335 lines
13 KiB
Python
335 lines
13 KiB
Python
# Copyright (c) 2025 ASLP-LAB
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# 2025 Ziqian Ning (ningziqian@mail.nwpu.edu.cn)
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# 2025 Huakang Chen (huakang@mail.nwpu.edu.cn)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" This implementation is adapted from github repo:
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https://github.com/SWivid/F5-TTS.
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"""
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from __future__ import annotations
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import os
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import gc
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from tqdm import tqdm
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import wandb
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import torch
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from torch.optim import AdamW
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from torch.optim.lr_scheduler import LinearLR, SequentialLR, ConstantLR
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from accelerate import Accelerator
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from accelerate.utils import DistributedDataParallelKwargs
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from diffrhythm.dataset.dataset import DiffusionDataset
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from torch.utils.data import DataLoader
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from ema_pytorch import EMA
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from diffrhythm.model import CFM
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from diffrhythm.model.utils import exists, default
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class Trainer:
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def __init__(
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self,
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model: CFM,
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args,
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epochs,
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learning_rate,
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num_warmup_updates=20000,
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save_per_updates=1000,
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checkpoint_path=None,
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batch_size=32,
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batch_size_type: str = "sample",
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max_samples=32,
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grad_accumulation_steps=1,
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max_grad_norm=1.0,
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noise_scheduler: str | None = None,
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duration_predictor: torch.nn.Module | None = None,
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wandb_project="test_e2-tts",
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wandb_run_name="test_run",
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wandb_resume_id: str = None,
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last_per_steps=None,
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accelerate_kwargs: dict = dict(),
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ema_kwargs: dict = dict(),
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bnb_optimizer: bool = False,
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reset_lr: bool = False,
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use_style_prompt: bool = False,
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grad_ckpt: bool = False
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):
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self.args = args
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ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=False, )
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logger = "wandb" if wandb.api.api_key else None
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self.accelerator = Accelerator(
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log_with=logger,
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kwargs_handlers=[ddp_kwargs],
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gradient_accumulation_steps=grad_accumulation_steps,
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**accelerate_kwargs,
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)
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if logger == "wandb":
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if exists(wandb_resume_id):
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init_kwargs = {"wandb": {"resume": "allow", "name": wandb_run_name, "id": wandb_resume_id}}
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else:
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init_kwargs = {"wandb": {"resume": "allow", "name": wandb_run_name}}
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self.accelerator.init_trackers(
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project_name=wandb_project,
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init_kwargs=init_kwargs,
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config={
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"epochs": epochs,
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"learning_rate": learning_rate,
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"num_warmup_updates": num_warmup_updates,
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"batch_size": batch_size,
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"batch_size_type": batch_size_type,
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"max_samples": max_samples,
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"grad_accumulation_steps": grad_accumulation_steps,
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"max_grad_norm": max_grad_norm,
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"gpus": self.accelerator.num_processes,
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"noise_scheduler": noise_scheduler,
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},
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)
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self.precision = self.accelerator.state.mixed_precision
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self.precision = self.precision.replace("no", "fp32")
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self.model = model
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if self.is_main:
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self.ema_model = EMA(model, include_online_model=False, **ema_kwargs)
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self.ema_model.to(self.accelerator.device)
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if self.accelerator.state.distributed_type in ["DEEPSPEED", "FSDP"]:
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self.ema_model.half()
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self.epochs = epochs
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self.num_warmup_updates = num_warmup_updates
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self.save_per_updates = save_per_updates
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self.last_per_steps = default(last_per_steps, save_per_updates * grad_accumulation_steps)
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self.checkpoint_path = default(checkpoint_path, "ckpts/test_e2-tts")
