Making CTC training example more general (#28582)

* add w2v2bert compatibility

* Update examples/pytorch/speech-recognition/run_speech_recognition_ctc.py

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

---------

Co-authored-by: amyeroberts <22614925+amyeroberts@users.noreply.github.com>

examples/pytorch/speech-recognition/run_speech_recognition_ctc.py

@@ -132,10 +132,17 @@ class ModelArguments:
     ctc_loss_reduction: Optional[str] = field(
         default="mean", metadata={"help": "The way the ctc loss should be reduced. Should be one of 'mean' or 'sum'."}
     )
+    ctc_zero_infinity: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": "Whether to zero infinite losses and the associated gradients of `torch.nn.CTCLoss`. Infinite losses mainly"
+            " occur when the inputs are too short to be aligned to the targets."
+        },
+    )
     add_adapter: Optional[bool] = field(
         default=False,
         metadata={
-            "help": "Whether a convolutional attention network should be stacked on top of the Wav2Vec2BERT Encoder. Can be very"
+            "help": "Whether a convolutional attention network should be stacked on top of the Wav2Vec2Bert Encoder. Can be very"
             "useful to downsample the output length."
         },
     )
@@ -316,11 +323,14 @@ class DataCollatorCTCWithPadding:
     padding: Union[bool, str] = "longest"
     pad_to_multiple_of: Optional[int] = None
     pad_to_multiple_of_labels: Optional[int] = None
+    feature_extractor_input_name: Optional[str] = "input_values"
 
     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]
+        input_features = [
+            {self.feature_extractor_input_name: feature[self.feature_extractor_input_name]} for feature in features
+        ]
         label_features = [{"input_ids": feature["labels"]} for feature in features]
 
         batch = self.processor.pad(
@@ -606,6 +616,7 @@ def remove_special_characters(batch):
             "gradient_checkpointing": training_args.gradient_checkpointing,
             "layerdrop": model_args.layerdrop,
             "ctc_loss_reduction": model_args.ctc_loss_reduction,
+            "ctc_zero_infinity": model_args.ctc_zero_infinity,
             "pad_token_id": tokenizer.pad_token_id,
             "vocab_size": len(tokenizer),
             "activation_dropout": model_args.activation_dropout,
@@ -643,6 +654,7 @@ def remove_special_characters(batch):
     min_input_length = data_args.min_duration_in_seconds * feature_extractor.sampling_rate
     audio_column_name = data_args.audio_column_name
     num_workers = data_args.preprocessing_num_workers
+    feature_extractor_input_name = feature_extractor.model_input_names[0]
 
     # `phoneme_language` is only relevant if the model is fine-tuned on phoneme classification
     phoneme_language = data_args.phoneme_language
@@ -654,8 +666,9 @@ def prepare_dataset(batch):
         sample = batch[audio_column_name]
 
         inputs = feature_extractor(sample["array"], sampling_rate=sample["sampling_rate"])
-        batch["input_values"] = inputs.input_values[0]
-        batch["input_length"] = len(batch["input_values"])
+        batch[feature_extractor_input_name] = getattr(inputs, feature_extractor_input_name)[0]
+        # take length of raw audio waveform
+        batch["input_length"] = len(sample["array"].squeeze())
 
         # encode targets
         additional_kwargs = {}
@@ -736,7 +749,9 @@ def compute_metrics(pred):
         processor = Wav2Vec2Processor.from_pretrained(training_args.output_dir)
 
     # Instantiate custom data collator
-    data_collator = DataCollatorCTCWithPadding(processor=processor)
+    data_collator = DataCollatorCTCWithPadding(
+        processor=processor, feature_extractor_input_name=feature_extractor_input_name
+    )
 
     # Initialize Trainer
     trainer = Trainer(