from os.path import basename, join
from pathlib import Path

import librosa
import numpy as np
import pandas as pd
from datasets import Audio, Dataset, DatasetDict

from leviticus import normalize

MAX_DURATION_IN_SECONDS = 10.0
MIN_DURATION_IN_SECONDS = 1.0
MAX_LEN = 50
MIN_LEN = 5


def duration_filter(item):
    return MIN_DURATION_IN_SECONDS < item < MAX_DURATION_IN_SECONDS


def text_filter(item):
    return MIN_LEN < len([i for i in item.split(" ") if len(i) > 0]) < MAX_LEN


def create_dataset(item):
    dataset = Dataset.from_pandas(item)
    dataset = dataset.cast_column("audio", Audio(sampling_rate=16_000))
    dataset = dataset.filter(text_filter, input_columns=["text"])
    dataset = dataset.filter(duration_filter, input_columns=["duration"])
    return dataset


def main():
    repo_dir = Path(__file__).resolve().parent.parent
    data_dir = join(repo_dir, "data")
    kendex_dir = join(data_dir, "Kendex")
    audio_dir = join(kendex_dir, "wavs")

    metadata = pd.read_csv(join(kendex_dir, "metadata.csv"), delimiter="|", header=None)
    wavs = pd.Series([join(audio_dir, f"{f}.wav") for f in metadata[0]])
    data = {
        "audio": wavs,
        "file": [basename(w) for w in wavs],
        "text": metadata[1],
        "norm": metadata[1].map(lambda x: normalize(x)),
        "duration": [librosa.get_duration(path=w) for w in wavs],
    }

    df = pd.DataFrame(data).sample(frac=1, random_state=666).reset_index(drop=True)

    train, test = np.split(df, [int(0.9 * len(df))])

    train_dataset = create_dataset(train)
    test_dataset = create_dataset(test)

    full_dataset = DatasetDict({"train": train_dataset, "test": test_dataset})
    full_dataset.push_to_hub("michaelnetbiz/Kendex")


if __name__ == "__main__":
    main()