mirror of
https://github.com/w-okada/voice-changer.git
synced 2025-01-23 21:45:00 +03:00
update
This commit is contained in:
wataru
parent
48846aad7f
commit
7f1cdb9ddc
@ -63,7 +63,7 @@ class MMVC_Rest_Fileuploader:
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def post_update_settings(
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self, key: str = Form(...), val: Union[int, str, float] = Form(...)
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):
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print("post_update_settings", key, val)
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print("[Voice Changer] update configuration:", key, val)
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info = self.voiceChangerManager.update_settings(key, val)
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json_compatible_item_data = jsonable_encoder(info)
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return JSONResponse(content=json_compatible_item_data)
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@ -70,7 +70,7 @@ def _setInfoByONNX(slot: ModelSlot, file: str):
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try:
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metadata = json.loads(modelmeta.custom_metadata_map["metadata"])
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slot.modelType = metadata["modelType"]
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# slot.modelType = metadata["modelType"]
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slot.embChannels = metadata["embChannels"]
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if "embedder" not in metadata:
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@ -1,5 +1,6 @@
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import sys
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import os
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from Exceptions import NoModeLoadedException
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from voice_changer.RVC.ModelSlot import ModelSlot
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from voice_changer.RVC.deviceManager.DeviceManager import DeviceManager
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@ -42,7 +43,7 @@ import torch
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import traceback
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import faiss
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from const import TMP_DIR, UPLOAD_DIR, EnumEmbedderTypes
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from const import UPLOAD_DIR, EnumEmbedderTypes
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from voice_changer.RVC.custom_vc_infer_pipeline import VC
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@ -89,9 +90,6 @@ class RVC:
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f"[Voice Changer] RVC new model is uploaded,{target_slot_idx}",
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asdict(modelSlot),
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)
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"""
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[Voice Changer] RVC new model is uploaded,0 {'pyTorchModelFile': 'upload_dir/0/kurage.pth', 'onnxModelFile': None, 'featureFile': None, 'indexFile': None, 'defaultTrans': 16, 'isONNX': False, 'modelType': <EnumInferenceTypes.pyTorchWebUI: 'pyTorchWebUI'>, 'samplingRate': 48000, 'f0': True, 'embChannels': 768, 'deprecated': False, 'embedder': 'hubert-base-japanese'}
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"""
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# 初回のみロード
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if self.initialLoad:
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@ -139,7 +137,6 @@ class RVC:
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# Embedder 生成
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try:
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print("AFASFDAFDAFDASDFASDFSADFASDFA", half, self.settings.gpu)
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embedder = EmbedderManager.getEmbedder(
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modelSlot.embedder,
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emmbedderFilename,
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@ -179,8 +176,14 @@ class RVC:
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def prepareModel(self, slot: int):
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if slot < 0:
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return self.get_info()
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print("[Voice Changer] Prepare Model of slot:", slot)
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modelSlot = self.settings.modelSlots[slot]
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inferencerFilename = (
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modelSlot.onnxModelFile if modelSlot.isONNX else modelSlot.pyTorchModelFile
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)
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if inferencerFilename == "":
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return self.get_info()
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print("[Voice Changer] Prepare Model of slot:", slot)
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# Inferencer, embedderのロード
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inferencer, embedder = self.createPipeline(modelSlot)
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@ -240,13 +243,13 @@ class RVC:
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and self.embedder.isHalf == half
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):
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print(
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"NOT NEED CHAGE TO NEW PIPELINE!!!!!!!!!!!!!!!!!!!!!!!!!!!",
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"USE EXSISTING PIPELINE",
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half,
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)
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self.embedder.setDevice(dev)
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self.inferencer.setDevice(dev)
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else:
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print("CHAGE TO NEW PIPELINE!!!!!!!!!!!!!!!!!!!!!!!!!!!", half)
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print("CHAGE TO NEW PIPELINE", half)
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self.prepareModel(self.settings.modelSlotIndex)
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elif key in self.settings.floatData:
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setattr(self.settings, key, float(val))
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@ -307,7 +310,9 @@ class RVC:
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)
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raise NoModeLoadedException("model_common")
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if self.needSwitch:
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print(f"Switch model {self.currentSlot} -> {self.settings.modelSlotIndex}")
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print(
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f"[Voice Changer] Switch model {self.currentSlot} -> {self.settings.modelSlotIndex}"
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)
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self.currentSlot = self.settings.modelSlotIndex
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self.switchModel()
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self.needSwitch = False
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@ -363,8 +368,8 @@ class RVC:
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return result
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def __del__(self):
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del self.net_g
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del self.onnx_session
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del self.inferencer
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del self.embedder
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print("---------- REMOVING ---------------")
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@ -383,57 +388,17 @@ class RVC:
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pass
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def export2onnx(self):
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if hasattr(self, "net_g") is False or self.net_g is None:
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print("[Voice Changer] export2onnx, No pyTorch session.")
