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RVC-Boss
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@ -18,6 +18,10 @@ from fairseq import checkpoint_utils
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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if torch.cuda.is_available():device="cuda"
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elif torch.backends.mps.is_available():device="mps"
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else:device="cpu"
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f = open("%s/extract_f0_feature.log" % exp_dir, "a+")
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@ -60,7 +64,7 @@ models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
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model = models[0]
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model = model.to(device)
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printt("move model to %s" % device)
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if device != "cpu":
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if device not in ["mps","cpu"]:
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model = model.half()
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model.eval()
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@ -83,7 +87,7 @@ else:
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padding_mask = torch.BoolTensor(feats.shape).fill_(False)
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inputs = {
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"source": feats.half().to(device)
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if device != "cpu"
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if device not in ["mps", "cpu"]
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else feats.to(device),
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"padding_mask": padding_mask.to(device),
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"output_layer": 9, # layer 9
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169
infer-web.py
169
infer-web.py
@ -11,6 +11,8 @@ now_dir = os.getcwd()
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sys.path.append(now_dir)
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tmp = os.path.join(now_dir, "TEMP")
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shutil.rmtree(tmp, ignore_errors=True)
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shutil.rmtree("%s/runtime/Lib/site-packages/infer_pack"%(now_dir), ignore_errors=True)
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shutil.rmtree("%s/runtime/Lib/site-packages/uvr5_pack"%(now_dir) , ignore_errors=True)
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os.makedirs(tmp, exist_ok=True)
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os.makedirs(os.path.join(now_dir, "logs"), exist_ok=True)
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os.makedirs(os.path.join(now_dir, "weights"), exist_ok=True)
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@ -114,10 +116,16 @@ def load_hubert():
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weight_root = "weights"
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weight_uvr5_root = "uvr5_weights"
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index_root = "logs"
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names = []
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for name in os.listdir(weight_root):
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if name.endswith(".pth"):
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names.append(name)
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index_paths=[]
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for root, dirs, files in os.walk(index_root, topdown=False):
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for name in files:
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if name.endswith(".index") and "trained" not in name:
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index_paths.append("%s/%s"%(root,name))
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uvr5_names = []
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for name in os.listdir(weight_uvr5_root):
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if name.endswith(".pth"):
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@ -126,20 +134,23 @@ for name in os.listdir(weight_uvr5_root):
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def vc_single(
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sid,
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input_audio,
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input_audio_path,
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f0_up_key,
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f0_file,
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f0_method,
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file_index,
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file_index2,
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# file_big_npy,
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index_rate,
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filter_radius,
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resample_sr,
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): # spk_item, input_audio0, vc_transform0,f0_file,f0method0
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global tgt_sr, net_g, vc, hubert_model
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if input_audio is None:
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if input_audio_path is None:
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return "You need to upload an audio", None
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f0_up_key = int(f0_up_key)
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try:
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audio = load_audio(input_audio, 16000)
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audio = load_audio(input_audio_path, 16000)
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times = [0, 0, 0]
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if hubert_model == None:
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load_hubert()
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@ -151,7 +162,7 @@ def vc_single(
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.strip('"')
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.strip(" ")
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.replace("trained", "added")
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) # 防止小白写错，自动帮他替换掉
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)if file_index!=""else file_index2 # 防止小白写错，自动帮他替换掉
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# file_big_npy = (
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# file_big_npy.strip(" ").strip('"').strip("\n").strip('"').strip(" ")
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# )
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@ -160,6 +171,7 @@ def vc_single(
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net_g,
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sid,
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audio,
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input_audio_path,
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times,
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f0_up_key,
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f0_method,
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@ -167,12 +179,15 @@ def vc_single(
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# file_big_npy,
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index_rate,
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if_f0,
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filter_radius,
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tgt_sr,
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resample_sr,
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f0_file=f0_file,
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)
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print(
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"npy: ", times[0], "s, f0: ", times[1], "s, infer: ", times[2], "s", sep=""
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)
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return "Success", (tgt_sr, audio_opt)
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if(resample_sr>=16000 and tgt_sr!=resample_sr):
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tgt_sr=resample_sr
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index_info="Using index:%s."%file_index if os.path.exists(file_index)else"Index not used."
