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

# Oruk Speech-to-Text

> OrukSTTService streams STT and phrase-emotion estimates through Oruk's realtime API, with VAD endpointing and optional speaker events.

export const CommunityMaintained = ({maintainer, maintainerUrl, repo}) => <Note>
    <strong>Community-maintained integration.</strong> This service is built and
    maintained by{" "}
    <a href={maintainerUrl} target="_blank" rel="noreferrer">
      {maintainer}
    </a>
    . Pipecat does not test or officially support it. Please report issues and
    request changes on the{" "}
    <a href={repo} target="_blank" rel="noreferrer">
      source repository
    </a>
    . Learn more about{" "}
    <a href="/api-reference/server/services/community-integrations">
      community integrations
    </a>
    .
  </Note>;

<CommunityMaintained maintainer="Oruk labs" maintainerUrl="https://github.com/Oruk-AI" repo="https://github.com/Oruk-AI/pipecat-oruk" />

## Overview

`OrukSTTService` streams audio to Oruk's realtime preview and returns interim
transcripts, final text, and independent phrase-emotion estimates. It uses
upstream Pipecat VAD events to commit utterances and can also accept explicitly
segmented audio. Optional speaker events identify boundaries within a turn.

Phrase estimates describe vocal expression. They are not verified assessments
of a person's internal emotional state.

## Installation

Install the separately maintained package and pin the release candidate:

```bash theme={null}
uv add "pipecat-oruk==0.1.0rc1"
```

For the repository's browser transport examples, use
`uv add "pipecat-oruk[agent]==0.1.0rc1"`.

## Prerequisites

Create an [Oruk account](https://oruk.ai/signup), obtain an API key from your
developer portal, and set `ORUK_API_KEY` in the server environment. Keep the key
on the server; it is used in the authenticated WebSocket upgrade.

## Configuration

### Constructor parameters

<ParamField path="api_key" type="str | None" default="None">
  API key. When omitted, reads `ORUK_API_KEY` from the environment.
</ParamField>

<ParamField path="vad" type="bool" default="True">
  Commit utterances on upstream VAD stop events. Add a `VADProcessor` before
  this service. For externally segmented audio, set this to `False` and queue
  `OrukCommitFrame()` after each utterance.
</ParamField>

<ParamField path="wait_for_emotions" type="bool" default="False">
  By default, final text is emitted immediately and later phrase estimates
  arrive separately. Set to `True` to wait for clean turn completion and include
  all returned phrase events in the final transcript's metadata. Interim text
  remains immediate.
</ParamField>

<ParamField path="options" type="RealtimeOptions | None" default="None">
  Initial language, phrase segmentation, diarization, and turn limits. See the
  [integration
  reference](https://github.com/Oruk-AI/pipecat-oruk#configure-and-troubleshoot)
  for configuration and buffer limits.
</ParamField>

<ParamField path="settings" type="STTSettings | None" default="None">
  Use `OrukSTTService.Settings(language="fr")` to set the language. The model is
  fixed to `oruk-realtime`; language updates are accepted only between turns.
</ParamField>

## Usage

This pipeline excerpt assumes your application supplies the transport,
aggregators, LLM, and TTS. Configure the pipeline's input audio as 16 kHz mono
PCM16 and avoid adding a second VAD inside the user aggregator.

```python theme={null}
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.audio.vad.vad_analyzer import VADParams
from pipecat.pipeline.pipeline import Pipeline
from pipecat.processors.audio.vad_processor import VADProcessor
from pipecat_oruk import OrukSTTService

vad = VADProcessor(
    vad_analyzer=SileroVADAnalyzer(params=VADParams(start_secs=0.2, stop_secs=0.2))
)
stt = OrukSTTService(wait_for_emotions=True)

pipeline = Pipeline([
    transport.input(),
    vad,
    stt,
    user_aggregator,
    llm,
    tts,
    transport.output(),
    assistant_aggregator,
])
```

The source repository includes a complete
[browser agent example](https://github.com/Oruk-AI/pipecat-oruk/blob/main/examples/pipecat_agent.py)
and a [speech-only WebRTC example](https://github.com/Oruk-AI/pipecat-oruk/blob/main/examples/pipecat_browser/README.md)
that requires only an Oruk key.

### Reading phrase estimates

Phrase estimates are independent of transcript deltas and can arrive after
final text. Read `OrukPhraseEmotionFrame` or register an `on_phrase_emotion`
handler. Match results by stream, turn, and phrase identity; do not attach an
estimate to the nearest transcript delta. Speaker IDs restart with each
connection. The integration does not provide word alignment or transcript
confidence scores.

See [result frames and event handlers](https://github.com/Oruk-AI/pipecat-oruk#read-results)
for the output fields and an event-handler example.

## Compatibility

Release candidate `0.1.0rc1` is tested with Pipecat **1.8.1** on Python
**3.11–3.14**. The package accepts Pipecat 1.8.x; other patch releases have not
been separately verified. The upstream Oruk realtime API is also in preview.

The [production demonstration](https://github.com/Oruk-AI/pipecat-oruk/blob/main/docs/DEMO.md)
records real WebRTC transcription, phrase estimates, cancellation, reconnection,
and metering. It does not establish emotion accuracy or a verified spoken
LLM/TTS conversation. See the
[changelog](https://github.com/Oruk-AI/pipecat-oruk/blob/main/CHANGELOG.md)
for subsequent compatibility checks.