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| 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 | |