Upload f5_tts/train/datasets/prepare_wenetspeech4tts.py with huggingface_hub

f5_tts/train/datasets/prepare_wenetspeech4tts.py

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+# generate audio text map for WenetSpeech4TTS
+# evaluate for vocab size
+
+import os
+import sys
+
+sys.path.append(os.getcwd())
+
+import json
+from concurrent.futures import ProcessPoolExecutor
+from importlib.resources import files
+from tqdm import tqdm
+
+import torchaudio
+from datasets import Dataset
+
+from f5_tts.model.utils import convert_char_to_pinyin
+
+
+def deal_with_sub_path_files(dataset_path, sub_path):
+    print(f"Dealing with: {sub_path}")
+
+    text_dir = os.path.join(dataset_path, sub_path, "txts")
+    audio_dir = os.path.join(dataset_path, sub_path, "wavs")
+    text_files = os.listdir(text_dir)
+
+    audio_paths, texts, durations = [], [], []
+    for text_file in tqdm(text_files):
+        with open(os.path.join(text_dir, text_file), "r", encoding="utf-8") as file:
+            first_line = file.readline().split("\t")
+        audio_nm = first_line[0]
+        audio_path = os.path.join(audio_dir, audio_nm + ".wav")
+        text = first_line[1].strip()
+
+        audio_paths.append(audio_path)
+
+        if tokenizer == "pinyin":
+            texts.extend(convert_char_to_pinyin([text], polyphone=polyphone))
+        elif tokenizer == "char":
+            texts.append(text)
+
+        audio, sample_rate = torchaudio.load(audio_path)
+        durations.append(audio.shape[-1] / sample_rate)
+
+    return audio_paths, texts, durations
+
+
+def main():
+    assert tokenizer in ["pinyin", "char"]
+
+    audio_path_list, text_list, duration_list = [], [], []
+
+    executor = ProcessPoolExecutor(max_workers=max_workers)
+    futures = []
+    for dataset_path in dataset_paths:
+        sub_items = os.listdir(dataset_path)
+        sub_paths = [item for item in sub_items if os.path.isdir(os.path.join(dataset_path, item))]
+        for sub_path in sub_paths:
+            futures.append(executor.submit(deal_with_sub_path_files, dataset_path, sub_path))
+    for future in tqdm(futures, total=len(futures)):
+        audio_paths, texts, durations = future.result()
+        audio_path_list.extend(audio_paths)
+        text_list.extend(texts)
+        duration_list.extend(durations)
+    executor.shutdown()
+
+    if not os.path.exists("data"):
+        os.makedirs("data")
+
+    print(f"\nSaving to {save_dir} ...")
+    dataset = Dataset.from_dict({"audio_path": audio_path_list, "text": text_list, "duration": duration_list})
+    dataset.save_to_disk(f"{save_dir}/raw", max_shard_size="2GB")  # arrow format
+
+    with open(f"{save_dir}/duration.json", "w", encoding="utf-8") as f:
+        json.dump(
+            {"duration": duration_list}, f, ensure_ascii=False
+        )  # dup a json separately saving duration in case for DynamicBatchSampler ease
+
+    print("\nEvaluating vocab size (all characters and symbols / all phonemes) ...")
+    text_vocab_set = set()
+    for text in tqdm(text_list):
+        text_vocab_set.update(list(text))
+
+    # add alphabets and symbols (optional, if plan to ft on de/fr etc.)
+    if tokenizer == "pinyin":
+        text_vocab_set.update([chr(i) for i in range(32, 127)] + [chr(i) for i in range(192, 256)])
+
+    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(text_list)}")
+    print(f"For {dataset_name}, vocab size is: {len(text_vocab_set)}\n")
+
+
+if __name__ == "__main__":
+    max_workers = 32
+
+    tokenizer = "pinyin"  # "pinyin" | "char"
+    polyphone = True
+    dataset_choice = 1  # 1: Premium, 2: Standard, 3: Basic
+
+    dataset_name = (
+        ["WenetSpeech4TTS_Premium", "WenetSpeech4TTS_Standard", "WenetSpeech4TTS_Basic"][dataset_choice - 1]
+        + "_"
+        + tokenizer
+    )
+    dataset_paths = [
+        "<SOME_PATH>/WenetSpeech4TTS/Basic",
+        "<SOME_PATH>/WenetSpeech4TTS/Standard",
+        "<SOME_PATH>/WenetSpeech4TTS/Premium",
+    ][-dataset_choice:]
+    save_dir = str(files("f5_tts").joinpath("../../")) + f"/data/{dataset_name}"
+    print(f"\nChoose Dataset: {dataset_name}, will save to {save_dir}\n")
+
+    main()
+
+    # Results (if adding alphabets with accents and symbols):
+    # WenetSpeech4TTS       Basic     Standard     Premium
+    # samples count       3932473      1941220      407494
+    # pinyin vocab size      1349         1348        1344   (no polyphone)
+    #                           -            -        1459   (polyphone)
+    # char   vocab size      5264         5219        5042
+
+    # vocab size may be slightly different due to jieba tokenizer and pypinyin (e.g. way of polyphoneme)
+    # please be careful if using pretrained model, make sure the vocab.txt is same