ONNX models + card

README.md

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+---
+language: [en, hi]
+license: bsd-2-clause
+tags:
+  - turn-detection
+  - endpointing
+  - voice-activity-detection
+  - audio-classification
+  - hinglish
+  - onnx
+pipeline_tag: audio-classification
+---
+
+# Smart Turn Hinglish (whisper-tiny)
+
+Audio-only turn detection for voice agents: has the speaker **finished their turn**, or are
+they just pausing? Built for English + Hindi/Hinglish from the
+[pipecat smart-turn-v3.2](https://huggingface.co/datasets/pipecat-ai/smart-turn-data-v3.2-train)
+data. ~7.8M params. The ONNX graph takes a raw 16k waveform and returns `P(turn complete)`,
+with the log-mel frontend baked in, so inference needs only `onnxruntime` + `numpy`.
+
+**Code, training, ablations and the full write-up:**
+[github.com/CodeWithMoin/smart-turn-hinglish](https://github.com/CodeWithMoin/smart-turn-hinglish)
+
+## Files
+
+| file | window | notes |
+|---|---|---|
+| `smart_turn_8s.onnx` | 8s | shipped model. accuracy pick, ~38 ms/clip CPU |
+| `smart_turn_4s.onnx` | 4s | fast variant, ~17 ms/clip CPU |
+
+## Results (official frozen `smart-turn-v3.2-test`, accuracy @ 0.5)
+
+| | this model (8s) | Smart Turn v3.2 |
+|---|---|---|
+| Hindi | **93.9%** | 92.8% |
+| English | 93.7% | 94.7% |
+| Human / real | 94.4% | 95.5% |
+
+Beats the reference on Hindi and sits within a point on English, at a fraction of the size.
+Overall test AUC 0.983.
+
+## Usage
+
+```python
+import numpy as np, onnxruntime as ort
+
+sess = ort.InferenceSession("smart_turn_8s.onnx", providers=["CPUExecutionProvider"])
+
+def p_turn_complete(wav_16k):                 # float32 mono @ 16 kHz
+    x = wav_16k[-128000:]                     # last 8s
+    buf = np.zeros((1, 128000), np.float32)
+    buf[0, 128000 - len(x):] = x              # right-align (left-pad with silence)
+    return float(sess.run(None, {"waveform": buf})[0][0])
+```
+
+In a live agent, run a cheap VAD (e.g. Silero) continuously; on a ~200 ms pause, call this on
+the last 8s. Threshold `P(complete)` — higher = more patient (fewer interruptions).
+
+## Limitations
+
+Trained mostly on synthetic TTS. Real casual conversational speech on a laptop/phone mic is
+out of distribution and where it's weakest. See the report for the honest failure analysis
+and the plan (real Indian-mic conversational data) to close it.
+
+## Provenance
+
+Fine-tuned from `openai/whisper-tiny` on the openly published pipecat smart-turn-v3.2 corpus.
+For commercial use, check the upstream terms.

smart_turn_4s.onnx

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smart_turn_8s.onnx

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