Audio8-TTS-ONNX

About

What Audio8-TTS-ONNX is

SOTA-class multilingual TTS at compact scale, packaged for low-resource CPU inference. [](https://github.com/Audio8-AI/Audio8TTS) [](https://huggingface.co/Audio8/Audio8-TTS-Preview-0.6b) [](https://audio8-ai.github.io/Audio8TTS/) [](https://onnxruntime.ai/) [](https://github.com/Audio8-AI/Audio8TTS/blob/master/LICENSE) Audio8 TTS Preview is a 0.6B-parameter multilingual text-to-speech model with zero-shot voice cloning. This repository provides its CPU-oriented ONNX deployment: weight-only INT4 DualAR models, an FP16 neural audio codec, the tokenizer, and the optional FP16 encoder used to register reference voices. Model files only. Inference, streaming service, and voice-registration code live in the Audio8 TTS repository. Why this ONNX release Precision and footprint Normal synthesis loads only the Slow AR, Fast AR, and codec decoder sessions. On a 16 GB Apple M2 MacBook Air with five ONNX Runtime threads, the service used about 1004 MiB after loading and approximately 1.1-1.2 GiB at synthesis peak. Voice registration releases the online sessions before loading the codec encoder; the measured registration peak was approximately 1.55 GiB. Actual memory use varies by platform and allocator behavior. The online model files occupy about 572 MiB. The complete repository, including the optional voice-registration encoder, is about 968 MiB. Supported Languages Preview status: Language coverage is intentionally limited in this release. For the best results, use one of the 11 recommended languages above. Broader multilingual coverage and Chinese dialect support are planned for future releases. Model Details Audio8 TTS uses a DualAR architecture inspired by Fish Audio S2 Pro. The slow AR transformer predicts one semantic token for each audio frame. The fast AR transformer predicts the frame's codec codebooks, conditioned on the slow hidden state and preceding codebooks. Quick Start Python 3.11 or newer is required. The current release is tested on macOS arm64. 1. Download the code and model The model files are stored at this Hugging Face repository's root. Downloading with --local-dir model creates the exact layout expected by the runtime: 2. Register a reference voice

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How audio & music work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys audio8-tts-onnx for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (audio8-tts-onnx below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/audio/transcriptions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -F model="audio8-tts-onnx" -F file=@audio.mp3

Details

Languages, data & research

Licence

Open weights

Sources

Weights & code

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