Datasets:

santhosh-005
/

tamil-eot

license: cc-by-4.0
language:
  - ta
task_categories:
  - audio-classification
tags:
  - end-of-turn
  - turn-detection
  - turn-taking
  - endpointing
  - voice-agent
  - tamil
  - south-indian
  - smart-turn
size_categories:
  - 10K<n<100K
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: dev
        path: data/dev-*
      - split: test
        path: data/test-*
dataset_info:
  features:
    - name: audio
      dtype: audio
    - name: endpoint_bool
      dtype: bool
    - name: language
      dtype: string
    - name: dataset
      dtype: string
    - name: sid
      dtype: string
    - name: call
      dtype: string
    - name: split
      dtype: string
    - name: source
      dtype: string
    - name: harvest
      dtype: string
    - name: gap
      dtype: float32
    - name: prev_dur
      dtype: float32
    - name: n_samples
      dtype: int32
    - name: label_pipeline
      dtype: bool
    - name: llm_verdict
      dtype: bool
    - name: dispute
      dtype: bool
    - name: llm_conf
      dtype: float32
  splits:
    - name: train
      num_examples: 11992
    - name: dev
      num_examples: 2325
    - name: test
      num_examples: 4168

tamil-eot

18,485 labelled end-of-turn boundaries from 116 Tamil telephone conversations.

Each row is up to 8 seconds of one speaker's audio ending at a turn boundary, labelled complete (the speaker finished) or incomplete (they paused mid-thought). Built for training semantic turn detectors for voice agents; Tamil is not covered by Smart Turn v3 or the LiveKit turn detector.

Schema matches pipecat-ai's Smart Turn datasets, so it trains with their loop unchanged.


clips	18,485 — 16 kHz mono FLAC, ≤ 8 s
complete / incomplete	12,069 / 6,416
source calls	116
train / dev / test	11,992 / 2,325 / 4,168
source audio	~36 h

Structure

Split by call, not by clip — no call appears in two splits. The test split is fixed and has not changed across releases.

field	meaning
`audio`	16 kHz mono FLAC
`endpoint_bool`	the label — true = speaker finished
`language`	`tam`
`dataset`	provenance tag
`sid`	stable clip id, `<call>_<L\|R>_<offset_ms>`
`call`	source call — group by this
`source`	boundary type: `change`, `change_midseg`, `hold_intra`, `hold_inter`
`harvest`	`core`, or `relaxed` for the widened-gate second pass
`gap`	silence at the boundary, seconds
`prev_dur`	preceding talk-spurt duration, seconds
`n_samples`	true length before padding
`label_pipeline`	first-pass acoustic label
`llm_verdict`	audio-LLM label
`dispute`	the two disagree (7,471 rows)
`llm_conf`	labeller confidence

source distinguishes speaker handovers (change, change_midseg) from same-speaker pauses (hold_intra, hold_inter). Handovers are ~93% single-class; hold_intra is near-balanced and is 58% of the test split.

from datasets import load_dataset
ds = load_dataset("santhosh-005/tamil-eot")
ds["train"][0]["audio"], ds["train"][0]["endpoint_bool"]

datasets ≥ 4 needs torchcodec to decode the audio column. To avoid that dependency, read the FLAC bytes directly:

import io, soundfile as sf
from datasets import load_dataset, Audio

ds = load_dataset("santhosh-005/tamil-eot").cast_column("audio", Audio(decode=False))
x, sr = sf.read(io.BytesIO(ds["train"][0]["audio"]["bytes"]))

Trained on this: santhosh-005/smart-turn-tamil — 83.71% (tiny) and 86.13% (base) on the held-out test split, against 70.30% zero-shot. Usable from LiveKit Agents via smart-turn-livekit.

Dataset creation

Source

Derived from SPRING_INX Tamil R1 (SPRING Lab, IIT Madras, CC BY 4.0), which ships each call as one audio file per speaker, separately transcribed. That makes the speaker label structural rather than inferred — no diarization, and no speaker-error rate to propagate.

