits = logits / temperature
+    # Top-p/top-k filtering
+    logits = top_k_top_p_filtering(logits, top_k=top_k, top_p=top_p)
+    # Sample
+    token = torch.multinomial(F.softmax(logits, dim=-1), num_samples=1)
+    logprobs = F.log_softmax(logits.float(), dim=-1)
+    current_logprobs = logprobs[torch.arange(logprobs.shape[0]), token.squeeze(1)]
+    return token, current_logprobs
diff --git a/apps/audio_cloning/vallex/models/visualizer.py b/apps/audio_cloning/vallex/models/visualizer.py
new file mode 100644
index 0000000000000000000000000000000000000000..05d056649b67ba10faa6e0293609eb4943e32ed1
--- /dev/null
+++ b/apps/audio_cloning/vallex/models/visualizer.py
@@ -0,0 +1,102 @@
+#!/usr/bin/env python3
+# Copyright    2023                           (authors: Feiteng Li)
+#
+# 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.
+
+
+from typing import Dict, List, Tuple, Union
+
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+
+
+def visualize(
+    predicts: Tuple[torch.Tensor],
+    batch: Dict[str, Union[List, torch.Tensor]],
+    output_dir: str,
+    limit: int = 4,
+) -> None:
+    text_tokens = batch["text_tokens"].to("cpu").detach().numpy()
+    text_tokens_lens = batch["text_tokens_lens"].to("cpu").detach().numpy()
+    audio_features = batch["audio_features"].to("cpu").detach().numpy()
+    audio_features_lens = batch["audio_features_lens"].to("cpu").detach().numpy()
+    assert text_tokens.ndim == 2
+
+    utt_ids, texts = batch["utt_id"], batch["text"]
+
+    encoder_outputs = predicts[0].to("cpu").type(torch.float32).detach().numpy()
+    decoder_outputs = predicts[1]
+    if isinstance(decoder_outputs, list):
+        decoder_outputs = decoder_outputs[-1]
+    decoder_outputs = decoder_outputs.to("cpu").type(torch.float32).detach().numpy()
+
+    vmin, vmax = 0, 1024  # Encodec
+    if decoder_outputs.dtype == np.float32:
+        vmin, vmax = -6, 0  # Fbank
+
+    num_figures = 3
+    for b, (utt_id, text) in enumerate(zip(utt_ids[:limit], texts[:limit])):
+        _ = plt.figure(figsize=(14, 8 * num_figures))
+
+        S = text_tokens_lens[b]
+        T = audio_features_lens[b]
+
+        # encoder
+        plt.subplot(num_figures, 1, 1)
+        plt.title(f"Text: {text}")
+        plt.imshow(
+            X=np.transpose(encoder_outputs[b]),
+            cmap=plt.get_cmap("jet"),
+            aspect="auto",
+            interpolation="nearest",
+        )
+        plt.gca().invert_yaxis()
+        plt.axvline(x=S - 0.4, linewidth=2, color="r")
+        plt.xlabel("Encoder Output")
+        plt.colorbar()
+
+        # decoder
+        plt.subplot(num_figures, 1, 2)
+        plt.imshow(
+            X=np.transpose(decoder_outputs[b]),
+            cmap=plt.get_cmap("jet"),
+            aspect="auto",
+            interpolation="nearest",
+            vmin=vmin,
+            vmax=vmax,
+        )
+        plt.gca().invert_yaxis()
+        plt.axvline(x=T - 0.4, linewidth=2, color="r")
+        plt.xlabel("Decoder Output")
+        plt.colorbar()
+
+        # target
+        plt.subplot(num_figures, 1, 3)
+        plt.imshow(
+            X=np.transpose(audio_features[b]),
+            cmap=plt.get_cmap("jet"),
+            aspect="auto",
+            interpolation="nearest",
+            vmin=vmin,
+            vmax=vmax,
+        )
+        plt.gca().invert_yaxis()
+        plt.axvline(x=T - 0.4, linewidth=2, color="r")
+        plt.xlabel("Decoder Target")
+        plt.colorbar()
+
+        plt.savefig(f"{output_dir}/{utt_id}.png")
+        plt.close()
diff --git a/apps/audio_cloning/vallex/modules/__init__.py b/apps/audio_cloning/vallex/modules/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/apps/audio_cloning/vallex/modules/activation.py b/apps/audio_cloning/vallex/modules/activation.py
new file mode 100644
index 0000000000000000000000000000000000000000..e51c8eb49a378b039ed819820444f784fdc52dba
--- /dev/null
+++ b/apps/audio_cloning/vallex/modules/activation.py
@@ -0,0 +1,612 @@
+from typing import Optional, Tuple, List
+import math
+
+import torch
+from torch import Tensor
+from torch.nn import Linear, Module
+from torch.nn import functional as F
+from torch.nn.init import constant_, xavier_normal_, xavier_uniform_
+from torch.nn.modules.linear import NonDynamicallyQuantizableLinear
+from torch.nn.parameter import Parameter
+
+def _in_projection_packed(
+    q: Tensor,
+    k: Tensor,
+    v: Tensor,
+    w: Tensor,
+    b: Optional[Tensor] = None,
+) -> List[Tensor]:
+    r"""
+    Performs the in-projection step of the attention operation, using packed weights.
+    Output is a triple containing projection tensors for query, key and value.
+
+    Args:
+        q, k, v: query, key and value tensors to be projected. For self-attention,
+            these are typically the same tensor; for encoder-decoder attention,
+            k and v are typically the same tensor. (We take advantage of these
+            identities for performance if they are present.) Regardless, q, k and v
+            must share a common embedding dimension; otherwise their shapes may vary.
+        w: projection weights for q, k and v, packed into a single tensor. Weights
+            are packed along dimension 0, in q, k, v order.
+        b: optional projection biases for q, k and v, packed into a single tensor
+            in q, k, v order.
+
+    Shape:
+        Inputs:
+        - q: :math:`(..., E)` where E is the embedding dimension
+        - k: :math:`(..., E)` where E is the embedding dimension
+        - v: :math:`(..., E)` where E is the embedding dimension
+        - w: :math:`(E * 3, E)` where E is the embedding dimension
+        - b: :math:`E * 3` where E is the embedding dimension
+
+        Output:
+        - in output list :math:`[q', k', v']`, each output tensor will have the
+            same shape as the corresponding input tensor.
+    """
+    E = q.size(-1)
+    if k is v:
+        if q is k:
+            # self-attention
+            return F.linear(q, w, b).chunk(3, dim=-1)
+        else:
+            # encoder-decoder attention
+            w_q, w_kv = w.split([E, E * 2])
+            if b is None:
+                b_q = b_kv = None
+            else:
+                b_q, b_kv = b.split([E, E * 2])
+            return (F.linear(q, w_q, b_q),) + F.linear(k, w_kv, b_kv).chunk(2, dim=-1)
+    else:
+        w_q, w_k, w_v = w.chunk(3)
+        if b is None:
+            b_q = b_k = b_v = None
+        else:
+            b_q, b_k, b_v = b.chunk(3)
+        return F.linear(q, w_q, b_q), F.linear(k, w_k, b_k), F.linear(v, w_v, b_v)
+
+def _scaled_dot_product_attention(
+    q: Tensor,
+    k: Tensor,
+    v: Tensor,
+    attn_mask: Optional[Tensor] = None,
+    dropout_p: float = 0.0,
+) -> Tuple[Tensor, Tensor]:
+    r"""
+    Computes scaled dot product attention on query, key and value tensors, using
+    an optional attention mask if passed, and applying dropout if a probability
+    greater than 0.0 is specified.
+    Returns a tensor pair containing attended values and attention weights.
+
+    Args:
+        q, k, v: query, key and value tensors. See Shape section for shape details.
+        attn_mask: optional tensor containing mask values to be added to calculated
+            attention. May be 2D or 3D; see Shape section for details.
+        dropout_p: dropout probability. If greater than 0.0, dropout is applied.
+
+    Shape:
+        - q: :math:`(B, Nt, E)` where B is batch size, Nt is the target sequence length,
+            and E is embedding dimension.
+        - key: :math:`(B, Ns, E)` where B is batch size, Ns is the source sequence length,
+            and E is embedding dimension.
+        - value: :math:`(B, Ns, E)` where B is batch size, Ns is the source sequence length,
+            and E is embedding dimension.
+        - attn_mask: either a 3D tensor of shape :math:`(B, Nt, Ns)` or a 2D tensor of
+            shape :math:`(Nt, Ns)`.
+
+        - Output: attention values have shape :math:`(B, Nt, E)`; attention weights
+            have shape :math:`(B, Nt, Ns)`
+    """
+    B, Nt, E = q.shape
+    q = q / math.sqrt(E)
+    # (B, Nt, E) x (B, E, Ns) -> (B, Nt, Ns)
+    if attn_mask is not None:
+        attn = torch.baddbmm(attn_mask, q, k.transpose(-2, -1))
+    else:
+        attn = torch.bmm(q, k.transpose(-2, -1))
+
+    attn = F.softmax(attn, dim=-1)
+    if dropout_p > 0.0:
+        attn = F.dropout(attn, p=dropout_p)
+    # (B, Nt, Ns) x (B, Ns, E) -> (B, Nt, E)
+    output = torch.bmm(attn, v)
+    return output, attn
+
+def multi_head_attention_forward(
+        x,
+        ipw,
+        ipb,
+        opw,
+        opb,
+        n_head,
+        attn_mask,
+        past_kv=None,
+        use_cache=False,
+):
+    # x = x.transpose(1, 0)
+    # tgt_len, bsz, embed_dim = x.shape
+    # head_dim = embed_dim // n_head
+    # q, k, v = _in_projection_packed(x, x, x, ipw, ipb)
+    # q = q.contiguous().view(tgt_len, bsz * n_head, head_dim).transpose(0, 1)
+    # k = k.contiguous().view(k.shape[0], bsz * n_head, head_dim).transpose(0, 1)
+    # v = v.contiguous().view(v.shape[0], bsz * n_head, head_dim).transpose(0, 1)
+
+    # new_attn_mask = torch.zeros_like(attn_mask, dtype=q.dtype)
+    # new_attn_mask.masked_fill_(attn_mask, float("-inf"))
+    # attn_mask = new_attn_mask
+    #
+    # attn_output, attn_output_weights = _scaled_dot_product_attention(q, k, v, attn_mask, 0.0)
+    # attn_output = attn_output.transpose(0, 1).contiguous().view(tgt_len * bsz, embed_dim)
+    # attn_output = torch._C._nn.linear(attn_output, opw, opb)
+    # attn_output = attn_output.view(tgt_len, bsz, attn_output.size(1))
+
+    B, T, C = x.size()
+
+    q, k, v = torch._C._nn.linear(x, ipw, ipb).chunk(3, dim=-1)
+    k = k.view(B, T, n_head, C // n_head).transpose(1, 2)  # (B, nh, T, hs)
+    q = q.view(B, T, n_head, C // n_head).transpose(1, 2)  # (B, nh, T, hs)
+    v = v.view(B, T, n_head, C // n_head).transpose(1, 2)  # (B, nh, T, hs)
+    if past_kv is not None:
+        past_key = past_kv[0]
+        past_value = past_kv[1]
+        k = torch.cat((past_key, k), dim=-2)
+        v = torch.cat((past_value, v), dim=-2)
+
+    FULL_T = k.shape[-2]
+
+    if use_cache is True:
+        present = (k, v)
+    else:
+        present = None
+
+    att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
+    att = att.masked_fill(attn_mask[FULL_T - T:FULL_T, :FULL_T], float('-inf'))
+    att = F.softmax(att, dim=-1)
+    y = att @ v  # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
+    y = y.transpose(1, 2).contiguous().view(B, T, C)  # re-assemble all head outputs side by side
+    y = torch._C._nn.linear(y, opw, opb)
+    return (y, present)
+
+
+class MultiheadAttention(Module):
+    r"""Allows the model to jointly attend to information
+    from different representation subspaces as described in the paper:
+    `Attention Is All You Need <https://arxiv.org/abs/1706.03762>`_.
+
+    Multi-Head Attention is defined as:
+
+    .. math::
+        \text{MultiHead}(Q, K, V) = \text{Concat}(head_1,\dots,head_h)W^O
+
+    where :math:`head_i = \text{Attention}(QW_i^Q, KW_i^K, VW_i^V)`.
+
+    ``forward()`` will use a special optimized implementation if all of the following
+    conditions are met:
+
+    - self attention is being computed (i.e., ``query``, ``key``, and ``value`` are the same tensor. This
+      restriction will be loosened in the future.)
+    - Either autograd is disabled (using ``torch.inference_mode`` or ``torch.no_grad``) or no tensor argument ``requires_grad``
+    - training is disabled (using ``.eval()``)
+    - dropout is 0
+    - ``add_bias_kv`` is ``False``
+    - ``add_zero_attn`` is ``False``
+    - ``batch_first`` is ``True`` and the input is batched
+    - ``kdim`` and ``vdim`` are equal to ``embed_dim``
+    - at most one of ``key_padding_mask`` or ``attn_mask`` is passed
+    - if a `NestedTensor <https://pytorch.org/docs/stable/nested.html>`_ is passed, neither ``key_padding_mask``
+      nor ``attn_mask`` is passed
+
+    If the optimized implementation is in use, a
+    `NestedTensor <https://pytorch.org/docs/stable/nested.html>`_ can be passed for
+    ``query``/``key``/``value`` to represent padding more efficiently than using a
+    padding mask. In this case, a `NestedTensor <https://pytorch.org/docs/stable/nested.html>`_
+    will be returned, and an additional speedup proportional to the fraction of the input
+    that is padding can be expected.
+
+    Args:
+        embed_dim: Total dimension of the model.
+        num_heads: Number of parallel attention heads. Note that ``embed_dim`` will be split
+            across ``num_heads`` (i.e. each head will have dimension ``embed_dim // num_heads``).
+        dropout: Dropout probability on ``attn_output_weights``. Default: ``0.0`` (no dropout).
+        bias: If specified, adds bias to input / output projection layers. Default: ``True``.
+        add_bias_kv: If specified, adds bias to the key and value sequences at dim=0. Default: ``False``.
+        add_zero_attn: If specified, adds a new batch of zeros to the key and value sequences at dim=1.
+            Default: ``False``.
+        kdim: Total number of features for keys. Default: ``None`` (uses ``kdim=embed_dim``).
+        vdim: Total number of features for values. Default: ``None`` (uses ``vdim=embed_dim``).
+        batch_first: If ``True``, then the input and output tensors are provided
+            as (batch, seq, feature). Default: ``False`` (seq, batch, feature).
+
+    Examples::
+
+        >>> # xdoctest: +SKIP
+        >>> multihead_attn = nn.MultiheadAttention(embed_dim, num_heads)
+        >>> attn_output, attn_output_weights = multihead_attn(query, key, value)
+
+    """
+    __constants__ = ["batch_first"]
+    bias_k: Optional[torch.Tensor]
+    bias_v: Optional[torch.Tensor]
+
+    def __init__(
+            self,
+            embed_dim,
+            num_heads,
+            dropout=0.0,
+            bias=True,
+            add_bias_kv=False,
+            add_zero_attn=False,
+            kdim=None,
+            vdim=None,
+            batch_first=False,
+            linear1_cls=Linear,
+            linear2_cls=Linear,
+            device=None,
+            dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super(MultiheadAttention, self).__init__()
+        self.embed_dim = embed_dim
+        self.kdim = kdim if kdim is not None else embed_dim
+        self.vdim = vdim if vdim is not None else embed_dim
+        self._qkv_same_embed_dim = (
+                self.kdim == embed_dim and self.vdim == embed_dim
+        )
+
+        self.num_heads = num_heads
+        self.dropout = dropout
+        self.batch_first = batch_first
+        self.head_dim = embed_dim // num_heads
+        assert (
+                self.head_dim * num_heads == self.embed_dim
+        ), "embed_dim must be divisible by num_heads"
+
+        if add_bias_kv:
+            self.bias_k = Parameter(
+                torch.empty((1, 1, embed_dim), **factory_kwargs)
+            )
+            self.bias_v = Parameter(
+                torch.empty((1, 1, embed_dim), **factory_kwargs)
+            )
+        else:
+            self.bias_k = self.bias_v = None
+
+        if linear1_cls == Linear:
+            if not self._qkv_same_embed_dim:
+                self.q_proj_weight = Parameter(
+                    torch.empty((embed_dim, embed_dim), **factory_kwargs)
+                )
+                self.k_proj_weight = Parameter(
+                    torch.empty((embed_dim, self.kdim), **factory_kwargs)
+                )
+                self.v_proj_weight = Parameter(
+                    torch.empty((embed_dim, self.vdim), **factory_kwargs)
+                )
+                self.register_parameter("in_proj_weight", None)
+            else:
+                self.in_proj_weight = Parameter(
+                    torch.empty((3 * embed_dim, embed_dim), **factory_kwargs)
+                )
+                self.register_parameter("q_proj_weight", None)
+                self.register_parameter("k_proj_weight", None)
+                self.register_parameter("v_proj_weight", None)
+
+            if bias:
+                self.in_proj_bias = Parameter(
+                    torch.empty(3 * embed_dim, **factory_kwargs)
+                )
+            else:
+                self.register_parameter("in_proj_bias", None)
+            self.out_proj = NonDynamicallyQuantizableLinear(
+                embed_dim, embed_dim, bias=bias, **factory_kwargs
+            )
+
+            self._reset_parameters()
+        else:
+            if not self._qkv_same_embed_dim:
+                raise NotImplementedError
+            else:
+                self.in_proj_linear = linear1_cls(
+                    embed_dim, 3 * embed_dim, bias=bias, **factory_kwargs
+                )
+                self.in_proj_weight = self.in_proj_linear.weight
+
+                self.register_parameter("q_proj_weight", None)
+                self.register_parameter("k_proj_weight", None)
+                self.register_parameter("v_proj_weight", None)
+
+                if bias:
+                    self.in_proj_bias = self.in_proj_linear.bias
+                else:
+                    self.register_parameter("in_proj_bias", None)
+
+            self.out_proj = linear2_cls(
+                embed_dim, embed_dim, bias=bias, **factory_kwargs
+            )
+
+            if self.bias_k is not None:
+                xavier_normal_(self.bias_k)
+            if self.bias_v is not None:
+                xavier_normal_(self.bias_v)
+
+        self.add_zero_attn = add_zero_attn
+
+    def _reset_parameters(self):
+        if self._qkv_same_embed_dim:
+            xavier_uniform_(self.in_proj_weight)
+        else:
+            xavier_uniform_(self.q_proj_weight)
+            xavier_uniform_(self.k_proj_weight)
+            xavier_uniform_(self.v_proj_weight)
+
+        if self.in_proj_bias is not None:
+            constant_(self.in_proj_bias, 0.0)
+            constant_(self.out_proj.bias, 0.0)
+
+        if self.bias_k is not None:
+            xavier_normal_(self.bias_k)
+        if self.bias_v is not None:
+            xavier_normal_(self.bias_v)
+
+    def __setstate__(self, state):
+        # Support loading old MultiheadAttention checkpoints generated by v1.1.0
+        if "_qkv_same_embed_dim" not in state:
+            state["_qkv_same_embed_dim"] = True
+
+        super(MultiheadAttention, self).__setstate__(state)
+
+    def forward(
+            self,
+            query: Tensor,
+            key: Tensor,
+            value: Tensor,
+            key_padding_mask: Optional[Tensor] = None,
+            need_weights: bool = True,
+            attn_mask: Optional[Tensor] = None,
+            average_attn_weights: bool = True,
+    ) -> Tuple[Tensor, Optional[Tensor]]:
+        r"""
+        Args:
+            query: Query embeddings of shape :math:`(L, E_q)` for unbatched input, :math:`(L, N, E_q)` when ``batch_first=False``
+                or :math:`(N, L, E_q)` when ``batch_first=True``, where :math:`L` is the target sequence length,
+                :math:`N` is the batch size, and :math:`E_q` is the query embedding dimension ``embed_dim``.
+                Queries are compared against key-value pairs to produce the output.
+                See "Attention Is All You Need" for more details.
+            key: Key embeddings of shape :math:`(S, E_k)` for unbatched input, :math:`(S, N, E_k)` when ``batch_first=False``
+                or :math:`(N, S, E_k)` when ``batch_first=True``, where :math:`S` is the source sequence length,
+                :math:`N` is the batch size, and :math:`E_k` is the key embedding dimension ``kdim``.
+                See "Attention Is All You Need" for more details.
+            value: Value embeddings of shape :math:`(S, E_v)` for unbatched input, :math:`(S, N, E_v)` when
+                ``batch_first=False`` or :math:`(N, S, E_v)` when ``batch_first=True``, where :math:`S` is the source
+                sequence length, :math:`N` is the batch size, and :math:`E_v` is the value embedding dimension ``vdim``.
+                See "Attention Is All You Need" for more details.
+            key_padding_mask: If specified, a mask of shape :math:`(N, S)` indicating which elements within ``key``
+                to ignore for the purpose of attention (i.e. treat as "padding"). For unbatched `query`, shape should be :math:`(S)`.
+                Binary and byte masks are supported.
+                For a binary mask, a ``True`` value indicates that the corresponding ``key`` value will be ignored for
+                the purpose of attention. For a float mask, it will be directly added to the corresponding ``key`` value.
+            need_weights: If specified, returns ``attn_output_weights`` in addition to ``attn_outputs``.
+                Default: ``True``.
+            attn_mask: If specified, a 2D or 3D mask preventing attention to certain positions. Must be of shape
+                :math:`(L, S)` or :math:`(N\cdot\text{num\_heads}, L, S)`, where :math:`N` is the batch size,
+                :math:`L` is the target sequence length, and :math:`S` is the source sequence length. A 2D mask will be
+                broadcasted across the batch while a 3D mask allows for a different mask for each entry in the batch.
+                Binary, byte, and float masks are supported. For a binary mask, a ``True`` value indicates that the
+                corresponding position is not allowed to attend. For a byte mask, a non-zero value indicates that the
+                corresponding position is not allowed to attend. For a float mask, the mask values will be added to
+                the attention weight.
+            average_attn_weights: If true, indicates that the returned ``attn_weights`` should be averaged across
+                heads. Otherwise, ``attn_weights`` are provided separately per head. Note that this flag only has an
+                effect when ``need_weights=True``. Default: ``True`` (i.e. average weights across heads)
+
+        Outputs:
+            - **attn_output** - Attention outputs of shape :math:`(L, E)` when input is unbatched,
+              :math:`(L, N, E)` when ``batch_first=False`` or :math:`(N, L, E)` when ``batch_first=True``,
+              where :math:`L` is the target sequence length, :math:`N` is the batch size, and :math:`E` is the
+              embedding dimension ``embed_dim``.
+            - **attn_output_weights** - Only returned when ``need_weights=True``. If ``average_attn_weights=True``,
+              returns attention weights averaged across heads of shape :math:`(L, S)` when input is unbatched or
+              :math:`(N, L, S)`, where :math:`N` is the batch size, :math:`L` is the target sequence length, and
+              :math:`S` is the source sequence length. If ``average_attn_weights=False``, returns attention weights per
+              head of shape :math:`(\text{num\_heads}, L, S)` when input is unbatched or :math:`(N, \text{num\_heads}, L, S)`.
+
+            .. note::
+                `batch_first` argument is ignored for unbatched inputs.
+        """
+        is_batched = query.dim() == 3
+        if key_padding_mask is not None:
+            _kpm_dtype = key_padding_mask.dtype
+            if _kpm_dtype != torch.bool and not torch.is_floating_point(
+                    key_padding_mask
+            ):
+                raise AssertionError(
+                    "only bool and floating types of key_padding_mask are supported"
+                )
+        why_not_fast_path = ""
+        if not is_batched:
+            why_not_fast_path = f"input not batched; expected query.dim() of 3 but got {query.dim()}"
+        elif query is not key or key is not value:
+            # When lifting this restriction, don't forget to either
+            # enforce that the dtypes all match or test cases where
+            # they don't!
+            why_not_fast_path = "non-self attention was used (query, key, and value are not the same Tensor)"
+        elif (
+                self.in_proj_bias is not None
+                and query.dtype != self.in_proj_bias.dtype
+        ):
+            why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_bias ({self.in_proj_bias.dtype}) don't match"
+        elif (
+                self.in_proj_weight is not None
+                and query.dtype != self.in_proj_weight.dtype
+        ):
+            # this case will fail anyway, but at least they'll get a useful error message.
+            why_not_fast_path = f"dtypes of query ({query.dtype}) and self.in_proj_weight ({self.in_proj_weight.dtype}) don't match"
+        elif self.training:
+            why_not_fast_path = "training is enabled"
+        elif not self.batch_first:
+            why_not_fast_path = "batch_first was not True"
+        elif self.bias_k is not None:
+            why_not_fast_path = "self.bias_k was not None"
+        elif self.bias_v is not None:
+            why_not_fast_path = "self.bias_v was not None"
+        elif self.dropout:
+            why_not_fast_path = f"dropout was {self.dropout}, required zero"
+        elif self.add_zero_attn:
+            why_not_fast_path = "add_zero_attn was enabled"
+        elif not self._qkv_same_embed_dim:
+            why_not_fast_path = "_qkv_same_embed_dim was not True"
+        elif attn_mask is not None:
+            why_not_fast_path = "attn_mask was not None"
+        elif query.is_nested and key_padding_mask is not None:
+            why_not_fast_path = (
+                "key_padding_mask is not supported with NestedTensor input"
+            )
+        elif self.num_heads % 2 == 1:
+            why_not_fast_path = "num_heads is odd"
+        elif torch.is_autocast_enabled():
+            why_not_fast_path = "autocast is enabled"
+
+        if not why_not_fast_path:
+            tensor_args = (
+                query,
+                key,
+                value,
+                self.in_proj_weight,
+                self.in_proj_bias,
+                self.out_proj.weight,
+                self.out_proj.bias,
+            )
+            # We have to use list comprehensions below because TorchScript does not support
+            # generator expressions.
+            if torch.overrides.has_torch_function(tensor_args):
+                why_not_fast_path = "some Tensor argument has_torch_function"
+            elif not all(
+                    [
+                        (x is None or x.is_cuda or "cpu" in str(x.device))
+                        for x in tensor_args
+                    ]
+            ):
+                why_not_fast_path = (
+                    "some Tensor argument is neither CUDA nor CPU"
+                )
+            elif torch.is_grad_enabled() and any(
+                    [x is not None and x.requires_grad for x in tensor_args]
+            ):
+                why_not_fast_path = (
+                    "grad is enabled and at least one of query or the "
+                    "input/output projection weights or biases requires_grad"
+                )
+            if not why_not_fast_path:
+                return torch._native_multi_head_attention(
+                    query,
+                    key,
+                    value,
+                    self.embed_dim,
+                    self.num_heads,
+                    self.in_proj_weight,
+                    self.in_proj_bias,
+                    self.out_proj.weight,
+                    self.out_proj.bias,
+                    key_padding_mask
+                    if key_padding_mask is not None
+                    else attn_mask,
+                    need_weights,
+                    average_attn_weights,
+                    1
+                    if key_padding_mask is not None
+                    else 0
+                    if attn_mask is not None
+                    else None,
+                )
+
+        any_nested = query.is_nested or key.is_nested or value.is_nested
+        assert not any_nested, (
+                "MultiheadAttention does not support NestedTensor outside of its fast path. "
+                + f"The fast path was not hit because {why_not_fast_path}"
+        )
+
+        if self.batch_first and is_batched:
+            # make sure that the transpose op does not affect the "is" property
+            if key is value:
+                if query is key:
+                    query = key = value = query.transpose(1, 0)
+                else:
+                    query, key = [x.transpose(1, 0) for x in (query, key)]
+                    value = key
+            else:
+                query, key, value = [
+                    x.transpose(1, 0) for x in (query, key, value)
+                ]
+
+        if not self._qkv_same_embed_dim:
+            attn_output, attn_output_weights = F.multi_head_attention_forward(
+                query,
+                key,
+                value,
+                self.embed_dim,
+                self.num_heads,
+                self.in_proj_weight,
+                self.in_proj_bias,
+                self.bias_k,
+                self.bias_v,
+                self.add_zero_attn,
+                self.dropout,
+                self.out_proj.weight,
+                self.out_proj.bias,
+                training=self.training,
+                key_padding_mask=key_padding_mask,
+                need_weights=need_weights,
+                attn_mask=attn_mask,
+                use_separate_proj_weight=True,
+                q_proj_weight=self.q_proj_weight,
+                k_proj_weight=self.k_proj_weight,
+                v_proj_weight=self.v_proj_weight,
+                average_attn_weights=average_attn_weights,
+            )
+        else:
+            attn_output, attn_output_weights = F.multi_head_attention_forward(
+                query,
+                key,
+                value,
+                self.embed_dim,
+                self.num_heads,
+                self.in_proj_weight,
+                self.in_proj_bias,
+                self.bias_k,
+                self.bias_v,
+                self.add_zero_attn,
+                self.dropout,
+                self.out_proj.weight,
+                self.out_proj.bias,
+                training=self.training,
+                key_padding_mask=key_padding_mask,
+                need_weights=need_weights,
+                attn_mask=attn_mask,
+                average_attn_weights=average_attn_weights,
+            )
+        if self.batch_first and is_batched:
+            return attn_output.transpose(1, 0), attn_output_weights
+        else:
+            return attn_output, attn_output_weights
+
+    def infer(self,
+              x: Tensor,
+              key_padding_mask: Optional[Tensor] = None,
+              need_weights: bool = True,
+              attn_mask: Optional[Tensor] = None,
+              average_attn_weights: bool = True,
+              past_kv = None,
+              use_cache = False
+              ):
+        # x = x.transpose(1, 0)
+        y, kv = multi_head_attention_forward(
+                x=x,
+                ipw=self.in_proj_weight,
+                ipb=self.in_proj_bias,
+                opw=self.out_proj.weight,
+                opb=self.out_proj.bias,
+                n_head=self.num_heads,
+                attn_mask=attn_mask,
+                past_kv=past_kv,
+                use_cache=use_cache,
+        )
+        return (y, kv)
diff --git a/apps/audio_cloning/vallex/modules/embedding.py b/apps/audio_cloning/vallex/modules/embedding.py
new file mode 100644
index 0000000000000000000000000000000000000000..17f6c316da3de6a432f4d43f9563800fdb6d58c4
--- /dev/null
+++ b/apps/audio_cloning/vallex/modules/embedding.py
@@ -0,0 +1,97 @@
+# Copyright    2023                             (authors: Feiteng Li)
+#
+# 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 math
+
+import torch
+import torch.nn as nn
+
+
+class TokenEmbedding(nn.Module):
+    def __init__(
+        self,
+        dim_model: int,
+        vocab_size: int,
+        dropout: float = 0.0,
+    ):
+        super().__init__()
+
+        self.vocab_size = vocab_size
+        self.dim_model = dim_model
+
+        self.dropout = torch.nn.Dropout(p=dropout)
+        self.word_embeddings = nn.Embedding(self.vocab_size, self.dim_model)
+
+    @property
+    def weight(self) -> torch.Tensor:
+        return self.word_embeddings.weight
+
+    def embedding(self, index: int) -> torch.Tensor:
+        return self.word_embeddings.weight[index : index + 1]
+
+    def forward(self, x: torch.Tensor):
+        X = self.word_embeddings(x)
+        X = self.dropout(X)
+
+        return X
+
+
+class SinePositionalEmbedding(nn.Module):
+    def __init__(
+        self,
+        dim_model: int,
+        dropout: float = 0.0,
+        scale: bool = False,
+        alpha: bool = False,
+    ):
+        super().__init__()
+        self.dim_model = dim_model
+        self.x_scale = math.sqrt(dim_model) if scale else 1.0
+        self.alpha = nn.Parameter(torch.ones(1), requires_grad=alpha)
+        self.dropout = torch.nn.Dropout(p=dropout)
+
+        self.reverse = False
+        self.pe = None
+        self.extend_pe(torch.tensor(0.0).expand(1, 4000))
+
+    def extend_pe(self, x):
+        """Reset the positional encodings."""
+        if self.pe is not None:
+            if self.pe.size(1) >= x.size(1):
+                if self.pe.dtype != x.dtype or self.pe.device != x.device:
+                    self.pe = self.pe.to(dtype=x.dtype, device=x.device)
+                return
+        pe = torch.zeros(x.size(1), self.dim_model)
+        if self.reverse:
+            position = torch.arange(
+                x.size(1) - 1, -1, -1.0, dtype=torch.float32
+            ).unsqueeze(1)
+        else:
+            position = torch.arange(
+                0, x.size(1), dtype=torch.float32
+            ).unsqueeze(1)
+        div_term = torch.exp(
+            torch.arange(0, self.dim_model, 2, dtype=torch.float32)
+            * -(math.log(10000.0) / self.dim_model)
+        )
+        pe[:, 0::2] = torch.sin(position * div_term)
+        pe[:, 1::2] = torch.cos(position * div_term)
+        pe = pe.unsqueeze(0)
+        self.pe = pe.to(device=x.device, dtype=x.dtype).detach()
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        self.extend_pe(x)
+        output = x.unsqueeze(-1) if x.ndim == 2 else x
+        output = output * self.x_scale + self.alpha * self.pe[:, : x.size(1)]
+        return self.dropout(output)
diff --git a/apps/audio_cloning/vallex/modules/optim.py b/apps/audio_cloning/vallex/modules/optim.py
new file mode 100644
index 0000000000000000000000000000000000000000..8e590836de83d7de8a70e85fb53f59d4440e05f7
--- /dev/null
+++ b/apps/audio_cloning/vallex/modules/optim.py
@@ -0,0 +1,1105 @@
+# Copyright      2022  Xiaomi Corp.        (authors: Daniel Povey)
+#
+# 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 contextlib
+import logging
+import random
+from collections import defaultdict
+from typing import List, Optional, Tuple, Union
+
+import torch
+from lhotse.utils import fix_random_seed
+from torch import Tensor
+from torch.optim import Optimizer
+
+
+class BatchedOptimizer(Optimizer):
+    """
+    This class adds to class Optimizer the capability to optimize parameters in batches:
+    it will stack the parameters and their grads for you so the optimizer can work
+    on tensors with an extra leading dimension.  This is intended for speed with GPUs,
+    as it reduces the number of kernels launched in the optimizer.
+
+    Args:
+      params:
+    """
+
+    def __init__(self, params, defaults):
+        super(BatchedOptimizer, self).__init__(params, defaults)
+
+    @contextlib.contextmanager
+    def batched_params(self, param_group, group_params_names):
+        """
+        This function returns (technically, yields) a list of
+          of tuples (p, state), where
+        p is a `fake` parameter that is stacked (over axis 0) from real parameters
+        that share the same shape, and its gradient is also stacked;
+        `state` is the state corresponding to this batch of parameters
+        (it will be physically located in the "state" for one of the real
+        parameters, the last one that has any particular shape and dtype).
+
+        This function is decorated as a context manager so that it can
+        write parameters back to their "real" locations.
+
+        The idea is, instead of doing:
+        <code>
+          for p in group["params"]:
+             state = self.state[p]
+             ...
+        </code>
+        you can do:
+        <code>
+          with self.batched_params(group["params"]) as batches:
+             for p, state, p_names in batches:
+                 ...
+        </code>
+
+        Args:
+          group: a parameter group, which is a list of parameters; should be
+                one of self.param_groups.
+          group_params_names: name for each parameter in group,
+                which is List[str].
