initial upload attempt

initial upload attempt

system/tts_engines/rvc/configs/config.py

@@ -0,0 +1,170 @@
+import torch
+import json
+import os
+from pathlib import Path
+
+version_config_list = [
+    "v1/32000.json",
+    "v1/40000.json",
+    "v1/48000.json",
+    "v2/48000.json",
+    "v2/32000.json",
+]
+
+
+def singleton_variable(func):
+    def wrapper(*args, **kwargs):
+        if not wrapper.instance:
+            wrapper.instance = func(*args, **kwargs)
+        return wrapper.instance
+
+    wrapper.instance = None
+    return wrapper
+
+
+@singleton_variable
+class Config:
+    def __init__(self):
+        self.device = "cuda:0"
+        self.is_half = True
+        self.use_jit = False
+        self.n_cpu = 0
+        self.gpu_name = None
+        self.json_config = self.load_config_json()
+        self.gpu_mem = None
+        self.instead = ""
+        self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
+
+    @staticmethod
+    def load_config_json() -> dict:
+        d = {}
+        base_path = Path(__file__).resolve().parent
+        for config_file in version_config_list:
+            config_path = base_path / config_file
+            with open(config_path, "r") as f:
+                d[config_file] = json.load(f)
+        return d
+
+    @staticmethod
+    def has_mps() -> bool:
+        if not torch.backends.mps.is_available():
+            return False
+        try:
+            torch.zeros(1).to(torch.device("mps"))
+            return True
+        except Exception:
+            return False
+
+    @staticmethod
+    def has_xpu() -> bool:
+        if hasattr(torch, "xpu") and torch.xpu.is_available():
+            return True
+        else:
+            return False
+
+    def use_fp32_config(self):
+        print(
+            f"Using FP32 config instead of FP16 due to GPU compatibility ({self.gpu_name})"
+        )
+        for config_file in version_config_list:
+            config_path = os.path.join(os.getcwd(), "system", "tts_engines", "rvc", "configs", config_file)
+            with open(config_path, "r") as f:
+                config_data = f.read().replace("true", "false")
+            with open(config_path, "w") as f:
+                f.write(config_data)
+        
+        preprocess_path = os.path.join(os.getcwd(), "system", "tts_engines", "rvc", "train", "preprocess", "preprocess.py")
+        with open(preprocess_path, "r") as f:
+            preprocess_data = f.read().replace("3.7", "3.0")
+        with open(preprocess_path, "w") as f:
+            f.write(preprocess_data)
+
+    def device_config(self) -> tuple:
+        if torch.cuda.is_available():
+            if self.has_xpu():
+                self.device = self.instead = "xpu:0"
+                self.is_half = True
+            i_device = int(self.device.split(":")[-1])
+            self.gpu_name = torch.cuda.get_device_name(i_device)
+            if (
+                ("16" in self.gpu_name and "V100" not in self.gpu_name.upper())
+                or "P40" in self.gpu_name.upper()
+                or "P10" in self.gpu_name.upper()
+                or "1060" in self.gpu_name
+                or "1070" in self.gpu_name
+                or "1080" in self.gpu_name
+            ):
+                self.is_half = False
+                self.use_fp32_config()
+            self.gpu_mem = int(
+                torch.cuda.get_device_properties(i_device).total_memory
+                / 1024
+                / 1024
+                / 1024
+                + 0.4
+            )
+            if self.gpu_mem <= 4:
+                preprocess_path = os.path.join(os.getcwd(), "system", "tts_engines", "rvc", "train", "preprocess", "preprocess.py")
+                with open(preprocess_path, "r") as f:
+                    strr = f.read().replace("3.7", "3.0")
+                with open(preprocess_path, "w") as f:
+                    f.write(strr)
+        elif self.has_mps():
+            print("No supported Nvidia GPU found")
+            self.device = self.instead = "mps"
+            self.is_half = False
+            self.use_fp32_config()
+        else:
+            print("No supported Nvidia GPU found")
+            self.device = self.instead = "cpu"
+            self.is_half = False
+            self.use_fp32_config()
+
+        if self.n_cpu == 0:
+            self.n_cpu = os.cpu_count()
+
+        if self.is_half:
+            x_pad = 3
+            x_query = 10
+            x_center = 60
+            x_max = 65
+        else:
+            x_pad = 1
+            x_query = 6
+            x_center = 38
+            x_max = 41
+
+        if self.gpu_mem is not None and self.gpu_mem <= 4:
+            x_pad = 1
+            x_query = 5
+            x_center = 30
+            x_max = 32
+
+        return x_pad, x_query, x_center, x_max
+
+
+def max_vram_gpu(gpu):
+    if torch.cuda.is_available():
+        gpu_properties = torch.cuda.get_device_properties(gpu)
+        total_memory_gb = round(gpu_properties.total_memory / 1024 / 1024 / 1024)
+        return total_memory_gb
+    else:
+        return "0"
+
+
+def get_gpu_info():
+    ngpu = torch.cuda.device_count()
+    gpu_infos = []
+    if torch.cuda.is_available() or ngpu != 0:
+        for i in range(ngpu):
+            gpu_name = torch.cuda.get_device_name(i)
+            mem = int(
+                torch.cuda.get_device_properties(i).total_memory / 1024 / 1024 / 1024
+                + 0.4
+            )
+            gpu_infos.append("%s: %s %s GB" % (i, gpu_name, mem))
+    if len(gpu_infos) > 0:
+        gpu_info = "\n".join(gpu_infos)
+    else:
+        gpu_info = "Unfortunately, there is no compatible GPU available to support your training."
+    return gpu_info