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import torch |
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from diffusers import DDIMScheduler, DDPMScheduler, PNDMScheduler |
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from models.svc.base import SVCInference |
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from models.svc.diffusion.diffusion_inference_pipeline import DiffusionInferencePipeline |
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from models.svc.diffusion.diffusion_wrapper import DiffusionWrapper |
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from modules.encoder.condition_encoder import ConditionEncoder |
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class DiffusionInference(SVCInference): |
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def __init__(self, args=None, cfg=None, infer_type="from_dataset"): |
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SVCInference.__init__(self, args, cfg, infer_type) |
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settings = { |
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**cfg.model.diffusion.scheduler_settings, |
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**cfg.inference.diffusion.scheduler_settings, |
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} |
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settings.pop("num_inference_timesteps") |
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if cfg.inference.diffusion.scheduler.lower() == "ddpm": |
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self.scheduler = DDPMScheduler(**settings) |
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self.logger.info("Using DDPM scheduler.") |
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elif cfg.inference.diffusion.scheduler.lower() == "ddim": |
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self.scheduler = DDIMScheduler(**settings) |
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self.logger.info("Using DDIM scheduler.") |
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elif cfg.inference.diffusion.scheduler.lower() == "pndm": |
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self.scheduler = PNDMScheduler(**settings) |
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self.logger.info("Using PNDM scheduler.") |
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else: |
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raise NotImplementedError( |
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"Unsupported scheduler type: {}".format( |
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cfg.inference.diffusion.scheduler.lower() |
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) |
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) |
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self.pipeline = DiffusionInferencePipeline( |
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self.model[1], |
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self.scheduler, |
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args.diffusion_inference_steps, |
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) |
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def _build_model(self): |
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self.cfg.model.condition_encoder.f0_min = self.cfg.preprocess.f0_min |
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self.cfg.model.condition_encoder.f0_max = self.cfg.preprocess.f0_max |
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self.condition_encoder = ConditionEncoder(self.cfg.model.condition_encoder) |
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self.acoustic_mapper = DiffusionWrapper(self.cfg) |
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model = torch.nn.ModuleList([self.condition_encoder, self.acoustic_mapper]) |
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return model |
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def _inference_each_batch(self, batch_data): |
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device = self.accelerator.device |
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for k, v in batch_data.items(): |
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batch_data[k] = v.to(device) |
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conditioner = self.model[0](batch_data) |
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noise = torch.randn_like(batch_data["mel"], device=device) |
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y_pred = self.pipeline(noise, conditioner) |
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return y_pred |
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