Format code (#275)

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>

extract_f0_print.py

@@ -1,4 +1,5 @@
 import os, traceback, sys, parselmouth
+
 now_dir = os.getcwd()
 sys.path.append(now_dir)
 from my_utils import load_audio
@@ -35,7 +36,7 @@ class FeatureInput(object):
         self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
 
     def compute_f0(self, path, f0_method):
-        x=load_audio(path,self.fs)
+        x = load_audio(path, self.fs)
         p_len = x.shape[0] // self.hop
         if f0_method == "pm":
             time_step = 160 / 16000 * 1000

extract_feature_print.py

@@ -18,9 +18,12 @@ from fairseq import checkpoint_utils
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-if torch.cuda.is_available():device="cuda"
-elif torch.backends.mps.is_available():device="mps"
-else:device="cpu"
+if torch.cuda.is_available():
+    device = "cuda"
+elif torch.backends.mps.is_available():
+    device = "mps"
+else:
+    device = "cpu"
 
 f = open("%s/extract_f0_feature.log" % exp_dir, "a+")
 
@@ -36,7 +39,9 @@ model_path = "hubert_base.pt"
 
 printt(exp_dir)
 wavPath = "%s/1_16k_wavs" % exp_dir
-outPath = "%s/3_feature256" % exp_dir if version=="v1"else "%s/3_feature768" % exp_dir
+outPath = (
+    "%s/3_feature256" % exp_dir if version == "v1" else "%s/3_feature768" % exp_dir
+)
 os.makedirs(outPath, exist_ok=True)
 
 
@@ -64,7 +69,7 @@ models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
 model = models[0]
 model = model.to(device)
 printt("move model to %s" % device)
-if device not in ["mps","cpu"]:
+if device not in ["mps", "cpu"]:
     model = model.half()
 model.eval()
 
@@ -90,11 +95,13 @@ else:
                     if device not in ["mps", "cpu"]
                     else feats.to(device),
                     "padding_mask": padding_mask.to(device),
-                    "output_layer": 9 if version=="v1"else 12,  # layer 9
+                    "output_layer": 9 if version == "v1" else 12,  # layer 9
                 }
                 with torch.no_grad():
                     logits = model.extract_features(**inputs)
-                    feats = model.final_proj(logits[0])if version=="v1"else logits[0]
+                    feats = (
+                        model.final_proj(logits[0]) if version == "v1" else logits[0]
+                    )
 
                 feats = feats.squeeze(0).float().cpu().numpy()
                 if np.isnan(feats).sum() == 0:

infer-web.py

@@ -1,5 +1,6 @@
 import torch, os, traceback, sys, warnings, shutil, numpy as np
-os.environ["no_proxy"]="localhost, 127.0.0.1, ::1"
+
+os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1"
 from multiprocessing import cpu_count
 import threading
 from time import sleep
@@ -11,8 +12,8 @@ now_dir = os.getcwd()
 sys.path.append(now_dir)
 tmp = os.path.join(now_dir, "TEMP")
 shutil.rmtree(tmp, ignore_errors=True)
-shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack"%(now_dir), ignore_errors=True)
-shutil.rmtree("%s/runtime/Lib/site-packages/uvr5_pack"%(now_dir) , ignore_errors=True)
+shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack" % (now_dir), ignore_errors=True)
+shutil.rmtree("%s/runtime/Lib/site-packages/uvr5_pack" % (now_dir), ignore_errors=True)
 os.makedirs(tmp, exist_ok=True)
 os.makedirs(os.path.join(now_dir, "logs"), exist_ok=True)
 os.makedirs(os.path.join(now_dir, "weights"), exist_ok=True)
@@ -70,7 +71,12 @@ else:
     gpu_info = i18n("很遗憾您这没有能用的显卡来支持您训练")
     default_batch_size = 1
 gpus = "-".join([i[0] for i in gpu_infos])
-from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono,SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono
+from infer_pack.models import (
+    SynthesizerTrnMs256NSFsid,
+    SynthesizerTrnMs256NSFsid_nono,
+    SynthesizerTrnMs768NSFsid,
+    SynthesizerTrnMs768NSFsid_nono,
+)
 from scipy.io import wavfile
 from fairseq import checkpoint_utils
 import gradio as gr
@@ -121,11 +127,11 @@ names = []
 for name in os.listdir(weight_root):
     if name.endswith(".pth"):
         names.append(name)
-index_paths=[]
+index_paths = []
 for root, dirs, files in os.walk(index_root, topdown=False):
     for name in files:
         if name.endswith(".index") and "trained" not in name:
-            index_paths.append("%s/%s"%(root,name))
+            index_paths.append("%s/%s" % (root, name))
 uvr5_names = []
 for name in os.listdir(weight_uvr5_root):
     if name.endswith(".pth"):
@@ -144,29 +150,33 @@ def vc_single(
     index_rate,
     filter_radius,
     resample_sr,
-    rms_mix_rate
+    rms_mix_rate,
 ):  # spk_item, input_audio0, vc_transform0,f0_file,f0method0
-    global tgt_sr, net_g, vc, hubert_model,version
+    global tgt_sr, net_g, vc, hubert_model, version
     if input_audio_path is None:
         return "You need to upload an audio", None
     f0_up_key = int(f0_up_key)
     try:
         audio = load_audio(input_audio_path, 16000)
-        audio_max=np.abs(audio).max()/0.95
-        if(audio_max>1):
-            audio/=audio_max
+        audio_max = np.abs(audio).max() / 0.95
+        if audio_max > 1:
+            audio /= audio_max
         times = [0, 0, 0]
         if hubert_model == None:
             load_hubert()
         if_f0 = cpt.get("f0", 1)
         file_index = (
+            (
                 file_index.strip(" ")
                 .strip('"')
                 .strip("\n")
                 .strip('"')
                 .strip(" ")
                 .replace("trained", "added")
-        )if file_index!=""else file_index2  # 防止小白写错，自动帮他替换掉
+            )
+            if file_index != ""
+            else file_index2
+        )  # 防止小白写错，自动帮他替换掉
         # file_big_npy = (
         #     file_big_npy.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
         # )
@@ -190,10 +200,19 @@ def vc_single(
             version,
             f0_file=f0_file,
         )
-        if(resample_sr>=16000 and tgt_sr!=resample_sr):
-            tgt_sr=resample_sr
-        index_info="Using index:%s."%file_index if os.path.exists(file_index)else"Index not used."
-        return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss"%(index_info,times[0],times[1],times[2]), (tgt_sr, audio_opt)
+        if resample_sr >= 16000 and tgt_sr != resample_sr:
+            tgt_sr = resample_sr
+        index_info = (
+            "Using index:%s." % file_index
+            if os.path.exists(file_index)
+            else "Index not used."
+        )
+        return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % (
+            index_info,
+            times[0],
+            times[1],
+            times[2],
+        ), (tgt_sr, audio_opt)
     except:
         info = traceback.format_exc()
         print(info)
@@ -213,7 +232,7 @@ def vc_multi(
     index_rate,
     filter_radius,
     resample_sr,
-    rms_mix_rate
+    rms_mix_rate,
 ):
     try:
         dir_path = (
@@ -243,9 +262,9 @@ def vc_multi(
                 index_rate,
                 filter_radius,
                 resample_sr,
-                rms_mix_rate
+                rms_mix_rate,
             )
-            if "Success"in info:
+            if "Success" in info:
                 try:
                     tgt_sr, audio_opt = opt
                     wavfile.write(
@@ -330,8 +349,8 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg):
 
