Format code (#989)
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10
MDXNet.py
10
MDXNet.py
@ -83,12 +83,13 @@ def get_models(device, dim_f, dim_t, n_fft):
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warnings.filterwarnings("ignore")
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import sys
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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from config import Config
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cpu = torch.device("cpu")
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device=Config().device
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device = Config().device
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# if torch.cuda.is_available():
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# device = torch.device("cuda:0")
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# elif torch.backends.mps.is_available():
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@ -104,10 +105,15 @@ class Predictor:
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device=cpu, dim_f=args.dim_f, dim_t=args.dim_t, n_fft=args.n_fft
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)
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import onnxruntime as ort
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print(ort.get_available_providers())
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self.model = ort.InferenceSession(
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os.path.join(args.onnx, self.model_.target_name + ".onnx"),
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providers=["CUDAExecutionProvider", "DmlExecutionProvider","CPUExecutionProvider"],
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providers=[
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"CUDAExecutionProvider",
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"DmlExecutionProvider",
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"CPUExecutionProvider",
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],
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)
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print("onnx load done")
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26
config.py
26
config.py
@ -36,7 +36,7 @@ class Config:
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self.iscolab,
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self.noparallel,
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self.noautoopen,
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self.dml
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self.dml,
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) = self.arg_parse()
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self.instead = ""
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self.x_pad, self.x_query, self.x_center, self.x_max = self.device_config()
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@ -71,7 +71,7 @@ class Config:
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cmd_opts.colab,
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cmd_opts.noparallel,
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cmd_opts.noautoopen,
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cmd_opts.dml
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cmd_opts.dml,
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)
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# has_mps is only available in nightly pytorch (for now) and MasOS 12.3+.
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@ -149,26 +149,38 @@ class Config:
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if self.dml:
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print("use DirectML instead")
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try:
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os.rename("runtime\Lib\site-packages\onnxruntime","runtime\Lib\site-packages\onnxruntime-cuda")
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os.rename(
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"runtime\Lib\site-packages\onnxruntime",
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"runtime\Lib\site-packages\onnxruntime-cuda",
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)
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except:
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pass
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try:
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os.rename("runtime\Lib\site-packages\onnxruntime-dml","runtime\Lib\site-packages\onnxruntime")
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os.rename(
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"runtime\Lib\site-packages\onnxruntime-dml",
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"runtime\Lib\site-packages\onnxruntime",
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)
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except:
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pass
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import torch_directml
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self.device = torch_directml.device(torch_directml.default_device())
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self.is_half = False
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else:
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if self.instead:
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print(f"use {self.instead} instead")
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try:
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os.rename("runtime\Lib\site-packages\onnxruntime","runtime\Lib\site-packages\onnxruntime-cuda")
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os.rename(
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"runtime\Lib\site-packages\onnxruntime",
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"runtime\Lib\site-packages\onnxruntime-cuda",
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)
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except:
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pass
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try:
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os.rename("runtime\Lib\site-packages\onnxruntime-dml","runtime\Lib\site-packages\onnxruntime")
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os.rename(
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"runtime\Lib\site-packages\onnxruntime-dml",
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"runtime\Lib\site-packages\onnxruntime",
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)
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except:
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pass
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return x_pad, x_query, x_center, x_max
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@ -10,6 +10,7 @@ logging.getLogger("numba").setLevel(logging.WARNING)
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exp_dir = sys.argv[1]
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import torch_directml
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device = torch_directml.device(torch_directml.default_device())
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f = open("%s/extract_f0_feature.log" % exp_dir, "a+")
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@ -3,7 +3,7 @@ import os, sys, traceback
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os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
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os.environ["PYTORCH_MPS_HIGH_WATERMARK_RATIO"] = "0.0"
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device=sys.argv[1]
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device = sys.argv[1]
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n_part = int(sys.argv[2])
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i_part = int(sys.argv[3])
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if len(sys.argv) == 6:
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@ -20,7 +20,7 @@ import soundfile as sf
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import numpy as np
