Build a voice-enabled Telegram Bot with the Gemini Interactions API

What if your Telegram bot could listen?

Not just read text — actually understand voice messages, reason about them, and talk back with a natural-sounding voice. That's what we're building today: a Telegram bot powered by Google's Gemini API that handles both text and voice, with multi-turn memory and text-to-speech replies.

Here's what it looks like in action:

You send a voice note in any language
Gemini understands the audio and generates a text response
The bot sends the text and speaks the reply back as a voice message

All in about 400 lines of Python. Let's build it.

What We're Using

python-telegram-bot — async Telegram Bot API wrapper
Gemini Interactions API — Google's unified API for text, audio, and multi-turn conversations
Gemini 3.1 Flash Lite — fast, cost-efficient model for reasoning
Gemini 3.1 Flash TTS — text-to-speech model with natural-sounding voices
pydub + ffmpeg — audio format conversion (PCM → OGG/Opus for Telegram)

Prerequisites

Python 3.11+
A Telegram Bot Token (create a bot via @botfather)
A Google AI API Key
ffmpeg installed (brew install ffmpeg on macOS, apt-get install ffmpeg on Linux)

Project Setup

Create a new directory and set up the basics:

mkdir telegram-gemini-voice-bot && cd telegram-gemini-voice-bot

# Create a virtual environment
python -m venv .venv && source .venv/bin/activate

# Install dependencies
pip install 'python-telegram-bot[webhooks]~=21.11' 'google-genai>=1.55.0' 'pydub~=0.25'

Create a .env file with your credentials:

# .env
TELEGRAM_BOT_TOKEN=your-telegram-bot-token
GOOGLE_API_KEY=your-google-api-key
TELEGRAM_SECRET_TOKEN=generate-a-random-string-here
VOICE_ENABLED=true

Step 1: The Skeleton

Create bot.py and start with imports and config:

import base64
import io
import logging
import os
import wave

from google import genai
from pydub import AudioSegment
from telegram import Update
from telegram.ext import (
    Application,
    CommandHandler,
    ContextTypes,
    MessageHandler,
    filters,
)

# Config
TELEGRAM_BOT_TOKEN = os.environ["TELEGRAM_BOT_TOKEN"]
GOOGLE_API_KEY = os.environ["GOOGLE_API_KEY"]
WEBHOOK_URL = os.environ.get("WEBHOOK_URL", "")
TELEGRAM_SECRET_TOKEN = os.environ.get("TELEGRAM_SECRET_TOKEN")
PORT = int(os.environ.get("PORT", "8080"))

REASONING_MODEL = "gemini-3.1-flash-lite-preview"
TTS_MODEL = "gemini-3.1-flash-tts-preview"
TTS_VOICE = "Kore"

logging.basicConfig(
    format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
    level=logging.INFO,
)
logger = logging.getLogger(__name__)

# Initialize the Gemini client
gemini_client = genai.Client(api_key=GOOGLE_API_KEY)

We're using two Gemini models:

Flash Lite for understanding text and audio — it's the fastest, cheapest model in the Gemini family, perfect for a chatbot.
Flash TTS for generating voice replies — it produces natural speech with configurable voices.

Step 2: Understanding Audio with the Interactions API

The Interactions API is Gemini's unified interface. Instead of juggling generateContent and manually tracking conversation history, you call interactions.create() and pass a previous_interaction_id for multi-turn — the server handles the rest.

Here's the core function that sends text or audio to Gemini:

# Track conversation state (in-memory, resets on restart)
last_interaction_ids: dict[int, str] = {}  # chat_id → interaction ID

async def gemini_interact(
    chat_id: int,
    text: str | None = None,
    audio_bytes: bytes | None = None,
) -> str:
    """Send text or audio to Gemini, return the text response."""

    input_parts: list = []

    if audio_bytes is not None:
        # Encode audio as base64 for the API
        audio_b64 = base64.b64encode(audio_bytes).decode("utf-8")
        input_parts.append(
            {"type": "audio", "data": audio_b64, "mime_type": "audio/ogg"}
        )
        input_parts.append(
            {"type": "text", "text": "Listen to this voice message and respond helpfully."}
        )

    if text is not None:
        input_parts.append({"type": "text", "text": text})