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return {"status": "ng", "path": ""}
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modelSlot = self.settings.modelSlots[self.settings.modelSlotIndex]
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pyTorchModelFile = modelSlot.pyTorchModelFile
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pyTorchModelFile = self.settings.modelSlots[
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self.settings.modelSlotIndex
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].pyTorchModelFile # inference前にexportできるようにcurrentSlotではなくslot
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if pyTorchModelFile is None:
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# PyTorchのファイルが存在しない場合はエラーを返す
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if pyTorchModelFile is None or pyTorchModelFile == "":
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print("[Voice Changer] export2onnx, No pyTorch filepath.")
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return {"status": "ng", "path": ""}
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import voice_changer.RVC.export2onnx as onnxExporter
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output_file = os.path.splitext(os.path.basename(pyTorchModelFile))[0] + ".onnx"
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output_file_simple = (
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os.path.splitext(os.path.basename(pyTorchModelFile))[0] + "_simple.onnx"
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)
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output_path = os.path.join(TMP_DIR, output_file)
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output_path_simple = os.path.join(TMP_DIR, output_file_simple)
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print(
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"embChannels",
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self.settings.modelSlots[self.settings.modelSlotIndex].embChannels,
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)
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metadata = {
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"application": "VC_CLIENT",
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"version": "1",
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"modelType": self.settings.modelSlots[
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self.settings.modelSlotIndex
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].modelType,
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"samplingRate": self.settings.modelSlots[
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self.settings.modelSlotIndex
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].samplingRate,
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"f0": self.settings.modelSlots[self.settings.modelSlotIndex].f0,
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"embChannels": self.settings.modelSlots[
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self.settings.modelSlotIndex
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].embChannels,
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"embedder": self.settings.modelSlots[self.settings.modelSlotIndex].embedder,
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}
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if torch.cuda.device_count() > 0:
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onnxExporter.export2onnx(
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pyTorchModelFile, output_path, output_path_simple, True, metadata
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)
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else:
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print(
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"[Voice Changer] Warning!!! onnx export with float32. maybe size is doubled."
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)
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onnxExporter.export2onnx(
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pyTorchModelFile, output_path, output_path_simple, False, metadata
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)
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output_file_simple = onnxExporter.export2onnx(modelSlot)
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return {
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"status": "ok",
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"path": f"/tmp/{output_file_simple}",
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@ -55,7 +55,7 @@ class VC(object):
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pitchf, device=self.device, dtype=torch.float
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).unsqueeze(0)
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# tensor
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# tensor型調整
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feats = torch.from_numpy(audio_pad)
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if self.is_half is True:
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feats = feats.half()
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@ -94,11 +94,9 @@ class VC(object):
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torch.from_numpy(npy).unsqueeze(0).to(self.device) * index_rate
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+ (1 - index_rate) * feats
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)
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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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# ピッチ抽出
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# ピッチサイズ調整
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p_len = audio_pad.shape[0] // self.window
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if feats.shape[1] < p_len:
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p_len = feats.shape[1]
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@ -108,47 +106,23 @@ class VC(object):
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p_len = torch.tensor([p_len], device=self.device).long()
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# 推論実行
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try:
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with torch.no_grad():
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audio1 = (
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(inferencer.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] * 32768)
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(
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inferencer.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0]
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* 32768
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)
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.data.cpu()
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.float()
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.numpy()
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.astype(np.int16)
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)
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# if pitch is not None:
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# print("INFERENCE 1 ")
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# audio1 = (
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# (
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# inferencer.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0]
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# * 32768
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# )
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# .data.cpu()
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# .float()
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# .numpy()
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# .astype(np.int16)
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# )
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# else:
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# if hasattr(inferencer, "infer_pitchless"):
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# print("INFERENCE 2 ")
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# audio1 = (
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# (inferencer.infer_pitchless(feats, p_len, sid)[0][0, 0] * 32768)
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# .data.cpu()
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# .float()
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# .numpy()
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# .astype(np.int16)
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# )
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# else:
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# print("INFERENCE 3 ")
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# audio1 = (
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# (inferencer.infer(feats, p_len, sid)[0][0, 0] * 32768)
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# .data.cpu()
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# .float()
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# .numpy()
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# .astype(np.int16)
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# )
