> ## Documentation Index
> Fetch the complete documentation index at: https://api-docs.ollang.com/llms.txt
> Use this file to discover all available pages before exploring further.

# OpenAI Text-to-Speech API

> OpenAI's steerable text-to-speech — instruction-controlled delivery and 13 built-in voices, available for AI Dubbing in Ollang.

**OpenAI Text-to-Speech** converts text into spoken audio using OpenAI's audio model line. Its defining feature is **steerability**: alongside the text you supply an instruction describing *how* it should be delivered, and the model adjusts accordingly. This was the first mainstream TTS API to expose delivery as an instruction rather than as a per-voice parameter set.

<Info>
  **Available in Ollang Workflows** as `openaiTts` — AI Dubbing. See the [Text-to-Speech catalog](/providers/text-to-speech).
</Info>

## Models and voices

| Model             | Description                                                                    | Voices |
| ----------------- | ------------------------------------------------------------------------------ | ------ |
| `gpt-4o-mini-tts` | Newest and most reliable; the only model supporting instruction-based steering | All 13 |
| `tts-1`           | Lower latency, lower quality                                                   | 9      |
| `tts-1-hd`        | Higher quality                                                                 | 9      |

The 13 built-in voices are **alloy, ash, ballad, coral, echo, fable, nova, onyx, sage, shimmer, verse, marin,** and **cedar**. `tts-1` and `tts-1-hd` support the subset alloy, ash, coral, echo, fable, onyx, nova, sage, and shimmer.

Output formats: MP3 (default), Opus, AAC, FLAC, WAV, and PCM.

## Steering

On `gpt-4o-mini-tts`, the `instructions` parameter controls:

* **accent**
* **emotional range**
* **intonation**
* **impressions**
* **speed of speech**
* **tone**
* **whispering**

An instruction such as *"speak in a warm, reassuring tone with occasional pauses for emphasis"* changes the performance without changing the voice. For dubbing this is the practical substitute for line-by-line direction.

## Where it fits in a workflow

This is the engine for **volume with direction**: internal communications, training modules, product walkthroughs, compliance content, and first-pass drafts, where delivery needs steering but a bespoke voice identity does not.

A useful pattern: run the full backlog through OpenAI TTS, review, and re-run only the segments that need a distinctive voice through [Cartesia](/apis/tts-apis/cartesia) or [ElevenLabs](/apis/tts-apis/elevenlabs).

## Trade-offs

* **No custom or cloned voices.** A character cannot be carried across a series with a distinctive voice identity the way ElevenLabs, Cartesia, or Azure custom neural voices allow. If brand voice consistency is a requirement, this is the disqualifying constraint.
* **Language support broadly follows Whisper's 50+ languages, but the voices are optimized for English.** Quality outside English is less consistent than the language count suggests — benchmark your specific target languages rather than assuming parity.
* **Narrower expressive ceiling** than [Gemini TTS](/apis/tts-apis/gemini-2-5-tts) or Cartesia on performed content.

## Reference

* [Text-to-Speech provider catalog](/providers/text-to-speech)
* [OpenAI text-to-speech guide](https://developers.openai.com/api/docs/guides/text-to-speech)