Yapdo-Mini is a sample of the Yapdo dataset, a conversational speech corpus drawn from 109,804 hours of recordings from 17,008 speakers across 67 languages. The source audio is natively recorded with separate speaker channels; the samples here are presented as combined conversations.

Yapdo Data Highlights


Total audio	109,804 hours
Unique speakers	17,008
Languages	67
Format	48 kHz, 16-bit PCM WAV per speaker
Channel separation	Each speaker on a dedicated, time-aligned track
Speech type	Spontaneous, unscripted, multi-party conversations
Code-switching	Yoruba-English, Hindi-English, Swahili-English ("Sheng"), Tagalog-Cebuano, and more
Mean SNR	~33 dB
Median RMS	-26 dBFS

Top 10 Languages (estimated hours)

Language	Hours	Language	Hours
English	31,660	Tagalog	2,014
Hindi	8,412	Spanish	1,651
Arabic	2,427	Nigerian Pidgin	1,382
Swahili	2,075	Tamil	1,288
Hausa	2,074	Cebuano	848

Note: These are estimated hours based on automated language detection. We are in the process of obtaining human-verified language and accent labels. The total number of languages and hours per language/accent are subject to change.

Hours by City (click to expand)

Self-reported locations from speaker profiles across the full Yapdo dataset. Hours are total approved speaker-hours. '(unspecified)' means the user entered a country name rather than a specific city.

Nigeria — 38,500 hours, ~7,917 speakers

City	Hours	Speakers
Nigeria (unspecified)	24,685.1	4,598
Lagos	5,564.9	1,355
Abuja	1,818.4	380
Port Harcourt	728.0	162
Aba	652.2	115
Kaduna	618.5	162
Ibadan	378.9	110
Enugu	365.2	77
Benin	352.7	50
Kano	266.2	70
Uyo	238.7	70
Benin City	224.5	64
Warri	206.8	51
Ilorin	194.3	44
Minna	194.2	49
Jos	186.0	48
Bauchi	179.0	21
Owerri	165.4	45
Delta	156.5	40
Katsina	156.1	37
Kwara	124.1	32
Abia	123.8	31
Calabar	110.3	49
Abeokuta	105.8	19
Asaba	102.7	12
Ogun	93.6	55
Akure	89.7	20
Oyo	60.4	24
Yenagoa	55.0	12
Anambra	53.6	28
Ekiti	42.3	13
Ondo	40.9	21
Borno	38.3	6
Nasarawa	34.1	5
Osogbo	27.9	12
Maiduguri	26.7	8
Sokoto	18.0	4
Makurdi	15.8	15
Plateau	5.4	3

India — 15,608 hours, ~2,110 speakers

City	Hours	Speakers
India (unspecified)	11,899.1	1,370
Delhi	786.3	172
Chennai	579.1	44
Mumbai	349.4	77
Hyderabad	324.2	91
Kolkata	273.6	72
Pune	205.8	43
Bangalore	193.4	38
Patna	140.9	28
Indore	125.5	16
Gurugram	106.3	6
Lucknow	94.1	28
Noida	88.5	25
Nagpur	76.0	11
Jaipur	62.3	16
Bhopal	52.1	5
Surat	37.4	7
Ranchi	32.1	10
Coimbatore	26.6	3
Varanasi	25.7	2
Kanpur	23.7	7
Chandigarh	23.6	9
Bengaluru	22.6	9
Ahmedabad	21.4	7
Visakhapatnam	12.6	3
Gwalior	8.4	1
Madurai	6.0	3
Kochi	4.8	1
Thiruvananthapuram	3.9	2
Mangalore	2.4	4

Philippines — 5,616 hours, ~664 speakers

City	Hours	Speakers
Philippines (unspecified)	4,492.0	460
Manila	415.9	60
Davao	301.3	70
Cagayan De Oro	133.6	18
Cebu	129.2	22
Quezon City	60.9	16
Bulacan	53.3	9
Iloilo	18.6	6
Pampanga	9.5	1
Taguig	2.1	2

United States — 3,529 hours, ~531 speakers

City	Hours	Speakers
United States (unspecified)	2,676.8	383
New York	658.1	121
California	54.4	8
Los Angeles	49.8	6
Florida	37.8	1
Miami	22.9	3
New Jersey	18.8	2
Virginia	3.2	2
Atlanta	2.9	2
Texas	2.6	1
Chicago	2.1	2