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import fairseq
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if("privateuseone"not in device):
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if "privateuseone" not in device:
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device = "cpu"
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if torch.cuda.is_available():
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device = "cuda"
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@ -28,12 +28,15 @@ if("privateuseone"not in device):
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device = "mps"
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else:
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import torch_directml
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device = torch_directml.device(torch_directml.default_device())
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def forward_dml(ctx, x, scale):
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ctx.scale = scale
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res = x.clone().detach()
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return res
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fairseq.modules.grad_multiply.GradMultiply.forward=forward_dml
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fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml
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f = open("%s/extract_f0_feature.log" % exp_dir, "a+")
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@ -1,5 +1,6 @@
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import os, sys,pdb
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os.environ["OMP_NUM_THREADS"]="2"
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import os, sys, pdb
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os.environ["OMP_NUM_THREADS"] = "2"
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if sys.platform == "darwin":
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os.environ["PYTORCH_ENABLE_MPS_FALLBACK"] = "1"
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@ -47,8 +48,9 @@ if __name__ == "__main__":
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import torchaudio.transforms as tat
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from i18n import I18nAuto
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import rvc_for_realtime
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i18n = I18nAuto()
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device=rvc_for_realtime.config.device
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device = rvc_for_realtime.config.device
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# device = torch.device(
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# "cuda"
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# if torch.cuda.is_available()
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@ -61,7 +63,6 @@ if __name__ == "__main__":
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for _ in range(n_cpu):
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Harvest(inp_q, opt_q).start()
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class GUIConfig:
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def __init__(self) -> None:
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self.pth_path: str = ""
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112
infer-web.py
112
infer-web.py
@ -43,9 +43,7 @@ logging.getLogger("numba").setLevel(logging.WARNING)
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now_dir = os.getcwd()
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tmp = os.path.join(now_dir, "TEMP")
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shutil.rmtree(tmp, ignore_errors=True)
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shutil.rmtree(
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"%s/runtime/Lib/site-packages/infer_pack" % (now_dir), ignore_errors=True
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)
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shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack" % (now_dir), ignore_errors=True)
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shutil.rmtree("%s/runtime/Lib/site-packages/uvr5_pack" % (now_dir), ignore_errors=True)
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os.makedirs(tmp, exist_ok=True)
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os.makedirs(os.path.join(now_dir, "logs"), exist_ok=True)
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@ -56,12 +54,14 @@ torch.manual_seed(114514)
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config = Config()
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if(config.dml==True):
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if config.dml == True:
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def forward_dml(ctx, x, scale):
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ctx.scale = scale
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res = x.clone().detach()
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return res
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fairseq.modules.grad_multiply.GradMultiply.forward=forward_dml
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fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml
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i18n = I18nAuto()
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i18n.print()
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# 判断是否有能用来训练和加速推理的N卡
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@ -451,15 +451,21 @@ def get_vc(sid, to_return_protect0, to_return_protect1):
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del net_g, cpt
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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return {"visible": False, "__type__": "update"},{
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"visible": True,
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"value": to_return_protect0,
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"__type__": "update",
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},{
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"visible": True,
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"value": to_return_protect1,
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"__type__": "update",
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},"",""
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return (
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{"visible": False, "__type__": "update"},
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{
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"visible": True,
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"value": to_return_protect0,
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"__type__": "update",
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},
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{
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"visible": True,
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"value": to_return_protect1,
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"__type__": "update",
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},
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"",
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"",
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)
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person = "%s/%s" % (weight_root, sid)
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print("loading %s" % person)
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@ -504,15 +510,13 @@ def get_vc(sid, to_return_protect0, to_return_protect1):
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net_g = net_g.float()
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vc = VC(tgt_sr, config)
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n_spk = cpt["config"][-3]
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index={
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"value":get_index_path_from_model(sid),
