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