103 lines
3.7 KiB
Python
103 lines
3.7 KiB
Python
# Copyright (c) 2023 Amphion.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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import torch
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from diffusers import DDPMScheduler
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from models.svc.base import SVCTrainer
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from modules.encoder.condition_encoder import ConditionEncoder
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from .diffusion_wrapper import DiffusionWrapper
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class DiffusionTrainer(SVCTrainer):
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r"""The base trainer for all diffusion models. It inherits from SVCTrainer and
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implements ``_build_model`` and ``_forward_step`` methods.
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"""
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def __init__(self, args=None, cfg=None):
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SVCTrainer.__init__(self, args, cfg)
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# Only for SVC tasks using diffusion
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self.noise_scheduler = DDPMScheduler(
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**self.cfg.model.diffusion.scheduler_settings,
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)
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self.diffusion_timesteps = (
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self.cfg.model.diffusion.scheduler_settings.num_train_timesteps
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)
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### Following are methods only for diffusion models ###
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def _build_model(self):
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r"""Build the model for training. This function is called in ``__init__`` function."""
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# TODO: sort out the config
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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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num_of_params_encoder = self.count_parameters(self.condition_encoder)
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num_of_params_am = self.count_parameters(self.acoustic_mapper)
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num_of_params = num_of_params_encoder + num_of_params_am
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log = "Diffusion Model's Parameters: #Encoder is {:.2f}M, #Diffusion is {:.2f}M. The total is {:.2f}M".format(
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num_of_params_encoder / 1e6, num_of_params_am / 1e6, num_of_params / 1e6
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)
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self.logger.info(log)
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return model
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def count_parameters(self, model):
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model_param = 0.0
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if isinstance(model, dict):
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for key, value in model.items():
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model_param += sum(p.numel() for p in model[key].parameters())
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else:
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model_param = sum(p.numel() for p in model.parameters())
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return model_param
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def _check_nan(self, batch, loss, y_pred, y_gt):
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if torch.any(torch.isnan(loss)):
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for k, v in batch.items():
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self.logger.info(k)
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self.logger.info(v)
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super()._check_nan(loss, y_pred, y_gt)
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def _forward_step(self, batch):
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r"""Forward step for training and inference. This function is called
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in ``_train_step`` & ``_test_step`` function.
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"""
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device = self.accelerator.device
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if self.online_features_extraction:
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# On-the-fly features extraction
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batch = self._extract_svc_features(batch)
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# To debug
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# for k, v in batch.items():
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# print(k, v.shape, v)
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# exit()
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mel_input = batch["mel"]
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noise = torch.randn_like(mel_input, device=device, dtype=torch.float32)
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batch_size = mel_input.size(0)
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timesteps = torch.randint(
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0,
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self.diffusion_timesteps,
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(batch_size,),
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device=device,
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dtype=torch.long,
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)
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noisy_mel = self.noise_scheduler.add_noise(mel_input, noise, timesteps)
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conditioner = self.condition_encoder(batch)
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y_pred = self.acoustic_mapper(noisy_mel, timesteps, conditioner)
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loss = self._compute_loss(self.criterion, y_pred, noise, batch["mask"])
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self._check_nan(batch, loss, y_pred, noise)
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return loss
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