common_voice_bn.py

import csv
import os
import urllib


import datasets
from datasets.utils.py_utils import size_str


import datasets
import requests
from datasets.utils.py_utils import size_str
from huggingface_hub import HfApi, HfFolder

# from .languages import LANGUAGES
#Used to get tar.gz file from mozilla website
from .release_stats import STATS



#Hard Links

_HOMEPAGE = "https://commonvoice.mozilla.org/en/datasets"

_LICENSE = "https://creativecommons.org/publicdomain/zero/1.0/"

_API_URL = "https://commonvoice.mozilla.org/api/v1"





class CommonVoiceConfig(datasets.BuilderConfig):
    """BuilderConfig for CommonVoice."""

    def __init__(self, name, version, **kwargs):
        self.language = "bn"  # kwargs.pop("language", None)
        self.release_date = "2022-04-27"  # kwargs.pop("release_date", None)
        self.num_clips = 231120  # kwargs.pop("num_clips", None)
        self.num_speakers = 19863  # kwargs.pop("num_speakers", None)
        self.validated_hr = 56.61  # kwargs.pop("validated_hr", None)
        self.total_hr = 399.47  # kwargs.pop("total_hr", None)
        self.size_bytes = 8262390506  # kwargs.pop("size_bytes", None)
        self.size_human = size_str(self.size_bytes)
        description = (
            f"Common Voice speech to text dataset in {self.language} released on {self.release_date}. "
            f"The dataset comprises {self.validated_hr} hours of validated transcribed speech data "
            f"out of {self.total_hr} hours in total from {self.num_speakers} speakers. "
            f"The dataset contains {self.num_clips} audio clips and has a size of {self.size_human}."
        )
        super(CommonVoiceConfig, self).__init__(
            name=name,
            version=datasets.Version(version),
            description=description,
            **kwargs,
        )


class CommonVoice(datasets.GeneratorBasedBuilder):
    #DEFAULT_CONFIG_NAME = "en"
    DEFAULT_CONFIG_NAME = "bn"
    DEFAULT_WRITER_BATCH_SIZE = 1000

    BUILDER_CONFIGS = [
        CommonVoiceConfig(
            name="bn"#lang,
            version= '9.0.0' #STATS["version"],
            language= "Bengali" #LANGUAGES[lang],
            release_date= "2022-04-27" #STATS["date"],
            num_clips= 231120 #lang_stats["clips"],
            num_speakers= 19863 #lang_stats["users"],
            validated_hr= float(56.61) #float(lang_stats["validHrs"]),
            total_hr= float(399.47) #float(lang_stats["totalHrs"]),
            size_bytes= int(8262390506) #int(lang_stats["size"]),
        )
        #for lang, lang_stats in STATS["locales"].items()
    ]

    def _info(self):
        # total_languages = len(STATS["locales"])
        # total_valid_hours = STATS["totalValidHrs"]
        total_languages = 1 #len(STATS["locales"])
        total_valid_hours = float(399.47) #STATS["totalValidHrs"]
        description = (
            "Common Voice Bangla is bengali AI's initiative to help teach machines how real people speak in Bangla. "
            f"The dataset is for initial training of a general speech recognition model for Bangla."
        )
        features = datasets.Features(
            {
                "client_id": datasets.Value("string"),
                "path": datasets.Value("string"),
                "audio": datasets.features.Audio(sampling_rate=48_000),
                "sentence": datasets.Value("string"),
                "up_votes": datasets.Value("int64"),
                "down_votes": datasets.Value("int64"),
                "age": datasets.Value("string"),
                "gender": datasets.Value("string"),
                "accent": datasets.Value("string"),
                "locale": 'bn',
                "segment": datasets.Value("string"),
            }
        )

        return datasets.DatasetInfo(
            description=description,
            features=features,
            supervised_keys=None,
            # homepage=_HOMEPAGE,
            license=_LICENSE,
            # citation=_CITATION,
            version=self.config.version,            
            #task_templates=[
            #    AutomaticSpeechRecognition(audio_file_path_column="path", transcription_column="sentence")
            #],
        )


