Update PT to TF CLI for audio models (#19465)

* Update PT to TF CLI model inputs

* Get padding strategy if specified

* Make False comparison explicit

src/transformers/commands/pt_to_tf.py

@@ -197,6 +197,20 @@ class PTtoTFCommand(BaseTransformersCLICommand):
             raw_samples = [x["array"] for x in speech_samples]
             return raw_samples
 
+        model_config_class = type(pt_model.config)
+        if model_config_class in PROCESSOR_MAPPING:
+            processor = AutoProcessor.from_pretrained(self._local_dir)
+            if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
+                processor.tokenizer.pad_token = processor.tokenizer.eos_token
+        elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
+            processor = AutoFeatureExtractor.from_pretrained(self._local_dir)
+        elif model_config_class in TOKENIZER_MAPPING:
+            processor = AutoTokenizer.from_pretrained(self._local_dir)
+            if processor.pad_token is None:
+                processor.pad_token = processor.eos_token
+        else:
+            raise ValueError(f"Unknown data processing type (model config type: {model_config_class})")
+
         model_forward_signature = set(inspect.signature(pt_model.forward).parameters.keys())
         processor_inputs = {}
         if "input_ids" in model_forward_signature:
@@ -211,24 +225,20 @@ class PTtoTFCommand(BaseTransformersCLICommand):
             sample_images = load_dataset("cifar10", "plain_text", split="test")[:2]["img"]
             processor_inputs.update({"images": sample_images})
         if "input_features" in model_forward_signature:
-            processor_inputs.update({"raw_speech": _get_audio_input(), "padding": True})
-        if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
-            processor_inputs.update({"raw_speech": _get_audio_input(), "padding": True})
-
-        model_config_class = type(pt_model.config)
-        if model_config_class in PROCESSOR_MAPPING:
-            processor = AutoProcessor.from_pretrained(self._local_dir)
-            if model_config_class in TOKENIZER_MAPPING and processor.tokenizer.pad_token is None:
-                processor.tokenizer.pad_token = processor.tokenizer.eos_token
-        elif model_config_class in FEATURE_EXTRACTOR_MAPPING:
-            processor = AutoFeatureExtractor.from_pretrained(self._local_dir)
-        elif model_config_class in TOKENIZER_MAPPING:
-            processor = AutoTokenizer.from_pretrained(self._local_dir)
-            if processor.pad_token is None:
-                processor.pad_token = processor.eos_token
+            feature_extractor_signature = inspect.signature(processor.feature_extractor).parameters
+            # Pad to the largest input length by default but take feature extractor default
+            # padding value if it exists e.g. "max_length" and is not False or None
+            if "padding" in feature_extractor_signature:
+                default_strategy = feature_extractor_signature["padding"].default
+                if default_strategy is not False and default_strategy is not None:
+                    padding_strategy = default_strategy
                 else:
-            raise ValueError(f"Unknown data processing type (model config type: {model_config_class})")
-
+                    padding_strategy = True
+            else:
+                padding_strategy = True
+            processor_inputs.update({"audio": _get_audio_input(), "padding": padding_strategy})
+        if "input_values" in model_forward_signature:  # Wav2Vec2 audio input
+            processor_inputs.update({"audio": _get_audio_input(), "padding": True})
         pt_input = processor(**processor_inputs, return_tensors="pt")
         tf_input = processor(**processor_inputs, return_tensors="tf")