    # Simplify input if it's just a single text part
    if len(input_parts) == 1 and input_parts[0]["type"] == "text":
        input_value = input_parts[0]["text"]
    else:
        input_value = input_parts

    kwargs = {
        "model": REASONING_MODEL,
        "input": input_value,
        "system_instruction": (
            "You are a helpful, concise AI assistant on Telegram. "
            "Keep responses short and informative. "
            "Always respond in the same language the user writes or speaks in."
        ),
    }

    # Chain to previous interaction for multi-turn context
    prev_id = last_interaction_ids.get(chat_id)
    if prev_id:
        kwargs["previous_interaction_id"] = prev_id

    interaction = gemini_client.interactions.create(**kwargs)

    # Store this interaction's ID for the next turn
    last_interaction_ids[chat_id] = interaction.id

    return interaction.outputs[-1].text or "(No response generated)"

What's happening here:

Audio input — We base64-encode the voice message bytes and pass them as an audio part alongside a text prompt telling the model what to do.
Multi-turn — We store the interaction.id from each response and pass it as previous_interaction_id on the next call. The server keeps the full conversation history — we don't need to.
Text input — For plain text messages, we send a simple string instead of a multipart array.

Step 3: Text-to-Speech with Gemini TTS

Gemini's TTS model returns raw PCM audio. Telegram voice messages require OGG/Opus format. So we need a conversion pipeline:

Text → Gemini TTS → raw PCM (24kHz, 16-bit, mono) → WAV → OGG/Opus → Telegram

Here's the implementation:

async def gemini_tts(text: str) -> bytes:
    """Convert text to OGG/Opus audio bytes via Gemini TTS."""
    interaction = gemini_client.interactions.create(
        model=TTS_MODEL,
        input=text,
        response_modalities=["AUDIO"],
        generation_config={
            "speech_config": {
                "voice": TTS_VOICE.lower(),
            }
        },
    )

    # Extract PCM audio from response
    pcm_audio = None
    for output in interaction.outputs:
        if output.type == "audio":
            pcm_audio = base64.b64decode(output.data)
            break

    if pcm_audio is None:
        raise RuntimeError("No audio output from TTS")

    # Convert raw PCM → WAV (pydub needs a container format)
    wav_buffer = io.BytesIO()
    with wave.open(wav_buffer, "wb") as wav_file:
        wav_file.setnchannels(1)        # mono
        wav_file.setsampwidth(2)        # 16-bit
        wav_file.setframerate(24000)    # 24kHz
        wav_file.writeframes(pcm_audio)

    wav_buffer.seek(0)
    audio_segment = AudioSegment.from_wav(wav_buffer)

    # WAV → OGG/Opus (Telegram's required format for voice messages)
    ogg_buffer = io.BytesIO()
    audio_segment.export(ogg_buffer, format="ogg", codec="libopus")
    ogg_buffer.seek(0)
    return ogg_buffer.read()

The key detail: Gemini TTS returns raw PCM samples at 24kHz, 16-bit, mono. We wrap it in a WAV header using Python's wave module, then use pydub (which calls ffmpeg under the hood) to re-encode as OGG/Opus — the format Telegram expects for reply_voice().

💡 Inline audio tags: Gemini TTS supports inline audio tags — square-bracket modifiers you can embed directly in your transcript to control delivery. For example, [whispers], [laughs], [excited], [sighs], or [shouting]. You can use these in the text you pass to TTS to make responses more expressive:
"[laughs] Oh that's a great question! [whispers] Let me tell you a secret..."
There's no fixed list — the model understands a wide range of emotions and expressions like [sarcastic], [panicked], [curious], and more.

Find a Gemini TTS prompting guide here: https://dev.to/googleai/how-to-prompt-gemini-31s-new-text-to-speech-model-24bb

Step 4: Telegram Handlers

Now wire it all together with Telegram's handler system. We need two handlers: one for text, one for voice.

