Sync preprocesses before loading the processor at run_speech_recognition_ctc.py (#21926)

* Update run_speech_recognition_ctc.py

Make sure all processes wait until data is saved before loading the processor from the output_dit

* Make sure all processes wait until data is saved before loading the processor from the output_dit

* Update run_speech_recognition_ctc.py

* Update run_speech_recognition_seq2seq.py

examples/pytorch/speech-recognition/run_speech_recognition_ctc.py

@@ -673,11 +673,14 @@ def main():
         return metrics
 
     # Now save everything to be able to create a single processor later
-    if is_main_process(training_args.local_rank):
-        # save feature extractor, tokenizer and config
-        feature_extractor.save_pretrained(training_args.output_dir)
-        tokenizer.save_pretrained(training_args.output_dir)
-        config.save_pretrained(training_args.output_dir)
+    # make sure all processes wait until data is saved
+    with training_args.main_process_first():
+        # only the main process saves them
+        if is_main_process(training_args.local_rank):
+            # save feature extractor, tokenizer and config
+            feature_extractor.save_pretrained(training_args.output_dir)
+            tokenizer.save_pretrained(training_args.output_dir)
+            config.save_pretrained(training_args.output_dir)
 
     try:
         processor = AutoProcessor.from_pretrained(training_args.output_dir)

examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py

@@ -506,11 +506,14 @@ def main():
         return {"wer": wer}
 
     # 9. Create a single speech processor
-    if is_main_process(training_args.local_rank):
-        # save feature extractor, tokenizer and config
-        feature_extractor.save_pretrained(training_args.output_dir)
-        tokenizer.save_pretrained(training_args.output_dir)
-        config.save_pretrained(training_args.output_dir)
+    # make sure all processes wait until data is saved
+    with training_args.main_process_first():
+        # only the main process saves them
+        if is_main_process(training_args.local_rank):
+            # save feature extractor, tokenizer and config
+            feature_extractor.save_pretrained(training_args.output_dir)
+            tokenizer.save_pretrained(training_args.output_dir)
+            config.save_pretrained(training_args.output_dir)
 
     processor = AutoProcessor.from_pretrained(training_args.output_dir)