| title | category | tags | difficulty | description | style | githubUrl | demonstrates | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
ElevenLabs Change Language |
pipeline-tts |
|
intermediate |
Shows how to use the ElevenLabs TTS model to change the language of the agent. |
step-by-step |
|
This example demonstrates how to build a multilingual voice agent that can switch between languages mid-call by updating ElevenLabs TTS and Deepgram STT on the fly. The agent greets callers in English, switches to Spanish, French, German, or Italian when asked, and replies with a native greeting in the new language.
- Add a
.envin this directory with your LiveKit and provider credentials:LIVEKIT_URL=your_livekit_url LIVEKIT_API_KEY=your_api_key LIVEKIT_API_SECRET=your_api_secret DEEPGRAM_API_KEY=your_deepgram_key ELEVENLABS_API_KEY=your_elevenlabs_key - Install dependencies:
pip install python-dotenv "livekit-agents[silero,deepgram,elevenlabs]"
Start by importing the necessary modules, loading your environment, and configuring logging for the agent.
import logging
from dotenv import load_dotenv
from livekit.agents import JobContext, JobProcess, Agent, AgentSession, AgentServer, cli, inference, function_tool
from livekit.plugins import deepgram, elevenlabs, silero
load_dotenv()
logger = logging.getLogger("language-switcher")
logger.setLevel(logging.INFO)
server = AgentServer()Preload VAD once per process to reduce connection latency. Configure the RTC session with Deepgram STT, ElevenLabs TTS, and an inference LLM.
import logging
from dotenv import load_dotenv
from livekit.agents import JobContext, JobProcess, Agent, AgentSession, AgentServer, cli, inference, function_tool
from livekit.plugins import deepgram, elevenlabs, silero
load_dotenv()
logger = logging.getLogger("language-switcher")
logger.setLevel(logging.INFO)
server = AgentServer()def prewarm(proc: JobProcess):
proc.userdata["vad"] = silero.VAD.load()
server.setup_fnc = prewarm
class LanguageSwitcherAgent(Agent):
def __init__(self) -> None:
super().__init__(
instructions="""
You are a helpful assistant communicating through voice.
You can switch to a different language if asked.
Don't use any unpronounceable characters.
"""
)
self.current_language = "en"
self.language_names = {
"en": "English",
"es": "Spanish",
"fr": "French",
"de": "German",
"it": "Italian",
}
self.deepgram_language_codes = {
"en": "en",
"es": "es",
"fr": "fr-CA",
"de": "de",
"it": "it",
}
self.greetings = {
"en": "Hello! I'm now speaking in English. How can I help you today?",
"es": "¡Hola! Ahora estoy hablando en español. ¿Cómo puedo ayudarte hoy?",
"fr": "Bonjour! Je parle maintenant en français. Comment puis-je vous aider aujourd'hui?",
"de": "Hallo! Ich spreche jetzt Deutsch. Wie kann ich Ihnen heute helfen?",
"it": "Ciao! Ora sto parlando in italiano. Come posso aiutarti oggi?",
}
async def on_enter(self):
await self.session.say(
"Hi there! I can speak in multiple languages including Spanish, French, German, and Italian. "
"Just ask me to switch to any of these languages. How can I help you today?"
)
@server.rtc_session()
async def entrypoint(ctx: JobContext):
ctx.log_context_fields = {"room": ctx.room.name}
session = AgentSession(
stt=deepgram.STT(model="nova-2-general", language="en"),
llm=inference.LLM(model="openai/gpt-4o"),
tts=elevenlabs.TTS(model="eleven_turbo_v2_5", language="en"),
vad=ctx.proc.userdata["vad"],
preemptive_generation=True,
)
await session.start(agent=LanguageSwitcherAgent(), room=ctx.room)
await ctx.connect()Next we'll add a helper to swap STT/TTS languages, and function tools that let the LLM trigger language changes.
import logging
from dotenv import load_dotenv
from livekit.agents import JobContext, JobProcess, Agent, AgentSession, AgentServer, cli, inference, function_tool
from livekit.plugins import deepgram, elevenlabs, silero
load_dotenv()
logger = logging.getLogger("language-switcher")
logger.setLevel(logging.INFO)
server = AgentServer()
def prewarm(proc: JobProcess):
proc.userdata["vad"] = silero.VAD.load()
server.setup_fnc = prewarm
class LanguageSwitcherAgent(Agent):
def __init__(self) -> None:
super().__init__(
instructions="""
You are a helpful assistant communicating through voice.
You can switch to a different language if asked.
Don't use any unpronounceable characters.
"""
)
self.current_language = "en"
self.language_names = {
"en": "English",
"es": "Spanish",
"fr": "French",
"de": "German",
"it": "Italian",
}
self.deepgram_language_codes = {
"en": "en",
"es": "es",
"fr": "fr-CA",
"de": "de",
"it": "it",
}
self.greetings = {
"en": "Hello! I'm now speaking in English. How can I help you today?",
"es": "¡Hola! Ahora estoy hablando en español. ¿Cómo puedo ayudarte hoy?",
"fr": "Bonjour! Je parle maintenant en français. Comment puis-je vous aider aujourd'hui?",
"de": "Hallo! Ich spreche jetzt Deutsch. Wie kann ich Ihnen heute helfen?",
"it": "Ciao! Ora sto parlando in italiano. Come posso aiutarti oggi?",
}
async def on_enter(self):
await self.session.say(
"Hi there! I can speak in multiple languages including Spanish, French, German, and Italian. "
"Just ask me to switch to any of these languages. How can I help you today?"
