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RVC-Boss
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@ -1,5 +1,7 @@
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import os, traceback, sys, parselmouth
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import librosa
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now_dir = os.getcwd()
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sys.path.append(now_dir)
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from my_utils import load_audio
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import pyworld
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from scipy.io import wavfile
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import numpy as np, logging
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@ -33,17 +35,14 @@ class FeatureInput(object):
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self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
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def compute_f0(self, path, f0_method):
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# default resample type of librosa.resample is "soxr_hq".
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# Quality: soxr_vhq > soxr_hq
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x, sr = librosa.load(path, self.fs) # , res_type='soxr_vhq'
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x=load_audio(path,self.fs)
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p_len = x.shape[0] // self.hop
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assert sr == self.fs
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if f0_method == "pm":
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time_step = 160 / 16000 * 1000
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f0_min = 50
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f0_max = 1100
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f0 = (
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parselmouth.Sound(x, sr)
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parselmouth.Sound(x, self.fs)
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.to_pitch_ac(
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time_step=time_step / 1000,
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voicing_threshold=0.6,
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@ -60,19 +59,19 @@ class FeatureInput(object):
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elif f0_method == "harvest":
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f0, t = pyworld.harvest(
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x.astype(np.double),
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fs=sr,
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fs=self.fs,
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f0_ceil=self.f0_max,
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f0_floor=self.f0_min,
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frame_period=1000 * self.hop / sr,
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frame_period=1000 * self.hop / self.fs,
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)
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f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.fs)
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elif f0_method == "dio":
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f0, t = pyworld.dio(
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x.astype(np.double),
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fs=sr,
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fs=self.fs,
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f0_ceil=self.f0_max,
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f0_floor=self.f0_min,
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frame_period=1000 * self.hop / sr,
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frame_period=1000 * self.hop / self.fs,
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)
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f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.fs)
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return f0
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@ -9,7 +9,7 @@ else:
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i_gpu = sys.argv[4]
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exp_dir = sys.argv[5]
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os.environ["CUDA_VISIBLE_DEVICES"] = str(i_gpu)
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version = sys.argv[6]
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import torch
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import torch.nn.functional as F
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import soundfile as sf
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@ -18,12 +18,9 @@ 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():
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device = "cuda"
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elif torch.backends.mps.is_available():
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device = "mps"
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else:
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device = "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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@ -39,7 +36,7 @@ model_path = "hubert_base.pt"
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printt(exp_dir)
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wavPath = "%s/1_16k_wavs" % exp_dir
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outPath = "%s/3_feature256" % exp_dir
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outPath = "%s/3_feature256" % exp_dir if version=="v1"else "%s/3_feature768" % exp_dir
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os.makedirs(outPath, exist_ok=True)
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@ -67,7 +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 not in ["mps", "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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@ -93,11 +90,11 @@ else:
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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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"output_layer": 9 if version=="v1"else 12, # layer 9
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}
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with torch.no_grad():
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logits = model.extract_features(**inputs)
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feats = model.final_proj(logits[0])
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feats = model.final_proj(logits[0])if version=="v1"else logits[0]
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feats = feats.squeeze(0).float().cpu().numpy()
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if np.isnan(feats).sum() == 0:
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356
infer-web.py
356
infer-web.py
@ -1,9 +1,9 @@
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import torch, os, traceback, sys, warnings, shutil, numpy as np
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os.environ["no_proxy"]="localhost, 127.0.0.1, ::1"
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from multiprocessing import cpu_count
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import threading
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from time import sleep
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from subprocess import Popen
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from time import sleep
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import torch, os, traceback, sys, warnings, shutil, numpy as np
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import faiss
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from random import shuffle
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@ -11,8 +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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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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@ -70,7 +70,7 @@ else:
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gpu_info = i18n("很遗憾您这没有能用的显卡来支持您训练")
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default_batch_size = 1
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gpus = "-".join([i[0] for i in gpu_infos])
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from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
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from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono,SynthesizerTrnMs768NSFsid, SynthesizerTrnMs768NSFsid_nono
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from scipy.io import wavfile
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from fairseq import checkpoint_utils
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import gradio as gr
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@ -121,11 +121,11 @@ 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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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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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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@ -144,29 +144,29 @@ def vc_single(
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index_rate,
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filter_radius,
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resample_sr,
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rms_mix_rate
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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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global tgt_sr, net_g, vc, hubert_model,version
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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_path, 16000)
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audio_max=np.abs(audio).max()/0.95
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if(audio_max>1):
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audio/=audio_max
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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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if_f0 = cpt.get("f0", 1)
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file_index = (
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(
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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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)
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if file_index != ""
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else file_index2
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) # 防止小白写错，自动帮他替换掉
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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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)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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@ -186,21 +186,14 @@ def vc_single(
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filter_radius,
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tgt_sr,
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resample_sr,
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rms_mix_rate,
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version,
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f0_file=f0_file,
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)
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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 = (
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"Using index:%s." % file_index
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if os.path.exists(file_index)
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else "Index not used."
