update prepare libritts script

src/f5_tts/train/README.md

@@ -13,6 +13,9 @@ python src/f5_tts/train/datasets/prepare_emilia.py
 
 # Prepare the Wenetspeech4TTS dataset
 python src/f5_tts/train/datasets/prepare_wenetspeech4tts.py
+
+# Prepare the LibriTTS dataset
+python src/f5_tts/train/datasets/prepare_libritts.py
 ```
 
 ### 2. Create custom dataset with metadata.csv

src/f5_tts/train/datasets/prepare_libritts.py

@@ -0,0 +1,92 @@
+import os
+import sys
+
+sys.path.append(os.getcwd())
+
+import json
+from concurrent.futures import ProcessPoolExecutor
+from importlib.resources import files
+from pathlib import Path
+from tqdm import tqdm
+import soundfile as sf
+from datasets.arrow_writer import ArrowWriter
+
+
+def deal_with_audio_dir(audio_dir):
+    sub_result, durations = [], []
+    vocab_set = set()
+    audio_lists = list(audio_dir.rglob("*.wav"))
+
+    for line in audio_lists:
+        text_path = line.with_suffix(".normalized.txt")
+        text = open(text_path, "r").read().strip()
+        duration = sf.info(line).duration
+        if duration < 0.4 or duration > 30:
+            continue
+        sub_result.append({"audio_path": str(line), "text": text, "duration": duration})
+        durations.append(duration)
+        vocab_set.update(list(text))
+    return sub_result, durations, vocab_set
+
+
+def main():
+    result = []
+    duration_list = []
+    text_vocab_set = set()
+
+    # process raw data
+    executor = ProcessPoolExecutor(max_workers=max_workers)
+    futures = []
+
+    for subset in tqdm(SUB_SET):
+        dataset_path = Path(os.path.join(dataset_dir, subset))
+        [
+            futures.append(executor.submit(deal_with_audio_dir, audio_dir))
+            for audio_dir in dataset_path.iterdir()
+            if audio_dir.is_dir()
+        ]
+    for future in tqdm(futures, total=len(futures)):
+        sub_result, durations, vocab_set = future.result()
+        result.extend(sub_result)
+        duration_list.extend(durations)
+        text_vocab_set.update(vocab_set)
+    executor.shutdown()
+
+    # 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
+    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__":
+    max_workers = 36
+
+    tokenizer = "char"  # "pinyin" | "char"
+
+    SUB_SET = ["train-clean-100", "train-clean-360", "train-other-500"]
+    dataset_dir = "<SOME_PATH>/LibriTTS"
+    dataset_name = f"LibriTTS_{'_'.join(SUB_SET)}_{tokenizer}".replace("train-clean-", "").replace("train-other-", "")
+    save_dir = str(files("f5_tts").joinpath("../../")) + f"/data/{dataset_name}"
+    print(f"\nPrepare for {dataset_name}, will save to {save_dir}\n")
+    main()
+
+    # For LibriTTS_100_360_500_char, sample count: 354218
+    # For LibriTTS_100_360_500_char, vocab size is: 78
+    # For LibriTTS_100_360_500_char, total 554.09 hours