mirror of
https://github.com/RVC-Boss/GPT-SoVITS.git
synced 2025-12-16 09:16:59 +08:00
d3b8f7e09e
This commit migrates the project from using NVIDIA CUDA to Intel XPU for GPU acceleration, based on the PyTorch 2.9 release. Key changes include: - Replaced `torch.cuda` with `torch.xpu` for device checks, memory management, and distributed training. - Updated device strings from "cuda" to "xpu" across the codebase. - Switched the distributed training backend from "nccl" to "ccl" for Intel GPUs. - Disabled custom CUDA kernels in the `BigVGAN` module by setting `use_cuda_kernel=False`. - Updated `requirements.txt` to include `torch==2.9` and `intel-extension-for-pytorch`. - Modified CI/CD pipelines and build scripts to remove CUDA dependencies and build for an XPU target.
225 lines
7.5 KiB
Python
225 lines
7.5 KiB
Python
import os
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import re
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import sys
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import torch
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from tools.i18n.i18n import I18nAuto
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i18n = I18nAuto(language=os.environ.get("language", "Auto"))
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pretrained_sovits_name = {
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"v1": "GPT_SoVITS/pretrained_models/s2G488k.pth",
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"v2": "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth",
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"v3": "GPT_SoVITS/pretrained_models/s2Gv3.pth", ###v3v4还要检查vocoder，算了。。。
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"v4": "GPT_SoVITS/pretrained_models/gsv-v4-pretrained/s2Gv4.pth",
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"v2Pro": "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2Pro.pth",
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"v2ProPlus": "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2ProPlus.pth",
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}
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pretrained_gpt_name = {
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"v1": "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt",
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"v2": "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt",
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"v3": "GPT_SoVITS/pretrained_models/s1v3.ckpt",
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"v4": "GPT_SoVITS/pretrained_models/s1v3.ckpt",
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"v2Pro": "GPT_SoVITS/pretrained_models/s1v3.ckpt",
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"v2ProPlus": "GPT_SoVITS/pretrained_models/s1v3.ckpt",
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}
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name2sovits_path = {
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# i18n("不训练直接推v1底模！"): "GPT_SoVITS/pretrained_models/s2G488k.pth",
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i18n("不训练直接推v2底模！"): "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s2G2333k.pth",
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# i18n("不训练直接推v3底模！"): "GPT_SoVITS/pretrained_models/s2Gv3.pth",
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# i18n("不训练直接推v4底模！"): "GPT_SoVITS/pretrained_models/gsv-v4-pretrained/s2Gv4.pth",
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i18n("不训练直接推v2Pro底模！"): "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2Pro.pth",
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i18n("不训练直接推v2ProPlus底模！"): "GPT_SoVITS/pretrained_models/v2Pro/s2Gv2ProPlus.pth",
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}
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name2gpt_path = {
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# i18n("不训练直接推v1底模！"):"GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt",
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i18n(
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"不训练直接推v2底模！"
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): "GPT_SoVITS/pretrained_models/gsv-v2final-pretrained/s1bert25hz-5kh-longer-epoch=12-step=369668.ckpt",
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i18n("不训练直接推v3底模！"): "GPT_SoVITS/pretrained_models/s1v3.ckpt",
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}
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SoVITS_weight_root = [
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"SoVITS_weights",
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"SoVITS_weights_v2",
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"SoVITS_weights_v3",
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"SoVITS_weights_v4",
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"SoVITS_weights_v2Pro",
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"SoVITS_weights_v2ProPlus",
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]
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GPT_weight_root = [
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"GPT_weights",
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"GPT_weights_v2",
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"GPT_weights_v3",
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"GPT_weights_v4",
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"GPT_weights_v2Pro",
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"GPT_weights_v2ProPlus",
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]
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SoVITS_weight_version2root = {
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"v1": "SoVITS_weights",
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"v2": "SoVITS_weights_v2",
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"v3": "SoVITS_weights_v3",
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"v4": "SoVITS_weights_v4",
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"v2Pro": "SoVITS_weights_v2Pro",
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"v2ProPlus": "SoVITS_weights_v2ProPlus",
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}
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GPT_weight_version2root = {
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"v1": "GPT_weights",
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"v2": "GPT_weights_v2",
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"v3": "GPT_weights_v3",
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"v4": "GPT_weights_v4",
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"v2Pro": "GPT_weights_v2Pro",
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"v2ProPlus": "GPT_weights_v2ProPlus",
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}
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def custom_sort_key(s):
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# 使用正则表达式提取字符串中的数字部分和非数字部分
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parts = re.split("(\d+)", s)
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# 将数字部分转换为整数，非数字部分保持不变
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parts = [int(part) if part.isdigit() else part for part in parts]
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return parts
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def get_weights_names():
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SoVITS_names = []
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for key in name2sovits_path:
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if os.path.exists(name2sovits_path[key]):
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SoVITS_names.append(key)
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for path in SoVITS_weight_root:
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if not os.path.exists(path):
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continue
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for name in os.listdir(path):
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if name.endswith(".pth"):
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SoVITS_names.append("%s/%s" % (path, name))
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if not SoVITS_names:
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SoVITS_names = [""]
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GPT_names = []
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for key in name2gpt_path:
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if os.path.exists(name2gpt_path[key]):
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GPT_names.append(key)