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except RuntimeError as e:
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if "HALF" in e.__str__().upper():
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raise HalfPrecisionChangingException()
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else:
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raise e
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del feats, p_len, padding_mask
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torch.cuda.empty_cache()
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@ -1,7 +1,10 @@
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import os
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import json
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import torch
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from onnxsim import simplify
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import onnx
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from const import TMP_DIR, EnumInferenceTypes
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from voice_changer.RVC.ModelSlot import ModelSlot
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from voice_changer.RVC.onnx.SynthesizerTrnMs256NSFsid_ONNX import (
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SynthesizerTrnMs256NSFsid_ONNX,
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@ -15,24 +18,60 @@ from voice_changer.RVC.onnx.SynthesizerTrnMsNSFsidNono_webui_ONNX import (
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from voice_changer.RVC.onnx.SynthesizerTrnMsNSFsid_webui_ONNX import (
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SynthesizerTrnMsNSFsid_webui_ONNX,
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)
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from .const import RVC_MODEL_TYPE_RVC, RVC_MODEL_TYPE_WEBUI
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def export2onnx(input_model, output_model, output_model_simple, is_half, metadata):
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def export2onnx(modelSlot: ModelSlot):
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pyTorchModelFile = modelSlot.pyTorchModelFile
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output_file = os.path.splitext(os.path.basename(pyTorchModelFile))[0] + ".onnx"
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output_file_simple = (
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os.path.splitext(os.path.basename(pyTorchModelFile))[0] + "_simple.onnx"
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)
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output_path = os.path.join(TMP_DIR, output_file)
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output_path_simple = os.path.join(TMP_DIR, output_file_simple)
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metadata = {
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"application": "VC_CLIENT",
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"version": "2",
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# ↓EnumInferenceTypesのままだとシリアライズできないのでテキスト化
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"modelType": modelSlot.modelType.value,
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"samplingRate": modelSlot.samplingRate,
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"f0": modelSlot.f0,
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"embChannels": modelSlot.embChannels,
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"embedder": modelSlot.embedder.value,
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}
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if torch.cuda.device_count() > 0:
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_export2onnx(pyTorchModelFile, output_path, output_path_simple, True, metadata)
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else:
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print(
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"[Voice Changer] Warning!!! onnx export with float32. maybe size is doubled."
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)
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_export2onnx(pyTorchModelFile, output_path, output_path_simple, False, metadata)
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return output_file_simple
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def _export2onnx(input_model, output_model, output_model_simple, is_half, metadata):
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cpt = torch.load(input_model, map_location="cpu")
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if is_half:
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dev = torch.device("cuda", index=0)
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else:
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dev = torch.device("cpu")
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if metadata["f0"] is True and metadata["modelType"] == RVC_MODEL_TYPE_RVC:
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# EnumInferenceTypesのままだとシリアライズできないのでテキスト化
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if metadata["modelType"] == EnumInferenceTypes.pyTorchRVC.value:
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net_g_onnx = SynthesizerTrnMs256NSFsid_ONNX(*cpt["config"], is_half=is_half)
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elif metadata["f0"] is True and metadata["modelType"] == RVC_MODEL_TYPE_WEBUI:
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elif metadata["modelType"] == EnumInferenceTypes.pyTorchWebUI.value:
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net_g_onnx = SynthesizerTrnMsNSFsid_webui_ONNX(**cpt["params"], is_half=is_half)
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elif metadata["f0"] is False and metadata["modelType"] == RVC_MODEL_TYPE_RVC:
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elif metadata["modelType"] == EnumInferenceTypes.pyTorchRVCNono.value:
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net_g_onnx = SynthesizerTrnMs256NSFsid_nono_ONNX(*cpt["config"])
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elif metadata["f0"] is False and metadata["modelType"] == RVC_MODEL_TYPE_WEBUI:
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elif metadata["modelType"] == EnumInferenceTypes.pyTorchWebUINono.value:
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net_g_onnx = SynthesizerTrnMsNSFsidNono_webui_ONNX(**cpt["params"])
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else:
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print(
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"unknwon::::: ",
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metadata["modelType"],
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EnumInferenceTypes.pyTorchWebUI.value,
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)
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net_g_onnx.eval().to(dev)
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net_g_onnx.load_state_dict(cpt["weight"], strict=False)
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@ -24,6 +24,7 @@ class OnnxRVCInferencer(Inferencer):
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self.isHalf = True
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self.model = onnx_session
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self.setDevice(dev)
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return self
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def infer(
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@ -37,9 +38,9 @@ class OnnxRVCInferencer(Inferencer):
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if pitch is None or pitchf is None:
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raise RuntimeError("[Voice Changer] Pitch or Pitchf is not found.")
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print("INFER1", self.model.get_providers())
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print("INFER2", self.model.get_provider_options())
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print("INFER3", self.model.get_session_options())
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# print("INFER1", self.model.get_providers())
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# print("INFER2", self.model.get_provider_options())
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# print("INFER3", self.model.get_session_options())
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if self.isHalf:
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audio1 = self.model.run(
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["audio"],
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@ -11,6 +11,7 @@ from infer_pack.models import ( # type:ignore
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class RVCInferencer(Inferencer):
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def loadModel(self, file: str, dev: device, isHalf: bool = True):
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super().setProps(EnumInferenceTypes.pyTorchRVC, file, dev, isHalf)
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print("load inf", file)
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cpt = torch.load(file, map_location="cpu")
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model = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=isHalf)
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