Pipecat is an open-source Python framework for real-time voice and multimodal conversational agents. The pipecat-plugins-munsit package bridges Munsit TTS into Pipecat pipelines, so you can build Arabic voice agents with low-latency streaming audio.
Your API key is only shown once. Save it securely — set it as the MUNSIT_API_KEY environment variable rather than hardcoding it.
2
Quick start
The service takes your API key and a shared aiohttp session. That's the whole handshake — drop the resulting tts processor into any Pipecat pipeline.
python
import aiohttp
from pipecat_plugins_faseeh import FaseehTTSService
async with aiohttp.ClientSession() as session:
tts = FaseehTTSService(
api_key="your-api-key",
aiohttp_session=session,
)
3
Where it sits in the pipeline
In a typical Pipecat pipeline, data flows through processors in sequence:
data flow
Microphone → Transport → Munsit STT → LLM → Munsit TTS → Transport → Speaker
The user speaks; the transport (e.g. Daily WebRTC) captures audio; Munsit STT converts speech to text; the LLM (e.g. OpenAI GPT-4o) generates a response; Munsit TTS converts it to Arabic audio — streaming PCM16 at 24 kHz — and the transport plays it back. Pipecat's TTSService base class aggregates LLM tokens into complete sentences before calling Munsit: each sentence triggers one HTTP streaming request, and PCM16 chunks are yielded back to the pipeline as they arrive.
4
Full example
A complete Daily-transported agent: auto-creates a Daily room, wires Munsit STT → GPT-4o → Munsit TTS, and speaks first when a participant joins.
agent.py
import asyncio
import os
import aiohttp
from dotenv import load_dotenv
from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.frames.frames import EndFrame, LLMMessagesUpdateFrame
from pipecat.pipeline.pipeline import Pipeline
from pipecat.pipeline.runner import PipelineRunner
from pipecat.pipeline.task import PipelineParams, PipelineTask
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.aggregators.llm_response_universal import (
LLMContextAggregatorPair,
LLMUserAggregatorParams,
)
from pipecat_plugins_munsit import MunsitSTTService
from pipecat.services.openai.llm import OpenAILLMService
from pipecat.transports.daily.transport import DailyParams, DailyTransport
from pipecat_plugins_faseeh import FaseehTTSService
load_dotenv(override=True)
async defmain():
async with aiohttp.ClientSession() as session:
# Auto-create a Daily roomfrom pipecat.transports.daily.utils import DailyRESTHelper, DailyRoomParams
daily_helper = DailyRESTHelper(
daily_api_key=os.getenv("DAILY_API_KEY", ""),
aiohttp_session=session,
)
room = await daily_helper.create_room(DailyRoomParams())
token = await daily_helper.get_token(room.url)
transport = DailyTransport(
room.url,
token,
"Munsit Bot",
DailyParams(
audio_in_enabled=True,
audio_out_enabled=True,
audio_out_sample_rate=48000,
),
)
stt = MunsitSTTService(api_key=os.getenv("MUNSIT_API_KEY", ""))
llm = OpenAILLMService(api_key=os.getenv("OPENAI_API_KEY", ""), model="gpt-4o")
tts = FaseehTTSService(
api_key=os.getenv("MUNSIT_API_KEY"),
aiohttp_session=session,
)
messages = [
{
"role": "system",
"content": "You are a helpful Arabic-speaking assistant. Respond in Arabic.",
}
]
context = LLMContext(messages=messages)
context_aggregator = LLMContextAggregatorPair(
context,
user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
)
pipeline = Pipeline([
transport.input(),
stt,
context_aggregator.user(),
llm,
tts,
transport.output(),
context_aggregator.assistant(),
])
task = PipelineTask(pipeline, params=PipelineParams(allow_interruptions=True))
@transport.event_handler("on_first_participant_joined")
async defon_joined(transport, participant):
await task.queue_frames([LLMMessagesUpdateFrame(messages, run_llm=True)])
@transport.event_handler("on_participant_left")
async defon_left(transport, participant, reason):
await task.queue_frame(EndFrame())
runner = PipelineRunner()
await runner.run(task)
if __name__ == "__main__":
asyncio.run(main())
5
Configuration
Constructor parameters on FaseehTTSService. To pick a voice, browse the Voice Library, listen, and click "Copy Voice ID" next to the one you want.
Parameter
Type
Default
Description
api_key
str
env MUNSIT_API_KEY
Munsit API key
voice_id
str
ar-hijazi-female-2
Voice identifier
model
str
faseeh-v1-preview
TTS model
stability
float
0.5
Voice consistency (0.0–1.0)
speed
float
1.0
Speech rate (0.7–1.2)
sample_rate
int
48000
Output PCM rate requested from the API. 48000 is the engine's native rate — no downsampling, no client-side resampling. Match your transport's audio_out_sample_rate to it.
base_url
str
https://api.munsit.com/api/v1
API base URL
You can also change voice, model, speed, or stability mid-conversation by queueing a TTSUpdateSettingsFrame:
pick a voice
tts = FaseehTTSService(
api_key="your-api-key",
voice_id="ar-uae-male-1", # Paste the copied voice ID here
aiohttp_session=session,
)
No audio output? Verify MUNSIT_API_KEY is set and valid, that voice_id exists in the voice library, and that the pipeline sample rate matches the plugin's (48000 Hz default). High latency? The plugin already uses HTTP streaming for lowest latency — check connectivity to api.munsit.com, or try a voice with faster generation characteristics. Import errors? Make sure both pipecat-ai and pipecat-plugins-munsit are installed; minimum Pipecat version is 0.0.100.
Working with an AI assistant? Every page is available as Markdown: add .md to the URL, or send an Accept: text/markdown header. For the whole documentation in one request, point it at llms-full.txt; the page index is llms.txt. Or use Copy Page, top right.