 # 一个选项卡全局只能有一个音色
 def get_vc(sid):
-    global n_spk, tgt_sr, net_g, vc, cpt,version
-    if sid == ""or sid==[]:
+    global n_spk, tgt_sr, net_g, vc, cpt, version
+    if sid == "" or sid == []:
         global hubert_model
         if hubert_model != None:  # 考虑到轮询, 需要加个判断看是否 sid 是由有模型切换到无模型的
             print("clean_empty_cache")
@@ -342,14 +361,18 @@ def get_vc(sid):
             ###楼下不这么折腾清理不干净
             if_f0 = cpt.get("f0", 1)
             version = cpt.get("version", "v1")
-            if (version == "v1"):
+            if version == "v1":
                 if if_f0 == 1:
-                    net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
+                    net_g = SynthesizerTrnMs256NSFsid(
+                        *cpt["config"], is_half=config.is_half
+                    )
                 else:
                     net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
-            elif (version == "v2"):
+            elif version == "v2":
                 if if_f0 == 1:
-                    net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
+                    net_g = SynthesizerTrnMs768NSFsid(
+                        *cpt["config"], is_half=config.is_half
+                    )
                 else:
                     net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
             del net_g, cpt
@@ -364,12 +387,12 @@ def get_vc(sid):
     cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0]  # n_spk
     if_f0 = cpt.get("f0", 1)
     version = cpt.get("version", "v1")
-    if(version=="v1"):
+    if version == "v1":
         if if_f0 == 1:
             net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
         else:
             net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
-    elif(version=="v2"):
+    elif version == "v2":
         if if_f0 == 1:
             net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
         else:
@@ -391,17 +414,21 @@ def change_choices():
     for name in os.listdir(weight_root):
         if name.endswith(".pth"):
             names.append(name)
-    index_paths=[]
+    index_paths = []
     for root, dirs, files in os.walk(index_root, topdown=False):
         for name in files:
             if name.endswith(".index") and "trained" not in name:
                 index_paths.append("%s/%s" % (root, name))
-    return {"choices": sorted(names), "__type__": "update"},{"choices": sorted(index_paths), "__type__": "update"}
+    return {"choices": sorted(names), "__type__": "update"}, {
+        "choices": sorted(index_paths),
+        "__type__": "update",
+    }
 
 
 def clean():
     return {"value": "", "__type__": "update"}
 
+
 sr_dict = {
     "32k": 32000,
     "40k": 40000,
@@ -468,7 +495,7 @@ def preprocess_dataset(trainset_dir, exp_dir, sr, n_p):
 
 
 # but2.click(extract_f0,[gpus6,np7,f0method8,if_f0_3,trainset_dir4],[info2])
-def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir,version19):
+def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19):
     gpus = gpus.split("-")
     os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True)
     f = open("%s/logs/%s/extract_f0_feature.log" % (now_dir, exp_dir), "w")
@@ -514,7 +541,10 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir,version19):
     leng = len(gpus)
     ps = []
     for idx, n_g in enumerate(gpus):
-        cmd = config.python_cmd + " extract_feature_print.py %s %s %s %s %s/logs/%s %s" % (
+        cmd = (
+            config.python_cmd
+            + " extract_feature_print.py %s %s %s %s %s/logs/%s %s"
+            % (
                 config.device,
                 leng,
                 idx,
@@ -523,6 +553,7 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir,version19):
                 exp_dir,
                 version19,
             )
+        )
         print(cmd)
         p = Popen(
             cmd, shell=True, cwd=now_dir
@@ -549,34 +580,47 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir,version19):
     yield log
 
