add memory endpoint to the server api code

docs/cn/inference_cpu.md

@@ -1,63 +0,0 @@
-# 推理
-
-## Windows
-
-### 使用cpu推理
-本文档介绍如何使用cpu进行推理,使用cpu的推理速度有点慢,但不是很慢
-
-#### 安装依赖
-```
-# 拉取项目代码
-git clone --depth=1 https://github.com/RVC-Boss/GPT-SoVITS
-cd GPT-SoVITS
-
-# 安装好 Miniconda 之后，先创建一个虚拟环境：
-conda create -n GPTSoVits python=3.9
-conda activate GPTSoVits
-
-# 安装依赖：
-pip install -r requirements.txt
-
-# (可选)如果网络环境不好，可以考虑换源(比如清华源)：
-pip install -i https://pypi.tuna.tsinghua.edu.cn/simple -r requirements.txt
-```
-
-#### 添加预训练模型
-```
-# 安装 huggingface-cli 用于和 huggingface hub 交互
-pip install huggingface_hub
-# 登录 huggingface-cli
-huggingface-cli login
-
-# 下载模型, 由于模型文件较大，可能需要一段时间
-# --local-dir-use-symlinks False 用于解决 macOS alias 文件的问题
-# 会下载到 GPT_SoVITS/pretrained_models 文件夹下
-huggingface-cli download --resume-download lj1995/GPT-SoVITS --local-dir GPT_SoVITS/pretrained_models --local-dir-use-symlinks False
-```
-
-#### 添加微调模型(可选)  
-笔者是将微调添加到了GPT-SoVITS/trained目录,内容如下,正常情况下包含 openai_alloy-e15.ckpt 和openai_alloy_e8_s112.pth 即可  
-如果仅仅测试合成效果,不添加微调模型 使用预训练模型作为微调模型也可以
-```
-├── .gitignore
-├── openai_alloy
-│   ├── infer_config.json
-│   ├── openai_alloy-e15.ckpt
-│   ├── openai_alloy_e8_s112.pth
-│   ├── output-2.txt
-│   ├── output-2.wav
-```
-
-#### 启动推理webtui
-```
-python.exe GPT_SoVITS/inference_webui.py
-```
-配置如下
-![](inference_cpu_files/1.jpg)
-
-### 使用gpu推理
-```
-pip uninstall torch torchaudio -y
-pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117
-```
-请根据你的环境选择合适的cuda版本

docs/cn/inference_cpu_files/memory.md

@@ -0,0 +1,18 @@
+加载模型后需要占用的内存容量如下,单位是MB
+```
+{
+    "memory_usage": {
+        "rss": 1029.30078125,
+        "vms": 4505.546875,
+        "percent": 6.5181980027997115
+    }
+}
+
+{
+    "gpu_memory_usage": {
+        "used_memory": 2640,
+        "total_memory": 4096,
+        "percent": 64.453125
+    }
+}
+```

requirements.txt

@@ -25,4 +25,4 @@ jieba_fast
 jieba
 LangSegment>=0.2.0
 Faster_Whisper
-wordsegment
+wordsegmentpsutil

server/handlers.py

@@ -1,4 +1,6 @@
 from fastapi import APIRouter, Request
+
+from memory_service import get_memory_usage, get_gpu_memory_usage
 from pyutils.logs import llog
 from tts_service import change_sovits_weights, change_gpt_weights, handle_control, handle_change, handle
 
@@ -70,3 +72,13 @@ async def tts_endpoint(
   cut_punc: str = None,
 ):
   return handle(refer_wav_path, prompt_text, prompt_language, text, text_language, cut_punc)
+
+@index_router.get("/memory-usage")
+def read_memory_usage():
+    memory_usage = get_memory_usage()
+    return {"memory_usage": memory_usage}
+
+@index_router.get("/gpu-memory-usage")
+def read_gpu_memory_usage():
+    gpu_memory_usage = get_gpu_memory_usage()
+    return {"gpu_memory_usage": gpu_memory_usage}

server/memory_service.py

@@ -0,0 +1,37 @@
+import psutil
+import subprocess
+
+
+def get_memory_usage():
+  process = psutil.Process()
+  mem_info = process.memory_info()
+  memory_usage = {
+    "rss": mem_info.rss / (1024 ** 2),  # Resident Set Size
+    "vms": mem_info.vms / (1024 ** 2),  # Virtual Memory Size
+    "percent": process.memory_percent()  # Percentage of memory usage
+  }
+  return memory_usage
+
+
+def get_gpu_memory_usage():
+  try:
+    result = subprocess.run(
+      ["nvidia-smi", "--query-gpu=memory.used,memory.total", "--format=csv,nounits,noheader"],
+      stdout=subprocess.PIPE,
+      stderr=subprocess.PIPE,
+      check=True,
+      text=True
+    )
+    output = result.stdout.strip()
+    if output:
+      used_memory, total_memory = map(int, output.split(', '))
+      gpu_memory_usage = {
+        "used_memory": used_memory,  # in MiB
+        "total_memory": total_memory,  # in MiB
+        "percent": (used_memory / total_memory) * 100  # Percentage of GPU memory usage
+      }
+      return gpu_memory_usage
+    else:
+      return {"error": "No GPU found or unable to query GPU memory usage."}
+  except Exception as e:
+    return {"error": str(e)}