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Introduction

Warning

We assume no responsibility for any illegal use of the codebase. Please refer to the local laws regarding DMCA (Digital Millennium Copyright Act) and other relevant laws in your area.
This codebase and all models are released under the CC-BY-NC-SA-4.0 license.

Requirements

  • GPU Memory: 4GB (for inference), 8GB (for fine-tuning)
  • System: Linux, Windows

Windows Setup

Professional Windows users may consider using WSL2 or Docker to run the codebase.

# Create a python 3.10 virtual environment, you can also use virtualenv
conda create -n fish-speech python=3.10
conda activate fish-speech

# Install pytorch
pip3 install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu121

# Install fish-speech
pip3 install -e .

# (Enable acceleration) Install triton-windows
pip install https://github.com/AnyaCoder/fish-speech/releases/download/v0.1.0/triton_windows-0.1.0-py3-none-any.whl

Non-professional Windows users can consider the following basic methods to run the project without a Linux environment (with model compilation capabilities, i.e., torch.compile):

  1. Extract the project package.
  2. Click install_env.bat to install the environment.
  3. If you want to enable compilation acceleration, follow this step:
    1. Download the LLVM compiler from the following links:
    2. Download and install the Microsoft Visual C++ Redistributable to solve potential .dll missing issues:
    3. Download and install Visual Studio Community Edition to get MSVC++ build tools and resolve LLVM's header file dependencies:
      • Visual Studio Download
      • After installing Visual Studio Installer, download Visual Studio Community 2022.
      • As shown below, click the Modify button and find the Desktop development with C++ option to select and download.
    4. Download and install CUDA Toolkit 12.x
  4. Double-click start.bat to open the training inference WebUI management interface. If needed, you can modify the API_FLAGS as prompted below.

Optional

Want to start the inference WebUI?

Edit the API_FLAGS.txt file in the project root directory and modify the first three lines as follows: 

 --infer
 # --api
 # --listen ...
 ...

Optional

Want to start the API server?

Edit the API_FLAGS.txt file in the project root directory and modify the first three lines as follows:

# --infer
--api
--listen ...
...

Optional

Double-click run_cmd.bat to enter the conda/python command line environment of this project.

Linux Setup

See pyproject.toml for details. 

# Create a python 3.10 virtual environment, you can also use virtualenv
conda create -n fish-speech python=3.10
conda activate fish-speech

# Install pytorch
pip3 install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1

# (Ubuntu / Debian User) Install sox + ffmpeg
apt install libsox-dev ffmpeg 

# (Ubuntu / Debian User) Install pyaudio 
apt install build-essential \
    cmake \
    libasound-dev \
    portaudio19-dev \
    libportaudio2 \
    libportaudiocpp0

# Install fish-speech
pip3 install -e .[stable]

macos setup

If you want to perform inference on MPS, please add the --device mps flag. Please refer to this PR for a comparison of inference speeds.

Warning

The compile option is not officially supported on Apple Silicon devices, so there is no guarantee that inference speed will improve.

# create a python 3.10 virtual environment, you can also use virtualenv
conda create -n fish-speech python=3.10
conda activate fish-speech
# install pytorch
pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1
# install fish-speech
pip install -e .[stable]

Docker Setup

  1. Install NVIDIA Container Toolkit:

    To use GPU for model training and inference in Docker, you need to install NVIDIA Container Toolkit:

    For Ubuntu users:

    # Add repository
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
    && curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
        sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
        sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
# Install nvidia-container-toolkit
sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit
# Restart Docker service
sudo systemctl restart docker

    For users of other Linux distributions, please refer to: NVIDIA Container Toolkit Install-guide.

  2. Pull and run the fish-speech image

    # Pull the image
docker pull fishaudio/fish-speech:latest-dev
# Run the image
docker run -it \
    --name fish-speech \
    --gpus all \
    -p 7860:7860 \
    fishaudio/fish-speech:latest-dev \
    zsh
# If you need to use a different port, please modify the -p parameter to YourPort:7860

  3. Download model dependencies

    Make sure you are in the terminal inside the docker container, then download the required vqgan and llama models from our huggingface repository.

    huggingface-cli download fishaudio/fish-speech-1.4 --local-dir checkpoints/fish-speech-1.4

  4. Configure environment variables and access WebUI

    In the terminal inside the docker container, enter export GRADIO_SERVER_NAME="0.0.0.0" to allow external access to the gradio service inside docker. Then in the terminal inside the docker container, enter python tools/webui.py to start the WebUI service.

    If you're using WSL or MacOS, visit http://localhost:7860 to open the WebUI interface.

    If it's deployed on a server, replace localhost with your server's IP.

Changelog

  • 2024/09/10: Updated Fish-Speech to 1.4 version, with an increase in dataset size and a change in the quantizer's n_groups from 4 to 8.
  • 2024/07/02: Updated Fish-Speech to 1.2 version, remove VITS Decoder, and greatly enhanced zero-shot ability.
  • 2024/05/10: Updated Fish-Speech to 1.1 version, implement VITS decoder to reduce WER and improve timbre similarity.
  • 2024/04/22: Finished Fish-Speech 1.0 version, significantly modified VQGAN and LLAMA models.
  • 2023/12/28: Added lora fine-tuning support.
  • 2023/12/27: Add gradient checkpointing, causual sampling, and flash-attn support.
  • 2023/12/19: Updated webui and HTTP API.
  • 2023/12/18: Updated fine-tuning documentation and related examples.
  • 2023/12/17: Updated text2semantic model, supporting phoneme-free mode.
  • 2023/12/13: Beta version released, includes VQGAN model and a language model based on LLAMA (phoneme support only).

Acknowledgements