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"__type__": "update"
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}
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index = {"value": get_index_path_from_model(sid), "__type__": "update"}
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return (
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{"visible": True, "maximum": n_spk, "__type__": "update"},
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to_return_protect0,
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to_return_protect1,
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index,index
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index,
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index,
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)
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@ -629,7 +633,7 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, gpus_rmvp
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),
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).start()
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else:
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if(gpus_rmvpe!="-"):
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if gpus_rmvpe != "-":
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gpus_rmvpe = gpus_rmvpe.split("-")
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leng = len(gpus_rmvpe)
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ps = []
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@ -647,7 +651,7 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, gpus_rmvp
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###煞笔gr, popen read都非得全跑完了再一次性读取, 不用gr就正常读一句输出一句;只能额外弄出一个文本流定时读
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done = [False]
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threading.Thread(
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target=if_done_multi,#
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target=if_done_multi, #
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args=(
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done,
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ps,
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@ -655,7 +659,8 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, gpus_rmvp
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).start()
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else:
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cmd = config.python_cmd + ' extract_f0_rmvpe_dml.py "%s/logs/%s" ' % (
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now_dir, exp_dir
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now_dir,
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exp_dir,
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)
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print(cmd)
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p = Popen(
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@ -671,9 +676,7 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, gpus_rmvp
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sleep(1)
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if done[0]:
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break
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with open(
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"%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r"
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) as f:
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with open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "r") as f:
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log = f.read()
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print(log)
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yield log
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@ -971,7 +974,7 @@ def click_train(
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# but4.click(train_index, [exp_dir1], info3)
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def train_index(exp_dir1, version19):
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# exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
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exp_dir = "logs/%s" % ( exp_dir1)
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exp_dir = "logs/%s" % (exp_dir1)
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os.makedirs(exp_dir, exist_ok=True)
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feature_dir = (
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"%s/3_feature256" % (exp_dir)
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@ -1115,17 +1118,21 @@ def train1key(
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p = Popen(cmd, shell=True, cwd=now_dir)
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p.wait()
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else:
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if(gpus_rmvpe!="-"):
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if gpus_rmvpe != "-":
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gpus_rmvpe = gpus_rmvpe.split("-")
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leng = len(gpus_rmvpe)
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ps = []
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for idx, n_g in enumerate(gpus_rmvpe):
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cmd = config.python_cmd + ' extract_f0_rmvpe.py %s %s %s "%s" %s ' % (
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leng,
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idx,
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n_g,
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model_log_dir,
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config.is_half,
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cmd = (
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config.python_cmd
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+ ' extract_f0_rmvpe.py %s %s %s "%s" %s '
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% (
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leng,
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idx,
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n_g,
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model_log_dir,
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config.is_half,
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)
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)
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yield get_info_str(cmd)
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p = Popen(
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@ -1134,7 +1141,7 @@ def train1key(
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ps.append(p)
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for p in ps:
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p.wait()
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else:#dml
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else: # dml
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cmd = config.python_cmd + ' extract_f0_rmvpe_dml.py "%s" ' % (
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model_log_dir
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)
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@ -1316,7 +1323,17 @@ def train1key(
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index_ivf = faiss.extract_index_ivf(index) #
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index_ivf.nprobe = 1
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index.train(big_npy)
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faiss.write_index(index,"%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index"% (model_log_dir.replace(now_dir+"/",""), n_ivf, index_ivf.nprobe, exp_dir1, version19))
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faiss.write_index(
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index,
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"%s/trained_IVF%s_Flat_nprobe_%s_%s_%s.index"
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% (
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model_log_dir.replace(now_dir + "/", ""),
