diff --git a/src/f5_tts/train/README.md b/src/f5_tts/train/README.md
index a6dfda3..420e2ff 100644
--- a/src/f5_tts/train/README.md
+++ b/src/f5_tts/train/README.md
@@ -16,6 +16,9 @@ python src/f5_tts/train/datasets/prepare_wenetspeech4tts.py
 
 # Prepare the LibriTTS dataset
 python src/f5_tts/train/datasets/prepare_libritts.py
+
+# Prepare the LJSpeech dataset
+python src/f5_tts/train/datasets/prepare_ljspeech.py
 ```
 
 ### 2. Create custom dataset with metadata.csv
diff --git a/src/f5_tts/train/datasets/prepare_ljspeech.py b/src/f5_tts/train/datasets/prepare_ljspeech.py
new file mode 100644
index 0000000..b1df290
--- /dev/null
+++ b/src/f5_tts/train/datasets/prepare_ljspeech.py
@@ -0,0 +1,64 @@
+import os
+import sys
+
+sys.path.append(os.getcwd())
+
+import json
+from importlib.resources import files
+from pathlib import Path
+from tqdm import tqdm
+import soundfile as sf
+from datasets.arrow_writer import ArrowWriter
+
+
+def main():
+    result = []
+    duration_list = []
+    text_vocab_set = set()
+
+    with open(meta_info, "r") as f:
+        lines = f.readlines()
+        for line in tqdm(lines):
+            uttr, text, norm_text = line.split("|")
+            wav_path = Path(dataset_dir) / "wavs" / f"{uttr}.wav"
+            duration = sf.info(wav_path).duration
+            if duration < 0.4 or duration > 30:
+                continue
+            result.append({"audio_path": str(wav_path), "text": norm_text, "duration": duration})
+            duration_list.append(duration)
+            text_vocab_set.update(list(norm_text))
+
+    # save preprocessed dataset to disk
+    if not os.path.exists(f"{save_dir}"):
+        os.makedirs(f"{save_dir}")
+    print(f"\nSaving to {save_dir} ...")
+
+    with ArrowWriter(path=f"{save_dir}/raw.arrow") as writer:
+        for line in tqdm(result, desc="Writing to raw.arrow ..."):
+            writer.write(line)
+
+    # dup a json separately saving duration in case for DynamicBatchSampler ease
+    with open(f"{save_dir}/duration.json", "w", encoding="utf-8") as f:
+        json.dump({"duration": duration_list}, f, ensure_ascii=False)
+
+    # vocab map, i.e. tokenizer
+    # add alphabets and symbols (optional, if plan to ft on de/fr etc.)
+    with open(f"{save_dir}/vocab.txt", "w") as f:
+        for vocab in sorted(text_vocab_set):
+            f.write(vocab + "\n")
+
+    print(f"\nFor {dataset_name}, sample count: {len(result)}")
+    print(f"For {dataset_name}, vocab size is: {len(text_vocab_set)}")
+    print(f"For {dataset_name}, total {sum(duration_list)/3600:.2f} hours")
+
+
+if __name__ == "__main__":
+    tokenizer = "char"  # "pinyin" | "char"
+
+    dataset_dir = "<SOME_PATH>/LJSpeech-1.1"
+    dataset_name = f"LJSpeech_{tokenizer}"
+    meta_info = os.path.join(dataset_dir, "metadata.csv")
+    save_dir = str(files("f5_tts").joinpath("../../")) + f"/data/{dataset_name}"
+    print(f"\nPrepare for {dataset_name}, will save to {save_dir}\n")
+
+    main()