mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2024-12-28 10:35:05 +08:00
chore(format): run black on dev (#1638)
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
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parent
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commit
5449f84f06
67
gui_v1.py
67
gui_v1.py
@ -38,10 +38,14 @@ def phase_vocoder(a, b, fade_out, fade_in):
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deltaphase = deltaphase - 2 * np.pi * torch.floor(deltaphase / 2 / np.pi + 0.5)
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w = 2 * np.pi * torch.arange(n // 2 + 1).to(a) + deltaphase
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t = torch.arange(n).unsqueeze(-1).to(a) / n
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result = a * (fade_out ** 2) + b * (fade_in ** 2) + torch.sum(absab * torch.cos(w * t + phia), -1) * window / n
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result = (
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a * (fade_out**2)
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+ b * (fade_in**2)
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+ torch.sum(absab * torch.cos(w * t + phia), -1) * window / n
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)
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return result
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class Harvest(multiprocessing.Process):
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def __init__(self, inp_q, opt_q):
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multiprocessing.Process.__init__(self)
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@ -592,11 +596,11 @@ if __name__ == "__main__":
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self.gui_config.pth_path = values["pth_path"]
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self.gui_config.index_path = values["index_path"]
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self.gui_config.sr_type = ["sr_model", "sr_device"][
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[
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values["sr_model"],
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values["sr_device"],
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].index(True)
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]
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[
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values["sr_model"],
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values["sr_device"],
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].index(True)
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]
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self.gui_config.threhold = values["threhold"]
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self.gui_config.pitch = values["pitch"]
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self.gui_config.block_time = values["block_time"]
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@ -633,7 +637,11 @@ if __name__ == "__main__":
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self.config,
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self.rvc if hasattr(self, "rvc") else None,
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)
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self.gui_config.samplerate = self.rvc.tgt_sr if self.gui_config.sr_type == "sr_model" else self.get_device_samplerate()
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self.gui_config.samplerate = (
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self.rvc.tgt_sr
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if self.gui_config.sr_type == "sr_model"
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else self.get_device_samplerate()
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)
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self.zc = self.gui_config.samplerate // 100
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self.block_frame = (
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int(
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@ -690,7 +698,9 @@ if __name__ == "__main__":
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2 * self.zc, device=self.config.device, dtype=torch.float32
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)
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self.skip_head = self.extra_frame // self.zc
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self.return_length = (self.block_frame + self.sola_buffer_frame + self.sola_search_frame) // self.zc
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self.return_length = (
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self.block_frame + self.sola_buffer_frame + self.sola_search_frame
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) // self.zc
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self.fade_in_window: torch.Tensor = (
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torch.sin(
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0.5
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@ -824,7 +834,11 @@ if __name__ == "__main__":
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# volume envelop mixing
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if self.gui_config.rms_mix_rate < 1 and self.function == "vc":
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rms1 = librosa.feature.rms(
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y=self.input_wav_res[160 * self.skip_head : 160 * (self.skip_head + self.return_length)]
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y=self.input_wav_res[
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160
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* self.skip_head : 160
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* (self.skip_head + self.return_length)
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]
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.cpu()
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.numpy(),
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frame_length=640,
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@ -871,21 +885,24 @@ if __name__ == "__main__":
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else:
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sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0])
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printt("sola_offset = %d", int(sola_offset))
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infer_wav = infer_wav[sola_offset :]
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infer_wav = infer_wav[sola_offset:]
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if "privateuseone" in str(self.config.device) or not self.gui_config.use_pv:
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infer_wav[: self.sola_buffer_frame] *= self.fade_in_window
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infer_wav[: self.sola_buffer_frame] += self.sola_buffer * self.fade_out_window
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infer_wav[: self.sola_buffer_frame] += (
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self.sola_buffer * self.fade_out_window
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)
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else:
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infer_wav[: self.sola_buffer_frame] = phase_vocoder(
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self.sola_buffer,
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infer_wav[: self.sola_buffer_frame],
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self.fade_out_window,
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self.fade_in_window)
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self.sola_buffer[:] = infer_wav[self.block_frame : self.block_frame + self.sola_buffer_frame]
