How Podcasters Use AI to Create Multilingual Episodes

If you’ve ever tried to reach a global audience, you know that language can be a major barrier. Translating scripts, hiring voice actors for every dialect, and coordinating recordings across time zones can turn a simple podcast into a logistical nightmare. The good news? Voice‑AI and text‑to‑speech (TTS) tech have matured fast enough that you can generate high‑quality, native‑sounding episodes in any language with just a few lines of code.

In this post, I’ll walk you through a practical workflow that shows how you can:

And if you’re ready to get started, the tool that makes all of this possible is ElevenLabs. You can sign up and try it out here: https://try.elevenlabs.io/kr07zfuqn1bp

You can swap out any of these for alternatives (Azure Translator, AWS Polly, etc.) but the example below sticks to the most developer‑friendly choices.

The starting point is a clean text file (episode.txt) with the episode’s transcript in your native language. You can add metadata (title, tags) in YAML front‑matter if you like, but the core is plain text.

Title: The Future of AI in Healthcare
Episode: 12
Author: Jane Doe
---
Today we’re talking about how AI is transforming diagnostics, treatment plans, and patient care...

We’ll use the Google Cloud Translate API because it’s straightforward and offers a generous free tier. Here’s a quick wrapper:

from google.cloud import translate_v1 as translate

def translate_text(text, target_lang):
    client = translate.Client()
    result = client.translate(text, target_language=target_lang)
    return result["translatedText"]

Call it for each language you want:

languages = ["es", "fr", "de"]  # Spanish, French, German
translations = {lang: translate_text(script_text, lang) for lang in languages}

ElevenLabs gives you two key features:

Below is a minimal example that uses the clone feature. Remember to replace YOUR_API_KEY with the key you get after signing up.

import requests
import json

API_KEY = "YOUR_API_KEY"
BASE_URL = "https://api.elevenlabs.io/v1"

def synthesize(text, voice_id, model_id="eleven_monolingual_v1"):
    headers = {
        "xi-api-key": API_KEY,
        "Content-Type": "application/json"
    }
    payload = {
        "text": text,
        "voice_settings": {"stability": 0.75, "similarity_boost": 0.75},
        "model_id": model_id,
        "voice_id": voice_id
    }
    response = requests.post(f"{BASE_URL}/text-to-speech/{voice_id}/stream", headers=headers, json=payload, stream=True)
    response.raise_for_status()
    return response.raw

def save_audio(stream, filename):
    with open(filename, "wb") as f:
        for chunk in stream.iter_content(chunk_size=4096):
            if chunk:
                f.write(chunk)

Using the clone voice

from eleven import synthesize, save_audio
from translate import translate_text

script_text = open("episode.txt").read()
languages = ["es", "fr", "de"]
voice_id = "YOUR_CLONED_VOICE_ID"  # obtained after training

for lang in languages:
    translated = translate_text(script_text, lang)
    audio_stream = synthesize(translated, voice_id)
    save_audio(audio_stream, f"episode_{lang}.mp3")

Tip: If you haven’t cloned a voice yet, visit https://try.elevenlabs.io/kr07zfuqn1bp to start the process. ElevenLabs offers a generous free trial, so you can experiment with a few voices before committing.

After you’ve got the raw MP3s, a quick FFmpeg pass normalizes volume and converts to a podcast‑friendly format.

for file in episode_*.mp3; do
  ffmpeg -i "$file" -af "loudnorm=I=-16:TP=-1.5:LRA=11" "${file%.mp3}.wav"
done

The loudnorm filter ensures every episode sits at the same perceived loudness, a requirement for many podcast directories.

Commit your script, translation configs, and a tiny workflow file and you’ll have a fully automated pipeline.

name: Generate Podcast

on:
  push:
    branches: [main]
    paths: ["episode.txt"]

jobs:
  build:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: "3.11"
      - name: Install dependencies
        run: pip install google-cloud-translate requests
      - name: Run pipeline
        env:
          ELEVEN_API_KEY: ${{ secrets.ELEVEN_API_KEY }}
        run: |
          python main.py
      - name: Upload to S3
        uses: jakejarvis/s3-sync-action@master
        with:
          args: --acl public-read
        env:
          AWS_S3_BUCKET: ${{ secrets.S3_BUCKET }}
          AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }}
          AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }}

All you need to do is push a new episode.txt, and the action will produce the translated audio, upload it, and you can link the files in your RSS feed.

Creating multilingual podcast episodes used to be a nightmare involving multiple editors, voice actors, and a lot of manual QA. With modern TTS services like ElevenLabs, you can:

All that remains is to drop a text file into your repo and let the pipeline do the heavy lifting.

If you’re ready to experiment, sign up at https://try.elevenlabs.io/kr07zfuqn1bp, create a clone voice, and watch your podcast reach a global audience in a fraction of the time. Happy podcasting!