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return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss"%(index_info,times[0],times[1],times[2]), (tgt_sr, audio_opt)
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except:
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info = traceback.format_exc()
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print(info)
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@ -187,8 +202,11 @@ def vc_multi(
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f0_up_key,
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f0_method,
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file_index,
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file_index2,
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# file_big_npy,
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index_rate,
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filter_radius,
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resample_sr,
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):
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try:
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dir_path = (
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@ -205,14 +223,6 @@ def vc_multi(
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traceback.print_exc()
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paths = [path.name for path in paths]
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infos = []
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file_index = (
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file_index.strip(" ")
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.strip('"')
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.strip("\n")
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.strip('"')
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.strip(" ")
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.replace("trained", "added")
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) # 防止小白写错，自动帮他替换掉
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for path in paths:
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info, opt = vc_single(
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sid,
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@ -221,17 +231,20 @@ def vc_multi(
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None,
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f0_method,
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file_index,
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file_index2,
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# file_big_npy,
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index_rate,
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filter_radius,
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resample_sr,
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)
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if info == "Success":
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if "Success"in info:
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try:
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tgt_sr, audio_opt = opt
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wavfile.write(
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"%s/%s" % (opt_root, os.path.basename(path)), tgt_sr, audio_opt
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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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infos.append("%s->%s" % (os.path.basename(path), info))
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yield "\n".join(infos)
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yield "\n".join(infos)
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@ -310,7 +323,7 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg):
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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):
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global n_spk, tgt_sr, net_g, vc, cpt
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if sid == "":
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if sid == ""or sid==[]:
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global hubert_model
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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
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print("clean_empty_cache")
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@ -358,7 +371,12 @@ def change_choices():
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for name in os.listdir(weight_root):
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if name.endswith(".pth"):
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names.append(name)
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return {"choices": sorted(names), "__type__": "update"}
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index_paths=[]
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for root, dirs, files in os.walk(index_root, topdown=False):
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for name in files:
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if name.endswith(".index") and "trained" not in name:
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index_paths.append("%s/%s" % (root, name))
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return {"choices": sorted(names), "__type__": "update"},{"choices": sorted(index_paths), "__type__": "update"}
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def clean():
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@ -412,7 +430,7 @@ def if_done_multi(done, ps):
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done[0] = True
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def preprocess_dataset(trainset_dir, exp_dir, sr, n_p=ncpu):
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def preprocess_dataset(trainset_dir, exp_dir, sr, n_p):
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sr = sr_dict[sr]
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os.makedirs("%s/logs/%s" % (now_dir, exp_dir), exist_ok=True)
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f = open("%s/logs/%s/preprocess.log" % (now_dir, exp_dir), "w")
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@ -684,7 +702,6 @@ def train_index(exp_dir1):
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infos.append("training")
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yield "\n".join(infos)
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index_ivf = faiss.extract_index_ivf(index) #
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# index_ivf.nprobe = int(np.power(n_ivf,0.3))
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index_ivf.nprobe = 1
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index.train(big_npy)
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faiss.write_index(
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@ -743,7 +760,7 @@ def train1key(
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cmd = (
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config.python_cmd
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+ " trainset_preprocess_pipeline_print.py %s %s %s %s "
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% (trainset_dir4, sr_dict[sr2], ncpu, model_log_dir)
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% (trainset_dir4, sr_dict[sr2], np7, model_log_dir)
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+ str(config.noparallel)
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)
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yield get_info_str(i18n("step1:正在处理数据"))
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@ -908,7 +925,6 @@ def train1key(
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index = faiss.index_factory(256, "IVF%s,Flat" % n_ivf)
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yield get_info_str("training index")
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index_ivf = faiss.extract_index_ivf(index) #
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# index_ivf.nprobe = int(np.power(n_ivf,0.3))
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index_ivf.nprobe = 1
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index.train(big_npy)
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faiss.write_index(
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@ -1044,8 +1060,7 @@ with gr.Blocks() as app:
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with gr.TabItem(i18n("模型推理")):
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with gr.Row():
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sid0 = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names))
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refresh_button = gr.Button(i18n("刷新音色列表"), variant="primary")
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refresh_button.click(fn=change_choices, inputs=[], outputs=[sid0])
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refresh_button = gr.Button(i18n("刷新音色列表和索引路径"), variant="primary")
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clean_button = gr.Button(i18n("卸载音色省显存"), variant="primary")
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spk_item = gr.Slider(
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minimum=0,
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@ -1073,7 +1088,7 @@ with gr.Blocks() as app:
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)
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input_audio0 = gr.Textbox(
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label=i18n("输入待处理音频文件路径(默认是正确格式示例)"),
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value="E:\\codes\\py39\\vits_vc_gpu_train\\todo-songs\\冬之花clip1.wav",
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value="E:\\codes\\py39\\test-20230416b\\todo-songs\\冬之花clip1.wav",
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)
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f0method0 = gr.Radio(
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label=i18n("选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比"),
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@ -1081,12 +1096,26 @@ with gr.Blocks() as app:
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value="pm",
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interactive=True,
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)
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with gr.Column():
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file_index1 = gr.Textbox(
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label=i18n("特征检索库文件路径"),
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value="E:\\codes\\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\added_IVF677_Flat_nprobe_7.index",
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filter_radius0=gr.Slider(
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minimum=0,
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maximum=7,
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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"),
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value=3,
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step=1,
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interactive=True,
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)
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with gr.Column():
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file_index1 = gr.Textbox(
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label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"),
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value="",
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interactive=True,
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)
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file_index2 = gr.Dropdown(
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label=i18n("自动检测index路径,下拉式选择(dropdown)"),
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choices=sorted(index_paths),
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interactive=True,
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)
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refresh_button.click(fn=change_choices, inputs=[], outputs=[sid0, file_index2])
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# file_big_npy1 = gr.Textbox(
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# label=i18n("特征文件路径"),
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# value="E:\\codes\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy",
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@ -1099,6 +1128,14 @@ with gr.Blocks() as app:
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value=0.76,
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interactive=True,
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)
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resample_sr0=gr.Slider(
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minimum=0,
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maximum=48000,
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label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
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value=0,
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step=1,
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interactive=True,
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)
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f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调"))
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but0 = gr.Button(i18n("转换"), variant="primary")
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with gr.Column():
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@ -1113,8 +1150,11 @@ with gr.Blocks() as app:
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f0_file,
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f0method0,
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file_index1,
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file_index2,
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# file_big_npy1,
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index_rate1,
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filter_radius0,
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resample_sr0
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],
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[vc_output1, vc_output2],
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)
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@ -1134,10 +1174,23 @@ with gr.Blocks() as app:
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value="pm",
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interactive=True,
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)
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filter_radius1=gr.Slider(
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minimum=0,
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maximum=7,
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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"),
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value=3,
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step=1,
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interactive=True,
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)
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with gr.Column():
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file_index2 = gr.Textbox(
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label=i18n("特征检索库文件路径"),
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value="E:\\codes\\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\added_IVF677_Flat_nprobe_7.index",
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file_index3 = gr.Textbox(
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label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"),
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value="",
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interactive=True,
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)
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file_index4 = gr.Dropdown(
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label=i18n("自动检测index路径,下拉式选择(dropdown)"),
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choices=sorted(index_paths),
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interactive=True,
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)
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# file_big_npy2 = gr.Textbox(
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@ -1152,10 +1205,18 @@ with gr.Blocks() as app:
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value=1,
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interactive=True,