Indonesia — 3,110 hours, ~43 speakers

City	Hours	Speakers
Indonesia (unspecified)	3,107.3	41
Jakarta	2.9	2

Kenya — 3,105 hours, ~483 speakers

City	Hours	Speakers
Kenya (unspecified)	2,136.4	226
Nairobi	888.9	230
Mombasa	41.4	11
Nakuru	28.3	6
Eldoret	8.1	8
Kisumu	1.6	2

Egypt — 2,435 hours, ~293 speakers

City	Hours	Speakers
Egypt (unspecified)	1,985.8	192
Cairo	364.7	71
Alexandria	67.5	18
Giza	17.3	12

Venezuela — 2,252 hours, ~92 speakers

City	Hours	Speakers
Venezuela (unspecified)	2,184.4	84
Valencia	62.1	3
Caracas	6.0	5

Italy — 2,028 hours, ~30 speakers

City	Hours	Speakers
Italy (unspecified)	1,833.5	29
Naples	194.8	1

Algeria — 504 hours, ~26 speakers

City	Hours	Speakers
Algeria (unspecified)	482.3	23
Algiers	21.4	3

United Kingdom — 434 hours, ~55 speakers

City	Hours	Speakers
United Kingdom (unspecified)	293.2	38
London	135.9	14
Birmingham	4.9	3

Pakistan — 327 hours, ~68 speakers

City	Hours	Speakers
Pakistan (unspecified)	267.3	46
Lahore	42.6	8
Karachi	13.8	11
Faisalabad	1.9	2
Islamabad	1.7	1

Ghana — 300 hours, ~56 speakers

City	Hours	Speakers
Ghana (unspecified)	161.9	34
Accra	134.9	18
Kumasi	2.7	4

South Africa — 200 hours, ~13 speakers

City	Hours	Speakers
South Africa (unspecified)	128.3	9
Johannesburg	36.1	2
Cape Town	32.2	1
Pretoria	2.9	1

Bangladesh — 159 hours, ~22 speakers

City	Hours	Speakers
Dhaka	107.0	17
Bangladesh (unspecified)	52.1	5

Colombia — 135 hours, ~13 speakers

City	Hours	Speakers
Bogota	112.3	8
Colombia (unspecified)	16.4	4
Medellin	5.9	1

Other countries

Country	Hours	Speakers
Malaysia	99.3	3
Mexico	63.0	6
Japan	20.3	9
Brazil	16.3	2
Cameroon	3.3	3
Morocco	1.7	1

Combined vs. Separated Audio

Each sample in this mini dataset is a combined mix of all speakers. The parent Yapdo corpus stores each speaker on a separate, time-aligned track. Here's what that difference sounds like — a Telugu conversation with 2 speakers:

Combined (all speakers mixed)

Speaker 1 (isolated track)

Speaker 2 (isolated track)

All 12 Samples

#	Language	Speakers	Variety	Transcript
1	sw	2	Nairobi urban	Yes
2	hi	2
3	tl	2	Central Visayas
4	sw	2	Nairobi urban	Yes
5	ar	3	Cairene
6	te	2	Karnataka/Bangalore	Yes
7	es	3	Venezuelan
8	pcm	2	Nigerian English	Yes
9	en	3	Egyptian Arabic	Yes
10	pcm	2	Nigerian English	Yes
11	tl	3	Mindoreño
12	en	3	Indian

Schema

Column	Type	Description
`audio`	`Audio(16kHz)`	Combined multi-speaker audio, 16 kHz mono
`text`	`string`	Timestamped transcript with speaker IDs (human-reviewed where available, otherwise empty). Full human-validated transcripts are available upon request.
`language`	`string`	Primary ISO 639-1 language code
`accent`	`string`	Accent or dialect label (e.g. "Nairobi urban", "Cairene", "Mindoreño")
`relationship`	`string`	Speaker relationship (friends, acquaintances, colleagues, etc.)
`topics`	`string`	Topics discussed
`speech_characteristics`	`string`	Notable audio features (code-switching, laughter, etc.)
`num_speakers`	`int`	Number of speakers in the clip
`duration_s`	`float`	Clip duration in seconds
`rms_dbfs`	`float`	RMS loudness in dBFS
`peak_amplitude`	`float`	Peak sample amplitude (0.0–1.0)
`speech_ratio`	`float`	Fraction of frames containing speech

Usage

from datasets import load_dataset

ds = load_dataset("liva-ai/yapdo-mini", split="train")

for example in ds:
    print(f"{example['language']:>3s} | {example['num_speakers']} speakers | {example['accent']}")
    print(f"   Transcript: {example['text'][:100]}...")
    print()

Downloads last month: 195