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audio
audioduration (s)
154
3.06k
country_from
stringlengths
2
2
country_to
stringclasses
15 values
topic
stringclasses
15 values
languages
listlengths
1
1
duration_seconds
float64
154
3.06k
turns_per_minute
float64
3.8
19.6
turn_taking_gap_ms
int64
-193
1.47k
speech_balance_percent
float64
20.4
49.9
overlap_percent
float64
4.9
25.2
language
stringclasses
5 values
NG
NG
business_finance
[ "en" ]
389.04
10.6
400
46.2
16.5
en
GH
GH
business_finance
[ "en" ]
666.576
13.1
239
45
15.4
en
NG
NG
education
[ "en" ]
1,352.544
11.1
234
41.1
19.6
en
NG
NG
technology_science
[ "en" ]
630.144
9.1
93
35.1
19
en
NG
NG
beliefs_values
[ "en" ]
555.96
8.6
321
49.5
13.4
en
NG
NG
business_finance
[ "en" ]
751.032
3.8
884
32
5.1
en
NG
NG
work_career
[ "en" ]
555.048
7.9
571
30.5
7.1
en
GH
GH
everyday_life
[ "en" ]
669.096
9.3
371
32.3
17.8
en
NG
NG
uncertain
[ "en" ]
500.928
11.7
534
43.4
10.7
en
NG
PK
business_finance
[ "en" ]
1,430.664
10.1
396
32.2
10.3
en
NG
PK
beliefs_values
[ "en" ]
1,129.656
9.9
773
40.6
5.6
en
NG
NG
work_career
[ "en" ]
985.608
9.2
771
46.7
9.5
en
CM
CM
mixed
[ "en" ]
1,477.44
10.1
206
49.7
18.1
en
NG
NG
news_society
[ "en" ]
638.28
7.8
89
22.2
16.9
en
NG
NG
uncertain
[ "en" ]
304.56
19.6
361
45.4
12.7
en
NG
NG
mixed
[ "en" ]
622.848
8.2
124
31.6
12.6
en
SN
NG
health
[ "en" ]
430.08
9.3
1,288
33.7
9.4
en
GQ
NG
news_society
[ "en" ]
1,364.232
10
443
41.7
11.9
en
NG
NG
education
[ "en" ]
848.088
10
579
38
10.7
en
NG
NG
business_finance
[ "en" ]
320.568
9.5
158
27.2
19.7
en
NG
NG
uncertain
[ "en" ]
395.616
6.3
292
33.4
9.8
en
NG
NG
business_finance
[ "en" ]
547.368
10.6
306
49.9
13.8
en
NG
NG
relationships
[ "en" ]
572.928
9.6
383
46.9
14.6
en
ET
ET
relationships
[ "en" ]
613.416
9.7
466
43.7
13.7
en
IN
IN
everyday_life
[ "hi" ]
581.736
16.9
397
35.1
16.4
hi
IN
IN
everyday_life
[ "hi" ]
1,305.072
8.9
661
38.4
14.8
hi
PK
PK
everyday_life
[ "hi" ]
623.496
14
399
47.1
10.7
hi
IN
IN
education
[ "hi" ]
336.96
17.9
181
43.3
15.6
hi
IN
IN
business_finance
[ "hi" ]
594.672
14.4
412
45.2
11.8
hi
IN
IN
business_finance
[ "hi" ]
658.584
12.9
382
47
18.7
hi
IN
IN
everyday_life
[ "hi" ]
606.552
16.3
595
38.8
12.4
hi
IN
IN
everyday_life
[ "hi" ]
304.32
14.1
450
41.3
9.7
hi
IN
IN
health
[ "hi" ]
434.28
16.4
363
36.3
10.9
hi
IN
IN
business_finance
[ "hi" ]
1,034.688
10.7
543
35.7
16.9
hi
IN
IN
business_finance
[ "hi" ]
677.448
9
93
27.5
15.6
hi
PK
PK
mixed
[ "hi" ]
1,319.568
10.9
397
31.6
15.8
hi
IN
IN
mixed
[ "hi" ]
1,145.424
8.9
1,200
49.1
9.3
hi
IN
IN
everyday_life
[ "hi" ]
1,225.656
11.6
565
45.1
15.8
hi
IN
IN
everyday_life