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self.max_samples = max_samples
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self.grad_accumulation_steps = grad_accumulation_steps
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self.max_grad_norm = max_grad_norm
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self.noise_scheduler = noise_scheduler
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self.duration_predictor = duration_predictor
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self.reset_lr = reset_lr
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self.use_style_prompt = use_style_prompt
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self.grad_ckpt = grad_ckpt
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if bnb_optimizer:
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import bitsandbytes as bnb
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self.optimizer = bnb.optim.AdamW8bit(model.parameters(), lr=learning_rate)
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else:
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self.optimizer = AdamW(model.parameters(), lr=learning_rate)
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if self.accelerator.state.distributed_type == "DEEPSPEED":
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self.accelerator.state.deepspeed_plugin.deepspeed_config['train_micro_batch_size_per_gpu'] = batch_size
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self.get_dataloader()
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self.get_scheduler()
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self.model, self.optimizer, self.scheduler, self.train_dataloader = self.accelerator.prepare(self.model, self.optimizer, self.scheduler, self.train_dataloader)
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def get_scheduler(self):
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warmup_steps = (
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self.num_warmup_updates * self.accelerator.num_processes
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) # consider a fixed warmup steps while using accelerate multi-gpu ddp
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total_steps = len(self.train_dataloader) * self.epochs / self.grad_accumulation_steps
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decay_steps = total_steps - warmup_steps
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warmup_scheduler = LinearLR(self.optimizer, start_factor=1e-8, end_factor=1.0, total_iters=warmup_steps)
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decay_scheduler = LinearLR(self.optimizer, start_factor=1.0, end_factor=1e-8, total_iters=decay_steps)
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self.scheduler = SequentialLR(
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self.optimizer, schedulers=[warmup_scheduler, decay_scheduler], milestones=[warmup_steps]
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)
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def get_constant_scheduler(self):
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total_steps = len(self.train_dataloader) * self.epochs / self.grad_accumulation_steps
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self.scheduler = ConstantLR(self.optimizer, factor=1, total_iters=total_steps)
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def get_dataloader(self):
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print(self.args)
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dd = DiffusionDataset(self.args.file_path, self.args.max_frames, self.args.min_frames, self.args.sampling_rate, self.args.downsample_rate, self.precision)
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self.train_dataloader = DataLoader(
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dataset=dd,
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batch_size=self.args.batch_size,
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shuffle=True,
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num_workers=4,
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pin_memory=True,
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collate_fn=dd.custom_collate_fn,
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persistent_workers=True
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)
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@property
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def is_main(self):
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return self.accelerator.is_main_process
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def save_checkpoint(self, step, last=False):
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self.accelerator.wait_for_everyone()
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if self.is_main:
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checkpoint = dict(
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model_state_dict=self.accelerator.unwrap_model(self.model).state_dict(),
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optimizer_state_dict=self.accelerator.unwrap_model(self.optimizer).state_dict(),
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ema_model_state_dict=self.ema_model.state_dict(),
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scheduler_state_dict=self.scheduler.state_dict(),
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step=step,
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)
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if not os.path.exists(self.checkpoint_path):
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os.makedirs(self.checkpoint_path)
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if last:
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self.accelerator.save(checkpoint, f"{self.checkpoint_path}/model_last.pt")
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print(f"Saved last checkpoint at step {step}")
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else:
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self.accelerator.save(checkpoint, f"{self.checkpoint_path}/model_{step}.pt")
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def load_checkpoint(self):
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if (
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not exists(self.checkpoint_path)
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or not os.path.exists(self.checkpoint_path)
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or not os.listdir(self.checkpoint_path)
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):
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return 0
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self.accelerator.wait_for_everyone()
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if "model_last.pt" in os.listdir(self.checkpoint_path):
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latest_checkpoint = "model_last.pt"
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else:
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latest_checkpoint = sorted(
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[f for f in os.listdir(self.checkpoint_path) if f.endswith(".pt")],
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key=lambda x: int("".join(filter(str.isdigit, x))),
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)[-1]
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checkpoint = torch.load(f"{self.checkpoint_path}/{latest_checkpoint}", map_location="cpu")
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if self.is_main:
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ema_dict = self.ema_model.state_dict()
