OPENGIT/Retrieval-based-Voice-Conversion-WebUI
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Merge pull request #1618 from CNChTu/main

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add fcpe for realtime
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RVC-Boss 2023-12-15 00:20:04 +08:00 committed by GitHub
commit d269d14768
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3 changed files with 1324 additions and 1295 deletions
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17
gui_v1.py
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@ -122,6 +122,7 @@ if __name__ == "__main__":
data["harvest"] = data["f0method"] == "harvest"
data["crepe"] = data["f0method"] == "crepe"
data["rmvpe"] = data["f0method"] == "rmvpe"
data["fcpe"] = data["f0method"] == "fcpe"
if data["sg_input_device"] not in input_devices:
data["sg_input_device"] = input_devices[sd.default.device[0]]
if data["sg_output_device"] not in output_devices:
@ -147,6 +148,7 @@ if __name__ == "__main__":
data["harvest"] = data["f0method"] == "harvest"
data["crepe"] = data["f0method"] == "crepe"
data["rmvpe"] = data["f0method"] == "rmvpe"
data["fcpe"] = data["f0method"] == "fcpe"
return data
def launcher(self):
@ -287,6 +289,13 @@ if __name__ == "__main__":
default=data.get("rmvpe", "") == True,
enable_events=True,
),
sg.Radio(
"fcpe",
"f0method",
key="fcpe",
default=data.get("fcpe", "") == True,
enable_events=True,
),
],
],
title=i18n("常规设置"),
@ -445,12 +454,13 @@ if __name__ == "__main__":
"n_cpu": values["n_cpu"],
# "use_jit": values["use_jit"],
"use_jit": False,
"f0method": ["pm", "harvest", "crepe", "rmvpe"][
"f0method": ["pm", "harvest", "crepe", "rmvpe", "fcpe"][
[
values["pm"],
values["harvest"],
values["crepe"],
values["rmvpe"],
values["fcpe"],
].index(True)
],
}
@ -484,7 +494,7 @@ if __name__ == "__main__":
self.rvc.change_index_rate(values["index_rate"])
elif event == "rms_mix_rate":
self.gui_config.rms_mix_rate = values["rms_mix_rate"]
elif event in ["pm", "harvest", "crepe", "rmvpe"]:
elif event in ["pm", "harvest", "crepe", "rmvpe", "fcpe"]:
self.gui_config.f0method = event
elif event == "I_noise_reduce":
self.gui_config.I_noise_reduce = values["I_noise_reduce"]
@ -531,12 +541,13 @@ if __name__ == "__main__":
self.gui_config.rms_mix_rate = values["rms_mix_rate"]
self.gui_config.index_rate = values["index_rate"]
self.gui_config.n_cpu = values["n_cpu"]
self.gui_config.f0method = ["pm", "harvest", "crepe", "rmvpe"][
self.gui_config.f0method = ["pm", "harvest", "crepe", "rmvpe", "fcpe"][
[
values["pm"],
values["harvest"],
values["crepe"],
values["rmvpe"],
values["fcpe"],
].index(True)
]
return True

1
requirements.txt
View File

@ -42,6 +42,7 @@ onnxruntime; sys_platform == 'darwin'
onnxruntime-gpu; sys_platform != 'darwin'
torchcrepe==0.0.20
fastapi==0.88
torchfcpe
ffmpy==0.3.1
python-dotenv>=1.0.0
av

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,