pretty_name: AfriSpeech-200
annotations_creators:
- expert-generated
language_creators:
- crowdsourced
- expert-generated
language:
- en
license:
- cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- automatic-speech-recognition
task_ids: []
dataset_info:
features:
- name: user_id
dtype: string
- name: path
dtype: string
- name: audio
dtype:
audio:
sampling_rate: 44100
- name: transcript
dtype: string
splits:
- name: train
num_bytes: 1722002133
num_examples: 58000
- name: dev
num_bytes: 86120227
num_examples: 3231
download_size: 1475540500
dataset_size: 1808122360
extra_gated_prompt: >-
By clicking on “Access repository” below, you also agree to not attempt to
determine the identity of speakers in the Common Voice dataset.
Dataset Card for AfriSpeech-200
Table of Contents
- Dataset Card for AfriSpeech-200
Dataset Description
- Homepage: https://github.com/intron-innovation/AfriSpeech-Dataset-Paper
- Repository: https://github.com/intron-innovation/AfriSpeech-Dataset-Paper
- Paper: AfriSpeech-200: Pan-African accented speech dataset for clinical and general domain ASR
- Leaderboard: [Needs More Information]
- Point of Contact: Intron Innovation
Dataset Summary
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.
How to use
The datasets
library allows you to load and pre-process your dataset in pure Python, at scale. The dataset can be downloaded and prepared in one call to your local drive by using the load_dataset
function.
from datasets import load_dataset
afrispeech = load_dataset("tobiolatunji/afrispeech-200", "all")
The entire dataset is ~120GB and may take about 2hrs to download depending on internet speed/bandwidth. If you have disk space or bandwidth limitations, you can use streaming
mode described below to work with smaller subsets of the data.
In future updates, you will be able to pass a config to the load_dataset
function and download only a subset of the data corresponding to a specific accent of interest. The example provided below is isizulu
.
For example, to download the isizulu config, simply specify the corresponding language config name. The list of supported accents is provided in accent list
section below:
from datasets import load_dataset
afrispeech = load_dataset("tobiolatunji/afrispeech-200", "isizulu", split="train")
Using the datasets library, you can also stream the dataset on-the-fly by adding a streaming=True
argument to the load_dataset
function call. Loading a dataset in streaming mode loads individual samples of the dataset at a time, rather than downloading the entire dataset to disk.
from datasets import load_dataset
afrispeech = load_dataset("tobiolatunji/afrispeech-200", "isizulu", split="train", streaming=True)
print(next(iter(afrispeech)))
print(list(afrispeech.take(5)))
Local
from datasets import load_dataset
from torch.utils.data.sampler import BatchSampler, RandomSampler
afrispeech = load_dataset("tobiolatunji/afrispeech-200", "isizulu", split="train")
batch_sampler = BatchSampler(RandomSampler(afrispeech), batch_size=32, drop_last=False)
dataloader = DataLoader(afrispeech, batch_sampler=batch_sampler)
Streaming
from datasets import load_dataset
from torch.utils.data import DataLoader
afrispeech = load_dataset("tobiolatunji/afrispeech-200", "isizulu", split="train", streaming=True)
dataloader = DataLoader(afrispeech, batch_size=32)
Fine-tuning Colab tutorial
To walk through a complete colab tutorial that finetunes a wav2vec2 model on the afrispeech-200 dataset with transformers
, take a look at this colab notebook afrispeech/wav2vec2-colab-tutorial.
Supported Tasks and Leaderboards
- Automatic Speech Recognition
- Speech Synthesis (Text-to-Speech)
Languages
English (Accented)
Dataset Structure
Data Instances
A typical data point comprises the path to the audio file, called path
and its transcription, called transcript
. Some additional information about the speaker is provided.
