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Edge0's Audio8 Clones Any Voice Across 23 Languages With Just 600M Parameters

Edge0 released Audio8-TTS-Realistic-Air , a 0.6B TTS model with zero-shot voice cloning, Apache 2.0 licensed. Supports 23 languages including Arabic, Hindi, Thai, Vietnamese, adding 12 to the previous Preview release. Scores 1.25% error on Seed-TTS, competitive with 4B to 8.5B parameter rivals.

Edge0's Audio8 Clones Any Voice Across 23 Languages With Just 600M Parameters
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AlphaSignal
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AlphaSignal
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Takeaways

  • Edge0 released Audio8-TTS-Realistic-Air , a 0.6B TTS model with zero-shot voice cloning, Apache 2.0 licensed.
  • Supports 23 languages including Arabic, Hindi, Thai, Vietnamese, adding 12 to the previous Preview release.
  • Scores 1.25% error on Seed-TTS, competitive with 4B to 8.5B parameter rivals.
  • DualAR architecture: slow transformer picks semantic tokens, fast transformer fills codec codebooks at 44.1 kHz.
  • Ships with SGLang Omni adapter exposing an OpenAI-compatible /v1/audio/speech endpoint.
  • Quality varies by language; Thai hits 48% WER while German and English stay under 3.3%.

Edge0 has released Audio8-TTS-Realistic-Air , a 601 million-parameter text-to-speech model that supports zero-shot voice cloning across 23 languages. Zero-shot cloning lets the model imitate a speaker from a reference recording and transcript without additional training.

Realistic Air fine-tunes Audio8-TTS-Preview-0.6B, with improved speech realism claimed by Edge0 while retaining the same compact architecture. The Apache 2.0 release bundles the model weights, tokenizer, custom Transformers code, and a neural audio codec that produces 44.1 kHz audio.

Dual autoregression keeps the model compact

Audio8 follows the DualAR architecture used by Fish Audio S2 Pro. A 24-layer autoregressive transformer predicts one semantic token for each audio frame, tracking content and timing across the utterance. A smaller four-layer transformer then predicts the codec codebooks that represent acoustic details such as timbre and intonation.

The codec reconstructs those codebook values into a waveform. Splitting sequence planning from frame-level detail allows the main model to remain relatively small while producing high-resolution audio.

Architecture and runtime specifications

  • Main model: 601 million parameters, excluding the codec
  • Slow transformer: 24 layers with a hidden width of 896
  • Fast transformer: 4 layers
  • Codec: 10 codebooks with 4,096 entries each
  • Audio: 44.1 kHz output at about 21.5 codec frames per second
  • Context: up to 2,048 combined text and audio positions
  • Precision: BF16
  • Tested hardware: NVIDIA H20

Text, reference audio, and generated audio share the 2,048-position context window, so longer prompts leave less room for synthesis. At 21.5 audio frames per second, 2,048 audio-only positions would represent roughly 95 seconds, although practical output limits are lower once the other inputs are included.

Reporting is indexed from AlphaSignal. Rights remain with the original publisher and cited sources.

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Sources

  1. 01huggingface.co
  2. 02github.com
  3. 03arxiv.org
  4. 04huggingface.co