fish-speech/docs/en/inference.md

Inference

Inference support command line, HTTP API and web UI.

!!! note

Overall, reasoning consists of several parts:

1. Encode a given ~10 seconds of voice using VQGAN.
2. Input the encoded semantic tokens and the corresponding text into the language model as an example.
3. Given a new piece of text, let the model generate the corresponding semantic tokens.
4. Input the generated semantic tokens into VITS / VQGAN to decode and generate the corresponding voice.

Command Line Inference

Download the required vqgan and llama models from our Hugging Face repository.

huggingface-cli download fishaudio/fish-speech-1.2 --local-dir checkpoints/fish-speech-1.2

1. Generate prompt from voice:

!!! note

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

python tools/vqgan/inference.py \
    -i "paimon.wav" \
    --checkpoint-path "checkpoints/fish-speech-1.2/firefly-gan-vq-fsq-4x1024-42hz-generator.pth"

You should get a fake.npy file.

2. Generate semantic tokens from text:

python tools/llama/generate.py \
    --text "The text you want to convert" \
    --prompt-text "Your reference text" \
    --prompt-tokens "fake.npy" \
    --checkpoint-path "checkpoints/fish-speech-1.2" \
    --num-samples 2 \
    --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 (~30 tokens/second -> ~500 tokens/second).
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.

!!! warning

If you are using your own fine-tuned model, please be sure to carry the `--speaker` parameter to ensure the stability of pronunciation.

3. Generate vocals from semantic tokens:

VQGAN Decoder (not recommended)

python tools/vqgan/inference.py \
    -i "codes_0.npy" \
    --checkpoint-path "checkpoints/fish-speech-1.2/firefly-gan-vq-fsq-4x1024-42hz-generator.pth"

HTTP API Inference

We provide a HTTP API for inference. You can use the following command to start the server:

python -m tools.api \
    --listen 0.0.0.0:8000 \
    --llama-checkpoint-path "checkpoints/fish-speech-1.2" \
    --decoder-checkpoint-path "checkpoints/fish-speech-1.2/firefly-gan-vq-fsq-4x1024-42hz-generator.pth" \
    --decoder-config-name firefly_gan_vq

If you want to speed up inference, you can add the --compile parameter.

After that, you can view and test the API at http://127.0.0.1:8000/.  

## WebUI Inference

You can start the WebUI using the following command:

```bash
python -m tools.webui \
    --llama-checkpoint-path "checkpoints/fish-speech-1.2" \
    --decoder-checkpoint-path "checkpoints/fish-speech-1.2/firefly-gan-vq-fsq-4x1024-42hz-generator.pth" \
    --decoder-config-name firefly_gan_vq

!!! note

You can use Gradio environment variables, such as `GRADIO_SHARE`, `GRADIO_SERVER_PORT`, `GRADIO_SERVER_NAME` to configure WebUI.

Enjoy!