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)
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return "Success.\n %s\nTime:\n npy:%ss, f0:%ss, infer:%ss" % (
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index_info,
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times[0],
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times[1],
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times[2],
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), (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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@ -220,6 +213,7 @@ def vc_multi(
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index_rate,
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filter_radius,
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resample_sr,
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rms_mix_rate
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):
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try:
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dir_path = (
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@ -249,8 +243,9 @@ def vc_multi(
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index_rate,
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filter_radius,
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resample_sr,
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rms_mix_rate
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)
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if "Success" in info:
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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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@ -335,8 +330,8 @@ 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 == "" or sid == []:
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global n_spk, tgt_sr, net_g, vc, cpt,version
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if sid == ""or sid==[]:
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global hubert_model
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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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@ -346,12 +341,17 @@ def get_vc(sid):
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torch.cuda.empty_cache()
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###楼下不这么折腾清理不干净
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if_f0 = cpt.get("f0", 1)
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if if_f0 == 1:
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net_g = SynthesizerTrnMs256NSFsid(
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*cpt["config"], is_half=config.is_half
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)
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else:
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net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
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version = cpt.get("version", "v1")
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if (version == "v1"):
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if if_f0 == 1:
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net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
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else:
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net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
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elif (version == "v2"):
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if if_f0 == 1:
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net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
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else:
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net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
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del net_g, cpt
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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@ -363,12 +363,19 @@ def get_vc(sid):
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tgt_sr = cpt["config"][-1]
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cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk
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if_f0 = cpt.get("f0", 1)
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if if_f0 == 1:
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net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
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else:
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net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
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version = cpt.get("version", "v1")
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if(version=="v1"):
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if if_f0 == 1:
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net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=config.is_half)
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else:
