> ## Documentation Index
> Fetch the complete documentation index at: https://doc.rapida.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Pipecat Smart Turn EOS

> Configure Pipecat Smart Turn end-of-speech detection in assistant-api.

[Pipecat Smart Turn](https://github.com/pipecat-ai/smart-turn) uses a Whisper-based audio model (\~8 MB) to predict turn completion directly from speech audio. It detects prosodic cues — falling intonation, speech rate changes — that indicate a caller has finished speaking.

**Provider identifier:** `pipecat_smart_turn_eos`

***

## Source Location

```
api/assistant-api/internal/end_of_speech/internal/pipecat/
├── pipecat_end_of_speech.go   # Main implementation
├── turn_detector.go           # ONNX inference wrapper
├── models/
│   └── smart-turn-v3.2-cpu.onnx   # Downloaded at Docker build (~8 MB)
```

***

## How It Works

1. `UserAudioPacket` audio is accumulated in a rolling float32 buffer (max \~5 seconds at 16 kHz)
2. When a final `SpeechToTextPacket` arrives, the model runs inference on the buffered audio
3. The model outputs a turn-completion probability (0.0 – 1.0)
4. If `probability >= threshold` → set timer to `quick_timeout` (caller is likely done)
5. If `probability < threshold` → set timer to `silence_timeout` (caller is likely still speaking)
6. Interim transcripts reset the timer to `fallback_timeout`
7. When the timer fires, `EndOfSpeechPacket` is emitted

```
Audio → buffer (rolling ~5s)
STT final → model inference → P(complete) = 0.73
  0.73 >= 0.5 (threshold) → timer = 200ms (quick)
  ...silence...
  Timer fires → EndOfSpeechPacket
```

***

## Parameters

| Option Key                       | Default   | Range       | Description                                                     |
| -------------------------------- | --------- | ----------- | --------------------------------------------------------------- |
| `microphone.eos.threshold`       | `0.5`     | 0.1–0.9     | Turn completion probability threshold                           |
| `microphone.eos.quick_timeout`   | `250 ms`  | 50–1000 ms  | Silence buffer when model says "done"                           |
| `microphone.eos.silence_timeout` | `2000 ms` | 500–5000 ms | Silence duration when model says "still speaking"               |
| `microphone.eos.timeout`         | `500 ms`  | 500–4000 ms | Fallback timeout for interim transcripts and inference failures |

***

## Model Setup

### Docker

The model is downloaded from Hugging Face during the Docker build and patched for ONNX Runtime compatibility. No manual action required.

```env theme={null}
# Set automatically in the Dockerfile
PIPECAT_TURN_MODEL_PATH=./models/pipecat_turn/smart-turn-v3.2-cpu.onnx
```

### From Source

Download the model manually:

```bash theme={null}
mkdir -p api/assistant-api/internal/end_of_speech/internal/pipecat/models

curl -fsSL -o api/assistant-api/internal/end_of_speech/internal/pipecat/models/smart-turn-v3.2-cpu.onnx \
    "https://huggingface.co/pipecat-ai/smart-turn-v3/resolve/main/smart-turn-v3.2-cpu.onnx"
```

If you encounter ONNX opset errors, patch the model:

```bash theme={null}
pip install onnx
python3 -c "
import onnx
m = onnx.load('api/assistant-api/internal/end_of_speech/internal/pipecat/models/smart-turn-v3.2-cpu.onnx')
m.ir_version = 9
used = {n.domain for n in m.graph.node}
keep = [o for o in m.opset_import if o.domain == '' or o.domain in used]
del m.opset_import[:]
m.opset_import.extend(keep)
onnx.save(m, 'api/assistant-api/internal/end_of_speech/internal/pipecat/models/smart-turn-v3.2-cpu.onnx')
"
```

To override the model path:

```bash theme={null}
export PIPECAT_TURN_MODEL_PATH=/path/to/smart-turn-v3.2-cpu.onnx
```

Requires ONNX Runtime (`libonnxruntime`) — same dependency as Silero/FireRed VAD.