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

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4 changed files with 102 additions and 71 deletions
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67
gui_v1.py
View File

@ -38,10 +38,14 @@ 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
class Harvest(multiprocessing.Process):
def __init__(self, inp_q, opt_q):
multiprocessing.Process.__init__(self)
@ -592,11 +596,11 @@ if __name__ == "__main__":
self.gui_config.pth_path = values["pth_path"]
self.gui_config.index_path = values["index_path"]
self.gui_config.sr_type = ["sr_model", "sr_device"][
[
values["sr_model"],
values["sr_device"],
].index(True)
]
[
values["sr_model"],
values["sr_device"],
].index(True)
]
self.gui_config.threhold = values["threhold"]
self.gui_config.pitch = values["pitch"]
self.gui_config.block_time = values["block_time"]
@ -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,
@ -871,21 +885,24 @@ if __name__ == "__main__":
else:
sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0])
printt("sola_offset = %d", int(sola_offset))
infer_wav = infer_wav[sola_offset :]
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.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]
else:
outdata[:] = (
infer_wav[: self.block_frame].repeat(2, 1).t().cpu().numpy()
@ -930,7 +947,7 @@ if __name__ == "__main__":
input_devices_indices,
output_devices_indices,
)
def set_devices(self, input_device, output_device):
"""设置输出设备"""
(
@ -947,8 +964,10 @@ if __name__ == "__main__":
]
printt("Input device: %s:%s", str(sd.default.device[0]), input_device)
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()

20
infer/lib/infer_pack/models.py
View File

@ -795,9 +795,9 @@ class SynthesizerTrnMs256NSFsid(nn.Module):
assert isinstance(return_length, torch.Tensor)
head = int(skip_head.item())
length = int(return_length.item())
z_p = z_p[:, :, head: head + length]
x_mask = x_mask[:, :, head: head + length]
nsff0 = nsff0[:, head: head + length]
z_p = z_p[:, :, head : head + length]
x_mask = x_mask[:, :, head : head + length]
nsff0 = nsff0[:, head : head + length]
z = self.flow(z_p, x_mask, g=g, reverse=True)
o = self.dec(z * x_mask, nsff0, g=g)
return o, x_mask, (z, z_p, m_p, logs_p)
@ -957,9 +957,9 @@ class SynthesizerTrnMs768NSFsid(nn.Module):
assert isinstance(return_length, torch.Tensor)
head = int(skip_head.item())
length = int(return_length.item())
z_p = z_p[:, :, head: head + length]
x_mask = x_mask[:, :, head: head + length]
nsff0 = nsff0[:, head: head + length]
z_p = z_p[:, :, head : head + length]
x_mask = x_mask[:, :, head : head + length]
nsff0 = nsff0[:, head : head + length]
z = self.flow(z_p, x_mask, g=g, reverse=True)
o = self.dec(z * x_mask, nsff0, g=g)
return o, x_mask, (z, z_p, m_p, logs_p)
@ -1108,8 +1108,8 @@ class SynthesizerTrnMs256NSFsid_nono(nn.Module):
assert isinstance(return_length, torch.Tensor)
head = int(skip_head.item())
length = int(return_length.item())
z_p = z_p[:, :, head: head + length]
x_mask = x_mask[:, :, head: head + length]
z_p = z_p[:, :, head : head + length]
x_mask = x_mask[:, :, head : head + length]
z = self.flow(z_p, x_mask, g=g, reverse=True)
o = self.dec(z * x_mask, g=g)
return o, x_mask, (z, z_p, m_p, logs_p)
@ -1258,8 +1258,8 @@ class SynthesizerTrnMs768NSFsid_nono(nn.Module):
assert isinstance(return_length, torch.Tensor)
head = int(skip_head.item())
length = int(return_length.item())
z_p = z_p[:, :, head: head + length]
x_mask = x_mask[:, :, head: head + length]
z_p = z_p[:, :, head : head + length]
x_mask = x_mask[:, :, head : head + length]
z = self.flow(z_p, x_mask, g=g, reverse=True)
o = self.dec(z * x_mask, g=g)
return o, x_mask, (z, z_p, m_p, logs_p)

