SILMA TTS for Pipecat

A Pipecat text-to-speech integration for SILMA AI — English and Arabic speech synthesis, with Modern Standard Arabic and the Saudi (Najdi) dialect.

Maintained by SILMA AI. We build the TTS service this integration talks to, so it stays current with the API.

Tested with Pipecat v1.8.1.

Installation

uv add pipecat-silma
# or
pip install pipecat-silma

Prerequisites

An API key from app.silma.ai:

export SILMA_API_KEY="..."

Usage

from pipecat_silma import SilmaTTSService

tts = SilmaTTSService(
    api_key=os.environ["SILMA_API_KEY"],
    settings=SilmaTTSService.Settings(
        model="silma-tts-v2-english",
        voice="emma",
    ),
)

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

Models and voices

SILMA selects the language by model rather than by a language parameter. You can still pass a Pipecat Language, in settings or in a TTSUpdateSettingsFrame, and it will switch the model for you.

Configuration

Constructor arguments, all also available on SilmaTTSService.Settings:

creativity and speed are omitted from the request unless you set them, so SILMA's own defaults apply.

Settings can be changed while the pipeline runs, via Pipecat's TTSUpdateSettingsFrame:

await task.queue_frame(TTSUpdateSettingsFrame(settings=SilmaTTSService.Settings(voice="salma")))

Cloned voices

Upload a voice under Custom Voices at app.silma.ai/voices, then pass its id together with your user id:

SilmaTTSService(
    settings=SilmaTTSService.Settings(
        model="silma-tts-v2-ksa",
        voice="sarah",
        user_id="...",
        custom_audio_id="voice_1769817467123",
    ),
)

Pronunciation hints

SILMA reads phone numbers, emails and links correctly when they are tagged:

You can reach us on <STAG_PN>92005455</STAG_PN> or at <STAG_EMAIL>hi@silma.ai</STAG_EMAIL>.

The integration keeps these tags intact when it splits text, so a tag is never cut in half across two requests. Instruct your LLM to emit them — the example shows how.

Account-level pronunciation overrides configured at app.silma.ai/control apply when you pass user_id and enable_server_pronunciation_overrides=True.

Running the example

examples/foundational.py is a complete voice bot over a local WebRTC transport — no telephony account, no cloud room.

uv add pipecat-silma "pipecat-ai[webrtc,silero,openai,runner]"

export SILMA_API_KEY="..."
export OPENAI_API_KEY="..."

python examples/foundational.py

Open the URL it prints and start talking. For English:

LANGUAGE=en python examples/foundational.py

How it works

SilmaTTSService extends Pipecat's InterruptibleTTSService, which is the right base for a websocket service with no context id and no cancel message: the way to stop SILMA mid-utterance is to drop the socket and reconnect, and that base class does exactly that on an interruption.

Audio. SILMA returns a 24 kHz mono float32 waveform. The service converts it to the 16-bit PCM Pipecat carries in TTSAudioRawFrame, buffering samples that straddle a chunk boundary so a split never produces a click. Because the API takes no sample-rate parameter, the service pins its rate at 24000 and rejects a request for anything else rather than mislabelling the audio — let the output transport resample if your transport needs a different rate.

Text limits. The API caps a request at 250 characters, so a longer aggregation is split on word boundaries and sent as sequential requests over the same socket; their audio arrives in order and is concatenated into one context.

Connection. One websocket is held open and reused across turns, with a keepalive ping every keepalive_interval_s so an idle connection is not closed by the proxy in front of the API. On an interruption the socket is dropped and replaced — SILMA has no cancel message, so that is how speech stops promptly, including when the user talks over the very start of a reply. The replacement is not waited for: the old socket is detached at once and closed in the background, because Pipecat forwards the frame that stops playback only once the service's interruption handler returns.

Development

uv sync --extra dev
uv run pytest          # 40 tests, no API key or network needed
uv run ruff check .
uv run ruff format --check .

Two tests drive a real Pipecat pipeline to check barge-in behaviour, and Pipecat's sentence aggregator needs NLTK's sentence data for that. They skip themselves if the data is absent; to run them:

uv run python -c "import nltk; nltk.download('punkt_tab')"

License

BSD 2-Clause, matching Pipecat. See LICENSE.