Introduce LRU cache for RVC
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ekoc committed
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@@ -75,7 +75,7 @@ from sklearn.metrics import (
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)
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class VC(object):
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def __init__(self, tgt_sr, config, data=None, d=None, train_data=None, debug_rvc=False):
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def __init__(self, tgt_sr, config, version, if_f0, net_g, data=None, d=None, train_data=None, debug_rvc=False):
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branding = "AllTalk "
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self.x_pad, self.x_query, self.x_center, self.x_max, self.is_half = (
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config.x_pad,
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@@ -85,6 +85,10 @@ class VC(object):
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config.is_half,
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)
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self.sr = 16000
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self.tgt_sr = tgt_sr
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self.__version = version
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self.__if_f0 = if_f0
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self.__net_g = net_g
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self.window = 160
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self.t_pad = self.sr * self.x_pad
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self.t_pad_tgt = tgt_sr * self.x_pad
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@@ -417,7 +421,6 @@ class VC(object):
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def vc(
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self,
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model,
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net_g,
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sid,
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audio0,
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pitch,
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@@ -425,7 +428,6 @@ class VC(object):
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index,
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big_npy,
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index_rate,
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version,
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protect,
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):
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#print("ENTERING pipeline vc")
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@@ -453,7 +455,7 @@ class VC(object):
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inputs = {
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"source": feats.to(self.device),
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"padding_mask": padding_mask,
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"output_layer": 9 if version == "v1" else 12,
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"output_layer": 9 if self.__version == "v1" else 12,
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}
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#print(f"inputs source shape: {inputs['source'].shape}")
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#print(f"inputs source device: {inputs['source'].device}")
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@@ -465,7 +467,7 @@ class VC(object):
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#print(f"logits type: {type(logits)}")
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#print(f"logits length: {len(logits)}")
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#print(f"logits[0] shape: {logits[0].shape}")
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feats = model.final_proj(logits[0]) if version == "v1" else logits[0]
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feats = model.final_proj(logits[0]) if self.__version == "v1" else logits[0]
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#print(f"feats shape after final_proj or logits[0]: {feats.shape}")
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#print("Pipeline vc STEP 1a:")
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if protect < 0.5 and pitch is not None and pitchf is not None:
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@@ -549,14 +551,14 @@ class VC(object):
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with torch.no_grad():
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if pitch is not None and pitchf is not None:
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audio1 = (
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(net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0])
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(self.__net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0])
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.data.cpu()
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.float()
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.numpy()
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)
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else:
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audio1 = (
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(net_g.infer(feats, p_len, sid)[0][0, 0]).data.cpu().float().numpy()
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(self.__net_g.infer(feats, p_len, sid)[0][0, 0]).data.cpu().float().numpy()
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)
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#print(f"audio1 shape: {audio1.shape}")
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del feats, p_len, padding_mask
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@@ -573,7 +575,6 @@ class VC(object):
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def pipeline(
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self,
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model,
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net_g,
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sid,
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audio,
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input_audio_path,
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@@ -581,12 +582,10 @@ class VC(object):
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f0_method,
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file_index,
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index_rate,
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if_f0,
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filter_radius,
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tgt_sr,
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resample_sr,
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rms_mix_rate,
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version,
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protect,
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hop_length,
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f0autotune,
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@@ -636,7 +635,7 @@ class VC(object):
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print(error)
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sid = torch.tensor(sid, device=self.device).unsqueeze(0).long()
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pitch, pitchf = None, None
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if if_f0 == 1:
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if self.__if_f0 == 1:
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pitch, pitchf = self.get_f0(
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input_audio_path,
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audio_pad,
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@@ -657,11 +656,10 @@ class VC(object):
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t2 = ttime()
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for t in opt_ts:
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t = t // self.window * self.window
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if if_f0 == 1:
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if self.__if_f0 == 1:
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audio_opt.append(
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self.vc(
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model,
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net_g,
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sid,
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audio_pad[s : t + self.t_pad2 + self.window],
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pitch[:, s // self.window : (t + self.t_pad2) // self.window],
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@@ -669,7 +667,6 @@ class VC(object):
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index,
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big_npy,
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index_rate,
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version,
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protect,
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)[self.t_pad_tgt : -self.t_pad_tgt]
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)
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@@ -677,7 +674,6 @@ class VC(object):
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audio_opt.append(
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self.vc(
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model,
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net_g,
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sid,
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audio_pad[s : t + self.t_pad2 + self.window],
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None,
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@@ -685,16 +681,14 @@ class VC(object):
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index,
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big_npy,
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index_rate,
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version,
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protect,
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)[self.t_pad_tgt : -self.t_pad_tgt]
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)
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s = t
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if if_f0 == 1:
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if self.__if_f0 == 1:
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audio_opt.append(
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self.vc(
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model,
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net_g,
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sid,
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audio_pad[t:],
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pitch[:, t // self.window :] if t is not None else pitch,
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@@ -702,7 +696,6 @@ class VC(object):
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index,
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big_npy,
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index_rate,
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version,
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protect,
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)[self.t_pad_tgt : -self.t_pad_tgt]
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)
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@@ -710,7 +703,6 @@ class VC(object):
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audio_opt.append(
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self.vc(
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model,
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net_g,
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sid,
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audio_pad[t:],
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None,
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@@ -718,7 +710,6 @@ class VC(object):
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index,
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big_npy,
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index_rate,
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version,
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protect,
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)[self.t_pad_tgt : -self.t_pad_tgt]
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)
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