parent
d634c2727e
commit
8ffdcb0128
2
.env
2
.env
@ -4,5 +4,5 @@ no_proxy = localhost, 127.0.0.1, ::1
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# You can change the location of the model, etc. by changing here
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weight_root = assets/weights
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weight_uvr5_root = assets/uvr5_weights
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index_root = output
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index_root = logs
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rmvpe_root = assets/rmvpe
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@ -160,7 +160,7 @@ class Config:
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x_center = 30
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x_max = 32
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if self.dml:
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print("use DirectML instead")
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print("Use DirectML instead")
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if (
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os.path.exists(
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"runtime\Lib\site-packages\onnxruntime\capi\DirectML.dll"
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@ -188,7 +188,7 @@ class Config:
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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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print(f"Use {self.instead} instead")
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if (
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os.path.exists(
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"runtime\Lib\site-packages\onnxruntime\capi\onnxruntime_providers_cuda.dll"
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10
gui_v1.py
10
gui_v1.py
@ -356,7 +356,7 @@ if __name__ == "__main__":
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)
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if event == "start_vc" and self.flag_vc == False:
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if self.set_values(values) == True:
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print("using_cuda:" + str(torch.cuda.is_available()))
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print("Use CUDA:" + str(torch.cuda.is_available()))
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self.start_vc()
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settings = {
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"pth_path": values["pth_path"],
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@ -623,7 +623,7 @@ if __name__ == "__main__":
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sola_offset = sola_offset.item()
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else:
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sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0])
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print("sola offset: " + str(int(sola_offset)))
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print("sola_offset =" + str(int(sola_offset)))
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self.output_wav[:] = infer_wav[sola_offset : sola_offset + self.block_frame]
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self.output_wav[: self.crossfade_frame] *= self.fade_in_window
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self.output_wav[: self.crossfade_frame] += self.sola_buffer[:]
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@ -663,7 +663,7 @@ if __name__ == "__main__":
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outdata[:] = self.output_wav[:].repeat(2, 1).t().cpu().numpy()
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total_time = time.perf_counter() - start_time
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self.window["infer_time"].update(int(total_time * 1000))
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print("infer time:" + str(total_time))
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print("Infer time:" + str(total_time))
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def get_devices(self, update: bool = True):
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"""获取设备列表"""
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@ -716,9 +716,9 @@ if __name__ == "__main__":
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sd.default.device[1] = output_device_indices[
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output_devices.index(output_device)
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]
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print("input device:" + str(sd.default.device[0]) + ":" + str(input_device))
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print("Input device:" + str(sd.default.device[0]) + ":" + str(input_device))
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print(
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"output device:" + str(sd.default.device[1]) + ":" + str(output_device)
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"Output device:" + str(sd.default.device[1]) + ":" + str(output_device)
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)
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gui = GUI()
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10
infer-web.py
10
infer-web.py
@ -124,7 +124,7 @@ class ToolButton(gr.Button, gr.components.FormComponent):
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weight_root = os.getenv("weight_root")
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weight_uvr5_root = os.getenv("weight_uvr5_root")
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index_root = "logs"
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index_root = os.getenv("index_root")
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names = []
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for name in os.listdir(weight_root):
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@ -556,14 +556,14 @@ def click_train(
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shuffle(opt)
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with open("%s/filelist.txt" % exp_dir, "w") as f:
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f.write("\n".join(opt))
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print("write filelist done")
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print("Write filelist done")
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# 生成config#无需生成config
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# cmd = python_cmd + " train_nsf_sim_cache_sid_load_pretrain.py -e mi-test -sr 40k -f0 1 -bs 4 -g 0 -te 10 -se 5 -pg pretrained/f0G40k.pth -pd pretrained/f0D40k.pth -l 1 -c 0"
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print("use gpus:", gpus16)
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print("Use gpus:", gpus16)
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if pretrained_G14 == "":
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print("no pretrained Generator")
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print("No pretrained Generator")
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if pretrained_D15 == "":
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print("no pretrained Discriminator")
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print("No pretrained Discriminator")
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if gpus16:
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cmd = get_quoted_python_cmd() + ' infer/modules/train/train.py -e "%s" -sr %s -f0 %s -bs %s -g %s -te %s -se %s %s %s -l %s -c %s -sw %s -v %s' % (
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exp_dir1,
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@ -6,7 +6,7 @@ import soundfile
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class ContentVec:
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def __init__(self, vec_path="pretrained/vec-768-layer-12.onnx", device=None):
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print("load model(s) from {}".format(vec_path))
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print("Load model(s) from {}".format(vec_path))
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if device == "cpu" or device is None:
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providers = ["CPUExecutionProvider"]
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elif device == "cuda":
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@ -51,9 +51,9 @@ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False)
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"""
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# Validation
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if torch.min(y) < -1.07:
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print("min value is ", torch.min(y))
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print("spectrogram_torch min value is ", torch.min(y))
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if torch.max(y) > 1.07:
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print("max value is ", torch.max(y))
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print("spectrogram_torch max value is ", torch.max(y))
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# Window - Cache if needed
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global hann_window
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@ -34,7 +34,7 @@ def load_checkpoint_d(checkpoint_path, combd, sbd, optimizer=None, load_opt=1):
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new_state_dict[k] = saved_state_dict[k]
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if saved_state_dict[k].shape != state_dict[k].shape:
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print(
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"shape-%s-mismatch|need-%s|get-%s"
