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 torchvision torchaudio --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
):
- Extract the project package.
- Click
install_env.bat
to install the environment. - If you want to enable compilation acceleration, follow this step:
- Download the LLVM compiler from the following links:
- LLVM-17.0.6 (Official Site Download)
- LLVM-17.0.6 (Mirror Site Download)
- After downloading
LLVM-17.0.6-win64.exe
, double-click to install, select an appropriate installation location, and most importantly, check theAdd Path to Current User
option to add the environment variable. - Confirm that the installation is complete.
- Download and install the Microsoft Visual C++ Redistributable to solve potential .dll missing issues:
- 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 theDesktop development with C++
option to select and download.
- Download and install CUDA Toolkit 12.x
- Download the LLVM compiler from the following links:
- Double-click
start.bat
to open the training inference WebUI management interface. If needed, you can modify theAPI_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:
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:
Optional
Double-click run_cmd.bat
to enter the conda/python command line environment of this project.
Linux Setup
# 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 torchvision torchaudio
# Install fish-speech
pip3 install -e .[stable]
# (Ubuntu / Debian User) Install sox + ffmpeg
apt install libsox-dev ffmpeg
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 torchvision torchaudio
# install fish-speech
pip install -e .[stable]
Docker Setup
-
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.
-
Pull and run the fish-speech image
-
Download model dependencies
Make sure you are in the terminal inside the docker container, then download the required
vqgan
andllama
models from our huggingface repository. -
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, enterpython 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
, andflash-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).