From 6a3eaef090ce1e0b14ebfd70fdaaabab23b54f65 Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" <41898282+github-actions[bot]@users.noreply.github.com> Date: Sun, 14 May 2023 07:52:36 +0000 Subject: [PATCH] Format code (#275) Co-authored-by: github-actions[bot] --- extract_f0_print.py | 3 +- extract_feature_print.py | 21 +- infer-web.py | 297 ++++++++++++++--------- infer_pack/models.py | 10 +- train/process_ckpt.py | 6 +- train/utils.py | 6 +- train_nsf_sim_cache_sid_load_pretrain.py | 20 +- vc_infer_pipeline.py | 90 ++++--- 8 files changed, 295 insertions(+), 158 deletions(-) diff --git a/extract_f0_print.py b/extract_f0_print.py index fc689f7..7685ba5 100644 --- a/extract_f0_print.py +++ b/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 diff --git a/extract_feature_print.py b/extract_feature_print.py index 37cc4e2..fa46afb 100644 --- a/extract_feature_print.py +++ b/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: diff --git a/infer-web.py b/infer-web.py index 200f2c1..df2f9a4 100644 --- a/infer-web.py +++ b/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 + ( + 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 # 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") @@ -514,14 +541,18 @@ 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" % ( - config.device, - leng, - idx, - n_g, - now_dir, - exp_dir, - version19, + cmd = ( + config.python_cmd + + " extract_feature_print.py %s %s %s %s %s/logs/%s %s" + % ( + config.device, + leng, + idx, + n_g, + now_dir, + exp_dir, + version19, + ) ) print(cmd) p = Popen( @@ -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()) diff --git a/infer_pack/models.py b/infer_pack/models.py index 085d26c..724bd88 100644 --- a/infer_pack/models.py +++ b/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 + [ diff --git a/train/process_ckpt.py b/train/process_ckpt.py index 63cca61..9d3d7e9 100644 --- a/train/process_ckpt.py +++ b/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): diff --git a/train/utils.py b/train/utils.py index 9b8f92c..434ed7a 100644 --- a/train/utils.py +++ b/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" diff --git a/train_nsf_sim_cache_sid_load_pretrain.py b/train_nsf_sim_cache_sid_load_pretrain.py index 494c561..481f754 100644 --- a/train_nsf_sim_cache_sid_load_pretrain.py +++ b/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) diff --git a/vc_infer_pipeline.py b/vc_infer_pipeline.py index 7470a91..a8f0540 100644 --- a/vc_infer_pipeline.py +++ b/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()