fish-speech/docs/en/inference.md

Inference

The Fish Audio S2 model requires a large amount of VRAM. We recommend using a GPU with at least 24GB for inference.

Download Weights

First, you need to download the model weights:

hf download fishaudio/s2-pro --local-dir checkpoints/s2-pro

Command Line Inference

!!! note

If you plan to let the model randomly choose a voice timbre, you can skip this step.

1. Get VQ tokens from reference audio

python fish_speech/models/dac/inference.py \
    -i "test.wav" \
    --checkpoint-path "checkpoints/s2-pro/codec.pth"

You should get a fake.npy and a fake.wav.

2. Generate Semantic tokens from text:

python fish_speech/models/text2semantic/inference.py \
    --text "The text you want to convert" \
    --prompt-text "Your reference text" \
    --prompt-tokens "fake.npy" \
    # --compile

This command will create a codes_N file in the working directory, where N is an integer starting from 0.

!!! note

You may want to use `--compile` to fuse CUDA kernels for faster inference. However, we recommend using our sglang inference acceleration optimization.
Correspondingly, if you do not plan to use acceleration, you can comment out the `--compile` parameter.

!!! info

For GPUs that do not support bf16, you may need to use the `--half` parameter.

3. Generate vocals from semantic tokens:

python fish_speech/models/dac/inference.py \
    -i "codes_0.npy" \

After that, you will get a fake.wav file.

WebUI Inference

1. Gradio WebUI

For compatibility, we still maintain the Gradio WebUI.

python tools/run_webui.py # --compile if you need acceleration

2. Awesome WebUI

Awesome WebUI is a modernized Web interface built with TypeScript, offering richer features and a better user experience.

Build WebUI:

You need to have Node.js and npm installed on your local machine or server.

1. Enter the awesome_webui directory:

   cd awesome_webui
   

   1. Install dependencies: bash npm install

2. Build the WebUI:

   npm run build
   

   Start Backend Server:

   After building the WebUI, return to the project root and start the API server:

   python tools/api_server.py --listen 0.0.0.0:8888 --compile
   

Access:

Once the server is running, you can access it via your browser: http://localhost:8888/ui