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n_ivf,
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index_ivf.nprobe,
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exp_dir1,
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version19,
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),
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)
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yield get_info_str("adding index")
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batch_size_add = 8192
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for i in range(0, big_npy.shape[0], batch_size_add):
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@ -1324,7 +1341,13 @@ def train1key(
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faiss.write_index(
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index,
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"%s/added_IVF%s_Flat_nprobe_%s_%s_%s.index"
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% (model_log_dir.replace(now_dir+"/",""), n_ivf, index_ivf.nprobe, exp_dir1, version19),
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% (
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model_log_dir.replace(now_dir + "/", ""),
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n_ivf,
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index_ivf.nprobe,
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exp_dir1,
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version19,
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),
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)
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yield get_info_str(
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"成功构建索引, added_IVF%s_Flat_nprobe_%s_%s_%s.index"
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@ -1351,6 +1374,8 @@ def change_info_(ckpt_path):
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F0GPUVisible = config.dml == False
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def change_f0_method(f0method8):
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if f0method8 == "rmvpe_gpu":
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visible = F0GPUVisible
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@ -1450,7 +1475,9 @@ with gr.Blocks(title="RVC WebUI") as app:
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label=i18n(
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"选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU"
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),
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choices=["pm", "harvest", "crepe", "rmvpe"]if config.dml==False else ["pm", "harvest", "rmvpe"],
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choices=["pm", "harvest", "crepe", "rmvpe"]
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if config.dml == False
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else ["pm", "harvest", "rmvpe"],
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value="pm",
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interactive=True,
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)
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@ -1556,7 +1583,9 @@ with gr.Blocks(title="RVC WebUI") as app:
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label=i18n(
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"选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU,rmvpe效果最好且微吃GPU"
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),
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choices=["pm", "harvest", "crepe", "rmvpe"]if config.dml==False else ["pm", "harvest", "rmvpe"],
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choices=["pm", "harvest", "crepe", "rmvpe"]
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if config.dml == False
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else ["pm", "harvest", "rmvpe"],
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value="pm",
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interactive=True,
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)
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@ -1790,9 +1819,12 @@ with gr.Blocks(title="RVC WebUI") as app:
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gpus6 = gr.Textbox(
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label=i18n("以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"),
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value=gpus,
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interactive=True,visible=F0GPUVisible
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interactive=True,
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visible=F0GPUVisible,
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)
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gpu_info9 = gr.Textbox(
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label=i18n("显卡信息"), value=gpu_info, visible=F0GPUVisible
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)
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gpu_info9 = gr.Textbox(label=i18n("显卡信息"), value=gpu_info,visible=F0GPUVisible)
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with gr.Column():
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f0method8 = gr.Radio(
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label=i18n(
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@ -315,8 +315,8 @@ class SineGen(torch.nn.Module):
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# generate uv signal
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uv = torch.ones_like(f0)
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uv = uv * (f0 > self.voiced_threshold)
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if(uv.device.type=="privateuseone"):#for DirectML
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uv=uv.float()
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if uv.device.type == "privateuseone": # for DirectML
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uv = uv.float()
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return uv
|
||||
|
||||
def forward(self, f0, upp):
|
||||
|
||||
142
lib/rmvpe.py
142
lib/rmvpe.py
@ -1,14 +1,23 @@
|
||||
import torch, numpy as np,pdb
|
||||
import torch, numpy as np, pdb
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as F
|
||||
import torch,pdb
|
||||
import torch, pdb
|
||||
import numpy as np
|
||||
import torch.nn.functional as F
|
||||
from scipy.signal import get_window
|
||||
from librosa.util import pad_center, tiny,normalize
|
||||
from librosa.util import pad_center, tiny, normalize
|
||||
|
||||
|
||||
###stft codes from https://github.com/pseeth/torch-stft/blob/master/torch_stft/util.py
|
||||
def window_sumsquare(window, n_frames, hop_length=200, win_length=800,
|
||||
n_fft=800, dtype=np.float32, norm=None):
|
||||
def window_sumsquare(
|
||||
window,
|
||||
n_frames,
|
||||
hop_length=200,
|
||||
win_length=800,
|
||||
n_fft=800,
|
||||
dtype=np.float32,
|
||||
norm=None,
|
||||
):
|
||||
"""
|
||||
# from librosa 0.6
|
||||
Compute the sum-square envelope of a window function at a given hop length.
|
||||
@ -41,18 +50,20 @@ def window_sumsquare(window, n_frames, hop_length=200, win_length=800,
|
||||
|
||||
# Compute the squared window at the desired length
|
||||
win_sq = get_window(window, win_length, fftbins=True)
|
||||
win_sq = normalize(win_sq, norm=norm)**2
|
||||
win_sq = normalize(win_sq, norm=norm) ** 2
|
||||
win_sq = pad_center(win_sq, n_fft)
|
||||
|
||||
# Fill the envelope
|
||||
for i in range(n_frames):
|
||||
sample = i * hop_length
|
||||
x[sample:min(n, sample + n_fft)] += win_sq[:max(0, min(n_fft, n - sample))]
|
||||
x[sample : min(n, sample + n_fft)] += win_sq[: max(0, min(n_fft, n - sample))]
|
||||
return x
|
||||
|
||||
|
||||
class STFT(torch.nn.Module):
|
||||
def __init__(self, filter_length=1024, hop_length=512, win_length=None,
|
||||
window='hann'):
|
||||
def __init__(
|
||||
self, filter_length=1024, hop_length=512, win_length=None, window="hann"
|
||||
):
|
||||
"""
|
||||
This module implements an STFT using 1D convolution and 1D transpose convolutions.
