Merge 0cc6b8aaef4c637c674f8a6660ce8f59124a240e 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/1-get-text.py

@@ -1,6 +1,9 @@
 # -*- coding: utf-8 -*-
 
 import os
+import re
+import LangSegment
+from text import chinese
 
 inp_text = os.environ.get("inp_text")
 inp_wav_dir = os.environ.get("inp_wav_dir")
@@ -83,24 +86,104 @@ if os.path.exists(txt_path) == False:
 
         return phone_level_feature.T
 
+    def get_bert_inf(phones:list, word2ph:list, norm_text:str, language:str):
+        language=language.replace("all_","")
+        if language == "zh":
+            feature = get_bert_feature(norm_text, word2ph).to(device)
+        else:
+            feature = torch.zeros(
+                (1024, len(phones)),
+                dtype=torch.float32,
+            ).to(device)
+
+        return feature
+
+    def get_phones_and_bert(text:str, language:str, version:str, final:bool=False):
+        if language in {"en", "all_zh", "all_ja", "all_ko", "all_yue"}:
+            language = language.replace("all_","")
+            if language == "en":
+                LangSegment.setfilters(["en"])
+                formattext = " ".join(tmp["text"] for tmp in LangSegment.getTexts(text))
+            else:
+                # 因无法区别中日韩文汉字,以用户输入为准
+                formattext = text
+            while "  " in formattext:
+                formattext = formattext.replace("  ", " ")
+            if language == "zh":
+                if re.search(r'[A-Za-z]', formattext):
+                    formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext)
+                    formattext = chinese.mix_text_normalize(formattext)
+                    return get_phones_and_bert(formattext,"zh",version)
+                else:
+                    phones, word2ph, norm_text = clean_text(formattext, language, version)
+                    bert = get_bert_feature(norm_text, word2ph).to(device)
+            elif language == "yue" and re.search(r'[A-Za-z]', formattext):
+                    formattext = re.sub(r'[a-z]', lambda x: x.group(0).upper(), formattext)
+                    formattext = chinese.mix_text_normalize(formattext)
+                    return get_phones_and_bert(formattext,"yue",version)
+            else:
+                phones, word2ph, norm_text = clean_text(formattext, language, version)
+                bert = torch.zeros(
+                    (1024, len(phones)),
+                    dtype=torch.float32,
+                ).to(device)
+        elif language in {"zh", "ja", "ko", "yue", "auto", "auto_yue"}:
+            textlist=[]
+            langlist=[]
+            LangSegment.setfilters(["zh","ja","en","ko"])
+            if language == "auto":
+                for tmp in LangSegment.getTexts(text):
+                    langlist.append(tmp["lang"])
+                    textlist.append(tmp["text"])
+            elif language == "auto_yue":
+                for tmp in LangSegment.getTexts(text):
+                    if tmp["lang"] == "zh":
+                        tmp["lang"] = "yue"
+                    langlist.append(tmp["lang"])
+                    textlist.append(tmp["text"])
+            else:
+                for tmp in LangSegment.getTexts(text):
+                    if tmp["lang"] == "en":
+                        langlist.append(tmp["lang"])
+                    else:
+                        # 因无法区别中日韩文汉字,以用户输入为准
+                        langlist.append(language)
+                    textlist.append(tmp["text"])
+            # print(textlist)
+            # print(langlist)
+            phones_list = []
+            bert_list = []
+            norm_text_list = []
+            for i in range(len(textlist)):
+                lang = langlist[i]
+                phones, word2ph, norm_text = clean_text(textlist[i], lang, version)
+                bert = get_bert_inf(phones, word2ph, norm_text, lang)
+                phones_list.append(phones)
+                norm_text_list.append(norm_text)
+                bert_list.append(bert)
+            bert = torch.cat(bert_list, dim=1)
+            phones = sum(phones_list, [])
+            norm_text = ''.join(norm_text_list)
+
+        return phones, bert, norm_text
+
     def process(data, res):
         for name, text, lan in data:
             try:
                 name=clean_path(name)
                 name = os.path.basename(name)
                 print(name)
-                phones, word2ph, norm_text = clean_text(
-                    text.replace("%", "-").replace("￥", ","), lan, version
+                phones, bert_feature, norm_text = get_phones_and_bert(
+                    text.replace("%", "-").replace("￥", ","), lan, 'v2'
                 )
                 path_bert = "%s/%s.pt" % (bert_dir, name)
                 if os.path.exists(path_bert) == False and lan == "zh":
-                    bert_feature = get_bert_feature(norm_text, word2ph)
                     assert bert_feature.shape[-1] == len(phones)
                     # torch.save(bert_feature, path_bert)
                     my_save(bert_feature, path_bert)
                 phones = " ".join(phones)
                 # res.append([name,phones])
-                res.append([name, phones, word2ph, norm_text])
+                res.append([name, phones, None, norm_text])
             except:
                 print(name, text, traceback.format_exc())
 

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
         )
     )
 

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):