 
-def change_sr2(sr2, if_f0_3,version19):
-    vis_v=True if sr2=="40k"else False
-    if(sr2!="40k"):version19="v1"
-    path_str=""if version19=="v1"else "_v2"
-    version_state={"visible": vis_v, "__type__": "update"}
-    if(vis_v==False):version_state["value"]="v1"
-    f0_str="f0"if if_f0_3 else""
-    return "pretrained%s/%sG%s.pth" % (path_str,f0_str,sr2), "pretrained%s/%sD%s.pth" % (path_str,f0_str,sr2),version_state
+def change_sr2(sr2, if_f0_3, version19):
+    vis_v = True if sr2 == "40k" else False
+    if sr2 != "40k":
+        version19 = "v1"
+    path_str = "" if version19 == "v1" else "_v2"
+    version_state = {"visible": vis_v, "__type__": "update"}
+    if vis_v == False:
+        version_state["value"] = "v1"
+    f0_str = "f0" if if_f0_3 else ""
+    return (
+        "pretrained%s/%sG%s.pth" % (path_str, f0_str, sr2),
+        "pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2),
+        version_state,
+    )
 
-def change_version19(sr2,if_f0_3,version19):
-    path_str=""if version19=="v1"else "_v2"
-    f0_str="f0"if if_f0_3 else""
-    return "pretrained%s/%sG%s.pth" % (path_str,f0_str,sr2), "pretrained%s/%sD%s.pth" % (path_str,f0_str,sr2)
 
-def change_f0(if_f0_3, sr2,version19):  # f0method8,pretrained_G14,pretrained_D15
-    path_str=""if version19=="v1"else "_v2"
+def change_version19(sr2, if_f0_3, version19):
+    path_str = "" if version19 == "v1" else "_v2"
+    f0_str = "f0" if if_f0_3 else ""
+    return "pretrained%s/%sG%s.pth" % (
+        path_str,
+        f0_str,
+        sr2,
+    ), "pretrained%s/%sD%s.pth" % (path_str, f0_str, sr2)
+
+
+def change_f0(if_f0_3, sr2, version19):  # f0method8,pretrained_G14,pretrained_D15
+    path_str = "" if version19 == "v1" else "_v2"
     if if_f0_3:
         return (
             {"visible": True, "__type__": "update"},
-            "pretrained%s/f0G%s.pth" % (path_str,sr2),
-            "pretrained%s/f0D%s.pth" % (path_str,sr2),
+            "pretrained%s/f0G%s.pth" % (path_str, sr2),
+            "pretrained%s/f0D%s.pth" % (path_str, sr2),
         )
     return (
         {"visible": False, "__type__": "update"},
-        "pretrained%s/G%s.pth" % (path_str,sr2),
-        "pretrained%s/D%s.pth" % (path_str,sr2),
+        "pretrained%s/G%s.pth" % (path_str, sr2),
+        "pretrained%s/D%s.pth" % (path_str, sr2),
     )
 
+
 # but3.click(click_train,[exp_dir1,sr2,if_f0_3,save_epoch10,total_epoch11,batch_size12,if_save_latest13,pretrained_G14,pretrained_D15,gpus16])
 def click_train(
     exp_dir1,
@@ -598,7 +642,11 @@ def click_train(
     exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
     os.makedirs(exp_dir, exist_ok=True)
     gt_wavs_dir = "%s/0_gt_wavs" % (exp_dir)
-    feature_dir = "%s/3_feature256" % (exp_dir)if version19=="v1"else "%s/3_feature768" % (exp_dir)
+    feature_dir = (
+        "%s/3_feature256" % (exp_dir)
+        if version19 == "v1"
+        else "%s/3_feature768" % (exp_dir)
+    )
     if if_f0_3:
         f0_dir = "%s/2a_f0" % (exp_dir)
         f0nsf_dir = "%s/2b-f0nsf" % (exp_dir)
@@ -640,18 +688,18 @@ def click_train(
                     spk_id5,
                 )
             )
-    fea_dim = 256 if version19 == "v1"else 768
+    fea_dim = 256 if version19 == "v1" else 768
     if if_f0_3:
         for _ in range(2):
             opt.append(
                 "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
-                % (now_dir, sr2, now_dir,fea_dim, now_dir, now_dir, spk_id5)
+                % (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5)
             )
     else:
         for _ in range(2):
             opt.append(
                 "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s"
-                % (now_dir, sr2, now_dir,fea_dim, spk_id5)
+                % (now_dir, sr2, now_dir, fea_dim, spk_id5)
             )
     shuffle(opt)
     with open("%s/filelist.txt" % exp_dir, "w") as f:
@@ -706,10 +754,14 @@ def click_train(
 