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ema_checkpoint_dict = checkpoint["ema_model_state_dict"]
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filtered_ema_dict = {
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k: v for k, v in ema_checkpoint_dict.items()
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if k in ema_dict and ema_dict[k].shape == v.shape
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}
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self.ema_model.load_state_dict(filtered_ema_dict, strict=False)
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model_dict = self.accelerator.unwrap_model(self.model).state_dict()
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checkpoint_model_dict = checkpoint["model_state_dict"]
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filtered_model_dict = {
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k: v for k, v in checkpoint_model_dict.items()
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if k in model_dict and model_dict[k].shape == v.shape
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}
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self.accelerator.unwrap_model(self.model).load_state_dict(filtered_model_dict, strict=False)
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if "step" in checkpoint:
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if self.scheduler and not self.reset_lr:
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self.scheduler.load_state_dict(checkpoint["scheduler_state_dict"])
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step = checkpoint["step"]
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else:
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step = 0
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del checkpoint
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gc.collect()
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print("Checkpoint loaded at step", step)
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return step
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def train(self, resumable_with_seed: int = None):
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train_dataloader = self.train_dataloader
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start_step = self.load_checkpoint()
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global_step = start_step
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if resumable_with_seed > 0:
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orig_epoch_step = len(train_dataloader)
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skipped_epoch = int(start_step // orig_epoch_step)
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skipped_batch = start_step % orig_epoch_step
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skipped_dataloader = self.accelerator.skip_first_batches(train_dataloader, num_batches=skipped_batch)
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else:
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skipped_epoch = 0
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for epoch in range(skipped_epoch, self.epochs):
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self.model.train()
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if resumable_with_seed > 0 and epoch == skipped_epoch:
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progress_bar = tqdm(
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skipped_dataloader,
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desc=f"Epoch {epoch+1}/{self.epochs}",
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unit="step",
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disable=not self.accelerator.is_local_main_process,
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initial=skipped_batch,
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total=orig_epoch_step,
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smoothing=0.15
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)
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else:
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progress_bar = tqdm(
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train_dataloader,
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desc=f"Epoch {epoch+1}/{self.epochs}",
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unit="step",
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disable=not self.accelerator.is_local_main_process,
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smoothing=0.15
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)
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for batch in progress_bar:
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with self.accelerator.accumulate(self.model):
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text_inputs = batch["lrc"]
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mel_spec = batch["latent"].permute(0, 2, 1)
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mel_lengths = batch["latent_lengths"]
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style_prompt = batch["prompt"]
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style_prompt_lens = batch["prompt_lengths"]
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start_time = batch["start_time"]
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loss, cond, pred = self.model(
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mel_spec, text=text_inputs, lens=mel_lengths, noise_scheduler=self.noise_scheduler,
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style_prompt=style_prompt,
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style_prompt_lens=style_prompt_lens,
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grad_ckpt=self.grad_ckpt, start_time=start_time
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)
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self.accelerator.backward(loss)
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if self.max_grad_norm > 0 and self.accelerator.sync_gradients:
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self.accelerator.clip_grad_norm_(self.model.parameters(), self.max_grad_norm)
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self.optimizer.step()
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self.scheduler.step()
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self.optimizer.zero_grad()
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if self.is_main:
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self.ema_model.update()
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global_step += 1
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if self.accelerator.is_local_main_process:
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self.accelerator.log({"loss": loss.item(), "lr": self.scheduler.get_last_lr()[0]}, step=global_step)
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progress_bar.set_postfix(step=str(global_step), loss=loss.item())
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if global_step % (self.save_per_updates * self.grad_accumulation_steps) == 0:
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self.save_checkpoint(global_step)
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if global_step % self.last_per_steps == 0:
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self.save_checkpoint(global_step, last=True)
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self.save_checkpoint(global_step, last=True)
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self.accelerator.end_training()
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