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import datetime
import json
import os
import pickle as pickle_tts
import shutil
from typing import Any, Callable, Dict, Union
import fsspec
import torch
from coqpit import Coqpit
from TTS.utils.generic_utils import get_user_data_dir
try:
from torch import serialization as _torch_serialization
except ImportError: # pragma: no cover - older torch versions
_torch_serialization = None
_SAFE_GLOBALS_REGISTERED = False
def _ensure_torch_safe_globals():
"""Allow torch.load to unpickle trusted XTTS config types when weights_only=True."""
global _SAFE_GLOBALS_REGISTERED
if _SAFE_GLOBALS_REGISTERED or _torch_serialization is None:
return
add_safe_globals = getattr(_torch_serialization, "add_safe_globals", None)
if add_safe_globals is None:
_SAFE_GLOBALS_REGISTERED = True
return
safe_types = []
try: # XTTS config
from TTS.tts.configs.xtts_config import XttsConfig
safe_types.append(XttsConfig)
except ImportError:
pass
try: # XTTS model args
from TTS.tts.models.xtts import XttsArgs, XttsAudioConfig
safe_types.extend([XttsArgs, XttsAudioConfig])
except ImportError:
pass
try: # Shared dataset configs referenced in checkpoints
from TTS.config.shared_configs import BaseDatasetConfig
safe_types.append(BaseDatasetConfig)
except ImportError:
pass
if safe_types:
add_safe_globals(safe_types)
_SAFE_GLOBALS_REGISTERED = True
class RenamingUnpickler(pickle_tts.Unpickler):
"""Overload default pickler to solve module renaming problem"""
def find_class(self, module, name):
return super().find_class(module.replace("mozilla_voice_tts", "TTS"), name)
class AttrDict(dict):
"""A custom dict which converts dict keys
to class attributes"""
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.__dict__ = self
def copy_model_files(config: Coqpit, out_path, new_fields=None):
"""Copy config.json and other model files to training folder and add
new fields.
Args:
config (Coqpit): Coqpit config defining the training run.
out_path (str): output path to copy the file.
new_fields (dict): new fileds to be added or edited
in the config file.
"""
copy_config_path = os.path.join(out_path, "config.json")
# add extra information fields
if new_fields:
config.update(new_fields, allow_new=True)
# TODO: Revert to config.save_json() once Coqpit supports arbitrary paths.
with fsspec.open(copy_config_path, "w", encoding="utf8") as f:
json.dump(config.to_dict(), f, indent=4)
# copy model stats file if available
if config.audio.stats_path is not None:
copy_stats_path = os.path.join(out_path, "scale_stats.npy")
filesystem = fsspec.get_mapper(copy_stats_path).fs
if not filesystem.exists(copy_stats_path):
with fsspec.open(config.audio.stats_path, "rb") as source_file:
with fsspec.open(copy_stats_path, "wb") as target_file:
shutil.copyfileobj(source_file, target_file)
def load_fsspec(
path: str,
map_location: Union[str, Callable, torch.device, Dict[Union[str, torch.device], Union[str, torch.device]]] = None,
cache: bool = True,
**kwargs,
) -> Any:
"""Like torch.load but can load from other locations (e.g. s3:// , gs://).
Args:
path: Any path or url supported by fsspec.
map_location: torch.device or str.
cache: If True, cache a remote file locally for subsequent calls. It is cached under `get_user_data_dir()/tts_cache`. Defaults to True.
**kwargs: Keyword arguments forwarded to torch.load.
Returns:
Object stored in path.
"""
is_local = os.path.isdir(path) or os.path.isfile(path)
if cache and not is_local:
with fsspec.open(
f"filecache::{path}",
filecache={"cache_storage": str(get_user_data_dir("tts_cache"))},
mode="rb",
) as f:
_ensure_torch_safe_globals()
return torch.load(f, map_location=map_location, **kwargs)
else:
with fsspec.open(path, "rb") as f:
_ensure_torch_safe_globals()
return torch.load(f, map_location=map_location, **kwargs)
def load_checkpoint(
model, checkpoint_path, use_cuda=False, eval=False, cache=False
): # pylint: disable=redefined-builtin
try:
state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"), cache=cache)
except ModuleNotFoundError:
pickle_tts.Unpickler = RenamingUnpickler
state = load_fsspec(checkpoint_path, map_location=torch.device("cpu"), pickle_module=pickle_tts, cache=cache)
model.load_state_dict(state["model"])
if use_cuda:
model.cuda()
if eval:
model.eval()
return model, state
def save_fsspec(state: Any, path: str, **kwargs):
"""Like torch.save but can save to other locations (e.g. s3:// , gs://).
Args:
state: State object to save
path: Any path or url supported by fsspec.
**kwargs: Keyword arguments forwarded to torch.save.
"""
with fsspec.open(path, "wb") as f:
torch.save(state, f, **kwargs)
def save_model(config, model, optimizer, scaler, current_step, epoch, output_path, **kwargs):
if hasattr(model, "module"):
model_state = model.module.state_dict()
else:
model_state = model.state_dict()
if isinstance(optimizer, list):
optimizer_state = [optim.state_dict() for optim in optimizer]
elif optimizer.__class__.__name__ == "CapacitronOptimizer":
optimizer_state = [optimizer.primary_optimizer.state_dict(), optimizer.secondary_optimizer.state_dict()]
else:
optimizer_state = optimizer.state_dict() if optimizer is not None else None
if isinstance(scaler, list):
scaler_state = [s.state_dict() for s in scaler]
else:
scaler_state = scaler.state_dict() if scaler is not None else None
if isinstance(config, Coqpit):
config = config.to_dict()
state = {
"config": config,
"model": model_state,
"optimizer": optimizer_state,
"scaler": scaler_state,
"step": current_step,
"epoch": epoch,
"date": datetime.date.today().strftime("%B %d, %Y"),
}
state.update(kwargs)
save_fsspec(state, output_path)
def save_checkpoint(
config,
model,
optimizer,
scaler,
current_step,
epoch,
output_folder,
**kwargs,
):
file_name = "checkpoint_{}.pth".format(current_step)
checkpoint_path = os.path.join(output_folder, file_name)
print("\n > CHECKPOINT : {}".format(checkpoint_path))
save_model(
config,
model,
optimizer,
scaler,
current_step,
epoch,
checkpoint_path,
**kwargs,
)
def save_best_model(
current_loss,
best_loss,
config,
model,
optimizer,
scaler,
current_step,
epoch,
out_path,
keep_all_best=False,
keep_after=10000,
**kwargs,
):
if current_loss < best_loss:
best_model_name = f"best_model_{current_step}.pth"
checkpoint_path = os.path.join(out_path, best_model_name)
print(" > BEST MODEL : {}".format(checkpoint_path))
save_model(
config,
model,
optimizer,
scaler,
current_step,
epoch,
checkpoint_path,
model_loss=current_loss,
**kwargs,
)
fs = fsspec.get_mapper(out_path).fs
# only delete previous if current is saved successfully
if not keep_all_best or (current_step < keep_after):
model_names = fs.glob(os.path.join(out_path, "best_model*.pth"))
for model_name in model_names:
if os.path.basename(model_name) != best_model_name:
fs.rm(model_name)
# create a shortcut which always points to the currently best model
shortcut_name = "best_model.pth"
shortcut_path = os.path.join(out_path, shortcut_name)
fs.copy(checkpoint_path, shortcut_path)
best_loss = current_loss
return best_loss
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