> ## 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.

# Deepgram

> Speech-to-text service implementations using Deepgram's real-time transcription and Flux APIs

## Overview

Deepgram provides four STT service implementations:

* `DeepgramSTTService` for real-time speech recognition using Deepgram's standard WebSocket API with support for interim results, language detection, and voice activity detection (VAD)
* `DeepgramFluxSTTService` for advanced conversational AI with Flux capabilities including intelligent turn detection, eager end-of-turn events, multilingual support (with `flux-general-multi` model), and enhanced speech processing for improved response timing
* `DeepgramSageMakerSTTService` for real-time speech recognition using Deepgram Nova models deployed on AWS SageMaker endpoints via HTTP/2 bidirectional streaming
* `DeepgramFluxSageMakerSTTService` for advanced conversational AI using Deepgram Flux models deployed on AWS SageMaker endpoints with native turn detection and low-latency streaming

<CardGroup cols={2}>
  <Card title="Deepgram STT API Reference" icon="code" href="https://reference-server.pipecat.ai/en/latest/api/pipecat.services.deepgram.stt.html">
    Pipecat's API methods for standard Deepgram STT
  </Card>

  <Card title="Deepgram Flux API Reference" icon="code" href="https://reference-server.pipecat.ai/en/latest/api/pipecat.services.deepgram.flux.stt.html">
    Pipecat's API methods for Deepgram Flux STT
  </Card>

  <Card title="Standard STT Example" icon="play" href="https://github.com/pipecat-ai/pipecat/blob/main/examples/voice/voice-deepgram.py">
    Complete example with standard Deepgram STT
  </Card>

  <Card title="Flux STT Example" icon="play" href="https://github.com/pipecat-ai/pipecat/blob/main/examples/voice/voice-deepgram-flux.py">
    Complete example with Deepgram Flux STT
  </Card>

  <Card title="SageMaker Example" icon="play" href="https://github.com/pipecat-ai/pipecat/blob/main/examples/voice/voice-deepgram-sagemaker.py">
    Complete example with Deepgram Nova on SageMaker
  </Card>

  <Card title="Flux SageMaker Example" icon="play" href="https://github.com/pipecat-ai/pipecat/blob/main/examples/voice/voice-deepgram-flux-sagemaker.py">
    Complete example with Deepgram Flux on SageMaker
  </Card>

  <Card title="Deepgram Documentation" icon="book" href="https://developers.deepgram.com/docs/pre-recorded-audio">
    Official Deepgram documentation and features
  </Card>

  <Card title="Deepgram Console" icon="microphone" href="https://console.deepgram.com/signup">
    Access API keys and transcription models
  </Card>
</CardGroup>

## Installation

To use Deepgram STT services, install the required dependencies:

```bash theme={null}
uv add "pipecat-ai[deepgram]"
```

For the SageMaker variant, install both the Deepgram and SageMaker dependencies:

```bash theme={null}
uv add "pipecat-ai[deepgram,sagemaker]"
```

## Prerequisites

### Deepgram Account Setup

Before using `DeepgramSTTService` or `DeepgramFluxSTTService`, you need:

1. **Deepgram Account**: Sign up at [Deepgram Console](https://console.deepgram.com/signup)
2. **API Key**: Generate an API key from your console dashboard
3. **Model Selection**: Choose from available transcription models and features

### Required Environment Variables

* `DEEPGRAM_API_KEY`: Your Deepgram API key for authentication

### AWS SageMaker Setup

Before using `DeepgramSageMakerSTTService` or `DeepgramFluxSageMakerSTTService`, you need:

1. **AWS Account**: With credentials configured (via environment variables, AWS CLI, or instance metadata)
2. **SageMaker Endpoint**: A deployed SageMaker endpoint with a [Deepgram model](https://developers.deepgram.com/docs/deploy-amazon-sagemaker) (Nova for standard service, Flux for advanced turn detection)
3. **Deepgram SDK**: The Deepgram SDK may be needed for certain advanced configurations

## DeepgramSTTService

<ParamField path="api_key" type="str" required>
  Deepgram API key for authentication.
</ParamField>

<ParamField path="base_url" type="str" default="&#x22;&#x22;">
  Custom Deepgram API base URL. Leave empty for the default endpoint. Supports
  `wss://`, `https://`, `ws://`, `http://`, or bare hostname (defaults to
  secure). Preserves the specified scheme, useful for air-gapped or private
  deployments that don't use TLS.
</ParamField>

