pipecat-gnani

Pipecat service integration for Gnani — high-accuracy Speech-to-Text and low-latency Text-to-Speech for Indian languages.

Gnani is a production-ready speech AI platform supporting 10+ Indian languages, real-time streaming, and multilingual transcription.

This integration is maintained by Gnani.ai.

Installation

pip install pipecat-gnani

Or with uv:

uv add pipecat-gnani

This will also install the gnani-vachana (>= 0.7.9) core SDK as a dependency. The Python import package name remains gnani.

The WebRTC quickstart below needs the example extra (Pipecat runner, Silero VAD, WebRTC stack, and Groq LLM):

pip install "pipecat-gnani[example]"
# or: uv add "pipecat-gnani[example]"

From source:

git clone https://github.com/Gnani-AI-Mintlify/pipecat-gnani.git
cd pipecat-gnani
uv pip install -e ".[example]"

Prerequisites

You need a Gnani API key. Gnani APIs have this.

Set your credentials as environment variables:

export GNANI_API_KEY="your-api-key"

Quickstart example

The example lives in this repository's examples/foundational/ directory (not in the PyPI wheel). Clone the repo, then run a small WebRTC bot: Gnani WebSocket STT/TTS, OpenAI LLM, and the Pipecat runner CLI.

1. Copy examples/foundational/env.example to examples/foundational/.env and set GNANI_API_KEY and GROQ_API_KEY.
2. With the example extra installed (see Install), from the clone root:

cd examples/foundational
python agent.py -t webrtc

If you use uv in a git checkout, from the repository root:

uv sync --extra example
cd examples/foundational
uv run --extra example python agent.py -t webrtc

3. Open the Pipecat playground at http://localhost:7860/client, connect, and speak.

Environment variables

Quick Start — Pipeline snippet

The snippet below shows the core Pipeline([...]) wiring used in the foundational example. See examples/foundational/agent.py for the full runnable version.

import os

from pipecat.audio.vad.silero import SileroVADAnalyzer
from pipecat.pipeline.pipeline import Pipeline
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.services.groq.llm import GroqLLMService
from pipecat.transcriptions.language import Language
from pipecat_gnani import GnaniSTTService, GnaniTTSService

# transport = ...  # WebRTC — see examples/foundational/agent.py

stt = GnaniSTTService(
    api_key=os.environ["GNANI_API_KEY"],
    settings=GnaniSTTService.Settings(language=Language.HI_IN),
)

tts = GnaniTTSService(
    api_key=os.environ["GNANI_API_KEY"],
    settings=GnaniTTSService.Settings(voice="Pranav"),
)

llm = GroqLLMService(
    api_key=os.environ["GROQ_API_KEY"],
    settings=GroqLLMService.Settings(
        model="llama-3.1-8b-instant",
    ),
)

context = LLMContext()
aggregators = LLMContextAggregatorPair(
    context,
    user_params=LLMUserAggregatorParams(vad_analyzer=SileroVADAnalyzer()),
)

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

task = PipelineTask(
    pipeline,
    params=PipelineParams(enable_metrics=True),
)

Swap service classes in agent.py for REST or SSE variants — see the file for options (WebSocket STT + TTS is the default for lowest latency and interruption support).

Service Construction

Speech-to-Text (REST)

from pipecat_gnani import GnaniHttpSTTService
from pipecat.transcriptions.language import Language

stt = GnaniHttpSTTService(
    api_key="your-api-key",
    aiohttp_session=session,
    settings=GnaniHttpSTTService.Settings(
        language=Language.HI_IN,
    ),
)

Speech-to-Text (Streaming WebSocket)

from pipecat_gnani import GnaniSTTService
from pipecat.transcriptions.language import Language

stt = GnaniSTTService(
    api_key="your-api-key",
    settings=GnaniSTTService.Settings(
        language=Language.HI_IN,
    ),
)

Text-to-Speech (REST)

from pipecat_gnani import GnaniHttpTTSService

tts = GnaniHttpTTSService(
    api_key="your-api-key",
    aiohttp_session=session,
    settings=GnaniHttpTTSService.Settings(
        voice="Pranav",
    ),
)

Text-to-Speech (SSE Streaming)

from pipecat_gnani import GnaniSSETTSService

tts = GnaniSSETTSService(
    api_key="your-api-key",
    aiohttp_session=session,
    settings=GnaniSSETTSService.Settings(
        voice="Pranav",
    ),
)

Text-to-Speech (WebSocket Streaming)

from pipecat_gnani import GnaniTTSService

tts = GnaniTTSService(
    api_key="your-api-key",
    settings=GnaniTTSService.Settings(
        voice="Pranav",
    ),
)

Services

STT Services

Streaming PCM Specification

All streaming audio must be sent as raw PCM binary frames — no container format (WAV, MP3) mid-stream.

Frames must be sent at real-time cadence. See STT Realtime — PCM Specification for full details.

TTS Services

Supported Languages

STT Languages (Speech-to-Text)

STT uses BCP-47 locale codes (e.g. hi-IN, bn-IN). Note: STT uses the -IN suffix (unlike TTS).

For the full list of supported languages, see STT — Supported Languages.

TTS Languages (Text-to-Speech)

TTS uses ISO 639 language codes (e.g. hi, bn). Note: TTS does not use the -IN suffix.

For the full list of supported languages, see TTS — Supported Languages.

Available Voices

See the official voice list for the latest supported voices.

Architecture

gnani-vachana (>=0.7.9)  ← Core SDK on PyPI (import as `gnani`)
        ↑
pipecat-gnani            ← This package (Pipecat service adapters)
  ├── STT: REST + WebSocket
  └── TTS: REST + SSE + WebSocket

This package wraps the gnani SDK into Pipecat's SegmentedSTTService, STTService, TTSService, and InterruptibleTTSService base classes.

Documentation

• Gnani API Docs
• Pipecat Docs
• gnani-vachana SDK on PyPI
• STT REST API
• STT Realtime WebSocket
• TTS REST API
• TTS Streaming (SSE)
• TTS Realtime WebSocket

Pipecat Compatibility

Tested with Pipecat v1.5.0.

License

BSD 2-Clause — see LICENSE.