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if sys.platform == "darwin":
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outdata[:] = (
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infer_wav[: self.block_frame].cpu().numpy()[:, np.newaxis]
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self.sola_buffer,
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infer_wav[: self.sola_buffer_frame],
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self.fade_out_window,
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self.fade_in_window,
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)
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self.sola_buffer[:] = infer_wav[
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self.block_frame : self.block_frame + self.sola_buffer_frame
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]
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if sys.platform == "darwin":
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outdata[:] = infer_wav[: self.block_frame].cpu().numpy()[:, np.newaxis]
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else:
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outdata[:] = (
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infer_wav[: self.block_frame].repeat(2, 1).t().cpu().numpy()
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@ -930,7 +947,7 @@ if __name__ == "__main__":
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input_devices_indices,
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output_devices_indices,
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)
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def set_devices(self, input_device, output_device):
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"""设置输出设备"""
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(
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@ -947,8 +964,10 @@ if __name__ == "__main__":
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]
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printt("Input device: %s:%s", str(sd.default.device[0]), input_device)
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printt("Output device: %s:%s", str(sd.default.device[1]), output_device)
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def get_device_samplerate(self):
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return int(sd.query_devices(device=sd.default.device[0])['default_samplerate'])
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return int(
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sd.query_devices(device=sd.default.device[0])["default_samplerate"]
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)
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gui = GUI()
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@ -795,9 +795,9 @@ class SynthesizerTrnMs256NSFsid(nn.Module):
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assert isinstance(return_length, torch.Tensor)
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head = int(skip_head.item())
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length = int(return_length.item())
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z_p = z_p[:, :, head: head + length]
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x_mask = x_mask[:, :, head: head + length]
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nsff0 = nsff0[:, head: head + length]
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z_p = z_p[:, :, head : head + length]
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x_mask = x_mask[:, :, head : head + length]
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nsff0 = nsff0[:, head : head + length]
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z = self.flow(z_p, x_mask, g=g, reverse=True)
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o = self.dec(z * x_mask, nsff0, g=g)
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return o, x_mask, (z, z_p, m_p, logs_p)
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@ -957,9 +957,9 @@ class SynthesizerTrnMs768NSFsid(nn.Module):
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assert isinstance(return_length, torch.Tensor)
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head = int(skip_head.item())
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length = int(return_length.item())
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z_p = z_p[:, :, head: head + length]
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x_mask = x_mask[:, :, head: head + length]
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nsff0 = nsff0[:, head: head + length]
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z_p = z_p[:, :, head : head + length]
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x_mask = x_mask[:, :, head : head + length]
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nsff0 = nsff0[:, head : head + length]
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z = self.flow(z_p, x_mask, g=g, reverse=True)
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o = self.dec(z * x_mask, nsff0, g=g)
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return o, x_mask, (z, z_p, m_p, logs_p)
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@ -1108,8 +1108,8 @@ class SynthesizerTrnMs256NSFsid_nono(nn.Module):
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assert isinstance(return_length, torch.Tensor)
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head = int(skip_head.item())
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length = int(return_length.item())
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z_p = z_p[:, :, head: head + length]
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x_mask = x_mask[:, :, head: head + length]
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z_p = z_p[:, :, head : head + length]
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x_mask = x_mask[:, :, head : head + length]
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z = self.flow(z_p, x_mask, g=g, reverse=True)
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o = self.dec(z * x_mask, g=g)
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return o, x_mask, (z, z_p, m_p, logs_p)
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@ -1258,8 +1258,8 @@ class SynthesizerTrnMs768NSFsid_nono(nn.Module):
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assert isinstance(return_length, torch.Tensor)
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head = int(skip_head.item())
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length = int(return_length.item())
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z_p = z_p[:, :, head: head + length]
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x_mask = x_mask[:, :, head: head + length]
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z_p = z_p[:, :, head : head + length]
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x_mask = x_mask[:, :, head : head + length]
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z = self.flow(z_p, x_mask, g=g, reverse=True)
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o = self.dec(z * x_mask, g=g)
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return o, x_mask, (z, z_p, m_p, logs_p)
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@ -38,6 +38,7 @@ def spectral_de_normalize_torch(magnitudes):
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mel_basis = {}
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hann_window = {}
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def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
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"""Convert waveform into Linear-frequency Linear-amplitude spectrogram.
