Merge 855c369a17f04d300f7005708c92d60b8590563d into ee4a466f79b4f643251aa2f873f541f85df11d91

* Added the instruction for AMD GPU in English

* Added the instruction for AMD GPU in Chinese

* Update install.sh, now it will check wether user are using cuda or rocm

* 恢复原来的readme，已经更新了install.sh

* 恢复中文readme

* 将n卡的判断条件由nvcc改成nvidia-smi

GPT_SoVITS/AR/modules/patched_mha_with_cache.py

@@ -12,33 +12,33 @@ import torch
 
 
 def multi_head_attention_forward_patched(
-    query: Tensor,
-    key: Tensor,
-    value: Tensor,
-    embed_dim_to_check: int,
-    num_heads: int,
-    in_proj_weight: Optional[Tensor],
-    in_proj_bias: Optional[Tensor],
-    bias_k: Optional[Tensor],
-    bias_v: Optional[Tensor],
-    add_zero_attn: bool,
+    query,
+    key,
+    value,
+    embed_dim_to_check,
+    num_heads,
+    in_proj_weight,
+    in_proj_bias,
+    bias_k,
+    bias_v,
+    add_zero_attn,
     dropout_p: float,
-    out_proj_weight: Tensor,
-    out_proj_bias: Optional[Tensor],
-    training: bool = True,
-    key_padding_mask: Optional[Tensor] = None,
-    need_weights: bool = True,
-    attn_mask: Optional[Tensor] = None,
-    use_separate_proj_weight: bool = False,
-    q_proj_weight: Optional[Tensor] = None,
-    k_proj_weight: Optional[Tensor] = None,
-    v_proj_weight: Optional[Tensor] = None,
-    static_k: Optional[Tensor] = None,
-    static_v: Optional[Tensor] = None,
-    average_attn_weights: bool = True,
-    is_causal: bool = False,
+    out_proj_weight,
+    out_proj_bias,
+    training = True,
+    key_padding_mask = None,
+    need_weights = True,
+    attn_mask = None,
+    use_separate_proj_weight = False,
+    q_proj_weight = None,
+    k_proj_weight = None,
+    v_proj_weight = None,
+    static_k = None,
+    static_v = None,
+    average_attn_weights = True,
+    is_causal = False,
     cache=None,
-) -> Tuple[Tensor, Optional[Tensor]]:
+):
     r"""
     Args:
         query, key, value: map a query and a set of key-value pairs to an output.

GPT_SoVITS/prepare_datasets/3-get-semantic.py

@@ -81,7 +81,7 @@ if os.path.exists(semantic_path) == False:
     # utils.load_checkpoint(pretrained_s2G, vq_model, None, True)
     print(
         vq_model.load_state_dict(
-            torch.load(pretrained_s2G, map_location="cpu")["weight"], strict=False
+            torch.load(pretrained_s2G, map_location="cpu", weights_only=False)["weight"], strict=False
         )
     )
 

README.md

@@ -43,6 +43,8 @@ https://github.com/RVC-Boss/GPT-SoVITS/assets/129054828/05bee1fa-bdd8-4d85-9350-
 
 For users in China, you can [click here](https://www.codewithgpu.com/i/RVC-Boss/GPT-SoVITS/GPT-SoVITS-Official) to use AutoDL Cloud Docker to experience the full functionality online.
 
+You can also deploy on [Alibaba Cloud Function Compute](https://www.alibabacloud.com/en/product/function-compute) by [clicking here](https://fcnext.console.aliyun.com/applications/ai/create?template=fc-gpt-sovits).
+
 ### Tested Environments
 
 - Python 3.9, PyTorch 2.0.1, CUDA 11

docs/cn/README.md

@@ -42,6 +42,9 @@ https://github.com/RVC-Boss/GPT-SoVITS/assets/129054828/05bee1fa-bdd8-4d85-9350-
 
 中国地区的用户可[点击此处](https://www.codewithgpu.com/i/RVC-Boss/GPT-SoVITS/GPT-SoVITS-Official)使用 AutoDL 云端镜像进行体验。
 
