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Latestnineninesix1.0
nineninesixText → Speech

Gepard 1.0 brings bilingual TTS with voice cloning

An open, autoregressive text-to-speech model targets English and Spanish under a permissive Apache 2.0 license.

Jun 22, 2026
UpdateApache 2.0
Gepard 1.0

A team publishing under the handle nineninesix has released Gepard 1.0, an autoregressive text-to-speech model that supports voice cloning across English and Spanish. The model arrives on Hugging Face under the permissive Apache 2.0 license, which allows both commercial and research use.

Gepard uses an autoregressive approach to generation, producing speech token by token rather than in a single pass. That design has become common in recent open TTS work, trading some efficiency for expressive, natural-sounding output and the flexibility to adapt to a target voice from a reference sample.

Why it matters

Open speech synthesis is a fast-moving corner of the AI ecosystem, and bilingual coverage remains relatively scarce compared to English-only releases. A model that handles both English and Spanish with cloning support lowers the barrier for developers building voice assistants, dubbing tools, and accessibility features across two of the world's most widely spoken languages.

A few practical notes for anyone evaluating the release:

  • Languages: English and Spanish
  • Capability: voice cloning from a reference sample
  • License: Apache 2.0, permissive for commercial use

The repository does not publish detailed benchmark figures or parameter counts, so teams will want to test Gepard against their own audio quality and latency requirements before deploying. As an initial 1.0 release, it establishes a baseline the project can build on.

Sources

  • nineninesix/gepard-1.0

    Hugging Face

    Visit
OlderAgents-A1: A 35B MoE Built for Agentic ScalingInternScience · Reasoning · 4 months agoNewerBaidu's PP-OCRv6 packs 50-language OCR into tiny modelsBaidu · Vision-Language · 4 months ago

Get the model

Hugging Face

Specs

Languagesen, es
Size1.1 GB
PrecisionBF16
ArchitectureQwen3_5ForCausalLM
LicenseAPACHE-2.0
Downloads1.3K
Likes134

Can you run it?

Runs on just about anything.

  • BF16 (as published)

    Any modern laptop · 8 GB RAM

    2.1 GB
  • FP8

    Any modern laptop · 8 GB RAM

    1.6 GB
Your machine

Based on Hugging Face weights metadata. Assumes an 8K context and ~1 GB runtime overhead; actual needs vary. How we estimate


Modalities

Text → Speech

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