) async def _switch_language(self, language_code: str) -> None:
"""Helper method to switch the language"""
if language_code == self.current_language:
await self.session.say(f"I'm already speaking in {self.language_names[language_code]}.")
return
if self.session.tts is not None:
self.session.tts.update_options(language=language_code)
if self.session.stt is not None:
deepgram_language = self.deepgram_language_codes.get(language_code, language_code)
self.session.stt.update_options(language=deepgram_language)
self.current_language = language_code
await self.session.say(self.greetings[language_code])
@function_tool
async def switch_to_english(self):
"""Switch to speaking English"""
await self._switch_language("en")
@function_tool
async def switch_to_spanish(self):
"""Switch to speaking Spanish"""
await self._switch_language("es")
@function_tool
async def switch_to_french(self):
"""Switch to speaking French"""
await self._switch_language("fr")
@function_tool
async def switch_to_german(self):
"""Switch to speaking German"""
await self._switch_language("de")
@function_tool
async def switch_to_italian(self):
"""Switch to speaking Italian"""
await self._switch_language("it")@server.rtc_session()
async def entrypoint(ctx: JobContext):
ctx.log_context_fields = {"room": ctx.room.name}
session = AgentSession(
stt=deepgram.STT(model="nova-2-general", language="en"),
llm=inference.LLM(model="openai/gpt-4o"),
tts=elevenlabs.TTS(model="eleven_turbo_v2_5", language="en"),
vad=ctx.proc.userdata["vad"],
preemptive_generation=True,
)
await session.start(agent=LanguageSwitcherAgent(), room=ctx.room)
await ctx.connect()Use the CLI runner to start the agent server so it can respond to language-change requests.
import logging
from dotenv import load_dotenv
from livekit.agents import JobContext, JobProcess, Agent, AgentSession, AgentServer, cli, inference, function_tool
from livekit.plugins import deepgram, elevenlabs, silero
load_dotenv()
logger = logging.getLogger("language-switcher")
logger.setLevel(logging.INFO)
server = AgentServer()
def prewarm(proc: JobProcess):
proc.userdata["vad"] = silero.VAD.load()
server.setup_fnc = prewarm
class LanguageSwitcherAgent(Agent):
def __init__(self) -> None:
super().__init__(
instructions="""
You are a helpful assistant communicating through voice.
You can switch to a different language if asked.
Don't use any unpronounceable characters.
"""
)
self.current_language = "en"
self.language_names = {
"en": "English",
"es": "Spanish",
"fr": "French",
"de": "German",
"it": "Italian",
}
self.deepgram_language_codes = {
"en": "en",
"es": "es",
"fr": "fr-CA",
"de": "de",
"it": "it",
}
self.greetings = {
"en": "Hello! I'm now speaking in English. How can I help you today?",
"es": "¡Hola! Ahora estoy hablando en español. ¿Cómo puedo ayudarte hoy?",
"fr": "Bonjour! Je parle maintenant en français. Comment puis-je vous aider aujourd'hui?",
"de": "Hallo! Ich spreche jetzt Deutsch. Wie kann ich Ihnen heute helfen?",
"it": "Ciao! Ora sto parlando in italiano. Come posso aiutarti oggi?",
}
async def on_enter(self):
await self.session.say(
"Hi there! I can speak in multiple languages including Spanish, French, German, and Italian. "
"Just ask me to switch to any of these languages. How can I help you today?"
)
async def _switch_language(self, language_code: str) -> None:
"""Helper method to switch the language"""
if language_code == self.current_language:
await self.session.say(f"I'm already speaking in {self.language_names[language_code]}.")
return
if self.session.tts is not None:
self.session.tts.update_options(language=language_code)
if self.session.stt is not None:
deepgram_language = self.deepgram_language_codes.get(language_code, language_code)
self.session.stt.update_options(language=deepgram_language)
self.current_language = language_code
await self.session.say(self.greetings[language_code])
@function_tool
async def switch_to_english(self):
"""Switch to speaking English"""
await self._switch_language("en")
@function_tool
async def switch_to_spanish(self):
"""Switch to speaking Spanish"""
await self._switch_language("es")
@function_tool
async def switch_to_french(self):
"""Switch to speaking French"""
await self._switch_language("fr")
@function_tool
async def switch_to_german(self):
"""Switch to speaking German"""
await self._switch_language("de")
@function_tool
async def switch_to_italian(self):
"""Switch to speaking Italian"""
await self._switch_language("it")
@server.rtc_session()
async def entrypoint(ctx: JobContext):
ctx.log_context_fields = {"room": ctx.room.name}
session = AgentSession(
stt=deepgram.STT(model="nova-2-general", language="en"),
llm=inference.LLM(model="openai/gpt-4o"),
tts=elevenlabs.TTS(model="eleven_turbo_v2_5", language="en"),
vad=ctx.proc.userdata["vad"],
preemptive_generation=True,
)
await session.start(agent=LanguageSwitcherAgent(), room=ctx.room)
await ctx.connect()if __name__ == "__main__":
cli.run_app(server)python elevenlabs_change_language.py consoleTry saying:
- "Switch to Spanish"
- "Can you speak French?"
