!pip install pysrt""" Babelbox Voice Dataset"""

import os 
import csv
import codecs
import datasets
from typing import List
from pathlib import Path
from tqdm import tqdm

logger = datasets.logging.get_logger(__name__)

_CITATION = """\
@inproceedings{babelboxvoice:2022,
  author = {Andersson, O. and Bjelkenhed, M. and Bielsa, M. et al},
  title = {Babelbox Voice: A Speech Corpus for training Whisper},
  year = 2022
}
"""

_HF_REPO_PATH = "https://huggingface.co/datasets/babelbox/babelbox_voice/"

class BabelboxVoiceConfig(datasets.BuilderConfig):
    """BuilderConfig for BabelboxVoice."""

    def __init__(self, name, version, **kwargs):
        self.name = name
        self.version = version
        self.features = kwargs.pop("features", None)
        self.description = kwargs.pop("description", None)
        self.data_url = kwargs.pop("data_url", None)
        self.nb_data_shards = kwargs.pop("nb_data_shards", None)
        self.metadata_url = kwargs.pop("metadata_url", None)

        description = (
            f"Babelbox Voice speech to text dataset."
        )
        super(BabelboxVoiceConfig, self).__init__(
            name=name,
            version=version,
            **kwargs,
        )


class BabelboxVoice(datasets.GeneratorBasedBuilder):
    
    VERSION = datasets.Version("1.0.0")
    
    BUILDER_CONFIGS = [
        BabelboxVoiceConfig(
                name="nst",
                version=VERSION,
                description="This part of Babel Voice includes data from National Library of Norway",
                features=["path", "audio", "sentence"],
                data_url= _HF_REPO_PATH + "resolve/main/data/nst/nst-data-{:0>3d}.tar.gz",
                nb_data_shards = 42,
                metadata_url= _HF_REPO_PATH + "resolve/main/data/nst/metadata.tar.gz"
            )
    ]
         
    DEFAULT_CONFIG_NAME = "nst"

    def _info(self):
        description = (
            "Babelbox Voice is an initiative to help teach machines how real people speak. "
        )
        if self.config.name == "nst":
            features = datasets.Features(
                {
                    "path": datasets.Value("string"),
                    "audio": datasets.features.Audio(sampling_rate=16_000),
                    "sentence": datasets.Value("string")
                }
            )
        else:
            features = datasets.Features(
                {
                    "path": datasets.Value("string"),
                    "audio": datasets.features.Audio(sampling_rate=16_000),
                    "sentence": datasets.Value("string")
                }
            )
      
        return datasets.DatasetInfo(
            description=description,
            features=features,
            supervised_keys=None,
            version=self.config.version
        )
    
    def get_metadata(self, dl_manager, metadata_url):
        if metadata_url == None: return None
        
        metadata_path = dl_manager.download(metadata_url)
        local_extracted_metadata_path = dl_manager.extract(metadata_path) if not dl_manager.is_streaming else None
        
        metadata_archive = dl_manager.iter_archive(metadata_path)
        metadata = {}
        for path, file in metadata_archive:
            reader = csv.DictReader(codecs.iterdecode(file, 'utf-8'))
            for row in tqdm(reader, desc="Reading metadata..."):
                        filename = row['filename_channel_1']
                        sentence = row['text']
                        metadata[filename] = sentence
                        
        return metadata
        

    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
        
        download_urls = [self.config.data_url.format(i) for i in range(1, self.config.nb_data_shards + 1) ]
            
        archive_paths = dl_manager.download(download_urls)
        local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}
            
        metadata = self.get_metadata(dl_manager, self.config.metadata_url)
                                
        return [
            datasets.SplitGenerator(name=datasets.Split.TRAIN, 
                gen_kwargs={
                    "local_extracted_archive_paths": local_extracted_archive_paths,
                    "archives": [dl_manager.iter_archive(path) for path in archive_paths],
                    "metadata": metadata
                })
        ]
    
    def _generate_examples(self, local_extracted_archive_paths, archives, metadata):
           
            sampling_rate = 16000
                              
            for i, audio_archive in enumerate(archives):
                for path, file in audio_archive:
                    if local_extracted_archive_paths == False:
                        path = os.path.join(local_extracted_archive_paths[i], path) 
                    result = dict()          
                    result["path"] = path
                    result["audio"] = {"path": path, "bytes": file.read()}
                    result["sentence"] = metadata[path] 
                    yield path, result