# coding=utf-8
# Copyright 2021 Artem Ploujnikov


# Lint as: python3
import json

import datasets

_DESCRIPTION = """\
This is a public domain speech dataset consisting of 13,100 short audio
clips of a single speaker reading passages from 7 non-fiction books. A
transcription is provided for each clip. Clips vary in length from 1 to 10
seconds and have a total length of approximately 24 hours.
"""

_BASE_URL = "https://huggingface.co/datasets/flexthink/librig2p-nostress-space/resolve/main"

_HOMEPAGE_URL = "https://huggingface.co/datasets/flexthink/ljspeech"

_PHONEMES = [
    "AA",
    "AE",
    "AH",
    "AO",
    "AW",
    "AY",
    "B",
    "CH",
    "D",
    "DH",
    "EH",
    "ER",
    "EY",
    "F",
    "G",
    "HH",
    "IH",
    "IY",
    "JH",
    "K",
    "L",
    "M",
    "N",
    "NG",
    "OW",
    "OY",
    "P",
    "R",
    "S",
    "SH",
    "T",
    "TH",
    "UH",
    "UW",
    "V",
    "W",
    "Y",
    "Z",
    "ZH",
    " "
]
_SPLITS = ["train", "valid", "test"]

class LJSpeech(datasets.GeneratorBasedBuilder):
    def __init__(self, base_url=None, splits=None, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.base_url = base_url or _BASE_URL
        self.splits = splits or _SPLITS

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "char": datasets.Value("string"),
                    "phn_raw": datasets.Sequence(datasets.Value("string")),
                    "phn": datasets.Sequence(datasets.ClassLabel(names=_PHONEMES)),
                    "wav": datasets.Value("string"),
                },
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE_URL,
        )

    def _get_url(self, split):
        return f'{self.base_url}/ljspeech_{split}.json'

    def _split_generator(self, dl_manager, split):
        url = self._get_url(split)
        path = dl_manager.download_and_extract(url)
        return datasets.SplitGenerator(
            name=split,
            gen_kwargs={"datapath": path, "datatype": split},
        )

    def _split_generators(self, dl_manager):
        return [
            self._split_generator(dl_manager, split)
            for split in self.splits
        ]

    def _generate_examples(self, datapath, datatype):
        with open(datapath, encoding="utf-8") as f:
            data = json.load(f)

        for item_id, item in data.items():
            resp = {
                "char": item["char"],
                "phn": item["phn"],
                "phn_raw": item["phn"],
                "wav": item["wav"]
            }
            yield item_id, resp