fumiama/Retrieval-based-Voice-Conversion-WebUI
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optimize(crepe): move crepe into rvc.f0

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源文雨
2024-06-14 14:29:36 +09:00
parent f79b925ee2
commit e298fde29c
7 changed files with 106 additions and 54 deletions
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38
infer/modules/vc/pipeline.py
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@@ -12,10 +12,9 @@ import librosa
import numpy as np
import torch
import torch.nn.functional as F
import torchcrepe
from scipy import signal
from rvc.f0 import PM, Harvest, RMVPE
from rvc.f0 import PM, Harvest, RMVPE, CRePE, Dio
now_dir = os.getcwd()
sys.path.append(now_dir)
@@ -81,31 +80,24 @@ class Pipeline(object):
if not hasattr(self, "pm"):
self.pm = PM(self.window, f0_min, f0_max, self.sr)
f0 = self.pm.compute_f0(x, p_len=p_len)
if f0_method == "dio":
if not hasattr(self, "dio"):
self.dio = Dio(self.window, f0_min, f0_max, self.sr)
f0 = self.dio.compute_f0(x, p_len=p_len)
elif f0_method == "harvest":
if not hasattr(self, "harvest"):
self.harvest = Harvest(self.window, f0_min, f0_max, self.sr)
f0 = self.harvest.compute_f0(x, p_len=p_len, filter_radius=filter_radius)
elif f0_method == "crepe":
model = "full"
# Pick a batch size that doesn't cause memory errors on your gpu
batch_size = 512
# Compute pitch using first gpu
audio = torch.tensor(np.copy(x))[None].float()
f0, pd = torchcrepe.predict(
audio,
self.sr,
self.window,
f0_min,
f0_max,
model,
batch_size=batch_size,
device=self.device,
return_periodicity=True,
)
pd = torchcrepe.filter.median(pd, 3)
f0 = torchcrepe.filter.mean(f0, 3)
f0[pd < 0.1] = 0
f0 = f0[0].cpu().numpy()
if not hasattr(self, "crepe"):
self.crepe = CRePE(
self.window,
f0_min,
f0_max,
self.sr,
self.device,
)
f0 = self.crepe.compute_f0(x, p_len=p_len)
elif f0_method == "rmvpe":
if not hasattr(self, "rmvpe"):
logger.info(
@@ -117,7 +109,7 @@ class Pipeline(object):
device=self.device,
# use_jit=self.config.use_jit,
)
f0 = self.rmvpe.compute_f0(x, filter_radius=0.03)
f0 = self.rmvpe.compute_f0(x, p_len=p_len, filter_radius=0.03)
if "privateuseone" in str(self.device): # clean ortruntime memory
del self.rmvpe.model