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README.md

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+---
+license: cc-by-4.0
+language: [ta]
+task_categories: [audio-classification]
+tags: [end-of-turn, turn-detection, turn-taking, endpointing, voice-agent, tamil, dravidian, 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 call-centre calls.**
+
+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.
+
+```python
+from datasets import load_dataset
+ds = load_dataset("santhosh-005/tamil-eot")
+ds["train"][0]["audio"], ds["train"][0]["endpoint_bool"]
+```
+
+Trained model: [`santhosh-005/smart-turn-tamil`](https://huggingface.co/santhosh-005/smart-turn-tamil)
+
+## 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 call-centre telephony, two-party, 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](https://github.com/santhosh-005), released under
+**CC BY 4.0**.
+
+```bibtex
+@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/dataset>
+
+Method and ablations: <https://github.com/santhosh-005/tamil-eot>

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