 |
← you are here — Pretrain (~1.5M h). Fine-tuning, full CFG / NFE control. |
 |
+ Self-corrective Alignment. Highest zero-shot fidelity and speaker similarity; also recommended for fine-tuning. |
 |
+ MeanFlow distillation. Few-step inference (NFE = 4), low latency. |
---
## Quick Start
### Installation
```bash
conda create -n dots_tts python=3.10 -y
conda activate dots_tts
python -m pip install --upgrade pip
python -m pip install "git+https://github.com/rednote-hilab/dots.tts.git" \
-c "https://raw.githubusercontent.com/rednote-hilab/dots.tts/main/constraints/recommended.txt"
```
### CLI
```bash
# Continuation voice cloning (reference audio + transcript) — recommended
dots.tts \
--model-name-or-path rednote-hilab/dots.tts-base \
--text "Hello, this is a zero-shot voice cloning demonstration." \
--prompt-audio /path/to/reference.wav \
--prompt-text "The exact transcript of the reference audio." \
--output clone.wav
```
### Python API
```python
from dots_tts.runtime import DotsTtsRuntime
import soundfile as sf
runtime = DotsTtsRuntime.from_pretrained(
"rednote-hilab/dots.tts-base",
precision="bfloat16",
)
result = runtime.generate(
text="Hello, this is a quick speech synthesis test.",
prompt_audio_path="/path/to/reference.wav",
prompt_text="The exact transcript of the reference audio.",
num_steps=10,
guidance_scale=1.2,
)
sf.write("output.wav", result["audio"].float().cpu().squeeze().numpy(), result["sample_rate"])
```
### Recommended sampling settings
| Flag | Recommended | Notes |
|---|---:|---|
| `--num-steps` | `10`–`32` | Flow-matching sampling steps; higher = better quality, slower |
| `--guidance-scale` | `1.2` (default) | Standard CFG; raise modestly for stronger text/timbre adherence |
### Fine-tuning
`dots.tts-base` is the recommended starting point for fine-tuning. See the [training script](https://github.com/rednote-hilab/dots.tts/blob/main/scripts/train_dots_tts.py) and [smoke config](https://github.com/rednote-hilab/dots.tts/blob/main/configs/dots_tts.yaml) in the source repository:
```bash
accelerate launch scripts/train_dots_tts.py --config configs/dots_tts.yaml
```
---
## Architecture
A frozen **AudioVAE** encodes 48 kHz mono waveform into a continuous latent and decodes it back via a BigVGAN-style causal decoder. An **autoregressive backbone** predicts that latent one patch at a time:
- **Semantic encoder** — re-encodes each newly generated VAE patch into a compact embedding for the LLM, stripping high-variance acoustic detail.
- **LLM** — initialized from **Qwen2.5-1.5B-Base**, consumes BPE text directly (no phonemes), emits one hidden state per audio step.
- **AR flow-matching head** — a DiT that conditions on the LLM hidden state and the AR prefix to denoise the next VAE patch, with a frozen CAM++ speaker x-vector as side input.
---
## Performance — `dots.tts-base`
### Seed-TTS-Eval (zero-shot, ~3 s reference)
| Model | Params | test-en WER↓ / SIM↑ | test-zh WER↓ / SIM↑ | test-zh-hard WER↓ / SIM↑ | **Avg WER↓ / SIM↑** |
|---|---:|:---:|:---:|:---:|:---:|
| Seed-TTS | — | 2.25 / 76.2 | 1.12 / 79.6 | 7.59 / 77.6 | 3.65 / 77.8 |
| Qwen3-TTS | 1.7B | **1.23** / 71.7 | 1.22 / 77.0 | 6.76 / 74.8 | 3.07 / 74.5 |
| VoxCPM 2 | 2B | 1.84 / 75.3 | 0.97 / 79.5 | 8.13 / 75.3 | 3.65 / 76.7 |
| **dots.tts-base** | **2B** | 1.34 / **76.8** | **0.96** / **80.5** | **6.46** / **79.2** | **2.92** / **78.8** |
### MiniMax Multilingual (24 languages, average)
| Model | Avg WER↓ | Avg SIM↑ |
|---|:---:|:---:|
| MiniMax | **2.8** | 76.6 |
| Fish-Audio S2 | 3.7 | 78.0 |
| VoxCPM 2 | 5.7 | 82.3 |
| **dots.tts-base** | 6.6 | **83.5** |
See the [project README](https://github.com/rednote-hilab/dots.tts#-performance) for the full per-language breakdown, CV3-Eval and EmergentTTS-Eval results.
---
## Risks and Limitations
- **Misuse risk.** High-fidelity zero-shot voice cloning can produce highly realistic synthetic speech. This checkpoint is intended for research and authorized deployment. Do **not** use it for impersonation, fraud, or disinformation. Combine downstream use with consent-aware reference-audio policies, robust synthetic-speech detection, and content watermarking. Clearly mark AI-generated audio.
- **Low-resource WER gap.** A BPE backbone inherits the text LLM's language coverage at the cost of a higher data appetite. On script-divergent and under-represented languages (Arabic, Hindi, Turkish, Vietnamese) WER is higher than on high-resource languages; speaker similarity is preserved.
- **Speech-heavy training.** The backbone is trained on a speech-heavy mixture. Singing and unified speech + sound generation are not covered.
---
## Citation
```bibtex
@article{dotstts2026,
title = {dots.tts Technical Report},
author = {dots.tts Team},
journal = {arXiv preprint},
year = {2026},
}
```
## License
Released under [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0).