<ParamField path="encoding" type="str" default="linear16">
  Audio encoding format.
</ParamField>

<ParamField path="channels" type="int" default="1">
  Number of audio channels.
</ParamField>

<ParamField path="multichannel" type="bool" default="False">
  Transcribe each audio channel independently.
</ParamField>

<ParamField path="sample_rate" type="int" default="None">
  Audio sample rate in Hz. When `None`, uses the pipeline's configured sample
  rate.
</ParamField>

<ParamField path="callback" type="str" default="None">
  Callback URL for async transcription delivery.
</ParamField>

<ParamField path="callback_method" type="str" default="None">
  HTTP method for the callback (`"GET"` or `"POST"`).
</ParamField>

<ParamField path="tag" type="Any" default="None">
  Custom billing tag.
</ParamField>

<ParamField path="mip_opt_out" type="bool" default="None">
  Opt out of the Deepgram Model Improvement Program.
</ParamField>

<ParamField path="live_options" type="LiveOptions" default="None" deprecated>
  Legacy configuration options. *Deprecated in v0.0.105. Use
  `settings=DeepgramSTTService.Settings(...)` for runtime-updatable fields and
  direct constructor parameters for connection-level config instead.*
</ParamField>

<ParamField path="settings" type="DeepgramSTTService.Settings" default="None">
  Runtime-configurable settings for the STT service. See [Settings](#settings)
  below.
</ParamField>

<ParamField path="addons" type="Dict" default="None">
  Additional Deepgram features to enable.
</ParamField>

<ParamField path="ttfs_p99_latency" type="float" default="DEEPGRAM_TTFS_P99">
  P99 latency from speech end to final transcript in seconds. Override for your
  deployment.
</ParamField>

### Settings

Runtime-configurable settings passed via the `settings` constructor argument using `DeepgramSTTService.Settings(...)`. These can be updated mid-conversation with `STTUpdateSettingsFrame`. See [Service Settings](/pipecat/fundamentals/service-settings) for details.

| Parameter          | Type              | Default            | Description                                                  |
| ------------------ | ----------------- | ------------------ | ------------------------------------------------------------ |
| `model`            | `str`             | `"nova-3-general"` | Deepgram model to use. *(Inherited from base STT settings.)* |
| `language`         | `Language \| str` | `Language.EN`      | Recognition language. *(Inherited from base STT settings.)*  |
| `detect_entities`  | `bool`            | `False`            | Enable named entity detection.                               |
| `diarize`          | `bool`            | `False`            | Enable speaker diarization.                                  |
| `dictation`        | `bool`            | `False`            | Enable dictation mode (converts commands to punctuation).    |
| `endpointing`      | `int \| bool`     | `None`             | Endpointing sensitivity in ms, or `False` to disable.        |
| `interim_results`  | `bool`            | `True`             | Stream partial recognition results.                          |
| `keyterm`          | `str \| list`     | `None`             | Keyterms to boost recognition accuracy.                      |
| `keywords`         | `str \| list`     | `None`             | Keywords to boost (str or list of str).                      |
| `numerals`         | `bool`            | `False`            | Convert spoken numbers to numerals.                          |
| `profanity_filter` | `bool`            | `True`             | Filter profanity from transcripts.                           |
| `punctuate`        | `bool`            | `True`             | Add punctuation to transcripts.                              |
| `redact`           | `str \| list`     | `None`             | Redact sensitive information.                                |
| `replace`          | `str \| list`     | `None`             | Word replacement rules.                                      |
| `search`           | `str \| list`     | `None`             | Search terms to highlight.                                   |
| `smart_format`     | `bool`            | `False`            | Apply smart formatting to transcripts.                       |
| `utterance_end_ms` | `int`             | `None`             | Silence duration in ms before an utterance-end event.        |

### Usage

```python theme={null}
from pipecat.services.deepgram.stt import DeepgramSTTService

stt = DeepgramSTTService(
    api_key=os.getenv("DEEPGRAM_API_KEY"),
)
```

