From 824ecbd7222b9b9ada77c4fbbd7ae7f491049f21 Mon Sep 17 00:00:00 2001 From: 34j <55338215+34j@users.noreply.github.com> Date: Sun, 9 Apr 2023 00:54:58 +0900 Subject: [PATCH] feat: migrate to lightning (#246) --- .gitignore | 2 + README.md | 1 + poetry.lock | 2117 +++++++++++------ pyproject.toml | 2 + src/so_vits_svc_fork/__main__.py | 13 +- .../{data_utils.py => dataset.py} | 9 +- src/so_vits_svc_fork/f0.py | 4 +- src/so_vits_svc_fork/gui.py | 27 +- src/so_vits_svc_fork/inference/core.py | 3 +- src/so_vits_svc_fork/inference/main.py | 5 +- .../modules/decoders/hifigan/__init__.py | 4 +- .../modules/decoders/hifigan/_models.py | 2 +- .../modules/decoders/mb_istft/_generators.py | 2 +- .../modules/decoders/mb_istft/_pqmf.py | 8 +- .../modules/decoders/mb_istft/_stft.py | 6 +- src/so_vits_svc_fork/modules/synthesizers.py | 4 +- .../preprocessing/preprocess_hubert_f0.py | 8 +- src/so_vits_svc_fork/train.py | 681 +++--- src/so_vits_svc_fork/utils.py | 53 +- 19 files changed, 1815 insertions(+), 1136 deletions(-) rename src/so_vits_svc_fork/{data_utils.py => dataset.py} (94%) diff --git a/.gitignore b/.gitignore index f8ad11ee..bc70261f 100644 --- a/.gitignore +++ b/.gitignore @@ -151,6 +151,8 @@ tests/**/*.download tests/**/*.lab tests/**/*.pdf tests/**/*.csv +tests/**/*.ckpt +tests/**/*.yaml *.tfevents.* *.pt user_gui_presets.json diff --git a/README.md b/README.md index 8d2cc828..9fb371f9 100644 --- a/README.md +++ b/README.md @@ -41,6 +41,7 @@ A fork of [`so-vits-svc`](https://github.com/svc-develop-team/so-vits-svc) with - Fixed misuse of `ContentVec` in the original repository.[^c] - More accurate pitch estimation using [`CREPE`](https://github.com/marl/crepe/). - GUI and unified CLI available +- ~2x faster training - Ready to use just by installing with `pip`. - Automatically download pretrained models. - Code completely formatted with black, isort, autoflake etc. diff --git a/poetry.lock b/poetry.lock index 458f4f80..5ddf1b6a 100644 --- a/poetry.lock +++ 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"jaraco.itertools", "more [metadata] lock-version = "2.0" python-versions = ">=3.8,<3.11" -content-hash = "7cba7e37acc9d2aa1793ab026733c98e9cb41245bf3d42fe8151d09dcab0f6f3" +content-hash = "2589dee32ec6fd858b356159201be7dfdf7b5848539bd35cff4db2ce4671dc12" diff --git a/pyproject.toml b/pyproject.toml index 7f06b1e9..527e7c13 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -63,6 +63,8 @@ pysimplegui = ">=4.6" pebble = ">=5.0" torchcrepe = ">=0.0.17" unidecode = "^1.3.6" +lightning = "^2.0.1" +fastapi = "<0.89" [tool.poetry.group.dev.dependencies] pre-commit = ">=3" diff --git a/src/so_vits_svc_fork/__main__.py b/src/so_vits_svc_fork/__main__.py index ff16ee6d..1cab7512 100644 --- a/src/so_vits_svc_fork/__main__.py +++ b/src/so_vits_svc_fork/__main__.py @@ -10,6 +10,7 @@ import torch from so_vits_svc_fork import __version__ +from so_vits_svc_fork.utils import get_optimal_device LOG = getLogger(__name__) @@ -190,7 +191,7 @@ def train( "-d", "--device", type=str, - default="cuda" if torch.cuda.is_available() else "cpu", + default=get_optimal_device(), help="device", ) @click.option("-ch", "--chunk-seconds", type=float, default=0.5, help="chunk seconds") @@ -220,7 +221,7 @@ def infer( pad_seconds: float = 0.5, chunk_seconds: float = 0.5, absolute_thresh: bool = False, - device: Literal["cpu", "cuda"] = "cuda" if torch.cuda.is_available() else "cpu", + device: str | torch.device = get_optimal_device(), ): """Inference""" from so_vits_svc_fork.inference.main import infer @@ -339,7 +340,7 @@ def infer( "-d", "--device", type=str, - default="cuda" if torch.cuda.is_available() else "cpu", + default=get_optimal_device(), help="device", ) @click.option("-s", "--speaker", type=str, default=None, help="speaker name") @@ -378,7 +379,7 @@ def vc( version: int, input_device: int | str | None, output_device: int | str | None, - device: Literal["cpu", "cuda"], + device: torch.device, passthrough_original: bool = False, ) -> None: """Realtime inference from microphone""" @@ -759,7 +760,9 @@ def clean(): default="cpu", help="device to use", ) -def onnx(input_path: Path, output_path: Path, config_path: Path, device: str) -> None: +def onnx( + input_path: Path, output_path: Path, config_path: Path, device: torch.device | str +) -> None: raise NotImplementedError("ONNX export is not yet supported") """Export model to onnx""" input_path = Path(input_path) diff --git a/src/so_vits_svc_fork/data_utils.py b/src/so_vits_svc_fork/dataset.py similarity index 94% rename from src/so_vits_svc_fork/data_utils.py rename to src/so_vits_svc_fork/dataset.py index 4caacd28..6553394f 100644 --- a/src/so_vits_svc_fork/data_utils.py +++ b/src/so_vits_svc_fork/dataset.py @@ -5,13 +5,14 @@ from typing import Sequence import torch +import torch.nn as nn import torch.nn.functional as F -import torch.utils.data +from torch.utils.data import Dataset from .hparams import HParams -class