# Copyright (c) 2024 NVIDIA CORPORATION.
#   Licensed under the MIT license.

import os, glob


def get_wav_and_text_filelist(data_root, data_type, subsample=1):
    wav_list = sorted(
        [
            path.replace(data_root, "")[1:]
            for path in glob.glob(os.path.join(data_root, data_type, "**/**/*.wav"))
        ]
    )
    wav_list = wav_list[::subsample]
    txt_filelist = [path.replace(".wav", ".normalized.txt") for path in wav_list]

    txt_list = []
    for txt_file in txt_filelist:
        with open(os.path.join(data_root, txt_file), "r") as f_txt:
            text = f_txt.readline().strip("\n")
            txt_list.append(text)
    wav_list = [path.replace(".wav", "") for path in wav_list]

    return wav_list, txt_list


def write_filelist(output_path, wav_list, txt_list):
    with open(output_path, "w") as f:
        for i in range(len(wav_list)):
            filename = wav_list[i] + "|" + txt_list[i]
            f.write(filename + "\n")


if __name__ == "__main__":

    data_root = "filelists/LibriTTS"

    # Dev and test sets. subsample each sets to get ~100 utterances
    data_type_list = ["dev-clean", "dev-other", "test-clean", "test-other"]
    subsample_list = [50, 50, 50, 50]
    for data_type, subsample in zip(data_type_list, subsample_list):
        print(f"processing {data_type}")
        data_path = os.path.join(data_root, data_type)
        assert os.path.exists(data_path), (
            f"path {data_path} not found. make sure the path is accessible by creating the symbolic link using the following command: "
            f"ln -s /path/to/your/{data_path} {data_path}"
        )
        wav_list, txt_list = get_wav_and_text_filelist(data_root, data_type, subsample)
        write_filelist(os.path.join(data_root, data_type + ".txt"), wav_list, txt_list)

    # Training and seen speaker validation datasets (libritts-full): train-clean-100 + train-clean-360 + train-other-500
    wav_list_train, txt_list_train = [], []
    for data_type in ["train-clean-100", "train-clean-360", "train-other-500"]:
        print(f"processing {data_type}")
        data_path = os.path.join(data_root, data_type)
        assert os.path.exists(data_path), (
            f"path {data_path} not found. make sure the path is accessible by creating the symbolic link using the following command: "
            f"ln -s /path/to/your/{data_path} {data_path}"
        )
        wav_list, txt_list = get_wav_and_text_filelist(data_root, data_type)
        wav_list_train.extend(wav_list)
        txt_list_train.extend(txt_list)

    # Split the training set so that the seen speaker validation set contains ~100 utterances
    subsample_val = 3000
    wav_list_val, txt_list_val = (
        wav_list_train[::subsample_val],
        txt_list_train[::subsample_val],
    )
    del wav_list_train[::subsample_val]
    del txt_list_train[::subsample_val]
    write_filelist(
        os.path.join(data_root, "train-full.txt"), wav_list_train, txt_list_train
    )
    write_filelist(os.path.join(data_root, "val-full.txt"), wav_list_val, txt_list_val)

    print("done")