I've been experimenting with ElevenLabs for about six months now, primarily for generating synthetic voiceovers for internal training materials. The quality, especially for English, has been impressive enough that I started looking for a more integrated, automated use case within our development workflows. The result is a tool I've just open-sourced that automates the translation and vocalization of written team status updates.
The core problem it solves is asynchronous communication for a distributed team across three time zones. Written updates in a shared document lack nuance and are often skimmed. My hypothesis was that a brief, AI-generated voice summary in each listener's native language would increase engagement and clarity. The tool's pipeline is straightforward but leverages several services:
1. A scheduled process (AWS EventBridge + Lambda) fetches new updates from a source (initially a dedicated Slack channel, but designed for extensibility).
2. The text is cleaned and, if needed, translated via Amazon Translate. The translation step is crucial, as ElevenLabs' multilingual models are good but not always perfect for technical jargon; I prefer to feed it a high-quality translation.
3. The translated text is sent to the ElevenLabs API for speech synthesis. I'm using the `eleven_multilingual_v2` model with specific voice IDs for consistency.
4. The generated audio file is stored in S3, and a signed URL is posted back to the destination (e.g., a different Slack thread, or an SQS queue for other consumers).
Here is the key function that handles the ElevenLabs synthesis. Note the emphasis on controlling cost through explicit character counting and model selection.
```python
import requests
from datetime import datetime
def generate_speech(text: str, voice_id: str, api_key: str) -> bytes:
"""
Calls the ElevenLabs API to synthesize speech.
Includes character count logging for cost tracking.
"""
url = f"https://api.elevenlabs.io/v1/text-to-speech/{voice_id}"
headers = {
"Accept": "audio/mpeg",
"Content-Type": "application/json",
"xi-api-key": api_key
}
data = {
"text": text,
"model_id": "eleven_multilingual_v2",
"voice_settings": {
"stability": 0.4,
"similarity_boost": 0.75
}
}
# Critical for cost governance: log character usage per request.
char_count = len(text)
print(f"[{datetime.utcnow().isoformat()}] Generating speech for {char_count} chars.")
response = requests.post(url, json=data, headers=headers)
response.raise_for_status()
return response.content
```
From a cost-optimization perspective, this project provided concrete data on ElevenLabs' pricing model. We process approximately 5,000 characters per week across all updates. At the scale of a small team, the cost is negligible (under $1/month), but the architectural pattern is designed to scale. The Terraform module for the project includes detailed CloudWatch dashboards tracking characters processed per day and associated estimated costs, mapped from the ElevenLabs price per million characters.
The primary technical challenge was tuning the voice settings for optimal clarity in different languages without losing the speaker's characteristic warmth. The `stability` and `similarity_boost` parameters required significant iteration. Too high stability made the delivery monotonous for a status update; too low introduced unacceptable inconsistencies. The settings in the code block above represent our team's preferred balance.
I'm interested in hearing from others who have integrated ElevenLabs into automated, production-like systems. Specifically:
* Have you encountered issues with long-term voice consistency when using the API over months?
* What strategies are you employing for error handling and retries when the API is intermittently unavailable?
* For those using similar translation-plus-synthesis pipelines, how do you audit and ensure translation accuracy for critical terminology?
The repository includes the full Terraform IAC, the Lambda code, and a setup guide. It's a niche tool, but I believe the pattern of adding asynchronous, multilingual voice layers to text-based workflows has broader applicability.
Oh, I love this specific use case. Using Amazon Translate as a preprocessing step before ElevenLabs is smart. I've found the same thing with other TTS services, especially when dealing with domain-specific terms. Their "multilingual" models often just guess the pronunciation if the word isn't in their core training data.
Have you considered adding a small dictionary or phrase substitution step for team-specific acronyms? It's saved me a ton of weird pronunciations. Something simple, like a YAML file the translation step can reference, so `"K8s"` doesn't turn into "kates" in the final audio 😅
Excited to check out the repo!
Clean code, happy life
Absolutely right about the pronunciation mapping. In my old logging system, I had to do the same thing for alert summaries. The TTS engine would butcher server hostnames like `nyc-web-03` into "nick wee bee zero three".
A YAML file is clean, but you'll hit scaling issues fast if you have dynamic terms. I ended up with a simple regex substitution stage in the pipeline, fed by a key-value store that the team could update via a PR. It's less elegant than a config file, but it meant the ops team could add a new product code name without a deploy.
The real problem is when the translation service itself mangles the term before it even gets to the TTS. Have you seen that happen?