#### With Custom Settings

```python theme={null}
from pipecat.services.deepgram.stt import DeepgramSTTService

stt = DeepgramSTTService(
    api_key=os.getenv("DEEPGRAM_API_KEY"),
    settings=DeepgramSTTService.Settings(
        model="nova-3-general",
        language="es",
        punctuate=True,
        smart_format=True,
    ),
)
```

### Notes

* **Finalize on VAD stop**: When the pipeline's VAD detects the user has stopped speaking, the service sends a [finalize](https://developers.deepgram.com/docs/finalize) request to Deepgram for faster final transcript delivery.
* **Multilingual support**: Deepgram Nova models support many languages. The default is `Language.EN` (English). Set `language="multi"` in settings to enable multilingual transcription, which will detect and transcribe multiple languages within the same audio stream.
* **Runtime settings updates**: Changing settings via `STTUpdateSettingsFrame` triggers a reconnection with the new parameters. To avoid audio loss, reconnection is deferred until the current user turn ends (i.e., until `UserStoppedSpeakingFrame` is received). Audio frames arriving during the reconnect are buffered and replayed once the new connection is ready.

### Event Handlers

Supports the standard [service connection events](/api-reference/server/events/service-events) (`on_connected`, `on_disconnected`, `on_connection_error`).

## DeepgramFluxSTTService

<Note>
  Since Deepgram Flux provides its own user turn start and end detection, you
  should use `ExternalUserTurnStrategies` to let Flux handle turn management.
  See [User Turn
  Strategies](/api-reference/server/utilities/turn-management/user-turn-strategies)
  for configuration details.
</Note>

<ParamField path="api_key" type="str" required>
  Deepgram API key for authentication.
</ParamField>

<ParamField path="url" type="str" default="wss://api.deepgram.com/v2/listen">
  WebSocket URL for the Deepgram Flux API.
</ParamField>

<ParamField path="sample_rate" type="int" default="None">
  Audio sample rate in Hz. When `None`, uses the pipeline's configured sample
  rate.
</ParamField>

<ParamField path="model" type="str" default="flux-general-en" deprecated>
  Deepgram Flux model to use for transcription. *Deprecated in v0.0.105. Use
  `settings=DeepgramFluxSTTService.Settings(...)` instead.*
</ParamField>

<ParamField path="mip_opt_out" type="bool" default="None">
  Opt out of the Deepgram Model Improvement Program.
</ParamField>

<ParamField path="flux_encoding" type="str" default="linear16">
  Audio encoding format required by the Flux API. Must be `"linear16"`.
</ParamField>

<ParamField path="tag" type="list" default="None">
  Tags to label requests for identification during usage reporting.
</ParamField>

<ParamField path="params" type="InputParams" default="None" deprecated>
  Legacy configuration options. *Deprecated in v0.0.105. Use
  `settings=DeepgramFluxSTTService.Settings(...)` instead.*
</ParamField>

<ParamField path="settings" type="DeepgramFluxSTTService.Settings" default="None">
  Configuration settings for the Flux API. See [Settings](#settings-2) below.
</ParamField>

<ParamField path="should_interrupt" type="bool" default="True">
  Whether the bot should be interrupted when Flux detects user speech.
</ParamField>

### Settings

Runtime-configurable settings passed via the `settings` constructor argument using `DeepgramFluxSTTService.Settings(...)`. These can be updated mid-conversation with `STTUpdateSettingsFrame`. See [Service Settings](/pipecat/fundamentals/service-settings) for details.

| Parameter             | Type              | Default             | Description                                                                                                                                               | On-the-fly |
| --------------------- | ----------------- | ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------- |
| `model`               | `str`             | `"flux-general-en"` | Deepgram Flux model to use. *(Inherited from base STT settings.)*                                                                                         |            |
| `language`            | `Language \| str` | `None`              | Recognition language. *(Inherited from base STT settings.)*                                                                                               |            |
| `eager_eot_threshold` | `float`           | `None`              | EagerEndOfTurn threshold. Lower values trigger faster responses with more LLM calls; higher values are more conservative. `None` disables EagerEndOfTurn. | ✓          |
| `eot_threshold`       | `float`           | `None`              | End-of-turn confidence threshold (default 0.7). Lower = faster turn endings.                                                                              | ✓          |
| `eot_timeout_ms`      | `int`             | `None`              | Time in ms after speech to finish a turn regardless of confidence (default 5000).                                                                         | ✓          |
| `keyterm`             | `list`            | `[]`                | Key terms to boost recognition accuracy for specialized terminology.                                                                                      | ✓          |
| `min_confidence`      | `float`           | `None`              | Minimum average confidence required to produce a `TranscriptionFrame`.                                                                                    |            |
| `language_hints`      | `list[Language]`  | `None`              | Languages to bias transcription toward. Only honored by `flux-general-multi`. Empty list clears hints; `None` means auto-detect.                          | ✓          |

