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ElevenLabs Text-to-Speech produces some of the most natural-sounding synthetic voices available. Eleven Multilingual v2 is its model for content work, and the property that matters most for localization is not raw naturalness but voice consistency: a voice keeps its identity, personality, and accent across every language it speaks, which is what lets one character sound like the same person in eight dubs.
Available in Ollang Workflows as elevenlabs — AI Dubbing · Default voice engine. See the Text-to-Speech catalog.

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The current ElevenLabs model line

Ollang’s elevenlabs provider runs Multilingual v2. Eleven Turbo v2.5 and Turbo v2 are deprecated in favor of the Flash models.

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Coverage

Multilingual v2’s 29 languages: English (US, UK, Australian, Canadian), Spanish (Spain, Mexico), Portuguese (Brazil, Portugal), French (France, Canada), Arabic (Saudi Arabia, UAE), German, Italian, Dutch, Polish, Swedish, Danish, Finnish, Czech, Slovak, Croatian, Romanian, Bulgarian, Greek, Ukrainian, Russian, Turkish, Hindi, Tamil, Japanese, Korean, Chinese, Indonesian, Malay, and Filipino.

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Key capabilities

  • Cross-language voice identity — the property that makes multi-language character work coherent, and the main reason this remains the default.
  • Voice cloning, so a specific character or brand voice can be carried across an entire catalog rather than recast per language.
  • Emotional range in its main languages, with intonation drawn from the surrounding text.
  • Stability and similarity settings to trade consistency against expressiveness per project.
  • Pronunciation dictionaries to enforce correct rendering of brand names, product names, and technical terms.

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Where it fits in a workflow

ElevenLabs is the default because it is the safest choice across the widest range of narrative content. It earns its place on voiceover, audiobook, and character-driven material in widely spoken languages, particularly where the same voice has to appear across several target languages. It is over-specified for instructional and corporate content, where OpenAI TTS or Deepgram will not be perceptibly worse to most audiences.

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Trade-offs

  • 29 languages is the second-narrowest coverage in the catalog, ahead of only Deepgram. Cartesia covers 44 and Gemini TTS reaches 99.
  • Quality falls off outside the main languages, where the model loses subtlety and reviewers report tonal drift within a single render.

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Reference