scripts/prepare_wenetspeech4tts.py

# generate audio text map for WenetSpeech4TTS
# evaluate for vocab size

import sys, os
sys.path.append(os.getcwd())

import json
from tqdm import tqdm
from concurrent.futures import ProcessPoolExecutor

import torchaudio
from datasets import Dataset

from 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 data/{dataset_name}_{tokenizer} ...")
    dataset = Dataset.from_dict({"audio_path": audio_path_list, "text": text_list, "duration": duration_list})
    dataset.save_to_disk(f"data/{dataset_name}_{tokenizer}/raw", max_shard_size="2GB")  # arrow format

    with open(f"data/{dataset_name}_{tokenizer}/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"data/{dataset_name}_{tokenizer}/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]
    dataset_paths = [
        "<SOME_PATH>/WenetSpeech4TTS/Basic",
        "<SOME_PATH>/WenetSpeech4TTS/Standard",
        "<SOME_PATH>/WenetSpeech4TTS/Premium",
        ][-dataset_choice:]
    print(f"\nChoose Dataset: {dataset_name}\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