diff --git a/pyproject.toml b/pyproject.toml
index c6e8761..4a7a96e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -22,6 +22,7 @@ license = {file = "LICENSE"}
 [project.scripts]
 convert_dataset_to_jsonl = "torch_wae.cli.convert_dataset_to_jsonl:app"
 convert_dataset_to_web = "torch_wae.cli.convert_dataset_to_web:app"
+convert_pair_to_web = "torch_wae.cli.convert_pair_to_web:app"
 copy_dataset = "torch_wae.cli.copy_dataset:app"
 export_to_onnx = "torch_wae.cli.export_to_onnx:app"
 generate_pair = "torch_wae.cli.generate_pair:app"
diff --git a/src/torch_wae/cli/convert_pair_to_web.py b/src/torch_wae/cli/convert_pair_to_web.py
new file mode 100644
index 0000000..0346c53
--- /dev/null
+++ b/src/torch_wae/cli/convert_pair_to_web.py
@@ -0,0 +1,163 @@
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Any
+
+import torch
+import torchaudio
+import typer
+from torchaudio import functional as FA
+
+app = typer.Typer()
+
+
+@app.command()
+def main(
+    n_workers: int = typer.Option(
+        ...,
+        help="the number of workers used to transform dataset",
+    ),
+    root: Path = typer.Option(
+        ...,
+        help="the root path of a directory which contains datasets.",
+    ),
+    annotation: Path = typer.Option(
+        ...,
+        help="the path of a dataset to be encoded.",
+    ),
+    size_shard: int = typer.Option(
+        200,
+        help="the max size of a shard (unit: MB)",
+    ),
+    output: Path = typer.Option(
+        ...,
+        help="the output path of a directory which stores shards of WebDataset,",
+    ),
+) -> None:
+    from torch.utils.data import DataLoader
+    from tqdm import tqdm
+    from webdataset.writer import ShardWriter
+
+    from torch_wae.dataset import JsonLinesDataset, transformed_iter
+
+    dataset = JsonLinesDataset(annotation)
+
+    n = 0
+    for _ in dataset:
+        n += 1
+
+    loader = DataLoader(
+        transformed_iter(
+            dataset,
+            Transform(
+                root=root,
+                resample_rate=16000,
+                durations=1,
+            ),
+        ),
+        batch_size=1,
+        shuffle=False,
+        num_workers=n_workers,
+    )
+
+    output.mkdir(parents=True, exist_ok=True)
+    pattern = str(output / "%04d.tar")
+    max_size = size_shard * 1024**2
+
+    with tqdm(total=n) as progress:
+        with ShardWriter(pattern, maxsize=max_size, verbose=0) as w:
+            for i, (anchor, positive, key, class_id, melspec, ignore) in enumerate(
+                loader
+            ):
+                if n <= i:
+                    break
+
+                ignore = bool(ignore.detach().numpy()[0])
+                if ignore:
+                    continue
+
+                anchor = anchor[0]
+                positive = positive[0]
+                key = key[0]
+                class_id = int(class_id.detach().numpy()[0])
+                melspec = melspec.detach().numpy()[0]
+
+                w.write(
+                    {
+                        "__key__": key,
+                        "npy": melspec,
+                        "json": {
+                            "class_id": class_id,
+                            "anchor": anchor,
+                            "positive": positive,
+                        },
+                    }
+                )
+
+                progress.update(1)
+
+
+class Transform:
+    def __init__(
+        self,
+        root: Path,
+        resample_rate: int,
+        durations: int,
+    ) -> None:
+        from torch_wae.network import Preprocess
+
+        super().__init__()
+
+        self.__root = root
+        self.__resample_rate = resample_rate
+        self.__durations = durations
+        self.__preprocess = Preprocess()
+
+    def __call__(self, example: Any) -> tuple[str, str, str, int, torch.Tensor, bool]:
+        from torch_wae import fs
+
+        root = self.__root
+
+        anchor = Path(example["anchor"])
+        positive = Path(example["positive"])
+
+        path = anchor.parent
+        basename_anchor = fs.basename(anchor)
+        basename_positive = fs.basename(positive)
+
+        key = str(path / f"{basename_anchor}-{basename_positive}")
+        class_id = int(example["class_id"])
+
+        mel_anchor, ignore_anchor = self.convert_to_melspec(root / anchor)
+        mel_positive, ignore_positive = self.convert_to_melspec(root / positive)
+        melspec = torch.stack((mel_anchor, mel_positive))
+
+        ignore = ignore_anchor or ignore_positive
+
+        return str(anchor), str(positive), key, class_id, melspec, ignore
+
+    def convert_to_melspec(self, path: Path) -> tuple[torch.Tensor, bool]:
+        from torch_wae.audio import crop_or_pad_last
+
+        resample_rate = self.__resample_rate
+        durations = self.__durations
+        preprocess = self.__preprocess
+
+        waveform, sample_rate = torchaudio.load(path)
+        waveform = torch.mean(waveform, dim=0).unsqueeze(0)
+
+        if resample_rate != sample_rate:
+            waveform = FA.resample(waveform, sample_rate, resample_rate)
+
+        frames = waveform.shape[-1]
+
+        waveform = crop_or_pad_last(resample_rate, durations, waveform)
+        melspec = preprocess(waveform)[0]
+
+        ignore = frames > durations * resample_rate
+
+        return melspec, ignore
+
+
+if __name__ == "__main__":
+    app()