diff --git a/extract_feature_print.py b/extract_feature_print.py index c6b1c63..9cc5f06 100644 --- a/extract_feature_print.py +++ b/extract_feature_print.py @@ -64,8 +64,11 @@ def readwave(wav_path, normalize=False): # HuBERT model printt("load model(s) from {}".format(model_path)) # if hubert model is exist -if (os.access(model_path, os.F_OK) == False): - printt("Error: Extracting is shut down because %s does not exist, you may download it from https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main" % model_path) +if os.access(model_path, os.F_OK) == False: + printt( + "Error: Extracting is shut down because %s does not exist, you may download it from https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main" + % model_path + ) exit(0) models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task( [model_path], diff --git a/gui.py b/gui.py index 16c51f6..1e5e5d9 100644 --- a/gui.py +++ b/gui.py @@ -469,21 +469,21 @@ class GUI: self.flag_vc = False def set_values(self, values): - if(len(values["pth_path"].strip()) == 0): - sg.popup(i18n('请选择pth文件')) + if len(values["pth_path"].strip()) == 0: + sg.popup(i18n("请选择pth文件")) return False - if(len(values["index_path"].strip()) == 0): - sg.popup(i18n('请选择index文件')) + if len(values["index_path"].strip()) == 0: + sg.popup(i18n("请选择index文件")) return False pattern = re.compile("[^\x00-\x7F]+") - if(pattern.findall(values["hubert_path"])): - sg.popup(i18n('hubert模型路径不可包含中文')) + if pattern.findall(values["hubert_path"]): + sg.popup(i18n("hubert模型路径不可包含中文")) return False - if(pattern.findall(values["pth_path"])): - sg.popup(i18n('pth文件路径不可包含中文')) + if pattern.findall(values["pth_path"]): + sg.popup(i18n("pth文件路径不可包含中文")) return False - if(pattern.findall(values["index_path"])): - sg.popup(i18n('index文件路径不可包含中文')) + if pattern.findall(values["index_path"]): + sg.popup(i18n("index文件路径不可包含中文")) return False self.set_devices(values["sg_input_device"], values["sg_output_device"]) self.config.hubert_path = os.path.join(current_dir, "hubert_base.pt") diff --git a/infer-web.py b/infer-web.py index daca13d..e08315a 100644 --- a/infer-web.py +++ b/infer-web.py @@ -1,9 +1,10 @@ import os import shutil import sys + now_dir = os.getcwd() sys.path.append(now_dir) -import traceback,pdb +import traceback, pdb import warnings import numpy as np @@ -396,7 +397,7 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format # 一个选项卡全局只能有一个音色 -def get_vc(sid,to_return_protect0,to_return_protect1): +def get_vc(sid, to_return_protect0, to_return_protect1): global n_spk, tgt_sr, net_g, vc, cpt, version if sid == "" or sid == []: global hubert_model @@ -434,11 +435,23 @@ def get_vc(sid,to_return_protect0,to_return_protect1): tgt_sr = cpt["config"][-1] cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk if_f0 = cpt.get("f0", 1) - if(if_f0==0): - to_return_protect0=to_return_protect1={"visible": False, "value": 0.5, "__type__": "update"} + if if_f0 == 0: + to_return_protect0 = to_return_protect1 = { + "visible": False, + "value": 0.5, + "__type__": "update", + } else: - to_return_protect0 ={"visible": True, "value": to_return_protect0, "__type__": "update"} - to_return_protect1 ={"visible": True, "value": to_return_protect1, "__type__": "update"} + to_return_protect0 = { + "visible": True, + "value": to_return_protect0, + "__type__": "update", + } + to_return_protect1 = { + "visible": True, + "value": to_return_protect1, + "__type__": "update", + } version = cpt.get("version", "v1") if version == "v1": if if_f0 == 1: @@ -459,7 +472,11 @@ def get_vc(sid,to_return_protect0,to_return_protect1): net_g = net_g.float() vc = VC(tgt_sr, config) n_spk = cpt["config"][-3] - return {"visible": True, "maximum": n_spk, "__type__": "update"},to_return_protect0,to_return_protect1 + return ( + {"visible": True, "maximum": n_spk, "__type__": "update"}, + to_return_protect0, + to_return_protect1, + ) def change_choices(): @@ -665,8 +682,13 @@ def change_sr2(sr2, if_f0_3, version19): def change_version19(sr2, if_f0_3, version19): path_str = "" if version19 == "v1" else "_v2" - if(sr2=="32k"and version19=="v1"):sr2="40k" - to_return_sr2= {"choices": ["40k","48k"], "__type__": "update"} if version19=="v1"else {"choices": ["32k","40k","48k"], "__type__": "update"} + if sr2 == "32k" and version19 == "v1": + sr2 = "40k" + to_return_sr2 = ( + {"choices": ["40k", "48k"], "__type__": "update"} + if version19 == "v1" + else {"choices": ["32k", "40k", "48k"], "__type__": "update"} + ) f0_str = "f0" if if_f0_3 else "" if_pretrained_generator_exist = os.access( "pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2), os.F_OK @@ -691,7 +713,7 @@ def change_version19(sr2, if_f0_3, version19): "pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2) if if_pretrained_discriminator_exist else "", - to_return_sr2 + to_return_sr2, ) @@ -893,14 +915,24 @@ def train_index(exp_dir1, version19): big_npy_idx = np.arange(big_npy.shape[0]) np.random.shuffle(big_npy_idx) big_npy = big_npy[big_npy_idx] - if(big_npy.shape[0]>2e5): - # if(1): - infos.append("Trying doing kmeans %s shape to 10k centers."%big_npy.shape[0]) + if big_npy.shape[0] > 2e5: + # if(1): + infos.append("Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0]) yield "\n".join(infos) try: - big_npy = MiniBatchKMeans(n_clusters=10000, verbose=True, batch_size=256 * config.n_cpu, compute_labels=False, init="random").fit(big_npy).cluster_centers_ + big_npy = ( + MiniBatchKMeans( + n_clusters=10000, + verbose=True, + batch_size=256 * config.n_cpu, + compute_labels=False, + init="random", + ) + .fit(big_npy) + .cluster_centers_ + ) except: - info=traceback.format_exc() + info = traceback.format_exc() print(info) infos.append(info) yield "\n".join(infos) @@ -1147,15 +1179,25 @@ def train1key( np.random.shuffle(big_npy_idx) big_npy = big_npy[big_npy_idx] - if(big_npy.shape[0]>2e5): - # if(1): - info="Trying doing kmeans %s shape to 10k centers."%big_npy.shape[0] + if big_npy.shape[0] > 2e5: + # if(1): + info = "Trying doing kmeans %s shape to 10k centers." % big_npy.shape[0] print(info) yield get_info_str(info) try: - big_npy = MiniBatchKMeans(n_clusters=10000, verbose=True, batch_size=256 * config.n_cpu, compute_labels=False, init="random").fit(big_npy).cluster_centers_ + big_npy = ( + MiniBatchKMeans( + n_clusters=10000, + verbose=True, + batch_size=256 * config.n_cpu, + compute_labels=False, + init="random", + ) + .fit(big_npy) + .cluster_centers_ + ) except: - info=traceback.format_exc() + info = traceback.format_exc() print(info) yield get_info_str(info) @@ -1207,11 +1249,10 @@ def change_info_(ckpt_path): return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"} - def export_onnx(ModelPath, ExportedPath): cpt = torch.load(ModelPath, map_location="cpu") cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] - vec_channels = 256 if cpt.get("version","v1")=="v1"else 768 + vec_channels = 256 if cpt.get("version", "v1") == "v1" else 768 test_phone = torch.rand(1, 200, vec_channels) # hidden unit test_phone_lengths = torch.tensor([200]).long() # hidden unit 长度（貌似没啥用） @@ -1223,7 +1264,7 @@ def export_onnx(ModelPath, ExportedPath): device = "cpu" # 导出时设备（不影响使用模型） net_g = SynthesizerTrnMsNSFsidM( - *cpt["config"], is_half=False,version=cpt.get("version","v1") + *cpt["config"], is_half=False, version=cpt.get("version", "v1") ) # 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） net_g.load_state_dict(cpt["weight"], strict=False) input_names = ["phone", "phone_lengths", "pitch", "pitchf", "ds", "rnd"] @@ -1504,8 +1545,8 @@ with gr.Blocks() as app: ) sid0.change( fn=get_vc, - inputs=[sid0,protect0,protect1], - outputs=[spk_item,protect0,protect1], + inputs=[sid0, protect0, protect1], + outputs=[spk_item, protect0, protect1], ) with gr.TabItem(i18n("伴奏人声分离&去混响&去回声")): with gr.Group(): @@ -1604,7 +1645,7 @@ with gr.Blocks() as app: maximum=config.n_cpu, step=1, label=i18n("提取音高和处理数据使用的CPU进程数"), - value=int(np.ceil(config.n_cpu/1.5)), + value=int(np.ceil(config.n_cpu / 1.5)), interactive=True, ) with gr.Group(): # 暂时单人的, 后面支持最多4人的#数据处理 @@ -1722,7 +1763,7 @@ with gr.Blocks() as app: version19.change( change_version19, [sr2, if_f0_3, version19], - [pretrained_G14, pretrained_D15,sr2], + [pretrained_G14, pretrained_D15, sr2], ) if_f0_3.change( change_f0, @@ -1915,7 +1956,7 @@ with gr.Blocks() as app: [ckpt_path2, save_name, sr__, if_f0__, info___, version_1], info7, ) - + with gr.TabItem(i18n("Onnx导出")): with gr.Row(): ckpt_dir = gr.Textbox(label=i18n("RVC模型路径"), value="", interactive=True) diff --git a/infer_pack/onnx_inference.py b/infer_pack/onnx_inference.py index 7502543..fb583a4 100644 --- a/infer_pack/onnx_inference.py +++ b/infer_pack/onnx_inference.py @@ -3,6 +3,7 @@ import librosa import numpy as np import soundfile + class ContentVec: def __init__(self, vec_path="pretrained/vec-768-layer-12.onnx", device=None): print("load model(s) from {}".format(vec_path)) diff --git a/train_nsf_sim_cache_sid_load_pretrain.py b/train_nsf_sim_cache_sid_load_pretrain.py index 0d36fe6..2949bc4 100644 --- a/train_nsf_sim_cache_sid_load_pretrain.py +++ b/train_nsf_sim_cache_sid_load_pretrain.py @@ -192,7 +192,6 @@ def run(rank, n_gpus, hps): epoch_str = 1 global_step = 0 if hps.pretrainG != "": - if rank == 0: logger.info("loaded pretrained %s" % (hps.pretrainG)) print( @@ -201,7 +200,6 @@ def run(rank, n_gpus, hps): ) ) ##测试不加载优化器 if hps.pretrainD != "": - if rank == 0: logger.info("loaded pretrained %s" % (hps.pretrainD)) print( diff --git a/trainset_preprocess_pipeline_print.py b/trainset_preprocess_pipeline_print.py index 55f6db5..6188c86 100644 --- a/trainset_preprocess_pipeline_print.py +++ b/trainset_preprocess_pipeline_print.py @@ -53,9 +53,9 @@ class PreProcess: os.makedirs(self.wavs16k_dir, exist_ok=True) def norm_write(self, tmp_audio, idx0, idx1): - tmp_max=np.abs(tmp_audio).max() - if(tmp_max>2.5): - print("%s-%s-%s-filtered"%(idx0,idx1,tmp_max)) + tmp_max = np.abs(tmp_audio).max() + if tmp_max > 2.5: + print("%s-%s-%s-filtered" % (idx0, idx1, tmp_max)) return tmp_audio = (tmp_audio / tmp_max * (self.max * self.alpha)) + ( 1 - self.alpha diff --git a/vc_infer_pipeline.py b/vc_infer_pipeline.py index 759bf90..8e530fc 100644 --- a/vc_infer_pipeline.py +++ b/vc_infer_pipeline.py @@ -184,7 +184,7 @@ class VC(object): with torch.no_grad(): logits = model.extract_features(**inputs) feats = model.final_proj(logits[0]) if version == "v1" else logits[0] - if protect < 0.5 and pitch!=None and pitchf!=None: + if protect < 0.5 and pitch != None and pitchf != None: feats0 = feats.clone() if ( isinstance(index, type(None)) == False @@ -211,7 +211,7 @@ class VC(object): ) feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1) - if protect < 0.5 and pitch!=None and pitchf!=None: + if protect < 0.5 and pitch != None and pitchf != None: feats0 = F.interpolate(feats0.permute(0, 2, 1), scale_factor=2).permute( 0, 2, 1 ) @@ -223,7 +223,7 @@ class VC(object): pitch = pitch[:, :p_len] pitchf = pitchf[:, :p_len] - if protect < 0.5 and pitch!=None and pitchf!=None: + if protect < 0.5 and pitch != None and pitchf != None: pitchff = pitchf.clone() pitchff[pitchf > 0] = 1 pitchff[pitchf < 1] = protect