<Note>
  Parameters marked with ✓ in the "On-the-fly" column can be updated mid-stream
  using `STTUpdateSettingsFrame` without requiring a WebSocket reconnect.
</Note>

### Usage

```python theme={null}
from pipecat.services.deepgram.flux import DeepgramFluxSTTService

stt = DeepgramFluxSTTService(
    api_key=os.getenv("DEEPGRAM_API_KEY"),
)
```

#### With EagerEndOfTurn

```python theme={null}
from pipecat.services.deepgram.flux import DeepgramFluxSTTService

stt = DeepgramFluxSTTService(
    api_key=os.getenv("DEEPGRAM_API_KEY"),
    settings=DeepgramFluxSTTService.Settings(
        eager_eot_threshold=0.5,
        eot_threshold=0.8,
        keyterm=["Pipecat", "Deepgram"],
    ),
)
```

#### Multilingual Support

```python theme={null}
from pipecat.services.deepgram.flux import DeepgramFluxSTTService
from pipecat.transcriptions.language import Language

# Use flux-general-multi with language hints
stt = DeepgramFluxSTTService(
    api_key=os.getenv("DEEPGRAM_API_KEY"),
    settings=DeepgramFluxSTTService.Settings(
        model="flux-general-multi",
        language_hints=[Language.EN, Language.ES, Language.FR],
    ),
)
```

#### Updating Settings Mid-Stream

The `keyterm`, `eot_threshold`, `eager_eot_threshold`, `eot_timeout_ms`, and `language_hints` settings can be updated on-the-fly using `STTUpdateSettingsFrame`:

```python theme={null}
from pipecat.frames.frames import STTUpdateSettingsFrame
from pipecat.services.deepgram.flux import DeepgramFluxSTTService
from pipecat.transcriptions.language import Language

# During pipeline execution, update settings without reconnecting
await task.queue_frame(
    STTUpdateSettingsFrame(
        delta=DeepgramFluxSTTService.Settings(
            eot_threshold=0.8,
            keyterm=["Pipecat", "Deepgram"],
        )
    )
)

# Detect-then-lock: narrow language hints mid-stream
await task.queue_frame(
    STTUpdateSettingsFrame(
        delta=DeepgramFluxSTTService.Settings(
            language_hints=[Language.ES],
        )
    )
)
```

This sends a `Configure` message to Deepgram over the existing WebSocket connection, allowing you to adjust turn detection behavior, key terms, and language hints without interrupting the conversation.

### Notes

* **Turn management**: Flux provides its own turn detection via `StartOfTurn`/`EndOfTurn` events and broadcasts `UserStartedSpeakingFrame`/`UserStoppedSpeakingFrame` directly. Use `ExternalUserTurnStrategies` to avoid conflicting VAD-based turn management.
* **VAD is optional**: When Flux drives turn detection, a VAD (such as `SileroVADAnalyzer`) in your transport is not required for core functionality. Include one if you want useful STT metrics; omit it otherwise.
* **On-the-fly configuration**: Supports updating `keyterm`, `eot_threshold`, `eager_eot_threshold`, `eot_timeout_ms`, and `language_hints` mid-stream via `STTUpdateSettingsFrame`. These updates are sent as `Configure` messages over the existing WebSocket connection without requiring a reconnect.
* **EagerEndOfTurn**: Enabling `eager_eot_threshold` provides faster response times by predicting end-of-turn before it is confirmed. EagerEndOfTurn transcripts are pushed as `InterimTranscriptionFrame`s. If the user resumes speaking, a `TurnResumed` event is fired.
* **Multilingual support**: Use the `flux-general-multi` model with `language_hints` to bias transcription toward specific languages (EN, ES, FR, DE, HI, RU, PT, JA, IT, NL). `TranscriptionFrame.language` reflects the detected language for each turn. Omit hints for auto-detection or pass a subset to bias toward expected languages.

