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 5-10 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 VQGAN to decode and generate the corresponding voice.

Command Line Inference

Download the required vqgan and text2semantic models from our Hugging Face repository.

huggingface-cli download fishaudio/speech-lm-v1 vqgan-v1.pth --local-dir checkpoints
huggingface-cli download fishaudio/speech-lm-v1 text2semantic-400m-v0.2-4k.pth --local-dir checkpoints

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/vqgan-v1.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/text2semantic-400m-v0.2-4k.pth" \
    --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.

3. Generate vocals from semantic tokens:

python tools/vqgan/inference.py \
    -i "codes_0.npy" \
    --checkpoint-path "checkpoints/vqgan-v1.pth"

HTTP API Inference

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

python -m zibai tools.api_server:app --listen 127.0.0.1:8000

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

Generally, you need to first call PUT /v1/models/default to load the model, and then use POST /v1/models/default/invoke for inference. For specific parameters, please refer to the API documentation.

WebUI Inference

Before running the WebUI, you need to start the HTTP service as described above.

Then you can start the WebUI using the following command:

python fish_speech/webui/app.py

Enjoy!