mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2026-01-19 18:41:52 +00:00
Format code (#526)
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
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97
infer-web.py
97
infer-web.py
@@ -1,9 +1,10 @@
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import os
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import shutil
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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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import traceback,pdb
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import traceback, pdb
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import warnings
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import numpy as np
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@@ -396,7 +397,7 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format
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# 一个选项卡全局只能有一个音色
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def get_vc(sid,to_return_protect0,to_return_protect1):
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def get_vc(sid, to_return_protect0, to_return_protect1):
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global n_spk, tgt_sr, net_g, vc, cpt, version
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if sid == "" or sid == []:
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global hubert_model
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@@ -434,11 +435,23 @@ def get_vc(sid,to_return_protect0,to_return_protect1):
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tgt_sr = cpt["config"][-1]
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cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
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if_f0 = cpt.get("f0", 1)
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if(if_f0==0):
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to_return_protect0=to_return_protect1={"visible": False, "value": 0.5, "__type__": "update"}
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if if_f0 == 0:
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to_return_protect0 = to_return_protect1 = {
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"visible": False,
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"value": 0.5,
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"__type__": "update",
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}
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else:
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to_return_protect0 ={"visible": True, "value": to_return_protect0, "__type__": "update"}
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to_return_protect1 ={"visible": True, "value": to_return_protect1, "__type__": "update"}
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to_return_protect0 = {
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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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to_return_protect1 = {
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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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version = cpt.get("version", "v1")
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if version == "v1":
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if if_f0 == 1:
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@@ -459,7 +472,11 @@ 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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return {"visible": True, "maximum": n_spk, "__type__": "update"},to_return_protect0,to_return_protect1
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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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)
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def change_choices():
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@@ -665,8 +682,13 @@ def change_sr2(sr2, if_f0_3, version19):
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def change_version19(sr2, if_f0_3, version19):
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path_str = "" if version19 == "v1" else "_v2"
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if(sr2=="32k"and version19=="v1"):sr2="40k"
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to_return_sr2= {"choices": ["40k","48k"], "__type__": "update"} if version19=="v1"else {"choices": ["32k","40k","48k"], "__type__": "update"}
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if sr2 == "32k" and version19 == "v1":
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sr2 = "40k"
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to_return_sr2 = (
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{"choices": ["40k", "48k"], "__type__": "update"}
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if version19 == "v1"
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else {"choices": ["32k", "40k", "48k"], "__type__": "update"}
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)
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f0_str = "f0" if if_f0_3 else ""
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if_pretrained_generator_exist = os.access(
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"pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK
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@@ -691,7 +713,7 @@ def change_version19(sr2, if_f0_3, version19):
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"pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)
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if if_pretrained_discriminator_exist
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else "",
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to_return_sr2
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to_return_sr2,
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)
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@@ -893,14 +915,24 @@ def train_index(exp_dir1, version19):
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big_npy_idx = np.arange(big_npy.shape[0])
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np.random.shuffle(big_npy_idx)
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big_npy = big_npy[big_npy_idx]
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if(big_npy.shape[0]>2e5):
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# if(1):
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infos.append("Trying doing kmeans %s shape to 10k centers."%big_npy.shape[0])
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if big_npy.shape[0] > 2e5:
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# if(1):
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infos.append("Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0])
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yield "\n".join(infos)
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try:
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big_npy = MiniBatchKMeans(n_clusters=10000, verbose=True, batch_size=256 * config.n_cpu, compute_labels=False, init="random").fit(big_npy).cluster_centers_
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big_npy = (
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MiniBatchKMeans(
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n_clusters=10000,
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verbose=True,
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batch_size=256 * config.n_cpu,
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compute_labels=False,
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init="random",
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)
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.fit(big_npy)
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.cluster_centers_
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)
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except:
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info=traceback.format_exc()
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info = traceback.format_exc()
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print(info)
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infos.append(info)
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yield "\n".join(infos)
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@@ -1147,15 +1179,25 @@ def train1key(
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np.random.shuffle(big_npy_idx)
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big_npy = big_npy[big_npy_idx]
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if(big_npy.shape[0]>2e5):
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# if(1):
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info="Trying doing kmeans %s shape to 10k centers."%big_npy.shape[0]
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if big_npy.shape[0] > 2e5:
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# if(1):
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info = "Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0]
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print(info)
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yield get_info_str(info)
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try:
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big_npy = MiniBatchKMeans(n_clusters=10000, verbose=True, batch_size=256 * config.n_cpu, compute_labels=False, init="random").fit(big_npy).cluster_centers_
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big_npy = (
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MiniBatchKMeans(
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n_clusters=10000,
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verbose=True,
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batch_size=256 * config.n_cpu,
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compute_labels=False,
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init="random",
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)
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.fit(big_npy)
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.cluster_centers_
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)
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except:
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info=traceback.format_exc()
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info = traceback.format_exc()
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print(info)
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yield get_info_str(info)
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@@ -1207,11 +1249,10 @@ def change_info_(ckpt_path):
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return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
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def export_onnx(ModelPath, ExportedPath):
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cpt = torch.load(ModelPath, map_location="cpu")
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cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]
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vec_channels = 256 if cpt.get("version","v1")=="v1"else 768
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vec_channels = 256 if cpt.get("version", "v1") == "v1" else 768
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test_phone = torch.rand(1, 200, vec_channels) # hidden unit
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test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度（貌似没啥用）
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@@ -1223,7 +1264,7 @@ def export_onnx(ModelPath, ExportedPath):
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device = "cpu" # 导出时设备（不影响使用模型）
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net_g = SynthesizerTrnMsNSFsidM(
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*cpt["config"], is_half=False,version=cpt.get("version","v1")
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*cpt["config"], is_half=False, version=cpt.get("version", "v1")
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) # fp32导出（C++要支持fp16必须手动将内存重新排列所以暂时不用fp16）
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net_g.load_state_dict(cpt["weight"], strict=False)
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input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"]
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@@ -1504,8 +1545,8 @@ with gr.Blocks() as app:
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)
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sid0.change(
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fn=get_vc,
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inputs=[sid0,protect0,protect1],
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outputs=[spk_item,protect0,protect1],
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inputs=[sid0, protect0, protect1],
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outputs=[spk_item, protect0, protect1],
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)
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with gr.TabItem(i18n("伴奏人声分离&去混响&去回声")):
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with gr.Group():
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@@ -1604,7 +1645,7 @@ with gr.Blocks() as app:
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maximum=config.n_cpu,
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step=1,
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label=i18n("提取音高和处理数据使用的CPU进程数"),
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value=int(np.ceil(config.n_cpu/1.5)),
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value=int(np.ceil(config.n_cpu / 1.5)),
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interactive=True,
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)
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with gr.Group(): # 暂时单人的, 后面支持最多4人的#数据处理
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@@ -1722,7 +1763,7 @@ with gr.Blocks() as app:
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version19.change(
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change_version19,
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[sr2, if_f0_3, version19],
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[pretrained_G14, pretrained_D15,sr2],
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[pretrained_G14, pretrained_D15, sr2],
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)
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if_f0_3.change(
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change_f0,
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@@ -1915,7 +1956,7 @@ with gr.Blocks() as app:
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[ckpt_path2, save_name, sr__, if_f0__, info___, version_1],
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info7,
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)
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with gr.TabItem(i18n("Onnx导出")):
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with gr.Row():
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ckpt_dir = gr.Textbox(label=i18n("RVC模型路径"), value="", interactive=True)
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