Datasets:

intronhealth
/

afrispeech-dialog

Error code:   DatasetGenerationError
Exception:    TypeError
Message:      Mask must be a pyarrow.Array of type boolean
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1633, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 594, in finalize
                  self.write_examples_on_file()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 453, in write_examples_on_file
                  self.write_batch(batch_examples=batch_examples)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 567, in write_batch
                  self.write_table(pa_table, writer_batch_size)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 582, in write_table
                  pa_table = embed_table_storage(pa_table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2270, in embed_table_storage
                  arrays = [
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2271, in <listcomp>
                  embed_array_storage(table[name], feature) if require_storage_embed(feature) else table[name]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in wrapper
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1795, in <listcomp>
                  return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2140, in embed_array_storage
                  return feature.embed_storage(array)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/features/audio.py", line 276, in embed_storage
                  storage = pa.StructArray.from_arrays([bytes_array, path_array], ["bytes", "path"], mask=bytes_array.is_null())
                File "pyarrow/array.pxi", line 3257, in pyarrow.lib.StructArray.from_arrays
                File "pyarrow/array.pxi", line 3697, in pyarrow.lib.c_mask_inverted_from_obj
              TypeError: Mask must be a pyarrow.Array of type boolean
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1392, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1041, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 924, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1647, in _download_and_prepare
                  super()._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 999, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1485, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1642, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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transcript string	domain string	duration float64	age_group string	accent string	country string
00:00:97 [Speaker 1]: Okay, today's topic is, 00:04:97 00:04:98 [Speaker 2]: Are animals conscious? 00:06:98 00:07:01 [Speaker 1]: It's a very interesting topic, you know, because I feel that animals are conscious because they have feelings. Yes. 00:19:98 00:17:97 [Speaker 2]: they are just like humans 00:18:98 0...	general	127.520998	26-40	Sesotho	ZA
00:01:96 [Speaker 1]: Okay, so today's topic is the church, state and politics. The relationship between the church, the state and politics has been a subject of debate and controversy for centuries. The church as an institute upholds significant influence and power over the state and political decisions. In many socie...	general	373.306009	19-25	Yoruba	NG
00:00:02 [Speaker 1]: Yeah. So, rape, big topic for our society. Big, big topic. So what's your point of view about rape? 00:11:02 00:11:02 [Speaker 2]: I see rape as a very bad and heinous crime. I see it as something that sets the society ablaze to me. 00:23:00 00:23:98 [Speaker 1]: Okay. 00:23:100 00:24:00 [Speak...	general	866.233991	19-25	Yoruba	NG
00:01:97 [Speaker 1]: So in our society today, we have witnessed a rapid increase in the hike in oil prices, in price of oil around the world and in our country today. So what do you think has been the biggest factor? What do you think are the causes that's contributed to the increase in oil prices? 00:29:00 00:29:98 ...	general	473.714014	19-25	Igala	NG
00:00:00 [Speaker 1]: Alright, so our topic for today is technology. 00:06:02 [Speaker 2]: Does it bring people together? Does it bring people closer or does it drive them? Apartheid? What's your take on that? 00:15:03 [Speaker 1]: This is a very tricky one because in my opinion, technology bring people together and ...	general	886.976009	26-40	Sesotho	ZA
Speaker 1: Okay, so Mr Joel Solomon, what brought you here? Speaker 2: Since 4days ago, I’ve been having problem on my…cough problem and fever Speaker 1: Ok ok ok sorry, can you tell me more about it? Speaker 2: It usually happen every night, especially in the night, I can’t able to sleep. Speaker 1: Ok you feel it m...	medical	148.481996	19-25	Hausa	NG
00:01:00 [Speaker 2]: You're welcome. 00:01:02 00:02:01 [Speaker 1]: On today's topic, we'll be discussing about racism. I have several questions to ask about it. Mr Liam, could you kindly tell us what is racism? 00:11:03 00:12:02 [Speaker 2]: Racism, to me, is discrimination or by an individual, community, or insti...	general	311.810998	26-40	Swahili	KE
00:00:00 [Speaker 1]: Okay, what brought you here? 00:05:98 [Speaker 2]: I have a rectal bleeding and discomfort. 00:18:03 [Speaker 1]: How long does the bleeding started? 00:24:02 [Speaker 2]: It started two days ago. 00:31:98 [Speaker 1]: Okay, can you tell me more about this? 00:38:98 [Speaker 2]: The bleeding...	medical	209.908005	19-25	Isoko	NG
00:02:00 [Speaker 1]: So today's topic is who or what defines morality. Morality is a concept that is constantly debated and questioned with varying perspectives on what defines it. Some believe that morality is determined by religious beliefs or cultural norms, while others argue that it is based on personal values an...	general	1,196.053991	41-55	Yoruba	NG