- "Let's talk in German"
- "Change to Italian"
| Language | Code | Deepgram Code | Example Phrase |
|---|---|---|---|
| English | en | en | "Hello! How can I help you?" |
| Spanish | es | es | "¡Hola! ¿Cómo puedo ayudarte?" |
| French | fr | fr-CA | "Bonjour! Comment puis-je vous aider?" |
| German | de | de | "Hallo! Wie kann ich Ihnen helfen?" |
| Italian | it | it | "Ciao! Come posso aiutarti?" |
- The agent greets in English and waits for a language change request.
- A function tool routes to
_switch_language(), which updates both TTS and STT viaupdate_options(). - The agent tracks the current language to avoid redundant switches.
- A native greeting confirms the change, and the rest of the conversation stays in the selected language until switched again.
import logging
from dotenv import load_dotenv
from livekit.agents import JobContext, JobProcess, Agent, AgentSession, AgentServer, cli, inference, function_tool
from livekit.plugins import deepgram, elevenlabs, silero
load_dotenv()
logger = logging.getLogger("language-switcher")
logger.setLevel(logging.INFO)
server = AgentServer()
class LanguageSwitcherAgent(Agent):
def __init__(self) -> None:
super().__init__(
instructions="""
You are a helpful assistant communicating through voice.
You can switch to a different language if asked.
Don't use any unpronounceable characters.
"""
)
self.current_language = "en"
self.language_names = {
"en": "English",
"es": "Spanish",
"fr": "French",
"de": "German",
"it": "Italian",
}
self.deepgram_language_codes = {
"en": "en",
"es": "es",
"fr": "fr-CA",
"de": "de",
"it": "it",
}
self.greetings = {
"en": "Hello! I'm now speaking in English. How can I help you today?",
"es": "¡Hola! Ahora estoy hablando en español. ¿Cómo puedo ayudarte hoy?",
"fr": "Bonjour! Je parle maintenant en français. Comment puis-je vous aider aujourd'hui?",
"de": "Hallo! Ich spreche jetzt Deutsch. Wie kann ich Ihnen heute helfen?",
"it": "Ciao! Ora sto parlando in italiano. Come posso aiutarti oggi?",
}
async def on_enter(self):
await self.session.say(
"Hi there! I can speak in multiple languages including Spanish, French, German, and Italian. "
"Just ask me to switch to any of these languages. How can I help you today?"
)
async def _switch_language(self, language_code: str) -> None:
"""Helper method to switch the language"""
if language_code == self.current_language:
await self.session.say(f"I'm already speaking in {self.language_names[language_code]}.")
return
if self.session.tts is not None:
self.session.tts.update_options(language=language_code)
if self.session.stt is not None:
deepgram_language = self.deepgram_language_codes.get(language_code, language_code)
self.session.stt.update_options(language=deepgram_language)
self.current_language = language_code
await self.session.say(self.greetings[language_code])
@function_tool
async def switch_to_english(self):
"""Switch to speaking English"""
await self._switch_language("en")
@function_tool
async def switch_to_spanish(self):
"""Switch to speaking Spanish"""
await self._switch_language("es")
@function_tool
async def switch_to_french(self):
"""Switch to speaking French"""
await self._switch_language("fr")
@function_tool
async def switch_to_german(self):
"""Switch to speaking German"""
await self._switch_language("de")
@function_tool
async def switch_to_italian(self):
"""Switch to speaking Italian"""
await self._switch_language("it")
def prewarm(proc: JobProcess):
proc.userdata["vad"] = silero.VAD.load()
server.setup_fnc = prewarm
@server.rtc_session()
async def entrypoint(ctx: JobContext):
ctx.log_context_fields = {"room": ctx.room.name}
session = AgentSession(
stt=deepgram.STT(model="nova-2-general", language="en"),
llm=inference.LLM(model="openai/gpt-4o"),
tts=elevenlabs.TTS(model="eleven_turbo_v2_5", language="en"),
vad=ctx.proc.userdata["vad"],
preemptive_generation=True,
)
await session.start(agent=LanguageSwitcherAgent(), room=ctx.room)
await ctx.connect()
if __name__ == "__main__":
cli.run_app(server)Agent: "Hi there! I can speak in multiple languages..."
User: "Can you speak Spanish?"
Agent: "¡Hola! Ahora estoy hablando en español. ¿Cómo puedo ayudarte hoy?"
User: "¿Cuál es el clima?"
Agent: [Responds in Spanish about the weather]
User: "Now switch to French"
Agent: "Bonjour! Je parle maintenant en français. Comment puis-je vous aider aujourd'hui?"