# AI Models for Speech: Speech-to-Text and Text-to-Speech

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# AI Models for Speech

A structured overview of leading speech-to-text (STT) and text-to-speech (TTS) models, categorized by language support, quality, and access.

## Speech-to-Text (STT / Automatic Speech Recognition)

Open Source / API

### OpenAI Whisper

Highly robust model for transcription and translation in dozens of languages, trained on large amounts of diverse audio data.

- Language: Multilingual (including Dutch)

- Quality: High (estimated based on benchmarks)

- Access: Open source weights / Cloud API

Open Source

### Meta MMS (Massively Multilingual Speech)

Initially designed to make speech technology accessible for thousands of languages, including low-resource languages.

- Language: Vast coverage (hundreds of languages)

- Quality: Good to very good

- Access: Open source (Hugging Face)

## Text-to-Speech (TTS / Voice Synthesis)

Commercial / API

### ElevenLabs Multilingual

Offers highly realistic and emotionally expressive voices with advanced voice cloning functionalities.

- Language: Multilingual (fluent Dutch)

- Quality: Exceptionally high

- Access: Proprietary Cloud API

Open Source

### Coqui TTS (XTTS)

Flexible open-source framework that supports zero-shot voice cloning and can be run locally.

- Language: Multilingual (strong Dutch support)

- Quality: Good

- Access: Open source

Note on estimates: Quality ratings are based on qualitative benchmarks and community experiences, as exact metrics depend heavily on specific audio conditions and use cases. Also check out the [LLMNet Directory](https://directory.llmnet.nl/en/) for a wider range of AI tools.

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