import csv
import datasets
import os
import pandas as pd
import requests
import zipfile
import io
import soundfile as sf
from datasets import DatasetInfo, Dataset, ClassLabel, Features, Value
import datasets
_CITATION = """\
@inproceedings{luong-vu-2016-non,
    title = "A non-expert {K}aldi recipe for {V}ietnamese Speech Recognition System",
    author = "Luong, Hieu-Thi  and
      Vu, Hai-Quan",
    booktitle = "Proceedings of the Third International Workshop on Worldwide Language Service Infrastructure and Second Workshop on Open Infrastructures and Analysis Frameworks for Human Language Technologies ({WLSI}/{OIAF}4{HLT}2016)",
    month = dec,
    year = "2016",
    address = "Osaka, Japan",
    publisher = "The COLING 2016 Organizing Committee",
    url = "https://aclanthology.org/W16-5207",
    pages = "51--55",
}
"""
labels = ['abethr1',
 'abhori1',
 'abythr1',
 'afbfly1',
 'afdfly1',
 'afecuc1',
 'affeag1',
 'afgfly1',
 'afghor1',
 'afmdov1',
 'afpfly1',
 'afpkin1',
 'afpwag1',
 'afrgos1',
 'afrgrp1',
 'afrjac1',
 'afrthr1',
 'amesun2',
 'augbuz1',
 'bagwea1',
 'barswa',
 'bawhor2',
 'bawman1',
 'bcbeat1',
 'beasun2',
 'bkctch1',
 'bkfruw1',
 'blacra1',
 'blacuc1',
 'blakit1',
 'blaplo1',
 'blbpuf2',
 'blcapa2',
 'blfbus1',
 'blhgon1',
 'blhher1',
 'blksaw1',
 'blnmou1',
 'blnwea1',
 'bltapa1',
 'bltbar1',
 'bltori1',
 'blwlap1',
 'brcale1',
 'brcsta1',
 'brctch1',
 'brcwea1',
 'brican1',
 'brobab1',
 'broman1',
 'brosun1',
 'brrwhe3',
 'brtcha1',
 'brubru1',
 'brwwar1',
 'bswdov1',
 'btweye2',
 'bubwar2',
 'butapa1',
 'cabgre1',
 'carcha1',
 'carwoo1',
 'categr',
 'ccbeat1',
 'chespa1',
 'chewea1',
 'chibat1',
 'chtapa3',
 'chucis1',
 'cibwar1',
 'cohmar1',
 'colsun2',
 'combul2',
 'combuz1',
 'comsan',
 'crefra2',
 'crheag1',
 'crohor1',
 'darbar1',
 'darter3',
 'didcuc1',
 'dotbar1',
 'dutdov1',
 'easmog1',
 'eaywag1',
 'edcsun3',
 'egygoo',
 'equaka1',
 'eswdov1',
 'eubeat1',
 'fatrav1',
 'fatwid1',
 'fislov1',
 'fotdro5',
 'gabgos2',
 'gargan',
 'gbesta1',
 'gnbcam2',
 'gnhsun1',
 'gobbun1',
 'gobsta5',
 'gobwea1',
 'golher1',
 'grbcam1',
 'grccra1',
 'grecor',
 'greegr',
 'grewoo2',
 'grwpyt1',
 'gryapa1',
 'grywrw1',
 'gybfis1',
 'gycwar3',
 'gyhbus1',
 'gyhkin1',
 'gyhneg1',
 'gyhspa1',
 'gytbar1',
 'hadibi1',
 'hamerk1',
 'hartur1',
 'helgui',
 'hipbab1',
 'hoopoe',
 'huncis1',
 'hunsun2',
 'joygre1',
 'kerspa2',
 'klacuc1',
 'kvbsun1',
 'laudov1',
 'lawgol',
 'lesmaw1',
 'lessts1',
 'libeat1',
 'litegr',
 'litswi1',
 'litwea1',
 'loceag1',
 'lotcor1',
 'lotlap1',
 'luebus1',
 'mabeat1',
 'macshr1',
 'malkin1',
 'marsto1',
 'marsun2',
 'mcptit1',
 'meypar1',
 'moccha1',
 'mouwag1',
 'ndcsun2',
 'nobfly1',
 'norbro1',
 'norcro1',
 'norfis1',
 'norpuf1',
 'nubwoo1',
 'pabspa1',
 'palfly2',
 'palpri1',
 'piecro1',
 'piekin1',
 'pitwhy',
 'purgre2',
 'pygbat1',
 'quailf1',
 'ratcis1',
 'raybar1',
 'rbsrob1',
 'rebfir2',
 'rebhor1',
 'reboxp1',
 'reccor',
 'reccuc1',
 'reedov1',
 'refbar2',
 'refcro1',
 'reftin1',
 'refwar2',
 'rehblu1',
 'rehwea1',
 'reisee2',
 'rerswa1',
 'rewsta1',
 'rindov',
 'rocmar2',
 'rostur1',
 'ruegls1',
 'rufcha2',
 'sacibi2',
 'sccsun2',
 'scrcha1',
 'scthon1',
 'shesta1',
 'sichor1',
 'sincis1',
 'slbgre1',
 'slcbou1',
 'sltnig1',
 'sobfly1',
 'somgre1',
 'somtit4',
 'soucit1',
 'soufis1',
 'spemou2',
 'spepig1',
 'spewea1',
 'spfbar1',
 'spfwea1',
 'spmthr1',
 'spwlap1',
 'squher1',
 'strher',
 'strsee1',
 'stusta1',
 'subbus1',
 'supsta1',
 'tacsun1',
 'tafpri1',
 'tamdov1',
 'thrnig1',
 'trobou1',
 'varsun2',
 'vibsta2',
 'vilwea1',
 'vimwea1',
 'walsta1',
 'wbgbir1',
 'wbrcha2',
 'wbswea1',
 'wfbeat1',
 'whbcan1',
 'whbcou1',
 'whbcro2',
 'whbtit5',
 'whbwea1',
 'whbwhe3',
 'whcpri2',
 'whctur2',
 'wheslf1',
 'whhsaw1',
 'whihel1',
 'whrshr1',
 'witswa1',
 'wlwwar',
 'wookin1',
 'woosan',
 'wtbeat1',
 'yebapa1',
 'yebbar1',
 'yebduc1',
 'yebere1',
 'yebgre1',
 'yebsto1',
 'yeccan1',
 'yefcan',
 'yelbis1',
 'yenspu1',
 'yertin1',
 'yesbar1',
 'yespet1',
 'yetgre1',
 'yewgre1']

