हिन्दी
Hindi general conversation
Two-speaker general conversation in Hindi, recorded on one channel with speakers labelled. 81 hours. Transcripts are time-aligned and written in Devanagari script, with English words kept as spoken.
- ASR
- Full duplex
- Turn-taking
- Voice agents
- Diarisation
- TTS
The full set, measured.
Figures measured from the delivered files.
- of audio
- 81 h
- 259 conversations
- audio files
- 259
- FLAC · 16 kHz · 16-bit
- transcribed words
- 718,981
- 53,495 turns
- one channel
- 259
- bandwidth
- wideband
- every file measured
- median SNR
- 36.8 dB
- clean
Personal data. Numbers of 4 or more digits, digit strings spoken as words, and email addresses are masked as [pii] in text and replaced by a tone in audio. First names are not masked.
Sample 1 of 4
Open conversation
0:44 from a 4-minute conversation, at 0:20
Speaker 1
Male
Speaker 2
Male
Speakers
Across the whole dataset
- 177
- distinct voices
- 85 F · 82 M · 10 unknown
- by gender
Voices are grouped from the recordings themselves, so the count is an estimate. Gender is labelled, with voice-model checks.
Voices in the sample
In the transcripts
718,981 words in 53,495 turns
Devanagari script, English as spoken
Words are written in the script the speaker would use; English words stay in Latin script where they were said, so code-mixing is preserved as spoken.
Aligned per segment, speakers labelled
One segment per turn with its start and end time and the speaker, no word-level timestamps. Overlaps are kept as overlapping segments.
One tag vocabulary, in square brackets
Anything that is not a spoken word is a bracket tag from a single list: [pii] for masked personal data, [filler] for hesitations, [overlap] where both speak at once. Plain-text fields carry no tags.
Personal data masked, in text and audio
Numbers of 4 or more digits, digit strings spoken as words, and email addresses are masked as [pii] in text and replaced by a tone in audio. First names are not masked.
Delivery layouts are on formats.
Conversation profile
Across every conversation in the dataset
- Most-talking speaker 68%
- The other 32%
- Speech 72%
- Overlap 1%
- Silence 27%
- Latin script 14%
- Native script 86%
- 19.8 min
- median conversation
- 259
- conversations
- 8.6
- turns a minute
- 148
- words a minute
Audio quality
Across the whole dataset, then in the sample conversations.
Clean across the set. Bandwidth wideband (8 kHz), no telephone-band files.
Audible room and line noise. Right for recognition and agents meant to work in the real world; not a voice-model source. Noise floor -75.7 dBFS, speech at -15.3 dBFS.
Wideband speech. The full range a 16 kHz recording can hold is present.
- Background3.75
- Speech3.29
- Overall2.88
Clipping in 0.01% of the audio.
Start with a sample. License the full set when it fits.
The full set
Get a quote
Say what you will train and how many hours you need. We reply by email with the licence terms and a price for this set.
- Licensed per use, priced per dataset
- Delivered in the layout your training stack reads
- Every figure on this page comes with the delivery
Sample first
Get a sample by email
Ten to thirty minutes of this dataset, with transcripts, in the same files and fields as the full delivery. The link works for 24 hours.
- Real recordings from this dataset
- Same layout, naming and fields as the full set
- For evaluation only
What it is good for.
- Speech recognitionFitsTime-aligned transcripts in native script, English kept as spoken. 36.8 dB median SNR across the full set, wideband (53 dB in the sample conversations).
- Full-duplex speech to speechPartlyOne mixed channel. Turns are labelled, but overlapping speech cannot be separated, which these models need.
- Turn-taking and endpointingPartlyTurn boundaries come from the transcript, so gaps are approximate and overlap is marked, not separated.
- Voice agents for supportPartlyOpen conversation, not a support flow. Useful for language and prosody, not for task structure.
- Speaker diarisationFitsSpeaker-attributed segments across full conversations.
- Text to speechPartly36.8 dB median SNR across the full set, wideband (53 dB in the sample conversations): clean and wideband enough for conversational prosody data, though not a studio voice.
Specification
Figures marked measured were read from the audio. Anything we cannot confirm is listed under Ask us about.
Machine-readable
- id
- LK-SP-HIN-002
- type
- speech · conversational · general
- language
- हिन्दी · Hindi · hi-IN
- hours
- 81 h
- channels
- One channel, speakers labelled in the transcript
- files
- 259 files · 259 conversationscounted across the full set
- layouts
- 259 mono conversationcounted across the full set
- audio
- FLAC · 16 kHz · 16-bitmeasured across the full set
- bandwidth
- wideband (8 kHz)no telephone-band files
- snr
- 36.8 dB medianacross the full set
- release
- v1.0
- transcript
- time-aligned by segment · Devanagari script · delivered as JSON
- speakers
- 177 across the full set · id and gender per speaker
- pii
- Numbers of 4 or more digits, digit strings spoken as words, and email addresses are masked as [pii] in text and replaced by a tone in audio. First names are not masked.
- source
- Recorded for the dataset
- review
- Every recording has a complete, segment-level transcript
- personal data
- Redacted
- licence
- Customquoted per use
Not exactly what you need?
A different domain, more hours, another channel layout or speaker mix. Tell us, and it becomes a collection built to the same specification.
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