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A compact multilingual TTS with zero-shot voice cloning, packaged for low-resource CPU inference.
Audio8 TTS Preview is a compact multilingual text-to-speech model with zero-shot voice cloning. This repository provides its CPU-oriented ONNX deployment: INT8 per-token DualAR models, an FP16 neural audio codec, the tokenizer, the packaged reference voice, and the optional FP16 encoder used to register reference voices.
Model files only. Inference, streaming service, and voice-registration code live in the dedicated 0.1B INT8 ONNX Runtime. The older
onnx_runtime/directory targets the incompatible 0.6B INT4 graphs and must not be used with this model.
Acknowledgments
The per-token ONNX export design (stacked recurrent interface, valid-prefix attention) and the INT8 quantization strategy in this repository were adapted from the community conversion Masterx/Audio8-TTS-Preview-0.1B-ONNX-INT8. The graphs and tooling are independently produced by the Audio8 team from the official checkpoint and do not reuse that repository's artifacts. Thanks to Masterx and the community for exploring ONNX Runtime conversion of the 0.1B model.
The slow AR runs as a per-token recurrent graph (attention + Mamba states passed explicitly) so the CPU workload stays tiny: about 19 ms per slow-AR token and 8 ms per fast-AR frame on our test host (8 threads). A one-time prompt prefill costs about 19 ms per prompt token. Normal synthesis loads only the Slow AR, Fast AR, and codec decoder sessions; the online model files occupy about 0.6 GB. The complete repository, including the voice-registration encoder, is about 1.0 GB. Actual memory use varies by platform and allocator behavior.
Cantonese ·
Chinese ·
Dutch ·
English
French ·
German ·
Italian ·
Japanese
Korean ·
Polish ·
Spanish
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.
Audio8 TTS uses a DualAR architecture inspired by Fish Audio S2 Pro. The 0.1B model uses a Falcon-H1 hybrid slow AR (attention + Mamba) that predicts one semantic token per audio frame; the fast AR predicts the frame's codec codebooks, conditioned on the slow hidden state and preceding codebooks.
Python 3.10 or newer is required. The runtime uses ONNX Runtime's
CPUExecutionProvider; platform-specific ONNX Runtime wheels may be needed on
devices such as Jetson. The current release is tested on macOS arm64 and Linux
x86_64.
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:
The command writes outputs/example.wav and [10, T] codec codes to
outputs/example.npy.
Start the local service and open http://127.0.0.1:8024. The page provides text generation, voice selection, WAV playback/download, reference-voice registration, memory status, and runtime reload. The Swagger API documentation is available at http://127.0.0.1:8024/docs. Upload a 0.5-30 second reference recording, its exact transcript, and a voice name to register a new voice.
The same operation is available through HTTP:
See the complete 0.1B ONNX Runtime guide for streaming output, configuration, memory management, and service controls.
The source Audio8 TTS Preview 0.1B checkpoint is a compact multilingual model; see the base model card for details.
INT8 quantization can change sampled token sequences, so quality should be evaluated for each target language, voice, and deployment setting rather than assuming bit-for-bit equivalence with the source checkpoint. This repository contains the 0.1B INT8 graphs; use the dedicated runtime linked above when running them.
The code and model weights are released under the Apache License 2.0. See the upstream NOTICE for attribution details.
We thank the Fish Audio team for publishing the DualAR architecture used in Fish Audio S2 Pro.
git clone https://github.com/Audio8-AI/Audio8_TTS.gitcd Audio8_TTS/onnx_runtime_0_1b_int8
python3 -m pip install -U "huggingface_hub[cli]"hf download Audio8/audio8-TTS-0.1B-ONNX-INT8 --local-dir modelbash setup.sh"$PWD/.venv/bin/python" scripts/register_default_voice.py # packaged reference -> "default" voicemodel/├── slow_ar_int8.onnx # per-token slow AR (prefill + decode)├── fast_ar_int8.onnx # per-token fast AR├── codec_decoder_fp16.onnx(.data)├── runtime_manifest.json├── tokenizer/tokenizer.json├── reference_codes.npy # packaged fixed reference└── registration/ ├── codec_encoder_fp16.onnx(.data) └── registration_manifest.jsonbash run_infer.sh \ --voice default \ --text "这是一个零样本语音合成的中文测试，声音克隆效果怎么样？" \ --max-new-tokens 256 \ --output outputs/example.wavbash start_server.shcurl http://127.0.0.1:8024/api/voices/register \ -F 'audio=@/absolute/path/reference.wav' \ -F 'text=The exact transcript of the reference recording.' \ -F 'name=speaker_a' \ -F 'overwrite=false'curl http://127.0.0.1:8024/api/tts \ -H 'Content-Type: application/json' \ -d '{"text":"Welcome to Audio8 TTS.","voice_name":"speaker_a","max_new_tokens":256}' \ -o outputs/api.wavcurl http://127.0.0.1:8024/v1/audio/speech \ -H 'Content-Type: application/json' \ -d '{"model":"arktts","input":"Welcome to Audio8 TTS.","voice":"speaker_a","response_format":"wav"}' \ -o outputs/openai.wav