|
||||
This is a bit tricky so there are some cases that probably won't work as working
|
||||
@ -79,12 +90,15 @@ class STFT(torch.nn.Module):
|
||||
fourier_basis = np.fft.fft(np.eye(self.filter_length))
|
||||
|
||||
cutoff = int((self.filter_length / 2 + 1))
|
||||
fourier_basis = np.vstack([np.real(fourier_basis[:cutoff, :]),np.imag(fourier_basis[:cutoff, :])])
|
||||
fourier_basis = np.vstack(
|
||||
[np.real(fourier_basis[:cutoff, :]), np.imag(fourier_basis[:cutoff, :])]
|
||||
)
|
||||
forward_basis = torch.FloatTensor(fourier_basis[:, None, :])
|
||||
inverse_basis = torch.FloatTensor(
|
||||
np.linalg.pinv(scale * fourier_basis).T[:, None, :])
|
||||
np.linalg.pinv(scale * fourier_basis).T[:, None, :]
|
||||
)
|
||||
|
||||
assert (filter_length >= self.win_length)
|
||||
assert filter_length >= self.win_length
|
||||
# get window and zero center pad it to filter_length
|
||||
fft_window = get_window(window, self.win_length, fftbins=True)
|
||||
fft_window = pad_center(fft_window, size=filter_length)
|
||||
@ -94,8 +108,8 @@ class STFT(torch.nn.Module):
|
||||
forward_basis *= fft_window
|
||||
inverse_basis *= fft_window
|
||||
|
||||
self.register_buffer('forward_basis', forward_basis.float())
|
||||
self.register_buffer('inverse_basis', inverse_basis.float())
|
||||
self.register_buffer("forward_basis", forward_basis.float())
|
||||
self.register_buffer("inverse_basis", inverse_basis.float())
|
||||
|
||||
def transform(self, input_data):
|
||||
"""Take input data (audio) to STFT domain.
|
||||
@ -117,23 +131,25 @@ class STFT(torch.nn.Module):
|
||||
# similar to librosa, reflect-pad the input
|
||||
input_data = input_data.view(num_batches, 1, num_samples)
|
||||
# print(1234,input_data.shape)
|
||||
input_data = F.pad(input_data.unsqueeze(1),(self.pad_amount, self.pad_amount, 0, 0,0,0),mode='reflect').squeeze(1)
|
||||
input_data = F.pad(
|
||||
input_data.unsqueeze(1),
|
||||
(self.pad_amount, self.pad_amount, 0, 0, 0, 0),
|
||||
mode="reflect",
|
||||
).squeeze(1)
|
||||
# print(2333,input_data.shape,self.forward_basis.shape,self.hop_length)
|
||||
# pdb.set_trace()
|
||||
forward_transform = F.conv1d(
|
||||
input_data,
|
||||
self.forward_basis,
|
||||
stride=self.hop_length,
|
||||
padding=0)
|
||||
input_data, self.forward_basis, stride=self.hop_length, padding=0
|
||||
)
|
||||
|
||||
cutoff = int((self.filter_length / 2) + 1)
|
||||
real_part = forward_transform[:, :cutoff, :]
|
||||
imag_part = forward_transform[:, cutoff:, :]
|
||||
|
||||
magnitude = torch.sqrt(real_part ** 2 + imag_part ** 2)
|
||||
magnitude = torch.sqrt(real_part**2 + imag_part**2)
|
||||
# phase = torch.atan2(imag_part.data, real_part.data)
|
||||
|
||||
return magnitude#, phase
|
||||
return magnitude # , phase
|
||||
|
||||
def inverse(self, magnitude, phase):
|
||||
"""Call the inverse STFT (iSTFT), given magnitude and phase tensors produced
|
||||
@ -150,30 +166,39 @@ class STFT(torch.nn.Module):
|
||||
shape (num_batch, num_samples)
|
||||
"""
|
||||
recombine_magnitude_phase = torch.cat(
|
||||
[magnitude * torch.cos(phase), magnitude * torch.sin(phase)], dim=1)
