Hugging Face's logo

Datasets:
intronhealth
/
afrispeech-200

Tasks:
Automatic Speech Recognition
Languages:
English
Size:
10K<n<100K
License:
Dataset card Files Community
3
afrispeech-200 / README.md
tobiolatunji
add colab link
697b2fe
preview code
|
raw
history blame
17.3 kB
metadata
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 Description

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.

The entire dataset is ~120GB and may take about 2hrs to download. 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, list of supported accents 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)))

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")
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 sentence. Some additional information about the speaker and the passage which contains the transcription is provided.

{'user_id': 'ABCDXYZ01',
 'path': '/home/admin/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/vivos/train/waves/VIVOSSPK01/VIVOSSPK01_R001.wav',
 'audio': {'path': '/home/admin/.cache/huggingface/datasets/downloads/extracted/b7ded9969e09942ab65313e691e6fc2e12066192ee8527e21d634aca128afbe2/vivos/train/waves/VIVOSSPK01/VIVOSSPK01_R001.wav',
           'array': array([-0.00048828, -0.00018311, -0.00137329, ...,  0.00079346, 0.00091553,  0.00085449], dtype=float32),
           'sampling_rate': 16000},
 'transcirpt': 'The patient took the correct medication'}

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 to dataset.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 over dataset["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.