[ "hi" ]
353.232
10.4
-79
43.5
25.2
hi
IN
IN
shopping_services
[ "hi" ]
473.808
12.2
200
40
18.4
hi
IN
IN
business_finance
[ "hi" ]
717.624
8.3
179
41.9
16.3
hi
IN
IN
education
[ "hi" ]
496.248
11.5
1,066
34.1
8.9
hi
SA
IN
business_finance
[ "hi" ]
533.232
8.6
600
35.6
11.4
hi
IN
IN
business_finance
[ "hi" ]
901.92
9.1
250
42.4
15
hi
IN
IN
business_finance
[ "hi" ]
400.296
16
473
30.9
14.1
hi
ET
ET
mixed
[ "hi" ]
489.312
15
243
46.1
21.8
hi
NG
NG
uncertain
[ "id" ]
1,581
12.5
142
29.9
22.2
id
PH
PH
games_hobbies
[ "id" ]
680.856
10.1
268
32.6
12.9
id
ID
ID
mixed
[ "id" ]
698.568
11.1
556
42.9
9.1
id
ID
ID
uncertain
[ "id" ]
445.776
16.9
450
39.9
17.6
id
PH
PH
uncertain
[ "id" ]
2,706.12
10.7
-100
44.8
20.9
id
ID
ID
everyday_life
[ "id" ]
316.488
14.4
200
39.7
21.1
id
PH
PH
technology_science
[ "id" ]
772.728
8.9
694
46.6
10.3
id
ID
ID
uncertain
[ "id" ]
406.128
14.1
500
38.9
16.9
id
ID
ID
uncertain
[ "id" ]
748.128
10.7
846
33.9
11.4
id
ID
ID
mixed
[ "id" ]
682.44
12.9
250
48.9
19.8
id
PH
PH
uncertain
[ "id" ]
1,986.576
8.1
848
37.2
9.1
id
UA
UA
games_hobbies
[ "ru" ]
401.112
4.8
850
26.1
15
ru
RU
RU
work_career
[ "ru" ]
169.056
12.9
312
47.7
20
ru
DK
SK
everyday_life
[ "ru" ]
659.448
7.2
5
36
15.3
ru
UA
UA
work_career
[ "ru" ]
219.528
11.9
-67
30.2
18.7
ru
RU
RU
sports
[ "ru" ]
356.664
11.4
63
37.5
19.2
ru
RU
RU
sports
[ "ru" ]
359.712
10.8
-129
37
16.9
ru
KZ
KZ
everyday_life
[ "ru" ]
238.128
17.2
498
39.9
13.9
ru
UA
UA
everyday_life
[ "ru" ]
678.072
10.5
141
34.5
13
ru
RU
RU
mixed
[ "ru" ]
611.112
10
140
43
15.1
ru
UA
UA
shopping_services
[ "ru" ]
2,129.088
8.2
148
46.3
11.4
ru
UA
UA
everyday_life
[ "ru" ]
633.096
8.8
113
26.3
14.1
ru
UA
UA
games_hobbies
[ "ru" ]
415.56
10.4
150
48.2
10.9
ru
ME
ME
work_career
[ "ru" ]
200.16
12.8
105
20.4
11.7
ru
ME
ME
education
[ "ru" ]
153.864
4.8
457
30.6
4.9
ru
KZ
KZ
games_hobbies
[ "ru" ]
550.56
9.1
-193
30.2
18.1
ru
SK
DK
travel
[ "ru" ]
809.04
7.1
46
25.3
10.1
ru
KZ
KZ
mixed
[ "ru" ]
230.76
13.6
250
33.6
15.4
ru
UA
UA
games_hobbies
[ "ru" ]
469.488
7.9
588
39.8
16.5
ru
SK
DK
health
[ "ru" ]
1,163.016
5.1
150
28.4
8.5
ru
UA
UA
games_hobbies
[ "ru" ]
428.76
10.1
637
45
18.7
ru
VN
VN
business_finance
[ "vi" ]
763.632
10.5
378
39.4
15
vi
VN
VN
business_finance
[ "vi" ]
1,705.176
9.4
383
47.2
12.9
vi
VN
VN
games_hobbies
[ "vi" ]
1,329.72
10
973
43.8
9.7
vi
VN
VN
technology_science
[ "vi" ]
2,467.128
9.7
1,248
48.3
7.7
vi
VN
VN
mixed
[ "vi" ]
930.168
6.3
495
33.8
7.5
vi
VN
VN
games_hobbies
[ "vi" ]
1,198.968
7.9
1,465
43.2
7.7
vi
VN
VN
games_hobbies
[ "vi" ]
3,064.152
8
1,173
45.3
6
vi
VN
VN
mixed
[ "vi" ]
968.88
6.1
581
33.5
8.8
vi

