WIP: support rvc-webui
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wataru
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2fa33aad8d
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@ -32,7 +32,7 @@ import pyworld as pw
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from voice_changer.RVC.custom_vc_infer_pipeline import VC
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from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono
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from .models import SynthesizerTrnMsNSFsid as SynthesizerTrnMs768NSFsid
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from .const import RVC_MODEL_TYPE_NORMAL, RVC_MODEL_TYPE_PITCH_LESS, RVC_MODEL_TYPE_NORMAL_768, RVC_MODEL_TYPE_UNKNOWN
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from .const import RVC_MODEL_TYPE_NORMAL, RVC_MODEL_TYPE_PITCH_LESS, RVC_MODEL_TYPE_WEBUI_256_NORMAL, RVC_MODEL_TYPE_WEBUI_768_NORMAL, RVC_MODEL_TYPE_UNKNOWN
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from fairseq import checkpoint_utils
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providers = ['OpenVINOExecutionProvider', "CUDAExecutionProvider", "DmlExecutionProvider", "CPUExecutionProvider"]
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@ -155,34 +155,49 @@ class RVC:
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if pyTorchModelFile != None and pyTorchModelFile != "":
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cpt = torch.load(pyTorchModelFile, map_location="cpu")
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'''
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※ ノーマル or Pitchレス判定 ⇒ コンフィグのpsamplingrateの形状から判断
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■ ノーマル
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[1025, 32, 192, 192, 768, 2, 6, 3, 0, '1', [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 6, 2, 2, 2], 512, [16, 16, 4, 4, 4], 109, 256, 48000]
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■ピッチレス
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[1025, 32, 192, 192, 768, 2, 6, 3, 0, '1', [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 10, 2, 2], 512, [16, 16, 4, 4],109, 256, 40000]
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12番目の要素upsamplingrateの数で判定。4: ピッチレス, 5:ノーマル
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※ 256 or 768判定 ⇒ config全体の形状
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(1) オリジナルとrvc-webuiのモデル判定 ⇒ config全体の形状
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■ ノーマル256
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[1025, 32, 192, 192, 768, 2, 6, 3, 0, '1', [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 6, 2, 2, 2], 512, [16, 16, 4, 4, 4], 109, 256, 48000]
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■ ノーマル 768対応
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[1025, 32, 192, 192, 768, 2, 6, 3, 0, '1', [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 6, 2, 2, 2], 512, [16, 16, 4, 4, 4], 109, 256, 768, 48000]
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config全体の長さで判定 ⇒ config全体の形状
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⇒ 18: オリジナル, 19: rvc-webui
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(2-1) オリジナルのノーマルorPitchレス判定 ⇒ コンフィグのpsamplingrateの形状から判断
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■ ノーマル
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[1025, 32, 192, 192, 768, 2, 6, 3, 0, '1', [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 6, 2, 2, 2], 512, [16, 16, 4, 4, 4], 109, 256, 48000]
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■ピッチレス
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[1025, 32, 192, 192, 768, 2, 6, 3, 0, '1', [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 10, 2, 2], 512, [16, 16, 4, 4],109, 256, 40000]
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12番目の要素upsamplingrateの数で判定。4: ピッチレス, 5:ノーマル
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(2-2) rvc-webuiの、(256 or 768) x (ノーマルor pitchレス)判定 ⇒ 256, or 768 は17番目の要素で判定。
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■ 256 x ノーマル
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[1025, 32, 192, 192, 768, 2, 6, 3, 0, '1', [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 6, 2, 2, 2], 512, [16, 16, 4, 4, 4], 109, 256, 256, 48000]
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■ 256 x pitchレス
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[1025, 32, 192, 192, 768, 2, 6, 3, 0, '1', [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 6, 2, 2, 2], 512, [16, 16, 4, 4, 4], 109, 256, 256, 48000]
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■ 768 x ノーマル
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[1025, 32, 192, 192, 768, 2, 6, 3, 0, '1', [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 6, 2, 2, 2], 512, [16, 16, 4, 4, 4], 109, 256, 768, 48000]
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■ 768 x pitchレス
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[1025, 32, 192, 192, 768, 2, 6, 3, 0, '1', [3, 7, 11], [[1, 3, 5], [1, 3, 5], [1, 3, 5]], [10, 6, 2, 2, 2], 512, [16, 16, 4, 4, 4], 109, 256, 768, 48000]
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'''
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print("config shape:::::", cpt["config"])
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config_len = len(cpt["config"])
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upsamplingRateDims = len(cpt["config"][12])
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if config_len == 18 and upsamplingRateDims == 4:
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print("[Voice Changer] RVC Model Type: Pitch-Less")
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print("[Voice Changer] RVC Model Type: RVC_MODEL_TYPE_PITCH_LESS")
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self.settings.modelSlots[slot].modelType = RVC_MODEL_TYPE_PITCH_LESS
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elif config_len == 18 and upsamplingRateDims == 5:
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print("[Voice Changer] RVC Model Type: Normal")
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print("[Voice Changer] RVC Model Type: RVC_MODEL_TYPE_NORMAL")
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self.settings.modelSlots[slot].modelType = RVC_MODEL_TYPE_NORMAL
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elif config_len == 19:
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print("[Voice Changer] RVC Model Type: Normal_768")
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self.settings.modelSlots[slot].modelType = RVC_MODEL_TYPE_NORMAL_768
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embedding = cpt["config"][17]
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if embedding == 256:
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print("[Voice Changer] RVC Model Type: RVC_MODEL_TYPE_WEBUI_256_NORMAL")
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self.settings.modelSlots[slot].modelType = RVC_MODEL_TYPE_WEBUI_256_NORMAL
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else:
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print("[Voice Changer] RVC Model Type: RVC_MODEL_TYPE_WEBUI_768_NORMAL")
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self.settings.modelSlots[slot].modelType = RVC_MODEL_TYPE_WEBUI_768_NORMAL
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else:
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print("[Voice Changer] RVC Model Type: UNKNOWN")
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self.settings.modelSlots[slot].modelType = RVC_MODEL_TYPE_UNKNOWN
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@ -193,7 +208,7 @@ class RVC:
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net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=self.is_half)
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elif self.settings.modelSlots[slot].modelType == RVC_MODEL_TYPE_PITCH_LESS:
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net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"])
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elif self.settings.modelSlots[slot].modelType == RVC_MODEL_TYPE_NORMAL_768:
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elif self.settings.modelSlots[slot].modelType == RVC_MODEL_TYPE_WEBUI_256_NORMAL or self.settings.modelSlots[slot].modelType == RVC_MODEL_TYPE_WEBUI_768_NORMAL:
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net_g = SynthesizerTrnMs768NSFsid(**cpt["params"], is_half=self.is_half)
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else:
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print("unknwon")
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@ -1,4 +1,7 @@
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RVC_MODEL_TYPE_NORMAL = 0
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RVC_MODEL_TYPE_PITCH_LESS = 1
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RVC_MODEL_TYPE_NORMAL_768 = 2
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RVC_MODEL_TYPE_WEBUI_256_NORMAL = 2
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RVC_MODEL_TYPE_WEBUI_256_PITCHLESS = 3
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RVC_MODEL_TYPE_WEBUI_768_NORMAL = 4
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RVC_MODEL_TYPE_WEBUI_768_PITCHLESS = 5
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RVC_MODEL_TYPE_UNKNOWN = 99
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@ -10,7 +10,7 @@ import pyworld
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import os
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import traceback
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import faiss
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from .const import RVC_MODEL_TYPE_NORMAL, RVC_MODEL_TYPE_PITCH_LESS, RVC_MODEL_TYPE_NORMAL_768
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from .const import RVC_MODEL_TYPE_NORMAL, RVC_MODEL_TYPE_PITCH_LESS, RVC_MODEL_TYPE_WEBUI_256_NORMAL, RVC_MODEL_TYPE_WEBUI_768_NORMAL
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class VC(object):
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@ -94,7 +94,7 @@ class VC(object):
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assert feats.dim() == 1, feats.dim()
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feats = feats.view(1, -1)
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padding_mask = torch.BoolTensor(feats.shape).to(self.device).fill_(False)
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if modelType == RVC_MODEL_TYPE_NORMAL or modelType == RVC_MODEL_TYPE_PITCH_LESS:
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if modelType == RVC_MODEL_TYPE_NORMAL or modelType == RVC_MODEL_TYPE_PITCH_LESS or modelType == RVC_MODEL_TYPE_WEBUI_256_NORMAL:
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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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@ -109,7 +109,7 @@ class VC(object):
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t0 = ttime()
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with torch.no_grad():
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logits = model.extract_features(**inputs)
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if modelType == RVC_MODEL_TYPE_NORMAL or modelType == RVC_MODEL_TYPE_PITCH_LESS:
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if modelType == RVC_MODEL_TYPE_NORMAL or modelType == RVC_MODEL_TYPE_PITCH_LESS or modelType == RVC_MODEL_TYPE_WEBUI_256_NORMAL:
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feats = model.final_proj(logits[0])
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else:
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feats = logits[0]
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@ -136,7 +136,7 @@ class VC(object):
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p_len = torch.tensor([p_len], device=self.device).long()
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with torch.no_grad():
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if modelType == RVC_MODEL_TYPE_NORMAL or modelType == RVC_MODEL_TYPE_NORMAL_768:
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if modelType == RVC_MODEL_TYPE_NORMAL or modelType == RVC_MODEL_TYPE_WEBUI_256_NORMAL or modelType == RVC_MODEL_TYPE_WEBUI_768_NORMAL:
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audio1 = (net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768).data.cpu().float().numpy().astype(np.int16)
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else:
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audio1 = (net_g.infer(feats, p_len, sid)[0][0, 0] * 32768).data.cpu().float().numpy().astype(np.int16)
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