ElevenLabs + Twilio: Building Voice Call Apps
Learn how to combine ElevenLabs text-to-speech with Twilio and Flask to build interactive AI-powered voice call applications.
Stock photo for illustration only, not from the actual event
- Integrate Twilio and ElevenLabs for interactive AI voice calls
- Build a reusable Flask endpoint deployable on any cloud provider
- Leverage advanced voice cloning and TTS from ElevenLabs
- Expose local apps using ngrok to connect with Twilio webhooks
Transforming a regular phone call into an interactive, AI-powered experience is now more accessible than ever. Twilio provides the telecommunications infrastructure to make and receive voice calls, while ElevenLabs supplies state-of-the-art text-to-speech and voice cloning capabilities. Together, developers can build everything from personalized voicemail assistants to real-time phone translation.
The minimal yet functional Flask application setup is structured as follows:
from flask import Flask, request, Response
from twilio.twiml.voice_response import VoiceResponse, Gather
app = Flask(__name__)
@app.route("/voice", methods=["POST"])
def voice():
resp = VoiceResponse()
gather = Gather(input="speech", action="/process", method="POST", timeout=5)
gather.say("Hi! Tell me what you need help with.")
resp.append(gather)
resp.say("Sorry, I didn't catch that. Goodbye!")
resp.hangup()
return Response(str(resp), mimetype="application/xml")
Stock photo for illustration only, not from the actual event
Building voice-first AI applications previously required deep expertise in DSP libraries and self-hosted TTS engines. Modern cloud APIs lower the barrier to entry significantly, allowing developers to implement crystal-clear speech generation with just a few lines of code.
After running the app locally, developers can use ngrok to expose the HTTPS URL and paste it into the Twilio webhook field. Once the caller finishes speaking, Twilio sends a POST request containing the transcribed speech result for further processing.
Source: Dev.to
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