### Event Handlers

Supports the standard [service connection events](/api-reference/server/events/service-events) (`on_connected`, `on_disconnected`, `on_connection_error`), plus turn-level events for more granular conversation tracking:

| Event                  | Description                          |
| ---------------------- | ------------------------------------ |
| `on_start_of_turn`     | Start of a new turn detected         |
| `on_turn_resumed`      | A previously paused turn has resumed |
| `on_end_of_turn`       | End of turn detected                 |
| `on_eager_end_of_turn` | Early end-of-turn prediction         |
| `on_update`            | Transcript updated                   |

```python theme={null}
@stt.event_handler("on_start_of_turn")
async def on_start_of_turn(service, transcript):
    print(f"Turn started: {transcript}")

@stt.event_handler("on_end_of_turn")
async def on_end_of_turn(service, transcript):
    print(f"Turn ended: {transcript}")

@stt.event_handler("on_eager_end_of_turn")
async def on_eager_end_of_turn(service, transcript):
    print(f"Early end-of-turn prediction: {transcript}")
```

Turn events receive `(service, transcript)` where `transcript` is the current transcript text. The `on_turn_resumed` event receives only `(service)`.

## DeepgramSageMakerSTTService

<ParamField path="endpoint_name" type="str" required>
  Name of the SageMaker endpoint with Deepgram model deployed.
</ParamField>

<ParamField path="region" type="str" required>
  AWS region where the SageMaker endpoint is deployed (e.g., `"us-east-2"`).
</ParamField>

<ParamField path="encoding" type="str" default="linear16">
  Audio encoding format.
</ParamField>

<ParamField path="channels" type="int" default="1">
  Number of audio channels.
</ParamField>

<ParamField path="multichannel" type="bool" default="False">
  Transcribe each audio channel independently.
</ParamField>

<ParamField path="sample_rate" type="int" default="None">
  Audio sample rate in Hz. When `None`, uses the pipeline's configured sample
  rate.
</ParamField>

<ParamField path="mip_opt_out" type="bool" default="None">
  Opt out of the Deepgram Model Improvement Program.
</ParamField>

<ParamField path="live_options" type="LiveOptions" default="None" deprecated>
  Legacy configuration options. *Deprecated in v0.0.105. Use
  `settings=DeepgramSageMakerSTTService.Settings(...)` instead.*
</ParamField>

<ParamField path="settings" type="DeepgramSageMakerSTTService.Settings" default="None">
  Runtime-configurable settings for the STT service. See [Settings](#settings-3)
  below.
</ParamField>

<ParamField path="ttfs_p99_latency" type="float" default="DEEPGRAM_SAGEMAKER_TTFS_P99">
  P99 latency from speech end to final transcript in seconds. Override for your
  deployment.
</ParamField>

### Settings

Runtime-configurable settings passed via the `settings` constructor argument using `DeepgramSageMakerSTTService.Settings(...)`. These can be updated mid-conversation with `STTUpdateSettingsFrame`. See [Service Settings](/pipecat/fundamentals/service-settings) for details.

The SageMaker service inherits all settings from `DeepgramSTTService.Settings`. See [DeepgramSTTService Settings](#settings) above for the full list.

### Usage

```python theme={null}
from pipecat.services.deepgram.sagemaker.stt import DeepgramSageMakerSTTService

stt = DeepgramSageMakerSTTService(
    endpoint_name=os.getenv("SAGEMAKER_STT_ENDPOINT_NAME"),
    region=os.getenv("AWS_REGION"),
    settings=DeepgramSageMakerSTTService.Settings(
        model="nova-3",
        language="en",
        interim_results=True,
        punctuate=True,
    ),
)
```

### Notes

* **Finalize on VAD stop**: Like `DeepgramSTTService`, the SageMaker service sends a [finalize](https://developers.deepgram.com/docs/finalize) request when the pipeline's VAD detects the user has stopped speaking.
* **SageMaker deployment**: Requires a Deepgram model deployed to an AWS SageMaker endpoint. See the [Deepgram SageMaker deployment guide](https://developers.deepgram.com/docs/deploy-amazon-sagemaker) for setup instructions.
* **Keepalive**: Automatically sends KeepAlive messages every 5 seconds to maintain the connection during periods of silence.

### Event Handlers

Supports the standard [service connection events](/api-reference/server/events/service-events) (`on_connected`, `on_disconnected`, `on_connection_error`).

