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w-okada 2023-08-06 05:46:13 +09:00
parent 3e78db4318
commit 38d7ec8c4f
3 changed files with 21 additions and 16 deletions
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5
server/voice_changer/DiffusionSVC/inferencer/DiffusionSVCInferencer.py
View File

@ -119,11 +119,11 @@ class DiffusionSVCInferencer(Inferencer):
# print("[ ----Timer::1: ]", t.secs)
with Timer("pre-process", True) as t:
with Timer("pre-process", False) as t:
if skip_diffusion == 0:
out_mel = self.__call__(feats, pitch, volume, spk_id=sid, spk_mix_dict=None, aug_shift=0, gt_spec=gt_spec, infer_speedup=infer_speedup, method='dpm-solver', k_step=k_step, use_tqdm=False, spk_emb=None)
gt_spec = out_mel
print("[ ----Timer::2: ]", t.secs)
# print("[ ----Timer::2: ]", t.secs)
with Timer("pre-process", False) as t: # NOQA
@ -133,6 +133,7 @@ class DiffusionSVCInferencer(Inferencer):
out_wav *= mask
else:
out_wav = self.vocoder_onnx.infer(gt_spec, pitch, silence_front, mask)
# out_wav = self.vocoder_onnx.infer(gt_spec, pitch, 0, mask)
# print("[ ----Timer::3: ]", t.secs)
return out_wav.squeeze()

6
server/voice_changer/DiffusionSVC/pipeline/Pipeline.py
View File

@ -99,7 +99,8 @@ class Pipeline(object):
silence_front,
embOutputLayer,
useFinalProj,
protect=0.5
protect=0.5,
skip_diffusion=True,
):
# print("---------- pipe line --------------------")
with Timer("pre-process", False) as t:
@ -174,7 +175,8 @@ class Pipeline(object):
sid,
k_step,
infer_speedup,
silence_front=silence_front
silence_front=silence_front,
skip_diffusion=skip_diffusion
).to(dtype=torch.float32),
-1.0,
1.0,

26
server/voice_changer/RVC/pipeline/Pipeline.py
View File

@ -145,18 +145,20 @@ class Pipeline(object):
feats = feats.view(1, -1)
# embedding
with autocast(enabled=self.isHalf):
try:
feats = self.embedder.extractFeatures(feats, embOutputLayer, useFinalProj)
if torch.isnan(feats).all():
raise DeviceCannotSupportHalfPrecisionException()
except RuntimeError as e:
if "HALF" in e.__str__().upper():
raise HalfPrecisionChangingException()
elif "same device" in e.__str__():
raise DeviceChangingException()
else:
raise e
with Timer("main-process", False) as te:
with autocast(enabled=self.isHalf):
try:
feats = self.embedder.extractFeatures(feats, embOutputLayer, useFinalProj)
if torch.isnan(feats).all():
raise DeviceCannotSupportHalfPrecisionException()
except RuntimeError as e:
if "HALF" in e.__str__().upper():
raise HalfPrecisionChangingException()
elif "same device" in e.__str__():
raise DeviceChangingException()
else:
raise e
# print(f"[Embedding] {te.secs}")
# Index - feature抽出
# if self.index is not None and self.feature is not None and index_rate != 0: