mirror of
https://github.com/w-okada/voice-changer.git
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57 lines
1.8 KiB
Python
57 lines
1.8 KiB
Python
import traceback
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from data.ModelSlot import DiffusionSVCModelSlot
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from voice_changer.DiffusionSVC.inferencer.InferencerManager import InferencerManager
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from voice_changer.DiffusionSVC.pipeline.Pipeline import Pipeline
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from voice_changer.DiffusionSVC.pitchExtractor.PitchExtractorManager import PitchExtractorManager
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from voice_changer.RVC.deviceManager.DeviceManager import DeviceManager
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from voice_changer.RVC.embedder.EmbedderManager import EmbedderManager
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import torch
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from torchaudio.transforms import Resample
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def createPipeline(modelSlot: DiffusionSVCModelSlot, gpu: int, f0Detector: str, inputSampleRate: int, outputSampleRate: int):
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dev = DeviceManager.get_instance().getDevice(gpu)
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# half = DeviceManager.get_instance().halfPrecisionAvailable(gpu)
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half = False
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# Inferencer 生成
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try:
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inferencer = InferencerManager.getInferencer(modelSlot.modelType, modelSlot.modelFile, gpu)
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except Exception as e:
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print("[Voice Changer] exception! loading inferencer", e)
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traceback.print_exc()
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# Embedder 生成
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try:
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embedder = EmbedderManager.getEmbedder(
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modelSlot.embedder,
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# emmbedderFilename,
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half,
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dev,
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)
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except Exception as e:
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print("[Voice Changer] exception! loading embedder", e)
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traceback.print_exc()
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# pitchExtractor
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pitchExtractor = PitchExtractorManager.getPitchExtractor(f0Detector, gpu)
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resamplerIn = Resample(inputSampleRate, 16000, dtype=torch.int16).to(dev)
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resamplerOut = Resample(modelSlot.samplingRate, outputSampleRate, dtype=torch.int16).to(dev)
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pipeline = Pipeline(
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embedder,
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inferencer,
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pitchExtractor,
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modelSlot.samplingRate,
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dev,
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half,
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resamplerIn,
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resamplerOut
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)
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return pipeline
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