{
'speaker_id': 'b545a4ca235a7b72688a1c0b3eb6bde6',
'path': 'aad9bd69-7ca0-4db1-b650-1eeea17a0153/5dcb6ee086e392376cd3b7131a250397.wav',
'audio_id': 'aad9bd69-7ca0-4db1-b650-1eeea17a0153/5dcb6ee086e392376cd3b7131a250397',
'audio': {
'path': 'aad9bd69-7ca0-4db1-b650-1eeea17a0153/5dcb6ee086e392376cd3b7131a250397.wav',
'array': array([0.00018311, 0.00061035, 0.00012207, ..., 0.00192261, 0.00195312, 0.00216675]),
'sampling_rate': 44100},
'transcript': 'His mother is in her 50 s and has hypertension .',
'age_group': '26-40',
'gender': 'Male',
'accent': 'yoruba',
'domain': 'clinical',
'country': 'US',
'duration': 3.241995464852608
}
Data Fields
speaker_id: An id for which speaker (voice) made the recording
path: The path to the audio file
audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column:
dataset[0]["audio"]
the audio file is automatically decoded and resampled todataset.features["audio"].sampling_rate
. Decoding and resampling of a large number of audio files might take a significant amount of time. Thus it is important to first query the sample index before the"audio"
column, i.e.dataset[0]["audio"]
should always be preferred overdataset["audio"][0]
.transcript: The sentence the user was prompted to speak
Data Splits
The speech material has been subdivided into portions for train, dev, and test.
Speech was recorded in a quiet environment with high quality microphone, speakers were asked to read one sentence at a time.
- Total Number of Unique Speakers: 2,463
- Female/Male/Other Ratio: 57.11/42.41/0.48
- Data was first split on speakers. Speakers in Train/Dev/Test do not cross partitions
Train | Dev | Test | |
---|---|---|---|
# Speakers | 1466 | 247 | 750 |
# Seconds | 624228.83 | 31447.09 | 67559.10 |
# Hours | 173.4 | 8.74 | 18.77 |
# Accents | 71 | 45 | 108 |
Avg secs/speaker | 425.81 | 127.32 | 90.08 |
Avg num clips/speaker | 39.56 | 13.08 | 8.46 |
Avg num speakers/accent | 20.65 | 5.49 | 6.94 |
Avg secs/accent | 8791.96 | 698.82 | 625.55 |
# clips general domain | 21682 | 1407 | 2723 |
# clips clinical domain | 36318 | 1824 | 3623 |
Dataset Creation
Curation Rationale
Africa has a very low doctor-to-patient ratio. At very busy clinics, doctors could see 30+ patients per day-- a heavy patient burden compared with developed countries-- but productivity tools such as clinical automatic speech recognition (ASR) are lacking for these overworked clinicians. However, clinical ASR is mature, even ubiquitous, in developed nations, and clinician-reported performance of commercial clinical ASR systems is generally satisfactory. Furthermore, the recent performance of general domain ASR is approaching human accuracy. However, several gaps exist. Several publications have highlighted racial bias with speech-to-text algorithms and performance on minority accents lags significantly. To our knowledge, there is no publicly available research or benchmark on accented African clinical ASR, and speech data is non-existent for the majority of African accents. We release AfriSpeech, 200hrs of Pan-African speech, 67,577 clips from 2,463 unique speakers, across 120 indigenous accents from 13 countries for clinical and general domain ASR, a benchmark test set, with publicly available pre-trained models with SOTA performance on the AfriSpeech benchmark.
Source Data
Country Stats
Country | Clips | Speakers | Duration (seconds) | Duration (hrs) |
---|---|---|---|---|
NG | 45875 | 1979 | 512646.88 | 142.40 |
KE | 8304 | 137 | 75195.43 | 20.89 |
ZA | 7870 | 223 | 81688.11 | 22.69 |
GH | 2018 | 37 | 18581.13 | 5.16 |
BW | 1391 | 38 | 14249.01 | 3.96 |
UG | 1092 | 26 | 10420.42 | 2.89 |
RW | 469 | 9 | 5300.99 | 1.47 |
US | 219 | 5 | 1900.98 | 0.53 |
TR | 66 | 1 | 664.01 | 0.18 |
ZW | 63 | 3 | 635.11 | 0.18 |
MW | 60 | 1 | 554.61 | 0.15 |
TZ | 51 | 2 | 645.51 | 0.18 |
LS | 7 | 1 | 78.40 | 0.02 |
Accent Stats
Accent | Clips | Speakers | Duration (s) | Country | Splits |
---|---|---|---|---|---|
yoruba | 15407 | 683 | 161587.55 | US,NG | train,test,dev |
igbo | 8677 | 374 | 93035.79 | US,NG,ZA | train,test,dev |
swahili | 6320 | 119 | 55932.82 | KE,TZ,ZA,UG | train,test,dev |
hausa | 5765 | 248 | 70878.67 | NG | train,test,dev |
ijaw | 2499 | 105 | 33178.9 | NG | train,test,dev |
afrikaans | 2048 | 33 | 20586.49 | ZA | train,test,dev |
idoma | 1877 | 72 | 20463.6 | NG | train,test,dev |
zulu | 1794 | 52 | 18216.97 | ZA,TR,LS | dev,train,test |
setswana | 1588 | 39 | 16553.22 | BW,ZA | dev,test,train |
twi | 1566 | 22 | 14340.12 | GH | test,train,dev |
isizulu | 1048 | 48 | 10376.09 | ZA | test,train,dev |
igala | 919 | 31 | 9854.72 | NG | train,test |
izon | 838 | 47 | 9602.53 | NG | train,dev,test |
kiswahili | 827 | 6 | 8988.26 | KE | train,test |
ebira | 757 | 42 | 7752.94 | NG | train,test,dev |
luganda | 722 | 22 | 6768.19 | UG,BW,KE | test,dev,train |
urhobo | 646 | 32 | 6685.12 | NG | train,dev,test |
nembe | 578 | 16 | 6644.72 | NG | train,test,dev |
ibibio | 570 | 39 | 6489.29 | NG | train,test,dev |
pidgin | 514 | 20 | 5871.57 | NG | test,train,dev |
luhya | 508 | 4 | 4497.02 | KE | train,test |
kinyarwanda | 469 | 9 | 5300.99 | RW | train,test,dev |
xhosa | 392 | 12 | 4604.84 | ZA | train,dev,test |
tswana | 387 | 18 | 4148.58 | ZA,BW | train,test,dev |
esan | 380 | 13 | 4162.63 | NG | train,test,dev |
alago | 363 | 8 | 3902.09 | NG | train,test |
tshivenda | 353 | 5 | 3264.77 | ZA | test,train |
fulani | 312 | 18 | 5084.32 | NG | test,train |
isoko | 298 | 16 | 4236.88 | NG | train,test,dev |
akan (fante) | 295 | 9 | 2848.54 | GH | train,dev,test |
ikwere | 293 | 14 | 3480.43 | NG | test,train,dev |
sepedi | 275 | 10 | 2751.68 | ZA | dev,test,train |
efik | 269 | 11 | 2559.32 | NG | test,train,dev |
edo | 237 | 12 | 1842.32 | NG | train,test,dev |
luo | 234 | 4 | 2052.25 | UG,KE | test,train,dev |
kikuyu | 229 | 4 | 1949.62 | KE | train,test,dev |
bekwarra | 218 | 3 | 2000.46 | NG | train,test |
isixhosa | 210 | 9 | 2100.28 | ZA | train,dev,test |
hausa/fulani | 202 | 3 | 2213.53 | NG | test,train |
epie | 202 | 6 | 2320.21 | NG | train,test |
isindebele | 198 | 2 | 1759.49 | ZA | train,test |
venda and xitsonga | 188 | 2 | 2603.75 | ZA | train,test |
sotho | 182 | 4 | 2082.21 | ZA | dev,test,train |
akan | 157 | 6 | 1392.47 | GH | test,train |
nupe | 156 | 9 | 1608.24 | NG | dev,train,test |
anaang | 153 | 8 | 1532.56 | NG | test,dev |
english | 151 | 11 | 2445.98 | NG | dev,test |
afemai | 142 | 2 | 1877.04 | NG | train,test |
shona | 138 | 8 | 1419.98 | ZA,ZW | test,train,dev |
eggon | 137 | 5 | 1833.77 | NG | test |
luganda and kiswahili | 134 | 1 | 1356.93 | UG | train |
ukwuani | 133 | 7 | 1269.02 | NG | test |
sesotho | 132 | 10 | 1397.16 | ZA | train,dev,test |
benin | 124 | 4 | 1457.48 | NG | train,test |
kagoma | 123 | 1 | 1781.04 | NG | train |
nasarawa eggon | 120 | 1 | 1039.99 | NG | train |
tiv | 120 | 14 | 1084.52 | NG | train,test,dev |
south african english | 119 | 2 | 1643.82 | ZA | train,test |
