refactor: use save_checkpoint()/save_best_model() from Trainer

TTS/bin/train_encoder.py

@@ -8,12 +8,12 @@ import traceback
 
 import torch
 from torch.utils.data import DataLoader
-from trainer.io import copy_model_files
+from trainer.io import copy_model_files, save_best_model, save_checkpoint
 from trainer.torch import NoamLR
 from trainer.trainer_utils import get_optimizer
 
 from TTS.encoder.dataset import EncoderDataset
-from TTS.encoder.utils.generic_utils import save_best_model, save_checkpoint, setup_encoder_model
+from TTS.encoder.utils.generic_utils import setup_encoder_model
 from TTS.encoder.utils.training import init_training
 from TTS.encoder.utils.visual import plot_embeddings
 from TTS.tts.datasets import load_tts_samples
@@ -222,7 +222,9 @@ def train(model, optimizer, scheduler, criterion, data_loader, eval_data_loader,
 
             if global_step % c.save_step == 0:
                 # save model
-                save_checkpoint(model, optimizer, criterion, loss.item(), OUT_PATH, global_step, epoch)
+                save_checkpoint(
+                    c, model, optimizer, None, global_step, epoch, OUT_PATH, criterion=criterion.state_dict()
+                )
 
             end_time = time.time()
 
@@ -245,7 +247,18 @@ def train(model, optimizer, scheduler, criterion, data_loader, eval_data_loader,
                 flush=True,
             )
             # save the best checkpoint
-            best_loss = save_best_model(model, optimizer, criterion, eval_loss, best_loss, OUT_PATH, global_step, epoch)
+            best_loss = save_best_model(
+                eval_loss,
+                best_loss,
+                c,
+                model,
+                optimizer,
+                None,
+                global_step,
+                epoch,
+                OUT_PATH,
+                criterion=criterion.state_dict(),
+            )
             model.train()
 
     return best_loss, global_step

TTS/encoder/utils/generic_utils.py

@@ -1,11 +1,9 @@
-import datetime
 import glob
 import os
 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
@@ -136,41 +134,3 @@ def setup_encoder_model(config: "Coqpit"):
             audio_config=config.audio,
         )
     return model
-
-
-def save_checkpoint(model, optimizer, criterion, model_loss, out_path, current_step, epoch):
-    checkpoint_path = "checkpoint_{}.pth".format(current_step)
-    checkpoint_path = os.path.join(out_path, checkpoint_path)
-    print(" | | > Checkpoint saving : {}".format(checkpoint_path))
-
-    new_state_dict = model.state_dict()
-    state = {
-        "model": new_state_dict,
-        "optimizer": optimizer.state_dict() if optimizer is not None else None,
-        "criterion": criterion.state_dict(),
-        "step": current_step,
-        "epoch": epoch,
-        "loss": model_loss,
-        "date": datetime.date.today().strftime("%B %d, %Y"),
-    }
-    save_fsspec(state, checkpoint_path)
-
-
-def save_best_model(model, optimizer, criterion, model_loss, best_loss, out_path, current_step, epoch):
-    if model_loss < best_loss:
-        new_state_dict = model.state_dict()
-        state = {
-            "model": new_state_dict,
-            "optimizer": optimizer.state_dict(),
-            "criterion": criterion.state_dict(),
-            "step": current_step,
-            "epoch": epoch,
-            "loss": model_loss,
-            "date": datetime.date.today().strftime("%B %d, %Y"),
-        }
-        best_loss = model_loss
-        bestmodel_path = "best_model.pth"
-        bestmodel_path = os.path.join(out_path, bestmodel_path)
-        print("\n > BEST MODEL ({0:.5f}) : {1:}".format(model_loss, bestmodel_path))
-        save_fsspec(state, bestmodel_path)
-    return best_loss

TTS/encoder/utils/io.py

@@ -1,38 +0,0 @@
-import datetime
-import os
-
-from trainer.io import save_fsspec
-
-
-def save_checkpoint(model, optimizer, model_loss, out_path, current_step):
-    checkpoint_path = "checkpoint_{}.pth".format(current_step)
-    checkpoint_path = os.path.join(out_path, checkpoint_path)
-    print(" | | > Checkpoint saving : {}".format(checkpoint_path))
-
-    new_state_dict = model.state_dict()
-    state = {
-        "model": new_state_dict,
-        "optimizer": optimizer.state_dict() if optimizer is not None else None,
-        "step": current_step,
-        "loss": model_loss,
-        "date": datetime.date.today().strftime("%B %d, %Y"),
-    }
-    save_fsspec(state, checkpoint_path)
-
-
-def save_best_model(model, optimizer, model_loss, best_loss, out_path, current_step):
-    if model_loss < best_loss:
-        new_state_dict = model.state_dict()
-        state = {
-            "model": new_state_dict,
-            "optimizer": optimizer.state_dict(),
-            "step": current_step,
-            "loss": model_loss,
-            "date": datetime.date.today().strftime("%B %d, %Y"),
-        }
-        best_loss = model_loss
-        bestmodel_path = "best_model.pth"
-        bestmodel_path = os.path.join(out_path, bestmodel_path)
-        print("\n > BEST MODEL ({0:.5f}) : {1:}".format(model_loss, bestmodel_path))
-        save_fsspec(state, bestmodel_path)
-    return best_loss

tests/aux_tests/test_embedding_manager.py

@@ -3,11 +3,11 @@ import unittest
 
 import numpy as np
 import torch
+from trainer.io import save_checkpoint
 
 from tests import get_tests_input_path
 from TTS.config import load_config
 from TTS.encoder.utils.generic_utils import setup_encoder_model
-from TTS.encoder.utils.io import save_checkpoint
 from TTS.tts.utils.managers import EmbeddingManager
 from TTS.utils.audio import AudioProcessor
 
@@ -31,7 +31,7 @@ class EmbeddingManagerTest(unittest.TestCase):
 
         # create a dummy speaker encoder
         model = setup_encoder_model(config)
-        save_checkpoint(model, None, None, get_tests_input_path(), 0)
+        save_checkpoint(config, model, None, None, 0, 0, get_tests_input_path())
 
         # load audio processor and speaker encoder
         manager = EmbeddingManager(encoder_model_path=encoder_model_path, encoder_config_path=encoder_config_path)

tests/aux_tests/test_speaker_manager.py

@@ -3,11 +3,11 @@ import unittest
 
 import numpy as np
 import torch
+from trainer.io import save_checkpoint
 
 from tests import get_tests_input_path
 from TTS.config import load_config
 from TTS.encoder.utils.generic_utils import setup_encoder_model
-from TTS.encoder.utils.io import save_checkpoint
 from TTS.tts.utils.speakers import SpeakerManager
 from TTS.utils.audio import AudioProcessor
 
@@ -30,7 +30,7 @@ class SpeakerManagerTest(unittest.TestCase):
 
         # create a dummy speaker encoder
         model = setup_encoder_model(config)
-        save_checkpoint(model, None, None, get_tests_input_path(), 0)
+        save_checkpoint(config, model, None, None, 0, 0, get_tests_input_path())
 
         # load audio processor and speaker encoder
         ap = AudioProcessor(**config.audio)

tests/inference_tests/test_synthesizer.py

@@ -1,10 +1,11 @@
 import os
 import unittest
 
+from trainer.io import save_checkpoint
+
 from tests import get_tests_input_path
 from TTS.config import load_config
 from TTS.tts.models import setup_model
-from TTS.utils.io import save_checkpoint
 from TTS.utils.synthesizer import Synthesizer