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"shape-%s-mismatch. need: %s, get: %s"
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% (k, state_dict[k].shape, saved_state_dict[k].shape)
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) #
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raise KeyError
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@ -85,7 +85,7 @@ class FeatureInput(object):
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if hasattr(self, "model_rmvpe") == False:
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from infer.lib.rmvpe import RMVPE
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print("loading rmvpe model")
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print("Loading rmvpe model")
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self.model_rmvpe = RMVPE(
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"assets/rmvpe/rmvpe.pt", is_half=False, device="cpu"
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)
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@ -48,7 +48,7 @@ class FeatureInput(object):
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if hasattr(self, "model_rmvpe") == False:
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from infer.lib.rmvpe import RMVPE
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print("loading rmvpe model")
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print("Loading rmvpe model")
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self.model_rmvpe = RMVPE(
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"assets/rmvpe/rmvpe.pt", is_half=is_half, device="cuda"
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)
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@ -46,7 +46,7 @@ class FeatureInput(object):
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if hasattr(self, "model_rmvpe") == False:
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from infer.lib.rmvpe import RMVPE
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print("loading rmvpe model")
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print("Loading rmvpe model")
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self.model_rmvpe = RMVPE(
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"assets/rmvpe/rmvpe.pt", is_half=False, device=device
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)
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@ -101,7 +101,7 @@ class Predictor:
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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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print("ONNX load done")
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def demix(self, mix):
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samples = mix.shape[-1]
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@ -90,7 +90,7 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format
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del pre_fun
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except:
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traceback.print_exc()
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print("clean_empty_cache")
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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print("Executed torch.cuda.empty_cache()")
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yield "\n".join(infos)
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@ -31,7 +31,7 @@ class VC:
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def get_vc(self, sid, *to_return_protect):
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person = f'{os.getenv("weight_root")}/{sid}'
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print(f"loading {person}")
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print(f"Loading: {person}")
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self.cpt = torch.load(person, map_location="cpu")
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self.tgt_sr = self.cpt["config"][-1]
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@ -77,6 +77,7 @@ class VC:
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self.pipeline = Pipeline(self.tgt_sr, self.config)
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n_spk = self.cpt["config"][-3]
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index = {"value": get_index_path_from_model(sid), "__type__": "update"}
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print("Select index:", index["value"])
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return (
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(
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@ -140,7 +140,7 @@ class Pipeline(object):
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from infer.lib.rmvpe import RMVPE
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print(
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"loading rmvpe model,%s" % "%s/rmvpe.pt" % os.environ["rmvpe_root"]
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"Loading rmvpe model,%s" % "%s/rmvpe.pt" % os.environ["rmvpe_root"]
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)
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self.model_rmvpe = RMVPE(
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"%s/rmvpe.pt" % os.environ["rmvpe_root"],
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@ -152,7 +152,7 @@ class Pipeline(object):
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if "privateuseone" in str(self.device): # clean ortruntime memory
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del self.model_rmvpe.model
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del self.model_rmvpe
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print("cleaning ortruntime memory")
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print("Cleaning ortruntime memory")
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f0 *= pow(2, f0_up_key / 12)
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# with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
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@ -262,17 +262,12 @@ class Pipeline(object):
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feats = feats.to(feats0.dtype)
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p_len = torch.tensor([p_len], device=self.device).long()
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with torch.no_grad():
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if pitch is not None and pitchf is not None:
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audio1 = (
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(net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0])
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.data.cpu()
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.float()
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.numpy()
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)
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else:
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audio1 = (
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(net_g.infer(feats, p_len, sid)[0][0, 0]).data.cpu().float().numpy()
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)
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hasp = pitch is not None and pitchf is not None
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arg = (feats, p_len, pitch, pitchf, sid) if hasp else (feats, p_len, sid)
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audio1 = (
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(net_g.infer(*arg)[0][0, 0]).data.cpu().float().numpy()
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)
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del hasp, arg
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del feats, p_len, padding_mask
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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@ -2,33 +2,20 @@ import os
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from fairseq import checkpoint_utils
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### don't modify the code before you test it
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# def get_index_path_from_model(sid):
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# return next(
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# (
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# f
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# for f in [
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# os.path.join(root, name)
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# for root, dirs, files in os.walk(os.getenv("index_root"), topdown=False)
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# for name in files
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# if name.endswith(".index") and "trained" not in name
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# ]
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# if sid.split(".")[0] in f
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# ),
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# "",
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# )
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def get_index_path_from_model(sid):
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sel_index_path = ""
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name = os.path.join("logs", sid.split(".")[0], "")
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# print(name)
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for f in index_paths:
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if name in f:
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# print("selected index path:", f)
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sel_index_path = f
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break
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return sel_index_path
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return next(
