Available in Ollang Workflows as
deepgram — AI Dubbing. See the Text-to-Speech catalog. Deepgram’s speech recognition side is documented separately under Deepgram.Voices and languages
Aura-2 provides 88 voices across 7 languages:
Voices are addressed as
[model]-[voice]-[language], for example aura-2-thalia-en. Aura 1 remains available as a legacy generation with 12 English voices.
Key capabilities
- Accent depth within languages — five English accents and four Spanish accents mean regional variants can be cast properly rather than approximated. For an English-to-Spanish program targeting several Latin American markets, this is a genuine advantage over engines with one Spanish voice.
- Range of speaking styles across genders, age groups, and delivery — warm conversational, confident professional, and characterful storytelling.
- Low latency at scale — the architecture is built for real-time voice agents, which translates into fast batch throughput for dubbing.
- Efficient batch throughput, which suits large instructional and corporate libraries.
Where it fits in a workflow
Deepgram is a good fit when your target languages sit inside its seven and the content is instructional, corporate, or informational. For an English-to-Spanish training library the accent depth is a real product advantage rather than a rounding difference.Trade-offs
- Seven languages is the narrowest coverage in the catalog. Confirm your language pair before selecting it — this is the most common reason a Deepgram Workflow has to be reconfigured. French in particular has only two voices.
- Built for voice agents, not narration. On emotionally driven or performed content it reads accurately but does not act.
- No voice cloning.