Yapdo
Yapdo is a conversational speech corpus containing over 50,000 hours of conversational voice data. This dataset card details information for 28,693 hours of recordings from 9,165 speakers across 67 languages, with the rest of the hours still undergoing QA. The source audio is natively recorded with separate speaker channels; the samples here are presented as combined conversations.
This dataset is valuable for speech understanding research, particularly in modeling how humans listen and respond in natural contexts. Recorded among friends in unfiltered everyday settings, it preserves the spontaneity of real dialogue and supports the development of AI companions that feel genuinely conversational. It also reflects friendly interactions across cultures and captures realistic turn-taking dynamics, which are essential for training models that sound natural rather than scripted.
Dataset Overview
| Total audio | 28,693 hours |
| Unique conversations | 29,713 |
| Unique speakers | 9,165 |
| Languages | 67 |
| Speakers per conversation | 2–13 (avg 2.7) |
| Conversation duration | 19s – 24.4 hrs (avg ~58 min) |
| Code-switching | 28.1% of conversations |
| Speech type | Spontaneous, unscripted, multi-party conversations |
| Quality score (NISQA) | 0.6-4.8 (avg 2.35) |
| Common topics | Video games, daily life (jobs, school, relationships, earning money) |
Languages
Language labels for each conversation were reviewed by a native human speaker.
Monolingual Conversations
17 languages with over 10 hours of monolingual conversation data.
| Language | Code | Conversations | Hours |
|---|---|---|---|
| English | en | 13,339 | 10,860.1 |
| Egyptian Arabic | arz | 1,647 | 1,537.5 |
| Spanish | es | 1,016 | 1,388.1 |
| Swahili | sw | 1,358 | 850.3 |
| Nigerian Pidgin | pcm | 744 | 698.8 |
| Arabic | ar | 559 | 447.4 |
| Hindi | hi | 795 | 434.1 |
| Tagalog | tl | 150 | 229.9 |
| Tamil | ta | 138 | 163.8 |
| Yoruba | yo | 181 | 147.4 |
| Italian | it | 255 | 145.4 |
| Hausa | ha | 188 | 131.7 |
| French | fr | 32 | 49.0 |
| English (alt) | eng | 29 | 32.1 |
| Igbo | ig | 33 | 18.9 |
| Telugu | te | 15 | 12.0 |
| Kannada | kn | 14 | 10.0 |
Code-Switching Conversations
25 language combinations with over 10 hours of code-switching data, spanning 28.1% of all conversations.
| Language Group | Conversations | Hours |
|---|---|---|
| English + Nigerian Pidgin | 4,206 | 5,018.7 |
| English + Tagalog | 1,537 | 2,298.9 |
| Cebuano + English + Tagalog | 727 | 1,070.2 |
| English + Swahili | 447 | 504.7 |
| English + Yoruba | 238 | 235.0 |
| English + Hausa | 174 | 232.7 |
| English + Nigerian Pidgin + Yoruba | 88 | 103.9 |
| English + Hindi | 148 | 91.4 |
| Hausa + Swahili | 70 | 92.3 |
| English + Hiligaynon + Tagalog | 52 | 79.1 |
| English + Hausa + Swahili | 36 | 63.7 |
| Nigerian Pidgin + Yoruba | 66 | 60.2 |
| Arabic + English | 58 | 53.3 |
| Hindi + Urdu | 41 | 50.5 |
| English + Tamil | 42 | 44.1 |
| English + Igbo + Nigerian Pidgin | 17 | 33.0 |
| English + Hausa + Nigerian Pidgin | 21 | 32.6 |
| English + Spanish | 22 | 28.3 |
| English + Igbo | 29 | 28.1 |
| English + Telugu | 25 | 25.2 |
| Cebuano + English + Hiligaynon + Tagalog | 12 | 23.0 |
| Igbo + Nigerian Pidgin | 19 | 21.7 |
| Nigerian Pidgin + Swahili | 16 | 19.9 |
| English + Nigerian Pidgin + Swahili | 10 | 15.4 |
| Hausa + Nigerian Pidgin | 11 | 11.1 |
Labels
Language labels were assigned at the speaker-track level by native speakers who reviewed each individual track within a conversation. This means a single conversation may carry multiple language labels when speakers use different languages. Accent labels are derived from each speaker's self-reported city of origin, providing a natural geographic proxy for dialect and accent variation.
Technical Analysis
| Sample rate | 48 kHz |
| Bit depth | 16-bit PCM |
| File format | WAV |
| Mean SNR | ~33 dB |
| Median RMS | -26 dBFS |
| Average speech ratio | 0.35 |
| Spectral centroid | ~0.66 kHz |
| Frequency content | 3.3 kHz (averaged over 10–30 second clips) |
Sample Details
| Language | Accent | Relationship | Speakers | Duration (s) | RMS dBFS | Peak Amplitude | Speech Ratio | NISQA |
|---|---|---|---|---|---|---|---|---|
| sw | Nairobi urban | friends | 2 | 169 | -26.14 | 0.6900 | 0.461 | 2.999 |
| hi | – | friends | 2 | 65 | -23.86 | 0.6433 | 0.431 | 3.056 |
| ceb | Central Visayas | friends | 2 | 68 | -25.46 | 0.4780 | 0.452 | 3.029 |
| sw | Nairobi urban | friends | 2 | 66 | -25.04 | 0.4833 | 0.456 | 3.093 |
| ar | Cairene | friends | 3 | 142 | -31.32 | 0.3628 | 0.263 | 2.716 |
| te | Karnataka/Bangalore | friends | 2 | 83 | -26.07 | 0.4963 | 0.503 | 2.618 |
| es | Venezuelan | friends | 3 | 300 | -29.19 | 0.4287 | 0.366 | 3.056 |
| pcm | Nigerian English | acquaintances | 2 | 59 | -25.40 | 0.4831 | 0.341 | 2.524 |
| en | Egyptian Arabic | friends | 3 | 81 | -30.27 | 0.4270 | 0.365 | 3.318 |
| pcm | Nigerian English | friends | 2 | 60 | -23.45 | 0.6199 | 0.605 | 3.024 |
| tl | Mindoreño | friends | 3 | 60 | -32.16 | 0.3339 | 0.310 | 3.029 |
| en | Indian | friends | 3 | 89 | -28.98 | 0.3935 | 0.408 | 2.678 |
Combined vs. Separated Audio
Each sample in this 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)
Audio Artifacts
Source audio passes through a Discord/Opus VoIP pipeline.
| Artifact | Prevalence |
|---|---|
| Dropouts / packet loss | 98.6% |
| Bandwidth ceiling (< 4 kHz) | 97.2% |
| Clicks / pops | 93.6% |
| Mains hum (50/60 Hz) | 82.4% |
| Silence / dead air | 34.6% |
| Frame repetition | 18.2% |
| Echo | 15.2% |
| Low signal level | 5.8% |
| Onset transients | 5.2% |
| Clipping | 0.6% |
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