README.md

---
license: mit
---

# Amphion Vocoder Pretrained Models

We provide a [DiffWave](https://github.com/open-mmlab/Amphion/tree/main/egs/vocoder/diffusion) pretrained checkpoint, which is trained on 125 hours of speech data and 80 hours of singing voice data.

## Quick Start

To utilize these pretrained vocoders, just run the following commands:

### Step1: Download the checkpoint
```bash
git lfs install
git clone https://huggingface.co/amphion/diffwave
```

### Step2: Clone the Amphion's Source Code of GitHub
```bash
git clone https://github.com/open-mmlab/Amphion.git
```

### Step3: Specify the checkpoint's path
Use the soft link to specify the downloaded checkpoint in the first step:

```bash
cd Amphion
mkdir -p ckpts/vocoder
ln -s "$(realpath ../diffwave/diffwave)" pretrained/diffwave
```

### Step4: Inference
For analysis synthesis on the processed dataset, raw waveform, or predicted mel spectrograms, you can follow the inference part of [this recipe](https://github.com/open-mmlab/Amphion/tree/main/egs/vocoder/diffusion).

```bash
sh egs/vocoder/diffusion/diffwave/run.sh --stage 3 \
	--infer_mode [Your chosen inference mode] \
	--infer_datasets [Datasets you want to inference, needed when infer_from_dataset] \
	--infer_feature_dir [Your path to your predicted acoustic features, needed when infer_from_feature] \
	--infer_audio_dir [Your path to your audio files, needed when infer_form_audio] \
	--infer_expt_dir Amphion/ckpts/vocoder/[YourExptName] \
	--infer_output_dir Amphion/ckpts/vocoder/[YourExptName]/result \
```