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Introduce Docker and Windows CI Workflow, Pre-commit Formatting, and Language Resource Auto-Download (#2351)

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---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
XXXXRT666
2025-05-26 05:45:14 +03:00
committed by GitHub
parent 13055fa569
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README.md
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@@ -44,15 +44,15 @@ For users in China, you can [click here](https://www.codewithgpu.com/i/RVC-Boss/
### Tested Environments
| Python Version | PyTorch Version | Device |
|----------------|------------------|-----------------|
| Python 3.9 | PyTorch 2.0.1 | CUDA 11.8 |
| Python 3.10.13 | PyTorch 2.1.2 | CUDA 12.3 |
| Python 3.10.17 | PyTorch 2.5.1 | CUDA 12.4 |
| Python 3.9 | PyTorch 2.5.1 | Apple silicon |
| Python 3.11 | PyTorch 2.6.0 | Apple silicon |
| Python 3.9 | PyTorch 2.2.2 | CPU |
| Python 3.9 | PyTorch 2.8.0dev | CUDA12.8(for Nvidia50x0) |
| Python Version | PyTorch Version | Device |
| -------------- | ---------------- | ------------- |
| Python 3.10 | PyTorch 2.5.1 | CUDA 12.4 |
| Python 3.11 | PyTorch 2.5.1 | CUDA 12.4 |
| Python 3.11 | PyTorch 2.7.0 | CUDA 12.8 |
| Python 3.9 | PyTorch 2.8.0dev | CUDA 12.8 |
| Python 3.9 | PyTorch 2.5.1 | Apple silicon |
| Python 3.11 | PyTorch 2.7.0 | Apple silicon |
| Python 3.9 | PyTorch 2.2.2 | CPU |
### Windows
@@ -63,31 +63,41 @@ If you are a Windows user (tested with win>=10), you can [download the integrate
### Linux
```bash
conda create -n GPTSoVits python=3.9
conda create -n GPTSoVits python=3.10
conda activate GPTSoVits
bash install.sh --source <HF|HF-Mirror|ModelScope> [--download-uvr5]
bash install.sh --device <CU126|CU128|ROCM|CPU> --source <HF|HF-Mirror|ModelScope> [--download-uvr5]
```
### macOS
**Note: The models trained with GPUs on Macs result in significantly lower quality compared to those trained on other devices, so we are temporarily using CPUs instead.**
1. Install Xcode command-line tools by running `xcode-select --install`.
2. Install the program by running the following commands:
Install the program by running the following commands:
```bash
conda create -n GPTSoVits python=3.9
conda create -n GPTSoVits python=3.10
conda activate GPTSoVits
bash install.sh --source <HF|HF-Mirror|ModelScope> [--download-uvr5]
bash install.sh --device <MPS|CPU> --source <HF|HF-Mirror|ModelScope> [--download-uvr5]
```
### Install Manually
#### Install Dependences
```bash
conda create -n GPTSoVits python=3.10
conda activate GPTSoVits
pip install -r extra-req.txt --no-deps
pip install -r requirements.txt
```
#### Install FFmpeg
##### Conda Users
```bash
conda activate GPTSoVits
conda install ffmpeg
```
@@ -96,14 +106,13 @@ conda install ffmpeg
```bash
sudo apt install ffmpeg
sudo apt install libsox-dev
conda install -c conda-forge 'ffmpeg<7'
```
##### Windows Users
Download and place [ffmpeg.exe](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffmpeg.exe) and [ffprobe.exe](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffprobe.exe) in the GPT-SoVITS root.
Download and place [ffmpeg.exe](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffmpeg.exe) and [ffprobe.exe](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffprobe.exe) in the GPT-SoVITS root
Install [Visual Studio 2017](https://aka.ms/vs/17/release/vc_redist.x86.exe) (Korean TTS Only)
Install [Visual Studio 2017](https://aka.ms/vs/17/release/vc_redist.x86.exe)
##### MacOS Users
@@ -111,36 +120,53 @@ Install [Visual Studio 2017](https://aka.ms/vs/17/release/vc_redist.x86.exe) (Ko
brew install ffmpeg
```
#### Install Dependences
### Running GPT-SoVITS with Docker
#### Docker Image Selection
Due to rapid development in the codebase and a slower Docker image release cycle, please:
- Check [Docker Hub](https://hub.docker.com/r/xxxxrt666/gpt-sovits) for the latest available image tags
- Choose an appropriate image tag for your environment
- `Lite` means the Docker image does not include ASR models and UVR5 models. You can manually download the UVR5 models, while the program will automatically download the ASR models as needed
- The appropriate architecture image (amd64/arm64) will be automatically pulled during Docker Compose
- Optionally, build the image locally using the provided Dockerfile for the most up-to-date changes
#### Environment Variables
- `is_half`: Controls whether half-precision (fp16) is enabled. Set to `true` if your GPU supports it to reduce memory usage.
