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added rvc model training

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SayanoAI
2024-08-24 12:22:18 -04:00
parent aa0a961d96
commit f6ff593ccc
11 changed files with 453 additions and 134 deletions
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preprocessing_utils.py
+11 -19
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@@ -1,6 +1,5 @@
import sys, os, multiprocessing
from threading import Thread
from scipy import signal
import numpy as np, os, traceback
from .lib.model_utils import load_hubert
from .lib.slicer2 import Slicer
@@ -14,7 +13,7 @@ from .config import config
import torch
class Preprocess:
def __init__(self, sr, exp_dir, noparallel=True, period=3.0, overlap=.3, max_volume=.95):
def __init__(self, sr, exp_dir, noparallel=True, period=3.0, overlap=.3, alpha=.75, max_volume=.95):
self.slicer = Slicer(
sr=sr,
threshold=-42,
@@ -24,12 +23,11 @@ class Preprocess:
max_sil_kept=500
)
self.sr = sr
self.bh, self.ah = signal.butter(N=5, Wn=48, btype="high", fs=self.sr)
self.per = period
self.overlap = overlap
self.tail = self.per + self.overlap
self.max = max_volume
self.alpha = 0.75
self.alpha = alpha
self.exp_dir = exp_dir
self.gt_wavs_dir = "%s/0_gt_wavs" % exp_dir
self.wavs16k_dir = "%s/1_16k_wavs" % exp_dir
@@ -70,10 +68,7 @@ class Preprocess:
def pipeline(self, path, idx0):
try:
audio = load_audio(path, self.sr)
# zero phased digital filter cause pre-ringing noise...
# audio = signal.filtfilt(self.bh, self.ah, audio)
# audio = signal.lfilter(self.bh, self.ah, audio)
audio,_ = load_input_audio(path, self.sr)
idx1 = 0
for audio in self.slicer.slice(audio):
@@ -121,7 +116,7 @@ class Preprocess:
self.println("Fail. %s" % traceback.format_exc())
class FeatureInput(FeatureExtractor):
def __init__(self, f0_method, exp_dir, samplerate=16000, hop_size=160, device="cpu", version="v2", if_f0=False):
def __init__(self, model, f0_method, exp_dir, samplerate=16000, hop_size=160, device="cpu", version="v2", if_f0=False):
self.sr = samplerate
self.hop = hop_size
self.f0_method = f0_method
@@ -136,7 +131,7 @@ class FeatureInput(FeatureExtractor):
self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700)
self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700)
self.model = load_hubert(config)
self.model = model
super().__init__(samplerate, config, onnx=False)
@@ -161,11 +156,8 @@ class FeatureInput(FeatureExtractor):
"padding_mask": padding_mask.to(self.device),
"output_layer": 9 if self.version == "v1" else 12, # layer 9
}
with torch.no_grad():
logits = self.model.extract_features(**inputs)
feats = (
self.model.final_proj(logits[0]) if self.version == "v1" else logits[0]
)
feats = self.model.extract_features(version=self.version,**inputs)
feats = feats.squeeze(0).float().cpu().numpy()
if np.isnan(feats).sum() == 0:
@@ -216,9 +208,9 @@ class FeatureInput(FeatureExtractor):
except:
self.printt("f0fail-%s-%s-%s" % (idx, inp_path, traceback.format_exc()))
def preprocess_trainset(inp_root, sr, n_p, exp_dir, period=3.0, overlap=.3):
def preprocess_trainset(inp_root, sr, n_p, exp_dir, period=3.0, overlap=.3, max_volume=1., alpha=.75):
try:
pp = Preprocess(sr, exp_dir, period=period, overlap=overlap)
pp = Preprocess(sr, exp_dir, period=period, overlap=overlap, max_volume=max_volume, alpha=alpha)
pp.println("start preprocess")
pp.println(sys.argv)
pp.pipeline_mp_inp_dir(inp_root, n_p)
@@ -229,9 +221,9 @@ def preprocess_trainset(inp_root, sr, n_p, exp_dir, period=3.0, overlap=.3):
except Exception as e:
return f"Failed to preprocess data: {e}"
def extract_features_trainset(exp_dir,n_p,f0method,device,version,if_f0):
def extract_features_trainset(hubert_model,exp_dir,n_p,f0method,device,version,if_f0):
try:
featureInput = FeatureInput(f0_method=f0method,exp_dir=exp_dir,device=device,version=version,if_f0=if_f0)
featureInput = FeatureInput(f0_method=f0method,exp_dir=exp_dir,device=device,version=version,if_f0=if_f0,model=hubert_model)
paths = []
inp_root = os.path.join(exp_dir,"1_16k_wavs")
opt_root1 = os.path.join(exp_dir,"2a_f0")