00:00:100 [Speaker 2]: You are welcome. 00:01:100 00:02:04 [Speaker 1]: On today's discussion, we'll be talking about good leadership. And before we dive in, I would like you to tell us what is good leadership? 00:11:04 00:13:99 [Speaker 2]: Good leadership is the ability to guide, inspire and motivate others towards...	general	653.401996	26-40	Swahili	KE
00:02:00 [Speaker 2]: Good afternoon Mr. Ade 00:03:100 00:04:99 [Speaker 1]: Good afternoon doctor 00:06:01 00:07:03 [Speaker 2]: Okay. Please what brought you to the hospital today? 00:11:01 00:13:99 [Speaker 1]: Okay. I've been having pain in my abdomen, lower abdomen area 00:21:99 00:24:98 [Speaker 2]: Okay. Sor...	medical	894.410998	19-25	Isoko	NG
00:01:99 [Speaker 1]: So, Fajilo, nice having you once again. Well, today we're going to be talking about which is more important, creativity or knowledge. So, you know, what is different when you think about creativity and knowledge? What strikes the difference between the two? 00:15:04 00:17:03 [Speaker 2]: I thin...	general	401.01	19-25	Ebira	NG
[Speaker 1]: Ok. And how old are you chidinma? [Speaker 2]: I am 25 years old [Speaker 1]: What tribe are you? [Speaker 2]: I am igbo [Speaker 1]: Your religion please? [Speaker 2]: Christianity [Speaker 1]: Ok. And what did you for a living? Your occupation [Speaker 2]: I am a student [Speaker 1]: Ok. Are you marr...	medical	941.933991	19-25	Hausa	NG
[Speaker 1]: Male or a female? [Speaker 2]: A male [Speaker 1]: How old are you? [Speaker 2]: I am 65 years old [Speaker 1]: Hope you don't mind if i address you as mr emeka [Speaker 2]: I don't mind [Speaker 1]: Ok. Thank you very much. So what brought you to the clinic today? [Speaker 2]: I am having difficulty in ur...	medical	247.107982	19-25	Hausa	NG
00:00:99 [Speaker 1]: All right, so today's topic is child marriage. 00:05:00 00:06:00 [Speaker 2]: Yo, that's a toughie. 00:07:97 00:08:00 [Speaker 1]: That's a tough one. Yeah. So child marriage, it was described as a harmful practice that refers to any formal marriage. Right. Or informal union between a child unde...	general	406.420998	26-40	Sesotho	ZA
00:01:01 [Speaker 1]: Thank you so much for joining in the conversation. The topic I would like us to discuss how the western culture has affected African dressing. You see, Mr Haruna, over the centuries, the western culture has had a significant impact on African dressing. Isn't it so? 00:28:99 00:29:02 [Speaker 2]: ...	general	629.427982	26-40	Unknown	NG
00:02:02 [Speaker 1]: So, What brought you here today? 00:04:99 00:06:100 [Speaker 2]: Doctor, I have been having shortness of breath, and I have been feeling fatigue for the past two weeks. The pain is just so much; you know how bad it is when you are struggling with your breath. Then another thing is fatigue. The fa...	medical	685.324989	19-25	Idoma	NG
00:00:02 [Speaker 1]: Alright thank you very much. 00:01:02 00:01:04 [Speaker 2]: You're welcome. 00:02:02 00:02:02 [Speaker 1]: Sir I have a few questions to ask you, you said your name was what? 00:04:05 00:05:01 [Speaker 2]: Johnny Doubra. 00:06:01 00:06:02 [Speaker 1]: Alright how old are you? 00:07:02 00:07:0...	medical	198.614014	19-25	Isoko	NG
00:01:94 [Speaker 1]: Right, Victor? You're welcome. Today we'll be talking about something that is popular, that is cut across almost all the cultures. Something that a lot of people know about. And many people always look forward to it. Victor, can you guess? 00:21:98 00:22:97 [Speaker 2]: I can't guess. 00:23:00 0...	general	609.401996	19-25	Igbo	NG
00:00:00 [Speaker 1]: Alright welcome to the clinic today. So what brought you to the hospital today? 00:08:98 [Speaker 2]: Fatigue and my skin and eye have been getting yellow for the past two weeks 00:18:99 [Speaker 1]: Alright. This fatigue you were persistently getting tired right? Yes. 00:25:02 [Speaker 2]: Yes...	medical	295.611996	19-25	Hausa	NG
00:00:00 [Speaker 2]: Yes please. You have my consent 00:01:100 [Speaker 1]: How old are you? 00:03:00 [Speaker 2]: I am 35 years 00:05:96 [Speaker 1]: Are you a male or a female? 00:06:01 [Speaker 2]: I am a female 00:07:100 [Speaker 1]: Ok. May i know what brought you to the hospital today? 00:10:100 [Speaker...	medical	204.438005	19-25	Hausa	NG
00:00:00 [Speaker 1]: Alright, Can I confirm your age and gender, please? 00:04:01 [Speaker 2]: I am a female and 35 years old. 00:07:97 [Speaker 1]: Okay. So, what brought you to our hospital today? 00:12:01 [Speaker 2]: I have been experiencing pain and swelling in my left leg, It started about two weeks ago and t...	medical	406.982993	19-25	Isoko	NG
00:00:00 [Speaker 2]: Ok. Doctor i have been experiencing rectal bleeding for the past two weeks and it is gotten worse. Starting off as a little bit of blood in my stool but now there's quite alot of bright red blood mixed in. I have also had change of this in my bowel cavity, going from regular to alternating between...	medical	542.016009	19-25	Hausa	NG
00:00:98 [Speaker 1]: I'm 45 years old. 00:02:00 00:02:97 [Speaker 2]: Male or Female? 00:03:00 00:03:02 [Speaker 1]: Female. 00:03:98 00:04:97 [Speaker 2]: Okay. Please what brings you here today? 00:06:02 00:07:97 [Speaker 1]: Okay, so overtime I notice I urinate more frequent than usual, and I get more thirsty o...	medical	334.227007	19-25	Isoko	NG
00:00:00 [Speaker 1]: So the topic is books. What's your favorite book? 00:05:99 [Speaker 2]: I don't think I have a favorite book because I like a lot of books. What is yours? 00:17:99 [Speaker 1]: My favorite book is I just like any book written by Colin Hoover and Karen Kingsbury and Francis Rivers. 00:25:97 [Spe...	general	900.173991	19-25	Yoruba	NG