_DESCRIPTION = """\
VIVOS is a free Vietnamese speech corpus consisting of 15 hours of recording speech prepared for
Vietnamese Automatic Speech Recognition task.
The corpus was prepared by AILAB, a computer science lab of VNUHCM - University of Science, with Prof. Vu Hai Quan is the head of.
We publish this corpus in hope to attract more scientists to solve Vietnamese speech recognition problems.
"""

_HOMEPAGE = "https://doi.org/10.5281/zenodo.7068130"

_LICENSE = "CC BY-NC-SA 4.0"

_DATA_URL = "https://www.dropbox.com/s/lsix4b7dl7z96xa/my_dataset.zip?dl=1"



class AudioDataset(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.1.0")

    def _info(self):
        return datasets.DatasetInfo(
            # This is the description that will appear on the datasets page.
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    #"path": datasets.Value("string"),
                    "audio": datasets.Audio(sampling_rate=32_000),
                    "label": ClassLabel(names=labels),
                }
            ),
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        archive_path = dl_manager.download_and_extract(_DATA_URL)
        #prompts_paths = dl_manager.download(_PROMPTS_URLS)
        #archive = dl_manager.download()
        #train_dir = "vivos/train"
        #test_dir = "vivos/test"
        audio_path = os.path.join(archive_path, "my_dataset", "train")
        text_path =  os.path.join(archive_path, "my_dataset", "metadata.csv")

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "audio_path": audio_path,
                    "text_paths": text_path,
                },
            )
        ]
        
    def _generate_examples(self, audio_path, text_paths):
        key = 0
        
        with open(text_paths, encoding="utf-8") as csv_file:
            csv_reader = csv.reader(csv_file, delimiter=",", skipinitialspace=True)
            next(csv_reader)
            for row in csv_reader:
                label, secondary_labels, type,latitude,longitude,scientific_name,common_name,author,license,rating,url,file_path = row
                #file_path, transcription, english_transcription, intent_class = row

                file_path = os.path.join(audio_path, *file_path.split("/"))
                yield key, {
                    #"path": file_path,
                    "audio": file_path,
                    "label": label,
                }
                key += 1