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

Coming soon.