TextAudioSpeakerLoader(torch.utils.data.Dataset): +class TextAudioDataset(Dataset): def __init__(self, hps: HParams, is_validation: bool = False): self.datapaths = [ Path(x).parent / (Path(x).name + ".data.pt") @@ -59,8 +60,8 @@ def _pad_stack(array: Sequence[torch.Tensor]) -> torch.Tensor: return torch.stack(x_padded) -class TextAudioCollate: - def __call__( +class TextAudioCollate(nn.Module): + def forward( self, batch: Sequence[dict[str, torch.Tensor]] ) -> tuple[torch.Tensor, ...]: batch = [b for b in batch if b is not None] diff --git a/src/so_vits_svc_fork/f0.py b/src/so_vits_svc_fork/f0.py index 255ad89e..990f2050 100644 --- a/src/so_vits_svc_fork/f0.py +++ b/src/so_vits_svc_fork/f0.py @@ -10,6 +10,8 @@ from numpy import dtype, float32, ndarray from torch import FloatTensor, Tensor +from so_vits_svc_fork.utils import get_optimal_device + LOG = getLogger(__name__) @@ -154,7 +156,7 @@ def compute_f0_crepe( p_len: None | int = None, sampling_rate: int = 44100, hop_length: int = 512, - device: str = "cuda" if torch.cuda.is_available() else "cpu", + device: str | torch.device = get_optimal_device(), model: Literal["full", "tiny"] = "full", ): audio = torch.from_numpy(wav_numpy).to(device, copy=True) diff --git a/src/so_vits_svc_fork/gui.py b/src/so_vits_svc_fork/gui.py index 406e8ff7..0fddaced 100644 --- a/src/so_vits_svc_fork/gui.py +++ b/src/so_vits_svc_fork/gui.py @@ -11,7 +11,7 @@ from pebble import ProcessFuture, ProcessPool from tqdm.tk import tqdm_tk -from .utils import ensure_pretrained_model +from .utils import ensure_pretrained_model, get_optimal_device GUI_DEFAULT_PRESETS_PATH = Path(__file__).parent / "default_gui_presets.json" GUI_PRESETS_PATH = Path("./user_gui_presets.json").absolute() @@ -411,21 +411,14 @@ def main(): [ sg.Checkbox( key="use_gpu", - default=( - torch.cuda.is_available() or torch.backends.mps.is_available() - ), + default=get_optimal_device() != torch.device("cpu"), text="Use GPU" + ( " (not available; if your device has GPU, make sure you installed PyTorch with CUDA support)" - if not ( - torch.cuda.is_available() - or torch.backends.mps.is_available() - ) + if get_optimal_device() == torch.device("cpu") else "" ), - disabled=not ( - torch.cuda.is_available() or torch.backends.mps.is_available() - ), + disabled=get_optimal_device() == torch.device("cpu"), ) ], [ @@ -579,15 +572,7 @@ def apply_preset(name: str) -> None: pad_seconds=values["pad_seconds"], chunk_seconds=values["chunk_seconds"], absolute_thresh=values["absolute_thresh"], - device="cpu" - if not values["use_gpu"] - else ( - "cuda" - if torch.cuda.is_available() - else "mps" - if torch.backends.mps.is_available() - else "cpu" - ), + device="cpu" if not values["use_gpu"] else get_optimal_device(), ) if values["auto_play"]: pool.schedule(play_audio, args=[output_path]) @@ -641,7 +626,7 @@ def apply_preset(name: str) -> None: output_device=output_device_indices[ window["output_device"].widget.current() ], - device="cuda" if values["use_gpu"] else "cpu", + device=get_optimal_device() if values["use_gpu"] else "cpu", passthrough_original=values["passthrough_original"], ), ) diff --git a/src/so_vits_svc_fork/inference/core.py b/src/so_vits_svc_fork/inference/core.py index 00185c2f..5052e346 100644 --- a/src/so_vits_svc_fork/inference/core.py +++ b/src/so_vits_svc_fork/inference/core.py @@ -16,6 +16,7 @@ from so_vits_svc_fork import cluster, utils from ..modules.synthesizers import SynthesizerTrn +from ..utils import get_optimal_device LOG = getLogger(__name__) @@ -98,7 +99,7 @@ def __init__( ): self.net_g_path = net_g_path if device is None: - self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + self.device = (get_optimal_device(),) else: self.device = torch.device(device) self.hps = utils.get_hparams(config_path) diff --git a/src/so_vits_svc_fork/inference/main.py b/src/so_vits_svc_fork/inference/main.py index 85852b26..ff726a94 100644 --- a/src/so_vits_svc_fork/inference/main.py +++ b/src/so_vits_svc_fork/inference/main.py @@ -11,6 +11,7 @@ from cm_time import timer from so_vits_svc_fork.inference.core import RealtimeVC, RealtimeVC2, Svc +from so_vits_svc_fork.utils import get_optimal_device LOG = getLogger(__name__) @@ -35,7 +36,7 @@ def infer( pad_seconds: float = 0.5, chunk_seconds: float = 0.5, absolute_thresh: bool = False, - device: Literal["cpu", "cuda"] = "cuda" if torch.cuda.is_available() else "cpu", + device: str | torch.device = get_optimal_device(), ): model_path = Path(model_path) output_path = Path(output_path) @@ -94,7 +95,7 @@ def realtime( version: int = 2, input_device: int | str | None = None, output_device: int | str | None = None, - device: Literal["cpu", "cuda"] = "cuda" if torch.cuda.is_available() else "cpu", + device: str | torch.device = get_optimal_device(), passthrough_original: bool = False, ): import sounddevice as sd diff --git a/src/so_vits_svc_fork/modules/decoders/hifigan/__init__.py b/src/so_vits_svc_fork/modules/decoders/hifigan/__init__.py