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.
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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

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. 
"""

_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 AfriSpeech(datasets.GeneratorBasedBuilder):
    DEFAULT_WRITER_BATCH_SIZE = 1000
    VERSION = datasets.Version("1.1.0")

    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

        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 = {}
        with open(meta_path, "r", encoding="utf-8") as f:
            reader = csv.DictReader(f)
            for row in tqdm(reader, desc="Reading metadata..."):
                row["speaker_id"] = row["user_ids"]
                audio_id = row["audio_paths"].split("/")[-1]
                # 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 audio_archive:
                _, filename = os.path.split(filename)
                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