+        """
+        batches = defaultdict(
+            list
+        )  # `batches` maps from tuple (dtype_as_str,*shape) to list of nn.Parameter
+        batches_names = defaultdict(
+            list
+        )  # `batches` maps from tuple (dtype_as_str,*shape) to list of str
+
+        assert len(param_group) == len(group_params_names)
+        for p, named_p in zip(param_group, group_params_names):
+            key = (str(p.dtype), *p.shape)
+            batches[key].append(p)
+            batches_names[key].append(named_p)
+
+        batches_names_keys = list(batches_names.keys())
+        sorted_idx = sorted(
+            range(len(batches_names)), key=lambda i: batches_names_keys[i]
+        )
+        batches_names = [
+            batches_names[batches_names_keys[idx]] for idx in sorted_idx
+        ]
+        batches = [batches[batches_names_keys[idx]] for idx in sorted_idx]
+
+        stacked_params_dict = dict()
+
+        # turn batches into a list, in deterministic order.
+        # tuples will contain tuples of (stacked_param, state, stacked_params_names),
+        # one for each batch in `batches`.
+        tuples = []
+
+        for batch, batch_names in zip(batches, batches_names):
+            p = batch[0]
+            # we arbitrarily store the state in the
+            # state corresponding to the 1st parameter in the
+            # group.  class Optimizer will take care of saving/loading state.
+            state = self.state[p]
+            p_stacked = torch.stack(batch)
+            grad = torch.stack(
+                [
+                    torch.zeros_like(p) if p.grad is None else p.grad
+                    for p in batch
+                ]
+            )
+            p_stacked.grad = grad
+            stacked_params_dict[key] = p_stacked
+            tuples.append((p_stacked, state, batch_names))
+
+        yield tuples  # <-- calling code will do the actual optimization here!
+
+        for ((stacked_params, _state, _names), batch) in zip(tuples, batches):
+            for i, p in enumerate(batch):  # batch is list of Parameter
+                p.copy_(stacked_params[i])
+
+
+class ScaledAdam(BatchedOptimizer):
+    """
+     Implements 'Scaled Adam', a variant of Adam where we scale each parameter's update
+     proportional to the norm of that parameter; and also learn the scale of the parameter,
+     in log space, subject to upper and lower limits (as if we had factored each parameter as
+     param = underlying_param * log_scale.exp())
+
+
+     Args:
+          params:  The parameters or param_groups to optimize (like other Optimizer subclasses)
+              lr:  The learning rate.  We will typically use a learning rate schedule that starts
+                   at 0.03 and decreases over time, i.e. much higher than other common
+                   optimizers.
+     clipping_scale: (e.g. 2.0)
+                   A scale for gradient-clipping: if specified, the normalized gradients
+                   over the whole model will be clipped to have 2-norm equal to
+                   `clipping_scale` times the median 2-norm over the most recent period
+                   of `clipping_update_period` minibatches.  By "normalized gradients",
+                   we mean after multiplying by the rms parameter value for this tensor
+                   [for non-scalars]; this is appropriate because our update is scaled
+                   by this quantity.
+            betas: beta1,beta2 are momentum constants for regular momentum, and moving sum-sq grad.
+                   Must satisfy 0 < beta <= beta2 < 1.
+     scalar_lr_scale: A scaling factor on the learning rate, that we use to update the
+                   scale of each parameter tensor and scalar parameters of the mode..
+                   If each parameter were decomposed
+                   as p * p_scale.exp(), where (p**2).mean().sqrt() == 1.0, scalar_lr_scale
+                   would be a the scaling factor on the learning rate of p_scale.
+              eps:  A general-purpose epsilon to prevent division by zero
+    param_min_rms: Minimum root-mean-square value of parameter tensor, for purposes of
+                   learning the scale on the parameters (we'll constrain the rms of each non-scalar
+                   parameter tensor to be >= this value)
+    param_max_rms: Maximum root-mean-square value of parameter tensor, for purposes of
+                   learning the scale on the parameters (we'll constrain the rms of each non-scalar
+                   parameter tensor to be <= this value)
+       scalar_max: Maximum absolute value for scalar parameters (applicable if your
+                   model has any parameters with numel() == 1).
+    size_update_period: The periodicity, in steps, with which we update the size (scale)
+                   of the parameter tensor.  This is provided to save a little time
+                   in the update.
+     clipping_update_period: if clipping_scale is specified, this is the period
+    """
+
+    def __init__(
+        self,
+        params,
+        lr=3e-02,
+        clipping_scale=None,
+        betas=(0.9, 0.98),
+        scalar_lr_scale=0.1,
+        eps=1.0e-08,
+        param_min_rms=1.0e-05,
+        param_max_rms=3.0,
+        scalar_max=10.0,
+        size_update_period=4,
+        clipping_update_period=100,
+        parameters_names=None,
+        show_dominant_parameters=True,
+    ):
+
+        assert parameters_names is not None, (
+            "Please prepare parameters_names,"
+            "which is a List[List[str]]. Each List[str] is for a group"
+            "and each str is for a parameter"
+        )
+        defaults = dict(
+            lr=lr,
+            clipping_scale=clipping_scale,
+            betas=betas,
+            scalar_lr_scale=scalar_lr_scale,
+            eps=eps,
+            param_min_rms=param_min_rms,
+            param_max_rms=param_max_rms,
+            scalar_max=scalar_max,
+            size_update_period=size_update_period,
+            clipping_update_period=clipping_update_period,
+        )
+
+        super(ScaledAdam, self).__init__(params, defaults)
+        assert len(self.param_groups) == len(parameters_names)
+        self.parameters_names = parameters_names
+        self.show_dominant_parameters = show_dominant_parameters
+
+    def __setstate__(self, state):
+        super(ScaledAdam, self).__setstate__(state)
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        """Performs a single optimization step.
+
+        Arguments:
+            closure (callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        batch = True
+
+        for group, group_params_names in zip(
+            self.param_groups, self.parameters_names
+        ):
+
+            with self.batched_params(
+                group["params"], group_params_names
+            ) as batches:
+
+                # batches is list of pairs (stacked_param, state).  stacked_param is like
+                # a regular parameter, and will have a .grad, but the 1st dim corresponds to
+                # a stacking dim, it is not a real dim.
+
+                if (
+                    len(batches[0][1]) == 0
+                ):  # if len(first state) == 0: not yet initialized
+                    clipping_scale = 1
+                else:
+                    clipping_scale = self._get_clipping_scale(group, batches)
+
+                for p, state, _ in batches:
+                    # Perform optimization step.
+                    # grad is not going to be None, we handled that when creating the batches.
+                    grad = p.grad
+                    if grad.is_sparse:
+                        raise RuntimeError(
+                            "ScaledAdam optimizer does not support sparse gradients"
+                        )
+                    # State initialization
+                    if len(state) == 0:
+                        self._init_state(group, p, state)
+
+                    self._step_one_batch(group, p, state, clipping_scale)
+
+        return loss
+
+    def _init_state(self, group: dict, p: Tensor, state: dict):
+        """
+        Initializes state dict for parameter 'p'.  Assumes that dim 0 of tensor p
+        is actually the batch dimension, corresponding to batched-together
+        parameters of a given shape.
+
+
+        Args:
+           group:   Dict to look up configuration values.
+               p: The parameter that we are initializing the state for
+           state: Dict from string to whatever state we are initializing
+        """
+        size_update_period = group["size_update_period"]
+
+        state["step"] = 0
+
+        kwargs = {"device": p.device, "dtype": p.dtype}
+
+        # 'delta' implements conventional momentum.  There are
+        # several different kinds of update going on, so rather than
+        # compute "exp_avg" like in Adam, we store and decay a
+        # parameter-change "delta", which combines all forms of
+        # update.  this is equivalent to how it's done in Adam,
+        # except for the first few steps.
+        state["delta"] = torch.zeros_like(
+            p, memory_format=torch.preserve_format
+        )
+
+        batch_size = p.shape[0]
+        numel = p.numel() // batch_size
+        numel = p.numel()
+
+        if numel > 1:
+            # "param_rms" just periodically records the scalar root-mean-square value of
+            # the parameter tensor.
+            # it has a shape like (batch_size, 1, 1, 1, 1)
+            param_rms = (
+                (p ** 2).mean(dim=list(range(1, p.ndim)), keepdim=True).sqrt()
+            )
+            state["param_rms"] = param_rms
+
+            state["scale_exp_avg_sq"] = torch.zeros_like(param_rms)
+            state["scale_grads"] = torch.zeros(
+                size_update_period, *param_rms.shape, **kwargs
+            )
+
+        # exp_avg_sq is the weighted sum of scaled gradients. as in Adam.
+        state["exp_avg_sq"] = torch.zeros_like(
+            p, memory_format=torch.preserve_format
+        )
+
+    def _get_clipping_scale(
+        self, group: dict, tuples: List[Tuple[Tensor, dict, List[str]]]
+    ) -> float:
+        """
+        Returns a scalar factor <= 1.0 that dictates gradient clipping, i.e. we will scale the gradients
+        by this amount before applying the rest of the update.
+
+        Args:
+           group: the parameter group, an item in self.param_groups
+           tuples: a list of tuples of (param, state, param_names)
+                where param is a batched set of parameters,
+                with a .grad (1st dim is batch dim)
+                and state is the state-dict where optimization parameters are kept.
+                param_names is a List[str] while each str is name for a parameter
+                in batched set of parameters "param".
+        """
+        assert len(tuples) >= 1
+        clipping_scale = group["clipping_scale"]
+        (first_p, first_state, _) = tuples[0]
+        step = first_state["step"]
+        if clipping_scale is None or step == 0:
+            # no clipping.  return early on step == 0 because the other
+            # parameters' state won't have been initialized yet.
+            return 1.0
+        clipping_update_period = group["clipping_update_period"]
+
+        tot_sumsq = torch.tensor(0.0, device=first_p.device)
+        for (p, state, param_names) in tuples:
+            grad = p.grad
+            if grad.is_sparse:
+                raise RuntimeError(
+                    "ScaledAdam optimizer does not support sparse gradients"
+                )
+            if p.numel() == p.shape[0]:  # a batch of scalars
+                tot_sumsq += (
+                    grad ** 2
+                ).sum()  # sum() to change shape [1] to []
+            else:
+                tot_sumsq += ((grad * state["param_rms"]) ** 2).sum()
+
+        tot_norm = tot_sumsq.sqrt()
+        if "model_norms" not in first_state:
+            first_state["model_norms"] = torch.zeros(
+                clipping_update_period, device=p.device
+            )
+        first_state["model_norms"][step % clipping_update_period] = tot_norm
+
+        if step % clipping_update_period == 0:
+            # Print some stats.
+            # We don't reach here if step == 0 because we would have returned
+            # above.
+            sorted_norms = first_state["model_norms"].sort()[0].to("cpu")
+            quartiles = []
+            for n in range(0, 5):
+                index = min(
+                    clipping_update_period - 1,
+                    (clipping_update_period // 4) * n,
+                )
+                quartiles.append(sorted_norms[index].item())
+
+            median = quartiles[2]
+            threshold = clipping_scale * median
+            first_state["model_norm_threshold"] = threshold
+            percent_clipped = (
+                first_state["num_clipped"] * 100.0 / clipping_update_period
+                if "num_clipped" in first_state
+                else 0.0
+            )
+            first_state["num_clipped"] = 0
+            quartiles = " ".join(["%.3e" % x for x in quartiles])
+            logging.info(
+                f"Clipping_scale={clipping_scale}, grad-norm quartiles {quartiles}, "
+                f"threshold={threshold:.3e}, percent-clipped={percent_clipped:.1f}"
+            )
+
+        if step < clipping_update_period:
+            return 1.0  # We have not yet estimated a norm to clip to.
+        else:
+            try:
+                model_norm_threshold = first_state["model_norm_threshold"]
+            except KeyError:
+                logging.info(
+                    "Warning: model_norm_threshold not in state: possibly "
+                    "you changed config when restarting, adding clipping_scale option?"
+                )
+                return 1.0
+            ans = min(1.0, (model_norm_threshold / (tot_norm + 1.0e-20)).item())
+            if ans < 1.0:
+                first_state["num_clipped"] += 1
+            if ans < 0.1:
+                logging.warn(
+                    f"Scaling gradients by {ans}, model_norm_threshold={model_norm_threshold}"
+                )
+                if self.show_dominant_parameters:
+                    assert p.shape[0] == len(param_names)
+                    self._show_gradient_dominating_parameter(tuples, tot_sumsq)
+            return ans
+
+    def _show_gradient_dominating_parameter(
+        self, tuples: List[Tuple[Tensor, dict, List[str]]], tot_sumsq: Tensor
+    ):
+        """
+        Show information of parameter wihch dominanting tot_sumsq.
+
+        Args:
+           tuples: a list of tuples of (param, state, param_names)
+                where param is a batched set of parameters,
+                with a .grad (1st dim is batch dim)
+                and state is the state-dict where optimization parameters are kept.
+                param_names is a List[str] while each str is name for a parameter
+                in batched set of parameters "param".
+            tot_sumsq: sumsq of all parameters. Though it's could be calculated
+                from tuples, we still pass it to save some time.
+        """
+        all_sumsq_orig = {}
+        for (p, state, batch_param_names) in tuples:
+            # p is a stacked batch parameters.
+            batch_grad = p.grad
+            if p.numel() == p.shape[0]:  # a batch of scalars
+                batch_sumsq_orig = batch_grad ** 2
+                # Dummpy values used by following `zip` statement.
+                batch_rms_orig = torch.ones(p.shape[0])
+            else:
+                batch_rms_orig = state["param_rms"]
+                batch_sumsq_orig = ((batch_grad * batch_rms_orig) ** 2).sum(
+                    dim=list(range(1, batch_grad.ndim))
+                )
+
+            for name, sumsq_orig, rms, grad in zip(
+                batch_param_names, batch_sumsq_orig, batch_rms_orig, batch_grad
+            ):
+
+                proportion_orig = sumsq_orig / tot_sumsq
+                all_sumsq_orig[name] = (proportion_orig, sumsq_orig, rms, grad)
+
+        assert torch.isclose(
+            sum([value[0] for value in all_sumsq_orig.values()]).cpu(),
+            torch.tensor(1.0),
+        )
+        sorted_by_proportion = {
+            k: v
+            for k, v in sorted(
+                all_sumsq_orig.items(),
+                key=lambda item: item[1][0],
+                reverse=True,
+            )
+        }
+        dominant_param_name = next(iter(sorted_by_proportion))
+        (
+            dominant_proportion,
+            dominant_sumsq,
+            dominant_rms,
+            dominant_grad,
+        ) = sorted_by_proportion[dominant_param_name]
+        logging.info(
+            f"Parameter Dominanting tot_sumsq {dominant_param_name}"
+            f" with proportion {dominant_proportion:.2f},"
+            f" where dominant_sumsq=(grad_sumsq*orig_rms_sq)"
+            f"={dominant_sumsq:.3e},"
+            f" grad_sumsq = {(dominant_grad**2).sum():.3e},"
+            f" orig_rms_sq={(dominant_rms**2).item():.3e}"
+        )
+
+    def _step_one_batch(
+        self, group: dict, p: Tensor, state: dict, clipping_scale: float
+    ):
+        """
+        Do the step for one parameter, which is actually going to be a batch of
+        `real` parameters, with dim 0 as the batch dim.
+        Args:
+                  group:  dict to look up configuration values
+                    p: parameter to update (actually multiple parameters stacked together
+                       as a batch)
+                  state: state-dict for p, to look up the optimizer state
+        """
+        lr = group["lr"]
+        size_update_period = group["size_update_period"]
+        beta1 = group["betas"][0]
+
+        grad = p.grad
+        if clipping_scale != 1.0:
+            grad = grad * clipping_scale
+        step = state["step"]
+        delta = state["delta"]
+
+        delta.mul_(beta1)
+        batch_size = p.shape[0]
+        numel = p.numel() // batch_size
+        if numel > 1:
+            # Update the size/scale of p, and set param_rms
+            scale_grads = state["scale_grads"]
+            scale_grads[step % size_update_period] = (p * grad).sum(
+                dim=list(range(1, p.ndim)), keepdim=True
+            )
+            if step % size_update_period == size_update_period - 1:
+                param_rms = state["param_rms"]  # shape: (batch_size, 1, 1, ..)
+                param_rms.copy_(
+                    (p ** 2)
+                    .mean(dim=list(range(1, p.ndim)), keepdim=True)
+                    .sqrt()
+                )
+                if step > 0:
+                    # self._size_update() learns the overall scale on the
+                    # parameter, by shrinking or expanding it.
+                    self._size_update(group, scale_grads, p, state)
+
+        if numel == 1:
+            # For parameters with 1 element we just use regular Adam.
+            # Updates delta.
+            self._step_scalar(group, p, state)
+        else:
+            self._step(group, p, state)
+
+        state["step"] = step + 1
+
+    def _size_update(
+        self, group: dict, scale_grads: Tensor, p: Tensor, state: dict
+    ) -> None:
+        """
+               Called only where p.numel() > 1, this updates the scale of the parameter.
+               If we imagine: p =  underlying_param * scale.exp(), and we are doing
+               gradient descent on underlying param and on scale, this function does the update
+               on `scale`.
+
+               Args:
+              group: dict to look up configuration values
+        scale_grads: a tensor of shape (size_update_period, batch_size, 1, 1,...) containing
+                      grads w.r.t. the scales.
+                  p:  The parameter to update
+               state: The state-dict of p
+        """
+
+        param_rms = state["param_rms"]
+        beta1, beta2 = group["betas"]
+        size_lr = group["lr"] * group["scalar_lr_scale"]
+        param_min_rms = group["param_min_rms"]
+        param_max_rms = group["param_max_rms"]
+        eps = group["eps"]
+        step = state["step"]
+        batch_size = p.shape[0]
+
+        size_update_period = scale_grads.shape[0]
+        # correct beta2 for the size update period: we will have
+        # faster decay at this level.
+        beta2_corr = beta2 ** size_update_period
+
+        scale_exp_avg_sq = state[
+            "scale_exp_avg_sq"
+        ]  # shape: (batch_size, 1, 1, ..)
+        scale_exp_avg_sq.mul_(beta2_corr).add_(
+            (scale_grads ** 2).mean(
+                dim=0
+            ),  # mean over dim `size_update_period`
+            alpha=1 - beta2_corr,
+        )  # shape is (batch_size, 1, 1, ...)
+
+        # The 1st time we reach here is when size_step == 1.
+        size_step = (step + 1) // size_update_period
+        bias_correction2 = 1 - beta2_corr ** size_step
+        # we don't bother with bias_correction1; this will help prevent divergence
+        # at the start of training.
+
+        denom = scale_exp_avg_sq.sqrt() + eps
+
+        scale_step = (
+            -size_lr
+            * (bias_correction2 ** 0.5)
+            * scale_grads.sum(dim=0)
+            / denom
+        )
+
+        is_too_small = param_rms < param_min_rms
+        is_too_large = param_rms > param_max_rms
+
+        # when the param gets too small, just don't shrink it any further.
+        scale_step.masked_fill_(is_too_small, 0.0)
+        # when it gets too large, stop it from getting any larger.
+        scale_step.masked_fill_(is_too_large, -size_lr * size_update_period)
+        delta = state["delta"]
+        # the factor of (1-beta1) relates to momentum.
+        delta.add_(p * scale_step, alpha=(1 - beta1))
+
+    def _step(self, group: dict, p: Tensor, state: dict):
+        """
+        This function does the core update of self.step(), in the case where the members of
+        the batch have more than 1 element.
+
+        Args:
+            group: A dict which will be used to look up configuration values
+                p: The parameter to be updated
+             grad: The grad of p
+            state: The state-dict corresponding to parameter p
+
+        This function modifies p.
+        """
+        grad = p.grad
+        lr = group["lr"]
+        beta1, beta2 = group["betas"]
+        eps = group["eps"]
+        param_min_rms = group["param_min_rms"]
+        step = state["step"]
+
+        exp_avg_sq = state["exp_avg_sq"]
+        exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=(1 - beta2))
+
+        this_step = state["step"] - (
+            state["zero_step"] if "zero_step" in state else 0
+        )
+        bias_correction2 = 1 - beta2 ** (this_step + 1)
+        if bias_correction2 < 0.99:
+            # note: not in-place.
+            exp_avg_sq = exp_avg_sq * (1.0 / bias_correction2)
+
+        denom = exp_avg_sq.sqrt()
+        denom += eps
+        grad = grad / denom
+
+        alpha = -lr * (1 - beta1) * state["param_rms"].clamp(min=param_min_rms)
+
+        delta = state["delta"]
+        delta.add_(grad * alpha)
+        p.add_(delta)
+
+    def _step_scalar(self, group: dict, p: Tensor, state: dict):
+        """
+        A simplified form of the core update for scalar tensors, where we cannot get a good
+        estimate of the parameter rms.
+        """
+        beta1, beta2 = group["betas"]
+        scalar_max = group["scalar_max"]
+        eps = group["eps"]
+        lr = group["lr"] * group["scalar_lr_scale"]
+        grad = p.grad
+
+        exp_avg_sq = state["exp_avg_sq"]  # shape: (batch_size,)
+        exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
+
+        # bias_correction2 is like in Adam.  Don't bother with bias_correction1;
+        # slower update at the start will help stability anyway.
+        bias_correction2 = 1 - beta2 ** (state["step"] + 1)
+        denom = (exp_avg_sq / bias_correction2).sqrt() + eps
+
+        delta = state["delta"]
+        delta.add_(grad / denom, alpha=-lr * (1 - beta1))
+        p.clamp_(min=-scalar_max, max=scalar_max)
+        p.add_(delta)
+
+
+class LRScheduler(object):
+    """
+    Base-class for learning rate schedulers where the learning-rate depends on both the
+    batch and the epoch.
+    """
+
+    def __init__(self, optimizer: Optimizer, verbose: bool = False):
+        # Attach optimizer
+        if not isinstance(optimizer, Optimizer):
+            raise TypeError(
+                "{} is not an Optimizer".format(type(optimizer).__name__)
+            )
+        self.optimizer = optimizer
+        self.verbose = verbose
+
+        for group in optimizer.param_groups:
+            group.setdefault("base_lr", group["lr"])
+
+        self.base_lrs = [group["base_lr"] for group in optimizer.param_groups]
+
+        self.epoch = 0
+        self.batch = 0
+
+    def state_dict(self):
+        """Returns the state of the scheduler as a :class:`dict`.
+
+        It contains an entry for every variable in self.__dict__ which
+        is not the optimizer.
+        """
+        return {
+            "base_lrs": self.base_lrs,
+            "epoch": self.epoch,
+            "batch": self.batch,
+        }
+
+    def load_state_dict(self, state_dict):
+        """Loads the schedulers state.
+
+        Args:
+            state_dict (dict): scheduler state. Should be an object returned
+                from a call to :meth:`state_dict`.
+        """
+        self.__dict__.update(state_dict)
+
+    def get_last_lr(self) -> List[float]:
+        """Return last computed learning rate by current scheduler.  Will be a list of float."""
+        return self._last_lr
+
+    def get_lr(self):
+        # Compute list of learning rates from self.epoch and self.batch and
+        # self.base_lrs; this must be overloaded by the user.
+        # e.g. return [some_formula(self.batch, self.epoch, base_lr) for base_lr in self.base_lrs ]
+        raise NotImplementedError
+
+    def step_batch(self, batch: Optional[int] = None) -> None:
+        # Step the batch index, or just set it.  If `batch` is specified, it
+        # must be the batch index from the start of training, i.e. summed over
+        # all epochs.
+        # You can call this in any order; if you don't provide 'batch', it should
+        # of course be called once per batch.
+        if batch is not None:
+            self.batch = batch
+        else:
+            self.batch = self.batch + 1
+        self._set_lrs()
+
+    def step_epoch(self, epoch: Optional[int] = None):
+        # Step the epoch index, or just set it.  If you provide the 'epoch' arg,
+        # you should call this at the start of the epoch; if you don't provide the 'epoch'
+        # arg, you should call it at the end of the epoch.
+        if epoch is not None:
+            self.epoch = epoch
+        else:
+            self.epoch = self.epoch + 1
+        self._set_lrs()
+
+    def _set_lrs(self):
+        values = self.get_lr()
+        assert len(values) == len(self.optimizer.param_groups)
+
+        for i, data in enumerate(zip(self.optimizer.param_groups, values)):
+            param_group, lr = data
+            param_group["lr"] = lr
+            self.print_lr(self.verbose, i, lr)
+        self._last_lr = [group["lr"] for group in self.optimizer.param_groups]
+
+    def print_lr(self, is_verbose, group, lr):
+        """Display the current learning rate."""
+        if is_verbose:
+            logging.info(
+                f"Epoch={self.epoch}, batch={self.batch}: adjusting learning rate"
+                f" of group {group} to {lr:.4e}."
+            )
+
+
+class Eden(LRScheduler):
+    """
+    Eden scheduler.
+    The basic formula (before warmup) is:
+      lr = base_lr * (((batch**2 + lr_batches**2) / lr_batches**2) ** -0.25 *
+                     (((epoch**2 + lr_epochs**2) / lr_epochs**2) ** -0.25)) * warmup
+    where `warmup` increases from linearly 0.5 to 1 over `warmup_batches` batches
+    and then stays constant at 1.
+
+
+     E.g. suggest base_lr = 0.04 (passed to optimizer) if used with ScaledAdam
+
+    Args:
+        optimizer: the optimizer to change the learning rates on
+        lr_batches: the number of batches after which we start significantly
+              decreasing the learning rate, suggest 5000.
+        lr_epochs: the number of epochs after which we start significantly
+              decreasing the learning rate, suggest 6 if you plan to do e.g.
+              20 to 40 epochs, but may need smaller number if dataset is huge
+              and you will do few epochs.
+    """
+
+    def __init__(
+        self,
+        optimizer: Optimizer,
+        lr_batches: Union[int, float],
+        lr_epochs: Union[int, float],
+        warmup_batches: Union[int, float] = 500.0,
+        verbose: bool = False,
+    ):
+        super(Eden, self).__init__(optimizer, verbose)
+        self.lr_batches = lr_batches
+        self.lr_epochs = lr_epochs
+        self.warmup_batches = warmup_batches
+
+    def get_lr(self):
+        factor = (
+            (self.batch ** 2 + self.lr_batches ** 2) / self.lr_batches ** 2
+        ) ** -0.25 * (
+            ((self.epoch ** 2 + self.lr_epochs ** 2) / self.lr_epochs ** 2)
+            ** -0.25
+        )
+        warmup_factor = (
+            1.0
+            if self.batch >= self.warmup_batches
+            else 0.5 + 0.5 * (self.batch / self.warmup_batches)
+        )
+
+        return [x * factor * warmup_factor for x in self.base_lrs]
+
+
+def _test_eden():
+    m = torch.nn.Linear(100, 100)
+    optim = ScaledAdam(m.parameters(), lr=0.03)
+
+    scheduler = Eden(optim, lr_batches=100, lr_epochs=2, verbose=True)
+
+    for epoch in range(10):
+        scheduler.step_epoch(epoch)  # sets epoch to `epoch`
+
+        for step in range(20):
+            x = torch.randn(200, 100).detach()
+            x.requires_grad = True
+            y = m(x)
+            dy = torch.randn(200, 100).detach()
+            f = (y * dy).sum()
+            f.backward()
+
+            optim.step()
+            scheduler.step_batch()
+            optim.zero_grad()
+
+    logging.info(f"last lr = {scheduler.get_last_lr()}")
+    logging.info(f"state dict = {scheduler.state_dict()}")
+
+
+# This is included mostly as a baseline for ScaledAdam.
+class Eve(Optimizer):
+    """
+    Implements Eve algorithm.  This is a modified version of AdamW with a special
+    way of setting the weight-decay / shrinkage-factor, which is designed to make the
+    rms of the parameters approach a particular target_rms (default: 0.1).  This is
+    for use with networks with 'scaled' versions of modules (see scaling.py), which
+    will be close to invariant to the absolute scale on the parameter matrix.
+
+    The original Adam algorithm was proposed in `Adam: A Method for Stochastic Optimization`_.
+    The AdamW variant was proposed in `Decoupled Weight Decay Regularization`_.
+    Eve is unpublished so far.
+
+    Arguments:
+        params (iterable): iterable of parameters to optimize or dicts defining
+            parameter groups
+        lr (float, optional): learning rate (default: 1e-3)
+        betas (Tuple[float, float], optional): coefficients used for computing
+            running averages of gradient and its square (default: (0.9, 0.999))
+        eps (float, optional): term added to the denominator to improve
+            numerical stability (default: 1e-8)
+        weight_decay (float, optional): weight decay coefficient (default: 3e-4;
+            this value means that the weight would decay significantly after
+            about 3k minibatches.  Is not multiplied by learning rate, but
+            is conditional on RMS-value of parameter being > target_rms.
+        target_rms (float, optional): target root-mean-square value of
+           parameters, if they fall below this we will stop applying weight decay.
+
+
+    .. _Adam: A Method for Stochastic Optimization:
+        https://arxiv.org/abs/1412.6980
+    .. _Decoupled Weight Decay Regularization:
+        https://arxiv.org/abs/1711.05101
+    .. _On the Convergence of Adam and Beyond:
+        https://openreview.net/forum?id=ryQu7f-RZ
+    """
+
+    def __init__(
+        self,
+        params,
+        lr=1e-3,
+        betas=(0.9, 0.98),
+        eps=1e-8,
+        weight_decay=1e-3,
+        target_rms=0.1,
+    ):
+        if not 0.0 <= lr:
+            raise ValueError("Invalid learning rate: {}".format(lr))
+        if not 0.0 <= eps:
+            raise ValueError("Invalid epsilon value: {}".format(eps))
+        if not 0.0 <= betas[0] < 1.0:
+            raise ValueError(
+                "Invalid beta parameter at index 0: {}".format(betas[0])
+            )
+        if not 0.0 <= betas[1] < 1.0:
+            raise ValueError(
+                "Invalid beta parameter at index 1: {}".format(betas[1])
+            )
+        if not 0 <= weight_decay <= 0.1:
+            raise ValueError(
+                "Invalid weight_decay value: {}".format(weight_decay)
+            )
+        if not 0 < target_rms <= 10.0:
+            raise ValueError("Invalid target_rms value: {}".format(target_rms))
+        defaults = dict(
+            lr=lr,
+            betas=betas,
+            eps=eps,
+            weight_decay=weight_decay,
+            target_rms=target_rms,
+        )
+        super(Eve, self).__init__(params, defaults)
+
+    def __setstate__(self, state):
+        super(Eve, self).__setstate__(state)
+
+    @torch.no_grad()
+    def step(self, closure=None):
+        """Performs a single optimization step.
+
+        Arguments:
+            closure (callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        loss = None
+        if closure is not None:
+            with torch.enable_grad():
+                loss = closure()
+
+        for group in self.param_groups:
+            for p in group["params"]:
+                if p.grad is None:
+                    continue
+
+                # Perform optimization step
+                grad = p.grad
+                if grad.is_sparse:
+                    raise RuntimeError(
+                        "AdamW does not support sparse gradients"
+                    )
+
+                state = self.state[p]
+
+                # State initialization
+                if len(state) == 0:
+                    state["step"] = 0
+                    # Exponential moving average of gradient values
+                    state["exp_avg"] = torch.zeros_like(
+                        p, memory_format=torch.preserve_format
+                    )
+                    # Exponential moving average of squared gradient values
+                    state["exp_avg_sq"] = torch.zeros_like(
+                        p, memory_format=torch.preserve_format
+                    )
+
+                exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
+
+                beta1, beta2 = group["betas"]
+
+                state["step"] += 1
+                bias_correction1 = 1 - beta1 ** state["step"]
+                bias_correction2 = 1 - beta2 ** state["step"]
+
+                # Decay the first and second moment running average coefficient
+                exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
+                exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
+                denom = (exp_avg_sq.sqrt() * (bias_correction2 ** -0.5)).add_(
+                    group["eps"]
+                )
+
+                step_size = group["lr"] / bias_correction1
+                target_rms = group["target_rms"]
+                weight_decay = group["weight_decay"]
+
+                if p.numel() > 1:
+                    # avoid applying this weight-decay on "scaling factors"
+                    # (which are scalar).
+                    is_above_target_rms = p.norm() > (
+                        target_rms * (p.numel() ** 0.5)
+                    )
+                    p.mul_(1 - (weight_decay * is_above_target_rms))
+
+                p.addcdiv_(exp_avg, denom, value=-step_size)
+
+                # if random.random() < 0.0005:
+                #     step = (exp_avg / denom) * step_size
+                #     logging.info(
+                #         f"Delta rms = {(step**2).mean().item()}, shape = {step.shape}"
+                #     )
+
+        return loss
+
+
+def _test_scaled_adam(hidden_dim: int):
+    import timeit
+
+    from scaling import ScaledLinear
+
+    E = 100
+    B = 4
+    T = 2
+    logging.info("in test_eve_cain")
+    # device = torch.device('cuda')
+    device = torch.device("cpu")
+    dtype = torch.float32
+
+    fix_random_seed(42)
+    # these input_magnitudes and output_magnitudes are to test that
+    # Abel is working as we expect and is able to adjust scales of
+    # different dims differently.
+    input_magnitudes = (1.0 * torch.randn(E, dtype=dtype, device=device)).exp()
+    output_magnitudes = (1.0 * torch.randn(E, dtype=dtype, device=device)).exp()
+
+    for iter in [1, 0]:
+        fix_random_seed(42)
+        Linear = torch.nn.Linear if iter == 0 else ScaledLinear
+
+        m = torch.nn.Sequential(
+            Linear(E, hidden_dim),
+            torch.nn.PReLU(),
+            Linear(hidden_dim, hidden_dim),
+            torch.nn.PReLU(),
+            Linear(hidden_dim, E),
+        ).to(device)
+
+        train_pairs = [
+            (
+                100.0
+                * torch.randn(B, T, E, device=device, dtype=dtype)
+                * input_magnitudes,
+                torch.randn(B, T, E, device=device, dtype=dtype)
+                * output_magnitudes,
+            )
+            for _ in range(20)
+        ]
+
+        if iter == 0:
+            optim = Eve(m.parameters(), lr=0.003)
+        elif iter == 1:
+            optim = ScaledAdam(m.parameters(), lr=0.03, clipping_scale=2.0)
+        scheduler = Eden(optim, lr_batches=200, lr_epochs=5, verbose=False)
+
+        start = timeit.default_timer()
+        avg_loss = 0.0
+        for epoch in range(180):
+            scheduler.step_epoch()
+            # if epoch == 100 and iter in [2,3]:
+            #    optim.reset_speedup()  # check it doesn't crash.