 
 # but4.click(train_index, [exp_dir1], info3)
-def train_index(exp_dir1,version19):
+def train_index(exp_dir1, version19):
     exp_dir = "%s/logs/%s" % (now_dir, exp_dir1)
     os.makedirs(exp_dir, exist_ok=True)
-    feature_dir = "%s/3_feature256" % (exp_dir)if version19=="v1"else "%s/3_feature768" % (exp_dir)
+    feature_dir = (
+        "%s/3_feature256" % (exp_dir)
+        if version19 == "v1"
+        else "%s/3_feature768" % (exp_dir)
+    )
     if os.path.exists(feature_dir) == False:
         return "请先进行特征提取!"
     listdir_res = list(os.listdir(feature_dir))
@@ -729,7 +781,7 @@ def train_index(exp_dir1,version19):
     infos = []
     infos.append("%s,%s" % (big_npy.shape, n_ivf))
     yield "\n".join(infos)
-    index = faiss.index_factory(256if version19=="v1"else 768, "IVF%s,Flat" % n_ivf)
+    index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf)
     # index = faiss.index_factory(256if version19=="v1"else 768, "IVF%s,PQ128x4fs,RFlat"%n_ivf)
     infos.append("training")
     yield "\n".join(infos)
@@ -738,7 +790,8 @@ def train_index(exp_dir1,version19):
     index.train(big_npy)
     faiss.write_index(
         index,
-        "%s/trained_IVF%s_Flat_nprobe_%s_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe,version19),
+        "%s/trained_IVF%s_Flat_nprobe_%s_%s.index"
+        % (exp_dir, n_ivf, index_ivf.nprobe, version19),
     )
     # faiss.write_index(index, '%s/trained_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
     infos.append("adding")
@@ -750,7 +803,10 @@ def train_index(exp_dir1,version19):
         index,
         "%s/added_IVF%s_Flat_nprobe_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe),
     )
-    infos.append("成功构建索引，added_IVF%s_Flat_nprobe_%s_%s.index" % (n_ivf, index_ivf.nprobe,version19))
+    infos.append(
+        "成功构建索引，added_IVF%s_Flat_nprobe_%s_%s.index"
+        % (n_ivf, index_ivf.nprobe, version19)
+    )
     # faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
     # infos.append("成功构建索引，added_IVF%s_Flat_FastScan_%s.index"%(n_ivf,version19))
     yield "\n".join(infos)
@@ -786,7 +842,11 @@ def train1key(
     preprocess_log_path = "%s/preprocess.log" % model_log_dir
     extract_f0_feature_log_path = "%s/extract_f0_feature.log" % model_log_dir
     gt_wavs_dir = "%s/0_gt_wavs" % model_log_dir
-    feature_dir = "%s/3_feature256" % model_log_dir if version19=="v1"else "%s/3_feature768" % model_log_dir
+    feature_dir = (
+        "%s/3_feature256" % model_log_dir
+        if version19 == "v1"
+        else "%s/3_feature768" % model_log_dir
+    )
 
     os.makedirs(model_log_dir, exist_ok=True)
     #########step1:处理数据
@@ -830,7 +890,8 @@ def train1key(
             leng,
             idx,
             n_g,
-            model_log_dir,version19,
+            model_log_dir,
+            version19,
         )
         yield get_info_str(cmd)
         p = Popen(
@@ -885,18 +946,18 @@ def train1key(
                     spk_id5,
                 )
             )
-    fea_dim=256 if version19=="v1"else 768
+    fea_dim = 256 if version19 == "v1" else 768
     if if_f0_3:
         for _ in range(2):
             opt.append(
                 "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
-                % (now_dir, sr2, now_dir,fea_dim, now_dir, now_dir, spk_id5)
+                % (now_dir, sr2, now_dir, fea_dim, now_dir, now_dir, spk_id5)
             )
     else:
         for _ in range(2):
             opt.append(
                 "%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature%s/mute.npy|%s"
-                % (now_dir, sr2, now_dir,fea_dim, spk_id5)
+                % (now_dir, sr2, now_dir, fea_dim, spk_id5)
             )
     shuffle(opt)
     with open("%s/filelist.txt" % model_log_dir, "w") as f:
@@ -961,7 +1022,7 @@ def train1key(
     # n_ivf =  big_npy.shape[0] // 39
     n_ivf = min(int(16 * np.sqrt(big_npy.shape[0])), big_npy.shape[0] // 39)
     yield get_info_str("%s,%s" % (big_npy.shape, n_ivf))
-    index = faiss.index_factory(256 if version19=="v1"else 768, "IVF%s,Flat" % n_ivf)
+    index = faiss.index_factory(256 if version19 == "v1" else 768, "IVF%s,Flat" % n_ivf)
     yield get_info_str("training index")
     index_ivf = faiss.extract_index_ivf(index)  #
     index_ivf.nprobe = 1
@@ -969,7 +1030,7 @@ def train1key(
     faiss.write_index(
         index,
         "%s/trained_IVF%s_Flat_nprobe_%s_%s.index"
-        % (model_log_dir, n_ivf, index_ivf.nprobe,version19),
+        % (model_log_dir, n_ivf, index_ivf.nprobe, version19),
     )
     yield get_info_str("adding index")
     batch_size_add = 8192
@@ -978,10 +1039,11 @@ def train1key(
     faiss.write_index(
         index,
         "%s/added_IVF%s_Flat_nprobe_%s_%s.index"
-        % (model_log_dir, n_ivf, index_ivf.nprobe,version19),
+        % (model_log_dir, n_ivf, index_ivf.nprobe, version19),
     )
     yield get_info_str(
-        "成功构建索引, added_IVF%s_Flat_nprobe_%s_%s.index" % (n_ivf, index_ivf.nprobe,version19)
+        "成功构建索引, added_IVF%s_Flat_nprobe_%s_%s.index"
+        % (n_ivf, index_ivf.nprobe, version19)
     )
     yield get_info_str(i18n("全流程结束！"))
 
