[examples] update whisper fine-tuning

examples/pytorch/speech-recognition/README.md

@@ -368,6 +368,7 @@ python run_speech_recognition_seq2seq.py \
 	--dataset_name="mozilla-foundation/common_voice_11_0" \
 	--dataset_config_name="hi" \
 	--language="hindi" \
+	--task="transcribe" \
 	--train_split_name="train+validation" \
 	--eval_split_name="test" \
 	--max_steps="5000" \
@@ -384,12 +385,10 @@ python run_speech_recognition_seq2seq.py \
 	--save_steps="1000" \
 	--generation_max_length="225" \
 	--preprocessing_num_workers="16" \
-	--length_column_name="input_length" \
 	--max_duration_in_seconds="30" \
 	--text_column_name="sentence" \
 	--freeze_feature_encoder="False" \
 	--gradient_checkpointing \
-	--group_by_length \
 	--fp16 \
 	--overwrite_output_dir \
 	--do_train \
@@ -399,7 +398,8 @@ python run_speech_recognition_seq2seq.py \
 ```
 On a single V100, training should take approximately 8 hours, with a final cross-entropy loss of **1e-4** and word error rate of **32.6%**.
 
-If training on a different language, you should be sure to change the `language` argument. The `language` argument should be omitted for English speech recognition.
+If training on a different language, you should be sure to change the `language` argument. The `language` and `task` 
+arguments should be omitted for English speech recognition.
 
 #### Multi GPU Whisper Training
 The following example shows how to fine-tune the [Whisper small](https://huggingface.co/openai/whisper-small) checkpoint on the Hindi subset of [Common Voice 11](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0) using 2 GPU devices in half-precision:
@@ -410,6 +410,7 @@ torchrun \
 	--dataset_name="mozilla-foundation/common_voice_11_0" \
 	--dataset_config_name="hi" \
 	--language="hindi" \
+	--task="transcribe" \
 	--train_split_name="train+validation" \
 	--eval_split_name="test" \
 	--max_steps="5000" \
@@ -425,12 +426,10 @@ torchrun \
 	--save_steps="1000" \
 	--generation_max_length="225" \
 	--preprocessing_num_workers="16" \
-	--length_column_name="input_length" \
 	--max_duration_in_seconds="30" \
 	--text_column_name="sentence" \
 	--freeze_feature_encoder="False" \
 	--gradient_checkpointing \
-	--group_by_length \
 	--fp16 \
 	--overwrite_output_dir \
 	--do_train \

examples/pytorch/speech-recognition/run_speech_recognition_seq2seq.py

@@ -119,17 +119,16 @@ class ModelArguments:
     )
     forced_decoder_ids: List[List[int]] = field(
         default=None,
-        metadata={
+        metadata={"help": "Deprecated. Please use the `language` and `task` arguments instead."},
+    )
+    suppress_tokens: 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."
+                "Deprecated. The use of `suppress_tokens` should not be required for the majority of fine-tuning examples."
+                "Should you need to use `suppress_tokens`, please manually update them in the fine-tuning script directly."
             )
         },
     )
-    suppress_tokens: List[int] = field(
-        default=None, metadata={"help": "A list of tokens that will be suppressed at generation."}
-    )
     apply_spec_augment: bool = field(
         default=False,
         metadata={
@@ -400,8 +399,6 @@ def main():
         trust_remote_code=model_args.trust_remote_code,
     )
 
-    config.update({"forced_decoder_ids": model_args.forced_decoder_ids, "suppress_tokens": model_args.suppress_tokens})
-
     # SpecAugment for whisper models
     if getattr(config, "model_type", None) == "whisper":
         config.update({"apply_spec_augment": model_args.apply_spec_augment})
@@ -440,9 +437,35 @@ def main():
         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)
+    if hasattr(model.generation_config, "is_multilingual") and model.generation_config.is_multilingual:
+        # We only need to set the language and task ids in a multilingual setting
         tokenizer.set_prefix_tokens(language=data_args.language, task=data_args.task)
+        model.generation_config.update(
+            **{
+                "language": data_args.language,
+                "task": data_args.task,
+            }
+        )
+    elif data_args.language is not None:
+        raise ValueError(
+            "Setting language token for an English-only checkpoint is not permitted. The language argument should "
+            "only be set for multilingual checkpoints."
+        )
+
+    # TODO (Sanchit): deprecate these arguments in v4.41
+    if model_args.forced_decoder_ids is not None:
+        logger.warning(
+            "The use of `forced_decoder_ids` is deprecated and will be removed in v4.41."
+            "Please use the `language` and `task` arguments instead"
+        )
+        model.generation_config.forced_decoder_ids = model_args.forced_decoder_ids
+
+    if model_args.suppress_tokens is not None:
+        logger.warning(
+            "The use of `suppress_tokens` is deprecated and will be removed in v4.41."
+            "Should you need `suppress_tokens`, please manually set them in the fine-tuning script."
+        )
+        model.generation_config.suppress_tokens = model_args.suppress_tokens
 
     # 6. Resample speech dataset if necessary
     dataset_sampling_rate = next(iter(raw_datasets.values())).features[data_args.audio_column_name].sampling_rate