Handling Text Messages

async def handle_text(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
    """Handle incoming text messages."""
    chat_id = update.effective_chat.id
    user_text = update.message.text

    logger.info("Text message from chat %s: %s", chat_id, user_text[:100])

    # Show typing indicator
    await update.message.chat.send_action("typing")

    # Get Gemini response
    response_text = await gemini_interact(chat_id, text=user_text)

    # Always send text
    await update.message.reply_text(response_text)

    # Also send voice reply
    try:
        await update.message.chat.send_action("record_voice")
        ogg_audio = await gemini_tts(response_text)
        await update.message.reply_voice(voice=ogg_audio)
    except Exception as e:
        logger.error("TTS failed: %s", e)

Handling Voice Messages

async def handle_voice(update: Update, context: ContextTypes.DEFAULT_TYPE) -> None:
    """Handle incoming voice messages."""
    chat_id = update.effective_chat.id

    logger.info("Voice message from chat %s", chat_id)

    await update.message.chat.send_action("typing")

    # Download voice file from Telegram (already in OGG/Opus format)
    voice = update.message.voice
    voice_file = await voice.get_file()
    audio_bytes = await voice_file.download_as_bytearray()

    # Send audio directly to Gemini — it understands OGG natively
    response_text = await gemini_interact(chat_id, audio_bytes=bytes(audio_bytes))

    # Send text response
    await update.message.reply_text(response_text)

    # Send voice response
    try:
        await update.message.chat.send_action("record_voice")
        ogg_audio = await gemini_tts(response_text)
        await update.message.reply_voice(voice=ogg_audio)
    except Exception as e:
        logger.error("TTS failed: %s", e)

The beautiful thing here: Telegram voice messages are already OGG/Opus, and Gemini understands that format directly. No transcoding needed on input — we just pass the raw bytes.

Step 5: Launching the Bot

Finally, set up the application with both polling (local dev) and webhook (production) support:

def main() -> None:
    """Start the bot."""
    app = Application.builder().token(TELEGRAM_BOT_TOKEN).build()

    # Register handlers
    app.add_handler(CommandHandler("start", start_command))
    app.add_handler(MessageHandler(filters.TEXT & ~filters.COMMAND, handle_text))
    app.add_handler(MessageHandler(filters.VOICE, handle_voice))

    if WEBHOOK_URL:
        # Webhook mode (production / Cloud Run)
        logger.info("Starting webhook on port %s → %s", PORT, WEBHOOK_URL)
        app.run_webhook(
            listen="0.0.0.0",
            port=PORT,
            url_path="webhook",
            webhook_url=f"{WEBHOOK_URL}/webhook",
            secret_token=TELEGRAM_SECRET_TOKEN,
        )
    else:
        # Polling mode (local dev — no public URL needed)
        logger.info("Starting polling mode (no WEBHOOK_URL set)")
        app.run_polling(allowed_updates=Update.ALL_TYPES)


if __name__ == "__main__":
    main()

Polling vs. Webhook:

Polling — The bot asks Telegram "any new messages?" in a loop. Simple, works anywhere. Great for local development.
Webhook — Telegram pushes messages to your URL. More efficient, required for serverless (Cloud Run). The python-telegram-bot library handles webhook registration automatically via run_webhook().

Running Locally

# Load environment variables
export $(cat .env | xargs)

# Start in polling mode (no WEBHOOK_URL = polling)
python bot.py

Open Telegram, find your bot, and send it a voice message. You should get back a text reply and a spoken response. 🎉

Deploy to Cloud Run

Want this running 24/7 with scale-to-zero? Here's the Dockerfile:

FROM python:3.12-slim

# Install ffmpeg for audio conversion (WAV → OGG/Opus)
RUN apt-get update && \
    apt-get install -y --no-install-recommends ffmpeg && \
    rm -rf /var/lib/apt/lists/*

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY bot.py .