#### Shared Memory Configuration
On Windows (Docker Desktop), the default shared memory size is small and may cause unexpected behavior. Increase `shm_size` (e.g., to `16g`) in your Docker Compose file based on your available system memory.
#### Choosing a Service
The `docker-compose.yaml` defines two services:
- `GPT-SoVITS-CU126` & `GPT-SoVITS-CU128`: Full version with all features.
- `GPT-SoVITS-CU126-Lite` & `GPT-SoVITS-CU128-Lite`: Lightweight version with reduced dependencies and functionality.
To run a specific service with Docker Compose, use:
```bash
pip install -r extra-req.txt --no-deps
pip install -r requirements.txt
docker compose run --service-ports <GPT-SoVITS-CU126-Lite|GPT-SoVITS-CU128-Lite|GPT-SoVITS-CU126|GPT-SoVITS-CU128>
```
### Using Docker
#### Building the Docker Image Locally
#### docker-compose.yaml configuration
If you want to build the image yourself, use:
0. Regarding image tags: Due to rapid updates in the codebase and the slow process of packaging and testing images, please check [Docker Hub](https://hub.docker.com/r/breakstring/gpt-sovits)(outdated) for the currently packaged latest images and select as per your situation, or alternatively, build locally using a Dockerfile according to your own needs.
1. Environment Variables:
- is_half: Controls half-precision/double-precision. This is typically the cause if the content under the directories 4-cnhubert/5-wav32k is not generated correctly during the "SSL extracting" step. Adjust to True or False based on your actual situation.
2. Volumes Configuration, The application's root directory inside the container is set to /workspace. The default docker-compose.yaml lists some practical examples for uploading/downloading content.
3. shm_size: The default available memory for Docker Desktop on Windows is too small, which can cause abnormal operations. Adjust according to your own situation.
4. Under the deploy section, GPU-related settings should be adjusted cautiously according to your system and actual circumstances.
#### Running with docker compose
```
docker compose -f "docker-compose.yaml" up -d
```bash
bash docker_build.sh --cuda <12.6|12.8> [--lite]
```
#### Running with docker command
#### Accessing the Running Container (Bash Shell)
As above, modify the corresponding parameters based on your actual situation, then run the following command:
Once the container is running in the background, you can access it using:
```
docker run --rm -it --gpus=all --env=is_half=False --volume=G:\GPT-SoVITS-DockerTest\output:/workspace/output --volume=G:\GPT-SoVITS-DockerTest\logs:/workspace/logs --volume=G:\GPT-SoVITS-DockerTest\SoVITS_weights:/workspace/SoVITS_weights --workdir=/workspace -p 9880:9880 -p 9871:9871 -p 9872:9872 -p 9873:9873 -p 9874:9874 --shm-size="16G" -d breakstring/gpt-sovits:xxxxx
```bash
docker exec -it <GPT-SoVITS-CU126-Lite|GPT-SoVITS-CU128-Lite|GPT-SoVITS-CU126|GPT-SoVITS-CU128> bash
```
## Pretrained Models
@@ -168,7 +194,9 @@ docker run --rm -it --gpus=all --env=is_half=False --volume=G:\GPT-SoVITS-Docker
The TTS annotation .list file format:
```
vocal_path|speaker_name|language|text
```
Language dictionary:
@@ -182,7 +210,9 @@ Language dictionary:
Example:
```
D:\GPT-SoVITS\xxx/xxx.wav|xxx|en|I like playing Genshin.
