refactor: use save_fsspec() from Trainer

TTS/encoder/utils/generic_utils.py

@@ -5,10 +5,10 @@ import random
 
 import numpy as np
 from scipy import signal
+from trainer.io import save_fsspec
 
 from TTS.encoder.models.lstm import LSTMSpeakerEncoder
 from TTS.encoder.models.resnet import ResNetSpeakerEncoder
-from TTS.utils.io import save_fsspec
 
 
 class AugmentWAV(object):

TTS/encoder/utils/io.py

@@ -1,7 +1,7 @@
 import datetime
 import os
 
-from TTS.utils.io import save_fsspec
+from trainer.io import save_fsspec
 
 
 def save_checkpoint(model, optimizer, model_loss, out_path, current_step):

TTS/utils/io.py

@@ -8,6 +8,7 @@ from typing import Any, Callable, Dict, Union
 import fsspec
 import torch
 from coqpit import Coqpit
+from trainer.io import save_fsspec
 
 from TTS.utils.generic_utils import get_user_data_dir
 
@@ -102,18 +103,6 @@ def load_checkpoint(
     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()