afrispeech-200 / afrispeech-200.py
tobiolatunji
unlock test set audios
4701e10
# coding=utf-8
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#
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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/resolve/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[self.config.name]:
accent = self.config.name
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