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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
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