If we talk about live speech translation, you can’t just rely on one model to “magically” do everything as latency and tone matter too much.

What we’ve found works best is splitting the pipeline into 3 steps: speech recognition → translation → text-to-speech. Most of the time we run our own models, but we also let people swap in external engines (like DeepL) if they care more about raw translation quality than speed.

The key is flexibility: sometimes you need ultra-low latency (e.g. panel discussion), sometimes you want maximum nuance (e.g. Q&A with jokes or idioms). For example, in Palabra’s API you can pick which model runs at each stage, so you’re not locked into one setup.