--- pretty_name: "Hindi speaker diarization" language: - hi language_details: hi-IN license: other license_name: lokah-custom license_link: https://kenpathlabs.com/lokah/licensing task_categories: - automatic-speech-recognition size_categories: - 1K Hindi speaker diarization data: whole call-centre and everyday two-speaker conversations with every speaker turn marked, 432,929 turns in RTTM, for training and scoring who spoke when. 321 hours across 1,394 recordings. `LK-SP-HIN-004` · [Get a quote](https://kenpathlabs.com/lokah/datasets/hindi-speaker-diarization#contact) · [Record as JSON](https://kenpathlabs.com/api/lokah/datasets/hindi-speaker-diarization) · [Croissant](https://kenpathlabs.com/lokah/datasets/hindi-speaker-diarization/croissant.json) Hindi speaker diarization data: whole call-centre and everyday two-speaker conversations with every speaker turn marked, 432,929 turns in RTTM, for training and scoring who spoke when. 321 hours across 1,394 recordings. Measured from 6 sample conversations (24 minutes): 16 kHz, 16-bit PCM WAV, one mono file per conversation. Across them 9% of transcript words are English written in Latin script, 17% of the time is silence, and there are 320 turns. Every recording has a complete, segment-level transcript. Personal data: redacted. Licence: custom, quoted per use. ## Specification | Field | Value | Note | | --- | --- | --- | | id | LK-SP-HIN-004 | | | type | speech · speaker diarization · call centre and general | | | language | हिन्दी · Hindi · hi-IN | | | hours | 321 h | | | channels | One channel, speakers labelled in the transcript | | | files | 1,394 files · 432,929 speaker turns | counted across the full set | | layouts | 1,394 mono conversation | counted across the full set | | audio | FLAC · 16 kHz · 16-bit | measured across the full set | | bandwidth | wideband (8 kHz) | | | snr | 30.3 dB median | across the full set | | release | v1.0 | | | transcript | time-aligned by segment · Devanagari script · delivered as JSON | | | speakers | 619 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 | Custom | quoted per use | ## What it is good for | Task | Fit | Why | | --- | --- | --- | | Speech recognition | partly | Whole recordings with time-aligned transcripts; built for who spoke when, not for utterance-level training. | | Full-duplex speech to speech | no | Single-channel recordings. | | Turn-taking | yes | Every change of speaker is marked: 432,929 turns. | | Voice agents | partly | Teaches an agent who is speaking, and when. | | Diarisation | yes | Built for it: RTTM turn files for 1,394 recordings. | | Text to speech | no | Conversational call audio, not studio voice. | ## Conversation profile Measured from 6 full conversations (24 minutes, 320 turns, 3402 words). | Measure | Value | | --- | --- | | Talk time, speaker 1 / speaker 2 | 43% / 57% | | Silence | 17% | | Overlapping speech | 9% | | English words, written in Latin script | 9% | | Speaking rate | 168 words a minute | One mixed channel: segment edges were placed by an annotator, so turn timing is approximate and overlap is an event label. ## Audio quality Frame RMS at 20 ms on the channels mixed to mono. Noise floor is the 10th percentile, speech level the 90th; SNR is their difference. Bandwidth is the highest frequency at which speech still rises 6 dB above the recording's own noise spectrum. | Measure | Value | Reading | | --- | --- | --- | | Speech above noise floor (SNR) | 54.4 dB (28.3 to 81.7) | clean | | Noise floor | -70.2 dBFS | | | Effective bandwidth | 8.0 kHz | wideband (8 kHz) across the full set | | Clipping | 0.009% of samples | | | DNSMOS P.835 (1 to 5) | background 3.42, speech 3.04, overall 2.56 | listener-rated quality, estimated | ## Samples 4 public excerpts, 44.855 seconds each, from different conversations in the dataset. - Sample 1, Open conversation, from a 4-minute conversation: [audio](https://kenpathlabs.com/lokah-samples/hindi-speaker-diarization--1.m4a) · [segments](https://kenpathlabs.com/lokah-samples/hindi-speaker-diarization--1.segments.json) - Sample 2, Open conversation, from a 4-minute conversation: [audio](https://kenpathlabs.com/lokah-samples/hindi-speaker-diarization--2.m4a) · [segments](https://kenpathlabs.com/lokah-samples/hindi-speaker-diarization--2.segments.json) - Sample 3, Open conversation, from a 4-minute conversation: [audio](https://kenpathlabs.com/lokah-samples/hindi-speaker-diarization--3.m4a) · [segments](https://kenpathlabs.com/lokah-samples/hindi-speaker-diarization--3.segments.json) - Sample 4, Open conversation, from a 4-minute conversation (Speaker 1: male; Speaker 2: male): [audio](https://kenpathlabs.com/lokah-samples/hindi-speaker-diarization--4.m4a) · [segments](https://kenpathlabs.com/lokah-samples/hindi-speaker-diarization--4.segments.json) ### Sample 1 transcript Open conversation, starting at 0:31. Audio sha256 `ce52248430a77ce3dac29efedbc75ee4ae26ec76c4c40b32530ed82d80fe535a`. | Start | Speaker | Text | | --- | --- | --- | | 0:00 | Speaker 1 | hello | | 0:01 | Speaker 2 | हाँ madam [filler] | | 0:03 | Speaker 1 | hello | | 0:05 | Speaker 2 | hello | | 0:06 | Speaker 1 | हाँ राजू | | 0:08 | Speaker 2 | हाँ | | 0:10 | Speaker 1 | बोलो | | 0:12 | Speaker 2 | [filler] हम तो यहाँ में है घर में तुम्हार | | 0:18 | Speaker 1 | hello | | 0:19 | Speaker 2 | hello [overlap] हाँ क्या कर रहा है? | | 0:22 | Speaker 1 | [overlap] हाँ | | 0:22 | Speaker 1 | hello | | 0:24 | Speaker 2 | हाँ क्या कर रहा है? | | 0:25 | Speaker 1 | अरे तुम बात मत करो ना. record कर रहा था तो hello | | 0:30 | Speaker 2 | hello | | 0:31 | Speaker 1 | हाँ राजू | | 0:33 | Speaker 2 | ओय | | 0:33 | Speaker 1 | हाँ बोलो | | 0:35 | Speaker 2 | क्या करना है? बोलके आप जब | | 0:37 | Speaker 1 | हं हम तो ऐसे बैठा रहा था घर पे | | 0:41 | Speaker 2 | हं अच्छा अच्छा कल का loanding में है ना. बस्ती में है. | ## Licence Licence: custom, quoted per use (training, evaluation or both; internal or commercial; exclusive or not). 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