diff --git a/src/transformers/commands/pt_to_tf.py b/src/transformers/commands/pt_to_tf.py
index 4df45f7f086a51..4002b5e0eb85a4 100644
--- a/src/transformers/commands/pt_to_tf.py
+++ b/src/transformers/commands/pt_to_tf.py
@@ -202,9 +202,7 @@ def get_inputs(self, pt_model, tf_dummy_inputs, config):
         """
 
         def _get_audio_input():
-            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")
             speech_samples = ds.sort("id").select(range(2))[:2]["audio"]
             raw_samples = [x["array"] for x in speech_samples]
             return raw_samples
diff --git a/src/transformers/generation/logits_process.py b/src/transformers/generation/logits_process.py
index c9a978f5ee8990..b226a059d106b1 100644
--- a/src/transformers/generation/logits_process.py
+++ b/src/transformers/generation/logits_process.py
@@ -1760,7 +1760,7 @@ class SuppressTokensAtBeginLogitsProcessor(LogitsProcessor):
 
     >>> processor = AutoProcessor.from_pretrained("openai/whisper-tiny.en")
     >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
-    >>> 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")
     >>> inputs = processor(ds[0]["audio"]["array"], return_tensors="pt")
 
     >>> # Whisper has `begin_suppress_tokens` set by default (= `[220, 50256]`). 50256 is the EOS token, so this means
@@ -1812,7 +1812,7 @@ class SuppressTokensLogitsProcessor(LogitsProcessor):
 
     >>> processor = AutoProcessor.from_pretrained("openai/whisper-tiny.en")
     >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
-    >>> 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")
     >>> inputs = processor(ds[0]["audio"]["array"], return_tensors="pt")
 
     >>> # Whisper has a long list of suppressed tokens. For instance, in this case, the token 1 is suppressed by default.
@@ -1901,7 +1901,7 @@ class WhisperTimeStampLogitsProcessor(LogitsProcessor):
 
     >>> processor = AutoProcessor.from_pretrained("openai/whisper-tiny.en")
     >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
-    >>> 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")
     >>> inputs = processor(ds[3]["audio"]["array"], return_tensors="pt")
     >>> input_features = inputs.input_features
 
diff --git a/src/transformers/models/clvp/modeling_clvp.py b/src/transformers/models/clvp/modeling_clvp.py
index 4124e380a3d73d..d53bed2a5d12fd 100644
--- a/src/transformers/models/clvp/modeling_clvp.py
+++ b/src/transformers/models/clvp/modeling_clvp.py
@@ -1681,7 +1681,7 @@ def get_speech_features(
 
         >>> # Define the Text and Load the Audio (We are taking an audio example from HuggingFace Hub using `datasets` library)
         >>> text = "This is an example text."
-        >>> ds = datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True)
+        >>> ds = datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         >>> ds = ds.cast_column("audio", datasets.Audio(sampling_rate=22050))
         >>> _, audio, sr = ds.sort("id").select(range(1))[:1]["audio"][0].values()
 
@@ -1754,7 +1754,7 @@ def forward(
         >>> # Define the Text and Load the Audio (We are taking an audio example from HuggingFace Hub using `datasets` library)
         >>> text = "This is an example text."
 
-        >>> ds = datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True)
+        >>> ds = datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         >>> ds = ds.cast_column("audio", datasets.Audio(sampling_rate=22050))
         >>> _, audio, sr = ds.sort("id").select(range(1))[:1]["audio"][0].values()
 
diff --git a/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py b/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py
index 4db60e0faeb4c1..8f1a8370933c91 100755
--- a/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py
+++ b/src/transformers/models/deprecated/speech_to_text_2/modeling_speech_to_text_2.py
@@ -831,7 +831,7 @@ def forward(
         >>> model.config.decoder_start_token_id = tokenizer.bos_token_id
         >>> # pre-process inputs and labels
 
-        >>> 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")
         >>> inputs = feature_extractor(
         ...     ds[0]["audio"]["array"], sampling_rate=ds[0]["audio"]["sampling_rate"], return_tensors="pt"
         ... )
diff --git a/src/transformers/models/hubert/modeling_hubert.py b/src/transformers/models/hubert/modeling_hubert.py
index fd0c271b668109..da79c2894877b4 100755
--- a/src/transformers/models/hubert/modeling_hubert.py
+++ b/src/transformers/models/hubert/modeling_hubert.py
@@ -1325,7 +1325,7 @@ def forward(
         ...     return batch
 
 
-        >>> 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")
         >>> ds = ds.map(map_to_array)
 
         >>> input_values = processor(ds["speech"][0], return_tensors="pt").input_values  # Batch size 1
diff --git a/src/transformers/models/hubert/modeling_tf_hubert.py b/src/transformers/models/hubert/modeling_tf_hubert.py
index 6c2a341927e200..2adfeea5b8b883 100644
--- a/src/transformers/models/hubert/modeling_tf_hubert.py
+++ b/src/transformers/models/hubert/modeling_tf_hubert.py
@@ -1471,7 +1471,7 @@ def call(
         ...     return batch
 
 
-        >>> 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")
         >>> ds = ds.map(map_to_array)
 
         >>> input_values = processor(ds["speech"][0], return_tensors="tf").input_values  # Batch size 1
@@ -1583,7 +1583,7 @@ def call(
         ...     return batch
 
 
-        >>> 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")
         >>> ds = ds.map(map_to_array)
 
         >>> input_values = processor(ds["speech"][0], return_tensors="tf").input_values  # Batch size 1
diff --git a/src/transformers/models/speech_encoder_decoder/modeling_speech_encoder_decoder.py b/src/transformers/models/speech_encoder_decoder/modeling_speech_encoder_decoder.py
index a46a1d62af1163..c2f5dd0259093b 100644
--- a/src/transformers/models/speech_encoder_decoder/modeling_speech_encoder_decoder.py
+++ b/src/transformers/models/speech_encoder_decoder/modeling_speech_encoder_decoder.py
@@ -464,7 +464,7 @@ def forward(
         >>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-xls-r-300m-en-to-15")
         >>> model = SpeechEncoderDecoderModel.from_pretrained("facebook/wav2vec2-xls-r-300m-en-to-15")
 