|
||||
[magnitude * torch.cos(phase), magnitude * torch.sin(phase)], dim=1
|
||||
)
|
||||
|
||||
inverse_transform = F.conv_transpose1d(
|
||||
recombine_magnitude_phase,
|
||||
self.inverse_basis,
|
||||
stride=self.hop_length,
|
||||
padding=0)
|
||||
padding=0,
|
||||
)
|
||||
|
||||
if self.window is not None:
|
||||
window_sum = window_sumsquare(
|
||||
self.window, magnitude.size(-1), hop_length=self.hop_length,
|
||||
win_length=self.win_length, n_fft=self.filter_length,
|
||||
dtype=np.float32)
|
||||
self.window,
|
||||
magnitude.size(-1),
|
||||
hop_length=self.hop_length,
|
||||
win_length=self.win_length,
|
||||
n_fft=self.filter_length,
|
||||
dtype=np.float32,
|
||||
)
|
||||
# remove modulation effects
|
||||
approx_nonzero_indices = torch.from_numpy(
|
||||
np.where(window_sum > tiny(window_sum))[0])
|
||||
np.where(window_sum > tiny(window_sum))[0]
|
||||
)
|
||||
window_sum = torch.from_numpy(window_sum).to(inverse_transform.device)
|
||||
inverse_transform[:, :, approx_nonzero_indices] /= window_sum[approx_nonzero_indices]
|
||||
inverse_transform[:, :, approx_nonzero_indices] /= window_sum[
|
||||
approx_nonzero_indices
|
||||
]
|
||||
|
||||
# scale by hop ratio
|
||||
inverse_transform *= float(self.filter_length) / self.hop_length
|
||||
|
||||
inverse_transform = inverse_transform[..., self.pad_amount:]
|
||||
inverse_transform = inverse_transform[..., :self.num_samples]
|
||||
inverse_transform = inverse_transform[..., self.pad_amount :]
|
||||
inverse_transform = inverse_transform[..., : self.num_samples]
|
||||
inverse_transform = inverse_transform.squeeze(1)
|
||||
|
||||
return inverse_transform
|
||||
@ -191,7 +216,11 @@ class STFT(torch.nn.Module):
|
||||
self.magnitude, self.phase = self.transform(input_data)
|
||||
reconstruction = self.inverse(self.magnitude, self.phase)
|
||||
return reconstruction
|
||||
|
||||
|
||||
from time import time as ttime
|
||||
|
||||
|
||||
class BiGRU(nn.Module):
|
||||
def __init__(self, input_features, hidden_features, num_layers):
|
||||
super(BiGRU, self).__init__()
|
||||
@ -509,14 +538,14 @@ class MelSpectrogram(torch.nn.Module):
|
||||
# print(1111111111)
|
||||
# print(222222222222222,audio.device,self.is_half)
|
||||
if hasattr(self, "stft") == False:
|
||||
# print(n_fft_new,hop_length_new,win_length_new,audio.shape)
|
||||
self.stft=STFT(
|
||||
# print(n_fft_new,hop_length_new,win_length_new,audio.shape)
|
||||
self.stft = STFT(
|
||||
filter_length=n_fft_new,
|
||||
hop_length=hop_length_new,
|
||||
win_length=win_length_new,
|
||||
window='hann'
|
||||
window="hann",
|
||||
).to(audio.device)
|
||||
magnitude = self.stft.transform(audio)#phase
|
||||
magnitude = self.stft.transform(audio) # phase
|
||||
# if (audio.device.type == "privateuseone"):
|
||||
# magnitude=magnitude.to(audio.device)
|
||||
if keyshift != 0:
|
||||
@ -544,10 +573,13 @@ class RMVPE:
|
||||
self.mel_extractor = MelSpectrogram(
|
||||
is_half, 128, 16000, 1024, 160, None, 30, 8000
|
||||
).to(device)
|
||||
if ("privateuseone" in str(device)):
|
||||
if "privateuseone" in str(device):
|
||||