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net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
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elif(version=="v2"):
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if if_f0 == 1:
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net_g = SynthesizerTrnMs768NSFsid(*cpt["config"], is_half=config.is_half)
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else:
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net_g = SynthesizerTrnMs768NSFsid_nono(*cpt["config"])
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del net_g.enc_q
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print(net_g.load_state_dict(cpt["weight"], strict=False)) # 不加这一行清不干净, 真奇葩
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print(net_g.load_state_dict(cpt["weight"], strict=False))
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net_g.eval().to(config.device)
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if config.is_half:
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net_g = net_g.half()
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@ -384,37 +391,17 @@ 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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index_paths = []
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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"}, {
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"choices": sorted(index_paths),
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"__type__": "update",
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}
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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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return {"value": "", "__type__": "update"}
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def change_f0(if_f0_3, sr2): # np7, f0method8,pretrained_G14,pretrained_D15
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if if_f0_3:
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return (
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{"visible": True, "__type__": "update"},
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{"visible": True, "__type__": "update"},
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"pretrained/f0G%s.pth" % sr2,
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"pretrained/f0D%s.pth" % sr2,
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)
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return (
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{"visible": False, "__type__": "update"},
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{"visible": False, "__type__": "update"},
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"pretrained/G%s.pth" % sr2,
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"pretrained/D%s.pth" % sr2,
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)
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sr_dict = {
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"32k": 32000,
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"40k": 40000,
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@ -481,7 +468,7 @@ def preprocess_dataset(trainset_dir, exp_dir, sr, n_p):
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# but2.click(extract_f0,[gpus6,np7,f0method8,if_f0_3,trainset_dir4],[info2])
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def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir):
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def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir,version19):
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gpus = gpus.split("-")
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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/extract_f0_feature.log" % (now_dir, exp_dir), "w")
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@ -527,13 +514,14 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir):
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leng = len(gpus)
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ps = []
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for idx, n_g in enumerate(gpus):
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cmd = config.python_cmd + " extract_feature_print.py %s %s %s %s %s/logs/%s" % (
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cmd = config.python_cmd + " extract_feature_print.py %s %s %s %s %s/logs/%s %s" % (
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config.device,
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leng,
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idx,
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n_g,
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now_dir,
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exp_dir,
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version19,
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)
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print(cmd)
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p = Popen(
