fumiama/Retrieval-based-Voice-Conversion-WebUI
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optimize(f0): move some f0s into rvc.f0

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源文雨
2024-06-13 00:10:22 +09:00
parent d44a942882
commit 77b371d615
15 changed files with 91 additions and 185 deletions
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2
infer/lib/jit/rmvpe.py
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@@ -2,7 +2,7 @@ import torch
def get_rmvpe(model_path="assets/rmvpe/rmvpe.pt", device=torch.device("cpu")):
from infer.lib.rmvpe import E2E
from rvc.f0.e2e import E2E
model = E2E(4, 1, (2, 2))
ckpt = torch.load(model_path, map_location=device)

27
infer/lib/rmvpe.py
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@@ -6,17 +6,6 @@ import torch
from infer.lib import jit
try:
# Fix "Torch not compiled with CUDA enabled"
import intel_extension_for_pytorch as ipex # pylint: disable=import-error, unused-import
if torch.xpu.is_available():
from infer.modules.ipex import ipex_init
ipex_init()
except Exception: # pylint: disable=broad-exception-caught
pass
import torch.nn as nn
import torch.nn.functional as F
import logging
@@ -127,13 +116,13 @@ class RMVPE:
return hidden[:, :n_frames]
def decode(self, hidden, thred=0.03):
cents_pred = self.to_local_average_cents(hidden, thred=thred)
cents_pred = self.to_local_average_cents(hidden, threshold=thred)
f0 = 10 * (2 ** (cents_pred / 1200))
f0[f0 == 10] = 0
# f0 = np.array([10 * (2 ** (cent_pred / 1200)) if cent_pred else 0 for cent_pred in cents_pred])
return f0
def infer_from_audio(self, audio, thred=0.03):
def infer_from_audio(self, audio, threshold=0.03):
# torch.cuda.synchronize()
# t0 = ttime()
if not torch.is_tensor(audio):
@@ -155,17 +144,15 @@ class RMVPE:
if self.is_half == True:
hidden = hidden.astype("float32")
f0 = self.decode(hidden, thred=thred)
f0 = self.decode(hidden, thred=threshold)
# torch.cuda.synchronize()
# t3 = ttime()
# print("hmvpe:%s\t%s\t%s\t%s"%(t1-t0,t2-t1,t3-t2,t3-t0))
return f0
def to_local_average_cents(self, salience, thred=0.05):
# t0 = ttime()
def to_local_average_cents(self, salience, threshold=0.05):
center = np.argmax(salience, axis=1) # 帧长#index
salience = np.pad(salience, ((0, 0), (4, 4))) # 帧长,368
# t1 = ttime()
center += 4
todo_salience = []
todo_cents_mapping = []
@@ -174,15 +161,11 @@ class RMVPE:
for idx in range(salience.shape[0]):
todo_salience.append(salience[:, starts[idx] : ends[idx]][idx])
todo_cents_mapping.append(self.cents_mapping[starts[idx] : ends[idx]])
# t2 = ttime()
todo_salience = np.array(todo_salience) # 帧长，9
todo_cents_mapping = np.array(todo_cents_mapping) # 帧长，9
product_sum = np.sum(todo_salience * todo_cents_mapping, 1)
weight_sum = np.sum(todo_salience, 1) # 帧长
devided = product_sum / weight_sum # 帧长
# t3 = ttime()
maxx = np.max(salience, axis=1) # 帧长
devided[maxx <= thred] = 0
# t4 = ttime()
# print("decode:%s\t%s\t%s\t%s" % (t1 - t0, t2 - t1, t3 - t2, t4 - t3))
devided[maxx <= threshold] = 0
return devided