Cool_Tools/Retrieval-based-Voice-Conversion-WebUI
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chore(format): run black on dev (#1638)

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github-actions[bot] 2023-12-26 22:03:02 +09:00 committed by GitHub
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4 changed files with 102 additions and 71 deletions
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41
gui_v1.py
View File

@ -38,7 +38,11 @@ def phase_vocoder(a, b, fade_out, fade_in):
deltaphase = deltaphase - 2 * np.pi * torch.floor(deltaphase / 2 / np.pi + 0.5)
w = 2 * np.pi * torch.arange(n // 2 + 1).to(a) + deltaphase
t = torch.arange(n).unsqueeze(-1).to(a) / n
result = a * (fade_out ** 2) + b * (fade_in ** 2) + torch.sum(absab * torch.cos(w * t + phia), -1) * window / n
result = (
a * (fade_out**2)
+ b * (fade_in**2)
+ torch.sum(absab * torch.cos(w * t + phia), -1) * window / n
)
return result
@ -633,7 +637,11 @@ if __name__ == "__main__":
self.config,
self.rvc if hasattr(self, "rvc") else None,
)
self.gui_config.samplerate = self.rvc.tgt_sr if self.gui_config.sr_type == "sr_model" else self.get_device_samplerate()
self.gui_config.samplerate = (
self.rvc.tgt_sr
if self.gui_config.sr_type == "sr_model"
else self.get_device_samplerate()
)
self.zc = self.gui_config.samplerate // 100
self.block_frame = (
int(
@ -690,7 +698,9 @@ if __name__ == "__main__":
2 * self.zc, device=self.config.device, dtype=torch.float32
)
self.skip_head = self.extra_frame // self.zc
self.return_length = (self.block_frame + self.sola_buffer_frame + self.sola_search_frame) // self.zc
self.return_length = (
self.block_frame + self.sola_buffer_frame + self.sola_search_frame
) // self.zc
self.fade_in_window: torch.Tensor = (
torch.sin(
0.5
@ -824,7 +834,11 @@ if __name__ == "__main__":
# volume envelop mixing
if self.gui_config.rms_mix_rate < 1 and self.function == "vc":
rms1 = librosa.feature.rms(
y=self.input_wav_res[160 * self.skip_head : 160 * (self.skip_head + self.return_length)]
y=self.input_wav_res[
160
* self.skip_head : 160
* (self.skip_head + self.return_length)
]
.cpu()
.numpy(),
frame_length=640,
@ -874,18 +888,21 @@ if __name__ == "__main__":
infer_wav = infer_wav[sola_offset:]
if "privateuseone" in str(self.config.device) or not self.gui_config.use_pv:
infer_wav[: self.sola_buffer_frame] *= self.fade_in_window
infer_wav[: self.sola_buffer_frame] += self.sola_buffer * self.fade_out_window
infer_wav[: self.sola_buffer_frame] += (
self.sola_buffer * self.fade_out_window
)
else:
infer_wav[: self.sola_buffer_frame] = phase_vocoder(
self.sola_buffer,
infer_wav[: self.sola_buffer_frame],
self.fade_out_window,
self.fade_in_window)
self.sola_buffer[:] = infer_wav[self.block_frame : self.block_frame + self.sola_buffer_frame]
if sys.platform == "darwin":
outdata[:] = (
infer_wav[: self.block_frame].cpu().numpy()[:, np.newaxis]
self.fade_in_window,
)
self.sola_buffer[:] = infer_wav[
self.block_frame : self.block_frame + self.sola_buffer_frame
]
if sys.platform == "darwin":
outdata[:] = infer_wav[: self.block_frame].cpu().numpy()[:, np.newaxis]
else:
outdata[:] = (
infer_wav[: self.block_frame].repeat(2, 1).t().cpu().numpy()
@ -949,6 +966,8 @@ if __name__ == "__main__":
printt("Output device: %s:%s", str(sd.default.device[1]), output_device)
def get_device_samplerate(self):
return int(sd.query_devices(device=sd.default.device[0])['default_samplerate'])
return int(
sd.query_devices(device=sd.default.device[0])["default_samplerate"]
)
gui = GUI()

2
infer/lib/train/mel_processing.py
View File

@ -38,6 +38,7 @@ def spectral_de_normalize_torch(magnitudes):
mel_basis = {}
hann_window = {}
def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
"""Convert waveform into Linear-frequency Linear-amplitude spectrogram.
@ -87,6 +88,7 @@ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False)
spec = torch.sqrt(spec.real.pow(2) + spec.imag.pow(2) + 1e-6)
return spec
def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
# MelBasis - Cache if needed
global mel_basis

24
tools/rvc_for_realtime.py
View File

@ -62,6 +62,7 @@ class RVC:
"""
try:
if config.dml == True:
def forward_dml(ctx, x, scale):
ctx.scale = scale
res = x.clone().detach()
@ -320,6 +321,7 @@ class RVC:
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")
if "privateuseone" in str(self.device):
self.device_fcpe = "cpu"
@ -329,7 +331,7 @@ class RVC:
f0 = self.model_fcpe.infer(
x.to(self.device_fcpe).unsqueeze(0).float(),
sr=16000,
decoder_mode='local_argmax',
decoder_mode="local_argmax",
threshold=0.006,
)
f0 *= pow(2, f0_up_key / 12)
@ -384,21 +386,29 @@ class RVC:
if self.if_f0 == 1:
f0_extractor_frame = block_frame_16k + 800
if f0method == "rmvpe":
f0_extractor_frame = (
5120 * ((f0_extractor_frame - 1) // 5120 + 1) - 160
f0_extractor_frame = 5120 * ((f0_extractor_frame - 1) // 5120 + 1) - 160
pitch, pitchf = self.get_f0(
input_wav[-f0_extractor_frame:], self.f0_up_key, self.n_cpu, f0method
)
pitch, pitchf = self.get_f0(input_wav[-f0_extractor_frame: ], self.f0_up_key, self.n_cpu, f0method)
start_frame = block_frame_16k // 160
end_frame = len(self.cache_pitch) - (pitch.shape[0] - 4) + start_frame
self.cache_pitch[:] = np.append(self.cache_pitch[start_frame: end_frame], pitch[3:-1])
self.cache_pitch[:] = np.append(
self.cache_pitch[start_frame:end_frame], pitch[3:-1]
)
self.cache_pitchf[:] = np.append(
self.cache_pitchf[start_frame:end_frame], pitchf[3:-1]
)
t4 = ttime()
p_len = input_wav.shape[0] // 160
if self.if_f0 == 1:
cache_pitch = torch.LongTensor(self.cache_pitch[-p_len: ]).to(self.device).unsqueeze(0)
cache_pitchf = torch.FloatTensor(self.cache_pitchf[-p_len: ]).to(self.device).unsqueeze(0)
cache_pitch = (
torch.LongTensor(self.cache_pitch[-p_len:]).to(self.device).unsqueeze(0)
)
cache_pitchf = (
torch.FloatTensor(self.cache_pitchf[-p_len:])
.to(self.device)
.unsqueeze(0)
)
feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
feats = feats[:, :p_len, :]
p_len = torch.LongTensor([p_len]).to(self.device)