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@ -561,12 +549,33 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir):
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yield log
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def change_sr2(sr2, if_f0_3):
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if if_f0_3:
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return "pretrained/f0G%s.pth" % sr2, "pretrained/f0D%s.pth" % sr2
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else:
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return "pretrained/G%s.pth" % sr2, "pretrained/D%s.pth" % sr2
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def change_sr2(sr2, if_f0_3,version19):
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vis_v=True if sr2=="40k"else False
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if(sr2!="40k"):version19="v1"
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path_str=""if version19=="v1"else "_v2"
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version_state={"visible": vis_v, "__type__": "update"}
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if(vis_v==False):version_state["value"]="v1"
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f0_str="f0"if if_f0_3 else""
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return "pretrained%s/%sG%s.pth" % (path_str,f0_str,sr2), "pretrained%s/%sD%s.pth" % (path_str,f0_str,sr2),version_state
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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),
|
||||
)
|
||||
return (
|
||||
{"visible": False, "__type__": "update"},
|
||||
"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(
|
||||
@ -582,24 +591,26 @@ def click_train(
|
||||
pretrained_D15,
|
||||
gpus16,
|
||||
if_cache_gpu17,
|
||||
if_save_every_weights18,
|
||||
version19,
|
||||
):
|
||||
# 生成filelist
|
||||
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)
|
||||
co256_dir = "%s/3_feature256" % (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)
|
||||
names = (
|
||||
set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)])
|
||||
& set([name.split(".")[0] for name in os.listdir(co256_dir)])
|
||||
& set([name.split(".")[0] for name in os.listdir(feature_dir)])
|
||||
& set([name.split(".")[0] for name in os.listdir(f0_dir)])
|
||||
& set([name.split(".")[0] for name in os.listdir(f0nsf_dir)])
|
||||
)
|
||||
else:
|
||||
names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set(
|
||||
[name.split(".")[0] for name in os.listdir(co256_dir)]
|
||||
[name.split(".")[0] for name in os.listdir(feature_dir)]
|
||||
)
|
||||
opt = []
|
||||
for name in names:
|
||||
@ -609,7 +620,7 @@ def click_train(
|
||||
% (
|
||||
gt_wavs_dir.replace("\\", "\\\\"),
|
||||
name,
|
||||
co256_dir.replace("\\", "\\\\"),
|
||||
feature_dir.replace("\\", "\\\\"),
|
||||
name,
|
||||
f0_dir.replace("\\", "\\\\"),
|
||||
name,
|
||||
@ -624,22 +635,23 @@ def click_train(
|
||||
% (
|
||||
gt_wavs_dir.replace("\\", "\\\\"),
|
||||
name,
|
||||
co256_dir.replace("\\", "\\\\"),
|
||||
feature_dir.replace("\\", "\\\\"),
|
||||
name,
|
||||
spk_id5,
|
||||
)
|
||||
)
|
||||
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_feature256/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
|
||||
% (now_dir, sr2, now_dir, now_dir, now_dir, spk_id5)
|
||||
"%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)
|
||||
)
|
||||
else:
|
||||
for _ in range(2):
|
||||
opt.append(
|
||||
"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature256/mute.npy|%s"
|
||||
% (now_dir, sr2, now_dir, spk_id5)
|
||||
"%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)
|
||||
)
|
||||
shuffle(opt)
|
||||
with open("%s/filelist.txt" % exp_dir, "w") as f:
|
||||
@ -651,7 +663,7 @@ def click_train(
|
||||
if gpus16:
|
||||
cmd = (
|
||||
config.python_cmd
|
||||
+ " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s -pg %s -pd %s -l %s -c %s"
|
||||
+ " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s -pg %s -pd %s -l %s -c %s -sw %s -v %s"
|
||||
% (
|
||||
exp_dir1,
|
||||
sr2,
|
||||
@ -664,12 +676,14 @@ def click_train(
|
||||
pretrained_D15,
|
||||
1 if if_save_latest13 == i18n("是") else 0,
|
||||
1 if if_cache_gpu17 == i18n("是") else 0,
|
||||
1 if if_save_every_weights18 == i18n("是") else 0,
|
||||
version19,
|
||||
)
|
||||
)
|
||||
else:
|
||||
cmd = (
|
||||
config.python_cmd
|
||||
+ " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s -pg %s -pd %s -l %s -c %s"
|
||||
+ " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s -pg %s -pd %s -l %s -c %s -sw %s -v %s"
|
||||
% (
|
||||
exp_dir1,
|
||||
sr2,
|
||||
@ -681,6 +695,8 @@ def click_train(
|
||||
pretrained_D15,
|
||||
1 if if_save_latest13 == i18n("是") else 0,
|
||||
1 if if_cache_gpu17 == i18n("是") else 0,
|
||||
1 if if_save_every_weights18 == i18n("是") else 0,
|
||||
version19,
|
||||
)
|
||||
)
|
||||
print(cmd)
|
||||
@ -690,10 +706,10 @@ def click_train(
|
||||
|
||||
|
||||
# but4.click(train_index, [exp_dir1], info3)
|
||||
def train_index(exp_dir1):
|
||||
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)
|
||||