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for path in GPT_weight_root:
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if not os.path.exists(path):
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continue
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for name in os.listdir(path):
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if name.endswith(".ckpt"):
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GPT_names.append("%s/%s" % (path, name))
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SoVITS_names = sorted(SoVITS_names, key=custom_sort_key)
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GPT_names = sorted(GPT_names, key=custom_sort_key)
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if not GPT_names:
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GPT_names = [""]
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return SoVITS_names, GPT_names
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def change_choices():
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SoVITS_names, GPT_names = get_weights_names()
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return {"choices": SoVITS_names, "__type__": "update"}, {
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"choices": GPT_names,
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"__type__": "update",
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}
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# 推理用的指定模型
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sovits_path = ""
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gpt_path = ""
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is_half_str = os.environ.get("is_half", "True")
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is_half = True if is_half_str.lower() == "true" else False
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is_share_str = os.environ.get("is_share", "False")
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is_share = True if is_share_str.lower() == "true" else False
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cnhubert_path = "GPT_SoVITS/pretrained_models/chinese-hubert-base"
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bert_path = "GPT_SoVITS/pretrained_models/chinese-roberta-wwm-ext-large"
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pretrained_sovits_path = "GPT_SoVITS/pretrained_models/s2G488k.pth"
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pretrained_gpt_path = "GPT_SoVITS/pretrained_models/s1bert25hz-2kh-longer-epoch=68e-step=50232.ckpt"
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exp_root = "logs"
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python_exec = sys.executable or "python"
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webui_port_main = 9874
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webui_port_uvr5 = 9873
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webui_port_infer_tts = 9872
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webui_port_subfix = 9871
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api_port = 9880
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# Thanks to the contribution of @Karasukaigan and @XXXXRT666
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# Modified for Intel GPU (XPU)
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def get_device_dtype_sm(idx: int) -> tuple[torch.device, torch.dtype, float, float]:
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cpu = torch.device("cpu")
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try:
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if not torch.xpu.is_available():
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return cpu, torch.float32, 0.0, 0.0
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except AttributeError:
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return cpu, torch.float32, 0.0, 0.0
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xpu_device = torch.device(f"xpu:{idx}")
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properties = torch.xpu.get_device_properties(idx)
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mem_bytes = properties.total_memory
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mem_gb = mem_bytes / (1024**3)
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# Simplified logic for XPU, assuming FP16/BF16 is generally supported.
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# The complex SM version check is CUDA-specific.
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if mem_gb < 4: # Example threshold
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return cpu, torch.float32, 0.0, 0.0
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# For Intel GPUs, we can generally assume float16 is available.
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# The 'sm_version' equivalent is not straightforward, so we use a placeholder value (e.g., 1.0)
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# for compatibility with the downstream logic that sorts devices.
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return xpu_device, torch.float16, 1.0, mem_gb
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IS_GPU = True
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GPU_INFOS: list[str] = []
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GPU_INDEX: set[int] = set()
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try:
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GPU_COUNT = torch.xpu.device_count() if torch.xpu.is_available() else 0
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except AttributeError:
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GPU_COUNT = 0
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CPU_INFO: str = "0\tCPU " + i18n("CPU训练,较慢")
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tmp: list[tuple[torch.device, torch.dtype, float, float]] = []
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memset: set[float] = set()
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for i in range(max(GPU_COUNT, 1)):
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tmp.append(get_device_dtype_sm(i))
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for j in tmp:
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device = j[0]
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memset.add(j[3])
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if device.type == "xpu":
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GPU_INFOS.append(f"{device.index}\t{torch.xpu.get_device_name(device.index)}")
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GPU_INDEX.add(device.index)
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if not GPU_INFOS:
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IS_GPU = False
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GPU_INFOS.append(CPU_INFO)
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GPU_INDEX.add(0)
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infer_device = max(tmp, key=lambda x: (x[2], x[3]))[0]
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is_half = any(dtype == torch.float16 for _, dtype, _, _ in tmp)
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class Config:
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def __init__(self):
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self.sovits_path = sovits_path
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self.gpt_path = gpt_path
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self.is_half = is_half
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self.cnhubert_path = cnhubert_path
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self.bert_path = bert_path
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self.pretrained_sovits_path = pretrained_sovits_path
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self.pretrained_gpt_path = pretrained_gpt_path
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self.exp_root = exp_root
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self.python_exec = python_exec
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self.infer_device = infer_device
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self.webui_port_main = webui_port_main
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self.webui_port_uvr5 = webui_port_uvr5
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self.webui_port_infer_tts = webui_port_infer_tts
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self.webui_port_subfix = webui_port_subfix
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self.api_port = api_port
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