+
+            # if epoch == 130:
+            #    opts = diagnostics.TensorDiagnosticOptions(
+            #        2 ** 22
+            #    )  # allow 4 megabytes per sub-module
+            #    diagnostic = diagnostics.attach_diagnostics(m, opts)
+
+            for n, (x, y) in enumerate(train_pairs):
+                y_out = m(x)
+                loss = ((y_out - y) ** 2).mean() * 100.0
+                if epoch == 0 and n == 0:
+                    avg_loss = loss.item()
+                else:
+                    avg_loss = 0.98 * avg_loss + 0.02 * loss.item()
+                if n == 0 and epoch % 5 == 0:
+                    # norm1 = '%.2e' % (m[0].weight**2).mean().sqrt().item()
+                    # norm1b = '%.2e' % (m[0].bias**2).mean().sqrt().item()
+                    # norm2 = '%.2e' % (m[2].weight**2).mean().sqrt().item()
+                    # norm2b = '%.2e' % (m[2].bias**2).mean().sqrt().item()
+                    # scale1 = '%.2e' % (m[0].weight_scale.exp().item())
+                    # scale1b = '%.2e' % (m[0].bias_scale.exp().item())
+                    # scale2 = '%.2e' % (m[2].weight_scale.exp().item())
+                    # scale2b = '%.2e' % (m[2].bias_scale.exp().item())
+                    lr = scheduler.get_last_lr()[0]
+                    logging.info(
+                        f"Iter {iter}, epoch {epoch}, batch {n}, avg_loss {avg_loss:.4g}, lr={lr:.4e}"
+                    )  # , norms={norm1,norm1b,norm2,norm2b}") # scales={scale1,scale1b,scale2,scale2b}
+                loss.log().backward()
+                optim.step()
+                optim.zero_grad()
+                scheduler.step_batch()
+
+        # diagnostic.print_diagnostics()
+
+        stop = timeit.default_timer()
+        logging.info(f"Iter={iter}, Time taken: {stop - start}")
+
+        logging.info(f"last lr = {scheduler.get_last_lr()}")
+        # logging.info("state dict = ", scheduler.state_dict())
+        # logging.info("optim state_dict = ", optim.state_dict())
+        logging.info(f"input_magnitudes = {input_magnitudes}")
+        logging.info(f"output_magnitudes = {output_magnitudes}")
+
+
+if __name__ == "__main__":
+    torch.set_num_threads(1)
+    torch.set_num_interop_threads(1)
+    logging.getLogger().setLevel(logging.INFO)
+    import subprocess
+
+    s = subprocess.check_output(
+        "git status -uno .; git log -1; git diff HEAD .", shell=True
+    )
+    logging.info(s)
+    import sys
+
+    if len(sys.argv) > 1:
+        hidden_dim = int(sys.argv[1])
+    else:
+        hidden_dim = 200
+
+    _test_scaled_adam(hidden_dim)
+    _test_eden()
diff --git a/apps/audio_cloning/vallex/modules/scaling.py b/apps/audio_cloning/vallex/modules/scaling.py
new file mode 100644
index 0000000000000000000000000000000000000000..e3e6fff1d0df2e88db9eaec76e36c6d10822ed4f
--- /dev/null
+++ b/apps/audio_cloning/vallex/modules/scaling.py
@@ -0,0 +1,1369 @@
+# Copyright    2022  Xiaomi Corp.        (authors: Daniel Povey)
+#
+# 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 logging
+import math
+import random
+from typing import Optional, Tuple, Union
+
+import torch
+import torch.nn as nn
+from torch import Tensor
+
+
+class Transpose(nn.Identity):
+    """(N, T, D) -> (N, D, T)"""
+
+    def forward(self, input: torch.Tensor) -> torch.Tensor:
+        return input.transpose(1, 2)
+
+
+class ActivationBalancerFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx,
+        x: Tensor,
+        scale_factor: Tensor,
+        sign_factor: Optional[Tensor],
+        channel_dim: int,
+    ) -> Tensor:
+        if channel_dim < 0:
+            channel_dim += x.ndim
+        ctx.channel_dim = channel_dim
+        xgt0 = x > 0
+        if sign_factor is None:
+            ctx.save_for_backward(xgt0, scale_factor)
+        else:
+            ctx.save_for_backward(xgt0, scale_factor, sign_factor)
+        return x
+
+    @staticmethod
+    def backward(ctx, x_grad: Tensor) -> Tuple[Tensor, None, None, None]:
+        if len(ctx.saved_tensors) == 3:
+            xgt0, scale_factor, sign_factor = ctx.saved_tensors
+            for _ in range(ctx.channel_dim, x_grad.ndim - 1):
+                scale_factor = scale_factor.unsqueeze(-1)
+                sign_factor = sign_factor.unsqueeze(-1)
+            factor = sign_factor + scale_factor * (xgt0.to(x_grad.dtype) - 0.5)
+        else:
+            xgt0, scale_factor = ctx.saved_tensors
+            for _ in range(ctx.channel_dim, x_grad.ndim - 1):
+                scale_factor = scale_factor.unsqueeze(-1)
+            factor = scale_factor * (xgt0.to(x_grad.dtype) - 0.5)
+        neg_delta_grad = x_grad.abs() * factor
+        return (
+            x_grad - neg_delta_grad,
+            None,
+            None,
+            None,
+        )
+
+
+def _compute_scale_factor(
+    x: Tensor,
+    channel_dim: int,
+    min_abs: float,
+    max_abs: float,
+    gain_factor: float,
+    max_factor: float,
+) -> Tensor:
+    if channel_dim < 0:
+        channel_dim += x.ndim
+    sum_dims = [d for d in range(x.ndim) if d != channel_dim]
+    x_abs_mean = torch.mean(x.abs(), dim=sum_dims).to(torch.float32)
+
+    if min_abs == 0.0:
+        below_threshold = 0.0
+    else:
+        # below_threshold is 0 if x_abs_mean > min_abs, can be at most max_factor if
+        # x_abs)_mean , min_abs.
+        below_threshold = ((min_abs - x_abs_mean) * (gain_factor / min_abs)).clamp(
+            min=0, max=max_factor
+        )
+
+    above_threshold = ((x_abs_mean - max_abs) * (gain_factor / max_abs)).clamp(
+        min=0, max=max_factor
+    )
+
+    return below_threshold - above_threshold
+
+
+def _compute_sign_factor(
+    x: Tensor,
+    channel_dim: int,
+    min_positive: float,
+    max_positive: float,
+    gain_factor: float,
+    max_factor: float,
+) -> Tensor:
+    if channel_dim < 0:
+        channel_dim += x.ndim
+    sum_dims = [d for d in range(x.ndim) if d != channel_dim]
+    proportion_positive = torch.mean((x > 0).to(torch.float32), dim=sum_dims)
+    if min_positive == 0.0:
+        factor1 = 0.0
+    else:
+        # 0 if proportion_positive >= min_positive, else can be
+        # as large as max_factor.
+        factor1 = (
+            (min_positive - proportion_positive) * (gain_factor / min_positive)
+        ).clamp_(min=0, max=max_factor)
+
+    if max_positive == 1.0:
+        factor2 = 0.0
+    else:
+        # 0 if self.proportion_positive <= max_positive, else can be
+        # as large as -max_factor.
+        factor2 = (
+            (proportion_positive - max_positive) * (gain_factor / (1.0 - max_positive))
+        ).clamp_(min=0, max=max_factor)
+    sign_factor = factor1 - factor2
+    # require min_positive != 0 or max_positive != 1:
+    assert not isinstance(sign_factor, float)
+    return sign_factor
+
+
+class ActivationScaleBalancerFunction(torch.autograd.Function):
+    """
+    This object is used in class ActivationBalancer when the user specified
+    min_positive=0, max_positive=1, so there are no constraints on the signs
+    of the activations and only the absolute value has a constraint.
+    """
+
+    @staticmethod
+    def forward(
+        ctx,
+        x: Tensor,
+        sign_factor: Tensor,
+        scale_factor: Tensor,
+        channel_dim: int,
+    ) -> Tensor:
+        if channel_dim < 0:
+            channel_dim += x.ndim
+        ctx.channel_dim = channel_dim
+        xgt0 = x > 0
+        ctx.save_for_backward(xgt0, sign_factor, scale_factor)
+        return x
+
+    @staticmethod
+    def backward(ctx, x_grad: Tensor) -> Tuple[Tensor, None, None, None]:
+        xgt0, sign_factor, scale_factor = ctx.saved_tensors
+        for _ in range(ctx.channel_dim, x_grad.ndim - 1):
+            sign_factor = sign_factor.unsqueeze(-1)
+            scale_factor = scale_factor.unsqueeze(-1)
+
+        factor = sign_factor + scale_factor * (xgt0.to(x_grad.dtype) - 0.5)
+        neg_delta_grad = x_grad.abs() * factor
+        return (
+            x_grad - neg_delta_grad,
+            None,
+            None,
+            None,
+        )
+
+
+class RandomClampFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx,
+        x: Tensor,
+        min: Optional[float],
+        max: Optional[float],
+        prob: float,
+        reflect: float,
+    ) -> Tensor:
+        x_clamped = torch.clamp(x, min=min, max=max)
+        mask = torch.rand_like(x) < prob
+        ans = torch.where(mask, x_clamped, x)
+        if x.requires_grad:
+            ctx.save_for_backward(ans == x)
+            ctx.reflect = reflect
+        if reflect != 0.0:
+            ans = ans * (1.0 + reflect) - (x * reflect)
+        return ans
+
+    @staticmethod
+    def backward(ctx, ans_grad: Tensor) -> Tuple[Tensor, None, None, None, None]:
+        (is_same,) = ctx.saved_tensors
+        x_grad = ans_grad * is_same.to(ans_grad.dtype)
+        reflect = ctx.reflect
+        if reflect != 0.0:
+            x_grad = x_grad * (1.0 + reflect) - (ans_grad * reflect)
+        return x_grad, None, None, None, None
+
+
+def random_clamp(
+    x: Tensor,
+    min: Optional[float] = None,
+    max: Optional[float] = None,
+    prob: float = 0.5,
+    reflect: float = 0.0,
+):
+    return RandomClampFunction.apply(x, min, max, prob, reflect)
+
+
+def random_cast_to_half(x: Tensor, min_abs: float = 5.0e-06) -> Tensor:
+    """
+    A randomized way of casting a floating point value to half precision.
+    """
+    if x.dtype == torch.float16:
+        return x
+    x_abs = x.abs()
+    is_too_small = x_abs < min_abs
+    # for elements where is_too_small is true, random_val will contain +-min_abs with
+    # probability (x.abs() / min_abs), and 0.0 otherwise.  [so this preserves expectations,
+    # for those elements].
+    random_val = min_abs * x.sign() * (torch.rand_like(x) * min_abs < x_abs)
+    return torch.where(is_too_small, random_val, x).to(torch.float16)
+
+
+class RandomGradFunction(torch.autograd.Function):
+    """
+    Does nothing in forward pass; in backward pass, gets rid of very small grads using
+    randomized approach that preserves expectations (intended to reduce roundoff).
+    """
+
+    @staticmethod
+    def forward(ctx, x: Tensor, min_abs: float) -> Tensor:
+        ctx.min_abs = min_abs
+        return x
+
+    @staticmethod
+    def backward(ctx, ans_grad: Tensor) -> Tuple[Tensor, None]:
+        if ans_grad.dtype == torch.float16:
+            return (
+                random_cast_to_half(ans_grad.to(torch.float32), min_abs=ctx.min_abs),
+                None,
+            )
+        else:
+            return ans_grad, None
+
+
+class RandomGrad(torch.nn.Module):
+    """
+    Gets rid of very small gradients using an expectation-preserving method, intended to increase
+    accuracy of training when using amp (automatic mixed precision)
+    """
+
+    def __init__(self, min_abs: float = 5.0e-06):
+        super(RandomGrad, self).__init__()
+        self.min_abs = min_abs
+
+    def forward(self, x: Tensor):
+        if torch.jit.is_scripting() or not self.training or torch.jit.is_tracing():
+            return x
+        else:
+            return RandomGradFunction.apply(x, self.min_abs)
+
+
+class SoftmaxFunction(torch.autograd.Function):
+    """
+    Tries to handle half-precision derivatives in a randomized way that should
+    be more accurate for training than the default behavior.
+    """
+
+    @staticmethod
+    def forward(ctx, x: Tensor, dim: int):
+        ans = x.softmax(dim=dim)
+        # if x dtype is float16, x.softmax() returns a float32 because
+        # (presumably) that op does not support float16, and autocast
+        # is enabled.
+        if torch.is_autocast_enabled():
+            ans = ans.to(torch.float16)
+        ctx.save_for_backward(ans)
+        ctx.x_dtype = x.dtype
+        ctx.dim = dim
+        return ans
+
+    @staticmethod
+    def backward(ctx, ans_grad: Tensor):
+        (ans,) = ctx.saved_tensors
+        with torch.cuda.amp.autocast(enabled=False):
+            ans_grad = ans_grad.to(torch.float32)
+            ans = ans.to(torch.float32)
+            x_grad = ans_grad * ans
+            x_grad = x_grad - ans * x_grad.sum(dim=ctx.dim, keepdim=True)
+            return x_grad, None
+
+
+def softmax(x: Tensor, dim: int):
+    if torch.jit.is_scripting() or torch.jit.is_tracing():
+        return x.softmax(dim)
+
+    return SoftmaxFunction.apply(x, dim)
+
+
+class MaxEigLimiterFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx,
+        x: Tensor,
+        coeffs: Tensor,
+        direction: Tensor,
+        channel_dim: int,
+        grad_scale: float,
+    ) -> Tensor:
+        ctx.channel_dim = channel_dim
+        ctx.grad_scale = grad_scale
+        ctx.save_for_backward(x.detach(), coeffs.detach(), direction.detach())
+        return x
+
+    @staticmethod
+    def backward(ctx, x_grad, *args):
+        with torch.enable_grad():
+            (x_orig, coeffs, new_direction) = ctx.saved_tensors
+            x_orig.requires_grad = True
+            num_channels = x_orig.shape[ctx.channel_dim]
+            x = x_orig.transpose(ctx.channel_dim, -1).reshape(-1, num_channels)
+            new_direction.requires_grad = False
+            x = x - x.mean(dim=0)
+            x_var = (x**2).mean()
+            x_residual = x - coeffs * new_direction
+            x_residual_var = (x_residual**2).mean()
+            # `variance_proportion` is the proportion of the variance accounted for
+            # by the top eigen-direction.  This is to be minimized.
+            variance_proportion = (x_var - x_residual_var) / (x_var + 1.0e-20)
+            variance_proportion.backward()
+        x_orig_grad = x_orig.grad
+        x_extra_grad = (
+            x_orig.grad
+            * ctx.grad_scale
+            * x_grad.norm()
+            / (x_orig_grad.norm() + 1.0e-20)
+        )
+        return x_grad + x_extra_grad.detach(), None, None, None, None
+
+
+class BasicNorm(torch.nn.Module):
+    """
+    This is intended to be a simpler, and hopefully cheaper, replacement for
+    LayerNorm.  The observation this is based on, is that Transformer-type
+    networks, especially with pre-norm, sometimes seem to set one of the
+    feature dimensions to a large constant value (e.g. 50), which "defeats"
+    the LayerNorm because the output magnitude is then not strongly dependent
+    on the other (useful) features.  Presumably the weight and bias of the
+    LayerNorm are required to allow it to do this.
+
+    So the idea is to introduce this large constant value as an explicit
+    parameter, that takes the role of the "eps" in LayerNorm, so the network
+    doesn't have to do this trick.  We make the "eps" learnable.
+
+    Args:
+       num_channels: the number of channels, e.g. 512.
+      channel_dim: the axis/dimension corresponding to the channel,
+        interprted as an offset from the input's ndim if negative.
+        shis is NOT the num_channels; it should typically be one of
+        {-2, -1, 0, 1, 2, 3}.
+       eps: the initial "epsilon" that we add as ballast in:
+             scale = ((input_vec**2).mean() + epsilon)**-0.5
+          Note: our epsilon is actually large, but we keep the name
+          to indicate the connection with conventional LayerNorm.
+       learn_eps: if true, we learn epsilon; if false, we keep it
+         at the initial value.
+    eps_min: float
+    eps_max: float
+    """
+
+    def __init__(
+        self,
+        num_channels: int,
+        channel_dim: int = -1,  # CAUTION: see documentation.
+        eps: float = 0.25,
+        learn_eps: bool = True,
+        eps_min: float = -3.0,
+        eps_max: float = 3.0,
+    ) -> None:
+        super(BasicNorm, self).__init__()
+        self.num_channels = num_channels
+        self.channel_dim = channel_dim
+        if learn_eps:
+            self.eps = nn.Parameter(torch.tensor(eps).log().detach())
+        else:
+            self.register_buffer("eps", torch.tensor(eps).log().detach())
+        self.eps_min = eps_min
+        self.eps_max = eps_max
+
+    def forward(self, x: Tensor) -> Tensor:
+        assert x.shape[self.channel_dim] == self.num_channels
+        eps = self.eps
+        if self.training and random.random() < 0.25:
+            # with probability 0.25, in training mode, clamp eps between the min
+            # and max; this will encourage it to learn parameters within the
+            # allowed range by making parameters that are outside the allowed
+            # range noisy.
+
+            # gradients to allow the parameter to get back into the allowed
+            # region if it happens to exit it.
+            eps = eps.clamp(min=self.eps_min, max=self.eps_max)
+        scales = (
+            torch.mean(x**2, dim=self.channel_dim, keepdim=True) + eps.exp()
+        ) ** -0.5
+        return x * scales
+
+
+def ScaledLinear(*args, initial_scale: float = 1.0, **kwargs) -> nn.Linear:
+    """
+    Behaves like a constructor of a modified version of nn.Linear
+    that gives an easy way to set the default initial parameter scale.
+
+    Args:
+        Accepts the standard args and kwargs that nn.Linear accepts
+        e.g. in_features, out_features, bias=False.
+
+        initial_scale: you can override this if you want to increase
+           or decrease the initial magnitude of the module's output
+           (affects the initialization of weight_scale and bias_scale).
+           Another option, if you want to do something like this, is
+           to re-initialize the parameters.
+    """
+    ans = nn.Linear(*args, **kwargs)
+    with torch.no_grad():
+        ans.weight[:] *= initial_scale
+        if ans.bias is not None:
+            torch.nn.init.uniform_(ans.bias, -0.1 * initial_scale, 0.1 * initial_scale)
+    return ans
+
+
+def ScaledConv1d(
+    *args,
+    initial_scale: float = 1.0,
+    kernel_size: int = 3,
+    padding: str = "same",
+    **kwargs,
+) -> nn.Conv1d:
+    """
+    Behaves like a constructor of a modified version of nn.Conv1d
+    that gives an easy way to set the default initial parameter scale.
+
+    Args:
+        Accepts the standard args and kwargs that nn.Linear accepts
+        e.g. in_features, out_features, bias=False.
+
+        initial_scale: you can override this if you want to increase
+           or decrease the initial magnitude of the module's output
+           (affects the initialization of weight_scale and bias_scale).
+           Another option, if you want to do something like this, is
+           to re-initialize the parameters.
+    """
+    ans = nn.Conv1d(*args, kernel_size=kernel_size, padding=padding, **kwargs)
+    with torch.no_grad():
+        ans.weight[:] *= initial_scale
+        if ans.bias is not None:
+            torch.nn.init.uniform_(ans.bias, -0.1 * initial_scale, 0.1 * initial_scale)
+    return ans
+
+
+def TransposeScaledConv1d(
+    *args,
+    initial_scale: float = 1.0,
+    kernel_size: int = 3,
+    padding: str = "same",
+    **kwargs,
+) -> nn.Sequential:
+    """
+    Transpose -> ScaledConv1d
+    """
+    return nn.Sequential(
+        Transpose(),
+        ScaledConv1d(
+            *args,
+            initial_scale=initial_scale,
+            kernel_size=kernel_size,
+            padding=padding,
+            **kwargs,
+        ),
+    )
+
+
+def ScaledConv1dTranspose(
+    *args,
+    initial_scale: float = 1.0,
+    kernel_size: int = 3,
+    padding: str = "same",
+    **kwargs,
+) -> nn.Sequential:
+    """
+    Transpose -> ScaledConv1d
+    """
+    return nn.Sequential(
+        ScaledConv1d(
+            *args,
+            initial_scale=initial_scale,
+            kernel_size=kernel_size,
+            padding=padding,
+            **kwargs,
+        ),
+        Transpose(),
+    )
+
+
+def TransposeConv1d(
+    *args, kernel_size: int = 3, padding: str = "same", **kwargs
+) -> nn.Sequential:
+    """
+    Transpose -> Conv1d
+    """
+    return nn.Sequential(
+        Transpose(),
+        nn.Conv1d(*args, kernel_size=kernel_size, padding=padding, **kwargs),
+    )
+
+
+def Conv1dTranspose(
+    *args, kernel_size: int = 3, padding: str = "same", **kwargs
+) -> nn.Sequential:
+    """
+    ScaledConv1d -> Transpose
+    """
+    return nn.Sequential(
+        nn.Conv1d(*args, kernel_size=kernel_size, padding=padding, **kwargs),
+        Transpose(),
+    )
+
+
+class SRLinear(nn.Linear):
+    """https://arxiv.org/abs/2303.06296
+    Stabilizing Transformer Training by Preventing Attention Entropy Collapse
+    """
+
+    def __init__(self, in_features, out_features, bias=True, **kwargs):
+        super().__init__(in_features, out_features, bias=bias, **kwargs)
+        self.register_buffer(
+            "u", nn.functional.normalize(torch.randn(in_features), dim=0)
+        )
+        with torch.no_grad():
+            sigma = self.get_sigma()
+        self.register_buffer("spectral_norm", sigma)
+        self.sigma = nn.Parameter(torch.ones(1))
+
+    def get_sigma(self):
+        with torch.no_grad():
+            u = self.u
+            v = self.weight.mv(u)
+            v = nn.functional.normalize(v, dim=0)
+            u = self.weight.T.mv(v)
+            u = nn.functional.normalize(u, dim=0)
+            self.u.data.copy_(u)
+        return torch.einsum("c,cd,d->", v, self.weight, u)
+
+    def get_weight(self):
+        sigma = self.get_sigma()
+        if self.training:
+            self.spectral_norm.data.copy_(sigma)
+        weight = (self.sigma / sigma) * self.weight
+        return weight
+
+    def forward(self, x):
+        return nn.functional.linear(x, self.get_weight(), self.bias)
+
+
+class SRConv1d(SRLinear):
+    def __init__(
+        self,
+        in_features,
+        out_features,
+        kernel_size,
+        stride: int = 1,
+        padding: str = "same",
+        bias: bool = True,
+        **kwargs,
+    ):
+        in_features = in_features * kernel_size
+        super().__init__(in_features, out_features, bias=bias, **kwargs)
+        nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.padding = padding
+
+    def forward(self, x):
+        in_features = self.in_features // self.kernel_size
+        weight = self.get_weight().view(
+            self.out_features, in_features, self.kernel_size
+        )
+        return nn.functional.conv1d(
+            x, weight, bias=self.bias, stride=self.stride, padding=self.padding
+        )
+
+
+def TransposeSRConv1d(
+    *args, kernel_size: int = 3, padding: str = "same", **kwargs
+) -> nn.Sequential:
+    """
+    Transpose -> SRConv1d
+    """
+    return nn.Sequential(
+        Transpose(),
+        SRConv1d(*args, kernel_size=kernel_size, padding=padding, **kwargs),
+    )
+
+
+def SRConv1dTranspose(
+    *args, kernel_size: int = 3, padding: str = "same", **kwargs
+) -> nn.Sequential:
+    """
+    SRConv1d -> Transpose
+    """
+    return nn.Sequential(
+        SRConv1d(*args, kernel_size=kernel_size, padding=padding, **kwargs),
+        Transpose(),
+    )
+
+
+class ActivationBalancer(torch.nn.Module):
+    """
+    Modifies the backpropped derivatives of a function to try to encourage, for
+    each channel, that it is positive at least a proportion `threshold` of the
+    time.  It does this by multiplying negative derivative values by up to
+    (1+max_factor), and positive derivative values by up to (1-max_factor),
+    interpolated from 1 at the threshold to those extremal values when none
+    of the inputs are positive.
+
+    Args:
+           num_channels: the number of channels
+           channel_dim: the dimension/axis corresponding to the channel, e.g.
+               -1, 0, 1, 2; will be interpreted as an offset from x.ndim if negative.
+           min_positive: the minimum, per channel, of the proportion of the time
+               that (x > 0), below which we start to modify the derivatives.
+           max_positive: the maximum, per channel, of the proportion of the time
+               that (x > 0), above which we start to modify the derivatives.
+           max_factor: the maximum factor by which we modify the derivatives for
+              either the sign constraint or the magnitude constraint;
+              e.g. with max_factor=0.02, the the derivatives would be multiplied by
+              values in the range [0.98..1.02].
+           sign_gain_factor: determines the 'gain' with which we increase the
+              change in gradient once the constraints on min_positive and max_positive
+              are violated.
+           scale_gain_factor: determines the 'gain' with which we increase the
+              change in gradient once the constraints on min_abs and max_abs
+              are violated.
+           min_abs:  the minimum average-absolute-value difference from the mean
+               value per channel, which we allow, before we start to modify
+               the derivatives to prevent this.
+           max_abs:  the maximum average-absolute-value difference from the mean
+               value per channel, which we allow, before we start to modify
+               the derivatives to prevent this.
+          min_prob: determines the minimum probability with which we modify the
+             gradients for the {min,max}_positive and {min,max}_abs constraints,
+             on each forward().  This is done randomly to prevent all layers
+             from doing it at the same time.  Early in training we may use
+             higher probabilities than this; it will decay to this value.
+    """
+
+    def __init__(
+        self,
+        num_channels: int,
+        channel_dim: int,
+        min_positive: float = 0.05,
+        max_positive: float = 0.95,
+        max_factor: float = 0.04,
+        sign_gain_factor: float = 0.01,
+        scale_gain_factor: float = 0.02,
+        min_abs: float = 0.2,
+        max_abs: float = 100.0,
+        min_prob: float = 0.1,
+    ):
+        super(ActivationBalancer, self).__init__()
+        self.num_channels = num_channels
+        self.channel_dim = channel_dim
+        self.min_positive = min_positive
+        self.max_positive = max_positive
+        self.max_factor = max_factor
+        self.min_abs = min_abs
+        self.max_abs = max_abs
+        self.min_prob = min_prob
+        self.sign_gain_factor = sign_gain_factor
+        self.scale_gain_factor = scale_gain_factor
+
+        # count measures how many times the forward() function has been called.
+        # We occasionally sync this to a tensor called `count`, that exists to
+        # make sure it is synced to disk when we load and save the model.
+        self.cpu_count = 0
+        self.register_buffer("count", torch.tensor(0, dtype=torch.int64))
+
+    def forward(self, x: Tensor) -> Tensor:
+        if torch.jit.is_scripting() or not x.requires_grad or torch.jit.is_tracing():
+            return _no_op(x)
+
+        count = self.cpu_count
+        self.cpu_count += 1
+
+        if random.random() < 0.01:
+            # Occasionally sync self.cpu_count with self.count.
+            # count affects the decay of 'prob'.  don't do this on every iter,
+            # because syncing with the GPU is slow.
+            self.cpu_count = max(self.cpu_count, self.count.item())
+            self.count.fill_(self.cpu_count)
+
+        # the prob of doing some work exponentially decreases from 0.5 till it hits
+        # a floor at min_prob (==0.1, by default)
+        prob = max(self.min_prob, 0.5 ** (1 + (count / 4000.0)))
+
+        if random.random() < prob:
+            sign_gain_factor = 0.5
+            if self.min_positive != 0.0 or self.max_positive != 1.0:
+                sign_factor = _compute_sign_factor(
+                    x,
+                    self.channel_dim,
+                    self.min_positive,
+                    self.max_positive,
+                    gain_factor=self.sign_gain_factor / prob,
+                    max_factor=self.max_factor,
+                )
+            else:
+                sign_factor = None
+
+            scale_factor = _compute_scale_factor(
+                x.detach(),
+                self.channel_dim,
+                min_abs=self.min_abs,
+                max_abs=self.max_abs,
+                gain_factor=self.scale_gain_factor / prob,
+                max_factor=self.max_factor,
+            )
+            return ActivationBalancerFunction.apply(
+                x,
+                scale_factor,
+                sign_factor,
+                self.channel_dim,
+            )
+        else:
+            return _no_op(x)
+
+
+def penalize_abs_values_gt(x: Tensor, limit: float, penalty: float) -> Tensor:
+    """
+    Returns x unmodified, but in backprop will put a penalty for the excess of
+    the absolute values of elements of x over the limit "limit".  E.g. if
+    limit == 10.0, then if x has any values over 10 it will get a penalty.
+
+    Caution: the value of this penalty will be affected by grad scaling used
+    in automatic mixed precision training.  For this reasons we use this,
+    it shouldn't really matter, or may even be helpful; we just use this
+    to disallow really implausible values of scores to be given to softmax.
+    """
+    x_sign = x.sign()
+    over_limit = (x.abs() - limit) > 0
+    # The following is a memory efficient way to penalize the absolute values of
+    # x that's over the limit.  (The memory efficiency comes when you think
+    # about which items torch needs to cache for the autograd, and which ones it
+    # can throw away).  The numerical value of aux_loss as computed here will
+    # actually be larger than it should be, by limit * over_limit.sum(), but it
+    # has the same derivative as the real aux_loss which is penalty * (x.abs() -
+    # limit).relu().
+    aux_loss = penalty * ((x_sign * over_limit).to(torch.int8) * x)
+    # note: we don't do sum() here on aux)_loss, but it's as if we had done
+    # sum() due to how with_loss() works.
+    x = with_loss(x, aux_loss)
+    # you must use x for something, or this will be ineffective.
+    return x
+
+
+def _diag(x: Tensor):  # like .diag(), but works for tensors with 3 dims.
+    if x.ndim == 2:
+        return x.diag()
+    else:
+        (batch, dim, dim) = x.shape
+        x = x.reshape(batch, dim * dim)
+        x = x[:, :: dim + 1]
+        assert x.shape == (batch, dim)
+        return x
+
+
+def _whitening_metric(x: Tensor, num_groups: int):
+    """
+    Computes the "whitening metric", a value which will be 1.0 if all the eigenvalues of
+    of the centered feature covariance are the same within each group's covariance matrix
+    and also between groups.
+    Args:
+        x: a Tensor of shape (*, num_channels)
+     num_groups:  the number of groups of channels, a number >=1 that divides num_channels
+    Returns:
+        Returns a scalar Tensor that will be 1.0 if the data is "perfectly white" and
+    greater than 1.0 otherwise.
+    """
+    assert x.dtype != torch.float16
+    x = x.reshape(-1, x.shape[-1])
+    (num_frames, num_channels) = x.shape
+    assert num_channels % num_groups == 0
+    channels_per_group = num_channels // num_groups
+    x = x.reshape(num_frames, num_groups, channels_per_group).transpose(0, 1)
+    # x now has shape (num_groups, num_frames, channels_per_group)
+    # subtract the mean so we use the centered, not uncentered, covariance.
+    # My experience has been that when we "mess with the gradients" like this,
+    # it's better not do anything that tries to move the mean around, because
+    # that can easily cause instability.
+    x = x - x.mean(dim=1, keepdim=True)
+    # x_covar: (num_groups, channels_per_group, channels_per_group)
+    x_covar = torch.matmul(x.transpose(1, 2), x)
+    x_covar_mean_diag = _diag(x_covar).mean()
+    # the following expression is what we'd get if we took the matrix product
+    # of each covariance and measured the mean of its trace, i.e.
+    # the same as _diag(torch.matmul(x_covar, x_covar)).mean().
+    x_covarsq_mean_diag = (x_covar**2).sum() / (num_groups * channels_per_group)
+    # this metric will be >= 1.0; the larger it is, the less 'white' the data was.
+    metric = x_covarsq_mean_diag / (x_covar_mean_diag**2 + 1.0e-20)
+    return metric
+
+
+class WhiteningPenaltyFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx,
+        x: Tensor,
+        num_groups: int,
+        whitening_limit: float,
+        grad_scale: float,
+    ) -> Tensor:
+        ctx.save_for_backward(x)
+        ctx.num_groups = num_groups
+        ctx.whitening_limit = whitening_limit
+        ctx.grad_scale = grad_scale
+        return x
+
+    @staticmethod
+    def backward(ctx, x_grad: Tensor):
+        (x_orig,) = ctx.saved_tensors
+        with torch.enable_grad():
+            with torch.cuda.amp.autocast(enabled=False):
+                x_detached = x_orig.to(torch.float32).detach()
+                x_detached.requires_grad = True
+
+                metric = _whitening_metric(x_detached, ctx.num_groups)
+
+                if random.random() < 0.005 or __name__ == "__main__":
+                    logging.info(
+                        f"Whitening: num_groups={ctx.num_groups}, num_channels={x_orig.shape[-1]}, "
+                        f"metric={metric.item():.2f} vs. limit={ctx.whitening_limit}"
+                    )
+
+                (metric - ctx.whitening_limit).relu().backward()
+                penalty_grad = x_detached.grad
+                scale = ctx.grad_scale * (
+                    x_grad.to(torch.float32).norm() / (penalty_grad.norm() + 1.0e-20)
+                )
+                penalty_grad = penalty_grad * scale
+        return x_grad + penalty_grad.to(x_grad.dtype), None, None, None
+
+
+class Whiten(nn.Module):
+    def __init__(
+        self,
+        num_groups: int,
+        whitening_limit: float,
+        prob: Union[float, Tuple[float, float]],
+        grad_scale: float,
+    ):
+        """
+        Args:
+          num_groups: the number of groups to divide the channel dim into before
+            whitening.  We will attempt to make the feature covariance
+            within each group, after mean subtraction, as "white" as possible,
+            while having the same trace across all groups.
+         whitening_limit: a value greater than 1.0, that dictates how much
+           freedom we have to violate the constraints.  1.0 would mean perfectly
+           white, with exactly the same trace across groups; larger values
+           give more freedom.  E.g. 2.0.
+         prob: the probability with which we apply the gradient modification
+           (also affects the grad scale).  May be supplied as a float,
+           or as a pair (min_prob, max_prob)
+
+          grad_scale: determines the scale on the gradient term from this object,
+            relative to the rest of the gradient on the attention weights.
+            E.g. 0.02 (you may want to use smaller values than this if prob is large)
+        """
+        super(Whiten, self).__init__()
+        assert num_groups >= 1
+        assert whitening_limit >= 1
+        assert grad_scale >= 0
+        self.num_groups = num_groups
+        self.whitening_limit = whitening_limit
+        if isinstance(prob, float):
+            assert 0 < prob <= 1
+            self.prob = prob
+        else:
+            (self.min_prob, self.max_prob) = prob
+            assert 0 < self.min_prob < self.max_prob <= 1
+            self.prob = self.max_prob
+
+        self.grad_scale = grad_scale
+
+    def forward(self, x: Tensor) -> Tensor:
+        """
+        In the forward pass, this function just returns the input unmodified.
+        In the backward pass, it will modify the gradients to ensure that the
+        distribution in each group has close to (lambda times I) as the covariance
+        after mean subtraction, with the same lambda across groups.
+        For whitening_limit > 1, there will be more freedom to violate this
+        constraint.
+
+        Args:
+           x: the input of shape (*, num_channels)
+
+        Returns:
+            x, unmodified.   You should make sure
+        you use the returned value, or the graph will be freed
+        and nothing will happen in backprop.
+        """
+        if not x.requires_grad or random.random() > self.prob or self.grad_scale == 0:
+            return _no_op(x)
+        else:
+            if hasattr(self, "min_prob") and random.random() < 0.25:
+                # occasionally switch between min_prob and max_prob, based on whether
+                # we are above or below the threshold.
+                if (
+                    _whitening_metric(x.to(torch.float32), self.num_groups)
+                    > self.whitening_limit
+                ):
+                    # there would be a change to the grad.
+                    self.prob = self.max_prob
+                else:
+                    self.prob = self.min_prob
+
+            return WhiteningPenaltyFunction.apply(
+                x, self.num_groups, self.whitening_limit, self.grad_scale
+            )
+
+
+class WithLoss(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x: Tensor, y: Tensor):
+        ctx.y_shape = y.shape
+        return x
+
+    @staticmethod
+    def backward(ctx, ans_grad: Tensor):
+        return ans_grad, torch.ones(
+            ctx.y_shape, dtype=ans_grad.dtype, device=ans_grad.device
+        )
+
+
+def with_loss(x, y):
+    if torch.jit.is_scripting() or torch.jit.is_tracing():
+        return x
+    # returns x but adds y.sum() to the loss function.
+    return WithLoss.apply(x, y)
+
+
+def _no_op(x: Tensor) -> Tensor:
+    if torch.jit.is_scripting() or torch.jit.is_tracing():
+        return x
+    else:
+        # a no-op function that will have a node in the autograd graph,
+        # to avoid certain bugs relating to backward hooks
+        return x.chunk(1, dim=-1)[0]
+
+
+class Identity(torch.nn.Module):
+    def __init__(self):
+        super(Identity, self).__init__()
+
+    def forward(self, x):
+        return _no_op(x)
+
+
+class MaxEig(torch.nn.Module):
+    """
+    Modifies the backpropped derivatives of a function to try to discourage
+    that any given direction in activation space accounts for more than
+    a specified proportion of the covariance (e.g. 0.2).