import onnxruntime as ort
|
||||
ort_session = ort.InferenceSession("rmvpe.onnx", providers=["DmlExecutionProvider"])
|
||||
self.model=ort_session
|
||||
|
||||
ort_session = ort.InferenceSession(
|
||||
"rmvpe.onnx", providers=["DmlExecutionProvider"]
|
||||
)
|
||||
self.model = ort_session
|
||||
else:
|
||||
model = E2E(4, 1, (2, 2))
|
||||
ckpt = torch.load(model_path, map_location="cpu")
|
||||
@ -566,10 +598,13 @@ class RMVPE:
|
||||
mel = F.pad(
|
||||
mel, (0, 32 * ((n_frames - 1) // 32 + 1) - n_frames), mode="reflect"
|
||||
)
|
||||
if("privateuseone" in str(self.device) ):
|
||||
if "privateuseone" in str(self.device):
|
||||
onnx_input_name = self.model.get_inputs()[0].name
|
||||
onnx_outputs_names = self.model.get_outputs()[0].name
|
||||
hidden = self.model.run([onnx_outputs_names], input_feed={onnx_input_name: mel.cpu().numpy()})[0]
|
||||
hidden = self.model.run(
|
||||
[onnx_outputs_names],
|
||||
input_feed={onnx_input_name: mel.cpu().numpy()},
|
||||
)[0]
|
||||
else:
|
||||
hidden = self.model(mel)
|
||||
return hidden[:, :n_frames]
|
||||
@ -583,25 +618,27 @@ class RMVPE:
|
||||
|
||||
def infer_from_audio(self, audio, thred=0.03):
|
||||
# torch.cuda.synchronize()
|
||||
t0=ttime()
|
||||
mel = self.mel_extractor(torch.from_numpy(audio).float().to(self.device).unsqueeze(0), center=True)
|
||||
t0 = ttime()
|
||||
mel = self.mel_extractor(
|
||||
torch.from_numpy(audio).float().to(self.device).unsqueeze(0), center=True
|
||||
)
|
||||
# print(123123123,mel.device.type)
|
||||
# torch.cuda.synchronize()
|
||||
t1=ttime()
|
||||
t1 = ttime()
|
||||
hidden = self.mel2hidden(mel)
|
||||
# torch.cuda.synchronize()
|
||||
t2=ttime()
|
||||
t2 = ttime()
|
||||
# print(234234,hidden.device.type)
|
||||
if("privateuseone" not in str(self.device)):
|
||||
if "privateuseone" not in str(self.device):
|
||||
hidden = hidden.squeeze(0).cpu().numpy()
|
||||
else:
|
||||
hidden=hidden[0]
|
||||
hidden = hidden[0]
|
||||
if self.is_half == True:
|
||||
hidden = hidden.astype("float32")
|
||||
|
||||
f0 = self.decode(hidden, thred=thred)
|
||||
# torch.cuda.synchronize()
|
||||
t3=ttime()
|
||||
t3 = ttime()
|
||||
# print("hmvpe:%s\t%s\t%s\t%s"%(t1-t0,t2-t1,t3-t2,t3-t0))
|
||||
return f0
|
||||
|
||||
@ -632,8 +669,9 @@ class RMVPE:
|
||||
return devided
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if __name__ == "__main__":
|
||||
import soundfile as sf, librosa
|
||||
|
||||
audio, sampling_rate = sf.read(r"C:\Users\liujing04\Desktop\Z\冬之花clip1.wav")
|
||||
if len(audio.shape) > 1:
|
||||
audio = librosa.to_mono(audio.transpose(1, 0))
|
||||
@ -642,13 +680,13 @@ if __name__ == '__main__':
|
||||
audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
|
||||
model_path = r"D:\BaiduNetdiskDownload\RVC-beta-v2-0727AMD_realtime\rmvpe.pt"
|
||||
thred = 0.03 # 0.01
|
||||
device = 'cuda' if torch.cuda.is_available() else 'cpu'
|
||||
rmvpe = RMVPE(model_path,is_half=False, device=device)
|
||||
t0=ttime()
|
||||
device = "cuda" if torch.cuda.is_available() else "cpu"
|
||||
rmvpe = RMVPE(model_path, is_half=False, device=device)
|
||||
t0 = ttime()