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))
|
||||
@ -713,8 +729,8 @@ def train_index(exp_dir1):
|
||||
infos = []
|
||||
infos.append("%s,%s" % (big_npy.shape, n_ivf))
|
||||
yield "\n".join(infos)
|
||||
index = faiss.index_factory(256, "IVF%s,Flat" % n_ivf)
|
||||
# index = faiss.index_factory(256, "IVF%s,PQ128x4fs,RFlat"%n_ivf)
|
||||
index = faiss.index_factory(256if 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)
|
||||
index_ivf = faiss.extract_index_ivf(index) #
|
||||
@ -722,9 +738,9 @@ def train_index(exp_dir1):
|
||||
index.train(big_npy)
|
||||
faiss.write_index(
|
||||
index,
|
||||
"%s/trained_IVF%s_Flat_nprobe_%s.index" % (exp_dir, n_ivf, index_ivf.nprobe),
|
||||
"%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.index'%(exp_dir,n_ivf))
|
||||
# faiss.write_index(index, '%s/trained_IVF%s_Flat_FastScan_%s.index'%(exp_dir,n_ivf,version19))
|
||||
infos.append("adding")
|
||||
yield "\n".join(infos)
|
||||
batch_size_add = 8192
|
||||
@ -734,9 +750,9 @@ def train_index(exp_dir1):
|
||||
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.index" % (n_ivf, index_ivf.nprobe))
|
||||
# faiss.write_index(index, '%s/added_IVF%s_Flat_FastScan.index'%(exp_dir,n_ivf))
|
||||
# infos.append("成功构建索引，added_IVF%s_Flat_FastScan.index"%(n_ivf))
|
||||
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)
|
||||
|
||||
|
||||
@ -757,6 +773,8 @@ def train1key(
|
||||
pretrained_D15,
|
||||
gpus16,
|
||||
if_cache_gpu17,
|
||||
if_save_every_weights18,
|
||||
version19,
|
||||
):
|
||||
infos = []
|
||||
|
||||
@ -768,7 +786,7 @@ 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
|
||||
feature256_dir = "%s/3_feature256" % 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
|
||||
@ -807,12 +825,12 @@ def train1key(
|
||||
leng = len(gpus)
|
||||
ps = []
|
||||
for idx, n_g in enumerate(gpus):
|
||||
cmd = config.python_cmd + " extract_feature_print.py %s %s %s %s %s" % (
|
||||
cmd = config.python_cmd + " extract_feature_print.py %s %s %s %s %s %s" % (
|
||||
config.device,
|
||||
leng,
|
||||
idx,
|
||||
n_g,
|
||||
model_log_dir,
|
||||
model_log_dir,version19,
|
||||
)
|
||||
yield get_info_str(cmd)
|
||||
p = Popen(
|
||||
@ -831,13 +849,13 @@ def train1key(
|
||||
f0nsf_dir = "%s/2b-f0nsf" % model_log_dir
|
||||
names = (
|
||||
set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)])
|
||||
& set([name.split(".")[0] for name in os.listdir(feature256_dir)])
|
||||
& set([name.split(".")[0] for name in os.listdir(feature_dir)])
|
||||
& set([name.split(".")[0] for name in os.listdir(f0_dir)])
|
||||
& set([name.split(".")[0] for name in os.listdir(f0nsf_dir)])
|
||||
)
|
||||
else:
|
||||
names = set([name.split(".")[0] for name in os.listdir(gt_wavs_dir)]) & set(
|
||||
[name.split(".")[0] for name in os.listdir(feature256_dir)]
|
||||
[name.split(".")[0] for name in os.listdir(feature_dir)]
|
||||
)
|
||||
opt = []
|
||||
for name in names:
|
||||
@ -847,7 +865,7 @@ def train1key(
|
||||
% (
|
||||
gt_wavs_dir.replace("\\", "\\\\"),
|
||||
name,
|
||||
feature256_dir.replace("\\", "\\\\"),
|
||||
feature_dir.replace("\\", "\\\\"),
|
||||
name,
|
||||
f0_dir.replace("\\", "\\\\"),
|
||||
name,
|
||||
@ -862,22 +880,23 @@ def train1key(
|
||||
% (
|
||||
gt_wavs_dir.replace("\\", "\\\\"),
|
||||
name,
|
||||
feature256_dir.replace("\\", "\\\\"),
|
||||
feature_dir.replace("\\", "\\\\"),
|
||||
name,
|
||||
spk_id5,
|
||||
)
|
||||
)
|
||||
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_feature256/mute.npy|%s/logs/mute/2a_f0/mute.wav.npy|%s/logs/mute/2b-f0nsf/mute.wav.npy|%s"
|
||||
% (now_dir, sr2, now_dir, now_dir, now_dir, spk_id5)
|
||||
"%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)
|
||||
)
|
||||
else:
|
||||
for _ in range(2):
|
||||
opt.append(
|
||||
"%s/logs/mute/0_gt_wavs/mute%s.wav|%s/logs/mute/3_feature256/mute.npy|%s"
|
||||
% (now_dir, sr2, now_dir, spk_id5)
|
||||
"%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)
|
||||
)
|
||||
shuffle(opt)
|
||||
with open("%s/filelist.txt" % model_log_dir, "w") as f:
|
||||
@ -886,7 +905,7 @@ def train1key(
|
||||
if gpus16:
|
||||
cmd = (
|
||||
config.python_cmd
|
||||
+ " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s -pg %s -pd %s -l %s -c %s"
|
||||
+ " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -g %s -te %s -se %s -pg %s -pd %s -l %s -c %s -sw %s -v %s"
|
||||
% (
|
||||
exp_dir1,
|
||||
sr2,
|
||||
@ -899,12 +918,14 @@ def train1key(
|
||||
pretrained_D15,
|
||||
1 if if_save_latest13 == i18n("是") else 0,
|
||||
1 if if_cache_gpu17 == i18n("是") else 0,
|
||||
1 if if_save_every_weights18 == i18n("是") else 0,
|
||||
version19,
|
||||
)
|
||||
)
|
||||
else:
|
||||
cmd = (
|
||||
config.python_cmd
|
||||
+ " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s -pg %s -pd %s -l %s -c %s"
|
||||
+ " train_nsf_sim_cache_sid_load_pretrain.py -e %s -sr %s -f0 %s -bs %s -te %s -se %s -pg %s -pd %s -l %s -c %s -sw %s -v %s"
|
||||
% (
|
||||
exp_dir1,
|
||||
sr2,
|
||||
@ -916,6 +937,8 @@ def train1key(
|
||||
pretrained_D15,
|
||||
1 if if_save_latest13 == i18n("是") else 0,
|
||||