+
+
+    Args:
+           num_channels: the number of channels
+           channel_dim: the dimension/axis corresponding to the channel, e.g.
+               -1, 0, 1, 2; will be interpreted as an offset from x.ndim if negative.
+           max_var_per_eig:  the maximum proportion of the variance of the
+               features/channels, after mean subtraction, that can come from
+               any given eigenvalue.
+           min_prob: the minimum probability with which we apply this during any invocation
+               of forward(), assuming last time we applied the constraint it was
+               not active; supplied for speed.
+           scale: determines the scale with which we modify the gradients, relative
+               to the existing / unmodified gradients
+    """
+
+    def __init__(
+        self,
+        num_channels: int,
+        channel_dim: int,
+        max_var_per_eig: float = 0.2,
+        min_prob: float = 0.01,
+        scale: float = 0.01,
+    ):
+        super(MaxEig, self).__init__()
+        self.num_channels = num_channels
+        self.channel_dim = channel_dim
+        self.scale = scale
+        assert max_var_per_eig == 0.0 or max_var_per_eig > 1.0 / num_channels
+        self.max_var_per_eig = max_var_per_eig
+
+        # we figure out the dominant direction using the power method: starting with
+        # a random vector, keep multiplying by the covariance and renormalizing.
+        with torch.no_grad():
+            # arbitrary.. would use randn() but want to leave the rest of the model's
+            # random parameters unchanged for comparison
+            direction = torch.arange(num_channels).to(torch.float)
+            direction = direction / direction.norm()
+            self.register_buffer("max_eig_direction", direction)
+
+        self.min_prob = min_prob
+        # cur_prob is the current probability we'll use to apply the ActivationBalancer.
+        # We'll regress this towards prob, each time we try to apply it and it is not
+        # active.
+        self.cur_prob = 1.0
+
+    def forward(self, x: Tensor) -> Tensor:
+        if (
+            torch.jit.is_scripting()
+            or self.max_var_per_eig <= 0
+            or random.random() > self.cur_prob
+            or torch.jit.is_tracing()
+        ):
+            return _no_op(x)
+
+        with torch.cuda.amp.autocast(enabled=False):
+            eps = 1.0e-20
+            orig_x = x
+            x = x.to(torch.float32)
+            with torch.no_grad():
+                x = x.transpose(self.channel_dim, -1).reshape(-1, self.num_channels)
+                x = x - x.mean(dim=0)
+                new_direction, coeffs = self._find_direction_coeffs(
+                    x, self.max_eig_direction
+                )
+                x_var = (x**2).mean()
+                x_residual = x - coeffs * new_direction
+                x_residual_var = (x_residual**2).mean()
+
+                # `variance_proportion` is the proportion of the variance accounted for
+                # by the top eigen-direction.
+                variance_proportion = (x_var - x_residual_var) / (x_var + 1.0e-20)
+
+                # ensure new direction is nonzero even if x == 0, by including `direction`.
+                self._set_direction(0.1 * self.max_eig_direction + new_direction)
+
+            if random.random() < 0.01 or __name__ == "__main__":
+                logging.info(
+                    f"variance_proportion = {variance_proportion.item()}, shape={tuple(orig_x.shape)}, cur_prob={self.cur_prob}"
+                )
+
+            if variance_proportion >= self.max_var_per_eig:
+                # The constraint is active.  Note, we should quite rarely
+                # reach here, only near the beginning of training if we are
+                # starting to diverge, should this constraint be active.
+                cur_prob = self.cur_prob
+                self.cur_prob = 1.0  # next time, do the update with probability 1.0.
+                return MaxEigLimiterFunction.apply(
+                    orig_x, coeffs, new_direction, self.channel_dim, self.scale
+                )
+            else:
+                # let self.cur_prob exponentially approach self.min_prob, as
+                # long as the constraint is inactive.
+                self.cur_prob = 0.75 * self.cur_prob + 0.25 * self.min_prob
+                return orig_x
+
+    def _set_direction(self, direction: Tensor):
+        """
+        Sets self.max_eig_direction to a normalized version of `direction`
+        """
+        direction = direction.detach()
+        direction = direction / direction.norm()
+        direction_sum = direction.sum().item()
+        if direction_sum - direction_sum == 0:  # no inf/nan
+            self.max_eig_direction[:] = direction
+        else:
+            logging.info(
+                f"Warning: sum of direction in MaxEig is {direction_sum}, "
+                "num_channels={self.num_channels}, channel_dim={self.channel_dim}"
+            )
+
+    def _find_direction_coeffs(
+        self, x: Tensor, prev_direction: Tensor
+    ) -> Tuple[Tensor, Tensor, Tensor]:
+        """
+            Figure out (an approximation to) the proportion of the variance of a set of
+            feature vectors that can be attributed to the top eigen-direction.
+            Args:
+             x: a Tensor of shape (num_frames, num_channels), with num_frames > 1.
+          prev_direction:  a Tensor of shape (num_channels,), that is our previous estimate
+                   of the top eigen-direction, or a random direction if this is the first
+                   iteration.  Does not have to be normalized, but should be nonzero.
+
+        Returns: (cur_direction, coeffs), where:
+             cur_direction: a Tensor of shape (num_channels,) that is the current
+                estimate of the top eigen-direction.
+             coeffs: a Tensor of shape (num_frames, 1) that minimizes, or
+                approximately minimizes, (x - coeffs * cur_direction).norm()
+        """
+        (num_frames, num_channels) = x.shape
+        assert num_channels > 1 and num_frames > 1
+        assert prev_direction.shape == (num_channels,)
+        # `coeffs` are the coefficients of `prev_direction` in x.
+        # actually represent the coeffs up to a constant positive factor.
+        coeffs = (x * prev_direction).sum(dim=1, keepdim=True) + 1.0e-10
+        cur_direction = (x * coeffs).sum(dim=0) / ((coeffs**2).sum() + 1.0e-20)
+        return cur_direction, coeffs
+
+
+class DoubleSwishFunction(torch.autograd.Function):
+    """
+      double_swish(x) = x * torch.sigmoid(x-1)
+    This is a definition, originally motivated by its close numerical
+    similarity to swish(swish(x)), where swish(x) =  x * sigmoid(x).
+
+    Memory-efficient derivative computation:
+     double_swish(x) = x * s, where s(x) = torch.sigmoid(x-1)
+     double_swish'(x) = d/dx double_swish(x) =  x * s'(x) + x' * s(x) = x * s'(x) + s(x).
+     Now, s'(x) = s(x) * (1-s(x)).
+     double_swish'(x) =  x * s'(x) + s(x).
+                      =  x * s(x) * (1-s(x)) + s(x).
+                     = double_swish(x) * (1-s(x)) + s(x)
+     ... so we just need to remember s(x) but not x itself.
+    """
+
+    @staticmethod
+    def forward(ctx, x: Tensor) -> Tensor:
+        requires_grad = x.requires_grad
+        x_dtype = x.dtype
+        if x.dtype == torch.float16:
+            x = x.to(torch.float32)
+
+        s = torch.sigmoid(x - 1.0)
+        y = x * s
+
+        if requires_grad:
+            deriv = y * (1 - s) + s
+            # notes on derivative of x * sigmoid(x - 1):
+            # https://www.wolframalpha.com/input?i=d%2Fdx+%28x+*+sigmoid%28x-1%29%29
+            # min \simeq -0.043638.  Take floor as -0.043637 so it's a lower bund
+            # max \simeq 1.1990.   Take ceil to be 1.2 so it's an upper bound.
+            # the combination of "+ torch.rand_like(deriv)" and casting to torch.uint8 (which
+            # floors), should be expectation-preserving.
+            floor = -0.043637
+            ceil = 1.2
+            d_scaled = (deriv - floor) * (255.0 / (ceil - floor)) + torch.rand_like(
+                deriv
+            )
+            if __name__ == "__main__":
+                # for self-testing only.
+                assert d_scaled.min() >= 0.0
+                assert d_scaled.max() < 256.0
+            d_int = d_scaled.to(torch.uint8)
+            ctx.save_for_backward(d_int)
+        if x.dtype == torch.float16 or torch.is_autocast_enabled():
+            y = y.to(torch.float16)
+        return y
+
+    @staticmethod
+    def backward(ctx, y_grad: Tensor) -> Tensor:
+        (d,) = ctx.saved_tensors
+        # the same constants as used in forward pass.
+        floor = -0.043637
+        ceil = 1.2
+        d = d * ((ceil - floor) / 255.0) + floor
+        return y_grad * d
+
+
+class DoubleSwish(torch.nn.Module):
+    def forward(self, x: Tensor) -> Tensor:
+        """Return double-swish activation function which is an approximation to Swish(Swish(x)),
+        that we approximate closely with x * sigmoid(x-1).
+        """
+        if torch.jit.is_scripting() or torch.jit.is_tracing():
+            return x * torch.sigmoid(x - 1.0)
+        return DoubleSwishFunction.apply(x)
+
+
+def BalancedDoubleSwish(
+    d_model, channel_dim=-1, max_abs=10.0, min_prob=0.25
+) -> nn.Sequential:
+    """
+    ActivationBalancer -> DoubleSwish
+    """
+    balancer = ActivationBalancer(
+        d_model, channel_dim=channel_dim, max_abs=max_abs, min_prob=min_prob
+    )
+    return nn.Sequential(
+        balancer,
+        DoubleSwish(),
+    )
+
+
+def _test_max_eig():
+    for proportion in [0.1, 0.5, 10.0]:
+        logging.info(f"proportion = {proportion}")
+        x = torch.randn(100, 128)
+        direction = torch.randn(128)
+        coeffs = torch.randn(100, 1)
+        x += proportion * direction * coeffs
+
+        x.requires_grad = True
+
+        num_channels = 128
+        m = MaxEig(
+            num_channels,
+            1,
+            0.5,
+            scale=0.1,  # channel_dim  # max_var_per_eig
+        )  # grad_scale
+
+        for _ in range(4):
+            y = m(x)
+
+        y_grad = torch.randn_like(x)
+        y.backward(gradient=y_grad)
+
+        if proportion < 0.2:
+            assert torch.allclose(x.grad, y_grad, atol=1.0e-02)
+        elif proportion > 1.0:
+            assert not torch.allclose(x.grad, y_grad)
+
+
+def _test_whiten():
+    for proportion in [0.1, 0.5, 10.0]:
+        logging.info(f"_test_whiten(): proportion = {proportion}")
+        x = torch.randn(100, 128)
+        direction = torch.randn(128)
+        coeffs = torch.randn(100, 1)
+        x += proportion * direction * coeffs
+
+        x.requires_grad = True
+
+        num_channels = 128
+        m = Whiten(
+            1,
+            5.0,
+            prob=1.0,
+            grad_scale=0.1,  # num_groups  # whitening_limit,
+        )  # grad_scale
+
+        for _ in range(4):
+            y = m(x)
+
+        y_grad = torch.randn_like(x)
+        y.backward(gradient=y_grad)
+
+        if proportion < 0.2:
+            assert torch.allclose(x.grad, y_grad)
+        elif proportion > 1.0:
+            assert not torch.allclose(x.grad, y_grad)
+
+
+def _test_activation_balancer_sign():
+    probs = torch.arange(0, 1, 0.01)
+    N = 1000
+    x = 1.0 * ((2.0 * (torch.rand(probs.numel(), N) < probs.unsqueeze(-1))) - 1.0)
+    x = x.detach()
+    x.requires_grad = True
+    m = ActivationBalancer(
+        probs.numel(),
+        channel_dim=0,
+        min_positive=0.05,
+        max_positive=0.95,
+        max_factor=0.2,
+        min_abs=0.0,
+    )
+
+    y_grad = torch.sign(torch.randn(probs.numel(), N))
+
+    y = m(x)
+    y.backward(gradient=y_grad)
+    print("_test_activation_balancer_sign: x = ", x)
+    print("_test_activation_balancer_sign: y grad = ", y_grad)
+    print("_test_activation_balancer_sign: x grad = ", x.grad)
+
+
+def _test_activation_balancer_magnitude():
+    magnitudes = torch.arange(0, 1, 0.01)
+    N = 1000
+    x = torch.sign(torch.randn(magnitudes.numel(), N)) * magnitudes.unsqueeze(-1)
+    x = x.detach()
+    x.requires_grad = True
+    m = ActivationBalancer(
+        magnitudes.numel(),
+        channel_dim=0,
+        min_positive=0.0,
+        max_positive=1.0,
+        max_factor=0.2,
+        min_abs=0.2,
+        max_abs=0.8,
+        min_prob=1.0,
+    )
+
+    y_grad = torch.sign(torch.randn(magnitudes.numel(), N))
+
+    y = m(x)
+    y.backward(gradient=y_grad)
+    print("_test_activation_balancer_magnitude: x = ", x)
+    print("_test_activation_balancer_magnitude: y grad = ", y_grad)
+    print("_test_activation_balancer_magnitude: x grad = ", x.grad)
+
+
+def _test_basic_norm():
+    num_channels = 128
+    m = BasicNorm(num_channels=num_channels, channel_dim=1)
+
+    x = torch.randn(500, num_channels)
+
+    y = m(x)
+
+    assert y.shape == x.shape
+    x_rms = (x**2).mean().sqrt()
+    y_rms = (y**2).mean().sqrt()
+    print("x rms = ", x_rms)
+    print("y rms = ", y_rms)
+    assert y_rms < x_rms
+    assert y_rms > 0.5 * x_rms
+
+
+def _test_double_swish_deriv():
+    x = torch.randn(10, 12, dtype=torch.double) * 3.0
+    x.requires_grad = True
+    m = DoubleSwish()
+
+    tol = (1.2 - (-0.043637)) / 255.0
+    torch.autograd.gradcheck(m, x, atol=tol)
+
+    # for self-test.
+    x = torch.randn(1000, 1000, dtype=torch.double) * 3.0
+    x.requires_grad = True
+    y = m(x)
+
+
+def _test_softmax():
+    a = torch.randn(2, 10, dtype=torch.float64)
+    b = a.clone()
+    a.requires_grad = True
+    b.requires_grad = True
+    a.softmax(dim=1)[:, 0].sum().backward()
+    print("a grad = ", a.grad)
+    softmax(b, dim=1)[:, 0].sum().backward()
+    print("b grad = ", b.grad)
+    assert torch.allclose(a.grad, b.grad)
+
+
+if __name__ == "__main__":
+    logging.getLogger().setLevel(logging.INFO)
+    torch.set_num_threads(1)
+    torch.set_num_interop_threads(1)
+    _test_softmax()
+    _test_whiten()
+    _test_max_eig()
+    _test_activation_balancer_sign()
+    _test_activation_balancer_magnitude()
+    _test_basic_norm()
+    _test_double_swish_deriv()
diff --git a/apps/audio_cloning/vallex/modules/scheduler.py b/apps/audio_cloning/vallex/modules/scheduler.py
new file mode 100644
index 0000000000000000000000000000000000000000..df8ffb4dc9af9b97270b608f0afed828da3985ff
--- /dev/null
+++ b/apps/audio_cloning/vallex/modules/scheduler.py
@@ -0,0 +1,78 @@
+#!/usr/bin/env python3
+# Copyright    2023                           (authors: Feiteng Li)
+#
+# 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 torch
+
+from modules.optim import Eden
+
+
+def calc_lr(step, dim_embed, warmup_steps):
+    return dim_embed ** (-0.5) * min(
+        step ** (-0.5), step * warmup_steps ** (-1.5)
+    )
+
+
+class NoamScheduler(torch.optim.lr_scheduler._LRScheduler):
+    def __init__(
+        self,
+        base_lr: float,
+        optimizer: torch.optim.Optimizer,
+        dim_embed: int,
+        warmup_steps: int,
+        last_epoch: int = -1,
+        verbose: bool = False,
+    ) -> None:
+
+        self.dim_embed = dim_embed
+        self.base_lr = base_lr
+        self.warmup_steps = warmup_steps
+        self.num_param_groups = len(optimizer.param_groups)
+
+        super().__init__(optimizer, last_epoch, verbose)
+
+    def get_lr(self) -> float:
+        lr = self.base_lr * calc_lr(
+            self._step_count, self.dim_embed, self.warmup_steps
+        )
+        return [lr] * self.num_param_groups
+
+    def set_step(self, step: int):
+        self._step_count = step
+
+
+def get_scheduler(params, optimizer):
+    if params.scheduler_name.lower() == "eden":
+        scheduler = Eden(optimizer, 5000, 4, warmup_batches=params.warmup_steps)
+    elif params.scheduler_name.lower() == "noam":
+        scheduler = NoamScheduler(
+            params.base_lr,
+            optimizer,
+            params.decoder_dim,
+            warmup_steps=params.warmup_steps,
+        )
+        # scheduler.set_step(params.start_batch or params.batch_idx_train)
+    elif params.scheduler_name.lower() == "cosine":
+        scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
+            params.warmup_steps,
+            optimizer,
+            eta_min=params.base_lr,
+        )
+    else:
+        raise NotImplementedError(f"{params.scheduler_name}")
+
+    return scheduler
diff --git a/apps/audio_cloning/vallex/modules/transformer.py b/apps/audio_cloning/vallex/modules/transformer.py
new file mode 100644
index 0000000000000000000000000000000000000000..ea8826b193c5053cb8ae74312f65ac95fe440350
--- /dev/null
+++ b/apps/audio_cloning/vallex/modules/transformer.py
@@ -0,0 +1,683 @@
+import copy
+import numbers
+from functools import partial
+from typing import Any, Callable, List, Optional, Tuple, Union
+
+import torch
+from torch import Tensor, nn
+from torch.nn import functional as F
+
+from .activation import MultiheadAttention
+from .scaling import ActivationBalancer, BalancedDoubleSwish
+from .scaling import BasicNorm as _BasicNorm
+
+_shape_t = Union[int, List[int], torch.Size]
+
+
+class LayerNorm(nn.Module):
+    __constants__ = ["normalized_shape", "eps", "elementwise_affine"]
+    normalized_shape: Tuple[int, ...]
+    eps: float
+    elementwise_affine: bool
+
+    def __init__(
+        self,
+        normalized_shape: _shape_t,
+        eps: float = 1e-5,
+        elementwise_affine: bool = True,
+        device=None,
+        dtype=None,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super(LayerNorm, self).__init__()
+        if isinstance(normalized_shape, numbers.Integral):
+            # mypy error: incompatible types in assignment
+            normalized_shape = (normalized_shape,)  # type: ignore[assignment]
+        self.normalized_shape = tuple(normalized_shape)  # type: ignore[arg-type]
+        self.eps = eps
+        self.elementwise_affine = elementwise_affine
+        if self.elementwise_affine:
+            self.weight = nn.Parameter(
+                torch.empty(self.normalized_shape, **factory_kwargs)
+            )
+            self.bias = nn.Parameter(
+                torch.empty(self.normalized_shape, **factory_kwargs)
+            )
+        else:
+            self.register_parameter("weight", None)
+            self.register_parameter("bias", None)
+
+        self.reset_parameters()
+
+    def reset_parameters(self) -> None:
+        if self.elementwise_affine:
+            nn.init.ones_(self.weight)
+            nn.init.zeros_(self.bias)
+
+    def forward(self, input: Tensor, embedding: Any = None) -> Tensor:
+        if isinstance(input, tuple):
+            input, embedding = input
+            return (
+                F.layer_norm(
+                    input,
+                    self.normalized_shape,
+                    self.weight,
+                    self.bias,
+                    self.eps,
+                ),
+                embedding,
+            )
+
+        assert embedding is None
+        return F.layer_norm(
+            input, self.normalized_shape, self.weight, self.bias, self.eps
+        )
+
+    def extra_repr(self) -> str:
+        return (
+            "{normalized_shape}, eps={eps}, "
+            "elementwise_affine={elementwise_affine}".format(**self.__dict__)
+        )
+
+
+class AdaptiveLayerNorm(nn.Module):
+    r"""Adaptive Layer Normalization"""
+
+    def __init__(self, d_model, norm) -> None:
+        super(AdaptiveLayerNorm, self).__init__()
+        self.project_layer = nn.Linear(d_model, 2 * d_model)
+        self.norm = norm
+        self.d_model = d_model
+        self.eps = self.norm.eps
+
+    def forward(self, input: Tensor, embedding: Tensor = None) -> Tensor:
+        if isinstance(input, tuple):
+            input, embedding = input
+            weight, bias = torch.split(
+                self.project_layer(embedding),
+                split_size_or_sections=self.d_model,
+                dim=-1,
+            )
+            return (weight * self.norm(input) + bias, embedding)
+
+        weight, bias = torch.split(
+            self.project_layer(embedding),
+            split_size_or_sections=self.d_model,
+            dim=-1,
+        )
+        return weight * self.norm(input) + bias
+
+
+class BasicNorm(_BasicNorm):
+    def __init__(
+        self,
+        d_model: int,
+        eps: float = 1e-5,
+        device=None,
+        dtype=None,
+    ):
+        super(BasicNorm, self).__init__(d_model, eps=eps)
+
+    def forward(self, input: Tensor, embedding: Any = None) -> Tensor:
+        if isinstance(input, tuple):
+            input, embedding = input
+            return (
+                super(BasicNorm, self).forward(input),
+                embedding,
+            )
+
+        assert embedding is None
+        return super(BasicNorm, self).forward(input)
+
+
+class BalancedBasicNorm(nn.Module):
+    def __init__(
+        self,
+        d_model: int,
+        eps: float = 1e-5,
+        device=None,
+        dtype=None,
+    ):
+        super(BalancedBasicNorm, self).__init__()
+        self.balancer = ActivationBalancer(
+            d_model,
+            channel_dim=-1,
+            min_positive=0.45,
+            max_positive=0.55,
+            max_abs=6.0,
+        )
+        self.norm = BasicNorm(d_model, eps, device=device, dtype=dtype)
+
+    def forward(self, input: Tensor, embedding: Any = None) -> Tensor:
+        if isinstance(input, tuple):
+            input, embedding = input
+            return self.norm((self.balancer(input), embedding))
+
+        assert embedding is None
+        return self.norm(self.balancer(input))
+
+
+class IdentityNorm(nn.Module):
+    def __init__(
+        self,
+        d_model: int,
+        eps: float = 1e-5,
+        device=None,
+        dtype=None,
+    ) -> None:
+        super(IdentityNorm, self).__init__()
+
+    def forward(self, input: Tensor, embedding: Any = None) -> Tensor:
+        if isinstance(input, tuple):
+            return input
+
+        assert embedding is None
+        return input
+
+
+class TransformerEncoderLayer(nn.Module):
+    __constants__ = ["batch_first", "norm_first"]
+
+    def __init__(
+        self,
+        d_model: int,
+        nhead: int,
+        dim_feedforward: int = 2048,
+        dropout: float = 0.1,
+        activation: Union[str, Callable[[Tensor], Tensor]] = F.relu,
+        batch_first: bool = False,
+        norm_first: bool = False,
+        device=None,
+        dtype=None,
+        linear1_self_attention_cls: nn.Module = nn.Linear,
+        linear2_self_attention_cls: nn.Module = nn.Linear,
+        linear1_feedforward_cls: nn.Module = nn.Linear,
+        linear2_feedforward_cls: nn.Module = nn.Linear,
+        layer_norm_cls: nn.Module = LayerNorm,
+        layer_norm_eps: float = 1e-5,
+        adaptive_layer_norm=False,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super(TransformerEncoderLayer, self).__init__()
+        self.self_attn = MultiheadAttention(
+            d_model,
+            nhead,
+            dropout=dropout,
+            batch_first=batch_first,
+            linear1_cls=linear1_self_attention_cls,
+            linear2_cls=linear2_self_attention_cls,
+            **factory_kwargs,
+        )
+
+        # Implementation of Feedforward model
+        self.linear1 = linear1_feedforward_cls(
+            d_model, dim_feedforward, **factory_kwargs
+        )
+        self.dropout = nn.Dropout(dropout)
+        self.linear2 = linear2_feedforward_cls(
+            dim_feedforward, d_model, **factory_kwargs
+        )
+
+        self.norm_first = norm_first
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(dropout)
+
+        # Legacy string support for activation function.
+        if isinstance(activation, str):
+            activation = _get_activation_fn(activation)
+        elif isinstance(activation, partial):
+            activation = activation(d_model)
+        elif activation == BalancedDoubleSwish:
+            activation = BalancedDoubleSwish(d_model)
+
+        # # We can't test self.activation in forward() in TorchScript,
+        # # so stash some information about it instead.
+        # if activation is F.relu or isinstance(activation, torch.nn.ReLU):
+        #     self.activation_relu_or_gelu = 1
+        # elif activation is F.gelu or isinstance(activation, torch.nn.GELU):
+        #     self.activation_relu_or_gelu = 2
+        # else:
+        #     self.activation_relu_or_gelu = 0
+        self.activation = activation
+
+        norm1 = layer_norm_cls(d_model, eps=layer_norm_eps, **factory_kwargs)
+        if layer_norm_cls == IdentityNorm:
+            norm2 = BalancedBasicNorm(
+                d_model, eps=layer_norm_eps, **factory_kwargs
+            )
+        else:
+            norm2 = layer_norm_cls(
+                d_model, eps=layer_norm_eps, **factory_kwargs
+            )
+
+        if adaptive_layer_norm:
+            self.norm1 = AdaptiveLayerNorm(d_model, norm1)
+            self.norm2 = AdaptiveLayerNorm(d_model, norm2)
+        else:
+            self.norm1 = norm1
+            self.norm2 = norm2
+
+    def __setstate__(self, state):
+        super(TransformerEncoderLayer, self).__setstate__(state)
+        if not hasattr(self, "activation"):
+            self.activation = F.relu
+
+    def forward(
+        self,
+        src: Tensor,
+        src_mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        r"""Pass the input through the encoder layer.
+
+        Args:
+            src: the sequence to the encoder layer (required).
+            src_mask: the mask for the src sequence (optional).
+            src_key_padding_mask: the mask for the src keys per batch (optional).
+
+        Shape:
+            see the docs in Transformer class.
+        """
+        x, stage_embedding = src, None
+        is_src_tuple = False
+        if isinstance(src, tuple):
+            x, stage_embedding = src
+            is_src_tuple = True
+
+        if src_key_padding_mask is not None:
+            _skpm_dtype = src_key_padding_mask.dtype
+            if _skpm_dtype != torch.bool and not torch.is_floating_point(
+                src_key_padding_mask
+            ):
+                raise AssertionError(
+                    "only bool and floating types of key_padding_mask are supported"
+                )
+
+        if self.norm_first:
+            x = x + self._sa_block(
+                self.norm1(x, stage_embedding),
+                src_mask,
+                src_key_padding_mask,
+            )
+            x = x + self._ff_block(self.norm2(x, stage_embedding))
+        else:
+            x = self.norm1(
+                x + self._sa_block(x, src_mask, src_key_padding_mask),
+                stage_embedding,
+            )
+            x = self.norm2(x + self._ff_block(x), stage_embedding)
+
+        if is_src_tuple:
+            return (x, stage_embedding)
+        return x
+
+    def infer(
+        self,
+        src: Tensor,
+        src_mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+        past_kv: Optional[Tensor] = None,
+        use_cache: bool = False,
+    ):
+        x, stage_embedding = src, None
+        is_src_tuple = False
+        if isinstance(src, tuple):
+            x, stage_embedding = src
+            is_src_tuple = True
+
+        if src_key_padding_mask is not None:
+            _skpm_dtype = src_key_padding_mask.dtype
+            if _skpm_dtype != torch.bool and not torch.is_floating_point(
+                src_key_padding_mask
+            ):
+                raise AssertionError(
+                    "only bool and floating types of key_padding_mask are supported"
+                )
+
+        if self.norm_first:
+            x_attn_out, kv = self.self_attn.infer(
+                self.norm1(x, stage_embedding),
+                attn_mask=src_mask,
+                key_padding_mask=src_key_padding_mask,
+                need_weights=False,
+                past_kv=past_kv,
+                use_cache=use_cache,
+            )
+            x = x + x_attn_out
+            x = x + self._ff_block(self.norm2(x, stage_embedding))
+
+        if is_src_tuple:
+            return (x, stage_embedding)
+        return (x, kv)
+
+    # self-attention block
+    def _sa_block(
+        self,
+        x: Tensor,
+        attn_mask: Optional[Tensor],
+        key_padding_mask: Optional[Tensor],
+    ) -> Tensor:
+        x = self.self_attn(
+            x,
+            x,
+            x,
+            attn_mask=attn_mask,
+            key_padding_mask=key_padding_mask,
+            need_weights=False,
+        )[0]
+        return self.dropout1(x)
+
+    # feed forward block
+    def _ff_block(self, x: Tensor) -> Tensor:
+        x = self.linear2(self.dropout(self.activation(self.linear1(x))))
+        return self.dropout2(x)
+
+
+class TransformerEncoder(nn.Module):
+    r"""TransformerEncoder is a stack of N encoder layers. Users can build the
+    BERT(https://arxiv.org/abs/1810.04805) model with corresponding parameters.
+
+    Args:
+        encoder_layer: an instance of the TransformerEncoderLayer() class (required).
+        num_layers: the number of sub-encoder-layers in the encoder (required).
+        norm: the layer normalization component (optional).
+        enable_nested_tensor: if True, input will automatically convert to nested tensor
+            (and convert back on output). This will improve the overall performance of
+            TransformerEncoder when padding rate is high. Default: ``True`` (enabled).
+
+    Examples::
+        >>> encoder_layer = TransformerEncoderLayer(d_model=512, nhead=8)
+        >>> transformer_encoder = TransformerEncoder(encoder_layer, num_layers=6)
+        >>> src = torch.rand(10, 32, 512)
+        >>> out = transformer_encoder(src)
+    """
+    __constants__ = ["norm"]
+
+    def __init__(self, encoder_layer, num_layers, norm=None):
+        super(TransformerEncoder, self).__init__()
+        self.layers = _get_clones(encoder_layer, num_layers)
+        self.num_layers = num_layers
+        self.norm = norm
+
+    def forward(
+        self,
+        src: Tensor,
+        mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+        return_layer_states: bool = False,
+    ) -> Tensor:
+        r"""Pass the input through the encoder layers in turn.
+
+        Args:
+            src: the sequence to the encoder (required).
+            mask: the mask for the src sequence (optional).
+            src_key_padding_mask: the mask for the src keys per batch (optional).
+            return_layer_states: return layers' state (optional).
+
+        Shape:
+            see the docs in Transformer class.
+        """
+        if return_layer_states:
+            layer_states = []  # layers' output
+            output = src
+            for mod in self.layers:
+                output = mod(
+                    output,
+                    src_mask=mask,
+                    src_key_padding_mask=src_key_padding_mask,
+                )
+                layer_states.append(output[0])
+
+            if self.norm is not None:
+                output = self.norm(output)
+
+            return layer_states, output
+
+        output = src
+        for mod in self.layers:
+            output = mod(
+                output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
+            )
+
+        if self.norm is not None:
+            output = self.norm(output)
+
+        return output
+
+    def infer(
+        self,
+        src: Tensor,
+        mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+        return_layer_states: bool = False,
+        past_kv: Optional[Tensor] = None,
+        use_cache: bool = False,
+    ):
+        if past_kv is None:
+            past_length = 0
+            past_kv = tuple([None] * self.num_layers)
+        else:
+            past_length = past_kv[0][0].size(-2)
+        new_kv = () if use_cache else None
+        output = src
+        for mod, past_layer_kv in zip(self.layers, past_kv):
+            output, kv = mod.infer(
+                output, src_mask=mask, src_key_padding_mask=src_key_padding_mask, past_kv=past_layer_kv, use_cache=use_cache
+            )
+            if use_cache:
+                new_kv = new_kv + (kv,)
+
+        if self.norm is not None:
+            output = self.norm(output)
+
+        return output, new_kv
+
+
+class TransformerDecoderLayer(nn.Module):
+    __constants__ = ["batch_first", "norm_first"]
+
+    def __init__(
+        self,
+        d_model: int,
+        nhead: int,
+        dim_feedforward: int = 2048,
+        dropout: float = 0.1,
+        activation: Union[str, Callable[[Tensor], Tensor]] = F.relu,
+        linear1_self_attention_cls: nn.Module = nn.Linear,
+        linear2_self_attention_cls: nn.Module = nn.Linear,
+        linear1_feedforward_cls: nn.Module = nn.Linear,
+        linear2_feedforward_cls: nn.Module = nn.Linear,
+        batch_first: bool = False,
+        norm_first: bool = False,
+        device=None,
+        dtype=None,
+        layer_norm_cls: nn.Module = LayerNorm,
+        layer_norm_eps: float = 1e-5,
+        adaptive_layer_norm=False,
+    ) -> None:
+        factory_kwargs = {"device": device, "dtype": dtype}
+        super(TransformerDecoderLayer, self).__init__()
+        self.self_attn = MultiheadAttention(
+            d_model,
+            nhead,
+            dropout=dropout,
+            batch_first=batch_first,
+            linear1_cls=linear1_self_attention_cls,
+            linear2_cls=linear2_self_attention_cls,
+            **factory_kwargs,
+        )
+        self.multihead_attn = MultiheadAttention(
+            d_model,
+            nhead,
+            dropout=dropout,
+            batch_first=batch_first,
+            linear1_cls=linear1_self_attention_cls,
+            linear2_cls=linear2_self_attention_cls,
+            **factory_kwargs,
+        )
+        # Implementation of Feedforward model
+        self.linear1 = linear1_feedforward_cls(
+            d_model, dim_feedforward, **factory_kwargs
+        )
+        self.dropout = nn.Dropout(dropout)
+        self.linear2 = linear2_feedforward_cls(
+            dim_feedforward, d_model, **factory_kwargs
+        )
+
+        self.norm_first = norm_first
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(dropout)
+        self.dropout3 = nn.Dropout(dropout)
+
+        # Legacy string support for activation function.
+        if isinstance(activation, str):
+            self.activation = _get_activation_fn(activation)
+        elif isinstance(activation, partial):
+            self.activation = activation(d_model)
+        elif activation == BalancedDoubleSwish:
+            self.activation = BalancedDoubleSwish(d_model)
+        else:
+            self.activation = activation
+
+        if adaptive_layer_norm:
+            norm1 = layer_norm_cls(
+                d_model, eps=layer_norm_eps, **factory_kwargs
+            )
+            norm2 = layer_norm_cls(
+                d_model, eps=layer_norm_eps, **factory_kwargs
+            )
+            norm3 = layer_norm_cls(
+                d_model, eps=layer_norm_eps, **factory_kwargs
+            )
+
+            self.norm1 = AdaptiveLayerNorm(d_model, norm1)
+            self.norm2 = AdaptiveLayerNorm(d_model, norm2)
+            self.norm3 = AdaptiveLayerNorm(d_model, norm3)
+        else:
+            self.norm1 = layer_norm_cls(
+                d_model, eps=layer_norm_eps, **factory_kwargs
+            )
+            self.norm2 = layer_norm_cls(
+                d_model, eps=layer_norm_eps, **factory_kwargs
+            )
+            if layer_norm_cls == IdentityNorm:
+                self.norm3 = BalancedBasicNorm(
+                    d_model, eps=layer_norm_eps, **factory_kwargs
+                )
+            else:
+                self.norm3 = layer_norm_cls(
+                    d_model, eps=layer_norm_eps, **factory_kwargs
+                )
+
+    def forward(
+        self,
+        tgt: Tensor,
+        memory: Tensor,
+        tgt_mask: Optional[Tensor] = None,
+        memory_mask: Optional[Tensor] = None,
+        tgt_key_padding_mask: Optional[Tensor] = None,
+        memory_key_padding_mask: Optional[Tensor] = None,
+    ) -> Tensor:
+        r"""Pass the inputs (and mask) through the decoder layer.
+
+        Args:
+            tgt: the sequence to the decoder layer (required).