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@ -51,7 +52,7 @@ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False)
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Returns:
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:: (B, Freq, Frame) - Linear-frequency Linear-amplitude spectrogram
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"""
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# Window - Cache if needed
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global hann_window
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dtype_device = str(y.dtype) + "_" + str(y.device)
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@ -60,7 +61,7 @@ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False)
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hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(
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dtype=y.dtype, device=y.device
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)
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# Padding
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y = torch.nn.functional.pad(
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y.unsqueeze(1),
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@ -68,7 +69,7 @@ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False)
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mode="reflect",
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)
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y = y.squeeze(1)
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# Complex Spectrogram :: (B, T) -> (B, Freq, Frame, RealComplex=2)
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spec = torch.stft(
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y,
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@ -82,11 +83,12 @@ def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False)
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onesided=True,
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return_complex=True,
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)
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# Linear-frequency Linear-amplitude spectrogram :: (B, Freq, Frame, RealComplex=2) -> (B, Freq, Frame)
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spec = torch.sqrt(spec.real.pow(2) + spec.imag.pow(2) + 1e-6)
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return spec
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def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
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# MelBasis - Cache if needed
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global mel_basis
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@ -46,22 +46,23 @@ def printt(strr, *args):
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# config.is_half=False########强制cpu测试
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class RVC:
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def __init__(
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self,
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key,
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pth_path,
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index_path,
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index_rate,
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n_cpu,
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inp_q,
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opt_q,
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config: Config,
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last_rvc=None,
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self,
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key,
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pth_path,
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index_path,
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index_rate,
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n_cpu,
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inp_q,
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opt_q,
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config: Config,
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last_rvc=None,
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) -> None:
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"""
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初始化
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"""
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try:
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if config.dml == True:
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def forward_dml(ctx, x, scale):
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ctx.scale = scale
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res = x.clone().detach()
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@ -92,7 +93,7 @@ class RVC:
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self.index_rate = index_rate
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self.cache_pitch: np.ndarray = np.zeros(1024, dtype="int32")
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self.cache_pitchf = np.zeros(1024, dtype="float32")
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if last_rvc is None:
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models, _, _ = fairseq.checkpoint_utils.load_model_ensemble_and_task(
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["assets/hubert/hubert_base.pt"],
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@ -201,7 +202,7 @@ class RVC:
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f0bak = f0.copy()
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f0_mel = 1127 * np.log(1 + f0 / 700)
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f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - self.f0_mel_min) * 254 / (
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self.f0_mel_max - self.f0_mel_min
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self.f0_mel_max - self.f0_mel_min
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) + 1
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f0_mel[f0_mel <= 1] = 1
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f0_mel[f0_mel > 255] = 255
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@ -258,7 +259,7 @@ class RVC:
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self.inp_q.put((idx, x[:tail], res_f0, n_cpu, ts))
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else:
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self.inp_q.put(
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(idx, x[part_length * idx - 320: tail], res_f0, n_cpu, ts)
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(idx, x[part_length * idx - 320 : tail], res_f0, n_cpu, ts)
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)
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while 1:
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res_ts = self.opt_q.get()
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@ -273,7 +274,7 @@ class RVC:
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else:
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f0 = f0[2:]
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f0bak[