@@ -999,8 +1061,8 @@ def change_info_(ckpt_path):
         ) as f:
             info = eval(f.read().strip("\n").split("\n")[0].split("\t")[-1])
             sr, f0 = info["sample_rate"], info["if_f0"]
-            version="v2"if("version"in info and info["version"]=="v2")else"v1"
-            return sr, str(f0),version
+            version = "v2" if ("version" in info and info["version"] == "v2") else "v1"
+            return sr, str(f0), version
     except:
         traceback.print_exc()
         return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
@@ -1136,7 +1198,7 @@ with gr.Blocks() as app:
                             value="pm",
                             interactive=True,
                         )
-                        filter_radius0=gr.Slider(
+                        filter_radius0 = gr.Slider(
                             minimum=0,
                             maximum=7,
                             label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"),
@@ -1155,7 +1217,9 @@ with gr.Blocks() as app:
                             choices=sorted(index_paths),
                             interactive=True,
                         )
-                        refresh_button.click(fn=change_choices, inputs=[], outputs=[sid0, file_index2])
+                        refresh_button.click(
+                            fn=change_choices, inputs=[], outputs=[sid0, file_index2]
+                        )
                         # file_big_npy1 = gr.Textbox(
                         #     label=i18n("特征文件路径"),
                         #     value="E:\\codes\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy",
@@ -1168,7 +1232,7 @@ with gr.Blocks() as app:
                             value=0.76,
                             interactive=True,
                         )
-                        resample_sr0=gr.Slider(
+                        resample_sr0 = gr.Slider(
                             minimum=0,
                             maximum=48000,
                             label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
@@ -1202,7 +1266,7 @@ with gr.Blocks() as app:
                             index_rate1,
                             filter_radius0,
                             resample_sr0,
-                            rms_mix_rate0
+                            rms_mix_rate0,
                         ],
                         [vc_output1, vc_output2],
                     )
@@ -1222,7 +1286,7 @@ with gr.Blocks() as app:
                             value="pm",
                             interactive=True,
                         )
-                        filter_radius1=gr.Slider(
+                        filter_radius1 = gr.Slider(
                             minimum=0,
                             maximum=7,
                             label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"),
@@ -1253,7 +1317,7 @@ with gr.Blocks() as app:
                             value=1,
                             interactive=True,
                         )
-                        resample_sr1=gr.Slider(
+                        resample_sr1 = gr.Slider(
                             minimum=0,
                             maximum=48000,
                             label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
@@ -1293,7 +1357,7 @@ with gr.Blocks() as app:
                             index_rate2,
                             filter_radius1,
                             resample_sr1,
-                            rms_mix_rate1
+                            rms_mix_rate1,
                         ],
                         [vc_output3],
                     )
@@ -1398,7 +1462,7 @@ with gr.Blocks() as app:
                     but1 = gr.Button(i18n("处理数据"), variant="primary")
                     info1 = gr.Textbox(label=i18n("输出信息"), value="")
                     but1.click(
-                        preprocess_dataset, [trainset_dir4, exp_dir1, sr2,np7], [info1]
+                        preprocess_dataset, [trainset_dir4, exp_dir1, sr2, np7], [info1]
                     )
             with gr.Group():
                 gr.Markdown(value=i18n("step2b: 使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号)"))
@@ -1423,7 +1487,7 @@ with gr.Blocks() as app:
                     info2 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
                     but2.click(
                         extract_f0_feature,
-                        [gpus6, np7, f0method8, if_f0_3, exp_dir1,version19],
+                        [gpus6, np7, f0method8, if_f0_3, exp_dir1, version19],
                         [info2],
                     )
             with gr.Group():
@@ -1468,9 +1532,7 @@ with gr.Blocks() as app:
                         interactive=True,
                     )
                     if_save_every_weights18 = gr.Radio(
-                        label=i18n(
-                            "是否在每次保存时间点将最终小模型保存至weights文件夹"
-                        ),
+                        label=i18n("是否在每次保存时间点将最终小模型保存至weights文件夹"),
                         choices=[i18n("是"), i18n("否")],
                         value=i18n("否"),
                         interactive=True,
@@ -1487,14 +1549,18 @@ with gr.Blocks() as app:
                         interactive=True,
                     )
                     sr2.change(
-                        change_sr2, [sr2, if_f0_3,version19], [pretrained_G14, pretrained_D15,version19]
+                        change_sr2,
+                        [sr2, if_f0_3, version19],
+                        [pretrained_G14, pretrained_D15, version19],
                     )
                     version19.change(
-                        change_version19, [sr2, if_f0_3,version19], [pretrained_G14, pretrained_D15]
+                        change_version19,
+                        [sr2, if_f0_3, version19],
+                        [pretrained_G14, pretrained_D15],
                     )
                     if_f0_3.change(
                         change_f0,
-                        [if_f0_3, sr2,version19],
+                        [if_f0_3, sr2, version19],
                         [f0method8, pretrained_G14, pretrained_D15],
                     )
                     gpus16 = gr.Textbox(
@@ -1526,7 +1592,7 @@ with gr.Blocks() as app:
                         ],
                         info3,
                     )
-                    but4.click(train_index, [exp_dir1,version19], info3)
+                    but4.click(train_index, [exp_dir1, version19], info3)
                     but5.click(
                         train1key,
                         [
@@ -1586,7 +1652,7 @@ with gr.Blocks() as app:
                         max_lines=1,
                         interactive=True,
                     )
-                    version_2=gr.Radio(
+                    version_2 = gr.Radio(
                         label=i18n("模型版本型号"),
                         choices=["v1", "v2"],
                         value="v1",
@@ -1597,7 +1663,16 @@ with gr.Blocks() as app:
                     info4 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
                 but6.click(
                     merge,
-                    [ckpt_a, ckpt_b, alpha_a, sr_, if_f0_, info__, name_to_save0,version_2],
+                    [
+                        ckpt_a,
+                        ckpt_b,
+                        alpha_a,
+                        sr_,
+                        if_f0_,
+                        info__,
+                        name_to_save0,
+                        version_2,
+                    ],
                     info4,
                 )  # def merge(path1,path2,alpha1,sr,f0,info):
             with gr.Group():
@@ -1655,7 +1730,7 @@ with gr.Blocks() as app:
                         value="1",
                         interactive=True,
                     )
-                    version_1=gr.Radio(
+                    version_1 = gr.Radio(
                         label=i18n("模型版本型号"),
                         choices=["v1", "v2"],
                         value="v1",
@@ -1666,10 +1741,12 @@ with gr.Blocks() as app:
                     )
                     but9 = gr.Button(i18n("提取"), variant="primary")
                     info7 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8)
-                    ckpt_path2.change(change_info_, [ckpt_path2], [sr__, if_f0__,version_1])
+                    ckpt_path2.change(
+                        change_info_, [ckpt_path2], [sr__, if_f0__, version_1]
+                    )
                 but9.click(
                     extract_small_model,
-                    [ckpt_path2, save_name, sr__, if_f0__, info___,version_1],
+                    [ckpt_path2, save_name, sr__, if_f0__, info___, version_1],
                     info7,
                 )
 
@@ -1687,16 +1764,16 @@ with gr.Blocks() as app:
                 butOnnx = gr.Button(i18n("导出Onnx模型"), variant="primary")
             butOnnx.click(export_onnx, [ckpt_dir, onnx_dir, moevs], infoOnnx)
 
-        tab_faq=i18n("常见问题解答")
+        tab_faq = i18n("常见问题解答")
         with gr.TabItem(tab_faq):
             try:
-                if(tab_faq=="常见问题解答"):
-                    with open("docs/faq.md","r",encoding="utf8")as f:info=f.read()
+                if tab_faq == "常见问题解答":
+                    with open("docs/faq.md", "r", encoding="utf8") as f:
+                        info = f.read()
                 else:
-                    with open("docs/faq_en.md", "r")as f:info = f.read()
-                gr.Markdown(
-                    value=info
-                )
+                    with open("docs/faq_en.md", "r") as f:
+                        info = f.read()
+                gr.Markdown(value=info)
             except:
                 gr.Markdown(traceback.format_exc())
 

infer_pack/models.py

@@ -59,6 +59,8 @@ class TextEncoder256(nn.Module):
 
         m, logs = torch.split(stats, self.out_channels, dim=1)
         return m, logs, x_mask
+
+
 class TextEncoder768(nn.Module):
     def __init__(
         self,
@@ -105,6 +107,7 @@ class TextEncoder768(nn.Module):
         m, logs = torch.split(stats, self.out_channels, dim=1)
         return m, logs, x_mask
 
+
 class ResidualCouplingBlock(nn.Module):
     def __init__(
         self,
@@ -635,6 +638,8 @@ class SynthesizerTrnMs256NSFsid(nn.Module):
         z = self.flow(z_p, x_mask, g=g, reverse=True)
         o = self.dec((z * x_mask)[:, :, :max_len], nsff0, g=g)
         return o, x_mask, (z, z_p, m_p, logs_p)
+
+
 class SynthesizerTrnMs768NSFsid(nn.Module):
     def __init__(
         self,
@@ -846,6 +851,8 @@ class SynthesizerTrnMs256NSFsid_nono(nn.Module):
         z = self.flow(z_p, x_mask, g=g, reverse=True)
         o = self.dec((z * x_mask)[:, :, :max_len], g=g)
         return o, x_mask, (z, z_p, m_p, logs_p)
+
+
 class SynthesizerTrnMs768NSFsid_nono(nn.Module):
     def __init__(
         self,
@@ -977,11 +984,12 @@ class MultiPeriodDiscriminator(torch.nn.Module):
 
         return y_d_rs, y_d_gs, fmap_rs, fmap_gs
 
+
 class MultiPeriodDiscriminatorV2(torch.nn.Module):
     def __init__(self, use_spectral_norm=False):
         super(MultiPeriodDiscriminatorV2, self).__init__()
         # periods = [2, 3, 5, 7, 11, 17]
-        periods = [2,3, 5, 7, 11, 17, 23, 37]
+        periods = [2, 3, 5, 7, 11, 17, 23, 37]
 
         discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
         discs = discs + [

train/process_ckpt.py

@@ -2,7 +2,7 @@ import torch, traceback, os, pdb
 from collections import OrderedDict
 