index 486c66fe..7272ec95 100644 --- a/src/so_vits_svc_fork/modules/decoders/hifigan/__init__.py +++ b/src/so_vits_svc_fork/modules/decoders/hifigan/__init__.py @@ -1,3 +1,3 @@ -from ._models import Generator +from ._models import NSFHifiGANGenerator -__all__ = ["Generator"] +__all__ = ["NSFHifiGANGenerator"] diff --git a/src/so_vits_svc_fork/modules/decoders/hifigan/_models.py b/src/so_vits_svc_fork/modules/decoders/hifigan/_models.py index 5161ed2e..fee8300c 100644 --- a/src/so_vits_svc_fork/modules/decoders/hifigan/_models.py +++ b/src/so_vits_svc_fork/modules/decoders/hifigan/_models.py @@ -214,7 +214,7 @@ def forward(self, x): return sine_merge, noise, uv -class Generator(torch.nn.Module): +class NSFHifiGANGenerator(torch.nn.Module): def __init__(self, h): super().__init__() self.h = h diff --git a/src/so_vits_svc_fork/modules/decoders/mb_istft/_generators.py b/src/so_vits_svc_fork/modules/decoders/mb_istft/_generators.py index 77f4b88a..31bbbd7f 100644 --- a/src/so_vits_svc_fork/modules/decoders/mb_istft/_generators.py +++ b/src/so_vits_svc_fork/modules/decoders/mb_istft/_generators.py @@ -360,7 +360,7 @@ def forward(self, x, g=None): y_mb_hat = F.conv_transpose1d( y_mb_hat, - self.updown_filter.cuda(x.device) * self.subbands, + self.updown_filter.to(x.device) * self.subbands, stride=self.subbands, ) diff --git a/src/so_vits_svc_fork/modules/decoders/mb_istft/_pqmf.py b/src/so_vits_svc_fork/modules/decoders/mb_istft/_pqmf.py index 981c1739..c6b40c33 100644 --- a/src/so_vits_svc_fork/modules/decoders/mb_istft/_pqmf.py +++ b/src/so_vits_svc_fork/modules/decoders/mb_istft/_pqmf.py @@ -85,17 +85,15 @@ def __init__(self, device, subbands=8, taps=62, cutoff_ratio=0.15, beta=9.0): ) # convert to tensor - analysis_filter = torch.from_numpy(h_analysis).float().unsqueeze(1).cuda(device) - synthesis_filter = ( - torch.from_numpy(h_synthesis).float().unsqueeze(0).cuda(device) - ) + analysis_filter = torch.from_numpy(h_analysis).float().unsqueeze(1).to(device) + synthesis_filter = torch.from_numpy(h_synthesis).float().unsqueeze(0).to(device) # register coefficients as buffer self.register_buffer("analysis_filter", analysis_filter) self.register_buffer("synthesis_filter", synthesis_filter) # filter for downsampling & upsampling - updown_filter = torch.zeros((subbands, subbands, subbands)).float().cuda(device) + updown_filter = torch.zeros((subbands, subbands, subbands)).float().to(device) for k in range(subbands): updown_filter[k, k, 0] = 1.0 self.register_buffer("updown_filter", updown_filter) diff --git a/src/so_vits_svc_fork/modules/decoders/mb_istft/_stft.py b/src/so_vits_svc_fork/modules/decoders/mb_istft/_stft.py index 8a111dca..26632bc2 100644 --- a/src/so_vits_svc_fork/modules/decoders/mb_istft/_stft.py +++ b/src/so_vits_svc_fork/modules/decoders/mb_istft/_stft.py @@ -182,11 +182,7 @@ def inverse(self, magnitude, phase): window_sum = torch.autograd.Variable( torch.from_numpy(window_sum), requires_grad=False ) - window_sum = ( - window_sum.to(inverse_transform.device()) - if magnitude.is_cuda - else window_sum - ) + window_sum = window_sum.to(inverse_transform.device()) inverse_transform[:, :, approx_nonzero_indices] /= window_sum[ approx_nonzero_indices ] diff --git a/src/so_vits_svc_fork/modules/synthesizers.py b/src/so_vits_svc_fork/modules/synthesizers.py index 0f796215..c96e021b 100644 --- a/src/so_vits_svc_fork/modules/synthesizers.py +++ b/src/so_vits_svc_fork/modules/synthesizers.py @@ -9,7 +9,7 @@ from so_vits_svc_fork.f0 import f0_to_coarse from so_vits_svc_fork.modules import commons as commons from so_vits_svc_fork.modules.decoders.f0 import F0Decoder -from so_vits_svc_fork.modules.decoders.hifigan import Generator +from so_vits_svc_fork.modules.decoders.hifigan import NSFHifiGANGenerator from so_vits_svc_fork.modules.decoders.mb_istft import ( Multiband_iSTFT_Generator, Multistream_iSTFT_Generator, @@ -110,7 +110,7 @@ def __init__( "upsample_kernel_sizes": upsample_kernel_sizes, "gin_channels": gin_channels, } - self.dec = Generator(h=hps) + self.dec = NSFHifiGANGenerator(h=hps) self.mb = False else: hps = { diff --git a/src/so_vits_svc_fork/preprocessing/preprocess_hubert_f0.py b/src/so_vits_svc_fork/preprocessing/preprocess_hubert_f0.py index 2dfb52d8..2315f0e2 100644 --- a/src/so_vits_svc_fork/preprocessing/preprocess_hubert_f0.py +++ b/src/so_vits_svc_fork/preprocessing/preprocess_hubert_f0.py @@ -18,7 +18,7 @@ from ..hparams import HParams from ..modules.mel_processing import spec_to_mel_torch, spectrogram_torch -from ..utils import get_total_gpu_memory +from ..utils import get_optimal_device, get_total_gpu_memory from .preprocess_utils import check_hubert_min_duration LOG = getLogger(__name__) @@ -30,7 +30,7 @@ def _process_one( *, filepath: Path, content_model: HubertModel, - device: Literal["cuda", "cpu"] = "cuda", + device: torch.device | str = get_optimal_device(), f0_method: Literal["crepe", "crepe-tiny", "parselmouth", "dio", "harvest"] = "dio", force_rebuild: bool = False, hps: HParams, @@ -54,7 +54,7 @@ def _process_one( uv = torch.from_numpy(uv).float() # Compute HuBERT content - audio = torch.from_numpy(audio).float().cuda() + audio = torch.from_numpy(audio).float().to(device) c = utils.get_content( content_model, audio, @@ -102,7 +102,7 @@ def _process_one( def _process_batch(filepaths: Iterable[Path], pbar_position: int, **kwargs): - content_model = utils.get_hubert_model("cuda") + content_model = utils.get_hubert_model(get_optimal_device()) for filepath in tqdm(filepaths, position=pbar_position): _process_one( diff --git a/src/so_vits_svc_fork/train.py b/src/so_vits_svc_fork/train.py index d4a025d5..2209de6b 100644 --- a/src/so_vits_svc_fork/train.py +++ b/src/so_vits_svc_fork/train.py @@ -1,28 +1,26 @@ from __future__ import annotations -import os -import time +import math +import warnings from logging import getLogger from pathlib import Path +from typing import Any +import lightning.pytorch as pl import torch -import torch.distributed as dist -import torch.multiprocessing as mp -from torch import nn -from torch.cuda.amp import GradScaler, autocast +from lightning.pytorch.accelerators import TPUAccelerator +from lightning.pytorch.loggers import TensorBoardLogger +from torch.cuda.amp import autocast from torch.nn import functional as F -from torch.nn.parallel import DistributedDataParallel as DDP from torch.utils.data import DataLoader -from torch.utils.tensorboard import SummaryWriter -from tqdm import trange +from torch.utils.tensorboard.writer import SummaryWriter import so_vits_svc_fork.f0 import so_vits_svc_fork.modules.commons as commons import so_vits_svc_fork.utils from . import utils -from .data_utils import TextAudioCollate, TextAudioSpeakerLoader -from .hparams import HParams +from .dataset import TextAudioCollate, TextAudioDataset from .modules.descriminators import MultiPeriodDiscriminator from .modules.losses import discriminator_loss, feature_loss, generator_loss, kl_loss from .modules.mel_processing import mel_spectrogram_torch @@ -30,296 +28,272 @@ LOG = getLogger(__name__) torch.backends.cudnn.benchmark = True -global_step = 0 -start_time = time.time() + + +class VCDataModule(pl.LightningDataModule): + def __init__(self, hparams: Any): + super().__init__() + self.__hparams = hparams + self.collate_fn = TextAudioCollate() + + # these should be called in setup(), but we need to calculate check_val_every_n_epoch + self.train_dataset = TextAudioDataset(self.__hparams, is_validation=False) + self.val_dataset = TextAudioDataset(self.__hparams, is_validation=True) + + def train_dataloader(self): + # since dataset just reads data from a file, set num_workers to 0 + return DataLoader( + self.train_dataset, + batch_size=self.__hparams.train.batch_size, + collate_fn=self.collate_fn, + ) + + def val_dataloader(self): + return DataLoader( + self.val_dataset, + batch_size=1, + collate_fn=self.collate_fn, + ) def train( config_path: Path | str, model_path: Path | str, reset_optimizer: bool = False ): - """Assume Single Node Multi GPUs Training Only""" config_path = Path(config_path) model_path = Path(model_path) - if not torch.cuda.is_available(): - raise RuntimeError("CUDA is not available.") - - hps = utils.get_backup_hparams(config_path, model_path) - utils.ensure_pretrained_model(model_path, hps.model.get("type_", "hifi-gan")) - n_gpus = torch.cuda.device_count() - os.environ["MASTER_ADDR"] = "localhost" - os.environ["MASTER_PORT"] = hps.train.port - - mp.spawn( - _run, - nprocs=n_gpus, - args=(n_gpus, hps, reset_optimizer), - ) - -def _run(rank: int, n_gpus: int, hps: HParams, reset_optimizer: bool = False): - global global_step - if rank == 0: - LOG.info(hps) - # utils.check_git_hash(hps.model_dir) - writer = SummaryWriter(log_dir=hps.model_dir) - writer_eval = SummaryWriter(log_dir=Path(hps.model_dir) / "eval") - - # for pytorch on win, backend use gloo - dist.init_process_group( - backend="gloo" if os.name == "nt" else "nccl", - init_method="env://", - world_size=n_gpus, - rank=rank, - ) - torch.manual_seed(hps.train.seed) - torch.cuda.set_device(rank) - collate_fn = TextAudioCollate() - train_dataset = TextAudioSpeakerLoader(hps) - train_loader = DataLoader( - train_dataset, - num_workers=0, - shuffle=False, - pin_memory=True, - batch_size=hps.train.batch_size, - collate_fn=collate_fn, + hparams = utils.get_backup_hparams(config_path, model_path) + utils.ensure_pretrained_model(model_path, hparams.model.get("type_", "hifi-gan")) + + datamodule = VCDataModule(hparams) + trainer = pl.Trainer( + logger=TensorBoardLogger(model_path), + # profiler="simple", + max_epochs=hparams.train.epochs, + check_val_every_n_epoch=math.ceil( + hparams.train.eval_interval + / len(datamodule.train_dataset) + * hparams.train.batch_size + ), ) - if rank == 0: - eval_dataset = TextAudioSpeakerLoader(hps, is_validation=True) - eval_loader = DataLoader( - eval_dataset, - num_workers=0, - shuffle=False, - batch_size=1, - pin_memory=False, - drop_last=False, - collate_fn=collate_fn, + model = VitsLightning(reset_optimizer=reset_optimizer, **hparams) + trainer.fit(model, datamodule=datamodule) + + +class VitsLightning(pl.LightningModule): + def on_train_start(self) -> None: + self.load(False) + + # check if using tpu + if isinstance(self.trainer.accelerator, TPUAccelerator): + # patch torch.stft to use cpu + LOG.warning("Using TPU. Patching torch.stft to use cpu.") + + def stft( + input: torch.Tensor, + n_fft: int, + hop_length: int | None = None, + win_length: int | None = None, + window: torch.Tensor | None = None, + center: bool = True, + pad_mode: str = "reflect", + normalized: bool = False, + onesided: bool | None = None, + return_complex: bool | None = None, + ) -> torch.Tensor: + device = input.device + input = input.cpu() + if window is not None: + window = window.cpu() + return torch.functional.stft( + input, + n_fft, + hop_length, + win_length, + window, + center, + pad_mode, + normalized, + onesided, + return_complex, + ).to(device) + + torch.stft = stft + + def set_current_epoch(self, epoch: int): + LOG.info(f"Setting current epoch to {epoch}") + self.trainer.fit_loop.epoch_progress.current.completed = epoch + assert self.current_epoch == epoch, f"{self.current_epoch} != {epoch}" + + def set_global_step(self, global_step: int): + LOG.info(f"Setting global step to {global_step}") + self.trainer.fit_loop.epoch_loop.manual_optimization.optim_step_progress.total.completed = ( + global_step + ) + self.trainer.fit_loop.epoch_loop.automatic_optimization.optim_progress.optimizer.step.total.completed = ( + global_step + ) + assert self.global_step == global_step, f"{self.global_step} != {global_step}" + + def load(self, reset_optimizer: bool = False): + latest_g_path = utils.latest_checkpoint_path(self.hparams.model_dir, "G_*.pth") + latest_d_path = utils.latest_checkpoint_path(self.hparams.model_dir, "D_*.pth") + if latest_g_path is not None and latest_d_path is not None: + try: + _, _, _, epoch = utils.load_checkpoint( + latest_g_path, + self.net_g, + self.optim_g, + reset_optimizer, + ) + _, _, _, epoch = utils.load_checkpoint( + latest_d_path, + self.net_d, + self.optim_d, + reset_optimizer, + ) + self.set_current_epoch(epoch) + global_step = epoch * len(self.trainer.train_dataloader) + self.set_global_step(global_step) + assert self.current_epoch == epoch, f"{self.current_epoch} != {epoch}" + self.scheduler_g.last_epoch = self.current_epoch - 1 + self.scheduler_d.last_epoch = self.current_epoch - 1 + except Exception as e: + raise RuntimeError("Failed to load checkpoint") from e + else: + LOG.warning("No checkpoint found. Start from scratch.") + + def __init__(self, reset_optimizer: bool = False, **hparams: Any): + super().__init__() + self.save_hyperparameters("reset_optimizer") + self.save_hyperparameters(*[k for k in hparams.keys()]) + torch.manual_seed(self.hparams.train.seed) + self.net_g = SynthesizerTrn( + self.hparams.data.filter_length // 2 + 1, + self.hparams.train.segment_size // self.hparams.data.hop_length, + **self.hparams.model, + ) + self.net_d = MultiPeriodDiscriminator(self.hparams.model.use_spectral_norm) + self.automatic_optimization = False + self.optim_g = torch.optim.AdamW( + self.net_g.parameters(), + self.hparams.train.learning_rate, + betas=self.hparams.train.betas, + eps=self.hparams.train.eps, + ) + self.optim_d = torch.optim.AdamW( + self.net_d.parameters(), + self.hparams.train.learning_rate, + betas=self.hparams.train.betas, + eps=self.hparams.train.eps, + ) + self.scheduler_g = torch.optim.lr_scheduler.ExponentialLR( + self.optim_g, gamma=self.hparams.train.lr_decay + ) + self.scheduler_d = torch.optim.lr_scheduler.ExponentialLR( + self.optim_d, gamma=self.hparams.train.lr_decay ) - net_g = SynthesizerTrn( - hps.data.filter_length // 2 + 1, - hps.train.segment_size // hps.data.hop_length, - **hps.model, - ).cuda(rank) - net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank) - optim_g = torch.optim.AdamW( - net_g.parameters(), - hps.train.learning_rate, - betas=hps.train.betas, - eps=hps.train.eps, - ) - optim_d = torch.optim.AdamW( - net_d.parameters(), - hps.train.learning_rate, - betas=hps.train.betas, - eps=hps.train.eps, - ) - net_g = DDP(net_g, device_ids=[rank]) - net_d = DDP(net_d, device_ids=[rank]) - - latest_g_path = utils.latest_checkpoint_path(hps.model_dir, "G_*.pth") - latest_d_path = utils.latest_checkpoint_path(hps.model_dir, "D_*.pth") - if latest_g_path is not None and latest_d_path is not None: - try: - _, _, _, epoch_str = utils.load_checkpoint( - latest_g_path, - net_g, - optim_g, - reset_optimizer, - ) - _, _, _, epoch_str = utils.load_checkpoint( - latest_d_path, - net_d, - optim_d, - reset_optimizer, + def configure_optimizers(self): + return [self.optim_g, self.optim_d], [self.scheduler_g, self.scheduler_d] + + def log_image_dict( + self, image_dict: dict[str, Any], dataformats: str = "HWC" + ) -> None: + if not isinstance(self.logger, TensorBoardLogger): + warnings.warn("Image logging is only supported with TensorBoardLogger.") + return + writer: SummaryWriter = self.logger.experiment + for k, v in image_dict.items(): + try: + writer.add_image(k, v, self.global_step, dataformats=dataformats) + except Exception as e: + warnings.warn(f"Failed to log image {k}: {e}") + + def log_audio_dict(self, audio_dict: dict[str, Any]) -> None: + if not isinstance(self.logger, TensorBoardLogger): + warnings.warn("Audio logging is only supported with TensorBoardLogger.") + return + writer: SummaryWriter = self.logger.experiment + for k, v in audio_dict.items(): + writer.add_audio( + k, v, self.global_step, sample_rate=self.hparams.data.sampling_rate ) - epoch_str = max(epoch_str, 1) - global_step = (epoch_str - 1) * len(train_loader) - except Exception as e: - raise RuntimeError("Failed to load checkpoint") from e - else: - LOG.warning("No checkpoint found. Start from scratch.") - epoch_str = 1 - global_step = 0 - if reset_optimizer: - epoch_str = 1 - global_step = 0 - - scheduler_g = torch.optim.lr_scheduler.ExponentialLR( - optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2 - ) - scheduler_d = torch.optim.lr_scheduler.ExponentialLR( - optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2 - ) - scaler = GradScaler(enabled=hps.train.fp16_run) - - LOG.info( - "Start training..." - "Note: You do not need to wait until the progress bar is full." - ) + def training_step(self, batch: dict[str, torch.Tensor], batch_idx: int) -> None: + self.net_g.train() + self.net_d.train() + + # get optims + optim_g, optim_d = self.optimizers() + + # Generator + # train + self.toggle_optimizer(optim_g) + c, f0, spec, mel, y, g, lengths, uv = batch + ( + y_hat, + y_hat_mb, + ids_slice, + z_mask, + (z, z_p, m_p, logs_p, m_q, logs_q), + pred_lf0, + norm_lf0, + lf0, + ) = self.net_g(c, f0, uv, spec, g=g, c_lengths=lengths, spec_lengths=lengths) + y_mel = commons.slice_segments( + mel, + ids_slice, + self.hparams.train.segment_size // self.hparams.data.hop_length, + ) + y_hat_mel = mel_spectrogram_torch(y_hat.squeeze(1), self.hparams) + y = commons.slice_segments( + y, + ids_slice * self.hparams.data.hop_length, + self.hparams.train.segment_size, + ) - for epoch in trange( - epoch_str, hps.train.epochs + 1, initial=epoch_str, total=hps.train.epochs - ): - if rank == 0: - _train_and_evaluate( - rank, - epoch, - hps, - (net_g, net_d), - (optim_g, optim_d), - (scheduler_g, scheduler_d), - scaler, - (train_loader, eval_loader), - (writer, writer_eval), - ) - else: - _train_and_evaluate( - rank, - epoch, - hps, - (net_g, net_d), - (optim_g, optim_d), - (scheduler_g, scheduler_d), - scaler, - (train_loader, None), - None, - ) - scheduler_g.step() - scheduler_d.step() - - -def _train_and_evaluate( - rank: int, - epoch: int, - hps: HParams, - nets: tuple[nn.Module, nn.Module], - optims: tuple[torch.optim.Optimizer, torch.optim.Optimizer], - schedulers: tuple[ - torch.optim.lr_scheduler.ExponentialLR, torch.optim.lr_scheduler.ExponentialLR - ], - scaler: GradScaler, - loaders: tuple[DataLoader, DataLoader | None], - writers: None | tuple[SummaryWriter, SummaryWriter], -): - net_g, net_d = nets - optim_g, optim_d = optims - scheduler_g, scheduler_d = schedulers - train_loader, eval_loader = loaders - if writers is not None: - writer, writer_eval = writers - - # train_loader.batch_sampler.set_epoch(epoch) - global global_step - - net_g.train() - net_d.train() - for batch_idx, items in enumerate(train_loader): - items = [x.cuda(rank, non_blocking=True) for x in items] - c, f0, spec, mel, y, g, lengths, uv = items - with autocast(enabled=hps.train.fp16_run): - ( - y_hat, - y_hat_mb, - ids_slice, - z_mask, - (z, z_p, m_p, logs_p, m_q, logs_q), - pred_lf0, - norm_lf0, - lf0, - ) = net_g(c, f0, uv, spec, g=g, c_lengths=lengths, spec_lengths=lengths) - - y_mel = commons.slice_segments( - mel, ids_slice, hps.train.segment_size // hps.data.hop_length + # generator loss + LOG.debug("Calculating generator loss") + with torch.no_grad(): + y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = self.net_d(y, y_hat) + with autocast(enabled=False): + loss_mel = F.l1_loss(y_mel, y_hat_mel) * self.hparams.train.c_mel + loss_kl = ( + kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * self.hparams.train.c_kl ) - y_hat_mel = mel_spectrogram_torch(y_hat.squeeze(1), hps) - y = commons.slice_segments( - y, ids_slice * hps.data.hop_length, hps.train.segment_size - ) # slice - - # Discriminator - y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach()) - - with autocast(enabled=False): - loss_disc, losses_disc_r, losses_disc_g = discriminator_loss( - y_d_hat_r, y_d_hat_g - ) - loss_disc_all = loss_disc - - optim_d.zero_grad() - scaler.scale(loss_disc_all).backward() - scaler.unscale_(optim_d) - grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None) - scaler.step(optim_d) - - with