f5_tts/train/datasets/prepare_emilia_v2.py

# put in src/f5_tts/train/datasets/prepare_emilia_v2.py
# prepares Emilia dataset with the new format w/ Emilia-YODAS

import json
import os
from concurrent.futures import ProcessPoolExecutor
from importlib.resources import files
from pathlib import Path

from datasets.arrow_writer import ArrowWriter
from tqdm import tqdm

from f5_tts.model.utils import repetition_found

# Define filters for exclusion
out_en = set()
en_filters = ["ا", "い", "て"]


def process_audio_directory(audio_dir):
    sub_result, durations, vocab_set = [], [], set()
    bad_case_en = 0

    for file in audio_dir.iterdir():
        if file.suffix == ".json":
            with open(file, "r") as f:
                obj = json.load(f)
                text = obj["text"]
                if any(f in text for f in en_filters) or repetition_found(
                    text, length=4
                ):
                    bad_case_en += 1
                    continue

                duration = obj["duration"]
                audio_file = file.with_suffix(".mp3")
                if audio_file.exists():
                    sub_result.append(
                        {
                            "audio_path": str(audio_file),
                            "text": text,
                            "duration": duration,
                        }
                    )
                    durations.append(duration)
                    vocab_set.update(list(text))

    return sub_result, durations, vocab_set, bad_case_en


def main():
    assert tokenizer in ["pinyin", "char"]
    result, duration_list, text_vocab_set = [], [], set()
    total_bad_case_en = 0

    executor = ProcessPoolExecutor(max_workers=max_workers)
    futures = []
    dataset_path = Path(dataset_dir)
    for sub_dir in dataset_path.iterdir():
        if sub_dir.is_dir():
            futures.append(executor.submit(process_audio_directory, sub_dir))

    for future in tqdm(futures, total=len(futures)):
        sub_result, durations, vocab_set, bad_case_en = future.result()
        result.extend(sub_result)
        duration_list.extend(durations)
        text_vocab_set.update(vocab_set)
        total_bad_case_en += bad_case_en

    executor.shutdown()

    if not os.path.exists(f"{save_dir}"):
        os.makedirs(f"{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)

    with open(f"{save_dir}/duration.json", "w", encoding="utf-8") as f:
        json.dump({"duration": duration_list}, f, ensure_ascii=False)

    with open(f"{save_dir}/vocab.txt", "w") as f:
        for vocab in sorted(text_vocab_set):
            f.write(vocab + "\n")

    print(f"For {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")
    print(f"Bad en transcription case: {total_bad_case_en}\n")


if __name__ == "__main__":
    max_workers = 32
    tokenizer = "char"
    dataset_dir = "/home/ubuntu/emilia-dataset/Emilia-YODAS/EN"
    dataset_name = f"Emilia_EN_{tokenizer}"
    # save_dir = os.path.expanduser(f"~/F5-TTS/data/{dataset_name}")
    save_dir = str(files("f5_tts").joinpath("../../")) + f"/data/{dataset_name}"

    print(f"Prepare for {dataset_name}, will save to {save_dir}\n")
    main()