|
||||
f0 = rmvpe.infer_from_audio(audio, thred=thred)
|
||||
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
|
||||
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
|
||||
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
|
||||
# f0 = rmvpe.infer_from_audio(audio, thred=thred)
|
||||
t1=ttime()
|
||||
print(f0.shape,t1-t0)
|
||||
t1 = ttime()
|
||||
print(f0.shape, t1 - t0)
|
||||
|
||||
@ -18,12 +18,16 @@ from multiprocessing import Manager as M
|
||||
|
||||
mm = M()
|
||||
config = Config()
|
||||
if(config.dml==True):
|
||||
if config.dml == True:
|
||||
|
||||
def forward_dml(ctx, x, scale):
|
||||
ctx.scale = scale
|
||||
res = x.clone().detach()
|
||||
return res
|
||||
fairseq.modules.grad_multiply.GradMultiply.forward=forward_dml
|
||||
|
||||
fairseq.modules.grad_multiply.GradMultiply.forward = forward_dml
|
||||
|
||||
|
||||
# config.device=torch.device("cpu")########强制cpu测试
|
||||
# config.is_half=False########强制cpu测试
|
||||
class RVC:
|
||||
@ -183,8 +187,8 @@ class RVC:
|
||||
return self.get_f0_post(f0bak)
|
||||
|
||||
def get_f0_crepe(self, x, f0_up_key):
|
||||
if self.device.type == "privateuseone":###不支持dml，cpu又太慢用不成，拿pm顶替
|
||||
return self.get_f0(x, f0_up_key,1,"pm")
|
||||
if self.device.type == "privateuseone": ###不支持dml，cpu又太慢用不成，拿pm顶替
|
||||
return self.get_f0(x, f0_up_key, 1, "pm")
|
||||
audio = torch.tensor(np.copy(x))[None].float()
|
||||
# print("using crepe,device:%s"%self.device)
|
||||
f0, pd = torchcrepe.predict(
|
||||
@ -209,12 +213,15 @@ class RVC:
|
||||
def get_f0_rmvpe(self, x, f0_up_key):
|
||||
if hasattr(self, "model_rmvpe") == False:
|
||||
from lib.rmvpe import RMVPE
|
||||
|
||||
print("loading rmvpe model")
|
||||
self.model_rmvpe = RMVPE(
|
||||
# "rmvpe.pt", is_half=self.is_half if self.device.type!="privateuseone" else False, device=self.device if self.device.type!="privateuseone"else "cpu"####dml时强制对rmvpe用cpu跑
|
||||
# "rmvpe.pt", is_half=False, device=self.device####dml配置
|
||||
# "rmvpe.pt", is_half=False, device="cpu"####锁定cpu配置
|
||||
"rmvpe.pt", is_half=self.is_half, device=self.device####正常逻辑
|
||||
# "rmvpe.pt", is_half=False, device="cpu"####锁定cpu配置
|
||||
"rmvpe.pt",
|
||||
is_half=self.is_half,
|
||||
device=self.device, ####正常逻辑
|
||||
)
|
||||
# self.model_rmvpe = RMVPE("aug2_58000_half.pt", is_half=self.is_half, device=self.device)
|
||||
f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
|
||||
|
||||
@ -130,13 +130,14 @@ class VC(object):
|
||||
elif f0_method == "rmvpe":
|
||||
if hasattr(self, "model_rmvpe") == False:
|
||||
from lib.rmvpe import RMVPE
|
||||
|
||||
print("loading rmvpe model")
|
||||
self.model_rmvpe = RMVPE(
|
||||
"rmvpe.pt", is_half=self.is_half, device=self.device
|
||||
)
|
||||
|
||||
f0 = self.model_rmvpe.infer_from_audio(x, thred=0.03)
|
||||
if("privateuseone"in str(self.device)):#clean ortruntime memory
|
||||
if "privateuseone" in str(self.device): # clean ortruntime memory
|
||||
del self.model_rmvpe.model
|
||||
del self.model_rmvpe
|
||||
print("cleaning ortruntime memory")
|
||||
|
||||
Loading…
Reference in New Issue
Block a user