Boundaries and cutting

Turn boundaries come from Silero VAD run per leg, not from transcript timestamps: the shipped segments are padded and sum to ~107% of call wall-clock, so their edges are not turn boundaries. Transcripts are used only at build time — filtering crosstalk bleed, counting words for the backchannel gate, and separating mid-segment boundaries — and are never part of a clip.

Of 50,532 candidate boundaries, 18,485 were kept. Gates require ≥1.0 s of speech ending at the cut (enough prosody to read), ≤0.05 s of crosstalk in the gap, handovers within 1.5 s followed by ≥3 words (rejecting backchannels such as um, okay), and pauses between 0.2 s and 2.0 s. A second pass widened the speech-duration and pause limits to 0.5 s and 5.0 s, adding 2,272 clips marked harvest = "relaxed".

Each clip is cut from one leg only — what a deployed detector receives is the inbound user stream, not a mixdown — and ends with 200 ms of real audio after the boundary, identical for both classes. The 0.2 s pause floor exists because the speaker who paused is the one who resumes: a shorter pause would place their resumed speech inside the clip. Measured trailing-window peak amplitude is 0.0021 median, so trailing-silence length carries no label information.

Labels

The first labelling pass derived labels from VAD boundaries plus transcript segment structure — a pause falling inside a transcript segment was treated as incomplete. Audited against a human listening to 197 clips blind, it scored 70.1% overall and 44.4% on same-speaker pauses, below chance, because "a pause fell inside a transcript segment" is not a judgement about whether a thought was finished.

Labels were rebuilt with an audio LLM listening to the clip only — no transcript, no sight of the first-pass label. Seven candidates were scored against the same 197 human-labelled clips before any were used:

labeller	agreement	`hold_intra`	precision on `incomplete`
`gemini-3.7-flash` (used)	97.5%	97.0%	93.9%
`gemini-3.6-flash`	96.4%	97.0%	88.7%
`gemini-3-flash-preview`	93.4%	90.9%	85.7%
first-pass acoustic	70.1%	44.4%	44.4%

gemini-3.7-flash tied statistically with gemini-3.1-pro; price broke the tie. Labelling the full set cost $5.69. Relabelling flipped 53% of the original negatives and removed a confound where the gates had drawn negatives from longer utterances (Cohen's d on prev_dur: −0.33 → −0.09).

Both labels ship on every row. endpoint_bool follows llm_verdict.

Considerations

One domain — narrowband two-party telephone conversations, task-oriented.
Positive-heavy at 65:35, not 50:50.
Label-noise ceiling ~97.1%. Above that you are fitting labeller error.
source is unbalanced. Report per-source accuracy against each bucket's own base rate; overall accuracy is dominated by the near single-class handover buckets.
Human reference labels are not gold — one pass by ear on isolated clips, enough to rank labellers, not authoritative on any single clip.
Audio is conversational speech from a public corpus; no personally identifying metadata is included beyond what SPRING_INX ships.

Licensing and citation

The dataset — boundary extraction, cut geometry, gates, labels, and splits — is by santhosh-005, released under CC BY 4.0.

@misc{tamil-eot,
  author = {santhosh-005},
  title  = {tamil-eot: labelled end-of-turn boundaries for Tamil},
  year   = {2026},
  url    = {https://github.com/santhosh-005/tamil-eot}
}

The source audio is from SPRING_INX Tamil R1 and remains the work of SPRING Lab, IIT Madras. It is redistributable here under CC BY 4.0, with attribution:

SPRING Lab, Indian Institute of Technology Madras. SPRING_INX Tamil R1. CC BY 4.0. https://asr.iitm.ac.in/SPRING_INX

How this was built — boundary extraction, the labeller bake-off, and every experiment including the ones that failed: https://github.com/santhosh-005/tamil-eot

The label tables ship in that repo under labels/, so the labeller bake-off above reproduces from a clone with no audio and no GPU.