afrispeech-200.py

# coding=utf-8
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#     http://www.apache.org/licenses/LICENSE-2.0
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""" AfriSpeech-200 Dataset"""

# Adapted from
# https://huggingface.co/datasets/vivos/blob/main/vivos.py
# https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/blob/main/common_voice_11_0.py
# https://huggingface.co/datasets/PolyAI/minds14/blob/main/minds14.py
# https://huggingface.co/docs/datasets/share#clone-the-repository

import csv
import os
import json

import datasets
from datasets.utils.py_utils import size_str
from tqdm import tqdm

from .accent_stats import ACCENT_STATS

_CITATION = """ TBD """

_DESCRIPTION = """\
AFRISPEECH-200 is a 200hr Pan-African speech corpus for clinical and general domain English accented ASR; 
a dataset with 120 African accents from 13 countries and 2,463 unique African speakers. 
Our goal is to raise awareness for and advance Pan-African English ASR research, 
especially for the clinical domain. 
"""

ACCENT_MAPPER = {
    'akan (fante)': 'akan-fante',
    'igbo and yoruba': 'igbo-and-yoruba',
    'ijaw(nembe)': 'ijaw-nembe',
    'luganda and kiswahili': 'luganda-and-kiswahili',
    'luo, swahili': 'luo-swahili',
    'nasarawa eggon': 'nasarawa-eggon',
    'south african english': 'south-african-english',
    'southern sotho': 'southern-sotho', 
    'swahili ,luganda ,arabic': 'swahili-luganda-arabic',
    'venda and xitsonga': 'venda-and-xitsonga',
    'yala mbembe': 'yala-mbembe',
    'yoruba, hausa': 'yoruba-hausa'
}
ACCENT_MAPPER_REVERSED = {v:k for k,v in ACCENT_MAPPER.items()}

_ALL_CONFIGS = [
       'yoruba', 'igbo', 'swahili', 'ijaw', 'xhosa', 'twi', 'luhya',
       'igala', 'urhobo', 'hausa', 'kiswahili', 'zulu', 'isizulu',
       'venda and xitsonga', 'borana', 'afrikaans', 'setswana', 'idoma',
       'izon', 'chichewa', 'ebira', 'tshivenda', 'isixhosa',
       'kinyarwanda', 'tswana', 'luganda', 'luo', 'venda', 'dholuo',
       'akan (fante)', 'sepedi', 'kikuyu', 'isindebele',
       'luganda and kiswahili', 'akan', 'sotho', 'south african english',
       'sesotho', 'swahili ,luganda ,arabic', 'shona', 'damara',
       'southern sotho', 'luo, swahili', 'ateso', 'meru', 'siswati',
       'portuguese', 'esan', 'nasarawa eggon', 'ibibio', 'isoko',
       'pidgin', 'alago', 'nembe', 'ngas', 'kagoma', 'ikwere', 'fulani',
       'bette', 'efik', 'edo', 'hausa/fulani', 'bekwarra', 'epie',
       'afemai', 'benin', 'nupe', 'tiv', 'okrika', 'etsako', 'ogoni',
       'kubi', 'gbagyi', 'brass', 'oklo', 'ekene', 'ika', 'berom', 'jaba',
       'itsekiri', 'ukwuani', 'yala mbembe', 'afo', 'english', 'ebiobo',
       'igbo and yoruba', 'okirika', 'kalabari', 'ijaw(nembe)', 'anaang',
       'eggon', 'bini', 'yoruba, hausa', 'ekpeye', 'bajju', 'kanuri',
       'delta', 'khana', 'ogbia', 'mada', 'mwaghavul', 'angas', 'ikulu',
       'eleme', 'igarra', 'etche', 'agatu', 'bassa', 'jukun', 'urobo',
       'ibani', 'obolo', 'idah', 'eket', 'nyandang', 'estako', 'ishan',
       'bassa-nge/nupe', 'bagi', 'gerawa'
    ]

_HOMEPAGE = "https://github.com/intron-innovation/AfriSpeech-Dataset-Paper"

_LICENSE = "http://creativecommons.org/licenses/by-nc-sa/4.0/"

# _BASE_URL = "https://huggingface.co/datasets/tobiolatunji/afrispeech-200/main/"

_AUDIO_URL = "audio/{accent}/{split}/{split}_{accent}_{shard_idx}.tar.gz"
_AUDIO_URL_ALL = "audio/{split}/{split}_{shard_idx}.tar.gz"

_TRANSCRIPT_URL = "transcripts/{accent}/{split}.csv"
_TRANSCRIPT_URL_ALL = "transcripts/{split}.csv"

_N_SHARDS_URL = "accents.json"