+            memory: the sequence from the last layer of the encoder (required).
+            tgt_mask: the mask for the tgt sequence (optional).
+            memory_mask: the mask for the memory sequence (optional).
+            tgt_key_padding_mask: the mask for the tgt keys per batch (optional).
+            memory_key_padding_mask: the mask for the memory keys per batch (optional).
+
+        Shape:
+            see the docs in Transformer class.
+        """
+        tgt_is_tuple = False
+        if isinstance(tgt, tuple):
+            x, stage_embedding = tgt
+            tgt_is_tuple = True
+        else:
+            x, stage_embedding = tgt, None
+
+        if self.norm_first:
+            x = x + self._sa_block(
+                self.norm1(x, stage_embedding), tgt_mask, tgt_key_padding_mask
+            )
+            x = x + self._mha_block(
+                self.norm2(x, stage_embedding),
+                memory,
+                memory_mask,
+                memory_key_padding_mask,
+            )
+            x = x + self._ff_block(self.norm3(x, stage_embedding))
+        else:
+            x = self.norm1(
+                x + self._sa_block(x, tgt_mask, tgt_key_padding_mask),
+                stage_embedding,
+            )
+            x = self.norm2(
+                x
+                + self._mha_block(
+                    x, memory, memory_mask, memory_key_padding_mask
+                ),
+                stage_embedding,
+            )
+            x = self.norm3(x + self._ff_block(x), stage_embedding)
+
+        if tgt_is_tuple:
+            return (x, stage_embedding)
+        return x
+
+    # self-attention block
+    def _sa_block(
+        self,
+        x: Tensor,
+        attn_mask: Optional[Tensor],
+        key_padding_mask: Optional[Tensor],
+    ) -> Tensor:
+        x = self.self_attn(
+            x,
+            x,
+            x,
+            attn_mask=attn_mask,
+            key_padding_mask=key_padding_mask,
+            need_weights=False,
+        )[0]
+        return self.dropout1(x)
+
+    # multihead attention block
+    def _mha_block(
+        self,
+        x: Tensor,
+        mem: Tensor,
+        attn_mask: Optional[Tensor],
+        key_padding_mask: Optional[Tensor],
+    ) -> Tensor:
+        x = self.multihead_attn(
+            x,
+            mem,
+            mem,
+            attn_mask=attn_mask,
+            key_padding_mask=key_padding_mask,
+            need_weights=False,
+        )[0]
+        return self.dropout2(x)
+
+    # feed forward block
+    def _ff_block(self, x: Tensor) -> Tensor:
+        x = self.linear2(self.dropout(self.activation(self.linear1(x))))
+        return self.dropout3(x)
+
+
+def _get_clones(module, N):
+    return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
+
+
+def _get_activation_fn(activation: str) -> Callable[[Tensor], Tensor]:
+    if activation == "relu":
+        return F.relu
+    elif activation == "gelu":
+        return F.gelu
+
+    raise RuntimeError(
+        "activation should be relu/gelu, not {}".format(activation)
+    )
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--- /dev/null
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@@ -0,0 +1,3 @@
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+size 15650
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--- /dev/null
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new file mode 100644
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--- /dev/null
+++ b/apps/audio_cloning/vallex/presets/vctk_4.npz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/apps/audio_cloning/vallex/presets/yaesakura.npz b/apps/audio_cloning/vallex/presets/yaesakura.npz
new file mode 100644
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--- /dev/null
+++ b/apps/audio_cloning/vallex/presets/yaesakura.npz
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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@@ -0,0 +1,3 @@
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@@ -0,0 +1,3 @@
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--- /dev/null
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+version https://git-lfs.github.com/spec/v1
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diff --git a/apps/audio_cloning/vallex/prompts/en-2.wav b/apps/audio_cloning/vallex/prompts/en-2.wav
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--- /dev/null
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diff --git a/apps/audio_cloning/vallex/prompts/ja-1.wav b/apps/audio_cloning/vallex/prompts/ja-1.wav
new file mode 100644
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--- /dev/null
+++ b/apps/audio_cloning/vallex/prompts/ja-1.wav
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:717dd5c1fd613224c6bebca21a90b14e16d0f76c0c80d20ca3ecd767ea0e1608
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diff --git a/apps/audio_cloning/vallex/prompts/ja-2.ogg b/apps/audio_cloning/vallex/prompts/ja-2.ogg
new file mode 100644
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--- /dev/null
+++ b/apps/audio_cloning/vallex/prompts/ja-2.ogg
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/apps/audio_cloning/vallex/prompts/ja-okuwaki-long.wav b/apps/audio_cloning/vallex/prompts/ja-okuwaki-long.wav
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--- /dev/null
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/apps/audio_cloning/vallex/prompts/ja-okuwaki.wav b/apps/audio_cloning/vallex/prompts/ja-okuwaki.wav
new file mode 100644
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--- /dev/null
+++ b/apps/audio_cloning/vallex/prompts/ja-okuwaki.wav
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b3d75c18b9d8d4f8974253b15fd158069874cc009e9895315214d0477cbc468f
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diff --git a/apps/audio_cloning/vallex/prompts/ja-zundamon.wav b/apps/audio_cloning/vallex/prompts/ja-zundamon.wav
new file mode 100644
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--- /dev/null
+++ b/apps/audio_cloning/vallex/prompts/ja-zundamon.wav
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:02d439e7d067b12f08537e2af8a05f55e5dd712bee7f774051e13aef65b3b65f
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diff --git a/apps/audio_cloning/vallex/prompts/ph.txt b/apps/audio_cloning/vallex/prompts/ph.txt
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diff --git a/apps/audio_cloning/vallex/prompts/zh-1.wav b/apps/audio_cloning/vallex/prompts/zh-1.wav
new file mode 100644
index 0000000000000000000000000000000000000000..783432aff93c25fef9c8d7c1da556553b492c519
--- /dev/null
+++ b/apps/audio_cloning/vallex/prompts/zh-1.wav
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2bd979af3595b75fe1f5b44bf7bf16f27cb25ca05e2e97d483080d0bb2b135c9
+size 176480
diff --git a/apps/audio_cloning/vallex/prompts/zh-2.wav b/apps/audio_cloning/vallex/prompts/zh-2.wav
new file mode 100644
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--- /dev/null
+++ b/apps/audio_cloning/vallex/prompts/zh-2.wav
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5f8627898b744d0893b91fd88feb9210794efe10fbd7c8c0cac8087083f22357
+size 271824
diff --git a/apps/dev/calculator.py b/apps/dev/calculator.py
new file mode 100644
index 0000000000000000000000000000000000000000..51e5ff335c57b0a0c264a16e5883b71b07132c52
--- /dev/null
+++ b/apps/dev/calculator.py
@@ -0,0 +1,30 @@
+import gradio as gr
+
+
+def calculator(num1, operation, num2):
+    if operation == "add":
+        return num1 + num2
+    elif operation == "subtract":
+        return num1 - num2
+    elif operation == "multiply":
+        return num1 * num2
+    elif operation == "divide":
+        if num2 == 0:
+            raise gr.Error("Cannot divide by zero!")
+        return num1 / num2
+
+
+def main():
+    gr.Interface(
+        calculator,
+        ["number", gr.Radio(["add", "subtract", "multiply", "divide"]), "number"],
+        "number",
+        examples=[
+            [45, "add", 3],
+            [3.14, "divide", 2],
+            [144, "multiply", 2.5],
+            [0, "subtract", 1.2],
+        ],
+        title="Toy Calculator",
+        description="Here's a sample toy calculator.",
+    )
diff --git a/apps/dev/dev.py b/apps/dev/dev.py
new file mode 100644
index 0000000000000000000000000000000000000000..ee59be1d251a4d47a5416d6752e337bc1596687c
--- /dev/null
+++ b/apps/dev/dev.py
@@ -0,0 +1,146 @@
+import random
+import time
+
+import gradio as gr
+import numpy as np
+
+
+def store_message(message: str, history: list[str]):
+    output = {"Current messages": message, "Previous messages": history[::-1]}
+    history.append(message)
+    return output, history
+
+
+def store_message_ui():
+    gr.Interface(
+        fn=store_message,
+        inputs=["textbox", gr.State(value=[])],
+        outputs=["json", gr.State()],
+        title="Session State Management",
+    )
+
+
+def fake_gan():
+    time.sleep(1)
+    images = [
+        "https://images.unsplash.com/photo-1507003211169-0a1dd7228f2d?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=387&q=80",
+        "https://images.unsplash.com/photo-1554151228-14d9def656e4?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8&auto=format&fit=crop&w=386&q=80",
+        "https://images.unsplash.com/photo-1542909168-82c3e7fdca5c?ixlib=rb-1.2.1&ixid=MnwxMjA3fDB8MHxzZWFyY2h8MXx8aHVtYW4lMjBmYWNlfGVufDB8fDB8fA%3D%3D&w=1000&q=80",
+    ]
+    return images
+
+
+def fake_gan_ui():
+    gr.Interface(
+        fn=fake_gan,
+        inputs=None,
+        outputs=gr.Gallery(label="Generated Images", columns=2),
+        title="FD-GAN",
+        description="This is a fake demo of a GAN. In reality, the images are randomly chosen from Unsplash.",
+    )
+
+
+def fake_diffusion(steps):
+    rng = np.random.default_rng()
+    for i in range(steps):
+        time.sleep(1)
+        image = rng.random(size=(600, 600, 3))
+        yield image
+
+    image = np.ones((1000, 1000, 3), np.uint8)
+    image[:] = [255, 124, 0]
+    yield image
+
+
+def fake_diffusion_ui():
+    gr.Interface(
+        fake_diffusion,
+        inputs=gr.Slider(1, 10, 3, step=1, label="Steps"),
+        outputs="image",
+        title="Fake Diffusion",
+        description="This is a fake demo of a diffusion model. In reality, the images are randomly chosen from Unsplash.",
+    )
+
+
+def fake_chat_ui():
+    with gr.Blocks(title="Fake Chat"):
+        chatbot = gr.Chatbot()
+        msg = gr.Textbox()
+        clear = gr.Button("Clear")
+
+        def user(user_message: str, history: list[list[str]]):
+            return "", history + [[user_message, None]]
+
+        def bot(history):
+            bot_message = random.choice(
+                ["How are you?", "I love you", "I'm very hungry"]
+            )
+            time.sleep(1)
+            user_message = history[-1][0]
+            history[-1][1] = f"reply to: {user_message}\n{bot_message}"
+            return history
+
+        msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(
+            bot, chatbot, chatbot
+        )
+        clear.click(lambda: None, None, chatbot, queue=False)
+
+
+def keep_repeating(audio_file):
+    for _ in range(10):
+        time.sleep(0.5)
+        print(audio_file)
+        yield audio_file
+
+
+def keep_repeating_ui():
+    gr.Interface(
+        keep_repeating,
+        gr.Audio(sources=["microphone"], type="filepath"),
+        gr.Audio(streaming=True, autoplay=True),
+        title="Keep Repeating",
+        description="Keep repeating the audio from the microphone",
+    )
+
+
+def flip(im):
+    return np.flipud(im)
+
+
+def streaming_cam_ui():
+    gr.Interface(
+        flip,
+        gr.Image(sources=["webcam"], streaming=True),
+        "image",
+        live=True,
+        title="Streaming Camera",
+    )
+
+
+def main():
+    # fake GAN
+    fake_gan_ui()
+
+    # keep repeating
+    keep_repeating_ui()
+
+    # fake chat
+    # fake_chat_ui()
+
+    # fake diffusion
+    fake_diffusion_ui()
+
+    # session state management
+    store_message_ui()
+
+    # streaming camera
+    streaming_cam_ui()
+
+    # static audio
+    gr.Interface(
+        fn=lambda x: x,
+        inputs=gr.Audio(sources=["microphone"]),
+        outputs=gr.Audio(sources=["microphone"]),
+        title="Static Audio",
+        description="Play the audio from the microphone",
+    )
diff --git a/apps/dev/image_gen.py b/apps/dev/image_gen.py
new file mode 100644
index 0000000000000000000000000000000000000000..edd829ddd00a085e108314436c7cd1f4c22dab9f
--- /dev/null
+++ b/apps/dev/image_gen.py
@@ -0,0 +1,27 @@
+import time
+
+import gradio as gr
+
+
+def image_gen(prompt, progress=gr.Progress()):
+    progress(0, desc="Starting")
+    time.sleep(1)
+    progress(0.5)
+    time.sleep(1)
+    progress(1)
+    return "https://www.gradio.app/_app/immutable/assets/gradio.CHB5adID.svg"
+
+
+def main():
+    prompt = gr.Textbox()
+    image = gr.Image()
+    generate_btn_1 = gr.Button("Generate Image via model 1")
+    generate_btn_1.click(
+        image_gen, prompt, image, concurrency_limit=2, concurrency_id="gpu_queue"
+    )
+
+    generate_btn_2 = gr.Button("Generate Image via model 2")
+    generate_btn_2.click(image_gen, prompt, image, concurrency_id="gpu_queue")
+
+    generate_btn_3 = gr.Button("Generate Image via model 3")
+    generate_btn_3.click(image_gen, prompt, image, concurrency_id="gpu_queue")
diff --git a/apps/dev/main.py b/apps/dev/main.py
new file mode 100644
index 0000000000000000000000000000000000000000..b54b814c2d3c5d8618eec86a7759cbaef3b461af
--- /dev/null
+++ b/apps/dev/main.py
@@ -0,0 +1,28 @@
+import gradio as gr
+
+from .calculator import main as calculator
+from .dev import main as dev
+from .image_gen import main as image_gen
+from .sepia_filter import main as sepia_filter
+
+# from .simple_chat import main as simple_chat
+
+
+def main():
+    with gr.Blocks(theme=gr.themes.Soft()):
+        gr.Markdown("# Prototypings")
+
+        with gr.Tab("Dev"):
+            dev()
+
+        # with gr.Tab("Simple Form"):
+        #     simple_chat()
+
+        with gr.Tab("Image Generation"):
+            image_gen()
+
+        with gr.Tab("Sepia Filter"):
+            sepia_filter()
+
+        with gr.Tab("Calculator"):
+            calculator()
diff --git a/apps/dev/sepia_filter.py b/apps/dev/sepia_filter.py
new file mode 100644
index 0000000000000000000000000000000000000000..81691570e0cf3a827dda11a4b532ef0851aae046
--- /dev/null
+++ b/apps/dev/sepia_filter.py
@@ -0,0 +1,22 @@
+import gradio as gr
+import numpy as np
+
+
+def sepia(input_img):
+    sepia_filter = np.array(
+        [[0.393, 0.769, 0.189], [0.349, 0.686, 0.168], [0.272, 0.534, 0.131]]
+    )
+    sepia_img = input_img.dot(sepia_filter.T)
+    sepia_img /= sepia_img.max()
+
+    return sepia_img
+
+
+def main():
+    gr.Interface(
+        fn=sepia,
+        inputs=gr.Image(),
+        outputs=gr.Image(),
+        title="Sepia Filter",
+        description="Apply a sepia filter to an image",
+    )
diff --git a/apps/dev/simple_chat.py b/apps/dev/simple_chat.py
new file mode 100644
index 0000000000000000000000000000000000000000..7895b038e900f9dc88e7d275db5679c8e433c3ed
--- /dev/null
+++ b/apps/dev/simple_chat.py
@@ -0,0 +1,13 @@
+import gradio as gr
+
+
+def greet(name, intensity):
+    return "Hello, " + name + "!" * int(intensity)
+
+
+def main():
+    gr.Interface(
+        fn=greet,
+        inputs=["text", "slider"],
+        outputs=["text"],
+    )
diff --git a/models/checkpoints/.gitkeep b/models/checkpoints/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/models/whisper/.gitkeep b/models/whisper/.gitkeep
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000000000000000000000000000000000000..99180ac3e032b7ecbd9cc42911e464a201b8911c
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,31 @@
+[project]
+name = "chx-prototype"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.13"
+dependencies = [
+    "cn2an>=0.5.23",
+    "eng-to-ipa>=0.0.2",
+    "gradio>=5.49.1",
+    "huggingface-hub>=0.35.3",
+    "inflect>=7.5.0",
+    "jieba>=0.42.1",
+    "langid>=1.1.6",
+    "matplotlib>=3.10.7",
+    "nltk>=3.9.2",
+    "openai-whisper>=20250625",
+    "pyopenjtalk>=0.4.1",
+    "scipy>=1.16.2",
+    "tokenizers>=0.22.1",
+    "torch>=2.9.0",
+    "torchaudio>=2.9.0",
+    "unidecode>=1.4.0",
+    "vocos>=0.1.0",
+    "wget>=3.2",
+]
+
+[dependency-groups]
+dev = [
+    "ruff>=0.14.1",
+]
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..335586fbb09d2a8a5779caa1a53097cd008cf77a
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,1208 @@
+# This file was autogenerated by uv via the following command:
+#    uv export --format requirements-txt
+aiofiles==24.1.0 \
+    --hash=sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c \
+    --hash=sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5
+    # via gradio
+annotated-doc==0.0.3 \
+    --hash=sha256:348ec6664a76f1fd3be81f43dffbee4c7e8ce931ba71ec67cc7f4ade7fbbb580 \
+    --hash=sha256:e18370014c70187422c33e945053ff4c286f453a984eba84d0dbfa0c935adeda
+    # via fastapi
+annotated-types==0.7.0 \
+    --hash=sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53 \
+    --hash=sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89
+    # via pydantic
+anyio==4.11.0 \
+    --hash=sha256:0287e96f4d26d4149305414d4e3bc32f0dcd0862365a4bddea19d7a1ec38c4fc \
+    --hash=sha256:82a8d0b81e318cc5ce71a5f1f8b5c4e63619620b63141ef8c995fa0db95a57c4
+    # via
+    #   gradio
+    #   httpx
+    #   starlette
+audioop-lts==0.2.2 \
+    --hash=sha256:0337d658f9b81f4cd0fdb1f47635070cc084871a3d4646d9de74fdf4e7c3d24a \
+    --hash=sha256:03f061a1915538fd96272bac9551841859dbb2e3bf73ebe4a23ef043766f5449 \
+    --hash=sha256:068aa17a38b4e0e7de771c62c60bbca2455924b67a8814f3b0dee92b5820c0b3 \
+    --hash=sha256:088327f00488cdeed296edd9215ca159f3a5a5034741465789cad403fcf4bec0 \
+    --hash=sha256:0d9385e96f9f6da847f4d571ce3cb15b5091140edf3db97276872647ce37efd7 \
+    --hash=sha256:106753a83a25ee4d6f473f2be6b0966fc1c9af7e0017192f5531a3e7463dce58 \
+    --hash=sha256:143fad0311e8209ece30a8dbddab3b65ab419cbe8c0dde6e8828da25999be911 \
+    --hash=sha256:15ab25dd3e620790f40e9ead897f91e79c0d3ce65fe193c8ed6c26cffdd24be7 \
+    --hash=sha256:167d3b62586faef8b6b2275c3218796b12621a60e43f7e9d5845d627b9c9b80e \
+    --hash=sha256:2b267b70747d82125f1a021506565bdc5609a2b24bcb4773c16d79d2bb260bbd \
+    --hash=sha256:3bcddaaf6cc5935a300a8387c99f7a7fbbe212a11568ec6cf6e4bc458c048636 \
+    --hash=sha256:3fc38008969796f0f689f1453722a0f463da1b8a6fbee11987830bfbb664f623 \
+    --hash=sha256:47eba38322370347b1c47024defbd36374a211e8dd5b0dcbce7b34fdb6f8847b \
+    --hash=sha256:48159d96962674eccdca9a3df280e864e8ac75e40a577cc97c5c42667ffabfc5 \
+    --hash=sha256:49ee1a41738a23e98d98b937a0638357a2477bc99e61b0f768a8f654f45d9b7a \
+    --hash=sha256:4a53aa7c16a60a6857e6b0b165261436396ef7293f8b5c9c828a3a203147ed4a \
+    --hash=sha256:4b4cd51a57b698b2d06cb9993b7ac8dfe89a3b2878e96bc7948e9f19ff51dba6 \
+    --hash=sha256:51c916108c56aa6e426ce611946f901badac950ee2ddaf302b7ed35d9958970d \
+    --hash=sha256:550c114a8df0aafe9a05442a1162dfc8fec37e9af1d625ae6060fed6e756f303 \
+    --hash=sha256:58cf54380c3884fb49fdd37dfb7a772632b6701d28edd3e2904743c5e1773602 \
+    --hash=sha256:5b00be98ccd0fc123dcfad31d50030d25fcf31488cde9e61692029cd7394733b \
+    --hash=sha256:5f93a5db13927a37d2d09637ccca4b2b6b48c19cd9eda7b17a2e9f77edee6a6f \
+    --hash=sha256:64d0c62d88e67b98a1a5e71987b7aa7b5bcffc7dcee65b635823dbdd0a8dbbd0 \
+    --hash=sha256:73f80bf4cd5d2ca7814da30a120de1f9408ee0619cc75da87d0641273d202a09 \
+    --hash=sha256:752d76472d9804ac60f0078c79cdae8b956f293177acd2316cd1e15149aee132 \
+    --hash=sha256:83c381767e2cc10e93e40281a04852facc4cd9334550e0f392f72d1c0a9c5753 \
+    --hash=sha256:8fefe5868cd082db1186f2837d64cfbfa78b548ea0d0543e9b28935ccce81ce9 \
+    --hash=sha256:9191d68659eda01e448188f60364c7763a7ca6653ed3f87ebb165822153a8547 \
+    --hash=sha256:96f19de485a2925314f5020e85911fb447ff5fbef56e8c7c6927851b95533a1c \
+    --hash=sha256:9a13dc409f2564de15dd68be65b462ba0dde01b19663720c68c1140c782d1d75 \
+    --hash=sha256:a2c2a947fae7d1062ef08c4e369e0ba2086049a5e598fda41122535557012e9e \
+    --hash=sha256:a2d4f1513d63c795e82948e1305f31a6d530626e5f9f2605408b300ae6095093 \
+    --hash=sha256:a5bf613e96f49712073de86f20dbdd4014ca18efd4d34ed18c75bd808337851b \
+    --hash=sha256:a6d2e0f9f7a69403e388894d4ca5ada5c47230716a03f2847cfc7bd1ecb589d6 \
+    --hash=sha256:b492c3b040153e68b9fdaff5913305aaaba5bb433d8a7f73d5cf6a64ed3cc1dd \
+    --hash=sha256:ba7c3a7e5f23e215cb271516197030c32aef2e754252c4c70a50aaff7031a2c8 \
+    --hash=sha256:c0022283e9556e0f3643b7c3c03f05063ca72b3063291834cca43234f20c60bb \
+    --hash=sha256:c174e322bb5783c099aaf87faeb240c8d210686b04bd61dfd05a8e5a83d88969 \
+    --hash=sha256:c9c8e68d8b4a56fda8c025e538e639f8c5953f5073886b596c93ec9b620055e7 \
+    --hash=sha256:cfcac6aa6f42397471e4943e0feb2244549db5c5d01efcd02725b96af417f3fe \
+    --hash=sha256:d5e73fa573e273e4f2e5ff96f9043858a5e9311e94ffefd88a3186a910c70917 \
+    --hash=sha256:def246fe9e180626731b26e89816e79aae2276f825420a07b4a647abaa84becc \
+    --hash=sha256:dfbbc74ec68a0fd08cfec1f4b5e8cca3d3cd7de5501b01c4b5d209995033cde9 \
+    --hash=sha256:e160bf9df356d841bb6c180eeeea1834085464626dc1b68fa4e1d59070affdc3 \
+    --hash=sha256:e541c3ef484852ef36545f66209444c48b28661e864ccadb29daddb6a4b8e5f5 \
+    --hash=sha256:f9b0b8a03ef474f56d1a842af1a2e01398b8f7654009823c6d9e0ecff4d5cfbf \
+    --hash=sha256:f9ee9b52f5f857fbaf9d605a360884f034c92c1c23021fb90b2e39b8e64bede6 \
+    --hash=sha256:fbdd522624141e40948ab3e8cdae6e04c748d78710e9f0f8d4dae2750831de19 \
+    --hash=sha256:fd3d4602dc64914d462924a08c1a9816435a2155d74f325853c1f1ac3b2d9800
+    # via gradio
+brotli==1.1.0 \
+    --hash=sha256:3c0ef38c7a7014ffac184db9e04debe495d317cc9c6fb10071f7fefd93100a4f \
+    --hash=sha256:43395e90523f9c23a3d5bdf004733246fba087f2948f87ab28015f12359ca6a0 \
+    --hash=sha256:6967ced6730aed543b8673008b5a391c3b1076d834ca438bbd70635c73775368 \
+    --hash=sha256:7bc37c4d6b87fb1017ea28c9508b36bbcb0c3d18b4260fcdf08b200c74a6aee8 \
+    --hash=sha256:7eedaa5d036d9336c95915035fb57422054014ebdeb6f3b42eac809928e40d0c \
+    --hash=sha256:81de08ac11bcb85841e440c13611c00b67d3bf82698314928d0b676362546724 \
+    --hash=sha256:832436e59afb93e1836081a20f324cb185836c617659b07b129141a8426973c7 \
+    --hash=sha256:8bf32b98b75c13ec7cf774164172683d6e7891088f6316e54425fde1efc276d5 \
+    --hash=sha256:9011560a466d2eb3f5a6e4929cf4a09be405c64154e12df0dd72713f6500e32b \
+    --hash=sha256:91d7cc2a76b5567591d12c01f019dd7afce6ba8cba6571187e21e2fc418ae648 \
+    --hash=sha256:a93dde851926f4f2678e704fadeb39e16c35d8baebd5252c9fd94ce8ce68c4a0 \
+    --hash=sha256:d487f5432bf35b60ed625d7e1b448e2dc855422e87469e3f450aa5552b0eb284 \
+    --hash=sha256:f0db75f47be8b8abc8d9e31bc7aad0547ca26f24a54e6fd10231d623f183d089
+    # via gradio
+certifi==2025.10.5 \
+    --hash=sha256:0f212c2744a9bb6de0c56639a6f68afe01ecd92d91f14ae897c4fe7bbeeef0de \
+    --hash=sha256:47c09d31ccf2acf0be3f701ea53595ee7e0b8fa08801c6624be771df09ae7b43
+    # via
+    #   httpcore
+    #   httpx
+    #   requests
+charset-normalizer==3.4.4 \
+    --hash=sha256:2b7d8f6c26245217bd2ad053761201e9f9680f8ce52f0fcd8d0755aeae5b2152 \
+    --hash=sha256:3162d5d8ce1bb98dd51af660f2121c55d0fa541b46dff7bb9b9f86ea1d87de72 \
+    --hash=sha256:362d61fd13843997c1c446760ef36f240cf81d3ebf74ac62652aebaf7838561e \
+    --hash=sha256:47cc91b2f4dd2833fddaedd2893006b0106129d4b94fdb6af1f4ce5a9965577c \
+    --hash=sha256:542d2cee80be6f80247095cc36c418f7bddd14f4a6de45af91dfad36d817bba2 \
+    --hash=sha256:554af85e960429cf30784dd47447d5125aaa3b99a6f0683589dbd27e2f45da44 \
+    --hash=sha256:5bd2293095d766545ec1a8f612559f6b40abc0eb18bb2f5d1171872d34036ede \
+    --hash=sha256:6b39f987ae8ccdf0d2642338faf2abb1862340facc796048b604ef14919e55ed \
+    --hash=sha256:74018750915ee7ad843a774364e13a3db91682f26142baddf775342c3f5b1133 \
+    --hash=sha256:74664978bb272435107de04e36db5a9735e78232b85b77d45cfb38f758efd33e \
+    --hash=sha256:752944c7ffbfdd10c074dc58ec2d5a8a4cd9493b314d367c14d24c17684ddd14 \
+    --hash=sha256:799a7a5e4fb2d5898c60b640fd4981d6a25f1c11790935a44ce38c54e985f828 \
+    --hash=sha256:7a32c560861a02ff789ad905a2fe94e3f840803362c84fecf1851cb4cf3dc37f \
+    --hash=sha256:81d5eb2a312700f4ecaa977a8235b634ce853200e828fbadf3a9c50bab278328 \
+    --hash=sha256:82004af6c302b5d3ab2cfc4cc5f29db16123b1a8417f2e25f9066f91d4411090 \
+    --hash=sha256:8a6562c3700cce886c5be75ade4a5db4214fda19fede41d9792d100288d8f94c \
+    --hash=sha256:8af65f14dc14a79b924524b1e7fffe304517b2bff5a58bf64f30b98bbc5079eb \
+    --hash=sha256:94537985111c35f28720e43603b8e7b43a6ecfb2ce1d3058bbe955b73404e21a \
+    --hash=sha256:99ae2cffebb06e6c22bdc25801d7b30f503cc87dbd283479e7b606f70aff57ec \
+    --hash=sha256:9a26f18905b8dd5d685d6d07b0cdf98a79f3c7a918906af7cc143ea2e164c8bc \
+    --hash=sha256:9b35f4c90079ff2e2edc5b26c0c77925e5d2d255c42c74fdb70fb49b172726ac \
+    --hash=sha256:a8a8b89589086a25749f471e6a900d3f662d1d3b6e2e59dcecf787b1cc3a1894 \
+    --hash=sha256:b435cba5f4f750aa6c0a0d92c541fb79f69a387c91e61f1795227e4ed9cece14 \
+    --hash=sha256:bc7637e2f80d8530ee4a78e878bce464f70087ce73cf7c1caf142416923b98f1 \
+    --hash=sha256:c0463276121fdee9c49b98908b3a89c39be45d86d1dbaa22957e38f6321d4ce3 \
+    --hash=sha256:c8ae8a0f02f57a6e61203a31428fa1d677cbe50c93622b4149d5c0f319c1d19e \