 
-def savee(ckpt, sr, if_f0, name, epoch,version):
+def savee(ckpt, sr, if_f0, name, epoch, version):
     try:
         opt = OrderedDict()
         opt["weight"] = {}
@@ -96,7 +96,7 @@ def show_info(path):
         return traceback.format_exc()
 
 
-def extract_small_model(path, name, sr, if_f0, info,version):
+def extract_small_model(path, name, sr, if_f0, info, version):
     try:
         ckpt = torch.load(path, map_location="cpu")
         if "model" in ckpt:
@@ -194,7 +194,7 @@ def change_info(path, info, name):
         return traceback.format_exc()
 
 
-def merge(path1, path2, alpha1, sr, f0, info, name,version):
+def merge(path1, path2, alpha1, sr, f0, info, name, version):
     try:
 
         def extract(ckpt):

train/utils.py

@@ -322,7 +322,11 @@ def get_hparams(init=True):
         "-sr", "--sample_rate", type=str, required=True, help="sample rate, 32k/40k/48k"
     )
     parser.add_argument(
-        "-sw", "--save_every_weights", type=str, default="0", help="save the extracted model in weights directory when saving checkpoints"
+        "-sw",
+        "--save_every_weights",
+        type=str,
+        default="0",
+        help="save the extracted model in weights directory when saving checkpoints",
     )
     parser.add_argument(
         "-v", "--version", type=str, required=True, help="model version"

train_nsf_sim_cache_sid_load_pretrain.py

@@ -31,7 +31,8 @@ from data_utils import (
     TextAudioCollate,
     DistributedBucketSampler,
 )
-if(hps.version=="v1"):
+
+if hps.version == "v1":
     from infer_pack.models import (
         SynthesizerTrnMs256NSFsid as RVC_Model_f0,
         SynthesizerTrnMs256NSFsid_nono as RVC_Model_nof0,
@@ -519,14 +520,25 @@ def train_and_evaluate(
                 epoch,
                 os.path.join(hps.model_dir, "D_{}.pth".format(2333333)),
             )
-        if(rank==0 and hps.save_every_weights=="1"):
+        if rank == 0 and hps.save_every_weights == "1":
             if hasattr(net_g, "module"):
                 ckpt = net_g.module.state_dict()
             else:
                 ckpt = net_g.state_dict()
             logger.info(
                 "saving ckpt %s_e%s:%s"
-                % (hps.name,epoch,savee(ckpt, hps.sample_rate, hps.if_f0, hps.name+"_e%s"%epoch, epoch,hps.version))
+                % (
+                    hps.name,
+                    epoch,
+                    savee(
+                        ckpt,
+                        hps.sample_rate,
+                        hps.if_f0,
+                        hps.name + "_e%s" % epoch,
+                        epoch,
+                        hps.version,
+                    ),
+                )
             )
 
     if rank == 0:
@@ -540,7 +552,7 @@ def train_and_evaluate(
             ckpt = net_g.state_dict()
         logger.info(
             "saving final ckpt:%s"
-            % (savee(ckpt, hps.sample_rate, hps.if_f0, hps.name, epoch,hps.version))
+            % (savee(ckpt, hps.sample_rate, hps.if_f0, hps.name, epoch, hps.version))
         )
         sleep(1)
         os._exit(2333333)

vc_infer_pipeline.py

@@ -2,16 +2,18 @@ import numpy as np, parselmouth, torch, pdb
 from time import time as ttime
 import torch.nn.functional as F
 import scipy.signal as signal
-import pyworld, os, traceback, faiss,librosa
+import pyworld, os, traceback, faiss, librosa
 from scipy import signal
 from functools import lru_cache
 
 bh, ah = signal.butter(N=5, Wn=48, btype="high", fs=16000)
 
-input_audio_path2wav={}
+input_audio_path2wav = {}
+
+
 @lru_cache
-def cache_harvest_f0(input_audio_path,fs,f0max,f0min,frame_period):
-    audio=input_audio_path2wav[input_audio_path]
+def cache_harvest_f0(input_audio_path, fs, f0max, f0min, frame_period):
+    audio = input_audio_path2wav[input_audio_path]
     f0, t = pyworld.harvest(
         audio,
         fs=fs,
@@ -22,18 +24,29 @@ def cache_harvest_f0(input_audio_path,fs,f0max,f0min,frame_period):
     f0 = pyworld.stonemask(audio, f0, t, fs)
     return f0
 
