Mirrors/Retrieval-based-Voice-Conversion-WebUI
1
0
Fork 0
mirror of https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git synced 2025-12-24 07:29:36 +01:00
Code Issues Packages Projects Releases Wiki Activity

Format code (#1169)

Browse Source 
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
This commit is contained in:
github-actions[bot]
2023-09-02 16:14:32 +08:00
committed by GitHub
parent 0fc160c03e
commit c7bbf0bfd0
8 changed files with 136 additions and 69 deletions
Download Patch File Download Diff File Expand all files Collapse all files

33
gui_v1.py
View File

@@ -455,14 +455,17 @@ if __name__ == "__main__":
inp_q,
opt_q,
device,
self.rvc if hasattr(self, "rvc") else None
self.rvc if hasattr(self, "rvc") else None,
)
self.config.samplerate = self.rvc.tgt_sr
self.config.crossfade_time = min(
self.config.crossfade_time, self.config.block_time
)
self.zc = self.rvc.tgt_sr // 100
self.block_frame = int(np.round(self.config.block_time * self.config.samplerate / self.zc)) * self.zc
self.block_frame = (
int(np.round(self.config.block_time * self.config.samplerate / self.zc))
* self.zc
)
self.block_frame_16k = 160 * self.block_frame // self.zc
self.crossfade_frame = int(
self.config.crossfade_time * self.config.samplerate
@@ -484,7 +487,9 @@ if __name__ == "__main__":
),
dtype="float32",
)
self.input_wav_res: torch.Tensor= torch.zeros(160 * len(self.input_wav) // self.zc)
self.input_wav_res: torch.Tensor = torch.zeros(
160 * len(self.input_wav) // self.zc
)
self.output_wav_cache: torch.Tensor = torch.zeros(
int(
np.ceil(
@@ -577,12 +582,18 @@ if __name__ == "__main__":
if db_threhold[i]:
indata[i * hop_length : (i + 1) * hop_length] = 0
self.input_wav[: -self.block_frame] = self.input_wav[self.block_frame :]
self.input_wav[-self.block_frame: ] = indata
self.input_wav[-self.block_frame :] = indata
# infer
inp = torch.from_numpy(self.input_wav[-self.block_frame-2*self.zc :]).to(device)
self.input_wav_res[ : -self.block_frame_16k] = self.input_wav_res[self.block_frame_16k :].clone()
self.input_wav_res[-self.block_frame_16k-160 :] = self.resampler(inp)[160 :]
inp = torch.from_numpy(
self.input_wav[-self.block_frame - 2 * self.zc :]
).to(device)
self.input_wav_res[: -self.block_frame_16k] = self.input_wav_res[
self.block_frame_16k :
].clone()
self.input_wav_res[-self.block_frame_16k - 160 :] = self.resampler(inp)[
160:
]
rate = (
self.crossfade_frame + self.sola_search_frame + self.block_frame
) / (
@@ -592,11 +603,11 @@ if __name__ == "__main__":
+ self.block_frame
)
f0_extractor_frame = self.block_frame_16k + 800
if self.config.f0method == 'rmvpe':
if self.config.f0method == "rmvpe":
f0_extractor_frame = 5120 * ((f0_extractor_frame - 1) // 5120 + 1)
res2 = self.rvc.infer(
self.input_wav_res,
self.input_wav_res[-f0_extractor_frame :].cpu().numpy(),
self.input_wav_res[-f0_extractor_frame:].cpu().numpy(),
self.block_frame_16k,
rate,
self.pitch,
@@ -720,9 +731,7 @@ if __name__ == "__main__":
sd.default.device[1] = output_device_indices[
output_devices.index(output_device)
]
logger.info(
"Input device: %s:%d", str(sd.default.device[0]), input_device
)
logger.info("Input device: %s:%d", str(sd.default.device[0]), input_device)
logger.info(
"Output device: %s:%d", str(sd.default.device[1]), output_device
)