Don't use deprecated Repository anymore

training/run_parler_tts_training.py

@@ -19,28 +19,27 @@
 import logging
 import os
 import re
-import sys
 import shutil
+import sys
 import time
-from multiprocess import set_start_method
+from dataclasses import dataclass, field
 from datetime import timedelta
-
-
-import evaluate
-from tqdm import tqdm
 from pathlib import Path
-from dataclasses import dataclass, field
-from typing import Dict, List, Optional, Union, Set
+from typing import Dict, List, Optional, Set, Union
 
 import datasets
+import evaluate
 import numpy as np
 import torch
-from torch.utils.data import DataLoader
-
-from datasets import DatasetDict, load_dataset, Dataset, IterableDataset, interleave_datasets, concatenate_datasets
-
-from huggingface_hub import Repository, create_repo
 import transformers
+from accelerate import Accelerator
+from accelerate.utils import AutocastKwargs, InitProcessGroupKwargs, TorchDynamoPlugin, set_seed
+from accelerate.utils.memory import release_memory
+from datasets import Dataset, DatasetDict, IterableDataset, concatenate_datasets, interleave_datasets, load_dataset
+from huggingface_hub import HfApi
+from multiprocess import set_start_method
+from torch.utils.data import DataLoader
+from tqdm import tqdm
 from transformers import (
     AutoFeatureExtractor,
     AutoModel,
@@ -48,26 +47,19 @@ from transformers import (
     AutoTokenizer,
     HfArgumentParser,
     Seq2SeqTrainingArguments,
+    pipeline,
 )
-from transformers.trainer_pt_utils import LengthGroupedSampler
-from transformers import pipeline
 from transformers.optimization import get_scheduler
+from transformers.trainer_pt_utils import LengthGroupedSampler
 from transformers.utils import send_example_telemetry
-from transformers import AutoModel
-
-
-from accelerate import Accelerator
-from accelerate.utils import set_seed, AutocastKwargs, InitProcessGroupKwargs, TorchDynamoPlugin
-from accelerate.utils.memory import release_memory
+from wandb import Audio
 
 from parler_tts import (
-    ParlerTTSForConditionalGeneration,
     ParlerTTSConfig,
+    ParlerTTSForConditionalGeneration,
     build_delay_pattern_mask,
 )
 
-from wandb import Audio
-
 
 logger = logging.getLogger(__name__)
 
@@ -1415,14 +1407,13 @@ def main():
 
     if accelerator.is_main_process:
         if training_args.push_to_hub:
-            # Retrieve of infer repo_name
+            api = HfApi(token=training_args.hub_token)
+
+            # Create repo (repo_name from args or inferred)
             repo_name = training_args.hub_model_id
             if repo_name is None:
                 repo_name = Path(training_args.output_dir).absolute().name
-            # Create repo and retrieve repo_id
-            repo_id = create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id
-            # Clone repo locally
-            repo = Repository(training_args.output_dir, clone_from=repo_id, token=training_args.hub_token)
+            repo_id = api.create_repo(repo_name, exist_ok=True).repo_id
 
             with open(os.path.join(training_args.output_dir, ".gitignore"), "w+") as gitignore:
                 if "wandb" not in gitignore:
@@ -1624,9 +1615,11 @@ def main():
                             unwrapped_model.save_pretrained(training_args.output_dir)
 
                         if training_args.push_to_hub:
-                            repo.push_to_hub(
+                            api.upload_folder(
+                                repo_id=repo_id,
+                                folder_path=training_args.output_dir,
                                 commit_message=f"Saving train state of step {cur_step}",
-                                blocking=False,
+                                run_as_future=True,
                             )
 
                 if training_args.do_eval and (cur_step % eval_steps == 0 or cur_step == total_train_steps):