+    --hash=sha256:cb6254dc36b47a990e59e1068afacdcd02958bdcce30bb50cc1700a8b9d624a6 \
+    --hash=sha256:d1f13550535ad8cff21b8d757a3257963e951d96e20ec82ab44bc64aeb62a191 \
+    --hash=sha256:da3326d9e65ef63a817ecbcc0df6e94463713b754fe293eaa03da99befb9a5bd \
+    --hash=sha256:de00632ca48df9daf77a2c65a484531649261ec9f25489917f09e455cb09ddb2 \
+    --hash=sha256:e1f185f86a6f3403aa2420e815904c67b2f9ebc443f045edd0de921108345794 \
+    --hash=sha256:ecaae4149d99b1c9e7b88bb03e3221956f68fd6d50be2ef061b2381b61d20838 \
+    --hash=sha256:f8bf04158c6b607d747e93949aa60618b61312fe647a6369f88ce2ff16043490 \
+    --hash=sha256:f9d332f8c2a2fcbffe1378594431458ddbef721c1769d78e2cbc06280d8155f9
+    # via requests
+click==8.3.0 \
+    --hash=sha256:9b9f285302c6e3064f4330c05f05b81945b2a39544279343e6e7c5f27a9baddc \
+    --hash=sha256:e7b8232224eba16f4ebe410c25ced9f7875cb5f3263ffc93cc3e8da705e229c4
+    # via
+    #   nltk
+    #   typer
+    #   typer-slim
+    #   uvicorn
+cn2an==0.5.23 \
+    --hash=sha256:b19ab3c53676765c038ccdab51f69b7efa4f0b888139c34088935769241f1cbf \
+    --hash=sha256:eda06a63e5eff4a64488d9f22e5f2a4ceca6eaa63416e4f771e67edecb1a5bdb
+    # via chx-prototype
+colorama==0.4.6 ; sys_platform == 'win32' \
+    --hash=sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44 \
+    --hash=sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6
+    # via
+    #   click
+    #   tqdm
+contourpy==1.3.3 \
+    --hash=sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880 \
+    --hash=sha256:13b68d6a62db8eafaebb8039218921399baf6e47bf85006fd8529f2a08ef33fc \
+    --hash=sha256:177fb367556747a686509d6fef71d221a4b198a3905fe824430e5ea0fda54eb5 \
+    --hash=sha256:1cadd8b8969f060ba45ed7c1b714fe69185812ab43bd6b86a9123fe8f99c3263 \
+    --hash=sha256:1fd43c3be4c8e5fd6e4f2baeae35ae18176cf2e5cced681cca908addf1cdd53b \
+    --hash=sha256:22e9b1bd7a9b1d652cd77388465dc358dafcd2e217d35552424aa4f996f524f5 \
+    --hash=sha256:283edd842a01e3dcd435b1c5116798d661378d83d36d337b8dde1d16a5fc9ba3 \
+    --hash=sha256:2a2a8b627d5cc6b7c41a4beff6c5ad5eb848c88255fda4a8745f7e901b32d8e4 \
+    --hash=sha256:2b7e9480ffe2b0cd2e787e4df64270e3a0440d9db8dc823312e2c940c167df7e \
+    --hash=sha256:33c82d0138c0a062380332c861387650c82e4cf1747aaa6938b9b6516762e772 \
+    --hash=sha256:348ac1f5d4f1d66d3322420f01d42e43122f43616e0f194fc1c9f5d830c5b286 \
+    --hash=sha256:3c30273eb2a55024ff31ba7d052dde990d7d8e5450f4bbb6e913558b3d6c2301 \
+    --hash=sha256:4debd64f124ca62069f313a9cb86656ff087786016d76927ae2cf37846b006c9 \
+    --hash=sha256:4feffb6537d64b84877da813a5c30f1422ea5739566abf0bd18065ac040e120a \
+    --hash=sha256:50ed930df7289ff2a8d7afeb9603f8289e5704755c7e5c3bbd929c90c817164b \
+    --hash=sha256:644a6853d15b2512d67881586bd03f462c7ab755db95f16f14d7e238f2852c67 \
+    --hash=sha256:655456777ff65c2c548b7c454af9c6f33f16c8884f11083244b5819cc214f1b5 \
+    --hash=sha256:66c8a43a4f7b8df8b71ee1840e4211a3c8d93b214b213f590e18a1beca458f7d \
+    --hash=sha256:6afc576f7b33cf00996e5c1102dc2a8f7cc89e39c0b55df93a0b78c1bd992b36 \
+    --hash=sha256:6c3d53c796f8647d6deb1abe867daeb66dcc8a97e8455efa729516b997b8ed99 \
+    --hash=sha256:87acf5963fc2b34825e5b6b048f40e3635dd547f590b04d2ab317c2619ef7ae8 \
+    --hash=sha256:88df9880d507169449d434c293467418b9f6cbe82edd19284aa0409e7fdb933d \
+    --hash=sha256:95b181891b4c71de4bb404c6621e7e2390745f887f2a026b2d99e92c17892339 \
+    --hash=sha256:a15459b0f4615b00bbd1e91f1b9e19b7e63aea7483d03d804186f278c0af2659 \
+    --hash=sha256:a22738912262aa3e254e4f3cb079a95a67132fc5a063890e224393596902f5a4 \
+    --hash=sha256:ab2fd90904c503739a75b7c8c5c01160130ba67944a7b77bbf36ef8054576e7f \
+    --hash=sha256:ab3074b48c4e2cf1a960e6bbeb7f04566bf36b1861d5c9d4d8ac04b82e38ba20 \
+    --hash=sha256:afe5a512f31ee6bd7d0dda52ec9864c984ca3d66664444f2d72e0dc4eb832e36 \
+    --hash=sha256:b20c7c9a3bf701366556e1b1984ed2d0cedf999903c51311417cf5f591d8c78d \
+    --hash=sha256:b7301b89040075c30e5768810bc96a8e8d78085b47d8be6e4c3f5a0b4ed478a0 \
+    --hash=sha256:b7448cb5a725bb1e35ce88771b86fba35ef418952474492cf7c764059933ff8b \
+    --hash=sha256:ca0fdcd73925568ca027e0b17ab07aad764be4706d0a925b89227e447d9737b7 \
+    --hash=sha256:ca658cd1a680a5c9ea96dc61cdbae1e85c8f25849843aa799dfd3cb370ad4fbe \
+    --hash=sha256:cbedb772ed74ff5be440fa8eee9bd49f64f6e3fc09436d9c7d8f1c287b121d77 \
+    --hash=sha256:cf9022ef053f2694e31d630feaacb21ea24224be1c3ad0520b13d844274614fd \
+    --hash=sha256:d002b6f00d73d69333dac9d0b8d5e84d9724ff9ef044fd63c5986e62b7c9e1b1 \
+    --hash=sha256:d06bb1f751ba5d417047db62bca3c8fde202b8c11fb50742ab3ab962c81e8216 \
+    --hash=sha256:d304906ecc71672e9c89e87c4675dc5c2645e1f4269a5063b99b0bb29f232d13 \
+    --hash=sha256:e4e6b05a45525357e382909a4c1600444e2a45b4795163d3b22669285591c1ae \
+    --hash=sha256:e74a9a0f5e3fff48fb5a7f2fd2b9b70a3fe014a67522f79b7cca4c0c7e43c9ae \
+    --hash=sha256:ea37e7b45949df430fe649e5de8351c423430046a2af20b1c1961cae3afcda77 \
+    --hash=sha256:f64836de09927cba6f79dcd00fdd7d5329f3fccc633468507079c829ca4db4e3 \
+    --hash=sha256:fd6ec6be509c787f1caf6b247f0b1ca598bef13f4ddeaa126b7658215529ba0f \
+    --hash=sha256:fd914713266421b7536de2bfa8181aa8c699432b6763a0ea64195ebe28bff6a9 \
+    --hash=sha256:fde6c716d51c04b1c25d0b90364d0be954624a0ee9d60e23e850e8d48353d07a
+    # via matplotlib
+cycler==0.12.1 \
+    --hash=sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30 \
+    --hash=sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c
+    # via matplotlib
+einops==0.8.1 \
+    --hash=sha256:919387eb55330f5757c6bea9165c5ff5cfe63a642682ea788a6d472576d81737 \
+    --hash=sha256:de5d960a7a761225532e0f1959e5315ebeafc0cd43394732f103ca44b9837e84
+    # via
+    #   encodec
+    #   vocos
+encodec==0.1.1 \
+    --hash=sha256:36dde98ccfe6c51a15576476cadfcb3b35a63507b8b8555abd69889a6fba6772
+    # via vocos
+eng-to-ipa==0.0.2 \
+    --hash=sha256:0e4fac8370b0ffeaf696193e971b3ff9bd3762e4d153c6d0d280147887e008b1
+    # via chx-prototype
+fastapi==0.120.1 \
+    --hash=sha256:0e8a2c328e96c117272d8c794d3a97d205f753cc2e69dd7ee387b7488a75601f \
+    --hash=sha256:b5c6217e9ddca6dfcf54c97986180d4a1955e10c693d74943fc5327700178bff
+    # via gradio
+ffmpy==0.6.4 \
+    --hash=sha256:21fbf9cd3116279e0629e0f2bf5892306b3336d588165c8db1b5f6361118d811 \
+    --hash=sha256:9533ad21f878b609ab1a5235668f5c514cb0953361e266e34d33614c3f6712d1
+    # via gradio
+filelock==3.20.0 \
+    --hash=sha256:339b4732ffda5cd79b13f4e2711a31b0365ce445d95d243bb996273d072546a2 \
+    --hash=sha256:711e943b4ec6be42e1d4e6690b48dc175c822967466bb31c0c293f34334c13f4
+    # via
+    #   huggingface-hub
+    #   torch
+fonttools==4.60.1 \
+    --hash=sha256:022beaea4b73a70295b688f817ddc24ed3e3418b5036ffcd5658141184ef0d0c \
+    --hash=sha256:026290e4ec76583881763fac284aca67365e0be9f13a7fb137257096114cb3bc \
+    --hash=sha256:1410155d0e764a4615774e5c2c6fc516259fe3eca5882f034eb9bfdbee056259 \
+    --hash=sha256:145daa14bf24824b677b9357c5e44fd8895c2a8f53596e1b9ea3496081dc692c \
+    --hash=sha256:154cb6ee417e417bf5f7c42fe25858c9140c26f647c7347c06f0cc2d47eff003 \
+    --hash=sha256:2299df884c11162617a66b7c316957d74a18e3758c0274762d2cc87df7bc0272 \
+    --hash=sha256:2409d5fb7b55fd70f715e6d34e7a6e4f7511b8ad29a49d6df225ee76da76dd77 \
+    --hash=sha256:5664fd1a9ea7f244487ac8f10340c4e37664675e8667d6fee420766e0fb3cf08 \
+    --hash=sha256:583b7f8e3c49486e4d489ad1deacfb8d5be54a8ef34d6df824f6a171f8511d99 \
+    --hash=sha256:66929e2ea2810c6533a5184f938502cfdaea4bc3efb7130d8cc02e1c1b4108d6 \
+    --hash=sha256:6f68576bb4bbf6060c7ab047b1574a1ebe5c50a17de62830079967b211059ebb \
+    --hash=sha256:875cb7764708b3132637f6c5fb385b16eeba0f7ac9fa45a69d35e09b47045801 \
+    --hash=sha256:8a44788d9d91df72d1a5eac49b31aeb887a5f4aab761b4cffc4196c74907ea85 \
+    --hash=sha256:906306ac7afe2156fcf0042173d6ebbb05416af70f6b370967b47f8f00103bbb \
+    --hash=sha256:9d0ced62b59e0430b3690dbc5373df1c2aa7585e9a8ce38eff87f0fd993c5b01 \
+    --hash=sha256:a184b2ea57b13680ab6d5fbde99ccef152c95c06746cb7718c583abd8f945ccc \
+    --hash=sha256:a3db56f153bd4c5c2b619ab02c5db5192e222150ce5a1bc10f16164714bc39ac \
+    --hash=sha256:a884aef09d45ba1206712c7dbda5829562d3fea7726935d3289d343232ecb0d3 \
+    --hash=sha256:b33a7884fabd72bdf5f910d0cf46be50dce86a0362a65cfc746a4168c67eb96c \
+    --hash=sha256:b6379e7546ba4ae4b18f8ae2b9bc5960936007a1c0e30b342f662577e8bc3299 \
+    --hash=sha256:c8651e0d4b3bdeda6602b85fdc2abbefc1b41e573ecb37b6779c4ca50753a199 \
+    --hash=sha256:e852d9dda9f93ad3651ae1e3bb770eac544ec93c3807888798eccddf84596537 \
+    --hash=sha256:eedacb5c5d22b7097482fa834bda0dafa3d914a4e829ec83cdea2a01f8c813c4 \
+    --hash=sha256:ef00af0439ebfee806b25f24c8f92109157ff3fac5731dc7867957812e87b8d9 \
+    --hash=sha256:f0e8817c7d1a0c2eedebf57ef9a9896f3ea23324769a9a2061a80fe8852705ed \
+    --hash=sha256:f3d5be054c461d6a2268831f04091dc82753176f6ea06dc6047a5e168265a987
+    # via matplotlib
+fsspec==2025.9.0 \
+    --hash=sha256:19fd429483d25d28b65ec68f9f4adc16c17ea2c7c7bf54ec61360d478fb19c19 \
+    --hash=sha256:530dc2a2af60a414a832059574df4a6e10cce927f6f4a78209390fe38955cfb7
+    # via
+    #   gradio-client
+    #   huggingface-hub
+    #   torch
+gradio==5.49.1 \
+    --hash=sha256:1b19369387801a26a6ba7fd2f74d46c5b0e2ac9ddef14f24ddc0d11fb19421b7 \
+    --hash=sha256:c06faa324ae06c3892c8b4b4e73c706c4520d380f6b9e52a3c02dc53a7627ba9
+    # via chx-prototype
+gradio-client==1.13.3 \
+    --hash=sha256:3f63e4d33a2899c1a12b10fe3cf77b82a6919ff1a1fb6391f6aa225811aa390c \
+    --hash=sha256:869b3e67e0f7a0f40df8c48c94de99183265cf4b7b1d9bd4623e336d219ffbe7
+    # via gradio
+groovy==0.1.2 \
+    --hash=sha256:25c1dc09b3f9d7e292458aa762c6beb96ea037071bf5e917fc81fb78d2231083 \
+    --hash=sha256:7f7975bab18c729a257a8b1ae9dcd70b7cafb1720481beae47719af57c35fa64
+    # via gradio
+h11==0.16.0 \
+    --hash=sha256:4e35b956cf45792e4caa5885e69fba00bdbc6ffafbfa020300e549b208ee5ff1 \
+    --hash=sha256:63cf8bbe7522de3bf65932fda1d9c2772064ffb3dae62d55932da54b31cb6c86
+    # via
+    #   httpcore
+    #   uvicorn
+hf-xet==1.2.0 ; platform_machine == 'AMD64' or platform_machine == 'aarch64' or platform_machine == 'amd64' or platform_machine == 'arm64' or platform_machine == 'x86_64' \
+    --hash=sha256:10bfab528b968c70e062607f663e21e34e2bba349e8038db546646875495179e \
+    --hash=sha256:210d577732b519ac6ede149d2f2f34049d44e8622bf14eb3d63bbcd2d4b332dc \
+    --hash=sha256:27df617a076420d8845bea087f59303da8be17ed7ec0cd7ee3b9b9f579dff0e4 \
+    --hash=sha256:293a7a3787e5c95d7be1857358a9130694a9c6021de3f27fa233f37267174382 \
+    --hash=sha256:29c8fc913a529ec0a91867ce3d119ac1aac966e098cf49501800c870328cc090 \
+    --hash=sha256:2a212e842647b02eb6a911187dc878e79c4aa0aa397e88dd3b26761676e8c1f8 \
+    --hash=sha256:30e06daccb3a7d4c065f34fc26c14c74f4653069bb2b194e7f18f17cbe9939c0 \
+    --hash=sha256:3651fd5bfe0281951b988c0facbe726aa5e347b103a675f49a3fa8144c7968fd \
+    --hash=sha256:46740d4ac024a7ca9b22bebf77460ff43332868b661186a8e46c227fdae01848 \
+    --hash=sha256:4c1428c9ae73ec0939410ec73023c4f842927f39db09b063b9482dac5a3bb737 \
+    --hash=sha256:66e159cbfcfbb29f920db2c09ed8b660eb894640d284f102ada929b6e3dc410a \
+    --hash=sha256:6de1fc44f58f6dd937956c8d304d8c2dea264c80680bcfa61ca4a15e7b76780f \
+    --hash=sha256:7d40b18769bb9a8bc82a9ede575ce1a44c75eb80e7375a01d76259089529b5dc \
+    --hash=sha256:9c91d5ae931510107f148874e9e2de8a16052b6f1b3ca3c1b12f15ccb491390f \
+    --hash=sha256:a55558084c16b09b5ed32ab9ed38421e2d87cf3f1f89815764d1177081b99865 \
+    --hash=sha256:a8c27070ca547293b6890c4bf389f713f80e8c478631432962bb7f4bc0bd7d7f \
+    --hash=sha256:b70218dd548e9840224df5638fdc94bd033552963cfa97f9170829381179c813 \
+    --hash=sha256:cd3a6027d59cfb60177c12d6424e31f4b5ff13d8e3a1247b3a584bf8977e6df5 \
+    --hash=sha256:ceeefcd1b7aed4956ae8499e2199607765fbd1c60510752003b6cc0b8413b649 \
+    --hash=sha256:d06fa97c8562fb3ee7a378dd9b51e343bc5bc8190254202c9771029152f5e08c \
+    --hash=sha256:e6584a52253f72c9f52f9e549d5895ca7a471608495c4ecaa6cc73dba2b24d69 \
+    --hash=sha256:f182f264ed2acd566c514e45da9f2119110e48a87a327ca271027904c70c5832
+    # via huggingface-hub
+httpcore==1.0.9 \
+    --hash=sha256:2d400746a40668fc9dec9810239072b40b4484b640a8c38fd654a024c7a1bf55 \
+    --hash=sha256:6e34463af53fd2ab5d807f399a9b45ea31c3dfa2276f15a2c3f00afff6e176e8
+    # via httpx
+httpx==0.28.1 \
+    --hash=sha256:75e98c5f16b0f35b567856f597f06ff2270a374470a5c2392242528e3e3e42fc \
+    --hash=sha256:d909fcccc110f8c7faf814ca82a9a4d816bc5a6dbfea25d6591d6985b8ba59ad
+    # via
+    #   gradio
+    #   gradio-client
+    #   huggingface-hub
+    #   safehttpx
+huggingface-hub==1.0.1 \
+    --hash=sha256:7e255cd9b3432287a34a86933057abb1b341d20b97fb01c40cbd4e053764ae13 \
+    --hash=sha256:87b506d5b45f0d1af58df7cf8bab993ded25d6077c2e959af58444df8b9589f3
+    # via
+    #   chx-prototype
+    #   gradio
+    #   gradio-client
+    #   tokenizers
+    #   vocos
+idna==3.11 \
+    --hash=sha256:771a87f49d9defaf64091e6e6fe9c18d4833f140bd19464795bc32d966ca37ea \
+    --hash=sha256:795dafcc9c04ed0c1fb032c2aa73654d8e8c5023a7df64a53f39190ada629902
+    # via
+    #   anyio
+    #   httpx
+    #   requests
+inflect==7.5.0 \
+    --hash=sha256:2aea70e5e70c35d8350b8097396ec155ffd68def678c7ff97f51aa69c1d92344 \
+    --hash=sha256:faf19801c3742ed5a05a8ce388e0d8fe1a07f8d095c82201eb904f5d27ad571f
+    # via chx-prototype
+jieba==0.42.1 \
+    --hash=sha256:055ca12f62674fafed09427f176506079bc135638a14e23e25be909131928db2
+    # via chx-prototype
+jinja2==3.1.6 \
+    --hash=sha256:0137fb05990d35f1275a587e9aee6d56da821fc83491a0fb838183be43f66d6d \
+    --hash=sha256:85ece4451f492d0c13c5dd7c13a64681a86afae63a5f347908daf103ce6d2f67
+    # via
+    #   gradio
+    #   torch
+joblib==1.5.2 \
+    --hash=sha256:3faa5c39054b2f03ca547da9b2f52fde67c06240c31853f306aea97f13647b55 \
+    --hash=sha256:4e1f0bdbb987e6d843c70cf43714cb276623def372df3c22fe5266b2670bc241
+    # via nltk
+kiwisolver==1.4.9 \
+    --hash=sha256:0749fd8f4218ad2e851e11cc4dc05c7cbc0cbc4267bdfdb31782e65aace4ee9c \
+    --hash=sha256:0763515d4df10edf6d06a3c19734e2566368980d21ebec439f33f9eb936c07b7 \
+    --hash=sha256:0856e241c2d3df4efef7c04a1e46b1936b6120c9bcf36dd216e3acd84bc4fb21 \
+    --hash=sha256:0ab74e19f6a2b027ea4f845a78827969af45ce790e6cb3e1ebab71bdf9f215ff \
+    --hash=sha256:0e4e2bf29574a6a7b7f6cb5fa69293b9f96c928949ac4a53ba3f525dffb87f9c \
+    --hash=sha256:1a12cf6398e8a0a001a059747a1cbf24705e18fe413bc22de7b3d15c67cffe3f \
+    --hash=sha256:1b11d6a633e4ed84fc0ddafd4ebfd8ea49b3f25082c04ad12b8315c11d504dc1 \
+    --hash=sha256:1fa333e8b2ce4d9660f2cda9c0e1b6bafcfb2457a9d259faa82289e73ec24891 \
+    --hash=sha256:2489e4e5d7ef9a1c300a5e0196e43d9c739f066ef23270607d45aba368b91f2d \
+    --hash=sha256:24c175051354f4a28c5d6a31c93906dc653e2bf234e8a4bbfb964892078898ce \
+    --hash=sha256:2635d352d67458b66fd0667c14cb1d4145e9560d503219034a18a87e971ce4f3 \
+    --hash=sha256:40092754720b174e6ccf9e845d0d8c7d8e12c3d71e7fc35f55f3813e96376f78 \
+    --hash=sha256:497d05f29a1300d14e02e6441cf0f5ee81c1ff5a304b0d9fb77423974684e08b \
+    --hash=sha256:4a48a2ce79d65d363597ef7b567ce3d14d68783d2b2263d98db3d9477805ba32 \
+    --hash=sha256:52a15b0f35dad39862d376df10c5230155243a2c1a436e39eb55623ccbd68185 \
+    --hash=sha256:5656aa670507437af0207645273ccdfee4f14bacd7f7c67a4306d0dcaeaf6eed \
+    --hash=sha256:5a0f2724dfd4e3b3ac5a82436a8e6fd16baa7d507117e4279b660fe8ca38a3a1 \
+    --hash=sha256:60c439763a969a6af93b4881db0eed8fadf93ee98e18cbc35bc8da868d0c4f0c \
+    --hash=sha256:61874cdb0a36016354853593cffc38e56fc9ca5aa97d2c05d3dcf6922cd55a11 \
+    --hash=sha256:72d0eb9fba308b8311685c2268cf7d0a0639a6cd027d8128659f72bdd8a024b4 \
+    --hash=sha256:767c23ad1c58c9e827b649a9ab7809fd5fd9db266a9cf02b0e926ddc2c680d58 \
+    --hash=sha256:77937e5e2a38a7b48eef0585114fe7930346993a88060d0bf886086d2aa49ef5 \
+    --hash=sha256:7cf974dd4e35fa315563ac99d6287a1024e4dc2077b8a7d7cd3d2fb65d283134 \
+    --hash=sha256:85bd218b5ecfbee8c8a82e121802dcb519a86044c9c3b2e4aef02fa05c6da370 \
+    --hash=sha256:92a2f997387a1b79a75e7803aa7ded2cfbe2823852ccf1ba3bcf613b62ae3197 \
+    --hash=sha256:9928fe1eb816d11ae170885a74d074f57af3a0d65777ca47e9aeb854a1fba386 \
+    --hash=sha256:9af39d6551f97d31a4deebeac6f45b156f9755ddc59c07b402c148f5dbb6482a \
+    --hash=sha256:a30fd6fdef1430fd9e1ba7b3398b5ee4e2887783917a687d86ba69985fb08748 \
+    --hash=sha256:a31d512c812daea6d8b3be3b2bfcbeb091dbb09177706569bcfc6240dcf8b41c \
+    --hash=sha256:a5d0432ccf1c7ab14f9949eec60c5d1f924f17c037e9f8b33352fa05799359b8 \
+    --hash=sha256:a60ea74330b91bd22a29638940d115df9dc00af5035a9a2a6ad9399ffb4ceca5 \
+    --hash=sha256:aedff62918805fb62d43a4aa2ecd4482c380dc76cd31bd7c8878588a61bd0369 \
+    --hash=sha256:b34e51affded8faee0dfdb705416153819d8ea9250bbbf7ea1b249bdeb5f1122 \
+    --hash=sha256:b67e6efbf68e077dd71d1a6b37e43e1a99d0bff1a3d51867d45ee8908b931098 \
+    --hash=sha256:bb4ae2b57fc1d8cbd1cf7b1d9913803681ffa903e7488012be5b76dedf49297f \
+    --hash=sha256:bdd1a81a1860476eb41ac4bc1e07b3f07259e6d55bbf739b79c8aaedcf512799 \
+    --hash=sha256:bfc08add558155345129c7803b3671cf195e6a56e7a12f3dde7c57d9b417f525 \
+    --hash=sha256:c3b22c26c6fd6811b0ae8363b95ca8ce4ea3c202d3d0975b2914310ceb1bcc4d \
+    --hash=sha256:cc9617b46837c6468197b5945e196ee9ca43057bb7d9d1ae688101e4e1dddf64 \
+    --hash=sha256:ce6a3a4e106cf35c2d9c4fa17c05ce0b180db622736845d4315519397a77beaf \
+    --hash=sha256:d0005b053977e7b43388ddec89fa567f43d4f6d5c2c0affe57de5ebf290dc552 \
+    --hash=sha256:d4188e73af84ca82468f09cadc5ac4db578109e52acb4518d8154698d3a87ca2 \
+    --hash=sha256:d75aa530ccfaa593da12834b86a0724f58bff12706659baa9227c2ccaa06264c \
+    --hash=sha256:d84cd4061ae292d8ac367b2c3fa3aad11cb8625a95d135fe93f286f914f3f5a6 \
+    --hash=sha256:d8aacd3d4b33b772542b2e01beb50187536967b514b00003bdda7589722d2a64 \
+    --hash=sha256:d976bbb382b202f71c67f77b0ac11244021cfa3f7dfd9e562eefcea2df711548 \
+    --hash=sha256:dba5ee5d3981160c28d5490f0d1b7ed730c22470ff7f6cc26cfcfaacb9896a07 \
+    --hash=sha256:dd0a578400839256df88c16abddf9ba14813ec5f21362e1fe65022e00c883d4d \
+    --hash=sha256:e2ea9f7ab7fbf18fffb1b5434ce7c69a07582f7acc7717720f1d69f3e806f90c \
+    --hash=sha256:e6b93f13371d341afee3be9f7c5964e3fe61d5fa30f6a30eb49856935dfe4fc3 \
+    --hash=sha256:efb3a45b35622bb6c16dbfab491a8f5a391fe0e9d45ef32f4df85658232ca0e2 \
+    --hash=sha256:f68e4f3eeca8fb22cc3d731f9715a13b652795ef657a13df1ad0c7dc0e9731df
+    # via matplotlib
+langid==1.1.6 \
+    --hash=sha256:044bcae1912dab85c33d8e98f2811b8f4ff1213e5e9a9e9510137b84da2cb293
+    # via chx-prototype
+llvmlite==0.45.1 \
+    --hash=sha256:080e6f8d0778a8239cd47686d402cb66eb165e421efa9391366a9b7e5810a38b \
+    --hash=sha256:09430bb9d0bb58fc45a45a57c7eae912850bedc095cd0810a57de109c69e1c32 \
+    --hash=sha256:3aa3dfceda4219ae39cf18806c60eeb518c1680ff834b8b311bd784160b9ce40 \
+    --hash=sha256:57c48bf2e1083eedbc9406fb83c4e6483017879714916fe8be8a72a9672c995a \
+    --hash=sha256:c9f3cadee1630ce4ac18ea38adebf2a4f57a89bd2740ce83746876797f6e0bfb \
+    --hash=sha256:d9ea9e6f17569a4253515cc01dade70aba536476e3d750b2e18d81d7e670eb15
+    # via numba
+markdown-it-py==4.0.0 \
+    --hash=sha256:87327c59b172c5011896038353a81343b6754500a08cd7a4973bb48c6d578147 \
+    --hash=sha256:cb0a2b4aa34f932c007117b194e945bd74e0ec24133ceb5bac59009cda1cb9f3
+    # via rich
+markupsafe==3.0.3 \
+    --hash=sha256:0eb9ff8191e8498cca014656ae6b8d61f39da5f95b488805da4bb029cccbfbaf \
+    --hash=sha256:1085e7fbddd3be5f89cc898938f42c0b3c711fdcb37d75221de2666af647c175 \
+    --hash=sha256:116bb52f642a37c115f517494ea5feb03889e04df47eeff5b130b1808ce7c219 \
+    --hash=sha256:12c63dfb4a98206f045aa9563db46507995f7ef6d83b2f68eda65c307c6829eb \
+    --hash=sha256:133a43e73a802c5562be9bbcd03d090aa5a1fe899db609c29e8c8d815c5f6de6 \
+    --hash=sha256:1353ef0c1b138e1907ae78e2f6c63ff67501122006b0f9abad68fda5f4ffc6ab \
+    --hash=sha256:1b4b79e8ebf6b55351f0d91fe80f893b4743f104bff22e90697db1590e47a218 \
+    --hash=sha256:1b52b4fb9df4eb9ae465f8d0c228a00624de2334f216f178a995ccdcf82c4634 \
+    --hash=sha256:218551f6df4868a8d527e3062d0fb968682fe92054e89978594c28e642c43a73 \
+    --hash=sha256:2713baf880df847f2bece4230d4d094280f4e67b1e813eec43b4c0e144a34ffe \
+    --hash=sha256:32001d6a8fc98c8cb5c947787c5d08b0a50663d139f1305bac5885d98d9b40fa \
+    --hash=sha256:3524b778fe5cfb3452a09d31e7b5adefeea8c5be1d43c4f810ba09f2ceb29d37 \
+    --hash=sha256:457a69a9577064c05a97c41f4e65148652db078a3a509039e64d3467b9e7ef97 \
+    --hash=sha256:4e885a3d1efa2eadc93c894a21770e4bc67899e3543680313b09f139e149ab19 \
+    --hash=sha256:4faffd047e07c38848ce017e8725090413cd80cbc23d86e55c587bf979e579c9 \
+    --hash=sha256:509fa21c6deb7a7a273d629cf5ec029bc209d1a51178615ddf718f5918992ab9 \
+    --hash=sha256:5678211cb9333a6468fb8d8be0305520aa073f50d17f089b5b4b477ea6e67fdc \
+    --hash=sha256:5a7d5dc5140555cf21a6fefbdbf8723f06fcd2f63ef108f2854de715e4422cb4 \
+    --hash=sha256:69c0b73548bc525c8cb9a251cddf1931d1db4d2258e9599c28c07ef3580ef354 \
+    --hash=sha256:722695808f4b6457b320fdc131280796bdceb04ab50fe1795cd540799ebe1698 \
+    --hash=sha256:729586769a26dbceff69f7a7dbbf59ab6572b99d94576a5592625d5b411576b9 \
+    --hash=sha256:795e7751525cae078558e679d646ae45574b47ed6e7771863fcc079a6171a0fc \
+    --hash=sha256:7e68f88e5b8799aa49c85cd116c932a1ac15caaa3f5db09087854d218359e485 \
+    --hash=sha256:8485f406a96febb5140bfeca44a73e3ce5116b2501ac54fe953e488fb1d03b12 \
+    --hash=sha256:8709b08f4a89aa7586de0aadc8da56180242ee0ada3999749b183aa23df95025 \
+    --hash=sha256:8f71bc33915be5186016f675cd83a1e08523649b0e33efdb898db577ef5bb009 \
+    --hash=sha256:915c04ba3851909ce68ccc2b8e2cd691618c4dc4c4232fb7982bca3f41fd8c3d \
+    --hash=sha256:9a1abfdc021a164803f4d485104931fb8f8c1efd55bc6b748d2f5774e78b62c5 \
+    --hash=sha256:9b79b7a16f7fedff2495d684f2b59b0457c3b493778c9eed31111be64d58279f \
+    --hash=sha256:a4afe79fb3de0b7097d81da19090f4df4f8d3a2b3adaa8764138aac2e44f3af1 \
+    --hash=sha256:ad2cf8aa28b8c020ab2fc8287b0f823d0a7d8630784c31e9ee5edea20f406287 \
+    --hash=sha256:b8512a91625c9b3da6f127803b166b629725e68af71f8184ae7e7d54686a56d6 \
+    --hash=sha256:bdc919ead48f234740ad807933cdf545180bfbe9342c2bb451556db2ed958581 \
+    --hash=sha256:bdd37121970bfd8be76c5fb069c7751683bdf373db1ed6c010162b2a130248ed \
+    --hash=sha256:c47a551199eb8eb2121d4f0f15ae0f923d31350ab9280078d1e5f12b249e0026 \
+    --hash=sha256:ccfcd093f13f0f0b7fdd0f198b90053bf7b2f02a3927a30e63f3ccc9df56b676 \
+    --hash=sha256:e1cf1972137e83c5d4c136c43ced9ac51d0e124706ee1c8aa8532c1287fa8795 \
+    --hash=sha256:e56b7d45a839a697b5eb268c82a71bd8c7f6c94d6fd50c3d577fa39a9f1409f5 \
+    --hash=sha256:e8afc3f2ccfa24215f8cb28dcf43f0113ac3c37c2f0f0806d8c70e4228c5cf4d \
+    --hash=sha256:eaa9599de571d72e2daf60164784109f19978b327a3910d3e9de8c97b5b70cfe \
+    --hash=sha256:ec15a59cf5af7be74194f7ab02d0f59a62bdcf1a537677ce67a2537c9b87fcda \
+    --hash=sha256:f190daf01f13c72eac4efd5c430a8de82489d9cff23c364c3ea822545032993e \
+    --hash=sha256:f34c41761022dd093b4b6896d4810782ffbabe30f2d443ff5f083e0cbbb8c737 \
+    --hash=sha256:f3e98bb3798ead92273dc0e5fd0f31ade220f59a266ffd8a4f6065e0a3ce0523 \
+    --hash=sha256:fed51ac40f757d41b7c48425901843666a6677e3e8eb0abcff09e4ba6e664f50
+    # via
+    #   gradio
+    #   jinja2
+matplotlib==3.10.7 \
+    --hash=sha256:07124afcf7a6504eafcb8ce94091c5898bbdd351519a1beb5c45f7a38c67e77f \
+    --hash=sha256:09d7945a70ea43bf9248f4b6582734c2fe726723204a76eca233f24cffc7ef67 \
+    --hash=sha256:1d9d3713a237970569156cfb4de7533b7c4eacdd61789726f444f96a0d28f57f \
+    --hash=sha256:1e4bbad66c177a8fdfa53972e5ef8be72a5f27e6a607cec0d8579abd0f3102b1 \
+    --hash=sha256:22df30ffaa89f6643206cf13877191c63a50e8f800b038bc39bee9d2d4957632 \
+    --hash=sha256:31963603041634ce1a96053047b40961f7a29eb8f9a62e80cc2c0427aa1d22a2 \
+    --hash=sha256:37a1fea41153dd6ee061d21ab69c9cf2cf543160b1b85d89cd3d2e2a7902ca4c \
+    --hash=sha256:4645fc5d9d20ffa3a39361fcdbcec731382763b623b72627806bf251b6388866 \
+    --hash=sha256:4a74f79fafb2e177f240579bc83f0b60f82cc47d2f1d260f422a0627207008ca \
+    --hash=sha256:5e38c2d581d62ee729a6e144c47a71b3f42fb4187508dbbf4fe71d5612c3433b \
+    --hash=sha256:702590829c30aada1e8cef0568ddbffa77ca747b4d6e36c6d173f66e301f89cc \
+    --hash=sha256:7146d64f561498764561e9cd0ed64fcf582e570fc519e6f521e2d0cfd43365e1 \
+    --hash=sha256:744991e0cc863dd669c8dc9136ca4e6e0082be2070b9d793cbd64bec872a6815 \
+    --hash=sha256:786656bb13c237bbcebcd402f65f44dd61ead60ee3deb045af429d889c8dbc67 \
+    --hash=sha256:90ad854c0a435da3104c01e2c6f0028d7e719b690998a2333d7218db80950722 \
+    --hash=sha256:9257be2f2a03415f9105c486d304a321168e61ad450f6153d77c69504ad764bb \
+    --hash=sha256:932c55d1fa7af4423422cb6a492a31cbcbdbe68fd1a9a3f545aa5e7a143b5355 \
+    --hash=sha256:a06ba7e2a2ef9131c79c49e63dad355d2d878413a0376c1727c8b9335ff731c7 \
+    --hash=sha256:aebed7b50aa6ac698c90f60f854b47e48cd2252b30510e7a1feddaf5a3f72cbf \
+    --hash=sha256:b3c4ea4948d93c9c29dc01c0c23eef66f2101bf75158c291b88de6525c55c3d1 \
+    --hash=sha256:b4d41379b05528091f00e1728004f9a8d7191260f3862178b88e8fd770206318 \
+    --hash=sha256:b69676845a0a66f9da30e87f48be36734d6748024b525ec4710be40194282c84 \
+    --hash=sha256:c17398b709a6cce3d9fdb1595c33e356d91c098cd9486cb2cc21ea2ea418e715 \
+    --hash=sha256:cb783436e47fcf82064baca52ce748af71725d0352e1d31564cbe9c95df92b9c \
+    --hash=sha256:d0b181e9fa8daf1d9f2d4c547527b167cb8838fc587deabca7b5c01f97199e84 \
+    --hash=sha256:d883460c43e8c6b173fef244a2341f7f7c0e9725c7fe68306e8e44ed9c8fb100 \
