Cool_Tools/Retrieval-based-Voice-Conversion-WebUI
1
0
Fork 0
mirror of synced 2024-11-24 07:30:16 +01:00
Code Releases Wiki Activity

add fcpe for realtime

Browse Source
This commit is contained in:
CN_ChiTu 2023-12-14 21:01:46 +08:00 committed by GitHub
parent ff093ad88f
commit e7e9d5934d
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
1 changed files with 438 additions and 421 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

23
tools/rvc_for_realtime.py
View File

@ -62,7 +62,6 @@ class RVC:
"""
try:
if config.dml == True:
def forward_dml(ctx, x, scale):
ctx.scale = scale
res = x.clone().detach()
@ -183,6 +182,8 @@ class RVC:
if last_rvc is not None and hasattr(last_rvc, "model_rmvpe"):
self.model_rmvpe = last_rvc.model_rmvpe
if last_rvc is not None and hasattr(last_rvc, "model_fcpe"):
self.model_fcpe = last_rvc.model_fcpe
except:
printt(traceback.format_exc())
@ -217,6 +218,8 @@ class RVC:
return self.get_f0_crepe(x, f0_up_key)
if method == "rmvpe":
return self.get_f0_rmvpe(x, f0_up_key)
if method == "fcpe":
return self.get_f0_fcpe(x, f0_up_key)
if method == "pm":
p_len = x.shape[0] // 160 + 1
f0_min = 65
@ -258,7 +261,7 @@ class RVC:
self.inp_q.put((idx, x[:tail], res_f0, n_cpu, ts))
else:
self.inp_q.put(
(idx, x[part_length * idx - 320 : tail], res_f0, n_cpu, ts)
(idx, x[part_length * idx - 320: tail], res_f0, n_cpu, ts)
)
while 1:
res_ts = self.opt_q.get()
@ -273,7 +276,7 @@ class RVC:
else:
f0 = f0[2:]
f0bak[
part_length * idx // 160 : part_length * idx // 160 + f0.shape[0]
part_length * idx // 160: part_length * idx // 160 + f0.shape[0]
] = f0
f0bak = signal.medfilt(f0bak, 3)
f0bak *= pow(2, f0_up_key / 12)
@ -322,6 +325,20 @@ class RVC:
f0 *= pow(2, f0_up_key / 12)
return self.get_f0_post(f0)
def get_f0_fcpe(self, x, f0_up_key):
if hasattr(self, "model_fcpe") == False:
from torchfcpe import spawn_bundled_infer_model
printt("Loading fcpe model")
self.model_fcpe = spawn_bundled_infer_model(self.device)
f0 = self.model_fcpe.infer(
torch.from_numpy(x).to(self.device).unsqueeze(0).float(),
sr=16000,
decoder_mode='local_argmax',
threshold=0.006,
).squeeze().cpu().numpy()
f0 *= pow(2, f0_up_key / 12)
return self.get_f0_post(f0)
def infer(
self,
feats: torch.Tensor,