ENV PORT=8080
EXPOSE 8080

CMD ["python", "bot.py"]

1. Initialize `gcloud` and Enable APIs

First, make sure your gcloud CLI is configured with the right project:

gcloud init --skip-diagnostics

Enable the required APIs — Secret Manager for storing credentials and Cloud Build for building your container:

gcloud services enable secretmanager.googleapis.com
gcloud services enable cloudbuild.googleapis.com

2. Store Secrets

Never put API keys in environment variables directly. Use Secret Manager:

echo -n "$(grep TELEGRAM_BOT_TOKEN .env | cut -d '=' -f2)" | \
  gcloud secrets create TELEGRAM_BOT_TOKEN --data-file=-
echo -n "$(grep GOOGLE_API_KEY .env | cut -d '=' -f2)" | \
  gcloud secrets create GOOGLE_API_KEY --data-file=-
echo -n "$(openssl rand -base64 32)" | \
  gcloud secrets create TELEGRAM_SECRET_TOKEN --data-file=-

Note: The echo -n flag strips the trailing newline so it's not included in the stored secret. If you see a % at the end of the output when echoing — that's just zsh indicating no trailing newline, not part of your secret.

3. Grant IAM Permissions

Cloud Run source deploys use the default Compute Engine service account to build and run your container. This account needs three additional roles that aren't granted by default:

# Get your project number
PROJECT_NUMBER=$(gcloud projects describe $(gcloud config get-value project) \
  --format='value(projectNumber)')

# Allow the service account to build containers
gcloud projects add-iam-policy-binding $(gcloud config get-value project) \
  --member="serviceAccount:${PROJECT_NUMBER}-compute@developer.gserviceaccount.com" \
  --role="roles/cloudbuild.builds.builder"

# Allow it to read uploaded source code from Cloud Storage
gcloud projects add-iam-policy-binding $(gcloud config get-value project) \
  --member="serviceAccount:${PROJECT_NUMBER}-compute@developer.gserviceaccount.com" \
  --role="roles/storage.objectViewer"

# Allow it to access secrets at runtime
gcloud projects add-iam-policy-binding $(gcloud config get-value project) \
  --member="serviceAccount:${PROJECT_NUMBER}-compute@developer.gserviceaccount.com" \
  --role="roles/secretmanager.secretAccessor"

Why are these needed? The default Compute Engine service account has the roles/editor role, but Editor doesn't include Cloud Build execution, fine-grained Cloud Storage read access, or Secret Manager access. This is a one-time setup per project.

4. Deploy

gcloud run deploy telegram-gemini-bot \
  --source . \
  --region us-central1 \
  --allow-unauthenticated \
  --set-secrets="TELEGRAM_BOT_TOKEN=TELEGRAM_BOT_TOKEN:latest,GOOGLE_API_KEY=GOOGLE_API_KEY:latest,TELEGRAM_SECRET_TOKEN=TELEGRAM_SECRET_TOKEN:latest" \
  --no-cpu-throttling

Note on --no-cpu-throttling: This tells Cloud Run to keep the CPU active even after the initial response is sent. Since the bot needs to process TTS and send a voice reply after acknowledging the message, this prevents the CPU from being throttled, which would otherwise cause the voice reply to be delayed or stall until the next message arrives.

Notice there's no WEBHOOK_URL here — and that's fine. The bot detects Cloud Run automatically via the K_SERVICE environment variable (which Cloud Run always sets) and starts the HTTP server on port 8080. It just won't register a webhook with Telegram yet, so it won't receive messages until Step 5.

5. Set the Real Webhook URL

Grab the actual service URL from the deploy output, then update the service:

gcloud run services update telegram-gemini-bot \
  --region us-central1 \
  --update-env-vars="WEBHOOK_URL=https://telegram-gemini-bot-xxxxx-uc.a.run.app"

Cloud Run gives you HTTPS, auto-scaling, and scale-to-zero — you only pay when someone actually messages the bot.

Troubleshooting Deployment

Error	Cause	Fix
`PERMISSION_DENIED: Build failed because the default service account is missing required IAM permissions`	Compute Engine service account lacks Cloud Build permissions	Grant `roles/cloudbuild.builds.builder` and `roles/storage.objectViewer` (see Step 3)
`Permission denied on secret`	Service account can't access Secret Manager	Grant `roles/secretmanager.secretAccessor` (see Step 3)
`API [secretmanager.googleapis.com] not enabled`	Secret Manager API hasn't been turned on	Run `gcloud services enable secretmanager.googleapis.com`
`API [cloudbuild.googleapis.com] not enabled`	Cloud Build API hasn't been turned on	Say `Y` when prompted, or run `gcloud services enable cloudbuild.googleapis.com`
`Voice replies are slow or delayed`	CPU is being throttled after the text response	Deploy with `--no-cpu-throttling` to keep CPU active for background tasks

The Key Architectural Ideas

1. Server-Side Conversation Memory

Traditional chatbot APIs make you manage the conversation history. You send the full history on every request, and your token costs grow with every turn.

The Interactions API flips this. You pass previous_interaction_id and the server keeps the context:

# Turn 1
i1 = client.interactions.create(model="gemini-3.1-flash-lite-preview", input="Hi, I'm Alex")

# Turn 2 — server remembers "Alex"
i2 = client.interactions.create(
    model="gemini-3.1-flash-lite-preview",
    input="What's my name?",
    previous_interaction_id=i1.id  # ← that's it
)

In our bot, we key this by chat_id, so each Telegram chat gets its own conversation thread.

2. Multimodal Input Without Transcription

Gemini understands audio natively. No whisper, no transcription step, no intermediate text. We send the OGG bytes directly:

input_parts = [
    {"type": "audio", "data": audio_b64, "mime_type": "audio/ogg"},
    {"type": "text", "text": "Listen and respond helpfully."},
]

This means the model hears tone, emphasis, and language — not just words. It can respond in the same language the user speaks, detect questions vs. statements, and pick up on nuance that'd be lost in transcription.

3. Two-Model Architecture

We use two different models for two different jobs:

Job	Model	Why
Understanding + reasoning	`gemini-3.1-flash-lite-preview`	Cheapest, fastest — ideal for a chatbot
Text-to-speech	`gemini-3.1-flash-tts-preview`	Purpose-built for natural speech synthesis

This is cheaper and better than using a single model for both. Flash Lite handles the thinking, TTS handles the speaking.

Going Further

The full source code extends this with:

Mode switching — Agent, Transcribe, and Translate modes with inline keyboards
Configurable voice toggle — /voice on|off to control TTS responses
Language selection — /language Spanish to set the translation target
Mode-specific system instructions — each mode has tailored prompts

These are all just variations on the same gemini_interact() function with different system_instruction values. The core voice pipeline stays the same.

TL;DR: Gemini's Interactions API makes voice bots surprisingly simple. Audio goes in as base64, text comes out, TTS converts it back to speech. The server tracks conversation state so you don't have to. Add a Dockerfile and you've got a production-ready voice assistant on Cloud Run.

Happy hacking! 🚀

Build a voice-enabled Telegram Bot with the Gemini Interactions API

What We're Using

Prerequisites

Project Setup

Step 1: The Skeleton

Step 2: Understanding Audio with the Interactions API

Step 3: Text-to-Speech with Gemini TTS

Step 4: Telegram Handlers

Handling Text Messages

Handling Voice Messages

Step 5: Launching the Bot

Running Locally

Deploy to Cloud Run

1. Initialize `gcloud` and Enable APIs

2. Store Secrets

3. Grant IAM Permissions

4. Deploy

5. Set the Real Webhook URL

Troubleshooting Deployment

The Key Architectural Ideas

1. Server-Side Conversation Memory

2. Multimodal Input Without Transcription

3. Two-Model Architecture

Going Further

Tags

Author

Stats

Published

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Build a voice-enabled Telegram Bot with the Gemini Interactions API

What We're Using

Prerequisites

Project Setup

Step 1: The Skeleton

Step 2: Understanding Audio with the Interactions API

Step 3: Text-to-Speech with Gemini TTS

Step 4: Telegram Handlers

Handling Text Messages

Handling Voice Messages

Step 5: Launching the Bot

Running Locally

Deploy to Cloud Run

1. Initialize gcloud and Enable APIs

2. Store Secrets

3. Grant IAM Permissions

4. Deploy

5. Set the Real Webhook URL

Troubleshooting Deployment

The Key Architectural Ideas

1. Server-Side Conversation Memory

2. Multimodal Input Without Transcription

3. Two-Model Architecture

Going Further

Tags

Author

Stats

Published

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Join the OpenClaw Challenge: $1,200 Prize Pool!

Congrats to the Notion MCP Challenge Winners!

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1. Initialize `gcloud` and Enable APIs