Datasets:
audio audioduration (s) 234 3.54k | country_from stringclasses 3
values | country_to stringclasses 3
values | topic stringclasses 7
values | languages listlengths 1 1 | duration_seconds float64 234 3.54k | turns_per_minute float64 7.1 21.6 | turn_taking_gap_ms int64 148 661 | speech_balance_percent float64 31.6 48.6 | overlap_percent float64 5.1 20.7 | language stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|---|
IN | IN | mixed | [
"hi"
] | 2,084.424 | 11.2 | 148 | 38.7 | 19.9 | hi | |
IN | IN | food | [
"hi"
] | 1,577.136 | 11.3 | 398 | 48.3 | 15.4 | hi | |
PK | PK | work_career | [
"hi"
] | 233.52 | 17.5 | 523 | 38.8 | 5.1 | hi | |
IN | IN | everyday_life | [
"hi"
] | 1,305.072 | 8.9 | 661 | 38.4 | 14.8 | hi | |
IN | IN | business_finance | [
"hi"
] | 240.168 | 21.6 | 385 | 42.6 | 14.4 | hi | |
PK | PK | mixed | [
"hi"
] | 1,319.568 | 10.9 | 397 | 31.6 | 15.8 | hi | |
IN | IN | business_finance | [
"hi"
] | 717.624 | 8.3 | 179 | 41.9 | 16.3 | hi | |
SA | SA | business_finance | [
"hi"
] | 3,122.088 | 7.1 | 281 | 39.1 | 16.4 | hi | |
IN | IN | games_hobbies | [
"hi"
] | 406.584 | 18.4 | 328 | 41.7 | 12.9 | hi | |
IN | IN | mixed | [
"hi"
] | 3,173.28 | 11 | 401 | 48.6 | 12.5 | hi | |
IN | IN | work_career | [
"hi"
] | 271.656 | 8.6 | 649 | 32.9 | 10.7 | hi | |
IN | IN | technology_science | [
"hi"
] | 3,536.952 | 11.3 | 361 | 46.1 | 20.7 | hi |
DataHive — Hindi Conversations
Unscripted conversations in Hindi 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 |
|---|---|
| Language | Hindi (hi) |
| Conversations | 12 |
| Speakers | 24 unique speakers |
| Total duration | 4h 59m 48s |
| Conversation length | 3m 54s–58m 57s |
| 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 (hi)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/hi_000001.mp3
preview/hi_000002.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.
Conversations are at least two minutes long. 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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