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)
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resample_sr1=gr.Slider(
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minimum=0,
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maximum=48000,
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label=i18n("后处理重采样至最终采样率，0为不进行重采样"),
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value=0,
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step=1,
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interactive=True,
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)
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with gr.Column():
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dir_input = gr.Textbox(
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label=i18n("输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)"),
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value="E:\codes\py39\\vits_vc_gpu_train\\todo-songs",
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value="E:\codes\py39\\test-20230416b\\todo-songs",
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)
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inputs = gr.File(
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file_count="multiple", label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹")
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@ -1171,9 +1232,12 @@ with gr.Blocks() as app:
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inputs,
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vc_transform1,
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f0method1,
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file_index2,
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file_index3,
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file_index4,
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# file_big_npy2,
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index_rate2,
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filter_radius1,
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resample_sr1
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],
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[vc_output3],
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)
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@ -1188,7 +1252,7 @@ with gr.Blocks() as app:
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with gr.Column():
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dir_wav_input = gr.Textbox(
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label=i18n("输入待处理音频文件夹路径"),
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value="E:\\codes\\py39\\vits_vc_gpu_train\\todo-songs",
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value="E:\\codes\\py39\\test-20230416b\\todo-songs\\todo-songs",
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)
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wav_inputs = gr.File(
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file_count="multiple", label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹")
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@ -1242,6 +1306,14 @@ with gr.Blocks() as app:
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value=True,
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interactive=True,
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)
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np7 = gr.Slider(
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minimum=0,
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maximum=ncpu,
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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=ncpu,
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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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gr.Markdown(
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value=i18n(
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@ -1263,7 +1335,7 @@ with gr.Blocks() as app:
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but1 = gr.Button(i18n("处理数据"), variant="primary")
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info1 = gr.Textbox(label=i18n("输出信息"), value="")
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but1.click(
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preprocess_dataset, [trainset_dir4, exp_dir1, sr2], [info1]
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preprocess_dataset, [trainset_dir4, exp_dir1, sr2,np7], [info1]
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)
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with gr.Group():
|
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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)"))
|
||||
@ -1276,14 +1348,6 @@ with gr.Blocks() as app:
|
||||
)
|
||||
gpu_info9 = gr.Textbox(label=i18n("显卡信息"), value=gpu_info)
|
||||
with gr.Column():
|
||||
np7 = gr.Slider(
|
||||
minimum=0,
|
||||
maximum=ncpu,
|
||||
step=1,
|
||||
label=i18n("提取音高使用的CPU进程数"),
|
||||
value=ncpu,
|
||||
interactive=True,
|
||||
)
|
||||
f0method8 = gr.Radio(
|
||||
label=i18n(
|
||||
"选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢"
|
||||
@ -1533,6 +1597,19 @@ 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")
|
||||
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()
|
||||
else:
|
||||
with open("docs/faq_en.md", "r")as f:info = f.read()
|
||||
gr.Markdown(
|
||||
value=info
|
||||
)
|
||||
except:
|
||||
gr.Markdown(traceback.format_exc())
|
||||
|
||||
# with gr.TabItem(i18n("招募音高曲线前端编辑器")):
|
||||
# gr.Markdown(value=i18n("加开发群联系我xxxxx"))
|
||||
# with gr.TabItem(i18n("点击查看交流、问题反馈群号")):
|
||||
|
||||
@ -1,6 +1,7 @@
|
||||
import sys, os
|
||||
|
||||
now_dir = os.getcwd()
|
||||
sys.path.append(os.path.join(now_dir))
|
||||
sys.path.append(os.path.join(now_dir, "train"))
|
||||
import utils
|
||||
|
||||
|
||||
@ -2,11 +2,25 @@ 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
|
||||
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={}
|
||||
@lru_cache
|
||||
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,
|
||||
f0_ceil=f0max,
|
||||
f0_floor=f0min,
|
||||
frame_period=frame_period,
|
||||
)
|
||||
f0 = pyworld.stonemask(audio, f0, t, fs)
|
||||
return f0
|
||||
|
||||
class VC(object):
|
||||
def __init__(self, tgt_sr, config):
|
||||
@ -27,7 +41,8 @@ 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, x, p_len, f0_up_key, f0_method, 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
|
||||
f0_max = 1100
|
||||
@ -49,16 +64,11 @@ class VC(object):
|
||||
f0 = np.pad(
|
||||
f0, [[pad_size, p_len - len(f0) - pad_size]], mode="constant"
|
||||
)
|
||||
else:
|
||||
f0, t = pyworld.harvest(
|
||||
x.astype(np.double),
|
||||
fs=self.sr,
|
||||
f0_ceil=f0_max,
|
||||
f0_floor=f0_min,
|
||||
frame_period=10,
|
||||
)
|
||||
f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr)
|
||||
f0 = signal.medfilt(f0, 3)
|
||||
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):
|
||||
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()]))
|
||||
tf0 = self.sr // self.window # 每秒f0点数
|
||||
@ -158,7 +168,6 @@ class VC(object):
|
||||
.data.cpu()
|
||||
.float()
|
||||
.numpy()
|
||||
.astype(np.int16)
|
||||
)
|
||||
else:
|
||||
audio1 = (
|
||||
@ -166,7 +175,6 @@ class VC(object):
|
||||
.data.cpu()
|
||||
.float()
|
||||
.numpy()
|
||||
.astype(np.int16)
|
||||
)
|
||||
del feats, p_len, padding_mask
|
||||
if torch.cuda.is_available():
|
||||
@ -182,6 +190,7 @@ class VC(object):
|
||||
net_g,
|
||||
sid,
|
||||
audio,
|
||||
input_audio_path,
|
||||
times,
|
||||
f0_up_key,
|
||||
f0_method,
|
||||
@ -189,6 +198,9 @@ class VC(object):
|
||||
# file_big_npy,
|
||||
index_rate,
|
||||
if_f0,
|
||||
filter_radius,
|
||||
tgt_sr,
|
||||
resample_sr,
|
||||
f0_file=None,
|
||||
):
|
||||
if (
|
||||
@ -243,7 +255,7 @@ 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(audio_pad, p_len, f0_up_key, f0_method, 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":
|
||||
@ -316,6 +328,11 @@ class VC(object):
|
||||
)[self.t_pad_tgt : -self.t_pad_tgt]
|
||||
)
|
||||
audio_opt = np.concatenate(audio_opt)
|
||||
if(resample_sr>=16000 and tgt_sr!=resample_sr):
|
||||
audio_opt = librosa.resample(
|
||||
audio_opt, orig_sr=tgt_sr, target_sr=resample_sr
|
||||
)
|
||||
audio_opt=audio_opt.astype(np.int16)
|
||||
del pitch, pitchf, sid
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user