    def _get_bundle_url(self, locale, url_template):
        # path = encodeURIComponent(path)
        # path = url_template.replace("{locale}", locale)
        path = url_template
        path = urllib.parse.quote(path.encode("utf-8"), safe="~()*!.'")
        # use_cdn = self.config.size_bytes < 20 * 1024 * 1024 * 1024
        # response = requests.get(f"{_API_URL}/bucket/dataset/{path}/{use_cdn}", timeout=10.0).json()
        response = requests.get(
            f"{_API_URL}/bucket/dataset/{path}", timeout=10.0
        ).json()
        return response["url"]

    def _log_download(self, locale, bundle_version, auth_token):
        if isinstance(auth_token, bool):
            auth_token = HfFolder().get_token()
        whoami = HfApi().whoami(auth_token)
        email = whoami["email"] if "email" in whoami else ""
        payload = {"email": email, "locale": locale, "dataset": bundle_version}
        requests.post(f"{_API_URL}/{locale}/downloaders", json=payload).json()

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        hf_auth_token = dl_manager.download_config.use_auth_token
        if hf_auth_token is None:
            raise ConnectionError(
                "Please set use_auth_token=True or use_auth_token='<TOKEN>' to download this dataset"
            )

        bundle_url_template = STATS["bundleURLTemplate"]
        bundle_version = bundle_url_template.split("/")[0]
        dl_manager.download_config.ignore_url_params = True

        self._log_download(self.config.name, bundle_version, hf_auth_token)
        archive_path = dl_manager.download(
            self._get_bundle_url(self.config.name, bundle_url_template)
        )
        local_extracted_archive = (
            dl_manager.extract(archive_path) if not dl_manager.is_streaming else None
        )

        if self.config.version < datasets.Version("5.0.0"):
            path_to_data = ""
        else:
            path_to_data = "/".join([bundle_version, self.config.name])
        path_to_clips = "/".join([path_to_data, "clips"]) if path_to_data else "clips"

        #we provide our custom csvs with the huggingface repo so,
        path_to_tsvs = "/" + "bengali_ai_tsv" + "/"

        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "local_extracted_archive": local_extracted_archive,
                    "archive_iterator": dl_manager.iter_archive(archive_path),
                    #"metadata_filepath": "/".join([path_to_data, "train.tsv"])
                    # if path_to_data
                    # else "train.tsv",
                    #custom train.tsv
                    "metadata_filepath": "/".join([path_to_tsvs, "train.tsv"]) if path_to_tsvs else "train.tsv",
                    "path_to_clips": path_to_clips,
                },
            ),
                        datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "local_extracted_archive": local_extracted_archive,
                    "archive_iterator": dl_manager.iter_archive(archive_path),
                    "metadata_filepath": "/".join([path_to_data, "test.tsv"]) if path_to_data else "test.tsv",
                    "path_to_clips": path_to_clips,
                    "mode":"test",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "local_extracted_archive": local_extracted_archive,
                    "archive_iterator": dl_manager.iter_archive(archive_path),
                    "metadata_filepath": "/".join([path_to_data, "dev.tsv"]) if path_to_data else "dev.tsv",
                    "path_to_clips": path_to_clips,
                    "mode":"dev",
                },
            ),
            # datasets.SplitGenerator(
            #     name=datasets.Split.TEST,
            #     gen_kwargs={
            #         "local_extracted_archive": local_extracted_archive,
            #         "archive_iterator": dl_manager.iter_archive(archive_path),
            #         #"metadata_filepath": "/".join([path_to_data, "test.tsv"])
            #         # if path_to_data
            #         # else "test.tsv",
            #         #custom test.tsv
            #         "metadata_filepath": "/".join([path_to_tsvs, "test.tsv"]),
            #         "path_to_clips": path_to_clips,
            #     },
            # ),
            # datasets.SplitGenerator(
            #     name=datasets.Split.VALIDATION,
            #     gen_kwargs={
            #         "local_extracted_archive": local_extracted_archive,
            #         "archive_iterator": dl_manager.iter_archive(archive_path),
            #         # "metadata_filepath": "/".join([path_to_data, "dev.tsv"])
            #         # if path_to_data
            #         # else "dev.tsv",
            #         #custom test.tsv
            #         "metadata_filepath": "/".join([path_to_tsvs, "dev.tsv"]),
            #         "path_to_clips": path_to_clips,
            #     },
            # ),
        ]