_SHARDS = {
    'train': 35,
    'dev': 2,
    'test': 4
}

    
class AfriSpeechConfig(datasets.BuilderConfig):
    """BuilderConfig for afrispeech"""

    def __init__(
        self, accent, **kwargs
    ):
        self.name = accent
        self.homepage = _HOMEPAGE
        self.num_clips = kwargs.pop("num_clips", None)
        self.num_speakers = kwargs.pop("num_speakers", None)
        self.duration_secs = kwargs.pop("duration", None)
        description = (
            f"AfriSpeech dataset in {accent} accent(s) with {self.num_clips} clips "
            f"{self.num_speakers} speakers and {self.duration_secs} seconds"  
        )
        super(AfriSpeechConfig, self).__init__(
            name=self.name,
            version=datasets.Version("1.0.0", ""),
            description=description,
            **kwargs,
        )


def _build_config(accent):
    return AfriSpeechConfig(
        accent=accent,
        num_clips=ACCENT_STATS[accent]["num_clips"] if ACCENT_STATS[accent]["num_clips"] else None,
        num_speakers=ACCENT_STATS[accent]["num_speakers"] if ACCENT_STATS[accent]["num_speakers"] else None,
        duration=round(ACCENT_STATS[accent]["duration"], 2) if ACCENT_STATS[accent]["duration"] else None,
    )

class AfriSpeech(datasets.GeneratorBasedBuilder):
    DEFAULT_WRITER_BATCH_SIZE = 1000
    VERSION = datasets.Version("1.0.0")
    BUILDER_CONFIGS = [_build_config(name) for name in ACCENT_STATS.keys()]

    def _info(self):
        description = _DESCRIPTION
        features = datasets.Features(
            {
                "speaker_id": datasets.Value("string"),
                "path": datasets.Value("string"),
                "audio_id": datasets.Value("string"),
                "audio": datasets.features.Audio(sampling_rate=44_100),
                "transcript": datasets.Value("string"),
                "age_group": datasets.Value("string"),
                "gender": datasets.Value("string"),
                "accent": datasets.Value("string"),
                "domain": datasets.Value("string"),
                "country": datasets.Value("string"),
                "duration": datasets.Value("float"),
            }
        )

        return datasets.DatasetInfo(
            description=description,
            features=features,
            supervised_keys=None,
            homepage=_HOMEPAGE,
            license=_LICENSE,
            citation=_CITATION,
            version=self.VERSION,
        )

    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
        
        accent = self.config.name
        n_shards = _SHARDS

        audio_urls = {}
        splits = ("train", "dev")  # , "test"
        for split in splits:
            if self.config.name == 'all':
                audio_urls[split] = []
                for accent in ACCENT_STATS:
                    if accent == "all" or split not in ACCENT_STATS[accent]:
                        continue
                    for i in range(ACCENT_STATS[accent][split]['shards']):
                        audio_urls[split].append(_AUDIO_URL.format(accent=accent, split=split, shard_idx=i))
            elif split in ACCENT_STATS[accent]:
                audio_urls[split] = [
                    _AUDIO_URL.format(accent=accent, split=split, 
                                      shard_idx=i) for i in range(ACCENT_STATS[accent][split]['shards'])
                ]
        archive_paths = dl_manager.download(audio_urls)
        local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}
        
        accent = self.config.name
        
        if accent == 'all':
            meta_urls = {split: _TRANSCRIPT_URL_ALL.format(split=split) for split in splits}
        else:
            meta_urls = {split: _TRANSCRIPT_URL.format(accent=accent, split=split) 
                         for split in splits if split in ACCENT_STATS[accent]}
        
        meta_paths = dl_manager.download_and_extract(meta_urls)

        split_generators = []
        split_names = {
            "train": datasets.Split.TRAIN,
            "dev": datasets.Split.VALIDATION,
            # "test": datasets.Split.TEST,
        }

        for split in splits:
            if split in ACCENT_STATS[self.config.name]:
                split_generators.append(
                    datasets.SplitGenerator(
                        name=split_names.get(split, split),
                        gen_kwargs={
                            "local_extracted_archive_paths": local_extracted_archive_paths.get(split),
                            "archives": [dl_manager.iter_archive(path) for path in archive_paths.get(split)],
                            "meta_path": meta_paths[split],
                        },
                    ),
                )

        return split_generators

    def _generate_examples(self, local_extracted_archive_paths, archives, meta_path):
        """Yields examples as (key, example) tuples."""
        # This method handles input defined in _split_generators to yield (key, example) tuples
        # from the dataset.
        # The `key` is here for legacy reason (tfds) and is not important in itself.
        data_fields = [key for key in self._info().features.keys() if key not in ["audio", "path"]]
        metadata = {}
        with open(meta_path, "r", encoding="utf-8") as f:
            reader = csv.DictReader(f)
            for row in tqdm(reader, desc="Reading metadata..."):
                accent = ACCENT_MAPPER_REVERSED.get(self.config.name, self.config.name)
                if (row['accent'] == accent) or (accent == 'all'):
                    row["speaker_id"] = row["user_ids"]
                    audio_id = "/".join(row["audio_paths"].split("/")[-2:])
                    # if data is incomplete, fill with empty values
                    metadata[audio_id] = {field: row.get(field, "") for field in data_fields}

        for i, audio_archive in enumerate(archives):
            #for filename, file in tqdm(audio_archive, desc=f"Searching and Extracting audios for config {self.config.name}..."):
            for filename, file in audio_archive:
                # _, filename = os.path.split(filename)
                filename = "/".join(filename.split("/")[-2:])
                if filename in metadata:
                    result = dict(metadata[filename])
                    # set the audio feature and the path to the extracted file
                    path = os.path.join(local_extracted_archive_paths[i], filename) if local_extracted_archive_paths else filename
                    result["audio"] = {"path": path, "bytes": file.read()}
                    result["audio_id"] = filename.replace(".wav", "")
                    # set path to None if the audio file doesn't exist locally (i.e. in streaming mode)
                    result["path"] = path if local_extracted_archive_paths else filename

                    yield path, result