1 if if_cache_gpu17 == i18n("是") else 0,
|
||||
1 if if_save_every_weights18 == i18n("是") else 0,
|
||||
version19,
|
||||
)
|
||||
)
|
||||
yield get_info_str(cmd)
|
||||
@ -924,9 +947,9 @@ def train1key(
|
||||
yield get_info_str(i18n("训练结束, 您可查看控制台训练日志或实验文件夹下的train.log"))
|
||||
#######step3b:训练索引
|
||||
npys = []
|
||||
listdir_res = list(os.listdir(feature256_dir))
|
||||
listdir_res = list(os.listdir(feature_dir))
|
||||
for name in sorted(listdir_res):
|
||||
phone = np.load("%s/%s" % (feature256_dir, name))
|
||||
phone = np.load("%s/%s" % (feature_dir, name))
|
||||
npys.append(phone)
|
||||
big_npy = np.concatenate(npys, 0)
|
||||
|
||||
@ -938,15 +961,15 @@ 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, "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
|
||||
index.train(big_npy)
|
||||
faiss.write_index(
|
||||
index,
|
||||
"%s/trained_IVF%s_Flat_nprobe_%s.index"
|
||||
% (model_log_dir, n_ivf, index_ivf.nprobe),
|
||||
"%s/trained_IVF%s_Flat_nprobe_%s_%s.index"
|
||||
% (model_log_dir, n_ivf, index_ivf.nprobe,version19),
|
||||
)
|
||||
yield get_info_str("adding index")
|
||||
batch_size_add = 8192
|
||||
@ -954,11 +977,11 @@ def train1key(
|
||||
index.add(big_npy[i : i + batch_size_add])
|
||||
faiss.write_index(
|
||||
index,
|
||||
"%s/added_IVF%s_Flat_nprobe_%s.index"
|
||||
% (model_log_dir, n_ivf, index_ivf.nprobe),
|
||||
"%s/added_IVF%s_Flat_nprobe_%s_%s.index"
|
||||
% (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.index" % (n_ivf, index_ivf.nprobe)
|
||||
"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！"))
|
||||
|
||||
@ -969,17 +992,18 @@ def change_info_(ckpt_path):
|
||||
os.path.exists(ckpt_path.replace(os.path.basename(ckpt_path), "train.log"))
|
||||
== False
|
||||
):
|
||||
return {"__type__": "update"}, {"__type__": "update"}
|
||||
return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
|
||||
try:
|
||||
with open(
|
||||
ckpt_path.replace(os.path.basename(ckpt_path), "train.log"), "r"
|
||||
) as f:
|
||||
info = eval(f.read().strip("\n").split("\n")[0].split("\t")[-1])
|
||||
sr, f0 = info["sample_rate"], info["if_f0"]
|
||||
return sr, str(f0)
|
||||
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"}
|
||||
return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"}
|
||||
|
||||
|
||||
from infer_pack.models_onnx_moess import SynthesizerTrnMs256NSFsidM
|
||||
@ -1112,7 +1136,7 @@ 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"),
|
||||
@ -1131,9 +1155,7 @@ 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",
|
||||
@ -1146,7 +1168,7 @@ 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"),
|
||||
@ -1154,6 +1176,13 @@ with gr.Blocks() as app:
|
||||
step=1,
|
||||
interactive=True,
|
||||
)
|
||||
rms_mix_rate0 = gr.Slider(
|
||||
minimum=0,
|
||||
maximum=1,
|
||||
label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"),
|
||||
value=1,
|
||||
interactive=True,
|
||||
)
|
||||
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"))
|
||||
but0 = gr.Button(i18n("转换"), variant="primary")
|
||||
with gr.Column():
|
||||
@ -1173,6 +1202,7 @@ with gr.Blocks() as app:
|
||||
index_rate1,
|
||||
filter_radius0,
|
||||
resample_sr0,
|
||||
rms_mix_rate0
|
||||
],
|
||||
[vc_output1, vc_output2],
|
||||
)
|
||||
@ -1192,7 +1222,7 @@ 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"),
|
||||
@ -1223,7 +1253,7 @@ 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"),
|
||||
@ -1231,6 +1261,13 @@ with gr.Blocks() as app:
|
||||
step=1,
|
||||
interactive=True,
|
||||
)
|
||||
rms_mix_rate1 = gr.Slider(
|
||||
minimum=0,
|
||||
maximum=1,
|
||||
label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"),
|
||||
value=1,
|
||||
interactive=True,
|
||||
)
|
||||
with gr.Column():
|
||||
dir_input = gr.Textbox(
|
||||
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)"),
|
||||
@ -1256,6 +1293,7 @@ with gr.Blocks() as app:
|
||||
index_rate2,
|
||||
filter_radius1,
|
||||
resample_sr1,
|
||||
rms_mix_rate1
|
||||
],
|
||||
[vc_output3],
|
||||
)
|
||||
@ -1324,6 +1362,13 @@ with gr.Blocks() as app:
|
||||
value=True,
|
||||
interactive=True,
|
||||
)
|
||||
version19 = gr.Radio(
|
||||
label=i18n("版本(目前仅40k支持了v2)"),
|
||||
choices=["v1", "v2"],
|
||||
value="v1",
|
||||
interactive=True,
|
||||
visible=True,
|
||||
)
|
||||
np7 = gr.Slider(
|
||||
minimum=0,
|
||||
maximum=ncpu,
|
||||
@ -1353,7 +1398,7 @@ 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)"))
|
||||
@ -1378,7 +1423,7 @@ 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],
|
||||
[gpus6, np7, f0method8, if_f0_3, exp_dir1,version19],
|
||||
[info2],
|
||||
)
|
||||
with gr.Group():
|
||||
@ -1422,6 +1467,14 @@ with gr.Blocks() as app:
|
||||
value=i18n("否"),
|
||||
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"
|
||||
),
|
||||
choices=[i18n("是"), i18n("否")],
|
||||
value=i18n("否"),
|
||||
interactive=True,
|
||||
)
|
||||
with gr.Row():
|
||||
pretrained_G14 = gr.Textbox(
|
||||
label=i18n("加载预训练底模G路径"),
|
||||
@ -1434,12 +1487,15 @@ with gr.Blocks() as app:
|
||||
interactive=True,
|
||||
)
|
||||
sr2.change(
|
||||
change_sr2, [sr2, if_f0_3], [pretrained_G14, pretrained_D15]