+    --hash=sha256:d8eb7194b084b12feb19142262165832fc6ee879b945491d1c3d4660748020c4 \
+    --hash=sha256:f79d5de970fc90cd5591f60053aecfce1fcd736e0303d9f0bf86be649fa68fb8 \
+    --hash=sha256:fba2974df0bf8ce3c995fa84b79cde38326e0f7b5409e7a3a481c1141340bcf7
+    # via chx-prototype
+mdurl==0.1.2 \
+    --hash=sha256:84008a41e51615a49fc9966191ff91509e3c40b939176e643fd50a5c2196b8f8 \
+    --hash=sha256:bb413d29f5eea38f31dd4754dd7377d4465116fb207585f97bf925588687c1ba
+    # via markdown-it-py
+more-itertools==10.8.0 \
+    --hash=sha256:52d4362373dcf7c52546bc4af9a86ee7c4579df9a8dc268be0a2f949d376cc9b \
+    --hash=sha256:f638ddf8a1a0d134181275fb5d58b086ead7c6a72429ad725c67503f13ba30bd
+    # via
+    #   inflect
+    #   openai-whisper
+mpmath==1.3.0 \
+    --hash=sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f \
+    --hash=sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c
+    # via sympy
+networkx==3.5 \
+    --hash=sha256:0030d386a9a06dee3565298b4a734b68589749a544acbb6c412dc9e2489ec6ec \
+    --hash=sha256:d4c6f9cf81f52d69230866796b82afbccdec3db7ae4fbd1b65ea750feed50037
+    # via torch
+nltk==3.9.2 \
+    --hash=sha256:0f409e9b069ca4177c1903c3e843eef90c7e92992fa4931ae607da6de49e1419 \
+    --hash=sha256:1e209d2b3009110635ed9709a67a1a3e33a10f799490fa71cf4bec218c11c88a
+    # via chx-prototype
+numba==0.62.1 \
+    --hash=sha256:44a1412095534a26fb5da2717bc755b57da5f3053965128fe3dc286652cc6a92 \
+    --hash=sha256:728f91a874192df22d74e3fd42c12900b7ce7190b1aad3574c6c61b08313e4c5 \
+    --hash=sha256:7b774242aa890e34c21200a1fc62e5b5757d5286267e71103257f4e2af0d5161 \
+    --hash=sha256:8c9460b9e936c5bd2f0570e20a0a5909ee6e8b694fd958b210e3bde3a6dba2d7 \
+    --hash=sha256:b72489ba8411cc9fdcaa2458d8f7677751e94f0109eeb53e5becfdc818c64afb \
+    --hash=sha256:bbf3f88b461514287df66bc8d0307e949b09f2b6f67da92265094e8fa1282dd8
+    # via openai-whisper
+numpy==2.3.4 \
+    --hash=sha256:035796aaaddfe2f9664b9a9372f089cfc88bd795a67bd1bfe15e6e770934cf64 \
+    --hash=sha256:043885b4f7e6e232d7df4f51ffdef8c36320ee9d5f227b380ea636722c7ed12e \
+    --hash=sha256:04a69abe45b49c5955923cf2c407843d1c85013b424ae8a560bba16c92fe44a0 \
+    --hash=sha256:0f2bcc76f1e05e5ab58893407c63d90b2029908fa41f9f1cc51eecce936c3365 \
+    --hash=sha256:15eea9f306b98e0be91eb344a94c0e630689ef302e10c2ce5f7e11905c704f9c \
+    --hash=sha256:15fb27364ed84114438fff8aaf998c9e19adbeba08c0b75409f8c452a8692c52 \
+    --hash=sha256:22758999b256b595cf0b1d102b133bb61866ba5ceecf15f759623b64c020c9ec \
+    --hash=sha256:2ec646892819370cf3558f518797f16597b4e4669894a2ba712caccc9da53f1f \
+    --hash=sha256:3634093d0b428e6c32c3a69b78e554f0cd20ee420dcad5a9f3b2a63762ce4197 \
+    --hash=sha256:3da3491cee49cf16157e70f607c03a217ea6647b1cea4819c4f48e53d49139b9 \
+    --hash=sha256:40cc556d5abbc54aabe2b1ae287042d7bdb80c08edede19f0c0afb36ae586f37 \
+    --hash=sha256:4ee6a571d1e4f0ea6d5f22d6e5fbd6ed1dc2b18542848e1e7301bd190500c9d7 \
+    --hash=sha256:56209416e81a7893036eea03abcb91c130643eb14233b2515c90dcac963fe99d \
+    --hash=sha256:5e199c087e2aa71c8f9ce1cb7a8e10677dc12457e7cc1be4798632da37c3e86e \
+    --hash=sha256:62b2198c438058a20b6704351b35a1d7db881812d8512d67a69c9de1f18ca05f \
+    --hash=sha256:6d9cd732068e8288dbe2717177320723ccec4fb064123f0caf9bbd90ab5be868 \
+    --hash=sha256:7c26b0b2bf58009ed1f38a641f3db4be8d960a417ca96d14e5b06df1506d41ff \
+    --hash=sha256:817e719a868f0dacde4abdfc5c1910b301877970195db9ab6a5e2c4bd5b121f7 \
+    --hash=sha256:81c3e6d8c97295a7360d367f9f8553973651b76907988bb6066376bc2252f24e \
+    --hash=sha256:838f045478638b26c375ee96ea89464d38428c69170360b23a1a50fa4baa3562 \
+    --hash=sha256:84f01a4d18b2cc4ade1814a08e5f3c907b079c847051d720fad15ce37aa930b6 \
+    --hash=sha256:85597b2d25ddf655495e2363fe044b0ae999b75bc4d630dc0d886484b03a5eb0 \
+    --hash=sha256:85d9fb2d8cd998c84d13a79a09cc0c1091648e848e4e6249b0ccd7f6b487fa26 \
+    --hash=sha256:85e071da78d92a214212cacea81c6da557cab307f2c34b5f85b628e94803f9c0 \
+    --hash=sha256:863e3b5f4d9915aaf1b8ec79ae560ad21f0b8d5e3adc31e73126491bb86dee1d \
+    --hash=sha256:86966db35c4040fdca64f0816a1c1dd8dbd027d90fca5a57e00e1ca4cd41b879 \
+    --hash=sha256:8b5a9a39c45d852b62693d9b3f3e0fe052541f804296ff401a72a1b60edafb29 \
+    --hash=sha256:8dc20bde86802df2ed8397a08d793da0ad7a5fd4ea3ac85d757bf5dd4ad7c252 \
+    --hash=sha256:962064de37b9aef801d33bc579690f8bfe6c5e70e29b61783f60bcba838a14d6 \
+    --hash=sha256:9cb177bc55b010b19798dc5497d540dea67fd13a8d9e882b2dae71de0cf09eb3 \
+    --hash=sha256:9d729d60f8d53a7361707f4b68a9663c968882dd4f09e0d58c044c8bf5faee7b \
+    --hash=sha256:a13fc473b6db0be619e45f11f9e81260f7302f8d180c49a22b6e6120022596b3 \
+    --hash=sha256:a700a4031bc0fd6936e78a752eefb79092cecad2599ea9c8039c548bc097f9bc \
+    --hash=sha256:a7d018bfedb375a8d979ac758b120ba846a7fe764911a64465fd87b8729f4a6a \
+    --hash=sha256:b6c231c9c2fadbae4011ca5e7e83e12dc4a5072f1a1d85a0a7b3ed754d145a40 \
+    --hash=sha256:bd0c630cf256b0a7fd9d0a11c9413b42fef5101219ce6ed5a09624f5a65392c7 \
+    --hash=sha256:c090d4860032b857d94144d1a9976b8e36709e40386db289aaf6672de2a81966 \
+    --hash=sha256:d5e081bc082825f8b139f9e9fe42942cb4054524598aaeb177ff476cc76d09d2 \
+    --hash=sha256:d7315ed1dab0286adca467377c8381cd748f3dc92235f22a7dfc42745644a96a \
+    --hash=sha256:e1708fac43ef8b419c975926ce1eaf793b0c13b7356cfab6ab0dc34c0a02ac0f \
+    --hash=sha256:e73d63fd04e3a9d6bc187f5455d81abfad05660b212c8804bf3b407e984cd2bc \
+    --hash=sha256:e8370eb6925bb8c1c4264fec52b0384b44f675f191df91cbe0140ec9f0955646 \
+    --hash=sha256:ecb63014bb7f4ce653f8be7f1df8cbc6093a5a2811211770f6606cc92b5a78fd \
+    --hash=sha256:fc8a63918b04b8571789688b2780ab2b4a33ab44bfe8ccea36d3eba51228c953 \
+    --hash=sha256:fea80f4f4cf83b54c3a051f2f727870ee51e22f0248d3114b8e755d160b38cfb
+    # via
+    #   contourpy
+    #   encodec
+    #   gradio
+    #   langid
+    #   matplotlib
+    #   numba
+    #   openai-whisper
+    #   pandas
+    #   pyopenjtalk
+    #   scipy
+    #   vocos
+nvidia-cublas-cu12==12.8.4.1 ; platform_machine == 'x86_64' and sys_platform == 'linux' \
+    --hash=sha256:8ac4e771d5a348c551b2a426eda6193c19aa630236b418086020df5ba9667142
+    # via
+    #   nvidia-cudnn-cu12
+    #   nvidia-cusolver-cu12
+    #   torch
+nvidia-cuda-cupti-cu12==12.8.90 ; platform_machine == 'x86_64' and sys_platform == 'linux' \
+    --hash=sha256:ea0cb07ebda26bb9b29ba82cda34849e73c166c18162d3913575b0c9db9a6182
+    # via torch
+nvidia-cuda-nvrtc-cu12==12.8.93 ; platform_machine == 'x86_64' and sys_platform == 'linux' \
+    --hash=sha256:a7756528852ef889772a84c6cd89d41dfa74667e24cca16bb31f8f061e3e9994
+    # via torch
+nvidia-cuda-runtime-cu12==12.8.90 ; platform_machine == 'x86_64' and sys_platform == 'linux' \
+    --hash=sha256:adade8dcbd0edf427b7204d480d6066d33902cab2a4707dcfc48a2d0fd44ab90
+    # via torch
+nvidia-cudnn-cu12==9.10.2.21 ; platform_machine == 'x86_64' and sys_platform == 'linux' \
+    --hash=sha256:949452be657fa16687d0930933f032835951ef0892b37d2d53824d1a84dc97a8
+    # via torch
+nvidia-cufft-cu12==11.3.3.83 ; platform_machine == 'x86_64' and sys_platform == 'linux' \
+    --hash=sha256:4d2dd21ec0b88cf61b62e6b43564355e5222e4a3fb394cac0db101f2dd0d4f74
+    # via torch
+nvidia-cufile-cu12==1.13.1.3 ; platform_machine == 'x86_64' and sys_platform == 'linux' \
+    --hash=sha256:1d069003be650e131b21c932ec3d8969c1715379251f8d23a1860554b1cb24fc
+    # via torch
+nvidia-curand-cu12==10.3.9.90 ; platform_machine == 'x86_64' and sys_platform == 'linux' \
+    --hash=sha256:b32331d4f4df5d6eefa0554c565b626c7216f87a06a4f56fab27c3b68a830ec9
+    # via torch
+nvidia-cusolver-cu12==11.7.3.90 ; platform_machine == 'x86_64' and sys_platform == 'linux' \
+    --hash=sha256:4376c11ad263152bd50ea295c05370360776f8c3427b30991df774f9fb26c450
+    # via torch
+nvidia-cusparse-cu12==12.5.8.93 ; platform_machine == 'x86_64' and sys_platform == 'linux' \
+    --hash=sha256:1ec05d76bbbd8b61b06a80e1eaf8cf4959c3d4ce8e711b65ebd0443bb0ebb13b
+    # via
+    #   nvidia-cusolver-cu12
+    #   torch
+nvidia-cusparselt-cu12==0.7.1 ; platform_machine == 'x86_64' and sys_platform == 'linux' \
+    --hash=sha256:f1bb701d6b930d5a7cea44c19ceb973311500847f81b634d802b7b539dc55623
+    # via torch
+nvidia-nccl-cu12==2.27.5 ; platform_machine == 'x86_64' and sys_platform == 'linux' \
+    --hash=sha256:ad730cf15cb5d25fe849c6e6ca9eb5b76db16a80f13f425ac68d8e2e55624457
+    # via torch
+nvidia-nvjitlink-cu12==12.8.93 ; platform_machine == 'x86_64' and sys_platform == 'linux' \
+    --hash=sha256:81ff63371a7ebd6e6451970684f916be2eab07321b73c9d244dc2b4da7f73b88
+    # via
+    #   nvidia-cufft-cu12
+    #   nvidia-cusolver-cu12
+    #   nvidia-cusparse-cu12
+    #   torch
+nvidia-nvshmem-cu12==3.3.20 ; platform_machine == 'x86_64' and sys_platform == 'linux' \
+    --hash=sha256:d00f26d3f9b2e3c3065be895e3059d6479ea5c638a3f38c9fec49b1b9dd7c1e5
+    # via torch
+nvidia-nvtx-cu12==12.8.90 ; platform_machine == 'x86_64' and sys_platform == 'linux' \
+    --hash=sha256:5b17e2001cc0d751a5bc2c6ec6d26ad95913324a4adb86788c944f8ce9ba441f
+    # via torch
+openai-whisper==20250625 \
+    --hash=sha256:37a91a3921809d9f44748ffc73c0a55c9f366c85a3ef5c2ae0cc09540432eb96
+    # via chx-prototype
+orjson==3.11.4 \
+    --hash=sha256:04b69c14615fb4434ab867bf6f38b2d649f6f300af30a6705397e895f7aec67a \
+    --hash=sha256:09bf242a4af98732db9f9a1ec57ca2604848e16f132e3f72edfd3c5c96de009a \
+    --hash=sha256:0a54d6635fa3aaa438ae32e8570b9f0de36f3f6562c308d2a2a452e8b0592db1 \
+    --hash=sha256:1469d254b9884f984026bd9b0fa5bbab477a4bfe558bba6848086f6d43eb5e73 \
+    --hash=sha256:1e539e382cf46edec157ad66b0b0872a90d829a6b71f17cb633d6c160a223155 \
+    --hash=sha256:26a20f3fbc6c7ff2cb8e89c4c5897762c9d88cf37330c6a117312365d6781d54 \
+    --hash=sha256:2d6737d0e616a6e053c8b4acc9eccea6b6cce078533666f32d140e4f85002534 \
+    --hash=sha256:38aa9e65c591febb1b0aed8da4d469eba239d434c218562df179885c94e1a3ad \
+    --hash=sha256:39485f4ab4c9b30a3943cfe99e1a213c4776fb69e8abd68f66b83d5a0b0fdc6d \
+    --hash=sha256:42d43a1f552be1a112af0b21c10a5f553983c2a0938d2bbb8ecd8bc9fb572803 \
+    --hash=sha256:5c8b2769dc31883c44a9cd126560327767f848eb95f99c36c9932f51090bfce9 \
+    --hash=sha256:639c3735b8ae7f970066930e58cf0ed39a852d417c24acd4a25fc0b3da3c39a6 \
+    --hash=sha256:68e44722541983614e37117209a194e8c3ad07838ccb3127d96863c95ec7f1e0 \
+    --hash=sha256:6c13879c0d2964335491463302a6ca5ad98105fc5db3565499dcb80b1b4bd839 \
+    --hash=sha256:6e3f20be9048941c7ffa8fc523ccbd17f82e24df1549d1d1fe9317712d19938e \
+    --hash=sha256:724ca721ecc8a831b319dcd72cfa370cc380db0bf94537f08f7edd0a7d4e1780 \
+    --hash=sha256:78b999999039db3cf58f6d230f524f04f75f129ba3d1ca2ed121f8657e575d3d \
+    --hash=sha256:89216ff3dfdde0e4070932e126320a1752c9d9a758d6a32ec54b3b9334991a6a \
+    --hash=sha256:8e7805fda9672c12be2f22ae124dcd7b03928d6c197544fe12174b86553f3196 \
+    --hash=sha256:977c393f2e44845ce1b540e19a786e9643221b3323dae190668a98672d43fb23 \
+    --hash=sha256:9daa26ca8e97fae0ce8aa5d80606ef8f7914e9b129b6b5df9104266f764ce436 \
+    --hash=sha256:a85f0adf63319d6c1ba06fb0dbf997fced64a01179cf17939a6caca662bf92de \
+    --hash=sha256:aac364c758dc87a52e68e349924d7e4ded348dedff553889e4d9f22f74785316 \
+    --hash=sha256:afb14052690aa328cc118a8e09f07c651d301a72e44920b887c519b313d892ff \
+    --hash=sha256:b13c478fa413d4b4ee606ec8e11c3b2e52683a640b006bb586b3041c2ca5f606 \
+    --hash=sha256:bfc2a484cad3585e4ba61985a6062a4c2ed5c7925db6d39f1fa267c9d166487f \
+    --hash=sha256:d5c54a6d76e3d741dcc3f2707f8eeb9ba2a791d3adbf18f900219b62942803b1 \
+    --hash=sha256:d63076d625babab9db5e7836118bdfa086e60f37d8a174194ae720161eb12394 \
+    --hash=sha256:e34dbd508cb91c54f9c9788923daca129fe5b55c5b4eebe713bf5ed3791280cf \
+    --hash=sha256:f28485bdca8617b79d44627f5fb04336897041dfd9fa66d383a49d09d86798bc \
+    --hash=sha256:f2cf4dfaf9163b0728d061bebc1e08631875c51cd30bf47cb9e3293bfbd7dcd5
+    # via gradio
+packaging==25.0 \
+    --hash=sha256:29572ef2b1f17581046b3a2227d5c611fb25ec70ca1ba8554b24b0e69331a484 \
+    --hash=sha256:d443872c98d677bf60f6a1f2f8c1cb748e8fe762d2bf9d3148b5599295b0fc4f
+    # via
+    #   gradio
+    #   gradio-client
+    #   huggingface-hub
+    #   matplotlib
+pandas==2.3.3 \
+    --hash=sha256:0242fe9a49aa8b4d78a4fa03acb397a58833ef6199e9aa40a95f027bb3a1b6e7 \
+    --hash=sha256:1611aedd912e1ff81ff41c745822980c49ce4a7907537be8692c8dbc31924593 \
+    --hash=sha256:1b07204a219b3b7350abaae088f451860223a52cfb8a6c53358e7948735158e5 \
+    --hash=sha256:2462b1a365b6109d275250baaae7b760fd25c726aaca0054649286bcfbb3e8ec \
+    --hash=sha256:2e3ebdb170b5ef78f19bfb71b0dc5dc58775032361fa188e814959b74d726dd5 \
+    --hash=sha256:318d77e0e42a628c04dc56bcef4b40de67918f7041c2b061af1da41dcff670ac \
+    --hash=sha256:3869faf4bd07b3b66a9f462417d0ca3a9df29a9f6abd5d0d0dbab15dac7abe87 \
+    --hash=sha256:4e0a175408804d566144e170d0476b15d78458795bb18f1304fb94160cabf40c \
+    --hash=sha256:56851a737e3470de7fa88e6131f41281ed440d29a9268dcbf0002da5ac366713 \
+    --hash=sha256:6253c72c6a1d990a410bc7de641d34053364ef8bcd3126f7e7450125887dffe3 \
+    --hash=sha256:6435cb949cb34ec11cc9860246ccb2fdc9ecd742c12d3304989017d53f039a78 \
+    --hash=sha256:6d2cefc361461662ac48810cb14365a365ce864afe85ef1f447ff5a1e99ea81c \
+    --hash=sha256:74ecdf1d301e812db96a465a525952f4dde225fdb6d8e5a521d47e1f42041e21 \
+    --hash=sha256:75ea25f9529fdec2d2e93a42c523962261e567d250b0013b16210e1d40d7c2e5 \
+    --hash=sha256:900f47d8f20860de523a1ac881c4c36d65efcb2eb850e6948140fa781736e110 \
+    --hash=sha256:93c2d9ab0fc11822b5eece72ec9587e172f63cff87c00b062f6e37448ced4493 \
+    --hash=sha256:a21d830e78df0a515db2b3d2f5570610f5e6bd2e27749770e8bb7b524b89b450 \
+    --hash=sha256:a45c765238e2ed7d7c608fc5bc4a6f88b642f2f01e70c0c23d2224dd21829d86 \
+    --hash=sha256:bdcd9d1167f4885211e401b3036c0c8d9e274eee67ea8d0758a256d60704cfe8 \
+    --hash=sha256:c46467899aaa4da076d5abc11084634e2d197e9460643dd455ac3db5856b24d6 \
+    --hash=sha256:c4fc4c21971a1a9f4bdb4c73978c7f7256caa3e62b323f70d6cb80db583350bc \
+    --hash=sha256:d051c0e065b94b7a3cea50eb1ec32e912cd96dba41647eb24104b6c6c14c5788 \
+    --hash=sha256:e05e1af93b977f7eafa636d043f9f94c7ee3ac81af99c13508215942e64c993b \
+    --hash=sha256:e32e7cc9af0f1cc15548288a51a3b681cc2a219faa838e995f7dc53dbab1062d \
+    --hash=sha256:ee15f284898e7b246df8087fc82b87b01686f98ee67d85a17b7ab44143a3a9a0 \
+    --hash=sha256:ee67acbbf05014ea6c763beb097e03cd629961c8a632075eeb34247120abcb4b \
+    --hash=sha256:f8bfc0e12dc78f777f323f55c58649591b2cd0c43534e8355c51d3fede5f4dee
+    # via gradio
+pillow==11.3.0 \
+    --hash=sha256:023f6d2d11784a465f09fd09a34b150ea4672e85fb3d05931d89f373ab14abb2 \
+    --hash=sha256:02a723e6bf909e7cea0dac1b0e0310be9d7650cd66222a5f1c571455c0a45214 \
+    --hash=sha256:05f6ecbeff5005399bb48d198f098a9b4b6bdf27b8487c7f38ca16eeb070cd59 \
+    --hash=sha256:068d9c39a2d1b358eb9f245ce7ab1b5c3246c7c8c7d9ba58cfa5b43146c06e50 \
+    --hash=sha256:0743841cabd3dba6a83f38a92672cccbd69af56e3e91777b0ee7f4dba4385632 \
+    --hash=sha256:0b275ff9b04df7b640c59ec5a3cb113eefd3795a8df80bac69646ef699c6981a \
+    --hash=sha256:0bce5c4fd0921f99d2e858dc4d4d64193407e1b99478bc5cacecba2311abde51 \
+    --hash=sha256:1019b04af07fc0163e2810167918cb5add8d74674b6267616021ab558dc98ced \
+    --hash=sha256:118ca10c0d60b06d006be10a501fd6bbdfef559251ed31b794668ed569c87e12 \
+    --hash=sha256:13f87d581e71d9189ab21fe0efb5a23e9f28552d5be6979e84001d3b8505abe8 \
+    --hash=sha256:155658efb5e044669c08896c0c44231c5e9abcaadbc5cd3648df2f7c0b96b9a6 \
+    --hash=sha256:1904e1264881f682f02b7f8167935cce37bc97db457f8e7849dc3a6a52b99580 \
+    --hash=sha256:1c627742b539bba4309df89171356fcb3cc5a9178355b2727d1b74a6cf155fbd \
+    --hash=sha256:1f85acb69adf2aaee8b7da124efebbdb959a104db34d3a2cb0f3793dbae422a8 \
+    --hash=sha256:2465a69cf967b8b49ee1b96d76718cd98c4e925414ead59fdf75cf0fd07df673 \
+    --hash=sha256:2a3117c06b8fb646639dce83694f2f9eac405472713fcb1ae887469c0d4f6788 \
+    --hash=sha256:2d6fcc902a24ac74495df63faad1884282239265c6839a0a6416d33faedfae7e \
+    --hash=sha256:30b7c02f3899d10f13d7a48163c8969e4e653f8b43416d23d13d1bbfdc93b9f8 \
+    --hash=sha256:3828ee7586cd0b2091b6209e5ad53e20d0649bbe87164a459d0676e035e8f523 \
+    --hash=sha256:41342b64afeba938edb034d122b2dda5db2139b9a4af999729ba8818e0056477 \
+    --hash=sha256:41742638139424703b4d01665b807c6468e23e699e8e90cffefe291c5832b027 \
+    --hash=sha256:45dfc51ac5975b938e9809451c51734124e73b04d0f0ac621649821a63852e7b \
+    --hash=sha256:4c834a3921375c48ee6b9624061076bc0a32a60b5532b322cc0ea64e639dd50e \
+    --hash=sha256:4c96f993ab8c98460cd0c001447bff6194403e8b1d7e149ade5f00594918128b \
+    --hash=sha256:59a03cdf019efbfeeed910bf79c7c93255c3d54bc45898ac2a4140071b02b4ae \
+    --hash=sha256:5e05688ccef30ea69b9317a9ead994b93975104a677a36a8ed8106be9260aa6d \
+    --hash=sha256:7859a4cc7c9295f5838015d8cc0a9c215b77e43d07a25e460f35cf516df8626f \
+    --hash=sha256:7966e38dcd0fa11ca390aed7c6f20454443581d758242023cf36fcb319b1a874 \
+    --hash=sha256:79ea0d14d3ebad43ec77ad5272e6ff9bba5b679ef73375ea760261207fa8e0aa \
+    --hash=sha256:7db51d222548ccfd274e4572fdbf3e810a5e66b00608862f947b163e613b67dd \
+    --hash=sha256:83e1b0161c9d148125083a35c1c5a89db5b7054834fd4387499e06552035236c \
+    --hash=sha256:857844335c95bea93fb39e0fa2726b4d9d758850b34075a7e3ff4f4fa3aa3b31 \
+    --hash=sha256:8797edc41f3e8536ae4b10897ee2f637235c94f27404cac7297f7b607dd0716e \
+    --hash=sha256:8924748b688aa210d79883357d102cd64690e56b923a186f35a82cbc10f997db \
+    --hash=sha256:93efb0b4de7e340d99057415c749175e24c8864302369e05914682ba642e5d77 \
+    --hash=sha256:98a9afa7b9007c67ed84c57c9e0ad86a6000da96eaa638e4f8abe5b65ff83f0a \
+    --hash=sha256:a1bc6ba083b145187f648b667e05a2534ecc4b9f2784c2cbe3089e44868f2b9b \
+    --hash=sha256:a418486160228f64dd9e9efcd132679b7a02a5f22c982c78b6fc7dab3fefb635 \
+    --hash=sha256:a4d336baed65d50d37b88ca5b60c0fa9d81e3a87d4a7930d3880d1624d5b31f3 \
+    --hash=sha256:a7bc6e6fd0395bc052f16b1a8670859964dbd7003bd0af2ff08342eb6e442cfe \
+    --hash=sha256:c37d8ba9411d6003bba9e518db0db0c58a680ab9fe5179f040b0463644bc9805 \
+    --hash=sha256:c96d333dcf42d01f47b37e0979b6bd73ec91eae18614864622d9b87bbd5bbf36 \
+    --hash=sha256:d9da3df5f9ea2a89b81bb6087177fb1f4d1c7146d583a3fe5c672c0d94e55e12 \
+    --hash=sha256:ec1ee50470b0d050984394423d96325b744d55c701a439d2bd66089bff963d3c \
+    --hash=sha256:ee92f2fd10f4adc4b43d07ec5e779932b4eb3dbfbc34790ada5a6669bc095aa6 \
+    --hash=sha256:f0f5d8f4a08090c6d6d578351a2b91acf519a54986c055af27e7a93feae6d3f1 \
+    --hash=sha256:f8a5827f84d973d8636e9dc5764af4f0cf2318d26744b3d902931701b0d46653 \
+    --hash=sha256:f944255db153ebb2b19c51fe85dd99ef0ce494123f21b9db4877ffdfc5590c7c
+    # via
+    #   gradio
+    #   matplotlib
+proces==0.1.7 \
+    --hash=sha256:308325bbc96877263f06e57e5e9c760c4b42cc722887ad60be6b18fc37d68762 \
+    --hash=sha256:70a05d9e973dd685f7a9092c58be695a8181a411d63796c213232fd3fdc43775
+    # via cn2an
+pydantic==2.11.10 \
+    --hash=sha256:802a655709d49bd004c31e865ef37da30b540786a46bfce02333e0e24b5fe29a \
+    --hash=sha256:dc280f0982fbda6c38fada4e476dc0a4f3aeaf9c6ad4c28df68a666ec3c61423
+    # via
+    #   fastapi
+    #   gradio
+pydantic-core==2.33.2 \
+    --hash=sha256:04a1a413977ab517154eebb2d326da71638271477d6ad87a769102f7c2488c56 \
+    --hash=sha256:0a9f2c9dd19656823cb8250b0724ee9c60a82f3cdf68a080979d13092a3b0fef \
+    --hash=sha256:0fb2d542b4d66f9470e8065c5469ec676978d625a8b7a363f07d9a501a9cb36a \
+    --hash=sha256:1082dd3e2d7109ad8b7da48e1d4710c8d06c253cbc4a27c1cff4fbcaa97a9e3f \
+    --hash=sha256:1ea40a64d23faa25e62a70ad163571c0b342b8bf66d5fa612ac0dec4f069d916 \
+    --hash=sha256:2b0a451c263b01acebe51895bfb0e1cc842a5c666efe06cdf13846c7418caa9a \
+    --hash=sha256:52fb90784e0a242bb96ec53f42196a17278855b0f31ac7c3cc6f5c1ec4811849 \
+    --hash=sha256:5c92edd15cd58b3c2d34873597a1e20f13094f59cf88068adb18947df5455b4e \
+    --hash=sha256:61c18fba8e5e9db3ab908620af374db0ac1baa69f0f32df4f61ae23f15e586ac \
+    --hash=sha256:65132b7b4a1c0beded5e057324b7e16e10910c106d43675d9bd87d4f38dde162 \
+    --hash=sha256:7cb8bc3605c29176e1b105350d2e6474142d7c1bd1d9327c4a9bdb46bf827acc \
+    --hash=sha256:95237e53bb015f67b63c91af7518a62a8660376a6a0db19b89acc77a4d6199f5 \
+    --hash=sha256:9fdac5d6ffa1b5a83bca06ffe7583f5576555e6c8b3a91fbd25ea7780f825f7d \
+    --hash=sha256:c083a3bdd5a93dfe480f1125926afcdbf2917ae714bdb80b36d34318b2bec5d9 \
+    --hash=sha256:c2fc0a768ef76c15ab9238afa6da7f69895bb5d1ee83aeea2e3509af4472d0b9 \
+    --hash=sha256:c8e7af2f4e0194c22b5b37205bfb293d166a7344a5b0d0eaccebc376546d77d5 \
+    --hash=sha256:e80b087132752f6b3d714f041ccf74403799d3b23a72722ea2e6ba2e892555b9 \
+    --hash=sha256:f517ca031dfc037a9c07e748cefd8d96235088b83b4f4ba8939105d20fa1dcd6
+    # via pydantic
+pydub==0.25.1 \
+    --hash=sha256:65617e33033874b59d87db603aa1ed450633288aefead953b30bded59cb599a6 \
+    --hash=sha256:980a33ce9949cab2a569606b65674d748ecbca4f0796887fd6f46173a7b0d30f
+    # via gradio
+pygments==2.19.2 \
+    --hash=sha256:636cb2477cec7f8952536970bc533bc43743542f70392ae026374600add5b887 \
+    --hash=sha256:86540386c03d588bb81d44bc3928634ff26449851e99741617ecb9037ee5ec0b
+    # via rich
+pyopenjtalk==0.4.1 \
+    --hash=sha256:d5ada46f7fc2b52c1c79c273eb9668ff6ad7ab276a8db9d8be119ef93440f0dc
+    # via chx-prototype
+pyparsing==3.2.5 \
+    --hash=sha256:2df8d5b7b2802ef88e8d016a2eb9c7aeaa923529cd251ed0fe4608275d4105b6 \
+    --hash=sha256:e38a4f02064cf41fe6593d328d0512495ad1f3d8a91c4f73fc401b3079a59a5e
+    # via matplotlib
+python-dateutil==2.9.0.post0 \
+    --hash=sha256:37dd54208da7e1cd875388217d5e00ebd4179249f90fb72437e91a35459a0ad3 \
+    --hash=sha256:a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427
+    # via
+    #   matplotlib
+    #   pandas
+python-multipart==0.0.20 \
+    --hash=sha256:8a62d3a8335e06589fe01f2a3e178cdcc632f3fbe0d492ad9ee0ec35aab1f104 \
+    --hash=sha256:8dd0cab45b8e23064ae09147625994d090fa46f5b0d1e13af944c331a7fa9d13
+    # via gradio
+pytz==2025.2 \
+    --hash=sha256:360b9e3dbb49a209c21ad61809c7fb453643e048b38924c765813546746e81c3 \
+    --hash=sha256:5ddf76296dd8c44c26eb8f4b6f35488f3ccbf6fbbd7adee0b7262d43f0ec2f00
+    # via pandas
+pyyaml==6.0.3 \
+    --hash=sha256:00c4bdeba853cc34e7dd471f16b4114f4162dc03e6b7afcc2128711f0eca823c \
+    --hash=sha256:02893d100e99e03eda1c8fd5c441d8c60103fd175728e23e431db1b589cf5ab3 \
+    --hash=sha256:0f29edc409a6392443abf94b9cf89ce99889a1dd5376d94316ae5145dfedd5d6 \
+    --hash=sha256:16249ee61e95f858e83976573de0f5b2893b3677ba71c9dd36b9cf8be9ac6d65 \
+    --hash=sha256:2283a07e2c21a2aa78d9c4442724ec1eb15f5e42a723b99cb3d822d48f5f7ad1 \
+    --hash=sha256:34d5fcd24b8445fadc33f9cf348c1047101756fd760b4dacb5c3e99755703310 \
+    --hash=sha256:4a2e8cebe2ff6ab7d1050ecd59c25d4c8bd7e6f400f5f82b96557ac0abafd0ac \
+    --hash=sha256:4ad1906908f2f5ae4e5a8ddfce73c320c2a1429ec52eafd27138b7f1cbe341c9 \
+    --hash=sha256:501a031947e3a9025ed4405a168e6ef5ae3126c59f90ce0cd6f2bfc477be31b7 \
+    --hash=sha256:5190d403f121660ce8d1d2c1bb2ef1bd05b5f68533fc5c2ea899bd15f4399b35 \
+    --hash=sha256:5498cd1645aa724a7c71c8f378eb29ebe23da2fc0d7a08071d89469bf1d2defb \
+    --hash=sha256:66e1674c3ef6f541c35191caae2d429b967b99e02040f5ba928632d9a7f0f065 \
+    --hash=sha256:6adc77889b628398debc7b65c073bcb99c4a0237b248cacaf3fe8a557563ef6c \
+    --hash=sha256:79005a0d97d5ddabfeeea4cf676af11e647e41d81c9a7722a193022accdb6b7c \
+    --hash=sha256:7c6610def4f163542a622a73fb39f534f8c101d690126992300bf3207eab9764 \
+    --hash=sha256:8d1fab6bb153a416f9aeb4b8763bc0f22a5586065f86f7664fc23339fc1c1fac \
+    --hash=sha256:8da9669d359f02c0b91ccc01cac4a67f16afec0dac22c2ad09f46bee0697eba8 \
+    --hash=sha256:93dda82c9c22deb0a405ea4dc5f2d0cda384168e466364dec6255b293923b2f3 \
+    --hash=sha256:a33284e20b78bd4a18c8c2282d549d10bc8408a2a7ff57653c0cf0b9be0afce5 \
+    --hash=sha256:a80cb027f6b349846a3bf6d73b5e95e782175e52f22108cfa17876aaeff93702 \
+    --hash=sha256:b3bc83488de33889877a0f2543ade9f70c67d66d9ebb4ac959502e12de895788 \
+    --hash=sha256:c1ff362665ae507275af2853520967820d9124984e0f7466736aea23d8611fba \
+    --hash=sha256:c458b6d084f9b935061bc36216e8a69a7e293a2f1e68bf956dcd9e6cbcd143f5 \
+    --hash=sha256:d0eae10f8159e8fdad514efdc92d74fd8d682c933a6dd088030f3834bc8e6b26 \
+    --hash=sha256:d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f \
+    --hash=sha256:ebc55a14a21cb14062aa4162f906cd962b28e2e9ea38f9b4391244cd8de4ae0b \
+    --hash=sha256:eda16858a3cab07b80edaf74336ece1f986ba330fdb8ee0d6c0d68fe82bc96be \
+    --hash=sha256:ee2922902c45ae8ccada2c5b501ab86c36525b883eff4255313a253a3160861c \
+    --hash=sha256:f7057c9a337546edc7973c0d3ba84ddcdf0daa14533c2065749c9075001090e6
+    # via
+    #   gradio
+    #   huggingface-hub
+    #   vocos
+regex==2025.10.23 \
+    --hash=sha256:08a15d40ce28362eac3e78e83d75475147869c1ff86bc93285f43b4f4431a741 \
+    --hash=sha256:08f52122c352eb44c3421dab78b9b73a8a77a282cc8314ae576fcaa92b780d10 \
+    --hash=sha256:0967c5b86f274800a34a4ed862dfab56928144d03cb18821c5153f8777947796 \
+    --hash=sha256:16b0f1c2e2d566c562d5c384c2b492646be0a19798532fdc1fdedacc66e3223f \
+    --hash=sha256:182c452279365a93a9f45874f7f191ec1c51e1f1eb41bf2b16563f1a40c1da3a \
+    --hash=sha256:185d2b18c062820b3a40d8fefa223a83f10b20a674bf6e8c4a432e8dfd844627 \
+    --hash=sha256:1a32d77aeaea58a13230100dd8797ac1a84c457f3af2fdf0d81ea689d5a9105b \
+    --hash=sha256:1a56a54be3897d62f54290190fbcd754bff6932934529fbf5b29933da28fcd43 \