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part_length * idx // 160: part_length * idx // 160 + f0.shape[0]
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part_length * idx // 160 : part_length * idx // 160 + f0.shape[0]
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] = f0
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f0bak = signal.medfilt(f0bak, 3)
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f0bak *= pow(2, f0_up_key / 12)
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@ -320,6 +321,7 @@ class RVC:
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def get_f0_fcpe(self, x, f0_up_key):
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if hasattr(self, "model_fcpe") == False:
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from torchfcpe import spawn_bundled_infer_model
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printt("Loading fcpe model")
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if "privateuseone" in str(self.device):
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self.device_fcpe = "cpu"
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@ -329,7 +331,7 @@ class RVC:
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f0 = self.model_fcpe.infer(
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x.to(self.device_fcpe).unsqueeze(0).float(),
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sr=16000,
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decoder_mode='local_argmax',
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decoder_mode="local_argmax",
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threshold=0.006,
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)
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f0 *= pow(2, f0_up_key / 12)
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@ -337,12 +339,12 @@ class RVC:
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return self.get_f0_post(f0)
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def infer(
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self,
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input_wav: torch.Tensor,
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block_frame_16k,
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skip_head,
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return_length,
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f0method,
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self,
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input_wav: torch.Tensor,
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block_frame_16k,
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skip_head,
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return_length,
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f0method,
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) -> np.ndarray:
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t1 = ttime()
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with torch.no_grad():
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@ -364,16 +366,16 @@ class RVC:
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t2 = ttime()
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try:
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if hasattr(self, "index") and self.index_rate != 0:
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npy = feats[0][skip_head // 2:].cpu().numpy().astype("float32")
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npy = feats[0][skip_head // 2 :].cpu().numpy().astype("float32")
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score, ix = self.index.search(npy, k=8)
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weight = np.square(1 / score)
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weight /= weight.sum(axis=1, keepdims=True)
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npy = np.sum(self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1)
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if self.config.is_half:
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npy = npy.astype("float16")
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feats[0][skip_head // 2:] = (
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torch.from_numpy(npy).unsqueeze(0).to(self.device) * self.index_rate
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+ (1 - self.index_rate) * feats[0][skip_head // 2:]
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feats[0][skip_head // 2 :] = (
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torch.from_numpy(npy).unsqueeze(0).to(self.device) * self.index_rate
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+ (1 - self.index_rate) * feats[0][skip_head // 2 :]
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)
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else:
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printt("Index search FAILED or disabled")
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@ -384,21 +386,29 @@ class RVC:
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if self.if_f0 == 1:
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f0_extractor_frame = block_frame_16k + 800
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if f0method == "rmvpe":
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f0_extractor_frame = (
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5120 * ((f0_extractor_frame - 1) // 5120 + 1) - 160
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)
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pitch, pitchf = self.get_f0(input_wav[-f0_extractor_frame: ], self.f0_up_key, self.n_cpu, f0method)
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f0_extractor_frame = 5120 * ((f0_extractor_frame - 1) // 5120 + 1) - 160
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pitch, pitchf = self.get_f0(
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input_wav[-f0_extractor_frame:], self.f0_up_key, self.n_cpu, f0method
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)
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start_frame = block_frame_16k // 160
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end_frame = len(self.cache_pitch) - (pitch.shape[0] - 4) + start_frame
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self.cache_pitch[:] = np.append(self.cache_pitch[start_frame: end_frame], pitch[3:-1])
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self.cache_pitch[:] = np.append(
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self.cache_pitch[start_frame:end_frame], pitch[3:-1]
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)
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self.cache_pitchf[:] = np.append(
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self.cache_pitchf[start_frame: end_frame], pitchf[3:-1]
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self.cache_pitchf[start_frame:end_frame], pitchf[3:-1]
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)
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t4 = ttime()
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p_len = input_wav.shape[0] // 160
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if self.if_f0 == 1:
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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)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user