10
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.
@ -51,7 +52,7 @@ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False)
Returns:
:: (B, Freq, Frame) - Linear-frequency Linear-amplitude spectrogram
"""
# Window - Cache if needed
global hann_window
dtype_device = str(y.dtype) + "_" + str(y.device)
@ -60,7 +61,7 @@ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False)
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(
dtype=y.dtype, device=y.device
)
# Padding
y = torch.nn.functional.pad(
y.unsqueeze(1),
@ -68,7 +69,7 @@ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False)
mode="reflect",
)
y = y.squeeze(1)
# Complex Spectrogram :: (B, T) -> (B, Freq, Frame, RealComplex=2)
spec = torch.stft(
y,
@ -82,11 +83,12 @@ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False)
onesided=True,
return_complex=True,
)
# Linear-frequency Linear-amplitude spectrogram :: (B, Freq, Frame, RealComplex=2) -> (B, Freq, Frame)
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

76
tools/rvc_for_realtime.py
View File

@ -46,22 +46,23 @@ def printt(strr, *args):
# config.is_half=False########强制cpu测试
class RVC:
def __init__(
self,
key,
pth_path,
index_path,
index_rate,
n_cpu,
inp_q,
opt_q,
config: Config,
last_rvc=None,
self,
key,
pth_path,
index_path,
index_rate,
n_cpu,
inp_q,
opt_q,
config: Config,
last_rvc=None,
) -> None:
"""
初始化
"""
try:
if config.dml == True:
def forward_dml(ctx, x, scale):
ctx.scale = scale
res = x.clone().detach()
@ -92,7 +93,7 @@ class RVC:
self.index_rate = index_rate
self.cache_pitch: np.ndarray = np.zeros(1024, dtype="int32")
self.cache_pitchf = np.zeros(1024, dtype="float32")
if last_rvc is None:
models, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
["assets/hubert/hubert_base.pt"],
@ -201,7 +202,7 @@ class RVC:
f0bak = f0.copy()
f0_mel = 1127 * np.log(1 + f0 / 700)
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - self.f0_mel_min) * 254 / (
self.f0_mel_max - self.f0_mel_min
self.f0_mel_max - self.f0_mel_min
) + 1
f0_mel[f0_mel <= 1] = 1
f0_mel[f0_mel > 255] = 255
@ -258,7 +259,7 @@ 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 +274,7 @@ 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)
@ -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)
@ -337,12 +339,12 @@ class RVC:
return self.get_f0_post(f0)
def infer(
self,
input_wav: torch.Tensor,
block_frame_16k,
skip_head,
return_length,
f0method,
self,
input_wav: torch.Tensor,
block_frame_16k,
skip_head,
return_length,
f0method,
) -> np.ndarray:
t1 = ttime()
with torch.no_grad():
@ -364,16 +366,16 @@ class RVC:
t2 = ttime()
try:
if hasattr(self, "index") and self.index_rate != 0:
npy = feats[0][skip_head // 2:].cpu().numpy().astype("float32")
npy = feats[0][skip_head // 2 :].cpu().numpy().astype("float32")
score, ix = self.index.search(npy, k=8)
weight = np.square(1 / score)
weight /= weight.sum(axis=1, keepdims=True)
npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
if self.config.is_half:
npy = npy.astype("float16")
feats[0][skip_head // 2:] = (
torch.from_numpy(npy).unsqueeze(0).to(self.device) * self.index_rate
+ (1 - self.index_rate) * feats[0][skip_head // 2:]
feats[0][skip_head // 2 :] = (
torch.from_numpy(npy).unsqueeze(0).to(self.device) * self.index_rate
+ (1 - self.index_rate) * feats[0][skip_head // 2 :]
)
else:
printt("Index search FAILED or disabled")
@ -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
)
pitch, pitchf = self.get_f0(input_wav[-f0_extractor_frame: ], self.f0_up_key, self.n_cpu, f0method)
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
)
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]
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)