Remove trust_remote_code when loading Libri Dummy (#31748)

* [whisper integration] use parquet dataset for testing

* propagate to others

* more propagation

* last one

tests/models/whisper/test_feature_extraction_whisper.py

@@ -215,9 +215,7 @@ class WhisperFeatureExtractionTest(SequenceFeatureExtractionTestMixin, unittest.
             self.assertTrue(pt_processed.input_features.dtype == torch.float32)
 
     def _load_datasamples(self, num_samples):
-        ds = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        )
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         # automatic decoding with librispeech
         speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
 

tests/models/whisper/test_modeling_flax_whisper.py

@@ -410,9 +410,7 @@ class FlaxWhisperModelIntegrationTest(unittest.TestCase):
         return WhisperProcessor.from_pretrained("openai/whisper-base")
 
     def _load_datasamples(self, num_samples):
-        ds = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        )
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         # automatic decoding with librispeech
         speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
 

tests/models/whisper/test_modeling_tf_whisper.py

@@ -704,7 +704,7 @@ class TFWhisperModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestC
 
 
 def _load_datasamples(num_samples):
-    ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True)
+    ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
     # automatic decoding with librispeech
     speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
 

tests/models/whisper/test_modeling_whisper.py

@@ -1835,9 +1835,7 @@ class WhisperModelIntegrationTests(unittest.TestCase):
         return WhisperProcessor.from_pretrained("openai/whisper-base")
 
     def _load_datasamples(self, num_samples):
-        ds = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        )
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         # automatic decoding with librispeech
         speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
 
@@ -2718,9 +2716,7 @@ class WhisperModelIntegrationTests(unittest.TestCase):
         )
         assistant_model.to(torch_device)
 
-        dataset = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        )
+        dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         sample = dataset[0]["audio"]
 
         input_features = processor(sample["array"], return_tensors="pt", sampling_rate=16_000).input_features
@@ -2769,9 +2765,7 @@ class WhisperModelIntegrationTests(unittest.TestCase):
         )
         assistant_model.to(torch_device)
 
-        dataset = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        )
+        dataset = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         sample = dataset[0]["audio"]
 
         input_features = processor(sample["array"], return_tensors="pt", sampling_rate=16_000).input_features
@@ -2812,7 +2806,7 @@ class WhisperModelIntegrationTests(unittest.TestCase):
         model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
         model = model.to(torch_device)
 
-        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", trust_remote_code=True)
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean")
         one_audio = np.concatenate([x["array"] for x in ds["validation"]["audio"]], dtype=np.float32)
 
         input_features = processor(
@@ -2848,9 +2842,7 @@ class WhisperModelIntegrationTests(unittest.TestCase):
         prompt = "Mr. Kilter, Brionno."  # let's force Quilter -> Kilter, Brion -> Brionno
         prompt_ids = processor.get_prompt_ids(prompt, return_tensors="pt").to(torch_device)
 
-        ds = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation[:-1]", trust_remote_code=True
-        )
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation[:-1]")
         one_audio = np.concatenate([x["array"] for x in ds["audio"]], dtype=np.float32)
 
         first_text = ds[0]["text"].lower()
@@ -2901,7 +2893,7 @@ class WhisperModelIntegrationTests(unittest.TestCase):
         model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
         model = model.to(torch_device)
 
-        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", trust_remote_code=True)
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean")
         one_audio = np.concatenate([x["array"] for x in ds["validation"]["audio"]], dtype=np.float32)
 
         input_features = processor(
@@ -2983,7 +2975,7 @@ class WhisperModelIntegrationTests(unittest.TestCase):
         model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
         model = model.to(torch_device)
 
-        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", trust_remote_code=True)
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean")
         one_audio = np.concatenate([x["array"] for x in ds["validation"]["audio"]], dtype=np.float32)
 
         input_features = processor(
@@ -3025,7 +3017,7 @@ class WhisperModelIntegrationTests(unittest.TestCase):
         model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
         model = model.to(torch_device)
 
-        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", trust_remote_code=True)
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean")
         one_audio = np.concatenate([x["array"] for x in ds["validation"]["audio"]], dtype=np.float32)
         audios = []
         audios.append(one_audio[110000:])
@@ -3079,7 +3071,7 @@ class WhisperModelIntegrationTests(unittest.TestCase):
         model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny")
         model = model.to(torch_device)
 
-        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", trust_remote_code=True)
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean")
         one_audio = np.concatenate([x["array"] for x in ds["validation"]["audio"]], dtype=np.float32)
         audios = []
         audios.append(one_audio[110000:])