Pipecat Gandr TTS

Official Gandr text-to-speech integration for Pipecat, the framework for building voice and multimodal conversational agents.

Note: This integration is maintained by Gandr. As the provider of the TTS service, we keep it current with Pipecat releases and with our own API.

Table of Contents

• Why Gandr
• Pipecat compatibility
• Installation
• Quick start
• Configuration
• Voices
• How the integration works
• Interruptions
• Long text
• Errors
• Metrics
• Pricing
• Environment variables
• Examples
• Requirements
• License
• Support

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Why Gandr

146 ms to first audio byte over the open internet. Client-measured p50, n=25 interleaved runs against each named competitor in the same hours, from a neutral US vantage over a held WebSocket. Every pairwise gap is significant (p=0.0009 / 0.0041 / <0.001).

Read that claim precisely: it is the first audio byte, the moment audio starts arriving on the socket — not the moment a listener hears speech. It is the number a pipeline can actually act on, and it is the one we publish.

116 ms server-side p50, min 104 / max 130.

WER 1.982%, against a 2.171% human baseline. One whisper-large-v3 scorer transcribed everything, including the human baseline, so the two numbers are comparable. n=1,088, zero render errors.

Under load, overflow spills to a fallback lane that can take longer on its first request.

Pipecat compatibility

Tested with Pipecat v1.7.0 on Python 3.12.11 (2026-08-07): clean virtual environment, pip install exit 0, imports, 11/11 unit tests pass, service constructs.

Built against the WebsocketTTSService base class and the audio-context API, so it supports pipecat-ai from 0.0.108 up to (but not including) 2.0.0. The declared floor is 0.0.108 because that is what it was written against; the tested figure above crosses a major version and about a year of API drift, which is the number worth trusting.

Not claimed: no live socket has been opened against the service in that test. It covers install, import, unit tests and construction. Rendering audio costs money, so it is left to whoever runs it.

Installation

uv pip install git+https://github.com/Gandr-AI/gandr-pipecat.git

or with pip:

pip install git+https://github.com/Gandr-AI/gandr-pipecat.git

From source

git clone https://github.com/Gandr-AI/gandr-pipecat.git
cd gandr-pipecat
pip install -e .

Quick start

1. Get an API key

Keys are gnd_… strings. See gandr.ai/docs.

2. Basic usage

import os

from pipecat_gandr import GandrTTSService

tts = GandrTTSService(
    api_key=os.getenv("GANDR_API_KEY"),
    params=GandrTTSService.InputParams(
        voice_id="gandr-mia",
        language="en",
        sample_rate=24000,
    ),
)

3. In a pipeline

pipeline = Pipeline(
    [
        transport.input(),
        stt,
        context_aggregator.user(),
        llm,
        tts,
        transport.output(),
        context_aggregator.assistant(),
    ]
)

A complete runnable pipeline is in examples/foundational/gandr_tts_basic.py.

Configuration

GandrTTSService.InputParams:

Parameter	Type	Default	Range / options	Description
voice_id	str	"gandr-mia"	a stock id, or a gnd: clone id	Voice for synthesis
language	str	"en"	ISO language code	Language of the input text
sample_rate	int	24000	8000, 16000, 22050, 24000	Output rate. Takes priority over a sample_rate passed to the constructor
speed	float	None	0.6 to 1.5	Playback speed, pitch preserving
volume	float	None	0.5 to 2.0	Output gain, soft-ceiling mastered
temperature	float	None	—	Expression control. Omit and the API chooses
cfg_weight	float	None	—	Expression control. Omit and nothing is sent
seed	int	None	—	Fixes the render for a reproducible result
voice_wav_b64	str	None	base64 WAV	Reference audio for a cloned voice

Constructor arguments beyond api_key and params:

Argument	Default	Description
url	wss://tts-west.gandr.ai/ws	Streaming endpoint
text_aggregation_mode	None	How Pipecat aggregates text before synthesis
utterance_timeout_s	30.0	How long to wait for an utterance's closing frame
busy_retry_s	0.5	Wait before retrying after the server answers busy
max_attempts	3	Attempts per utterance, retries included
reconnect_on_interruption	True	See Interruptions

On sample_rate: 24000 is the API's default output rate and the right choice for almost every agent, including telephony. If your transport needs narrowband, let the transport resample — Pipecat does it for free — rather than asking the server for a narrowband stream.

