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synced 2025-01-23 13:35:12 +03:00
WIP: configure diffusion svc
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w-okada
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3ffacaed97
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01291dc4e3
@ -112,7 +112,9 @@ class DiffusionSVCModelSlot(ModelSlot):
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sampleId: str = ""
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defaultTune: int = 0
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kstep: int = 100
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defaultKstep: int = 20
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defaultSpeedup: int = 10
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kStepMax: int = 100
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speakers: dict = field(default_factory=lambda: {1: "user"})
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embedder: EmbedderType = "hubert_base"
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samplingRate: int = 44100
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@ -42,7 +42,7 @@ class DiffusionSVC(VoiceChangerModel):
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# その他の設定
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self.settings.tran = self.slotInfo.defaultTune
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self.settings.dstId = self.slotInfo.dstId
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self.settings.kstep = self.slotInfo.kstep
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self.settings.kstep = self.slotInfo.defaultKstep
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print("[Voice Changer] [DiffusionSVC] Initializing... done")
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@ -190,18 +190,18 @@ class DiffusionSVC(VoiceChangerModel):
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# "filename": output_file_simple,
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# }
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# def get_model_current(self):
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# return [
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# {
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# "key": "defaultTune",
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# "val": self.settings.tran,
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# },
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# {
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# "key": "defaultIndexRatio",
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# "val": self.settings.indexRatio,
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# },
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# {
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# "key": "defaultProtect",
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# "val": self.settings.protect,
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# },
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# ]
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def get_model_current(self):
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return [
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{
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"key": "defaultTune",
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"val": self.settings.tran,
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},
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{
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"key": "defaultKstep",
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"val": self.settings.kstep,
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},
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{
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"key": "defaultSpeedup",
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"val": self.settings.speedup,
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},
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]
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@ -1,11 +1,11 @@
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import os
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from const import EnumInferenceTypes
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from dataclasses import asdict
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import torch
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import onnxruntime
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import json
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from data.ModelSlot import DiffusionSVCModelSlot, ModelSlot, RVCModelSlot
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from voice_changer.DiffusionSVC.inferencer.diffusion_svc_model.diffusion.unit2mel import load_model_vocoder_from_combo
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from voice_changer.utils.LoadModelParams import LoadModelParams
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from voice_changer.utils.ModelSlotGenerator import ModelSlotGenerator
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@ -23,90 +23,16 @@ class DiffusionSVCModelSlotGenerator(ModelSlotGenerator):
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slotInfo.iconFile = "/assets/icons/noimage.png"
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slotInfo.embChannels = 768
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# if slotInfo.isONNX:
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# slotInfo = cls._setInfoByONNX(slotInfo)
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# else:
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# slotInfo = cls._setInfoByPytorch(slotInfo)
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if slotInfo.isONNX:
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slotInfo = cls._setInfoByONNX(slotInfo)
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else:
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slotInfo = cls._setInfoByPytorch(slotInfo)
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return slotInfo
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@classmethod
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def _setInfoByPytorch(cls, slot: ModelSlot):
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cpt = torch.load(slot.modelFile, map_location="cpu")
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config_len = len(cpt["config"])
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version = cpt.get("version", "v1")
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slot = RVCModelSlot(**asdict(slot))
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if version == "voras_beta":
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slot.f0 = True if cpt["f0"] == 1 else False
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slot.modelType = EnumInferenceTypes.pyTorchVoRASbeta.value
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slot.embChannels = 768
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slot.embOutputLayer = cpt["embedder_output_layer"] if "embedder_output_layer" in cpt else 9
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slot.useFinalProj = False
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slot.embedder = cpt["embedder_name"]
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if slot.embedder.endswith("768"):
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slot.embedder = slot.embedder[:-3]
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# if slot.embedder == "hubert":
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# slot.embedder = "hubert"
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# elif slot.embedder == "contentvec":
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# slot.embedder = "contentvec"
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# elif slot.embedder == "hubert_jp":
