Mirrors/Retrieval-based-Voice-Conversion-WebUI
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optimize real-time vc

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yxlllc
2023-12-26 16:26:01 +08:00
parent d62e80fb83
commit d7fb651f7c
3 changed files with 58 additions and 57 deletions
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55
tools/rvc_for_realtime.py
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@@ -90,7 +90,9 @@ class RVC:
self.pth_path: str = pth_path
self.index_path = index_path
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"],
@@ -329,8 +331,9 @@ class RVC:
sr=16000,
decoder_mode='local_argmax',
threshold=0.006,
).squeeze().cpu().numpy()
)
f0 *= pow(2, f0_up_key / 12)
f0 = f0.squeeze().cpu().numpy()
return self.get_f0_post(f0)
def infer(
@@ -338,8 +341,7 @@ class RVC:
input_wav: torch.Tensor,
block_frame_16k,
skip_head,
cache_pitch,
cache_pitchf,
return_length,
f0method,
) -> np.ndarray:
t1 = ttime()
@@ -362,24 +364,22 @@ class RVC:
t2 = ttime()
try:
if hasattr(self, "index") and self.index_rate != 0:
leng_replace_head = int(rate * feats[0].shape[0])
npy = feats[0][-leng_replace_head:].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][-leng_replace_head:] = (
feats[0][skip_head // 2:] = (
torch.from_numpy(npy).unsqueeze(0).to(self.device) * self.index_rate
+ (1 - self.index_rate) * feats[0][-leng_replace_head:]
+ (1 - self.index_rate) * feats[0][skip_head // 2:]
)
else:
printt("Index search FAILED or disabled")
except:
traceback.print_exc()
printt("Index search FAILED")
feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
t3 = ttime()
if self.if_f0 == 1:
f0_extractor_frame = block_frame_16k + 800
@@ -387,40 +387,39 @@ class RVC:
f0_extractor_frame = (
5120 * ((f0_extractor_frame - 1) // 5120 + 1) - 160
)
input_wav = input_wav[-f0_extractor_frame:]
pitch, pitchf = self.get_f0(input_wav, 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(cache_pitch) - (pitch.shape[0] - 4) + start_frame
cache_pitch[:] = np.append(cache_pitch[start_frame:end_frame], pitch[3:-1])
cache_pitchf[:] = np.append(
cache_pitchf[start_frame:end_frame], pitchf[3:-1]
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_pitchf[:] = np.append(
self.cache_pitchf[start_frame: end_frame], pitchf[3:-1]
)
p_len = min(feats.shape[1], 13000, cache_pitch.shape[0])
else:
cache_pitch, cache_pitchf = None, None
p_len = min(feats.shape[1], 13000)
t4 = ttime()
feats = feats[:, :p_len, :]
p_len = input_wav.shape[0] // 160
if self.if_f0 == 1:
cache_pitch = torch.LongTensor(cache_pitch[:p_len]).to(self.device).unsqueeze(0)
cache_pitchf = torch.FloatTensor(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)
sid = torch.LongTensor([0]).to(self.device)
skip_head = torch.LongTensor([skip_head])
return_length = torch.LongTensor([return_length])
with torch.no_grad():
if self.if_f0 == 1:
infered_audio = self.net_g.infer(
infered_audio, _, _ = self.net_g.infer(
feats,
p_len,
cache_pitch,
cache_pitchf,
sid,
skip_head,
)[0][0, 0].data.float()
return_length,
)
else:
infered_audio = self.net_g.infer(
feats, p_len, sid, skip_head
)[0][0, 0].data.float()
infered_audio, _, _ = self.net_g.infer(
feats, p_len, sid, skip_head, return_length
)
t5 = ttime()
printt(
"Spent time: fea = %.3fs, index = %.3fs, f0 = %.3fs, model = %.3fs",
@@ -429,4 +428,4 @@ class RVC:
t4 - t3,
t5 - t4,
)
return infered_audio
return infered_audio.squeeze().float()