## DeepgramFluxSageMakerSTTService

<Note>
  Since Deepgram Flux provides its own user turn start and end detection, you
  should use `ExternalUserTurnStrategies` to let Flux handle turn management.
  See [User Turn
  Strategies](/api-reference/server/utilities/turn-management/user-turn-strategies)
  for configuration details.
</Note>

<ParamField path="endpoint_name" type="str" required>
  Name of the SageMaker endpoint with Deepgram Flux model deployed (e.g.,
  `"my-deepgram-flux-endpoint"`).
</ParamField>

<ParamField path="region" type="str" required>
  AWS region where the endpoint is deployed (e.g., `"us-east-2"`).
</ParamField>

<ParamField path="encoding" type="str" default="linear16">
  Audio encoding format. Must be `"linear16"`.
</ParamField>

<ParamField path="sample_rate" type="int" default="None">
  Audio sample rate in Hz. When `None`, uses the pipeline's configured sample
  rate.
</ParamField>

<ParamField path="mip_opt_out" type="bool" default="None">
  Opt out of the Deepgram Model Improvement Program.
</ParamField>

<ParamField path="tag" type="list" default="None">
  Tags to label requests for identification during usage reporting.
</ParamField>

<ParamField path="should_interrupt" type="bool" default="True">
  Whether to interrupt the bot when Flux detects user speech.
</ParamField>

<ParamField path="settings" type="DeepgramFluxSageMakerSTTService.Settings" default="None">
  Runtime-configurable settings. See [Settings](#settings-4) below.
</ParamField>

### Settings

Runtime-configurable settings passed via the `settings` constructor argument using `DeepgramFluxSageMakerSTTService.Settings(...)`. These can be updated mid-conversation with `STTUpdateSettingsFrame`. See [Service Settings](/pipecat/fundamentals/service-settings) for details.

The Flux SageMaker service inherits all settings from `DeepgramFluxSTTService.Settings` with the same on-the-fly configuration support:

| Parameter             | Type              | Default             | Description                                                                                                                                               | On-the-fly |
| --------------------- | ----------------- | ------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------- | ---------- |
| `model`               | `str`             | `"flux-general-en"` | Deepgram Flux model to use. *(Inherited from base STT settings.)*                                                                                         |            |
| `language`            | `Language \| str` | `None`              | Recognition language. *(Inherited from base STT settings.)*                                                                                               |            |
| `eager_eot_threshold` | `float`           | `None`              | EagerEndOfTurn threshold. Lower values trigger faster responses with more LLM calls; higher values are more conservative. `None` disables EagerEndOfTurn. | ✓          |
| `eot_threshold`       | `float`           | `None`              | End-of-turn confidence threshold (default 0.7). Lower = faster turn endings.                                                                              | ✓          |
| `eot_timeout_ms`      | `int`             | `None`              | Time in ms after speech to finish a turn regardless of confidence (default 5000).                                                                         | ✓          |
| `keyterm`             | `list`            | `[]`                | Key terms to boost recognition accuracy for specialized terminology.                                                                                      | ✓          |
| `min_confidence`      | `float`           | `None`              | Minimum average confidence required to produce a `TranscriptionFrame`.                                                                                    |            |
| `language_hints`      | `list[Language]`  | `None`              | Languages to bias transcription toward. Only honored by `flux-general-multi`. Empty list clears hints; `None` means auto-detect.                          | ✓          |

<Note>
  Parameters marked with ✓ in the "On-the-fly" column can be updated mid-stream
  using `STTUpdateSettingsFrame` without requiring a session restart. These
  updates are sent as `Configure` messages to the Flux model over the existing
  connection.
</Note>

### Usage

```python theme={null}
from pipecat.services.deepgram.flux.sagemaker.stt import DeepgramFluxSageMakerSTTService

stt = DeepgramFluxSageMakerSTTService(
    endpoint_name=os.getenv("SAGEMAKER_FLUX_ENDPOINT_NAME"),
    region=os.getenv("AWS_REGION"),
)
```