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# slot.embedder = "hubert_jp"
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else:
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raise RuntimeError("[Voice Changer][setInfoByONNX] unknown embedder")
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elif config_len == 18:
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# Original RVC
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slot.f0 = True if cpt["f0"] == 1 else False
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version = cpt.get("version", "v1")
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if version is None or version == "v1":
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slot.modelType = EnumInferenceTypes.pyTorchRVC.value if slot.f0 else EnumInferenceTypes.pyTorchRVCNono.value
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slot.embChannels = 256
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slot.embOutputLayer = 9
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slot.useFinalProj = True
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slot.embedder = "hubert_base"
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print("[Voice Changer] Official Model(pyTorch) : v1")
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else:
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slot.modelType = EnumInferenceTypes.pyTorchRVCv2.value if slot.f0 else EnumInferenceTypes.pyTorchRVCv2Nono.value
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slot.embChannels = 768
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slot.embOutputLayer = 12
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slot.useFinalProj = False
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slot.embedder = "hubert_base"
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print("[Voice Changer] Official Model(pyTorch) : v2")
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else:
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# DDPN RVC
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slot.f0 = True if cpt["f0"] == 1 else False
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slot.modelType = EnumInferenceTypes.pyTorchWebUI.value if slot.f0 else EnumInferenceTypes.pyTorchWebUINono.value
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slot.embChannels = cpt["config"][17]
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slot.embOutputLayer = cpt["embedder_output_layer"] if "embedder_output_layer" in cpt else 9
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if slot.embChannels == 256:
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slot.useFinalProj = True
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else:
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slot.useFinalProj = False
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# DDPNモデルの情報を表示
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if slot.embChannels == 256 and slot.embOutputLayer == 9 and slot.useFinalProj is True:
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print("[Voice Changer] DDPN Model(pyTorch) : Official v1 like")
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elif slot.embChannels == 768 and slot.embOutputLayer == 12 and slot.useFinalProj is False:
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print("[Voice Changer] DDPN Model(pyTorch): Official v2 like")
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else:
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print(f"[Voice Changer] DDPN Model(pyTorch): ch:{slot.embChannels}, L:{slot.embOutputLayer}, FP:{slot.useFinalProj}")
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slot.embedder = cpt["embedder_name"]
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if slot.embedder.endswith("768"):
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slot.embedder = slot.embedder[:-3]
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if "speaker_info" in cpt.keys():
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for k, v in cpt["speaker_info"].items():
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slot.speakers[int(k)] = str(v)
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slot.samplingRate = cpt["config"][-1]
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del cpt
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def _setInfoByPytorch(cls, slot: DiffusionSVCModelSlot):
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diff_model, diff_args, naive_model, naive_args, vocoder = load_model_vocoder_from_combo(slot.modelFile, device="cpu")
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slot.kStepMax = diff_args.model.k_step_max
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return slot
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@classmethod
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@ -11,7 +11,8 @@ class DiffusionSVCSettings:
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silentThreshold: float = 0.00001
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extraConvertSize: int = 1024 * 4
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kstep: int = 100
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kstep: int = 20
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speedup: int = 10
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silenceFront: int = 1 # 0:off, 1:on
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modelSamplingRate: int = 44100
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@ -39,9 +39,13 @@ class PitchExtractorManager(Protocol):
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elif pitchExtractorType == "crepe_full":
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return CrepeOnnxPitchExtractor(pitchExtractorType, cls.params.crepe_onnx_full, gpu)
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elif pitchExtractorType == "rmvpe":
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print("pitchExtractorType", pitchExtractorType)
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return RMVPEPitchExtractor(cls.params.rmvpe, gpu)
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else:
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# return hubert as default
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raise RuntimeError(
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"[Voice Changer] PitchExctractor not found", pitchExtractorType
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)
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print("[Voice Changer] PitchExctractor not found", pitchExtractorType)
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print(" fallback to dio")
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return DioPitchExtractor()
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# raise RuntimeError(
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# "[Voice Changer] PitchExctractor not found", pitchExtractorType
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# )
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@ -39,6 +39,6 @@ class PitchExtractorManager(Protocol):
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return CrepeOnnxPitchExtractor(pitchExtractorType, cls.params.crepe_onnx_full, gpu)
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else:
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# return hubert as default
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raise RuntimeError(
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"[Voice Changer] PitchExctractor not found", pitchExtractorType
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)
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print("[Voice Changer] PitchExctractor not found", pitchExtractorType)
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print(" fallback to dio")
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return DioPitchExtractor()
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