+还可以通过通过阿里云 [Serverless 应用中心](https://fcnext.console.aliyun.com/applications/ai/create?template=fc-gpt-sovits) ，
+  [![Deploy with Severless Devs](https://img.alicdn.com/imgextra/i1/O1CN01w5RFbX1v45s8TIXPz_!!6000000006118-55-tps-95-28.svg)](https://fcnext.console.aliyun.com/applications/ai/create?template=fc-gpt-sovits) 到云端进行体验。
+
 ### 测试通过的环境
 
 - Python 3.9，PyTorch 2.0.1，CUDA 11

install.sh

@@ -2,8 +2,13 @@
 
 # 安装构建工具
 # Install build tools
+echo "Installing GCC..."
 conda install -c conda-forge gcc=14
+
+echo "Installing G++..."
 conda install -c conda-forge gxx
+
+echo "Installing ffmpeg and cmake..."
 conda install ffmpeg cmake
 
 # 设置编译环境
@@ -12,10 +17,60 @@ export CMAKE_MAKE_PROGRAM="$CONDA_PREFIX/bin/cmake"
 export CC="$CONDA_PREFIX/bin/gcc"
 export CXX="$CONDA_PREFIX/bin/g++"
 
-conda install pytorch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 pytorch-cuda=11.8 -c pytorch -c nvidia
+echo "Checking for CUDA installation..."
+if command -v nvidia-smi &> /dev/null; then
+    USE_CUDA=true
+    echo "CUDA found."
+else
+    echo "CUDA not found."
+    USE_CUDA=false
+fi
+
+
+if [ "$USE_CUDA" = false ]; then
+    echo "Checking for ROCm installation..."
+    if [ -d "/opt/rocm" ]; then
+        USE_ROCM=true
+        echo "ROCm found."
+        if grep -qi "microsoft" /proc/version; then
+            echo "You are running WSL."
+            IS_WSL=true
+        else
+            echo "You are NOT running WSL."
+            IS_WSL=false
+        fi
+    else
+        echo "ROCm not found."
+        USE_ROCM=false
+    fi
+fi
+
+if [ "$USE_CUDA" = true ]; then
+    echo "Installing PyTorch with CUDA support..."
+    conda install pytorch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 pytorch-cuda=11.8 -c pytorch -c nvidia
+elif [ "$USE_ROCM" = true ] ; then
+    echo "Installing PyTorch with ROCm support..."
+    pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/rocm6.2
+else
+    echo "Installing PyTorch for CPU..."
+    conda install pytorch==2.1.1 torchvision==0.16.1 torchaudio==2.1.1 cpuonly -c pytorch
+fi
+
+
+echo "Installing Python dependencies from requirements.txt..."
 
 # 刷新环境
 # Refresh environment
 hash -r
+pip install -r requirements.txt
+
+if [ "$USE_ROCM" = true ] && [ "$IS_WSL" = true ] ; then
+    echo "Update to WSL compatible runtime lib..."
+    location=`pip show torch | grep Location | awk -F ": " '{print $2}'`
+    cd ${location}/torch/lib/
+    rm libhsa-runtime64.so*
+    cp /opt/rocm/lib/libhsa-runtime64.so.1.2 libhsa-runtime64.so
+fi
+
+echo "Installation completed successfully!"
 
-pip install -r requirements.txt

tools/my_utils.py

@@ -32,7 +32,7 @@ def clean_path(path_str:str):
     if path_str.endswith(('\\','/')):
         return clean_path(path_str[0:-1])
     path_str = path_str.replace('/', os.sep).replace('\\', os.sep)
-    return path_str.strip(" ").strip('\'').strip("\n").strip('"').strip(" ").strip("\u202a")
+    return path_str.strip(" \'\n\"\u202a")#path_str.strip(" ").strip('\'').strip("\n").strip('"').strip(" ").strip("\u202a")
 
 
 def check_for_existance(file_list:list=None,is_train=False,is_dataset_processing=False):