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-1.4 --local-dir checkpoints/fish-speech-1.4

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-1.4/firefly-gan-vq-fsq-8x1024-21hz-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-1.4" \
    --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

python tools/vqgan/inference.py \
    -i "codes_0.npy" \
    --checkpoint-path "checkpoints/fish-speech-1.4/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:

python -m tools.api \
    --listen 0.0.0.0:8080 \
    --llama-checkpoint-path "checkpoints/fish-speech-1.4" \
    --decoder-checkpoint-path "checkpoints/fish-speech-1.4/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/post_api.py.

python -m tools.post_api \
    --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.

python -m tools.post_api \
    --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.post_api -h

GUI Inference

Download client

WebUI Inference

You can start the WebUI using the following command:

python -m tools.webui \
    --llama-checkpoint-path "checkpoints/fish-speech-1.4" \
    --decoder-checkpoint-path "checkpoints/fish-speech-1.4/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!