|
||||
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]
|
||||
)
|
||||
if_f0_3.change(
|
||||
change_f0,
|
||||
[if_f0_3, sr2],
|
||||
[np7, f0method8, pretrained_G14, pretrained_D15],
|
||||
[if_f0_3, sr2,version19],
|
||||
[f0method8, pretrained_G14, pretrained_D15],
|
||||
)
|
||||
gpus16 = gr.Textbox(
|
||||
label=i18n("以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"),
|
||||
@ -1465,10 +1521,12 @@ with gr.Blocks() as app:
|
||||
pretrained_D15,
|
||||
gpus16,
|
||||
if_cache_gpu17,
|
||||
if_save_every_weights18,
|
||||
version19,
|
||||
],
|
||||
info3,
|
||||
)
|
||||
but4.click(train_index, [exp_dir1], info3)
|
||||
but4.click(train_index, [exp_dir1,version19], info3)
|
||||
but5.click(
|
||||
train1key,
|
||||
[
|
||||
@ -1487,6 +1545,8 @@ with gr.Blocks() as app:
|
||||
pretrained_D15,
|
||||
gpus16,
|
||||
if_cache_gpu17,
|
||||
if_save_every_weights18,
|
||||
version19,
|
||||
],
|
||||
info3,
|
||||
)
|
||||
@ -1526,12 +1586,18 @@ with gr.Blocks() as app:
|
||||
max_lines=1,
|
||||
interactive=True,
|
||||
)
|
||||
version_2=gr.Radio(
|
||||
label=i18n("模型版本型号"),
|
||||
choices=["v1", "v2"],
|
||||
value="v1",
|
||||
interactive=True,
|
||||
)
|
||||
with gr.Row():
|
||||
but6 = gr.Button(i18n("融合"), variant="primary")
|
||||
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],
|
||||
[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():
|
||||
@ -1589,15 +1655,21 @@ with gr.Blocks() as app:
|
||||
value="1",
|
||||
interactive=True,
|
||||
)
|
||||
version_1=gr.Radio(
|
||||
label=i18n("模型版本型号"),
|
||||
choices=["v1", "v2"],
|
||||
value="v1",
|
||||
interactive=True,
|
||||
)
|
||||
info___ = gr.Textbox(
|
||||
label=i18n("要置入的模型信息"), value="", max_lines=8, interactive=True
|
||||
)
|
||||
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__])
|
||||
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___],
|
||||
[ckpt_path2, save_name, sr__, if_f0__, info___,version_1],
|
||||
info7,
|
||||
)
|
||||
|
||||
@ -1615,16 +1687,16 @@ 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())
|
||||
|
||||
|
||||
@ -31,14 +31,21 @@ from data_utils import (
|
||||
TextAudioCollate,
|
||||
DistributedBucketSampler,
|
||||
)
|
||||
from infer_pack.models import (
|
||||
SynthesizerTrnMs256NSFsid,
|
||||
SynthesizerTrnMs256NSFsid_nono,
|
||||
MultiPeriodDiscriminator,
|
||||
)
|
||||
if(hps.version=="v1"):
|
||||
from infer_pack.models import (
|
||||
SynthesizerTrnMs256NSFsid as RVC_Model_f0,
|
||||
SynthesizerTrnMs256NSFsid_nono as RVC_Model_nof0,
|
||||
MultiPeriodDiscriminator,
|
||||
)
|
||||
else:
|
||||
from infer_pack.models import (
|
||||
SynthesizerTrnMs768NSFsid as RVC_Model_f0,
|
||||
SynthesizerTrnMs768NSFsid_nono as RVC_Model_nof0,
|
||||
MultiPeriodDiscriminatorV2 as MultiPeriodDiscriminator,
|
||||
)
|
||||
from losses import generator_loss, discriminator_loss, feature_loss, kl_loss
|
||||
from mel_processing import mel_spectrogram_torch, spec_to_mel_torch
|
||||
|
||||
from process_ckpt import savee
|
||||
|
||||
global_step = 0
|
||||
|
||||
@ -63,7 +70,7 @@ def run(rank, n_gpus, hps):
|
||||
if rank == 0:
|
||||
logger = utils.get_logger(hps.model_dir)
|
||||
logger.info(hps)
|
||||
utils.check_git_hash(hps.model_dir)
|
||||
# utils.check_git_hash(hps.model_dir)
|
||||
writer = SummaryWriter(log_dir=hps.model_dir)
|
||||
writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
|
||||
|
||||
@ -104,7 +111,7 @@ def run(rank, n_gpus, hps):
|
||||
prefetch_factor=8,
|
||||
)
|
||||
if hps.if_f0 == 1:
|
||||
net_g = SynthesizerTrnMs256NSFsid(
|
||||
net_g = RVC_Model_f0(
|
||||
hps.data.filter_length // 2 + 1,
|
||||
hps.train.segment_size // hps.data.hop_length,
|
||||
**hps.model,
|
||||
@ -112,7 +119,7 @@ def run(rank, n_gpus, hps):
|
||||
sr=hps.sample_rate,
|
||||
)
|
||||
else:
|
||||
net_g = SynthesizerTrnMs256NSFsid_nono(
|
||||
net_g = RVC_Model_nof0(
|
||||
hps.data.filter_length // 2 + 1,
|
||||
hps.train.segment_size // hps.data.hop_length,
|
||||
**hps.model,
|
||||
@ -343,7 +350,7 @@ def train_and_evaluate(
|
||||
spec = spec.cuda(rank, non_blocking=True)
|
||||
spec_lengths = spec_lengths.cuda(rank, non_blocking=True)
|
||||
wave = wave.cuda(rank, non_blocking=True)
|
||||
wave_lengths = wave_lengths.cuda(rank, non_blocking=True)
|
||||
# wave_lengths = wave_lengths.cuda(rank, non_blocking=True)
|
||||
|
||||
# Calculate
|
||||
with autocast(enabled=hps.train.fp16_run):
|
||||
@ -428,10 +435,10 @@ def train_and_evaluate(
|
||||
)
|
||||
)
|
||||
# Amor For Tensorboard display
|
||||
if loss_mel > 50:
|
||||
loss_mel = 50
|
||||
if loss_kl > 5:
|
||||
loss_kl = 5
|
||||
if loss_mel > 75:
|
||||
loss_mel = 75
|
||||
if loss_kl > 9:
|
||||
loss_kl = 9
|
||||
|
||||
logger.info([global_step, lr])
|
||||
logger.info(
|
||||
@ -512,12 +519,20 @@ 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 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))
|
||||
)
|
||||
|
||||
if rank == 0:
|
||||
logger.info("====> Epoch: {}".format(epoch))
|
||||
if epoch >= hps.total_epoch and rank == 0:
|
||||
logger.info("Training is done. The program is closed.")