-def change_rms(data1,sr1,data2,sr2,rate):#1是输入音频，2是输出音频,rate是2的占比
+
+def change_rms(data1, sr1, data2, sr2, rate):  # 1是输入音频，2是输出音频,rate是2的占比
     # print(data1.max(),data2.max())
-    rms1 = librosa.feature.rms(y=data1, frame_length=sr1//2*2, hop_length=sr1//2)#每半秒一个点
-    rms2 = librosa.feature.rms(y=data2, frame_length=sr2//2*2, hop_length=sr2//2)
-    rms1=torch.from_numpy(rms1)
-    rms1=F.interpolate(rms1.unsqueeze(0), size=data2.shape[0],mode='linear').squeeze()
-    rms2=torch.from_numpy(rms2)
-    rms2=F.interpolate(rms2.unsqueeze(0), size=data2.shape[0],mode='linear').squeeze()
-    rms2=torch.max(rms2,torch.zeros_like(rms2)+1e-6)
-    data2*=(torch.pow(rms1,torch.tensor(1-rate))*torch.pow(rms2,torch.tensor(rate-1))).numpy()
+    rms1 = librosa.feature.rms(
+        y=data1, frame_length=sr1 // 2 * 2, hop_length=sr1 // 2
+    )  # 每半秒一个点
+    rms2 = librosa.feature.rms(y=data2, frame_length=sr2 // 2 * 2, hop_length=sr2 // 2)
+    rms1 = torch.from_numpy(rms1)
+    rms1 = F.interpolate(
+        rms1.unsqueeze(0), size=data2.shape[0], mode="linear"
+    ).squeeze()
+    rms2 = torch.from_numpy(rms2)
+    rms2 = F.interpolate(
+        rms2.unsqueeze(0), size=data2.shape[0], mode="linear"
+    ).squeeze()
+    rms2 = torch.max(rms2, torch.zeros_like(rms2) + 1e-6)
+    data2 *= (
+        torch.pow(rms1, torch.tensor(1 - rate))
+        * torch.pow(rms2, torch.tensor(rate - 1))
+    ).numpy()
     return data2
 
+
 class VC(object):
     def __init__(self, tgt_sr, config):
         self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = (
@@ -53,7 +66,16 @@ class VC(object):
         self.t_max = self.sr * self.x_max  # 免查询时长阈值
         self.device = config.device
 
-    def get_f0(self, input_audio_path,x, p_len, f0_up_key, f0_method,filter_radius, inp_f0=None):
+    def get_f0(
+        self,
+        input_audio_path,
+        x,
+        p_len,
+        f0_up_key,
+        f0_method,
+        filter_radius,
+        inp_f0=None,
+    ):
         global input_audio_path2wav
         time_step = self.window / self.sr * 1000
         f0_min = 50
@@ -77,9 +99,9 @@ class VC(object):
                     f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
                 )
         elif f0_method == "harvest":
-            input_audio_path2wav[input_audio_path]=x.astype(np.double)
-            f0=cache_harvest_f0(input_audio_path,self.sr,f0_max,f0_min,10)
-            if(filter_radius>2):
+            input_audio_path2wav[input_audio_path] = x.astype(np.double)
+            f0 = cache_harvest_f0(input_audio_path, self.sr, f0_max, f0_min, 10)
+            if filter_radius > 2:
                 f0 = signal.medfilt(f0, 3)
         f0 *= pow(2, f0_up_key / 12)
         # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()]))
@@ -134,12 +156,12 @@ class VC(object):
         inputs = {
             "source": feats.to(self.device),
             "padding_mask": padding_mask,
-            "output_layer": 9if version=="v1"else 12,
+            "output_layer": 9 if version == "v1" else 12,
         }
         t0 = ttime()
         with torch.no_grad():
             logits = model.extract_features(**inputs)
-            feats = model.final_proj(logits[0])if version=="v1"else logits[0]
+            feats = model.final_proj(logits[0]) if version == "v1" else logits[0]
 
         if (
             isinstance(index, type(None)) == False
@@ -184,10 +206,7 @@ class VC(object):
                 )
             else:
                 audio1 = (
-                    (net_g.infer(feats, p_len, sid)[0][0, 0])
-                    .data.cpu()
-                    .float()
-                    .numpy()
+                    (net_g.infer(feats, p_len, sid)[0][0, 0]).data.cpu().float().numpy()
                 )
         del feats, p_len, padding_mask
         if torch.cuda.is_available():
@@ -270,7 +289,15 @@ class VC(object):
         sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
         pitch, pitchf = None, None
         if if_f0 == 1:
-            pitch, pitchf = self.get_f0(input_audio_path,audio_pad, p_len, f0_up_key, f0_method,filter_radius, inp_f0)
+            pitch, pitchf = self.get_f0(
+                input_audio_path,
+                audio_pad,
+                p_len,
+                f0_up_key,
+                f0_method,
+                filter_radius,
+                inp_f0,
+            )
             pitch = pitch[:p_len]
             pitchf = pitchf[:p_len]
             if self.device == "mps":
@@ -347,16 +374,17 @@ class VC(object):
                 )[self.t_pad_tgt : -self.t_pad_tgt]
             )
         audio_opt = np.concatenate(audio_opt)
-        if(rms_mix_rate!=1):
-            audio_opt=change_rms(audio,16000,audio_opt,tgt_sr,rms_mix_rate)
-        if(resample_sr>=16000 and tgt_sr!=resample_sr):
+        if rms_mix_rate != 1:
+            audio_opt = change_rms(audio, 16000, audio_opt, tgt_sr, rms_mix_rate)
+        if resample_sr >= 16000 and tgt_sr != resample_sr:
             audio_opt = librosa.resample(
                 audio_opt, orig_sr=tgt_sr, target_sr=resample_sr
             )
-        audio_max=np.abs(audio_opt).max()/0.99
-        max_int16=32768
-        if(audio_max>1):max_int16/=audio_max
-        audio_opt=(audio_opt * max_int16).astype(np.int16)
+        audio_max = np.abs(audio_opt).max() / 0.99
+        max_int16 = 32768
+        if audio_max > 1:
+            max_int16 /= audio_max
+        audio_opt = (audio_opt * max_int16).astype(np.int16)
         del pitch, pitchf, sid
         if torch.cuda.is_available():
             torch.cuda.empty_cache()