diff --git a/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py b/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py
index 0c944472091..c9de3577bb8 100755
--- a/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py
+++ b/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py
@@ -195,7 +195,7 @@ class DataCollatorSpeechSeq2SeqWithPadding:
     Data collator that will dynamically pad the inputs received.
     Args:
         processor ([`Wav2Vec2Processor`])
-            The processor used for proccessing the data.
+            The processor used for processing the data.
         decoder_start_token_id (`int`)
             The begin-of-sentence of the decoder.
     """
@@ -204,7 +204,7 @@ class DataCollatorSpeechSeq2SeqWithPadding:
     decoder_start_token_id: int
 
     def __call__(self, features: List[Dict[str, Union[List[int], torch.Tensor]]]) -> Dict[str, torch.Tensor]:
-        # split inputs and labels since they have to be of different lenghts and need
+        # split inputs and labels since they have to be of different lengths and need
         # different padding methods
         input_features = [{"input_values": feature["input_values"]} for feature in features]
         label_features = [{"input_ids": feature["labels"]} for feature in features]
@@ -271,7 +271,7 @@ def main():
         transformers.utils.logging.set_verbosity_info()
     logger.info("Training/evaluation parameters %s", training_args)
 
-    # 3. Detecting last checkpoint and eventualy continue from last checkpoint
+    # 3. Detecting last checkpoint and eventually continue from last checkpoint
     last_checkpoint = None
     if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
         last_checkpoint = get_last_checkpoint(training_args.output_dir)
@@ -360,7 +360,7 @@ def main():
     if model_args.freeze_feature_encoder:
         model.freeze_feature_encoder()
 
-    # 6. Resample speech dataset if necassary
+    # 6. Resample speech dataset if necessary
     dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate
     if dataset_sampling_rate != feature_extractor.sampling_rate:
         raw_datasets = raw_datasets.cast_column(