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A 0.6B-parameter multilingual text-to-speech model with zero-shot voice cloning.
Audio8 TTS Preview supports multilingual speech generation and zero-shot voice cloning. This repository contains the complete checkpoint, its 44.1 kHz neural audio codec, tokenizer, processor, and Hugging Face remote code.
Preview status: Language coverage is intentionally limited in this release. For the best results, use one of the 11 recommended languages below. Broader multilingual coverage and Chinese dialect support are planned for future releases.
Cantonese ·
Chinese ·
Dutch ·
English
French ·
German ·
Italian ·
Japanese
Korean ·
Polish ·
Spanish
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.
The bundled codec handles both reference-audio encoding and waveform decoding, so no additional codec checkpoint is required.
Python 3.10 or newer and a CUDA-capable GPU are recommended.
The model uses custom Transformers code. Review the files in this repository,
then load it with trust_remote_code=True.
The reference transcript must match the spoken content in the reference audio.
Omit reference_audio and reference_text when a cloned voice is not needed:
For command-line inference, batching, and supervised fine-tuning, see the Audio8 TTS repository.
Audio8-TTS-Preview-0.6B-ONNX-INT4 packages Audio8 TTS for low-resource CPU inference with ONNX Runtime. Slow and Fast AR weights use weight-only INT4, while activations, KV caches, and the neural audio codec use FP16.
Normal synthesis loads only the Slow AR, Fast AR, and codec decoder sessions. Voice registration releases those sessions before loading the optional codec encoder, keeping peak memory controlled.
Get the ONNX INT4 model and follow the CPU ONNX Runtime guide.
Audio8 TTS now includes an SGLang Omni adapter for production-oriented GPU serving. It is installed as an independent model plugin and does not overwrite SGLang Omni core files.
The released adapter is validated against a pinned SGLang Omni revision and supports both generation without a reference and zero-shot voice cloning. See the SGLang Omni deployment guide for tested versions, installation, server configuration, and API examples.
Audio8 TTS Preview is the smallest model in this comparison at just 0.6B parameters. Despite using only a fraction of the parameters of the other systems, it delivers results in the first tier of industry-leading SOTA TTS models on the benchmarks below. In particular, it achieves the best English WER and competitive Chinese CER on Seed-TTS, while remaining competitive across the CV3 multilingual evaluation.
Lower WER/CER is better; higher SIM is better. Seed-TTS similarity values are shown as percentages.
Parameter counts are calculated directly from the released weight tensors. MOSS-TTS contains 8,489,841,664 parameters. VoxCPM2's main model contains 2,290,004,544 parameters; the separate AudioVAE is not included in the parameter comparison.
Fish S2 Pro was reevaluated because its official evaluation uses its own normalizer. Higgs Audio v2 was evaluated locally because concrete values were unavailable. All other baseline values were collected from their official reports through the VoxCPM repository.
Different normalizers and evaluators make cross-project values reference comparisons rather than a strictly matched ranking. Evaluation coverage does not expand the Preview checkpoint's supported-language claim beyond the 11 languages listed above.
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.
pip install "torch>=2.5.0" "torchaudio>=2.5.0" \ "transformers>=4.57.0,<5" "soundfile>=0.12" "safetensors>=0.4"import soundfile as sfimport torchfrom transformers import AutoModel, AutoProcessor
model_id = "Audio8/Audio8-TTS-Preview-0.6b"device = "cuda" if torch.cuda.is_available() else "cpu"dtype = torch.bfloat16 if device == "cuda" else torch.float32
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)model = AutoModel.from_pretrained( model_id, trust_remote_code=True, dtype=dtype,).eval().to(device)
inputs = processor( text=["Welcome to Audio8 TTS."], reference_audio=["reference.wav"], reference_text=["The exact transcript of the reference recording."], return_tensors="pt",)inputs = {name: value.to(device) for name, value in inputs.items()}
with torch.inference_mode(): output = model.generate( **inputs, max_new_tokens=1024, temperature=0.8, top_p=0.95, top_k=50, do_sample=True, return_dict_in_generate=True, ) waveforms, waveform_lengths = model.decode_audio(output.codes)
audio = waveforms[0, : int(waveform_lengths[0])].float().cpu().numpy()sf.write("output.wav", audio, model.config.codec_sample_rate)inputs = processor( text=["This utterance does not use a reference voice."], return_tensors="pt",)