Changing voice mid-session

Voice, language and expression controls are read per utterance, so a TTSUpdateSettingsFrame takes effect on the next thing the bot says:

from pipecat.frames.frames import TTSUpdateSettingsFrame

await task.queue_frame(TTSUpdateSettingsFrame(settings={"voice": "gandr-leo"}))

Cloned voices

A cloned voice is registered per connection. Pass the reference audio once and the service attaches it to the first utterance on each connection, and re-attaches it automatically if the connection is reopened:

tts = GandrTTSService(
    api_key=os.getenv("GANDR_API_KEY"),
    params=GandrTTSService.InputParams(
        voice_id="gnd:your-clone-id",
        voice_wav_b64=reference_wav_base64,
    ),
)

If the server asks for a voice and none was configured, the service raises a clear error naming voice_wav_b64 rather than going silent.

Voices

Stock voices: gandr-mia, gandr-ava, gandr-jenny, gandr-dane, gandr-leo, gandr-lewis. Cloned voices are gnd: identifiers.

How the integration works

The service holds one WebSocket to wss://tts-west.gandr.ai/ws for the life of the pipeline. Each utterance is a JSON message; the server answers with binary frames of raw PCM16LE mono audio as it renders, then a JSON frame that closes the utterance.

The connection carries many utterances but renders one at a time, so the service serialises sends: run_tts queues an utterance and a single sender task delivers it, waits for the closing frame, and only then sends the next. That is what keeps the server's busy backpressure from ever becoming the normal case. If it happens anyway, the send is retried after busy_retry_s.

Audio is delivered through Pipecat's audio-context API, so frames stay bound to the turn that requested them and are dropped cleanly when that turn is cancelled.

Interruptions

The wire protocol has no cancel message. When the user barges in, audio already rendering is discarded client-side — but it would still occupy the connection, and the next turn's first byte would queue behind audio nobody is listening to.

So by default (reconnect_on_interruption=True) the service reopens the connection on interruption. Set it to False to keep the connection and accept that the next turn waits for the interrupted render to drain.

Long text

The API caps a single request's transcript at 2000 characters. Pipecat normally hands over a sentence at a time, so this rarely comes up; when it does, the service splits the text on the cleanest boundary it can find — sentence end first, then a word boundary — and sends the pieces back to back on the same connection under the same turn. Nothing is dropped, and only the last piece closes the turn.

Errors

Failures surface as Pipecat ErrorFrames through push_error, and the turn is always closed with a TTSStoppedFrame so the pipeline never hangs waiting for audio that is not coming. Connection loss is handled by reopening and, where the utterance had not yet reached the wire, resending it.

Metrics

can_generate_metrics() is True.

Time to first byte is stopped on the first audio byte off the wire — the same event the 146 ms claim above measures, so what your dashboard shows is directly comparable to what we publish. Usage metrics are reported per request from the full text of the turn.

The server reports its own timings on the frame that closes each utterance. This service neither consumes nor logs them, so the only latency it ever reports is the one it measured itself.

Pricing

• $10 per million characters, prepaid packs.
• $150 per stream per month.

See gandr.ai/pricing.

Environment variables

GANDR_API_KEY=your_gandr_api_key_here
OPENAI_API_KEY=your_openai_key_here      # if using with an LLM
DEEPGRAM_API_KEY=your_deepgram_key_here  # if using with STT

Examples

• examples/foundational/gandr_tts_basic.py — full pipeline with STT, LLM and TTS over the local audio transport.

uv add "pipecat-ai[deepgram,openai,silero,local]"
python examples/foundational/gandr_tts_basic.py

Tests

pytest tests

The transcript tests import pipecat_gandr._text directly and need neither Pipecat nor a network, so they run anywhere.

Requirements

• Python >= 3.11
• pipecat-ai >= 0.0.108, < 2.0.0
• websockets >= 15.0.1, < 16.0
• pydantic >= 2.0
• loguru >= 0.7.3
• python-dotenv >= 1.1.1

License

MIT. See LICENSE.

Support

• Documentation: gandr.ai/docs
• Email: contact@gandr.ai
• Website: gandr.ai