#### With Custom Settings

```python theme={null}
from pipecat.services.deepgram.flux.sagemaker.stt import DeepgramFluxSageMakerSTTService

stt = DeepgramFluxSageMakerSTTService(
    endpoint_name=os.getenv("SAGEMAKER_FLUX_ENDPOINT_NAME"),
    region=os.getenv("AWS_REGION"),
    settings=DeepgramFluxSageMakerSTTService.Settings(
        model="flux-general-en",
        eot_threshold=0.7,
        eager_eot_threshold=0.5,
        keyterm=["Pipecat", "AI"],
    ),
)
```

#### Updating Settings Mid-Stream

The `keyterm`, `eot_threshold`, `eager_eot_threshold`, `eot_timeout_ms`, and `language_hints` settings can be updated on-the-fly:

```python theme={null}
from pipecat.frames.frames import STTUpdateSettingsFrame
from pipecat.services.deepgram.flux.sagemaker.stt import DeepgramFluxSageMakerSTTService
from pipecat.transcriptions.language import Language

# Update settings without reconnecting
await task.queue_frame(
    STTUpdateSettingsFrame(
        delta=DeepgramFluxSageMakerSTTService.Settings(
            eot_threshold=0.8,
            keyterm=["Pipecat", "Deepgram", "SageMaker"],
            language_hints=[Language.EN],
        )
    )
)
```

### Notes

* **Turn management**: Flux provides native turn detection via `StartOfTurn`/`EndOfTurn` events and broadcasts `UserStartedSpeakingFrame`/`UserStoppedSpeakingFrame` directly. Use `ExternalUserTurnStrategies` to avoid conflicting VAD-based turn management.
* **VAD is optional**: When Flux drives turn detection, a VAD (such as `SileroVADAnalyzer`) in your transport is not required for core functionality. Include one if you want useful STT metrics; omit it otherwise.
* **On-the-fly configuration**: Supports updating `keyterm`, `eot_threshold`, `eager_eot_threshold`, `eot_timeout_ms`, and `language_hints` mid-stream via `STTUpdateSettingsFrame`. These updates are sent as `Configure` messages over the existing HTTP/2 connection without requiring a reconnect.
* **EagerEndOfTurn**: Enabling `eager_eot_threshold` provides faster response times by predicting end-of-turn before it is confirmed. EagerEndOfTurn transcripts are pushed as `InterimTranscriptionFrame`s. If the user resumes speaking, a `TurnResumed` event is fired.
* **Multilingual support**: Use the `flux-general-multi` model with `language_hints` to bias transcription toward specific languages (EN, ES, FR, DE, HI, RU, PT, JA, IT, NL). `TranscriptionFrame.language` reflects the detected language for each turn. Omit hints for auto-detection or pass a subset to bias toward expected languages.
* **SageMaker deployment**: Requires a Deepgram Flux model deployed to an AWS SageMaker endpoint. Unlike Nova models, Flux provides native turn detection and does not require external VAD.
* **No KeepAlive needed**: The Flux protocol uses a watchdog mechanism that sends silence when needed to maintain the connection, so manual KeepAlive messages are not required.

### Event Handlers

Supports the standard [service connection events](/api-reference/server/events/service-events) (`on_connected`, `on_disconnected`, `on_connection_error`), plus turn-level events for granular conversation tracking:

| Event                  | Description                          |
| ---------------------- | ------------------------------------ |
| `on_start_of_turn`     | Start of a new turn detected         |
| `on_turn_resumed`      | A previously paused turn has resumed |
| `on_end_of_turn`       | End of turn detected                 |
| `on_eager_end_of_turn` | Early end-of-turn prediction         |
| `on_update`            | Transcript updated                   |

```python theme={null}
@stt.event_handler("on_start_of_turn")
async def on_start_of_turn(service, transcript):
    print(f"Turn started: {transcript}")

@stt.event_handler("on_end_of_turn")
async def on_end_of_turn(service, transcript):
    print(f"Turn ended: {transcript}")

@stt.event_handler("on_eager_end_of_turn")
async def on_eager_end_of_turn(service, transcript):
    print(f"Early end-of-turn prediction: {transcript}")
```

Turn events receive `(service, transcript)` where `transcript` is the current transcript text. The `on_turn_resumed` event receives only `(service)`.

<Tip>
  The `InputParams` / `params=` / `live_options=` pattern is deprecated as of
  v0.0.105. Use `Settings` / `settings=` instead. See the [Service Settings
  guide](/pipecat/fundamentals/service-settings) for migration details.
</Tip>