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(
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f
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for f in [
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os.path.join(root, name)
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for root, _, files in os.walk(os.getenv("index_root"), topdown=False)
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for name in files
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if name.endswith(".index") and "trained" not in name
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]
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if sid.split(".")[0] in f
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),
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"",
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)
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def load_hubert(config):
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@ -23,7 +23,7 @@ vc = VC(config)
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weight_root = os.getenv("weight_root")
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weight_uvr5_root = os.getenv("weight_uvr5_root")
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index_root = "logs"
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index_root = os.getenv("index_root")
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names = []
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hubert_model = None
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for name in os.listdir(weight_root):
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@ -55,7 +55,7 @@ def main(path, root):
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torch.manual_seed(114514)
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model_a = torch.load(path, map_location="cpu")["weight"]
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print("query:\t\t%s\t%s" % (path, model_hash(path)))
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print("Query:\t\t%s\t%s" % (path, model_hash(path)))
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map_attn_a = {}
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map_rand_input = {}
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@ -83,7 +83,7 @@ def main(path, root):
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sims.append(sim)
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print(
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"reference:\t%s\t%s\t%s"
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"Reference:\t%s\t%s\t%s"
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% (path, model_hash(path), f"{torch.mean(torch.stack(sims)) * 1e2:.2f}%")
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)
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@ -34,7 +34,7 @@ from scipy.io import wavfile
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model_path = r"E:\codes\py39\vits_vc_gpu_train\assets\hubert\hubert_base.pt" #
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print("load model(s) from {}".format(model_path))
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print("Load model(s) from {}".format(model_path))
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models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
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[model_path],
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suffix="",
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@ -50,14 +50,14 @@ np.save("tools/infer/big_src_feature_mi.npy", big_npy)
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# big_npy=np.load("/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/inference_f0/big_src_feature_mi.npy")
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n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
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index = faiss.index_factory(768, "IVF%s,Flat" % n_ivf) # mi
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print("training")
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print("Training...")
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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(
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index, "tools/infer/trained_IVF%s_Flat_baseline_src_feat_v2.index" % (n_ivf)
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)
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print("adding")
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print("Adding...")
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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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index.add(big_npy[i : i + batch_size_add])
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@ -20,12 +20,12 @@ np.save("infer/big_src_feature_mi.npy", big_npy)
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# big_npy=np.load("/bili-coeus/jupyter/jupyterhub-liujing04/vits_ch/inference_f0/big_src_feature_mi.npy")
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print(big_npy.shape)
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index = faiss.index_factory(256, "IVF512,Flat") # mi
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print("training")
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print("Training...")
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index_ivf = faiss.extract_index_ivf(index) #
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index_ivf.nprobe = 9
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index.train(big_npy)
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faiss.write_index(index, "infer/trained_IVF512_Flat_mi_baseline_src_feat.index")
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print("adding")
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print("Adding...")
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index.add(big_npy)
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faiss.write_index(index, "infer/added_IVF512_Flat_mi_baseline_src_feat.index")
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"""
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@ -67,7 +67,7 @@ class RVC:
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if index_rate != 0:
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self.index = faiss.read_index(index_path)
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self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
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print("index search enabled")
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print("Index search enabled")
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self.index_path = index_path
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self.index_rate = index_rate
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models, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
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@ -120,7 +120,7 @@ class RVC:
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if new_index_rate != 0 and self.index_rate == 0:
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self.index = faiss.read_index(self.index_path)
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self.big_npy = self.index.reconstruct_n(0, self.index.ntotal)
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print("index search enabled")
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print("Index search enabled")
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self.index_rate = new_index_rate
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def get_f0_post(self, f0):
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@ -237,7 +237,7 @@ class RVC:
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if hasattr(self, "model_rmvpe") == False:
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from infer.lib.rmvpe import RMVPE
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print("loading rmvpe model")
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print("Loading rmvpe model")
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self.model_rmvpe = RMVPE(
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# "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
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# "rmvpe.pt", is_half=False, device=self.device####dml配置
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@ -295,10 +295,10 @@ class RVC:
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+ (1 - self.index_rate) * feats[0][-leng_replace_head:]
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)
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else:
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print("index search FAIL or disabled")
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print("Index search FAILED or disabled")
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except:
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traceback.print_exc()
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print("index search FAIL")
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print("Index search FAILED")
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feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
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t3 = ttime()
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if self.if_f0 == 1:
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@ -338,5 +338,5 @@ class RVC:
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.float()
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)
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t5 = ttime()
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print("time->fea-index-f0-model:", t2 - t1, t3 - t2, t4 - t3, t5 - t4)
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print("Spent time: fea =", t2 - t1, ", index =", t3 - t2, ", f0 =", t4 - t3, ", model =", t5 - t4)
|
||||
return infered_audio
|
||||
|
Loading…
Reference in New Issue
Block a user