Migrate metric to Evaluate in Pytorch examples (#18369)

* Migrate metric to Evaluate in pytorch examples

* Remove unused imports

examples/pytorch/speech-recognition/run_speech_recognition_ctc.py

@@ -28,8 +28,9 @@ from typing import Dict, List, Optional, Union
 import datasets
 import numpy as np
 import torch
-from datasets import DatasetDict, load_dataset, load_metric
+from datasets import DatasetDict, load_dataset
 
+import evaluate
 import transformers
 from transformers import (
     AutoConfig,
@@ -643,7 +644,7 @@ def main():
     # instantiate a data collator and the trainer
 
     # Define evaluation metrics during training, *i.e.* word error rate, character error rate
-    eval_metrics = {metric: load_metric(metric) for metric in data_args.eval_metrics}
+    eval_metrics = {metric: evaluate.load(metric) for metric in data_args.eval_metrics}
 
     # for large datasets it is advised to run the preprocessing on a
     # single machine first with ``args.preprocessing_only`` since there will mostly likely

examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py

@@ -27,8 +27,9 @@ from typing import Any, Dict, List, Optional, Union
 
 import datasets
 import torch
-from datasets import DatasetDict, load_dataset, load_metric
+from datasets import DatasetDict, load_dataset
 
+import evaluate
 import transformers
 from transformers import (
     AutoConfig,
@@ -425,7 +426,7 @@ def main():
         return
 
     # 8. Load Metric
-    metric = load_metric("wer")
+    metric = evaluate.load("wer")
 
     def compute_metrics(pred):
         pred_ids = pred.predictions