soniqo/Smart-Turn-v3.2-ONNX

Smart Turn v3.2 — ONNX

End-of-turn detection for voice agents. Given the last 8 seconds of the user's speech, the model returns the probability that the user has finished their turn, so an agent can reply promptly after a real endpoint and keep listening through a mid-sentence pause. It looks at the raw audio (prosody, pace, intonation), not a transcript, and covers 23 languages.

This is an audio-in export: the Whisper log-mel front-end is inside the graph, including the zero-mean / unit-variance waveform normalisation the upstream model was trained with. Feed 16 kHz PCM and read one probability.

Model

Property	Value
Parameters	8.0 M (Whisper-Tiny encoder + attention pooling + MLP head)
Input	audio float32 [1, 128000] — 16 kHz mono, most recent audio last, zeros at the front
Output	probability float32 [1, 1] — turn complete if > 0.5
Window	8 s (longer turns: keep the last 8 s)
Formats	float32 (33.0 MB), int8 (11.1 MB)
Opset	18
Languages	Arabic, Bengali, Chinese, Danish, Dutch, English, Finnish, French, German, Hindi, Indonesian, Italian, Japanese, Korean, Marathi, Norwegian, Polish, Portuguese, Russian, Spanish, Turkish, Ukrainian, Vietnamese

Files

File	Size	Description
smart-turn-v3.2.onnx	33.0 MB	float32 graph with the audio front-end embedded
smart-turn-v3.2-int8.onnx	11.1 MB	int8 weights (dynamic quantization) for encoder and head, float32 front-end
config.json	—	I/O contract, window size, upstream revision
LICENSE	—	BSD-2-Clause notice for the upstream weights

Performance

Accuracy on 1,000 clips from the upstream pipecat-ai/smart-turn-data-v3.2-test set (shard train-00000-of-00010.parquet, threshold 0.5). Latency is one 8-second window on Apple M5 Pro (macOS 26.5.2) (ONNX Runtime CPU with 2 intra-op threads, CoreML on CPU + Neural Engine). Higher accuracy / F1 is better; FPR is the share of unfinished turns wrongly cut off, FNR the share of finished turns the model kept waiting on.

Model	Accuracy	Precision	Recall	F1	FPR	FNR	Latency (mean)	Latency (p95)
Upstream smart-turn-v3.2-gpu.onnx (mel input)	92.90%	0.912	0.944	0.927	8.48%	5.61%	—	—
Upstream smart-turn-v3.2-cpu.onnx (mel input)	91.70%	0.889	0.946	0.916	10.98%	5.41%	—	—
smart-turn-v3.2.onnx	92.90%	0.912	0.944	0.927	8.48%	5.61%	36.3 ms	51.4 ms
smart-turn-v3.2-int8.onnx	93.00%	0.910	0.948	0.929	8.67%	5.20%	37.3 ms	50.6 ms

The re-export reproduces the upstream fp32 graph to within 1e-5 on the same mel input; the only difference is where the front-end runs. The int8 file quantizes the encoder and head weights (dynamic quantization of MatMul/Gemm); on the test clips its probabilities differ from fp32 by 0.007 on average and it flips the 0.5 decision on fewer than 1 % of clips. On Apple Silicon the int8 graph is not faster than fp32 under ONNX Runtime; it mainly saves download size. The encoder dominates the cost (the embedded front-end is about 4 ms single-threaded), and more threads help: about 20 ms with four.

Usage

Run it after a lightweight VAD reports silence. Pass the whole current turn (up to 8 s), not just the last chunk. If the user resumes speaking before the agent answers, run it again on the full turn.

import numpy as np
import onnxruntime as ort

session = ort.InferenceSession("smart-turn-v3.2-int8.onnx", providers=["CPUExecutionProvider"])

def turn_complete_probability(turn_audio_16k: np.ndarray) -> float:
    window = np.zeros(128000, dtype=np.float32)
    tail = turn_audio_16k[-128000:].astype(np.float32)
    window[128000 - len(tail):] = tail
    return float(session.run(None, {"audio": window[None]})[0][0, 0])

# speech-core CLI (after scripts/download_models.sh)
speech turn --audio utterance.wav

Source

Re-exported from pipecat-ai/smart-turn-v3 (revision f766f81d3cfd, smart-turn-v3.2-gpu.onnx), the open Smart Turn model from the Pipecat project, BSD-2-Clause. Training data and evaluation sets are published by Pipecat as pipecat-ai/smart-turn-data-v3.2-*.

Links

• speech-android — Android SDK
• Docs — Android setup docs
• speech-core — C++ runtime
• Docs — runtime docs
• soniqo.audio — website
• blog — blog