mirror of
https://github.com/RVC-Project/Retrieval-based-Voice-Conversion-WebUI.git
synced 2026-01-20 11:00:23 +00:00
chore(format): run black on dev (#1638)
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
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commit
5449f84f06
@@ -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)
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cache_pitchf = torch.FloatTensor(self.cache_pitchf[-p_len: ]).to(self.device).unsqueeze(0)
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cache_pitch = (
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torch.LongTensor(self.cache_pitch[-p_len:]).to(self.device).unsqueeze(0)
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)
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cache_pitchf = (
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torch.FloatTensor(self.cache_pitchf[-p_len:])
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.to(self.device)
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.unsqueeze(0)
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)
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feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1)
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feats = feats[:, :p_len, :]
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p_len = torch.LongTensor([p_len]).to(self.device)
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