+    --hash=sha256:1cb976810ac1416a67562c2e5ba0accf6f928932320fef302e08100ed681b38e \
+    --hash=sha256:1e11d3e5887b8b096f96b4154dfb902f29c723a9556639586cd140e77e28b313 \
+    --hash=sha256:1f5799ea1787aa6de6c150377d11afad39a38afd033f0c5247aecb997978c422 \
+    --hash=sha256:1fa1186966b2621b1769fd467c7b22e317e6ba2d2cdcecc42ea3089ef04a8521 \
+    --hash=sha256:2003cc82a579107e70d013482acce8ba773293f2db534fb532738395c557ff34 \
+    --hash=sha256:238e67264b4013e74136c49f883734f68656adf8257bfa13b515626b31b20f8e \
+    --hash=sha256:281d87fa790049c2b7c1b4253121edd80b392b19b5a3d28dc2a77579cb2a58ec \
+    --hash=sha256:2b841698f93db3ccc36caa1900d2a3be281d9539b822dc012f08fc80b46a3224 \
+    --hash=sha256:2ee3663f2c334959016b56e3bd0dd187cbc73f948e3a3af14c3caaa0c3035d10 \
+    --hash=sha256:37be9296598a30c6a20236248cb8b2c07ffd54d095b75d3a2a2ee5babdc51df1 \
+    --hash=sha256:39a7e8083959cb1c4ff74e483eecb5a65d3b3e1d821b256e54baf61782c906c6 \
+    --hash=sha256:3a36b4005770044bf08edecc798f0e41a75795b9e7c9c12fe29da8d792ef870c \
+    --hash=sha256:4f13450328a6634348d47a88367e06b64c9d84980ef6a748f717b13f8ce64e87 \
+    --hash=sha256:563824a08c7c03d96856d84b46fdb3bbb7cfbdf79da7ef68725cda2ce169c72a \
+    --hash=sha256:5b5cb5b6344c4c4c24b2dc87b0bfee78202b07ef7633385df70da7fcf6f7cec6 \
+    --hash=sha256:5c259cb363299a0d90d63b5c0d7568ee98419861618a95ee9d91a41cb9954462 \
+    --hash=sha256:63b81eef3656072e4ca87c58084c7a9c2b81d41a300b157be635a8a675aacfb8 \
+    --hash=sha256:7c9f2c68ac6cb3de94eea08a437a75eaa2bd33f9e97c84836ca0b610a5804368 \
+    --hash=sha256:7d6606524fa77b3912c9ef52a42ef63c6cfbfc1077e9dc6296cd5da0da286044 \
+    --hash=sha256:842d449a8fefe546f311656cf8c0d6729b08c09a185f1cad94c756210286d6a8 \
+    --hash=sha256:89f8b9ea3830c79468e26b0e21c3585f69f105157c2154a36f6b7839f8afb351 \
+    --hash=sha256:8cbaf8ceb88f96ae2356d01b9adf5e6306fa42fa6f7eab6b97794e37c959ac26 \
+    --hash=sha256:8f3e6d202fb52c2153f532043bbcf618fd177df47b0b306741eb9b60ba96edc3 \
+    --hash=sha256:956d89e0c92d471e8f7eee73f73fdff5ed345886378c45a43175a77538a1ffe4 \
+    --hash=sha256:98fd84c4e4ea185b3bb5bf065261ab45867d8875032f358a435647285c722673 \
+    --hash=sha256:99018c331fb2529084a0c9b4c713dfa49fafb47c7712422e49467c13a636c656 \
+    --hash=sha256:a0ec8bdd88d2e2659c3518087ee34b37e20bd169419ffead4240a7004e8ed03b \
+    --hash=sha256:a5b7a26b51a9df473ec16a1934d117443a775ceb7b39b78670b2e21893c330c9 \
+    --hash=sha256:a6ce7973384c37bdf0f371a843f95a6e6f4e1489e10e0cf57330198df72959c5 \
+    --hash=sha256:a93e97338e1c8ea2649e130dcfbe8cd69bba5e1e163834752ab64dcb4de6d5ed \
+    --hash=sha256:a9639ab7540cfea45ef57d16dcbea2e22de351998d614c3ad2f9778fa3bdd788 \
+    --hash=sha256:af7b2661dcc032da1fae82069b5ebf2ac1dfcd5359ef8b35e1367bfc92181432 \
+    --hash=sha256:b1249e9ff581c5b658c8f0437f883b01f1edcf424a16388591e7c05e5e9e8b0c \
+    --hash=sha256:b24b29402f264f70a3c81f45974323b41764ff7159655360543b7cabb73e7d2f \
+    --hash=sha256:b2eb48bd9848d66fd04826382f5e8491ae633de3233a3d64d58ceb4ecfa2113a \
+    --hash=sha256:b577601bfe1d33913fcd9276d7607bbac827c4798d9e14d04bf37d417a6c41cb \
+    --hash=sha256:b5d4ece8628d6e364302006366cea3ee887db397faebacc5dacf8ef19e064cf8 \
+    --hash=sha256:b7690f95404a1293923a296981fd943cca12c31a41af9c21ba3edd06398fc193 \
+    --hash=sha256:c037aadf4d64bdc38af7db3dbd34877a057ce6524eefcb2914d6d41c56f968cc \
+    --hash=sha256:c70dfe58b0a00b36aa04cdb0f798bf3e0adc31747641f69e191109fd8572c9a9 \
+    --hash=sha256:ce81c5544a5453f61cb6f548ed358cfb111e3b23f3cd42d250a4077a6be2a7b6 \
+    --hash=sha256:d36591ce06d047d0c0fe2fc5f14bfbd5b4525d08a7b6a279379085e13f0e3d0e \
+    --hash=sha256:d614986dc68506be8f00474f4f6960e03e4ca9883f7df47744800e7d7c08a494 \
+    --hash=sha256:d9a4953575f300a7bab71afa4cd4ac061c7697c89590a2902b536783eeb49a4f \
+    --hash=sha256:e9bf7f6699f490e4e43c44757aa179dab24d1960999c84ab5c3d5377714ed473 \
+    --hash=sha256:ea7a3c283ce0f06fe789365841e9174ba05f8db16e2fd6ae00a02df9572c04c0 \
+    --hash=sha256:ebf1baebef1c4088ad5a5623decec6b52950f0e4d7a0ae4d48f0a99f8c9cb7d7 \
+    --hash=sha256:f7ada5d9dceafaab92646aa00c10a9efd9b09942dd9b0d7c5a4b73db92cc7e61 \
+    --hash=sha256:fd8aba965604d70306eb90a35528f776e59112a7114a5162824d43b76fa27f58
+    # via
+    #   nltk
+    #   tiktoken
+requests==2.32.5 \
+    --hash=sha256:2462f94637a34fd532264295e186976db0f5d453d1cdd31473c85a6a161affb6 \
+    --hash=sha256:dbba0bac56e100853db0ea71b82b4dfd5fe2bf6d3754a8893c3af500cec7d7cf
+    # via tiktoken
+rich==14.2.0 \
+    --hash=sha256:73ff50c7c0c1c77c8243079283f4edb376f0f6442433aecb8ce7e6d0b92d1fe4 \
+    --hash=sha256:76bc51fe2e57d2b1be1f96c524b890b816e334ab4c1e45888799bfaab0021edd
+    # via typer
+ruff==0.14.2 \
+    --hash=sha256:0df3424aa5c3c08b34ed8ce099df1021e3adaca6e90229273496b839e5a7e1af \
+    --hash=sha256:1c505b389e19c57a317cf4b42db824e2fca96ffb3d86766c1c9f8b96d32048a7 \
+    --hash=sha256:2877f02119cdebf52a632d743a2e302dea422bfae152ebe2f193d3285a3a65df \
+    --hash=sha256:3bf8d2f9aa1602599217d82e8e0af7fd33e5878c4d98f37906b7c93f46f9a839 \
+    --hash=sha256:3d0bbeffb8d9f4fccf7b5198d566d0bad99a9cb622f1fc3467af96cb8773c9e3 \
+    --hash=sha256:41775927d287685e08f48d8eb3f765625ab0b7042cc9377e20e64f4eb0056ee9 \
+    --hash=sha256:5c8753dfa44ebb2cde10ce5b4d2ef55a41fb9d9b16732a2c5df64620dbda44a3 \
+    --hash=sha256:5ca36b4cb4db3067a3b24444463ceea5565ea78b95fe9a07ca7cb7fd16948770 \
+    --hash=sha256:61ae91a32c853172f832c2f40bd05fd69f491db7289fb85a9b941ebdd549781a \
+    --hash=sha256:7047f0c5a713a401e43a88d36843d9c83a19c584e63d664474675620aaa634a8 \
+    --hash=sha256:7cbe4e593505bdec5884c2d0a4d791a90301bc23e49a6b1eb642dd85ef9c64f1 \
+    --hash=sha256:8d54b561729cee92f8d89c316ad7a3f9705533f5903b042399b6ae0ddfc62e11 \
+    --hash=sha256:98da787668f239313d9c902ca7c523fe11b8ec3f39345553a51b25abc4629c96 \
+    --hash=sha256:a307fc45ebd887b3f26b36d9326bb70bf69b01561950cdcc6c0bdf7bb8e0f7cc \
+    --hash=sha256:b8264016f6f209fac16262882dbebf3f8be1629777cf0f37e7aff071b3e9b92e \
+    --hash=sha256:bc1967e40286f63ee23c615e8e7e98098dedc7301568bd88991f6e544d8ae096 \
+    --hash=sha256:e21be42d72e224736f0c992cdb9959a2fa53c7e943b97ef5d081e13170e3ffc5 \
+    --hash=sha256:e681c5bc777de5af898decdcb6ba3321d0d466f4cb43c3e7cc2c3b4e7b843a05 \
+    --hash=sha256:ea9d635e83ba21569fbacda7e78afbfeb94911c9434aff06192d9bc23fd5495a
+    # via gradio
+safehttpx==0.1.7 \
+    --hash=sha256:c4f4a162db6993464d7ca3d7cc4af0ffc6515a606dfd220b9f82c6945d869cde \
+    --hash=sha256:db201c0978c41eddb8bb480f3eee59dd67304fdd91646035e9d9a720049a9d23
+    # via gradio
+scipy==1.16.3 \
+    --hash=sha256:01e87659402762f43bd2fee13370553a17ada367d42e7487800bf2916535aecb \
+    --hash=sha256:03192a35e661470197556de24e7cb1330d84b35b94ead65c46ad6f16f6b28f2a \
+    --hash=sha256:062246acacbe9f8210de8e751b16fc37458213f124bef161a5a02c7a39284304 \
+    --hash=sha256:0c3b4dd3d9b08dbce0f3440032c52e9e2ab9f96ade2d3943313dfe51a7056959 \
+    --hash=sha256:0c623a54f7b79dd88ef56da19bc2873afec9673a48f3b85b18e4d402bdd29a5a \
+    --hash=sha256:16b8bc35a4cc24db80a0ec836a9286d0e31b2503cb2fd7ff7fb0e0374a97081d \
+    --hash=sha256:21d9d6b197227a12dcbf9633320a4e34c6b0e51c57268df255a0942983bac562 \
+    --hash=sha256:2d1ae2cf0c350e7705168ff2429962a89ad90c2d49d1dd300686d8b2a5af22fc \
+    --hash=sha256:3a4c460301fb2cffb7f88528f30b3127742cff583603aa7dc964a52c463b385d \
+    --hash=sha256:4aff59800a3b7f786b70bfd6ab551001cb553244988d7d6b8299cb1ea653b353 \
+    --hash=sha256:50a3dbf286dbc7d84f176f9a1574c705f277cb6565069f88f60db9eafdbe3ee2 \
+    --hash=sha256:57d01cb6f85e34f0946b33caa66e892aae072b64b034183f3d87c4025802a119 \
+    --hash=sha256:5803c5fadd29de0cf27fa08ccbfe7a9e5d741bf63e4ab1085437266f12460ff9 \
+    --hash=sha256:6020470b9d00245926f2d5bb93b119ca0340f0d564eb6fbaad843eaebf9d690f \
+    --hash=sha256:63d3cdacb8a824a295191a723ee5e4ea7768ca5ca5f2838532d9f2e2b3ce2135 \
+    --hash=sha256:663b8d66a8748051c3ee9c96465fb417509315b99c71550fda2591d7dd634234 \
+    --hash=sha256:7dc1360c06535ea6116a2220f760ae572db9f661aba2d88074fe30ec2aa1ff88 \
+    --hash=sha256:7f68154688c515cdb541a31ef8eb66d8cd1050605be9dcd74199cbd22ac739bc \
+    --hash=sha256:875555ce62743e1d54f06cdf22c1e0bc47b91130ac40fe5d783b6dfa114beeb6 \
+    --hash=sha256:8b3c820ddb80029fe9f43d61b81d8b488d3ef8ca010d15122b152db77dc94c22 \
+    --hash=sha256:8d09d72dc92742988b0e7750bddb8060b0c7079606c0d24a8cc8e9c9c11f9079 \
+    --hash=sha256:96491a6a54e995f00a28a3c3badfff58fd093bf26cd5fb34a2188c8c756a3a2c \
+    --hash=sha256:9b9c9c07b6d56a35777a1b4cc8966118fb16cfd8daf6743867d17d36cfad2d40 \
+    --hash=sha256:aadd23f98f9cb069b3bd64ddc900c4d277778242e961751f77a8cb5c4b946fb0 \
+    --hash=sha256:b7c5f1bda1354d6a19bc6af73a649f8285ca63ac6b52e64e658a5a11d4d69800 \
+    --hash=sha256:b81c27fc41954319a943d43b20e07c40bdcd3ff7cf013f4fb86286faefe546c4 \
+    --hash=sha256:bb61878c18a470021fb515a843dc7a76961a8daceaaaa8bad1332f1bf4b54657 \
+    --hash=sha256:cd13e354df9938598af2be05822c323e97132d5e6306b83a3b4ee6724c6e522e \
+    --hash=sha256:d2ec56337675e61b312179a1ad124f5f570c00f920cc75e1000025451b88241c \
+    --hash=sha256:d3837938ae715fc0fe3c39c0202de3a8853aff22ca66781ddc2ade7554b7e2cc \
+    --hash=sha256:d9f48cafc7ce94cf9b15c6bffdc443a81a27bf7075cf2dcd5c8b40f85d10c4e7 \
+    --hash=sha256:da7763f55885045036fabcebd80144b757d3db06ab0861415d1c3b7c69042146 \
+    --hash=sha256:e1d27cbcb4602680a49d787d90664fa4974063ac9d4134813332a8c53dbe667c \
+    --hash=sha256:e5d42a9472e7579e473879a1990327830493a7047506d58d73fc429b84c1d49d \
+    --hash=sha256:e7efa2681ea410b10dde31a52b18b0154d66f2485328830e45fdf183af5aefc6 \
+    --hash=sha256:eab43fae33a0c39006a88096cd7b4f4ef545ea0447d250d5ac18202d40b6611d \
+    --hash=sha256:f2622206f5559784fa5c4b53a950c3c7c1cf3e84ca1b9c4b6c03f062f289ca26 \
+    --hash=sha256:f379b54b77a597aa7ee5e697df0d66903e41b9c85a6dd7946159e356319158e8 \
+    --hash=sha256:f667a4542cc8917af1db06366d3f78a5c8e83badd56409f94d1eac8d8d9133fa \
+    --hash=sha256:fb4b29f4cf8cc5a8d628bc8d8e26d12d7278cd1f219f22698a378c3d67db5e4b \
+    --hash=sha256:ffa6eea95283b2b8079b821dc11f50a17d0571c92b43e2b5b12764dc5f9b285d
+    # via
+    #   chx-prototype
+    #   vocos
+semantic-version==2.10.0 \
+    --hash=sha256:bdabb6d336998cbb378d4b9db3a4b56a1e3235701dc05ea2690d9a997ed5041c \
+    --hash=sha256:de78a3b8e0feda74cabc54aab2da702113e33ac9d9eb9d2389bcf1f58b7d9177
+    # via gradio
+setuptools==80.9.0 \
+    --hash=sha256:062d34222ad13e0cc312a4c02d73f059e86a4acbfbdea8f8f76b28c99f306922 \
+    --hash=sha256:f36b47402ecde768dbfafc46e8e4207b4360c654f1f3bb84475f0a28628fb19c
+    # via torch
+shellingham==1.5.4 \
+    --hash=sha256:7ecfff8f2fd72616f7481040475a65b2bf8af90a56c89140852d1120324e8686 \
+    --hash=sha256:8dbca0739d487e5bd35ab3ca4b36e11c4078f3a234bfce294b0a0291363404de
+    # via
+    #   huggingface-hub
+    #   typer
+six==1.17.0 \
+    --hash=sha256:4721f391ed90541fddacab5acf947aa0d3dc7d27b2e1e8eda2be8970586c3274 \
+    --hash=sha256:ff70335d468e7eb6ec65b95b99d3a2836546063f63acc5171de367e834932a81
+    # via python-dateutil
+sniffio==1.3.1 \
+    --hash=sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2 \
+    --hash=sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc
+    # via anyio
+starlette==0.49.1 \
+    --hash=sha256:481a43b71e24ed8c43b11ea02f5353d77840e01480881b8cb5a26b8cae64a8cb \
+    --hash=sha256:d92ce9f07e4a3caa3ac13a79523bd18e3bc0042bb8ff2d759a8e7dd0e1859875
+    # via
+    #   fastapi
+    #   gradio
+sympy==1.14.0 \
+    --hash=sha256:d3d3fe8df1e5a0b42f0e7bdf50541697dbe7d23746e894990c030e2b05e72517 \
+    --hash=sha256:e091cc3e99d2141a0ba2847328f5479b05d94a6635cb96148ccb3f34671bd8f5
+    # via torch
+tiktoken==0.12.0 \
+    --hash=sha256:01d99484dc93b129cd0964f9d34eee953f2737301f18b3c7257bf368d7615baa \
+    --hash=sha256:04f0e6a985d95913cabc96a741c5ffec525a2c72e9df086ff17ebe35985c800e \
+    --hash=sha256:06a9f4f49884139013b138920a4c393aa6556b2f8f536345f11819389c703ebb \
+    --hash=sha256:0ee8f9ae00c41770b5f9b0bb1235474768884ae157de3beb5439ca0fd70f3e25 \
+    --hash=sha256:285ba9d73ea0d6171e7f9407039a290ca77efcdb026be7769dccc01d2c8d7fff \
+    --hash=sha256:399c3dd672a6406719d84442299a490420b458c44d3ae65516302a99675888f3 \
+    --hash=sha256:4a1a4fcd021f022bfc81904a911d3df0f6543b9e7627b51411da75ff2fe7a1be \
+    --hash=sha256:54c891b416a0e36b8e2045b12b33dd66fb34a4fe7965565f1b482da50da3e86a \
+    --hash=sha256:584c3ad3d0c74f5269906eb8a659c8bfc6144a52895d9261cdaf90a0ae5f4de0 \
+    --hash=sha256:5edb8743b88d5be814b1a8a8854494719080c28faaa1ccbef02e87354fe71ef0 \
+    --hash=sha256:604831189bd05480f2b885ecd2d1986dc7686f609de48208ebbbddeea071fc0b \
+    --hash=sha256:775c2c55de2310cc1bc9a3ad8826761cbdc87770e586fd7b6da7d4589e13dab3 \
+    --hash=sha256:8f317e8530bb3a222547b85a58583238c8f74fd7a7408305f9f63246d1a0958b \
+    --hash=sha256:981a81e39812d57031efdc9ec59fa32b2a5a5524d20d4776574c4b4bd2e9014a \
+    --hash=sha256:9baf52f84a3f42eef3ff4e754a0db79a13a27921b457ca9832cf944c6be4f8f3 \
+    --hash=sha256:a01b12f69052fbe4b080a2cfb867c4de12c704b56178edf1d1d7b273561db160 \
+    --hash=sha256:a90388128df3b3abeb2bfd1895b0681412a8d7dc644142519e6f0a97c2111646 \
+    --hash=sha256:b18ba7ee2b093863978fcb14f74b3707cdc8d4d4d3836853ce7ec60772139931 \
+    --hash=sha256:b4e7ed1c6a7a8a60a3230965bdedba8cc58f68926b835e519341413370e0399a \
+    --hash=sha256:b8a0cd0c789a61f31bf44851defbd609e8dd1e2c8589c614cc1060940ef1f697 \
+    --hash=sha256:c2c714c72bc00a38ca969dae79e8266ddec999c7ceccd603cc4f0d04ccd76365 \
+    --hash=sha256:cbb9a3ba275165a2cb0f9a83f5d7025afe6b9d0ab01a22b50f0e74fee2ad253e \
+    --hash=sha256:d186a5c60c6a0213f04a7a802264083dea1bbde92a2d4c7069e1a56630aef830 \
+    --hash=sha256:d5f89ea5680066b68bcb797ae85219c72916c922ef0fcdd3480c7d2315ffff16 \
+    --hash=sha256:da900aa0ad52247d8794e307d6446bd3cdea8e192769b56276695d34d2c9aa88 \
+    --hash=sha256:dc2dd125a62cb2b3d858484d6c614d136b5b848976794edfb63688d539b8b93f \
+    --hash=sha256:dfdfaa5ffff8993a3af94d1125870b1d27aed7cb97aa7eb8c1cefdbc87dbee63 \
+    --hash=sha256:f61c0aea5565ac82e2ec50a05e02a6c44734e91b51c10510b084ea1b8e633a71 \
+    --hash=sha256:fc530a28591a2d74bce821d10b418b26a094bf33839e69042a6e86ddb7a7fb27
+    # via openai-whisper
+tokenizers==0.22.1 \
+    --hash=sha256:19d2962dd28bc67c1f205ab180578a78eef89ac60ca7ef7cbe9635a46a56422a \
+    --hash=sha256:331d6d149fa9c7d632cde4490fb8bbb12337fa3a0232e77892be656464f4b446 \
+    --hash=sha256:38201f15cdb1f8a6843e6563e6e79f4abd053394992b9bbdf5213ea3469b4ae7 \
+    --hash=sha256:59fdb013df17455e5f950b4b834a7b3ee2e0271e6378ccb33aa74d178b513c73 \
+    --hash=sha256:607989f2ea68a46cb1dfbaf3e3aabdf3f21d8748312dbeb6263d1b3b66c5010a \
+    --hash=sha256:61de6522785310a309b3407bac22d99c4db5dba349935e99e4d15ea2226af2d9 \
+    --hash=sha256:65fd6e3fb11ca1e78a6a93602490f134d1fdeb13bcef99389d5102ea318ed138 \
+    --hash=sha256:8d4e484f7b0827021ac5f9f71d4794aaef62b979ab7608593da22b1d2e3c4edc \
+    --hash=sha256:a0f307d490295717726598ef6fa4f24af9d484809223bbc253b201c740a06390 \
+    --hash=sha256:afd7594a56656ace95cdd6df4cca2e4059d294c5cfb1679c57824b605556cb2f \
+    --hash=sha256:b5120eed1442765cd90b903bb6cfef781fd8fe64e34ccaecbae4c619b7b12a82 \
+    --hash=sha256:ba0a64f450b9ef412c98f6bcd2a50c6df6e2443b560024a09fa6a03189726879 \
+    --hash=sha256:d1cbe5454c9a15df1b3443c726063d930c16f047a3cc724b9e6e1a91140e5a21 \
+    --hash=sha256:e2ef6063d7a84994129732b47e7915e8710f27f99f3a3260b8a38fc7ccd083f4 \
+    --hash=sha256:e7d094ae6312d69cc2a872b54b91b309f4f6fbce871ef28eb27b52a98e4d0214
+    # via chx-prototype
+tomlkit==0.13.3 \
+    --hash=sha256:430cf247ee57df2b94ee3fbe588e71d362a941ebb545dec29b53961d61add2a1 \
+    --hash=sha256:c89c649d79ee40629a9fda55f8ace8c6a1b42deb912b2a8fd8d942ddadb606b0
+    # via gradio
+torch==2.9.0 \
+    --hash=sha256:64693568f5dc4dbd5f880a478b1cea0201cc6b510d91d1bc54fea86ac5d1a637 \
+    --hash=sha256:695ba920f234ad4170c9c50e28d56c848432f8f530e6bc7f88fcb15ddf338e75 \
+    --hash=sha256:71c7578984f5ec0eb645eb4816ac8435fcf3e3e2ae1901bcd2f519a9cafb5125 \
+    --hash=sha256:71d9309aee457bbe0b164bce2111cd911c4ed4e847e65d5077dbbcd3aba6befc \
+    --hash=sha256:7e614fae699838038d888729f82b687c03413c5989ce2a9481f9a7e7a396e0bb \
+    --hash=sha256:8f25033b8667b57857dfd01458fbf2a9e6a6df1f8def23aef0dc46292f6aa642 \
+    --hash=sha256:b3d29524993a478e46f5d598b249cd824b7ed98d7fba538bd9c4cde6c803948f \
+    --hash=sha256:c08fb654d783899e204a32cca758a7ce8a45b2d78eeb89517cc937088316f78e \
+    --hash=sha256:c30a17fc83eeab346913e237c64b15b5ba6407fff812f6c541e322e19bc9ea0e \
+    --hash=sha256:d037f1b4ffd25013be4a7bf3651a0a910c68554956c7b2c92ebe87c76475dece \
+    --hash=sha256:dfb5b8cd310ba3436c7e14e8b7833ef658cf3045e50d2bdaed23c8fc517065eb \
+    --hash=sha256:e4e5b5cba837a2a8d1a497ba9a58dae46fa392593eaa13b871c42f71847503a5 \
+    --hash=sha256:ec8feb0099b2daa5728fbc7abb0b05730fd97e0f359ff8bda09865aaa7bd7d4b \
+    --hash=sha256:eff527d4e4846e6f70d2afd8058b73825761203d66576a7e04ea2ecfebcb4ab8 \
+    --hash=sha256:f8877779cf56d1ce431a7636703bdb13307f5960bb1af49716d8b179225e0e6a \
+    --hash=sha256:f8ed31ddd7d10bfb3fbe0b9fe01b1243577f13d75e6f4a0839a283915ce3791e
+    # via
+    #   chx-prototype
+    #   encodec
+    #   openai-whisper
+    #   torchaudio
+    #   vocos
+torchaudio==2.9.0 \
+    --hash=sha256:0a234634e1142fb2652c49e935a98b4d9656fd0af9e4aa14b1b05a80c3cf8e78 \
+    --hash=sha256:1eb0d1dac8cefbc4a54afb21aac72a1c25a91f73e9c3bd85f6684930a4a1be5d \
+    --hash=sha256:266d304dd4ed738a10148b020e3d066e81272ee851f6f92193fe549df96af868 \
+    --hash=sha256:276871d6f5fed5268a87c5da303a13ca2e06b9d29a4c44663b960f0a2e2f46d7 \
+    --hash=sha256:3d5657d929d6ca07b08cfa005988f2ea8caacf9af42f20bc7eff10f88812ce30 \
+    --hash=sha256:3fa41447a21103fcde930b4ad2bd2634565a0becff1a5425535b4f0116c0d5df \
+    --hash=sha256:3fe9cac0c2ee713e07f8c88d09528d55e0fa74987b0122e27911dfb720f39054 \
+    --hash=sha256:5549c25db4c2da306e179e9aa99980e7f5b1826a8d2d7de08125f3943a5620b2 \
+    --hash=sha256:631b0f43564a25e27e615b217454c334f52162679f39ae10b9fa7562ed587dfc \
+    --hash=sha256:69f46f21bd67e90ade33a7d0f0cf98270cd61b98f5f8249d3893be0a16b3e31f \
+    --hash=sha256:742143d9d62769bc4b9a2977ca4f4720e0a5e922bdc5df585c155e0a1f545461 \
+    --hash=sha256:7d3926129389d934aa048bd6c6f68fbf3ef26828ebbbbeac99794ea00e90dc1c \
+    --hash=sha256:93358d8f2f24969ba3f368f4eec33295df830af54836c7fd3336740228f9af16 \
+    --hash=sha256:967d664477fb91dffad82ef64ea3695801c0cc35304baec71be875b569440872 \
+    --hash=sha256:cbf5d6da8fd2ed545c78218b39fd6aacaa4dd5e265c5f85b248a2fac223f0bd6 \
+    --hash=sha256:ed6df9f14431e13498b984dc87df1aabb2156b9ce0ce7268ce4a61650197310a
+    # via
+    #   chx-prototype
+    #   encodec
+    #   vocos
+tqdm==4.67.1 \
+    --hash=sha256:26445eca388f82e72884e0d580d5464cd801a3ea01e63e5601bdff9ba6a48de2 \
+    --hash=sha256:f8aef9c52c08c13a65f30ea34f4e5aac3fd1a34959879d7e59e63027286627f2
+    # via
+    #   huggingface-hub
+    #   nltk
+    #   openai-whisper
+    #   pyopenjtalk
+triton==3.5.0 ; (platform_machine == 'x86_64' and sys_platform == 'linux') or sys_platform == 'linux2' \
+    --hash=sha256:317fe477ea8fd4524a6a8c499fb0a36984a56d0b75bf9c9cb6133a1c56d5a6e7 \
+    --hash=sha256:c83f2343e1a220a716c7b3ab9fccfcbe3ad4020d189549200e2d2e8d5868bed9 \
+    --hash=sha256:da0fa67ccd76c3dcfb0bffe1b1c57c685136a6bd33d141c24d9655d4185b1289 \
+    --hash=sha256:e6bb9aa5519c084a333acdba443789e50012a4b851cd486c54f0b8dc2a8d3a12
+    # via
+    #   openai-whisper
+    #   torch
+typeguard==4.4.4 \
+    --hash=sha256:3a7fd2dffb705d4d0efaed4306a704c89b9dee850b688f060a8b1615a79e5f74 \
+    --hash=sha256:b5f562281b6bfa1f5492470464730ef001646128b180769880468bd84b68b09e
+    # via inflect
+typer==0.20.0 \
+    --hash=sha256:1aaf6494031793e4876fb0bacfa6a912b551cf43c1e63c800df8b1a866720c37 \
+    --hash=sha256:5b463df6793ec1dca6213a3cf4c0f03bc6e322ac5e16e13ddd622a889489784a
+    # via gradio
+typer-slim==0.20.0 \
+    --hash=sha256:9fc6607b3c6c20f5c33ea9590cbeb17848667c51feee27d9e314a579ab07d1a3 \
+    --hash=sha256:f42a9b7571a12b97dddf364745d29f12221865acef7a2680065f9bb29c7dc89d
+    # via huggingface-hub
+typing-extensions==4.15.0 \
+    --hash=sha256:0cea48d173cc12fa28ecabc3b837ea3cf6f38c6d1136f85cbaaf598984861466 \
+    --hash=sha256:f0fa19c6845758ab08074a0cfa8b7aecb71c999ca73d62883bc25cc018c4e548
+    # via
+    #   fastapi
+    #   gradio
+    #   gradio-client
+    #   huggingface-hub
+    #   pydantic
+    #   pydantic-core
+    #   torch
+    #   typeguard
+    #   typer
+    #   typer-slim
+    #   typing-inspection
+typing-inspection==0.4.2 \
+    --hash=sha256:4ed1cacbdc298c220f1bd249ed5287caa16f34d44ef4e9c3d0cbad5b521545e7 \
+    --hash=sha256:ba561c48a67c5958007083d386c3295464928b01faa735ab8547c5692e87f464
+    # via pydantic
+tzdata==2025.2 \
+    --hash=sha256:1a403fada01ff9221ca8044d701868fa132215d84beb92242d9acd2147f667a8 \
+    --hash=sha256:b60a638fcc0daffadf82fe0f57e53d06bdec2f36c4df66280ae79bce6bd6f2b9
+    # via pandas
+unidecode==1.4.0 \
+    --hash=sha256:c3c7606c27503ad8d501270406e345ddb480a7b5f38827eafe4fa82a137f0021 \
+    --hash=sha256:ce35985008338b676573023acc382d62c264f307c8f7963733405add37ea2b23
+    # via chx-prototype
+urllib3==2.5.0 \
+    --hash=sha256:3fc47733c7e419d4bc3f6b3dc2b4f890bb743906a30d56ba4a5bfa4bbff92760 \
+    --hash=sha256:e6b01673c0fa6a13e374b50871808eb3bf7046c4b125b216f6bf1cc604cff0dc
+    # via requests
+uvicorn==0.38.0 \
+    --hash=sha256:48c0afd214ceb59340075b4a052ea1ee91c16fbc2a9b1469cca0e54566977b02 \
+    --hash=sha256:fd97093bdd120a2609fc0d3afe931d4d4ad688b6e75f0f929fde1bc36fe0e91d
+    # via gradio
+vocos==0.1.0 \
+    --hash=sha256:0ac13eaef68596074301e912d781399b3defa4b4ca60b6bc52c8a4b9209ca235 \
+    --hash=sha256:b488224dbe398ff7d4790a027ad659478b4bc02e465db992c62c12b32ca043d8
+    # via chx-prototype
+websockets==15.0.1 \
+    --hash=sha256:0f3c1e2ab208db911594ae5b4f79addeb3501604a165019dd221c0bdcabe4db8 \
+    --hash=sha256:229cf1d3ca6c1804400b0a9790dc66528e08a6a1feec0d5040e8b9eb14422375 \
+    --hash=sha256:3c714d2fc58b5ca3e285461a4cc0c9a66bd0e24c5da9911e30158286c9b5be7f \
+    --hash=sha256:558d023b3df0bffe50a04e710bc87742de35060580a293c2a984299ed83bc4e4 \
+    --hash=sha256:595b6c3969023ecf9041b2936ac3827e4623bfa3ccf007575f04c5a6aa318c22 \
+    --hash=sha256:5a939de6b7b4e18ca683218320fc67ea886038265fd1ed30173f5ce3f8e85675 \
+    --hash=sha256:746ee8dba912cd6fc889a8147168991d50ed70447bf18bcda7039f7d2e3d9151 \
+    --hash=sha256:756c56e867a90fb00177d530dca4b097dd753cde348448a1012ed6c5131f8b7d \
+    --hash=sha256:82544de02076bafba038ce055ee6412d68da13ab47f0c60cab827346de828dee \
+    --hash=sha256:ba9e56e8ceeeedb2e080147ba85ffcd5cd0711b89576b83784d8605a7df455fa \
+    --hash=sha256:e09473f095a819042ecb2ab9465aee615bd9c2028e4ef7d933600a8401c79561 \
+    --hash=sha256:ee443ef070bb3b6ed74514f5efaa37a252af57c90eb33b956d35c8e9c10a1931 \
+    --hash=sha256:f7a866fbc1e97b5c617ee4116daaa09b722101d4a3c170c787450ba409f9736f
+    # via gradio-client
+wget==3.2 \
+    --hash=sha256:35e630eca2aa50ce998b9b1a127bb26b30dfee573702782aa982f875e3f16061
+    # via chx-prototype
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000000000000000000000000000000000000..5be52db646f7efd00d2d59339f67613e278db62d
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,1950 @@
+version = 1
+revision = 3
+requires-python = ">=3.13"
+
+[[package]]
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+version = "24.1.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/0b/03/a88171e277e8caa88a4c77808c20ebb04ba74cc4681bf1e9416c862de237/aiofiles-24.1.0.tar.gz", hash = "sha256:22a075c9e5a3810f0c2e48f3008c94d68c65d763b9b03857924c99e57355166c", size = 30247, upload-time = "2024-06-24T11:02:03.584Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/a5/45/30bb92d442636f570cb5651bc661f52b610e2eec3f891a5dc3a4c3667db0/aiofiles-24.1.0-py3-none-any.whl", hash = "sha256:b4ec55f4195e3eb5d7abd1bf7e061763e864dd4954231fb8539a0ef8bb8260e5", size = 15896, upload-time = "2024-06-24T11:02:01.529Z" },
+]
+
+[[package]]
+name = "annotated-doc"
+version = "0.0.3"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/d7/a6/dc46877b911e40c00d395771ea710d5e77b6de7bacd5fdcd78d70cc5a48f/annotated_doc-0.0.3.tar.gz", hash = "sha256:e18370014c70187422c33e945053ff4c286f453a984eba84d0dbfa0c935adeda", size = 5535, upload-time = "2025-10-24T14:57:10.718Z" }
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+]
+
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89", size = 16081, upload-time = "2024-05-20T21:33:25.928Z" }
+wheels = [
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+]
+
+[[package]]
+name = "anyio"
+version = "4.11.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "idna" },
+    { name = "sniffio" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/c6/78/7d432127c41b50bccba979505f272c16cbcadcc33645d5fa3a738110ae75/anyio-4.11.0.tar.gz", hash = "sha256:82a8d0b81e318cc5ce71a5f1f8b5c4e63619620b63141ef8c995fa0db95a57c4", size = 219094, upload-time = "2025-09-23T09:19:12.58Z" }
+wheels = [
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+]
+
+[[package]]
+name = "audioop-lts"
+version = "0.2.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/38/53/946db57842a50b2da2e0c1e34bd37f36f5aadba1a929a3971c5d7841dbca/audioop_lts-0.2.2.tar.gz", hash = "sha256:64d0c62d88e67b98a1a5e71987b7aa7b5bcffc7dcee65b635823dbdd0a8dbbd0", size = 30686, upload-time = "2025-08-05T16:43:17.409Z" }
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