[Test] Fix W2V-Conformer integration test (huggingface#17303)

* [Test] Fix W2V-Conformer integration test

* correct w2v2

* up

Author: patrickvonplaten

src/transformers/models/wav2vec2/modeling_wav2vec2.py

@@ -1414,7 +1414,6 @@ def forward(
         >>> from transformers import AutoFeatureExtractor, Wav2Vec2ForPreTraining
         >>> from transformers.models.wav2vec2.modeling_wav2vec2 import _compute_mask_indices
         >>> from datasets import load_dataset
-        >>> import soundfile as sf
 
         >>> feature_extractor = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base")
         >>> model = Wav2Vec2ForPreTraining.from_pretrained("facebook/wav2vec2-base")

src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py

@@ -1442,7 +1442,7 @@ def compute_contrastive_logits(
 
     @add_start_docstrings_to_model_forward(WAV2VEC2_CONFORMER_INPUTS_DOCSTRING)
     @replace_return_docstrings(output_type=Wav2Vec2ConformerForPreTrainingOutput, config_class=_CONFIG_FOR_DOC)
-    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTraining.forward with Wav2Vec2->Wav2Vec2Conformer,wav2vec2-base->wav2vec2-conformer-rel-pos-large,wav2vec2->wav2vec2_conformer
+    # Copied from transformers.models.wav2vec2.modeling_wav2vec2.Wav2Vec2ForPreTraining.forward with Wav2Vec2->Wav2Vec2Conformer,wav2vec2->wav2vec2_conformer,wav2vec2_conformer-base->wav2vec2-conformer-rel-pos-large
     def forward(
         self,
         input_values: Optional[torch.Tensor],
@@ -1470,14 +1470,9 @@ def forward(
         >>> from transformers import AutoFeatureExtractor, Wav2Vec2ConformerForPreTraining
         >>> from transformers.models.wav2vec2_conformer.modeling_wav2vec2_conformer import _compute_mask_indices
         >>> from datasets import load_dataset
-        >>> import soundfile as sf
-
-        >>> feature_extractor = AutoFeatureExtractor.from_pretrained(
-        ...     "facebook/wav2vec2_conformer-conformer-rel-pos-large"
-        ... )
-        >>> model = Wav2Vec2ConformerForPreTraining.from_pretrained(
-        ...     "facebook/wav2vec2_conformer-conformer-rel-pos-large"
-        ... )
+
+        >>> 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")
         >>> input_values = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt").input_values  # Batch size 1

tests/models/wav2vec2_conformer/test_modeling_wav2vec2_conformer.py

@@ -581,6 +581,10 @@ def _mock_init_weights(self, module):
             module.weight_v.data.fill_(3)
         if hasattr(module, "bias") and module.bias is not None:
             module.bias.data.fill_(3)
+        if hasattr(module, "pos_bias_u") and module.pos_bias_u is not None:
+            module.pos_bias_u.data.fill_(3)
+        if hasattr(module, "pos_bias_v") and module.pos_bias_v is not None:
+            module.pos_bias_v.data.fill_(3)
         if hasattr(module, "codevectors") and module.codevectors is not None:
             module.codevectors.data.fill_(3)
         if hasattr(module, "masked_spec_embed") and module.masked_spec_embed is not None: