afrispeech-200.py

# coding=utf-8
# Copyright 2023 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
""" 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

import csv
import os

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


_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. 
"""

_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/{split}/{split}_{shard_idx}.tar.gz"

_TRANSCRIPT_URL = "transcripts/{split}.csv"

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

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

    def __init__(
        self, name, description, homepage, data_url
    ):
        super(AfriSpeechConfig, self).__init__(
            name=self.name,
            version=datasets.Version("1.0.0", ""),
            description=self.description,
        )
        self.name = name
        self.description = description
        self.homepage = homepage
        self.data_url = data_url


def _build_config(name):
    return AfriSpeechConfig(
        name=name,
        description=_DESCRIPTION,
        homepage=_HOMEPAGE,
        data_url=_AUDIO_URL,
    )

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

    def _info(self):
        description = _DESCRIPTION
        features = datasets.Features(
            {
                "speaker_id": datasets.Value("string"),
                "path": 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
        
        langs = (
            _ALL_CONFIGS
            if self.config.name == "all"
            else [self.config.name]
        )
        
        n_shards = _SHARDS

        audio_urls = {}
        splits = ("train", "dev")  # , "test"
        for split in splits:
            audio_urls[split] = [
                _AUDIO_URL.format(split=split, shard_idx=i) for i in range(n_shards[split])
            ]
        archive_paths = dl_manager.download(audio_urls)
        local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else {}

        meta_urls = {split: _TRANSCRIPT_URL.format(split=split) for split in splits}
        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:
            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 = {}
        print("config:", self.config.name)
        with open(meta_path, "r", encoding="utf-8") as f:
            reader = csv.DictReader(f)
            for row in tqdm(reader, desc="Reading metadata..."):
                if (row['accent'] == self.config.name) or (self.config.name == '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}..."):
                # _, 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()}
                    # 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