autocast(enabled=hps.train.fp16_run): - # Generator - y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat) - with autocast(enabled=False): - loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel - loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl - loss_fm = feature_loss(fmap_r, fmap_g) - loss_gen, losses_gen = generator_loss(y_d_hat_g) - loss_lf0 = F.mse_loss(pred_lf0, lf0) - loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl + loss_lf0 - - # MB-iSTFT-VITS - loss_subband = torch.tensor(0.0) - if hps.model.get("type_") == "mb-istft": - from .modules.decoders.mb_istft import PQMF, subband_stft_loss - - y_mb = PQMF(y.device, hps.model.subbands).analysis(y) - loss_subband = subband_stft_loss(hps, y_mb, y_hat_mb) - loss_gen_all += loss_subband - - optim_g.zero_grad() - scaler.scale(loss_gen_all).backward() - scaler.unscale_(optim_g) - grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None) - scaler.step(optim_g) - scaler.update() - - if rank == 0: - if global_step % hps.train.log_interval == 0: - lr = optim_g.param_groups[0]["lr"] - losses = { - "discriminator": loss_disc.item(), - "generator": loss_gen.item(), - "feature_matching": loss_fm.item(), - "melspectrogram": loss_mel.item(), - "kl_divergence": loss_kl.item(), - } - if hps.model.get("type_") == "mb-istft": - losses["subband_stft"] = loss_subband.item() - LOG.info( - "Train Epoch: {} [{:.0f}%]".format( - epoch, 100.0 * batch_idx / len(train_loader) - ) - ) - LOG.info(f"Losses: {losses}, step: {global_step}, lr: {lr}") - - scalar_dict = { - "loss/g/total": loss_gen_all, - "loss/d/total": loss_disc_all, - "learning_rate": lr, - "grad_norm_d": grad_norm_d, - "grad_norm_g": grad_norm_g, - } - scalar_dict.update( - { - "loss/g/fm": loss_fm, - "loss/g/mel": loss_mel, - "loss/g/kl": loss_kl, - "loss/g/lf0": loss_lf0, - } - ) - if hps.model.get("type_") == "mb-istft": - scalar_dict["loss/g/subband"] = loss_subband - - # scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)}) - # scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)}) - # scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)}) - image_dict = { + loss_fm = feature_loss(fmap_r, fmap_g) + loss_gen, losses_gen = generator_loss(y_d_hat_g) + loss_lf0 = F.mse_loss(pred_lf0, lf0) + loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl + loss_lf0 + + # MB-iSTFT-VITS + loss_subband = torch.tensor(0.0) + if self.hparams.model.get("type_") == "mb-istft": + from .modules.decoders.mb_istft import PQMF, subband_stft_loss + + y_mb = PQMF(y.device, self.hparams.model.subbands).analysis(y) + loss_subband = subband_stft_loss(self.hparams, y_mb, y_hat_mb) + loss_gen_all += loss_subband + + # log loss + self.log("grad_norm_g", commons.clip_grad_value_(self.net_g.parameters(), None)) + self.log("lr", self.optim_g.param_groups[0]["lr"]) + self.log_dict( + { + "loss/g/total": loss_gen_all, + "loss/g/fm": loss_fm, + "loss/g/mel": loss_mel, + "loss/g/kl": loss_kl, + "loss/g/lf0": loss_lf0, + }, + prog_bar=True, + ) + if self.hparams.model.get("type_") == "mb-istft": + self.log("loss/g/subband", loss_subband) + if self.global_step % self.hparams.train.log_interval == 0: + self.log_image_dict( + { "slice/mel_org": utils.plot_spectrogram_to_numpy( y_mel[0].data.cpu().numpy() ), @@ -338,78 +312,71 @@ def _train_and_evaluate( norm_lf0[0, 0, :].detach().cpu().numpy(), ), } + ) - utils.summarize( - writer=writer, - global_step=global_step, - images=image_dict, - scalars=scalar_dict, - ) + # optimizer + self.manual_backward(loss_gen_all) + optim_g.step() + optim_g.zero_grad() + self.untoggle_optimizer(optim_g) - if global_step % hps.train.eval_interval == 0: - LOG.info("Saving checkpoints...") - _evaluate(hps, net_g, eval_loader, writer_eval) - utils.save_checkpoint( - net_g, - optim_g, - hps.train.learning_rate, - epoch, - Path(hps.model_dir) / f"G_{global_step}.pth", - ) - utils.save_checkpoint( - net_d, - optim_d, - hps.train.learning_rate, - epoch, - Path(hps.model_dir) / f"D_{global_step}.pth", - ) - keep_ckpts = getattr(hps.train, "keep_ckpts", 0) - if keep_ckpts > 0: - utils.clean_checkpoints( - path_to_models=hps.model_dir, - n_ckpts_to_keep=keep_ckpts, - sort_by_time=True, - ) - - global_step += 1 - - if rank == 0: - global start_time - now = time.time() - durtaion = format(now - start_time, ".2f") - LOG.info(f"====> Epoch: {epoch}, cost {durtaion} s") - start_time = now - - -def _evaluate( - hps: HParams, - generator: torch.nn.Module, - eval_loader: torch.utils.data.DataLoader, - writer_eval: SummaryWriter, -) -> None: - generator.eval() - image_dict = {} - audio_dict = {} - with torch.no_grad(): - for batch_idx, items in enumerate(eval_loader): - items = [item[:1].cuda(0) for item in items] - c, f0, _, mel, y, g, _, uv = items - y_hat = generator.module.infer(c, f0, uv, g=g) - y_hat_mel = mel_spectrogram_torch(y_hat.squeeze(1).float(), hps) - audio_dict.update( + # Discriminator + # train + self.toggle_optimizer(optim_d) + y_d_hat_r, y_d_hat_g, _, _ = self.net_d(y, y_hat.detach()) + + # discriminator loss + with autocast(enabled=False): + loss_disc, losses_disc_r, losses_disc_g = discriminator_loss( + y_d_hat_r, y_d_hat_g + ) + loss_disc_all = loss_disc + + # log loss + self.log("loss/d/total", loss_disc_all, prog_bar=True) + self.log("grad_norm_d", commons.clip_grad_value_(self.net_d.parameters(), None)) + + # optimizer + self.manual_backward(loss_disc_all) + optim_d.zero_grad() + optim_d.step() + self.untoggle_optimizer(optim_d) + + def validation_step(self, batch, batch_idx): + with torch.no_grad(): + self.net_g.eval() + c, f0, _, mel, y, g, _, uv = batch + y_hat = self.net_g.infer(c, f0, uv, g=g) + y_hat_mel = mel_spectrogram_torch(y_hat.squeeze(1).float(), self.hparams) + self.log_audio_dict( {f"gen/audio_{batch_idx}": y_hat[0], f"gt/audio_{batch_idx}": y[0]} ) - image_dict.update( - { - "gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy()), - "gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy()), - } - ) - utils.summarize( - writer=writer_eval, - global_step=global_step, - images=image_dict, - audios=audio_dict, - audio_sampling_rate=hps.data.sampling_rate, - ) - generator.train() + self.log_image_dict( + { + "gen/mel": utils.plot_spectrogram_to_numpy( + y_hat_mel[0].cpu().numpy() + ), + "gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy()), + } + ) + utils.save_checkpoint( + self.net_g, + self.optim_g, + self.hparams.train.learning_rate, + self.current_epoch, + Path(self.hparams.model_dir) / f"G_{self.global_step}.pth", + ) + utils.save_checkpoint( + self.net_d, + self.optim_d, + self.hparams.train.learning_rate, + self.current_epoch, + Path(self.hparams.model_dir) / f"D_{self.global_step}.pth", + ) + keep_ckpts = self.hparams.train.get("keep_ckpts", 0) + if keep_ckpts > 0: + utils.clean_checkpoints( + path_to_models=self.hparams.model_dir, + n_ckpts_to_keep=keep_ckpts, + sort_by_time=True, + ) diff --git a/src/so_vits_svc_fork/utils.py b/src/so_vits_svc_fork/utils.py index 98c9cf3c..3995a3dd 100644 --- a/src/so_vits_svc_fork/utils.py +++ b/src/so_vits_svc_fork/utils.py @@ -25,6 +25,18 @@ HUBERT_SAMPLING_RATE = 16000 +def get_optimal_device(index: int = 0) -> torch.device: + if torch.cuda.is_available(): + return torch.device(f"cuda:{index % torch.cuda.device_count()}") + else: + try: + return torch.device("xla") + # return xm.xla_device() + except ImportError: + pass + return torch.device("cpu") + + def download_file( url: str, filepath: Path | str, @@ -115,7 +127,7 @@ def ensure_pretrained_model( return -def get_hubert_model(device: torch.device) -> HubertModel: +def get_hubert_model(device: str | torch.device) -> HubertModel: (path,) = ensure_pretrained_model(Path("."), "contentvec") models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task( @@ -164,6 +176,13 @@ def get_content( def _substitute_if_same_shape(to_: dict[str, Any], from_: dict[str, Any]) -> None: + not_in_to = list(filter(lambda x: x not in to_, from_.keys())) + not_in_from = list(filter(lambda x: x not in from_, to_.keys())) + if not_in_to: + warnings.warn(f"Keys not found in model state dict:" f"{not_in_to}") + if not_in_from: + warnings.warn(f"Keys not found in checkpoint state dict:" f"{not_in_from}") + shape_missmatch = [] for k, v in from_.items(): if k not in to_: warnings.warn(f"Key {k} not found in model state dict") @@ -173,14 +192,16 @@ def _substitute_if_same_shape(to_: dict[str, Any], from_: dict[str, Any]) -> Non if to_[k].shape == v.shape: to_[k] = v else: - warnings.warn( - f"Shape mismatch for key {k}, {to_[k].shape} != {v.shape}" - ) + shape_missmatch.append((k, to_[k].shape, v.shape)) elif isinstance(v, dict): assert isinstance(to_[k], dict) _substitute_if_same_shape(to_[k], v) else: to_[k] = v + if shape_missmatch: + warnings.warn( + f"Shape mismatch: {[f'{k}: {v1} -> {v2}' for k, v1, v2 in shape_missmatch]}" + ) def safe_load(model: torch.nn.Module, state_dict: dict[str, Any]) -> None: @@ -289,30 +310,6 @@ def clean_checkpoints( to_delete.unlink() -from torch.utils.tensorboard.writer import SummaryWriter - - -def summarize( - writer: SummaryWriter, - global_step: int, - scalars: dict[str, float] = {}, - histograms: dict[str, ndarray] = {}, - images: dict[str, ndarray] = {}, - audios: dict[str, ndarray] = {}, - audio_sampling_rate: int | None = None, -) -> None: - for k, v in scalars.items(): - writer.add_scalar(k, v, global_step) - for k, v in histograms.items(): - writer.add_histogram(k, v, global_step) - for k, v in images.items(): - writer.add_image(k, v, global_step, dataformats="HWC") - for k, v in audios.items(): - if audio_sampling_rate is None: - raise ValueError("audio_sampling_rate must be provided") - writer.add_audio(k, v, global_step, audio_sampling_rate) - - def latest_checkpoint_path(dir_path: Path | str, regex: str = "G_*.pth") -> Path | None: dir_path = Path(dir_path) name_key = lambda p: int(re.match(r"._(\d+)\.pth", p.name).group(1))