-        >>> 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")
 
         >>> input_values = processor(ds[0]["audio"]["array"], return_tensors="pt").input_values
         >>> # Inference: Translate English speech to German
diff --git a/src/transformers/models/speech_to_text/modeling_speech_to_text.py b/src/transformers/models/speech_to_text/modeling_speech_to_text.py
index 9832987f4e6433..8353a172b2120f 100755
--- a/src/transformers/models/speech_to_text/modeling_speech_to_text.py
+++ b/src/transformers/models/speech_to_text/modeling_speech_to_text.py
@@ -1129,7 +1129,7 @@ def forward(
 
          >>> model = Speech2TextModel.from_pretrained("facebook/s2t-small-librispeech-asr")
          >>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/s2t-small-librispeech-asr")
-         >>> 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")
          >>> inputs = feature_extractor(
          ...     ds[0]["audio"]["array"], sampling_rate=ds[0]["audio"]["sampling_rate"], return_tensors="pt"
          ... )
@@ -1270,7 +1270,7 @@ def forward(
         >>> processor = Speech2TextProcessor.from_pretrained("facebook/s2t-small-librispeech-asr")
 
 
-        >>> 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")
 
         >>> inputs = processor(
         ...     ds[0]["audio"]["array"], sampling_rate=ds[0]["audio"]["sampling_rate"], return_tensors="pt"
diff --git a/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py b/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py
index 6ad680d4fc0725..bac1256ca4b672 100755
--- a/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py
+++ b/src/transformers/models/speech_to_text/modeling_tf_speech_to_text.py
@@ -1483,7 +1483,7 @@ def call(
         ...     return batch
 
 
-        >>> 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")
         >>> ds = ds.map(map_to_array)
         >>> ds.set_format(type="tf")
 
diff --git a/src/transformers/models/univnet/modeling_univnet.py b/src/transformers/models/univnet/modeling_univnet.py
index 887493fdcf55f3..5b0c659c302a7c 100644
--- a/src/transformers/models/univnet/modeling_univnet.py
+++ b/src/transformers/models/univnet/modeling_univnet.py
@@ -525,7 +525,7 @@ def forward(
          >>> model = UnivNetModel.from_pretrained("dg845/univnet-dev")
          >>> feature_extractor = UnivNetFeatureExtractor.from_pretrained("dg845/univnet-dev")
 
-         >>> 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")
          >>> # Resample the audio to the feature extractor's sampling rate.
          >>> ds = ds.cast_column("audio", Audio(sampling_rate=feature_extractor.sampling_rate))
          >>> inputs = feature_extractor(
diff --git a/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py b/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py
index 7a629e24572af3..9a24b9d39fdaac 100644
--- a/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py
+++ b/src/transformers/models/wav2vec2/modeling_flax_wav2vec2.py
@@ -1076,7 +1076,7 @@ class FlaxWav2Vec2Model(FlaxWav2Vec2PreTrainedModel):
     ...     return batch
 
 
-    >>> 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")
     >>> ds = ds.map(map_to_array)
 
     >>> input_values = processor(
@@ -1195,7 +1195,7 @@ class FlaxWav2Vec2ForCTC(FlaxWav2Vec2PreTrainedModel):
     ...     return batch
 
 
-    >>> 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")
     >>> ds = ds.map(map_to_array)
 
     >>> input_values = processor(
@@ -1396,7 +1396,7 @@ def __call__(
     ...     return batch
 
 
-    >>> 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")
     >>> ds = ds.map(map_to_array)
 
     >>> input_values = feature_extractor(ds["speech"][0], return_tensors="np").input_values  # Batch size 1
diff --git a/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py b/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py
index cc8478d5b3c06a..a8338e363d94a2 100644
--- a/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py
+++ b/src/transformers/models/wav2vec2/modeling_tf_wav2vec2.py
@@ -1542,7 +1542,7 @@ def call(
         ...     return batch
 
 
-        >>> 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")
         >>> ds = ds.map(map_to_array)
 
         >>> input_values = processor(ds["speech"][0], return_tensors="tf").input_values  # Batch size 1
@@ -1654,7 +1654,7 @@ def call(
         ...     return batch
 
 
-        >>> 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")
         >>> ds = ds.map(map_to_array)
 
         >>> input_values = processor(ds["speech"][0], return_tensors="tf").input_values  # Batch size 1
diff --git a/src/transformers/models/wav2vec2/modeling_wav2vec2.py b/src/transformers/models/wav2vec2/modeling_wav2vec2.py
index 16e50cc06c52ce..f1d021b58ee538 100755
--- a/src/transformers/models/wav2vec2/modeling_wav2vec2.py
+++ b/src/transformers/models/wav2vec2/modeling_wav2vec2.py
@@ -1938,7 +1938,7 @@ def forward(
         >>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base")
         >>> model = Wav2Vec2ForPreTraining.from_pretrained("facebook/wav2vec2-base")
 
-        >>> 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")
         >>> input_values = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt").input_values  # Batch size 1
 
         >>> # compute masked indices
diff --git a/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py b/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py
index 6f631e4683ad3a..c37dd980d4ed3b 100644
--- a/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py
+++ b/src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py
@@ -1453,7 +1453,7 @@ def forward(
         >>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large")
         >>> model = Wav2Vec2ConformerForPreTraining.from_pretrained("facebook/wav2vec2-conformer-rel-pos-large")
 
-        >>> 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")
         >>> input_values = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt").input_values  # Batch size 1
 
         >>> # compute masked indices
diff --git a/src/transformers/models/whisper/generation_whisper.py b/src/transformers/models/whisper/generation_whisper.py
index 0467362ea2c7ec..4a28eb9203852c 100644
--- a/src/transformers/models/whisper/generation_whisper.py
+++ b/src/transformers/models/whisper/generation_whisper.py
@@ -464,7 +464,7 @@ def generate(
         >>> processor = AutoProcessor.from_pretrained("openai/whisper-tiny.en")
         >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
 
-        >>> 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")
 
         >>> inputs = processor(ds[0]["audio"]["array"], return_tensors="pt")
         >>> input_features = inputs.input_features
diff --git a/src/transformers/models/whisper/modeling_flax_whisper.py b/src/transformers/models/whisper/modeling_flax_whisper.py
index 9da592c107daef..cc4483963c6309 100644
--- a/src/transformers/models/whisper/modeling_flax_whisper.py
+++ b/src/transformers/models/whisper/modeling_flax_whisper.py
@@ -985,7 +985,7 @@ def encode(
 
         >>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
         >>> model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", from_pt=True)
-        >>> 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")
         >>> inputs = processor(ds[0]["audio"]["array"], return_tensors="np")
         >>> input_features = inputs.input_features
         >>> encoder_outputs = model.encode(input_features=input_features)
@@ -1045,7 +1045,7 @@ def decode(
 
         >>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
         >>> model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", from_pt=True)
-        >>> 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")
         >>> input_features = processor(ds[0]["audio"]["array"], return_tensors="np").input_features
 
         >>> encoder_outputs = model.encode(input_features=input_features)
@@ -1297,7 +1297,7 @@ def decode(
 
         >>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
         >>> model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", from_pt=True)
-        >>> 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")
         >>> inputs = processor(ds[0]["audio"]["array"], return_tensors="np")
         >>> input_features = inputs.input_features
         >>> encoder_outputs = model.encode(input_features=input_features)
@@ -1516,7 +1516,7 @@ def update_inputs_for_generation(self, model_outputs, model_kwargs):
 
     >>> processor = WhisperProcessor.from_pretrained("openai/whisper-tiny.en")
     >>> model = FlaxWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en", from_pt=True)
-    >>> 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")
     >>> inputs = processor(ds[0]["audio"]["array"], return_tensors="np")
     >>> input_features = inputs.input_features
     >>> generated_ids = model.generate(input_ids=input_features)
diff --git a/src/transformers/models/whisper/modeling_tf_whisper.py b/src/transformers/models/whisper/modeling_tf_whisper.py
index 6f50141bff9f40..18f55dce8a2224 100644
--- a/src/transformers/models/whisper/modeling_tf_whisper.py
+++ b/src/transformers/models/whisper/modeling_tf_whisper.py
@@ -1147,7 +1147,7 @@ def call(
 
          >>> model = TFWhisperModel.from_pretrained("openai/whisper-base")
          >>> feature_extractor = AutoFeatureExtractor.from_pretrained("openai/whisper-base")
-         >>> 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")
          >>> inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="tf")
          >>> input_features = inputs.input_features
          >>> decoder_input_ids = tf.convert_to_tensor([[1, 1]]) * model.config.decoder_start_token_id
@@ -1283,7 +1283,7 @@ def call(
 
          >>> model = TFWhisperModel.from_pretrained("openai/whisper-base")
          >>> feature_extractor = AutoFeatureExtractor.from_pretrained("openai/whisper-base")
-         >>> 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")
          >>> inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="tf")
          >>> input_features = inputs.input_features
          >>> decoder_input_ids = tf.convert_to_tensor([[1, 1]]) * model.config.decoder_start_token_id
@@ -1413,7 +1413,7 @@ def call(
         >>> processor = AutoProcessor.from_pretrained("openai/whisper-tiny.en")
         >>> model = TFWhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
 
-        >>> 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")
 
         >>> inputs = processor(ds[0]["audio"]["array"], return_tensors="tf")
         >>> input_features = inputs.input_features
diff --git a/src/transformers/models/whisper/modeling_whisper.py b/src/transformers/models/whisper/modeling_whisper.py
index 7ba2af00ad81c0..6db7da4b957cdc 100644
--- a/src/transformers/models/whisper/modeling_whisper.py
+++ b/src/transformers/models/whisper/modeling_whisper.py
@@ -1555,7 +1555,7 @@ def forward(
 
          >>> model = WhisperModel.from_pretrained("openai/whisper-base")
          >>> feature_extractor = AutoFeatureExtractor.from_pretrained("openai/whisper-base")
-         >>> 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")
          >>> inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt")
          >>> input_features = inputs.input_features
          >>> decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id
@@ -1698,7 +1698,7 @@ def forward(
         >>> processor = AutoProcessor.from_pretrained("openai/whisper-tiny.en")
         >>> model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-tiny.en")
 
-        >>> 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")
 
         >>> inputs = processor(ds[0]["audio"]["array"], return_tensors="pt")
         >>> input_features = inputs.input_features
@@ -1959,7 +1959,7 @@ def forward(
 
         >>> assistant_model = WhisperForCausalLM.from_pretrained("distil-whisper/distil-large-v2")
 
-        >>> 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")
         >>> sample = ds[0]["audio"]
         >>> input_features = processor(
         ...     sample["array"], sampling_rate=sample["sampling_rate"], return_tensors="pt"
diff --git a/tests/models/audio_spectrogram_transformer/test_feature_extraction_audio_spectrogram_transformer.py b/tests/models/audio_spectrogram_transformer/test_feature_extraction_audio_spectrogram_transformer.py
index 967f1936215e97..fbe250908633db 100644
--- a/tests/models/audio_spectrogram_transformer/test_feature_extraction_audio_spectrogram_transformer.py
+++ b/tests/models/audio_spectrogram_transformer/test_feature_extraction_audio_spectrogram_transformer.py
@@ -153,9 +153,7 @@ def test_double_precision_pad(self):
     def _load_datasamples(self, num_samples):
         from datasets import load_dataset
 
-        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"]
 
diff --git a/tests/models/clap/test_feature_extraction_clap.py b/tests/models/clap/test_feature_extraction_clap.py
index 8f2d6df3cb6c78..d0e913df828b84 100644
--- a/tests/models/clap/test_feature_extraction_clap.py
+++ b/tests/models/clap/test_feature_extraction_clap.py
@@ -164,9 +164,7 @@ def test_double_precision_pad(self):
 
     # Copied from tests.models.whisper.test_feature_extraction_whisper.WhisperFeatureExtractionTest._load_datasamples
     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"]
 
diff --git a/tests/models/clap/test_modeling_clap.py b/tests/models/clap/test_modeling_clap.py
index 8e3392133f1f26..9f8cc62d2e0fc3 100644
--- a/tests/models/clap/test_modeling_clap.py
+++ b/tests/models/clap/test_modeling_clap.py
@@ -665,9 +665,7 @@ def test_integration_unfused(self):
             "repeat": 0.0023,
         }
 
-        librispeech_dummy = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        )
+        librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         audio_sample = librispeech_dummy[-1]
 
         model_id = "laion/clap-htsat-unfused"
@@ -694,9 +692,7 @@ def test_integration_fused(self):
             "pad": -0.000379,
         }
 
-        librispeech_dummy = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        )
+        librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         audio_sample = librispeech_dummy[-1]
 
         model_id = "laion/clap-htsat-fused"
@@ -723,9 +719,7 @@ def test_batched_fused(self):
             "pad": 0.0006,
         }
 
-        librispeech_dummy = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        )
+        librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         audio_samples = [sample["array"] for sample in librispeech_dummy[0:4]["audio"]]
 
         model_id = "laion/clap-htsat-fused"
@@ -752,9 +746,7 @@ def test_batched_unfused(self):
             "pad": 0.0019,
         }
 
-        librispeech_dummy = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        )
+        librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         audio_samples = [sample["array"] for sample in librispeech_dummy[0:4]["audio"]]
 
         model_id = "laion/clap-htsat-unfused"
diff --git a/tests/models/clvp/test_feature_extraction_clvp.py b/tests/models/clvp/test_feature_extraction_clvp.py
index 83be97e8675434..db641eaf6145cb 100644
--- a/tests/models/clvp/test_feature_extraction_clvp.py
+++ b/tests/models/clvp/test_feature_extraction_clvp.py
@@ -209,9 +209,7 @@ def test_double_precision_pad(self):
             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")
         ds = ds.cast_column("audio", Audio(sampling_rate=22050))
         # automatic decoding with librispeech
         speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
diff --git a/tests/models/clvp/test_modeling_clvp.py b/tests/models/clvp/test_modeling_clvp.py
index 5d17d3fed622cd..0cf89a74523364 100644
--- a/tests/models/clvp/test_modeling_clvp.py
+++ b/tests/models/clvp/test_modeling_clvp.py
@@ -371,9 +371,7 @@ def get_config(self):
     def prepare_config_and_inputs(self):
         _, input_ids, attention_mask = self.clvp_encoder_tester.prepare_config_and_inputs()
 
-        ds = datasets.load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        )
+        ds = datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         ds = ds.cast_column("audio", datasets.Audio(sampling_rate=22050))
         _, audio, sr = ds.sort("id").select(range(1))[:1]["audio"][0].values()
 
@@ -555,9 +553,7 @@ def test_model_from_pretrained(self):
 class ClvpIntegrationTest(unittest.TestCase):
     def setUp(self):
         self.text = "This is an example text."
-        ds = datasets.load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        )
+        ds = datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         ds = ds.cast_column("audio", datasets.Audio(sampling_rate=22050))
         _, self.speech_samples, self.sr = ds.sort("id").select(range(1))[:1]["audio"][0].values()
 
diff --git a/tests/models/data2vec/test_modeling_data2vec_audio.py b/tests/models/data2vec/test_modeling_data2vec_audio.py
index 8bb16760ce61e9..d43128286853a5 100644
--- a/tests/models/data2vec/test_modeling_data2vec_audio.py
+++ b/tests/models/data2vec/test_modeling_data2vec_audio.py
@@ -694,9 +694,7 @@ def test_compute_mask_indices_short_audio(self):
 @slow
 class Data2VecAudioModelIntegrationTest(unittest.TestCase):
     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").filter(
             lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
diff --git a/tests/models/encodec/test_feature_extraction_encodec.py b/tests/models/encodec/test_feature_extraction_encodec.py
index 73c5019b11edda..e56517ac410661 100644
--- a/tests/models/encodec/test_feature_extraction_encodec.py
+++ b/tests/models/encodec/test_feature_extraction_encodec.py
@@ -138,9 +138,7 @@ def test_double_precision_pad(self):
     def _load_datasamples(self, num_samples):
         from datasets import load_dataset
 
-        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
         audio_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
 
diff --git a/tests/models/encodec/test_modeling_encodec.py b/tests/models/encodec/test_modeling_encodec.py
index 0a023894d8a00a..cff297be8e0002 100644
--- a/tests/models/encodec/test_modeling_encodec.py
+++ b/tests/models/encodec/test_modeling_encodec.py
@@ -461,9 +461,7 @@ def test_integration_24kHz(self):
             "1.5": [371955],
             "24.0": [6659962],
         }
-        librispeech_dummy = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        )
+        librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         model_id = "facebook/encodec_24khz"
 
         model = EncodecModel.from_pretrained(model_id).to(torch_device)
@@ -517,9 +515,7 @@ def test_integration_48kHz(self):
             "3.0": [144259, 146765, 156435, 176871, 161971],
             "24.0": [1568553, 1294948, 1306190, 1464747, 1663150],
         }
-        librispeech_dummy = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        )
+        librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         model_id = "facebook/encodec_48khz"
 
         model = EncodecModel.from_pretrained(model_id).to(torch_device)
@@ -581,9 +577,7 @@ def test_batch_48kHz(self):
                 [85561, 81870, 76953, 48967, 79315, 85442, 81479, 107241],
             ],
         }
-        librispeech_dummy = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        )
+        librispeech_dummy = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         model_id = "facebook/encodec_48khz"
 
         model = EncodecModel.from_pretrained(model_id).to(torch_device)
diff --git a/tests/models/hubert/test_modeling_hubert.py b/tests/models/hubert/test_modeling_hubert.py
index cd801be41d7b3d..86f2b4119324ae 100644
--- a/tests/models/hubert/test_modeling_hubert.py
+++ b/tests/models/hubert/test_modeling_hubert.py
@@ -753,9 +753,7 @@ class HubertModelIntegrationTest(unittest.TestCase):
     def _load_datasamples(self, num_samples):
         from datasets import load_dataset
 
-        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").filter(
             lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
diff --git a/tests/models/hubert/test_modeling_tf_hubert.py b/tests/models/hubert/test_modeling_tf_hubert.py
index 35a8d98c233f77..3685e6598740c5 100644
--- a/tests/models/hubert/test_modeling_tf_hubert.py
+++ b/tests/models/hubert/test_modeling_tf_hubert.py
@@ -609,9 +609,7 @@ class TFHubertModelIntegrationTest(unittest.TestCase):
     def _load_datasamples(self, num_samples):
         from datasets import load_dataset
 
-        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").filter(
             lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
diff --git a/tests/models/pop2piano/test_feature_extraction_pop2piano.py b/tests/models/pop2piano/test_feature_extraction_pop2piano.py
index 5a4652ad577cd7..c6766147975962 100644
--- a/tests/models/pop2piano/test_feature_extraction_pop2piano.py
+++ b/tests/models/pop2piano/test_feature_extraction_pop2piano.py
@@ -136,9 +136,7 @@ def test_call(self):
         self.assertTrue(input_features.extrapolated_beatstep.ndim == 2)
 
     def test_integration(self):
-        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")
         speech_samples = ds.sort("id").select([0])["audio"]
         input_speech = [x["array"] for x in speech_samples][0]
         sampling_rate = [x["sampling_rate"] for x in speech_samples][0]
diff --git a/tests/models/pop2piano/test_processor_pop2piano.py b/tests/models/pop2piano/test_processor_pop2piano.py
index 634cdd26bd105c..06a8bacfd8a45c 100644
--- a/tests/models/pop2piano/test_processor_pop2piano.py
+++ b/tests/models/pop2piano/test_processor_pop2piano.py
@@ -111,9 +111,7 @@ def test_save_load_pretrained_additional_features(self):
 
     def get_inputs(self):
         """get inputs for both feature extractor and tokenizer"""
-        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")
         speech_samples = ds.sort("id").select([0])["audio"]
         input_speech = [x["array"] for x in speech_samples][0]
         sampling_rate = [x["sampling_rate"] for x in speech_samples][0]
diff --git a/tests/models/seamless_m4t/test_feature_extraction_seamless_m4t.py b/tests/models/seamless_m4t/test_feature_extraction_seamless_m4t.py
index d9919e0adea6cc..a8fca4b90ba941 100644
--- a/tests/models/seamless_m4t/test_feature_extraction_seamless_m4t.py
+++ b/tests/models/seamless_m4t/test_feature_extraction_seamless_m4t.py
@@ -258,9 +258,7 @@ def test_double_precision_pad(self):
             self.assertTrue(pt_processed.input_features.dtype == torch.float32)
 
     def _load_datasample(self, id):
-        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_sample = ds.sort("id")[id]["audio"]["array"]
 
diff --git a/tests/models/sew/test_modeling_sew.py b/tests/models/sew/test_modeling_sew.py
index fe10d994450be7..6b21c2e9f7128e 100644
--- a/tests/models/sew/test_modeling_sew.py
+++ b/tests/models/sew/test_modeling_sew.py
@@ -494,9 +494,7 @@ class SEWModelIntegrationTest(unittest.TestCase):
     def _load_datasamples(self, num_samples):
         from datasets import load_dataset
 
-        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").filter(
             lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
diff --git a/tests/models/sew_d/test_modeling_sew_d.py b/tests/models/sew_d/test_modeling_sew_d.py
index 9fd94fbfef2668..b2efdccdf07c3f 100644
--- a/tests/models/sew_d/test_modeling_sew_d.py
+++ b/tests/models/sew_d/test_modeling_sew_d.py
@@ -508,9 +508,7 @@ class SEWDModelIntegrationTest(unittest.TestCase):
     def _load_datasamples(self, num_samples):
         from datasets import load_dataset
 
-        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").filter(
             lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
diff --git a/tests/models/speech_to_text/test_feature_extraction_speech_to_text.py b/tests/models/speech_to_text/test_feature_extraction_speech_to_text.py
index 6c8861e3d8689f..9023e8467f736c 100644
--- a/tests/models/speech_to_text/test_feature_extraction_speech_to_text.py
+++ b/tests/models/speech_to_text/test_feature_extraction_speech_to_text.py
@@ -259,9 +259,7 @@ def test_double_precision_pad(self):
     def _load_datasamples(self, num_samples):
         from datasets import load_dataset
 
-        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"]
 
diff --git a/tests/models/speech_to_text/test_modeling_speech_to_text.py b/tests/models/speech_to_text/test_modeling_speech_to_text.py
index 44672f1c588f31..4f19cc01b33c70 100644
--- a/tests/models/speech_to_text/test_modeling_speech_to_text.py
+++ b/tests/models/speech_to_text/test_modeling_speech_to_text.py
@@ -793,9 +793,7 @@ def default_processor(self):
     def _load_datasamples(self, num_samples):
         from datasets import load_dataset
 
-        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"]
 
diff --git a/tests/models/speech_to_text/test_modeling_tf_speech_to_text.py b/tests/models/speech_to_text/test_modeling_tf_speech_to_text.py
index d12174533395df..c2fd215f388575 100644
--- a/tests/models/speech_to_text/test_modeling_tf_speech_to_text.py
+++ b/tests/models/speech_to_text/test_modeling_tf_speech_to_text.py
@@ -587,9 +587,7 @@ def default_processor(self):
     def _load_datasamples(self, num_samples):
         from datasets import load_dataset
 
-        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"]
 
diff --git a/tests/models/speecht5/test_feature_extraction_speecht5.py b/tests/models/speecht5/test_feature_extraction_speecht5.py
index f8f7f53cac200c..5ec632e7e76c63 100644
--- a/tests/models/speecht5/test_feature_extraction_speecht5.py
+++ b/tests/models/speecht5/test_feature_extraction_speecht5.py
@@ -380,9 +380,7 @@ def test_attention_mask_with_truncation_target(self):
     def _load_datasamples(self, num_samples):
         from datasets import load_dataset
 
-        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"]
 
diff --git a/tests/models/speecht5/test_modeling_speecht5.py b/tests/models/speecht5/test_modeling_speecht5.py
index 1d67bb4f8ab527..7a8aab83272bc8 100644
--- a/tests/models/speecht5/test_modeling_speecht5.py
+++ b/tests/models/speecht5/test_modeling_speecht5.py
@@ -744,9 +744,7 @@ def default_processor(self):
     def _load_datasamples(self, num_samples):
         from datasets import load_dataset
 
-        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"]
 
@@ -1771,9 +1769,7 @@ def default_processor(self):
     def _load_datasamples(self, num_samples):
         from datasets import load_dataset
 
-        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"]
 
diff --git a/tests/models/unispeech/test_modeling_unispeech.py b/tests/models/unispeech/test_modeling_unispeech.py
index 1804e2c95ef4ce..d0a1d352243b19 100644
--- a/tests/models/unispeech/test_modeling_unispeech.py
+++ b/tests/models/unispeech/test_modeling_unispeech.py
@@ -549,9 +549,7 @@ def test_model_from_pretrained(self):
 @slow
 class UniSpeechModelIntegrationTest(unittest.TestCase):
     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").filter(
             lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
diff --git a/tests/models/unispeech_sat/test_modeling_unispeech_sat.py b/tests/models/unispeech_sat/test_modeling_unispeech_sat.py
index f3d467f0795d11..1aa2da20d5ec85 100644
--- a/tests/models/unispeech_sat/test_modeling_unispeech_sat.py
+++ b/tests/models/unispeech_sat/test_modeling_unispeech_sat.py
@@ -806,9 +806,7 @@ def test_model_from_pretrained(self):
 @slow
 class UniSpeechSatModelIntegrationTest(unittest.TestCase):
     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").filter(
             lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
diff --git a/tests/models/univnet/test_feature_extraction_univnet.py b/tests/models/univnet/test_feature_extraction_univnet.py
index 673faaae9adaca..dfa335d15383ee 100644
--- a/tests/models/univnet/test_feature_extraction_univnet.py
+++ b/tests/models/univnet/test_feature_extraction_univnet.py
@@ -327,9 +327,7 @@ def test_double_precision_pad(self):
             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")
         ds = ds.cast_column("audio", Audio(sampling_rate=self.feat_extract_tester.sampling_rate))
         # automatic decoding with librispeech
         speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
diff --git a/tests/models/univnet/test_modeling_univnet.py b/tests/models/univnet/test_modeling_univnet.py
index 4dc28b3c168b1d..e160c799b786cb 100644
--- a/tests/models/univnet/test_modeling_univnet.py
+++ b/tests/models/univnet/test_modeling_univnet.py
@@ -216,9 +216,7 @@ def tearDown(self):
         torch.cuda.empty_cache()
 
     def _load_datasamples(self, num_samples, sampling_rate=24000):
-        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")
         ds = ds.cast_column("audio", Audio(sampling_rate=sampling_rate))
         # automatic decoding with librispeech
         speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
diff --git a/tests/models/wav2vec2/test_modeling_flax_wav2vec2.py b/tests/models/wav2vec2/test_modeling_flax_wav2vec2.py
index 18252a17524337..b91d66654de6ae 100644
--- a/tests/models/wav2vec2/test_modeling_flax_wav2vec2.py
+++ b/tests/models/wav2vec2/test_modeling_flax_wav2vec2.py
@@ -489,9 +489,7 @@ def test_sample_negatives_with_attn_mask(self):
 @slow
 class FlaxWav2Vec2ModelIntegrationTest(unittest.TestCase):
     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").filter(
             lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
diff --git a/tests/models/wav2vec2/test_modeling_tf_wav2vec2.py b/tests/models/wav2vec2/test_modeling_tf_wav2vec2.py
index 2f10e3378d736a..7ef97290e61c9f 100644
--- a/tests/models/wav2vec2/test_modeling_tf_wav2vec2.py
+++ b/tests/models/wav2vec2/test_modeling_tf_wav2vec2.py
@@ -716,9 +716,7 @@ def tearDown(self):
         gc.collect()
 
     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").filter(
             lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
diff --git a/tests/models/wav2vec2/test_modeling_wav2vec2.py b/tests/models/wav2vec2/test_modeling_wav2vec2.py
index 51d105a5ee3f80..ff7a85218d3a00 100644
--- a/tests/models/wav2vec2/test_modeling_wav2vec2.py
+++ b/tests/models/wav2vec2/test_modeling_wav2vec2.py
@@ -1464,9 +1464,7 @@ def tearDown(self):
         backend_empty_cache(torch_device)
 
     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").filter(
             lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
diff --git a/tests/models/wav2vec2_bert/test_modeling_wav2vec2_bert.py b/tests/models/wav2vec2_bert/test_modeling_wav2vec2_bert.py
index 0fbd000edc8c3d..80237fea9d1e43 100644
--- a/tests/models/wav2vec2_bert/test_modeling_wav2vec2_bert.py
+++ b/tests/models/wav2vec2_bert/test_modeling_wav2vec2_bert.py
@@ -855,9 +855,7 @@ def test_sample_negatives_with_mask(self):
 @slow
 class Wav2Vec2BertModelIntegrationTest(unittest.TestCase):
     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").filter(lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)])
         speech_samples = speech_samples[:num_samples]["audio"]
diff --git a/tests/models/wav2vec2_conformer/test_modeling_wav2vec2_conformer.py b/tests/models/wav2vec2_conformer/test_modeling_wav2vec2_conformer.py
index ae13a8ecba9dea..096d1368ed02cb 100644
--- a/tests/models/wav2vec2_conformer/test_modeling_wav2vec2_conformer.py
+++ b/tests/models/wav2vec2_conformer/test_modeling_wav2vec2_conformer.py
@@ -863,9 +863,7 @@ def test_sample_negatives_with_mask(self):
 @slow
 class Wav2Vec2ConformerModelIntegrationTest(unittest.TestCase):
     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").filter(lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)])
         speech_samples = speech_samples[:num_samples]["audio"]
diff --git a/tests/models/wavlm/test_modeling_wavlm.py b/tests/models/wavlm/test_modeling_wavlm.py
index 8f4d1e850e0056..b20792d83545d8 100644
--- a/tests/models/wavlm/test_modeling_wavlm.py
+++ b/tests/models/wavlm/test_modeling_wavlm.py
@@ -491,9 +491,7 @@ def test_model_from_pretrained(self):
 @slow
 class WavLMModelIntegrationTest(unittest.TestCase):
     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").filter(
             lambda x: x["id"] in [f"1272-141231-000{i}" for i in range(num_samples)]
diff --git a/tests/models/whisper/test_feature_extraction_whisper.py b/tests/models/whisper/test_feature_extraction_whisper.py
index 579c42519ae033..a8295542f4e377 100644
--- a/tests/models/whisper/test_feature_extraction_whisper.py
+++ b/tests/models/whisper/test_feature_extraction_whisper.py
@@ -215,9 +215,7 @@ def test_double_precision_pad(self):
             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"]
 
diff --git a/tests/models/whisper/test_modeling_flax_whisper.py b/tests/models/whisper/test_modeling_flax_whisper.py
index d5e18d22c2f3c8..4b8092e800ad69 100644
--- a/tests/models/whisper/test_modeling_flax_whisper.py
+++ b/tests/models/whisper/test_modeling_flax_whisper.py
@@ -410,9 +410,7 @@ def default_processor(self):
         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"]
 
diff --git a/tests/models/whisper/test_modeling_tf_whisper.py b/tests/models/whisper/test_modeling_tf_whisper.py
index 97143cc4df5120..b200671e048f0e 100644
--- a/tests/models/whisper/test_modeling_tf_whisper.py
+++ b/tests/models/whisper/test_modeling_tf_whisper.py
@@ -704,7 +704,7 @@ def test_generate_with_prompt_ids_and_forced_decoder_ids(self):
 
 
 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"]
 
diff --git a/tests/models/whisper/test_modeling_whisper.py b/tests/models/whisper/test_modeling_whisper.py
index a11097fe7dc391..5a59f7a72517a4 100644
--- a/tests/models/whisper/test_modeling_whisper.py
+++ b/tests/models/whisper/test_modeling_whisper.py
@@ -1835,9 +1835,7 @@ def default_processor(self):
         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 @@ def test_speculative_decoding_distil(self):
         )
         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 @@ def test_speculative_decoding_non_distil(self):
         )
         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 @@ def test_whisper_longform_single_batch(self):
         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 @@ def test_whisper_longform_prompt_ids(self):
         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 @@ def test_whisper_longform_single_batch_prev_cond(self):
         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 @@ def test_whisper_longform_single_batch_beam(self):
         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 @@ def test_whisper_longform_multi_batch(self):
         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 @@ def test_whisper_longform_multi_batch_prev_cond(self):
         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:])
diff --git a/tests/pipelines/test_pipelines_audio_classification.py b/tests/pipelines/test_pipelines_audio_classification.py
index a8c5deb2284452..1f403a8be05d2a 100644
--- a/tests/pipelines/test_pipelines_audio_classification.py
+++ b/tests/pipelines/test_pipelines_audio_classification.py
@@ -71,9 +71,7 @@ def run_torchaudio(self, audio_classifier):
         import datasets
 
         # test with a local file
-        dataset = datasets.load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        )
+        dataset = datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         audio = dataset[0]["audio"]["array"]
         output = audio_classifier(audio)
         self.assertEqual(
diff --git a/tests/pipelines/test_pipelines_automatic_speech_recognition.py b/tests/pipelines/test_pipelines_automatic_speech_recognition.py
index 82c5580f0ea2cc..d8810f67eec15e 100644
--- a/tests/pipelines/test_pipelines_automatic_speech_recognition.py
+++ b/tests/pipelines/test_pipelines_automatic_speech_recognition.py
@@ -294,9 +294,7 @@ def test_torch_large(self):
         output = speech_recognizer(waveform)
         self.assertEqual(output, {"text": ""})
 
-        ds = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        ).sort("id")
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
         filename = ds[40]["file"]
         output = speech_recognizer(filename)
         self.assertEqual(output, {"text": "A MAN SAID TO THE UNIVERSE SIR I EXIST"})
@@ -313,9 +311,7 @@ def test_torch_large_with_input_features(self):
         output = speech_recognizer(waveform)
         self.assertEqual(output, {"text": ""})
 
-        ds = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        ).sort("id")
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
         filename = ds[40]["file"]
         output = speech_recognizer(filename)
         self.assertEqual(output, {"text": "a man said to the universe sir i exist"})
@@ -545,9 +541,7 @@ def test_torch_whisper(self):
             model="openai/whisper-tiny",
             framework="pt",
         )
-        ds = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        ).sort("id")
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
         filename = ds[40]["file"]
         output = speech_recognizer(filename)
         self.assertEqual(output, {"text": " A man said to the universe, Sir, I exist."})
@@ -722,9 +716,7 @@ def test_find_longest_common_subsequence(self):
     @slow
     @require_torch
     def test_whisper_timestamp_prediction(self):
-        ds = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        ).sort("id")
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
         array = np.concatenate(
             [ds[40]["audio"]["array"], ds[41]["audio"]["array"], ds[42]["audio"]["array"], ds[43]["audio"]["array"]]
         )
@@ -822,9 +814,7 @@ def test_whisper_timestamp_prediction(self):
     @slow
     @require_torch
     def test_whisper_large_timestamp_prediction(self):
-        ds = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        ).sort("id")
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
         array = np.concatenate(
             [ds[40]["audio"]["array"], ds[41]["audio"]["array"], ds[42]["audio"]["array"], ds[43]["audio"]["array"]]
         )
@@ -918,9 +908,7 @@ def test_whisper_word_timestamps_batched(self):
             chunk_length_s=3,
             return_timestamps="word",
         )
-        data = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        )
+        data = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         sample = data[0]["audio"]
 
         # not the same output as test_simple_whisper_asr because of chunking
@@ -963,9 +951,7 @@ def test_whisper_large_word_timestamps_batched(self):
             model="openai/whisper-large-v3",
             return_timestamps="word",
         )
-        data = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        )
+        data = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
         sample = data[0]["audio"]
 
         # not the same output as test_simple_whisper_asr because of chunking
@@ -1010,9 +996,7 @@ def test_torch_speech_encoder_decoder(self):
             framework="pt",
         )
 
-        ds = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        ).sort("id")
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
         filename = ds[40]["file"]
         output = speech_recognizer(filename)
         self.assertEqual(output, {"text": 'Ein Mann sagte zum Universum : " Sir, ich existiert! "'})
@@ -1030,9 +1014,7 @@ def test_simple_wav2vec2(self):
         output = asr(waveform)
         self.assertEqual(output, {"text": ""})
 
-        ds = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        ).sort("id")
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
         filename = ds[40]["file"]
         output = asr(filename)
         self.assertEqual(output, {"text": "A MAN SAID TO THE UNIVERSE SIR I EXIST"})
@@ -1058,9 +1040,7 @@ def test_simple_s2t(self):
         output = asr(waveform)
         self.assertEqual(output, {"text": "(Applausi)"})
 
-        ds = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        ).sort("id")
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
         filename = ds[40]["file"]
         output = asr(filename)
         self.assertEqual(output, {"text": "Un uomo disse all'universo: \"Signore, io esisto."})
@@ -1080,9 +1060,7 @@ def test_simple_whisper_asr(self):
             model="openai/whisper-tiny.en",
             framework="pt",
         )
-        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")
         filename = ds[0]["file"]
         output = speech_recognizer(filename)
         self.assertEqual(
@@ -1151,9 +1129,7 @@ def test_simple_whisper_translation(self):
             model="openai/whisper-large",
             framework="pt",
         )
-        ds = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        ).sort("id")
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
         filename = ds[40]["file"]
         output = speech_recognizer(filename)
         self.assertEqual(output, {"text": " A man said to the universe, Sir, I exist."})
@@ -1188,9 +1164,7 @@ def test_whisper_language(self):
             model="openai/whisper-tiny.en",
             framework="pt",
         )
-        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")
         filename = ds[0]["file"]
 
         # 1. English-only model compatible with no language argument
@@ -1323,9 +1297,7 @@ def test_xls_r_to_en(self):
             framework="pt",
         )
 
-        ds = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        ).sort("id")
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
         filename = ds[40]["file"]
         output = speech_recognizer(filename)
         self.assertEqual(output, {"text": "A man said to the universe: “Sir, I exist."})
@@ -1341,9 +1313,7 @@ def test_xls_r_from_en(self):
             framework="pt",
         )
 
-        ds = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        ).sort("id")
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
         filename = ds[40]["file"]
         output = speech_recognizer(filename)
         self.assertEqual(output, {"text": "Ein Mann sagte zu dem Universum, Sir, ich bin da."})
@@ -1360,9 +1330,7 @@ def test_speech_to_text_leveraged(self):
             framework="pt",
         )
 
-        ds = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        ).sort("id")
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
         filename = ds[40]["file"]
 
         output = speech_recognizer(filename)
@@ -1379,9 +1347,7 @@ def test_wav2vec2_conformer_float16(self):
             framework="pt",
         )
 
-        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"]
 
         output = speech_recognizer(sample)
@@ -1398,9 +1364,7 @@ def test_chunking_fast(self):
             chunk_length_s=10.0,
         )
 
-        ds = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        ).sort("id")
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
         audio = ds[40]["audio"]["array"]
 
         n_repeats = 2
@@ -1416,9 +1380,7 @@ def test_return_timestamps_ctc_fast(self):
             model="hf-internal-testing/tiny-random-wav2vec2",
         )
 
-        ds = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        ).sort("id")
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
         # Take short audio to keep the test readable
         audio = ds[40]["audio"]["array"][:800]
 
@@ -1462,9 +1424,7 @@ def test_chunking_fast_with_lm(self):
             chunk_length_s=10.0,
         )
 
-        ds = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        ).sort("id")
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
         audio = ds[40]["audio"]["array"]
 
         n_repeats = 2
@@ -1492,9 +1452,7 @@ def test_with_lm_fast(self):
         )
         self.assertEqual(speech_recognizer.type, "ctc_with_lm")
 
-        ds = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        ).sort("id")
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
         audio = ds[40]["audio"]["array"]
 
         n_repeats = 2
@@ -1522,9 +1480,7 @@ def test_with_local_lm_fast(self):
         )
         self.assertEqual(speech_recognizer.type, "ctc_with_lm")
 
-        ds = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        ).sort("id")
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
         audio = ds[40]["audio"]["array"]
 
         n_repeats = 2
@@ -1608,9 +1564,7 @@ def test_seamless_v2(self):
             device=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"]
 
         result = pipe(sample, generate_kwargs={"tgt_lang": "eng"})
@@ -1633,9 +1587,7 @@ def test_chunking_and_timestamps(self):
             chunk_length_s=10.0,
         )
 
-        ds = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        ).sort("id")
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
         audio = ds[40]["audio"]["array"]
 
         n_repeats = 10
@@ -1747,9 +1699,7 @@ def test_chunking_with_lm(self):
             model="patrickvonplaten/wav2vec2-base-100h-with-lm",
             chunk_length_s=10.0,
         )
-        ds = load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        ).sort("id")
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
         audio = ds[40]["audio"]["array"]
 
         n_repeats = 10
diff --git a/tests/pipelines/test_pipelines_common.py b/tests/pipelines/test_pipelines_common.py
index d4dbff218558e1..95349a8335de6d 100644
--- a/tests/pipelines/test_pipelines_common.py
+++ b/tests/pipelines/test_pipelines_common.py
@@ -840,9 +840,7 @@ def test_cached_pipeline_has_minimum_calls_to_head(self):
     def test_chunk_pipeline_batching_single_file(self):
         # Make sure we have cached the pipeline.
         pipe = pipeline(model="hf-internal-testing/tiny-random-Wav2Vec2ForCTC")
-        ds = datasets.load_dataset(
-            "hf-internal-testing/librispeech_asr_dummy", "clean", split="validation", trust_remote_code=True
-        ).sort("id")
+        ds = datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
         audio = ds[40]["audio"]["array"]
 
         pipe = pipeline(model="hf-internal-testing/tiny-random-Wav2Vec2ForCTC")
diff --git a/tests/utils/test_audio_utils.py b/tests/utils/test_audio_utils.py
index 47c384870d4a04..3e417bf7e3b450 100644
--- a/tests/utils/test_audio_utils.py
+++ b/tests/utils/test_audio_utils.py
@@ -262,9 +262,7 @@ def test_window_function(self):
     def _load_datasamples(self, num_samples):
         from datasets import load_dataset
 
-        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")
         speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
         return [x["array"] for x in speech_samples]