```
## Finetune and inference
@@ -212,12 +242,12 @@ Or maunally switch version in WebUI
#### Path Auto-filling is now supported
1. Fill in the audio path
2. Slice the audio into small chunks
3. Denoise(optinal)
4. ASR
5. Proofreading ASR transcriptions
6. Go to the next Tab, then finetune the model
1. Fill in the audio path
2. Slice the audio into small chunks
3. Denoise(optinal)
4. ASR
5. Proofreading ASR transcriptions
6. Go to the next Tab, then finetune the model
### Open Inference WebUI
@@ -259,7 +289,7 @@ Use v2 from v1 environment:
2. Clone the latest codes from github.
3. Download v2 pretrained models from [huggingface](https://huggingface.co/lj1995/GPT-SoVITS/tree/main/gsv-v2final-pretrained) and put them into `GPT_SoVITS\pretrained_models\gsv-v2final-pretrained`.
3. Download v2 pretrained models from [huggingface](https://huggingface.co/lj1995/GPT-SoVITS/tree/main/gsv-v2final-pretrained) and put them into `GPT_SoVITS/pretrained_models/gsv-v2final-pretrained`.
Chinese v2 additional: [G2PWModel.zip(HF)](https://huggingface.co/XXXXRT/GPT-SoVITS-Pretrained/resolve/main/G2PWModel.zip)| [G2PWModel.zip(ModelScope)](https://www.modelscope.cn/models/XXXXRT/GPT-SoVITS-Pretrained/resolve/master/G2PWModel.zip)(Download G2PW models, unzip and rename to `G2PWModel`, and then place them in `GPT_SoVITS/text`.)
@@ -279,7 +309,7 @@ Use v3 from v2 environment:
2. Clone the latest codes from github.
3. Download v3 pretrained models (s1v3.ckpt, s2Gv3.pth and models--nvidia--bigvgan_v2_24khz_100band_256x folder) from [huggingface](https://huggingface.co/lj1995/GPT-SoVITS/tree/main) and put them into `GPT_SoVITS\pretrained_models`.
3. Download v3 pretrained models (s1v3.ckpt, s2Gv3.pth and models--nvidia--bigvgan_v2_24khz_100band_256x folder) from [huggingface](https://huggingface.co/lj1995/GPT-SoVITS/tree/main) and put them into `GPT_SoVITS/pretrained_models`.
additional: for Audio Super Resolution model, you can read [how to download](./tools/AP_BWE_main/24kto48k/readme.txt)
@@ -296,7 +326,7 @@ Use v4 from v1/v2/v3 environment:
2. Clone the latest codes from github.
3. Download v4 pretrained models (gsv-v4-pretrained/s2v4.ckpt, and gsv-v4-pretrained/vocoder.pth) from [huggingface](https://huggingface.co/lj1995/GPT-SoVITS/tree/main) and put them into `GPT_SoVITS\pretrained_models`.
3. Download v4 pretrained models (gsv-v4-pretrained/s2v4.ckpt, and gsv-v4-pretrained/vocoder.pth) from [huggingface](https://huggingface.co/lj1995/GPT-SoVITS/tree/main) and put them into `GPT_SoVITS/pretrained_models`.
## Todo List
@@ -322,7 +352,7 @@ Use v4 from v1/v2/v3 environment:
Use the command line to open the WebUI for UVR5
```
```bash
python tools/uvr5/webui.py "<infer_device>" <is_half> <webui_port_uvr5>
```
@@ -333,7 +363,7 @@ python mdxnet.py --model --input_root --output_vocal --output_ins --agg_level --
This is how the audio segmentation of the dataset is done using the command line
```
```bash
python audio_slicer.py \
--input_path "<path_to_original_audio_file_or_directory>" \
--output_root "<directory_where_subdivided_audio_clips_will_be_saved>" \
@@ -345,7 +375,7 @@ python audio_slicer.py \
This is how dataset ASR processing is done using the command line(Only Chinese)
```
```bash
python tools/asr/funasr_asr.py -i <input> -o <output>
```
@@ -353,7 +383,7 @@ ASR processing is performed through Faster_Whisper(ASR marking except Chinese)
(No progress bars, GPU performance may cause time delays)
```
```bash
python ./tools/asr/fasterwhisper_asr.py -i <input> -o <output> -l <language> -p <precision>
```