borana | 112 | 1 | 1090.71 | KE | train |
swahili ,luganda ,arabic | 109 | 1 | 929.46 | UG | train |
ogoni | 109 | 4 | 1629.7 | NG | train,test |
mada | 109 | 2 | 1786.26 | NG | test |
bette | 106 | 4 | 930.16 | NG | train,test |
berom | 105 | 4 | 1272.99 | NG | dev,test |
bini | 104 | 4 | 1499.75 | NG | test |
ngas | 102 | 3 | 1234.16 | NG | train,test |
etsako | 101 | 4 | 1074.53 | NG | train,test |
okrika | 100 | 3 | 1887.47 | NG | train,test |
venda | 99 | 2 | 938.14 | ZA | train,test |
siswati | 96 | 5 | 1367.45 | ZA | dev,train,test |
damara | 92 | 1 | 674.43 | NG | train |
yoruba, hausa | 89 | 5 | 928.98 | NG | test |
southern sotho | 89 | 1 | 889.73 | ZA | train |
kanuri | 86 | 7 | 1936.78 | NG | test,dev |
itsekiri | 82 | 3 | 778.47 | NG | test,dev |
ekpeye | 80 | 2 | 922.88 | NG | test |
mwaghavul | 78 | 2 | 738.02 | NG | test |
bajju | 72 | 2 | 758.16 | NG | test |
luo, swahili | 71 | 1 | 616.57 | KE | train |
dholuo | 70 | 1 | 669.07 | KE | train |
ekene | 68 | 1 | 839.31 | NG | test |
jaba | 65 | 2 | 540.66 | NG | test |
ika | 65 | 4 | 576.56 | NG | test,dev |
angas | 65 | 1 | 589.99 | NG | test |
ateso | 63 | 1 | 624.28 | UG | train |
brass | 62 | 2 | 900.04 | NG | test |
ikulu | 61 | 1 | 313.2 | NG | test |
eleme | 60 | 2 | 1207.92 | NG | test |
chichewa | 60 | 1 | 554.61 | MW | train |
oklo | 58 | 1 | 871.37 | NG | test |
meru | 58 | 2 | 865.07 | KE | train,test |
agatu | 55 | 1 | 369.11 | NG | test |
okirika | 54 | 1 | 792.65 | NG | test |
igarra | 54 | 1 | 562.12 | NG | test |
ijaw(nembe) | 54 | 2 | 537.56 | NG | test |
khana | 51 | 2 | 497.42 | NG | test |
ogbia | 51 | 4 | 461.15 | NG | test,dev |
gbagyi | 51 | 4 | 693.43 | NG | test |
portuguese | 50 | 1 | 525.02 | ZA | train |
delta | 49 | 2 | 425.76 | NG | test |
bassa | 49 | 1 | 646.13 | NG | test |
etche | 49 | 1 | 637.48 | NG | test |
kubi | 46 | 1 | 495.21 | NG | test |
jukun | 44 | 2 | 362.12 | NG | test |
igbo and yoruba | 43 | 2 | 466.98 | NG | test |
urobo | 43 | 3 | 573.14 | NG | test |
kalabari | 42 | 5 | 305.49 | NG | test |
ibani | 42 | 1 | 322.34 | NG | test |
obolo | 37 | 1 | 204.79 | NG | test |
idah | 34 | 1 | 533.5 | NG | test |
bassa-nge/nupe | 31 | 3 | 267.42 | NG | test,dev |
yala mbembe | 29 | 1 | 237.27 | NG | test |
eket | 28 | 1 | 238.85 | NG | test |
afo | 26 | 1 | 171.15 | NG | test |
ebiobo | 25 | 1 | 226.27 | NG | test |
nyandang | 25 | 1 | 230.41 | NG | test |
ishan | 23 | 1 | 194.12 | NG | test |
bagi | 20 | 1 | 284.54 | NG | test |
estako | 20 | 1 | 480.78 | NG | test |
gerawa | 13 | 1 | 342.15 | NG | test |
Initial Data Collection and Normalization
[Needs More Information]
Who are the source language producers?
[Needs More Information]
Annotations
Annotation process
[Needs More Information]
Who are the annotators?
[Needs More Information]
Personal and Sensitive Information
The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.
Considerations for Using the Data
Social Impact of Dataset
[More Information Needed]
Discussion of Biases
[More Information Needed]
Other Known Limitations
Dataset provided for research purposes only. Please check dataset license for additional information.
Additional Information
Dataset Curators
The dataset was initially prepared by Intron and refined for public release by CLAIR Lab.
Licensing Information
Public Domain, Creative Commons Attribution NonCommercial ShareAlike v4.0 (CC BY-NC-SA 4.0)
Citation Information
[More Information Needed]
Contributions
Thanks to @tobiolatunji for adding this dataset.