DataHive — Multilingual Conversations

Unscripted conversations in English, Hindi, Indonesian, Russian, and Vietnamese between people who already know each other. Shared to support research on natural conversational voice models.

Why These Conversations

Natural conversation depends on timing: when to respond, brief acknowledgments, pauses, and interruptions. People who know each other share context, leave thoughts unfinished, and switch turns without explicit signals. These recordings capture that.

How We Collect

Participants call people they know through the Hive Calls app (Google Play, App Store). They choose their own topics, with no scripts. Conversations take place in everyday environments and are recorded through LiveKit, with each participant saved separately using Opus compression in an Ogg container. These recordings are converted to stereo MP3 for this sample. Participants are paid for qualifying conversation time.

What’s in This Sample

Detail Value
Languages English (en), Hindi (hi), Indonesian (id), Russian (ru), Vietnamese (vi)
Conversations 85
Speakers 112 unique speakers
Total duration 18h 41m 35s
Conversation length 2m 34s–51m 4s
Audio Stereo MP3 · 48 kHz · 128 kbps
Channels Caller left, callee right, on a shared timeline
Metadata JSONL, one record per conversation
License CC BY 4.0

Metadata per conversation:

  • topic
  • language: conversation language code (en, hi, id, ru, or vi)
  • languages: detected language codes from the original export
  • duration
  • caller and callee country (from phone number)
  • turns per minute
  • median gap between turns
  • speech balance
  • overlap

Transcripts are not included.

Files

README.md
preview/metadata.jsonl
preview/en_000001.mp3
preview/hi_000001.mp3
preview/id_000001.mp3
preview/ru_000001.mp3
preview/vi_000001.mp3
...

Install the Hugging Face audio dependencies, then load the dataset from the extracted folder:

python -m pip install "datasets[audio]"
from datasets import load_dataset

ds = load_dataset("audiofolder", data_dir="preview")

Quality and Selection

Every conversation passes QA review and automated checks for:

  • Audio: noise, signal-to-noise ratio, clipping, silence.
  • Dynamics: both speakers participate, turn-taking, overlap, interruptions, gaps.
  • Content: the same language on both tracks, topic, explicit content.

English and Hindi conversations are 5–30 minutes long and were drawn at random with a recorded seed; the English and Hindi sample cards describe the method. Indonesian, Russian and Vietnamese conversations are at least two minutes long, and repeat appearances by the same speaker are limited. Labels and measurements are automatic.

Consent and Privacy

Participants explicitly consent to recording, sharing with customers, and AI model training. Conversations flagged for personal information are excluded. Metadata contains no names, phone numbers, or account IDs. Audio and transcript redaction is available on request.

Custom Collections

We collect conversations for voice-model labs by language, accent, and requirements. Deliverables can include separate speaker tracks, transcripts, and quality metadata. On request, we can arrange collection of audio captured on-device before lossy compression, delivered as PCM WAV or FLAC. Contact contact@datahive.ai.

License

Released under CC BY 4.0. Free for research and commercial use, including model training, with attribution to DataHive.

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