README.md

---
license: mit
pipeline_tag: text-to-speech
---

# DMOSpeech 2: Reinforcement Learning for Duration Prediction in Metric-Optimized Speech Synthesis

Diffusion-based text-to-speech (TTS) systems have made remarkable progress in zero-shot speech synthesis, yet optimizing all components for perceptual metrics remains challenging. Prior work with DMOSpeech demonstrated direct metric optimization for speech generation components, but duration prediction remained unoptimized. This paper presents DMOSpeech 2, which extends metric optimization to the duration predictor through a reinforcement learning approach. The proposed system implements a novel duration policy framework using group relative preference optimization (GRPO) with speaker similarity and word error rate as reward signals. By optimizing this previously unoptimized component, DMOSpeech 2 creates a more complete metric-optimized synthesis pipeline. Additionally, this paper introduces teacher-guided sampling, a hybrid approach leveraging a teacher model for initial denoising steps before transitioning to the student model, significantly improving output diversity while maintaining efficiency. Comprehensive evaluations demonstrate superior performance across all metrics compared to previous systems, while reducing sampling steps by half without quality degradation. These advances represent a significant step toward speech synthesis systems with metric optimization across multiple components.

This model was presented in the paper [DMOSpeech 2: Reinforcement Learning for Duration Prediction in Metric-Optimized Speech Synthesis](https://huggingface.co/papers/2507.14988).

For more details, visit the [project page](https://dmospeech2.github.io/) or the [GitHub repository](https://github.com/yl4579/DMOSpeech2).

## Inference

To use the DMOSpeech 2 model, follow the steps below, adapted from the official GitHub repository.

### Pre-requisites

1.  **Clone the repository:**
    ```bash
    git clone https://github.com/yl4579/DMOSpeech2.git
    cd DMOSpeech2
    ```
2.  **Set up environment and install packages:**
    ```bash
    conda create -n dmo2 python=3.10
    conda activate dmo2
    pip install -r requirements.txt
    ```

### Download Checkpoints

Download the pre-trained model checkpoints from Hugging Face to a `ckpts` folder in your cloned repository:

```bash
mkdir ckpts
cd ckpts
wget https://huggingface.co/yl4579/DMOSpeech2/resolve/main/model_85000.pt
wget https://huggingface.co/yl4579/DMOSpeech2/resolve/main/model_1500.pt
```

### Run Inference

You can run the inference and explore various synthesis schemes using the provided `demo.ipynb` notebook in the GitHub repository.

Refer to `src/demo.ipynb` in the [DMOSpeech 2 GitHub repository](https://github.com/yl4579/DMOSpeech2/blob/main/src/demo.ipynb) for detailed code examples and usage.