def _generate_examples(
        self,
        local_extracted_archive,
        archive_iterator,
        metadata_filepath,
        path_to_clips,
    ):
        """Yields examples."""
        data_fields = list(self._info().features.keys())
        metadata = {}
        metadata_found = True
        with open(metadata_filepath, "rb") as file_obj:
            lines = (line.decode("utf-8") for line in file_obj)
        #lines = (line.decode("utf-8") for line in f)
        reader = csv.DictReader(lines, delimiter="\t", quoting=csv.QUOTE_NONE)
        for row in reader:
            # set absolute path for mp3 audio file
            if not row["path"].endswith(".mp3"):
                row["path"] += ".mp3"
            row["path"] = os.path.join(path_to_clips, row["path"])
            # accent -> accents in CV 8.0
            if "accents" in row:
                row["accent"] = row["accents"]
                del row["accents"]
            # if data is incomplete, fill with empty values
            for field in data_fields:
                if field not in row:
                    row[field] = ""
            metadata[row["path"]] = row
        for path, f in archive_iterator:    
            if path.startswith(path_to_clips):
                assert metadata_found, "Found audio clips before the metadata TSV file."
                if not metadata:
                    break
                if path in metadata:
                    result = metadata[path]
                    # set the audio feature and the path to the extracted file
                    path = (
                        os.path.join(local_extracted_archive, path)
                        if local_extracted_archive
                        else path
                    )
                    result["audio"] = {"path": path, "bytes": f.read()}
                    # set path to None if the audio file doesn't exist locally (i.e. in streaming mode)
                    result["path"] = path if local_extracted_archive else None

                    yield path, result

    # def _generate_examples(
    #     self,
    #     local_extracted_archive,
    #     archive_iterator,
    #     metadata_filepath,
    #     path_to_clips,
    # ):
    #     """Yields examples."""
    #     data_fields = list(self._info().features.keys())
    #     metadata = {}
    #     metadata_found = False
    #     for path, f in archive_iterator:
    #         if path == metadata_filepath:
    #             metadata_found = True
    #             lines = (line.decode("utf-8") for line in f)
    #             reader = csv.DictReader(lines, delimiter="\t", quoting=csv.QUOTE_NONE)
    #             for row in reader:
    #                 # set absolute path for mp3 audio file
    #                 if not row["path"].endswith(".mp3"):
    #                     row["path"] += ".mp3"
    #                 row["path"] = os.path.join(path_to_clips, row["path"])
    #                 # accent -> accents in CV 8.0
    #                 if "accents" in row:
    #                     row["accent"] = row["accents"]
    #                     del row["accents"]
    #                 # if data is incomplete, fill with empty values
    #                 for field in data_fields:
    #                     if field not in row:
    #                         row[field] = ""
    #                 metadata[row["path"]] = row
    #         elif path.startswith(path_to_clips):
    #             assert metadata_found, "Found audio clips before the metadata TSV file."
    #             if not metadata:
    #                 break
    #             if path in metadata:
    #                 result = metadata[path]
    #                 # set the audio feature and the path to the extracted file
    #                 path = (
    #                     os.path.join(local_extracted_archive, path)
    #                     if local_extracted_archive
    #                     else path
    #                 )
    #                 result["audio"] = {"path": path, "bytes": f.read()}
    #                 # set path to None if the audio file doesn't exist locally (i.e. in streaming mode)
    #                 result["path"] = path if local_extracted_archive else None

    #                 yield path, result



# 'bn': {'duration': 1438112808, 'reportedSentences': 693, 'buckets': {'dev': 7748, 'invalidated': 5844, 'other': 192522,
# 'reported': 717, 'test': 7748, 'train': 14503, 'validated': 32754}, 'clips': 231120, 'splits': {'accent': {'': 1},
# 'age': {'thirties': 0.02, 'twenties': 0.22, '': 0.72, 'teens': 0.04, 'fourties': 0},
#  'gender': {'male': 0.24, '': 0.72, 'female': 0.04, 'other': 0}}, 'users': 19863, 'size': 8262390506,
#  'checksum': '599a5f7c9e55a297928da390345a19180b279a1f013081e7255a657fc99f98d5', 'avgDurationSecs': 6.222,
#   'validDurationSecs': 203807.316, 'totalHrs': 399.47, 'validHrs': 56.61},