fish-speech-start-local/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.

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

```bash
huggingface-cli download fishaudio/fish-speech-1.5 --local-dir checkpoints/fish-speech-1.5
```

## Command Line Inference
### 1. Generate prompt from voice:

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

!!! warning "Future Warning"
    We have kept the interface accessible from the original path (tools/vqgan/inference.py), but this interface may be removed in subsequent releases, so please change your code as soon as possible.

```bash
python fish_speech/models/vqgan/inference.py \
    -i "paimon.wav" \
    --checkpoint-path "checkpoints/fish-speech-1.5/firefly-gan-vq-fsq-8x1024-21hz-generator.pth"
```

You should get a `fake.npy` file.

### 2. Generate semantic tokens from text:

!!! warning "Future Warning"
    We have kept the interface accessible from the original path (tools/llama/generate.py), but this interface may be removed in subsequent releases, so please change your code as soon as possible.

```bash
python fish_speech/models/text2semantic/inference.py \
    --text "The text you want to convert" \
    --prompt-text "Your reference text" \
    --prompt-tokens "fake.npy" \
    --checkpoint-path "checkpoints/fish-speech-1.5" \
    --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:

#### VQGAN Decoder

!!! warning "Future Warning"
    We have kept the interface accessible from the original path (tools/vqgan/inference.py), but this interface may be removed in subsequent releases, so please change your code as soon as possible.

```bash
python fish_speech/models/vqgan/inference.py \
    -i "codes_0.npy" \
    --checkpoint-path "checkpoints/fish-speech-1.5/firefly-gan-vq-fsq-8x1024-21hz-generator.pth"
```

## HTTP API Inference

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

```bash
python -m tools.api_server \
    --listen 0.0.0.0:8080 \
    --llama-checkpoint-path "checkpoints/fish-speech-1.5" \
    --decoder-checkpoint-path "checkpoints/fish-speech-1.5/firefly-gan-vq-fsq-8x1024-21hz-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://127.0.0.1:8080/.

Below is an example of sending a request using `tools/api_client.py`.

```bash
python -m tools.api_client \
    --text "Text to be input" \
    --reference_audio "Path to reference audio" \
    --reference_text "Text content of the reference audio" \
    --streaming True
```

The above command indicates synthesizing the desired audio according to the reference audio information and returning it in a streaming manner.

The following example demonstrates that you can use **multiple** reference audio paths and reference audio texts at once. Separate them with spaces in the command.

```bash
python -m tools.api_client \
    --text "Text to input" \
    --reference_audio "reference audio path1" "reference audio path2" \
    --reference_text "reference audio text1" "reference audio text2"\
    --streaming False \
    --output "generated" \
    --format "mp3"
```

The above command synthesizes the desired `MP3` format audio based on the information from multiple reference audios and saves it as `generated.mp3` in the current directory.

You can also use `--reference_id` (only one can be used) instead of `--reference-audio` and `--reference_text`, provided that you create a `references/<your reference_id>` folder in the project root directory, which contains any audio and annotation text. 
The currently supported reference audio has a maximum total duration of 90 seconds.


!!! info 
    To learn more about available parameters, you can use the command `python -m tools.api_client -h`

## GUI Inference 
[Download client](https://github.com/AnyaCoder/fish-speech-gui/releases)

## WebUI Inference

You can start the WebUI using the following command:

```bash
python -m tools.run_webui \
    --llama-checkpoint-path "checkpoints/fish-speech-1.5" \
    --decoder-checkpoint-path "checkpoints/fish-speech-1.5/firefly-gan-vq-fsq-8x1024-21hz-generator.pth" \
    --decoder-config-name firefly_gan_vq
```
> If you want to speed up inference, you can add the `--compile` parameter.

!!! note
    You can save the label file and reference audio file in advance to the `references` folder in the main directory (which you need to create yourself), so that you can directly call them in the WebUI.

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

Enjoy!