End of preview.

AfriSpeech-Dialog v1: A Conversational Speech Dataset for African Accents

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Overview and Purpose

AfriSpeech-Dialog is a pan-African conversational speech dataset with 6 hours of recorded dialogue, designed to support speech recognition (ASR) and speaker diarization applications. Collected from diverse accents across Nigeria, Kenya, and South Africa, the dataset offers valuable insights into the varied linguistic and phonetic characteristics found in African-accented English. This release includes 50 conversations across both medical and general topics.

Dataset Statistics

	Medical	General
Counts	20	29
Timestamped Counts	9	21
Avg. Num. of Turns	78.6	30.55
Total Duration (hrs)	2.07	4.93
Avg. Word Count	725.3	1356.83
Num. of Countries	1	3
Num. of Accents	6	8
Genders (M, F)	(14,26)	(25,33)

Use Cases

This dataset is tailored for use in:

Automatic Speech Recognition (ASR) fine-tuning
Speaker Diarization training and testing

Dataset Composition

Languages and Accents: The dataset includes 11 accents: Hausa, Isoko, Idoma, Urhobo, Ijaw, Yoruba, Swahili, Sesotho, Igbo, Igala, and Ebira.
Domains: Conversations span two domains—20 medical conversations, simulating doctor-patient interactions, and 30 general-topic conversations.
Participants: The dataset includes both male and female speakers.
Structure of Conversations: Conversations are two-speaker free-form dialogues.

Data Collection and Processing

Collection Method: Conversations were collected remotely across various acoustic environments as stored as .wav files.
Annotation: Each conversation is annotated with speaker labels and timestamps, including start and end times for each speaker’s turn.

Key Columns and Fields

file_name: Path to the audio file.
transcript: Full transcript of the conversation with timestamps.
domain: Indicates the conversation type, either medical or general.
duration: Duration of the audio file, in seconds.
age_group: Age group of the speakers.
accent: Primary accent represented in the conversation.
country: Country of origin for the speakers.

Usage Instructions

Accessing the Dataset: The dataset can be accessed through Hugging Face:

from datasets import load_dataset
afrispeech_dialog = load_dataset("intronhealth/afrispeech-dialog")

Downloads last month: 38

Total file size:

2.22 GB

Collection including intronhealth/afrispeech-dialog

AfriSpeechMultiBench

Collection

A multi-domain collection of African accented benchmark datasets across health, legal, finance, and others domains rich with African named-entities • 6 items • Updated Jul 1, 2025 • 3