diff --git a/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py b/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py
index d32f50371e68..294965914d00 100755
--- a/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py
+++ b/examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py
@@ -97,6 +97,22 @@ class ModelArguments:
     freeze_feature_encoder: bool = field(
         default=True, metadata={"help": "Whether to freeze the feature encoder layers of the model."}
     )
+    freeze_encoder: bool = field(
+        default=False, metadata={"help": "Whether to freeze the entire encoder of the seq2seq model."}
+    )
+    forced_decoder_ids: List[List[int]] = field(
+        default=None,
+        metadata={
+            "help": (
+                "A list of pairs of integers which indicates a mapping from generation indices to token indices "
+                "that will be forced before sampling. For example, [[0, 123]] means the first generated token "
+                "will always be a token of index 123."
+            )
+        },
+    )
+    suppress_tokens: List[int] = field(
+        default=None, metadata={"help": "A list of tokens that will be suppressed at generation."}
+    )
 
 
 @dataclass
@@ -187,6 +203,19 @@ class DataTrainingArguments:
         default=True,
         metadata={"help": "Whether the target text should be lower cased."},
     )
+    language: str = field(
+        default=None,
+        metadata={
+            "help": (
+                "Language for multilingual fine-tuning. This argument should be set for multilingual fine-tuning "
+                "only. For English speech recognition, it should be set to `None`."
+            )
+        },
+    )
+    task: str = field(
+        default="transcribe",
+        metadata={"help": "Task, either `transcribe` for speech recognition or `translate` for speech translation."},
+    )
 
 
 @dataclass
@@ -194,7 +223,7 @@ class DataCollatorSpeechSeq2SeqWithPadding:
     """
     Data collator that will dynamically pad the inputs received.
     Args:
-        processor ([`Wav2Vec2Processor`])
+        processor ([`WhisperProcessor`])
             The processor used for processing the data.
         decoder_start_token_id (`int`)
             The begin-of-sentence of the decoder.
@@ -206,7 +235,8 @@ class DataCollatorSpeechSeq2SeqWithPadding:
     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 lengths and need
         # different padding methods
-        input_features = [{"input_values": feature["input_values"]} for feature in features]
+        model_input_name = self.processor.model_input_names[0]
+        input_features = [{model_input_name: feature[model_input_name]} for feature in features]
         label_features = [{"input_ids": feature["labels"]} for feature in features]
 
         batch = self.processor.feature_extractor.pad(input_features, return_tensors="pt")
@@ -333,6 +363,8 @@ def main():
         use_auth_token=True if model_args.use_auth_token else None,
     )
 
+    config.update({"forced_decoder_ids": model_args.forced_decoder_ids, "suppress_tokens": model_args.suppress_tokens})
+
     feature_extractor = AutoFeatureExtractor.from_pretrained(
         model_args.feature_extractor_name if model_args.feature_extractor_name else model_args.model_name_or_path,
         cache_dir=model_args.cache_dir,
@@ -360,6 +392,14 @@ def main():
     if model_args.freeze_feature_encoder:
         model.freeze_feature_encoder()
 
+    if model_args.freeze_encoder:
+        model.freeze_encoder()
+        model.model.encoder.gradient_checkpointing = False
+
+    if data_args.language is not None:
+        # We only need to set the task id when the language is specified (i.e. in a multilingual setting)
+        tokenizer.set_prefix_tokens(language=data_args.language, task=data_args.task)
+
     # 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:
@@ -388,8 +428,8 @@ def prepare_dataset(batch):
         sample = batch[audio_column_name]
         inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
         # process audio length
-        batch[model_input_name] = inputs.input_values[0]
-        batch["input_length"] = len(batch["input_values"])
+        batch[model_input_name] = inputs.get(model_input_name)[0]
+        batch["input_length"] = len(sample["array"])
 
         # process targets
         input_str = batch[text_column_name].lower() if do_lower_case else batch[text_column_name]
@@ -452,7 +492,8 @@ def compute_metrics(pred):
 
     # 10. Define data collator
     data_collator = DataCollatorSpeechSeq2SeqWithPadding(
-        processor=processor, decoder_start_token_id=model.config.decoder_start_token_id
+        processor=processor,
+        decoder_start_token_id=model.config.decoder_start_token_id,
     )
 
     # 11. Initialize Trainer
@@ -492,7 +533,9 @@ def compute_metrics(pred):
     if training_args.do_eval:
         logger.info("*** Evaluate ***")
         metrics = trainer.evaluate(
-            metric_key_prefix="eval", max_length=model.config.max_length, num_beams=model.config.num_beams
+            metric_key_prefix="eval",
+            max_length=training_args.generation_max_length,
+            num_beams=training_args.generation_num_beams,
         )
         max_eval_samples = (
             data_args.max_eval_samples if data_args.max_eval_samples is not None else len(vectorized_datasets["eval"])