dots-studio/dots.tts-mf-1step

🤗 Hugging Face 来源text-to-speechapache-2.02.2B 参数4.4 GBsafetensors✓ 5 个校验和今天更新
一条命令提交

在你的模型文件夹旁边运行它。它会制作种子、将文件与 Hugging Face 比对,然后提交。你只需开始做种,并粘贴你账户中的密钥。它只读取你的文件,绝不修改。如果愿意,可以先阅读脚本。

curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo dots-studio/dots.tts-mf-1step ./model-folder
需要做种者 →

dots.tts-mf-1step

This repository provides a standalone dots.tts artifact for fixed one-step inference. The sampling contract is stored in config.json and selected automatically. Sampling options should be omitted.

Quick start

Install dots.tts from its official repository, then load this artifact exactly like a standard dots.tts model:

from dots_tts.runtime import DotsTtsRuntime
import soundfile as sf

runtime = DotsTtsRuntime.from_pretrained(
    "dots-studio/dots.tts-mf-1step",
    precision="bfloat16",
)

result = runtime.generate(
    text="Hello, this is a one-step synthesis test.",
    prompt_audio_path="reference.wav",
    prompt_text="The exact transcript of the reference audio.",
)
sf.write("output.wav", result["audio"].float().cpu().squeeze().numpy(), result["sample_rate"])

Scope and limitations

This model was optimized for the fixed one-step path. Multi-step quality is not claimed. A fresh public benchmark evaluation is not included in this artifact. High-fidelity voice cloning must be used only with authorization and consent; do not use it for impersonation, fraud, or disinformation.


Citation

@article{dotstts2026,
  title         = {dots.tts Technical Report},
  author        = {dots.tts Team},
  year          = {2026},
  eprint        = {2606.07080},
  archivePrefix = {arXiv},
  primaryClass  = {cs.SD},
}

License

Released under Apache-2.0.