|
||||
from process_ckpt import savee # def savee(ckpt,sr,if_f0,name,epoch):
|
||||
|
||||
if hasattr(net_g, "module"):
|
||||
ckpt = net_g.module.state_dict()
|
||||
@ -525,7 +540,7 @@ 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))
|
||||
% (savee(ckpt, hps.sample_rate, hps.if_f0, hps.name, epoch,hps.version))
|
||||
)
|
||||
sleep(1)
|
||||
os._exit(2333333)
|
||||
|
||||
@ -32,19 +32,19 @@ class PreProcess:
|
||||
def __init__(self, sr, exp_dir):
|
||||
self.slicer = Slicer(
|
||||
sr=sr,
|
||||
threshold=-40,
|
||||
min_length=800,
|
||||
threshold=-42,
|
||||
min_length=1500,
|
||||
min_interval=400,
|
||||
hop_size=15,
|
||||
max_sil_kept=150,
|
||||
max_sil_kept=500,
|
||||
)
|
||||
self.sr = sr
|
||||
self.bh, self.ah = signal.butter(N=5, Wn=48, btype="high", fs=self.sr)
|
||||
self.per = 3.7
|
||||
self.overlap = 0.3
|
||||
self.tail = self.per + self.overlap
|
||||
self.max = 0.95
|
||||
self.alpha = 0.8
|
||||
self.max = 0.9
|
||||
self.alpha = 0.75
|
||||
self.exp_dir = exp_dir
|
||||
self.gt_wavs_dir = "%s/0_gt_wavs" % exp_dir
|
||||
self.wavs16k_dir = "%s/1_16k_wavs" % exp_dir
|
||||
|
||||
@ -2,18 +2,16 @@ 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,
|
||||
@ -24,6 +22,17 @@ 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
|
||||
# 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()
|
||||
return data2
|
||||
|
||||
class VC(object):
|
||||
def __init__(self, tgt_sr, config):
|
||||
@ -44,16 +53,7 @@ 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 +77,9 @@ 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()]))
|
||||
@ -118,6 +118,7 @@ class VC(object):
|
||||
index,
|
||||
big_npy,
|
||||
index_rate,
|
||||
version,
|
||||
): # ,file_index,file_big_npy
|
||||
feats = torch.from_numpy(audio0)
|
||||
if self.is_half:
|
||||
@ -133,12 +134,12 @@ class VC(object):
|
||||
inputs = {
|
||||
"source": feats.to(self.device),
|
||||
"padding_mask": padding_mask,
|
||||
"output_layer": 9, # layer 9
|
||||
"output_layer": 9if version=="v1"else 12,
|
||||
}
|
||||
t0 = ttime()
|
||||
with torch.no_grad():
|
||||
logits = model.extract_features(**inputs)
|
||||
feats = model.final_proj(logits[0])
|
||||
feats = model.final_proj(logits[0])if version=="v1"else logits[0]
|
||||
|
||||
if (
|
||||
isinstance(index, type(None)) == False
|
||||
@ -176,14 +177,14 @@ class VC(object):
|
||||
with torch.no_grad():
|
||||
if pitch != None and pitchf != None:
|
||||
audio1 = (
|
||||
(net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768)
|
||||
(net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0])
|
||||
.data.cpu()
|
||||
.float()
|
||||
.numpy()
|
||||
)
|
||||
else:
|
||||
audio1 = (
|
||||
(net_g.infer(feats, p_len, sid)[0][0, 0] * 32768)
|
||||
(net_g.infer(feats, p_len, sid)[0][0, 0])
|
||||
.data.cpu()
|
||||
.float()
|
||||
.numpy()
|
||||
@ -213,6 +214,8 @@ class VC(object):
|
||||
filter_radius,
|
||||
tgt_sr,
|
||||
resample_sr,
|
||||
rms_mix_rate,
|
||||
version,
|
||||
f0_file=None,
|
||||
):
|
||||
if (
|
||||
@ -267,15 +270,7 @@ 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(
|
||||
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":
|
||||
@ -299,6 +294,7 @@ class VC(object):
|
||||
index,
|
||||
big_npy,
|
||||
index_rate,
|
||||
version,
|
||||
)[self.t_pad_tgt : -self.t_pad_tgt]
|
||||
)
|
||||
else:
|
||||
@ -314,6 +310,7 @@ class VC(object):
|
||||
index,
|
||||
big_npy,
|
||||
index_rate,
|
||||
version,
|
||||
)[self.t_pad_tgt : -self.t_pad_tgt]
|
||||
)
|
||||
s = t
|
||||
@ -330,6 +327,7 @@ class VC(object):
|
||||
index,
|
||||
big_npy,
|
||||
index_rate,
|
||||
version,
|
||||
)[self.t_pad_tgt : -self.t_pad_tgt]
|
||||
)
|
||||
else:
|
||||
@ -345,14 +343,20 @@ class VC(object):
|
||||
index,
|
||||
big_npy,
|
||||
index_rate,
|
||||
version,
|
||||
)[self.t_pad_tgt : -self.t_pad_tgt]
|
||||
)
|
||||
audio_opt = np.concatenate(audio_opt)
|
||||
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_opt = audio_opt.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()
|
||||
|
||||
Loading…
Reference in New Issue
Block a user