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| # Copyright (c) 2023 Amphion. | |
| # | |
| # This source code is licensed under the MIT license found in the | |
| # LICENSE file in the root directory of this source tree. | |
| import torch | |
| from models.svc.base import SVCInference | |
| from modules.encoder.condition_encoder import ConditionEncoder | |
| from models.svc.comosvc.comosvc import ComoSVC | |
| class ComoSVCInference(SVCInference): | |
| def __init__(self, args, cfg, infer_type="from_dataset"): | |
| SVCInference.__init__(self, args, cfg, infer_type) | |
| def _build_model(self): | |
| # TODO: sort out the config | |
| self.cfg.model.condition_encoder.f0_min = self.cfg.preprocess.f0_min | |
| self.cfg.model.condition_encoder.f0_max = self.cfg.preprocess.f0_max | |
| self.condition_encoder = ConditionEncoder(self.cfg.model.condition_encoder) | |
| self.acoustic_mapper = ComoSVC(self.cfg) | |
| if self.cfg.model.comosvc.distill: | |
| self.acoustic_mapper.decoder.init_consistency_training() | |
| model = torch.nn.ModuleList([self.condition_encoder, self.acoustic_mapper]) | |
| return model | |
| def _inference_each_batch(self, batch_data): | |
| device = self.accelerator.device | |
| for k, v in batch_data.items(): | |
| batch_data[k] = v.to(device) | |
| cond = self.condition_encoder(batch_data) | |
| mask = batch_data["mask"] | |
| encoder_pred, decoder_pred = self.acoustic_mapper( | |
| mask, cond, self.cfg.inference.comosvc.inference_steps | |
| ) | |
| return decoder_pred | |