[CLAP] Support batched inputs for CLAP. Fixes pipeline issues (#21931)

* fix pipeline

* fix feature_extraction clap

* you can now batch the `is_longer` attribute

* add tests

* fixup

* add expected scores

* comment on is_longert

src/transformers/models/clap/feature_extraction_clap.py

@@ -347,6 +347,9 @@ class ClapFeatureExtractor(SequenceFeatureExtractor):
         if isinstance(input_mel[0], List):
             input_mel = [np.asarray(feature, dtype=np.float64) for feature in input_mel]
 
+        # is_longer is a list of bool
+        is_longer = [[longer] for longer in is_longer]
+
         input_features = {"input_features": input_mel, "is_longer": is_longer}
         input_features = BatchFeature(input_features)
 

src/transformers/pipelines/zero_shot_audio_classification.py

@@ -44,6 +44,7 @@ class ZeroShotAudioClassificationPipeline(Pipeline):
     >>> audio = next(iter(dataset["train"]["audio"]))["array"]
     >>> classifier = pipeline(task="zero-shot-audio-classification", model="laion/clap-htsat-unfused")
     >>> classifier(audio, candidate_labels=["Sound of a dog", "Sound of vaccum cleaner"])
+    [{'score': 0.9995999932289124, 'label': 'Sound of a dog'}, {'score': 0.00040007088682614267, 'label': 'Sound of vaccum cleaner'}]
     ```
 
 
@@ -118,6 +119,7 @@ class ZeroShotAudioClassificationPipeline(Pipeline):
         sequences = [hypothesis_template.format(x) for x in candidate_labels]
         text_inputs = self.tokenizer(sequences, return_tensors=self.framework, padding=True)
         inputs["text_inputs"] = [text_inputs]
+        return inputs
 
     def _forward(self, model_inputs):
         candidate_labels = model_inputs.pop("candidate_labels")
@@ -131,8 +133,8 @@ class ZeroShotAudioClassificationPipeline(Pipeline):
         outputs = self.model(**text_inputs, **model_inputs)
 
         model_outputs = {
-            "candidate_label": candidate_labels,
+            "candidate_labels": candidate_labels,
-            "logits_per_audio": outputs.logits_per_audio,
+            "logits": outputs.logits_per_audio,
         }
         return model_outputs
 

tests/models/clap/test_modeling_clap.py

@@ -665,3 +665,55 @@ class ClapModelIntegrationTest(unittest.TestCase):
             self.assertTrue(
                 torch.allclose(audio_embed.cpu().mean(), torch.tensor([expected_mean]), atol=1e-3, rtol=1e-3)
             )
+
+    def test_batched_fused(self):
+        EXPECTED_MEANS_FUSED = {
+            "repeatpad": 0.0010,
+            "repeat": 0.0020,
+            "pad": 0.0006,
+        }
+
+        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"
+
+        model = ClapModel.from_pretrained(model_id).to(torch_device)
+        processor = ClapProcessor.from_pretrained(model_id)
+
+        for padding in self.paddings:
+            inputs = processor(audios=audio_samples, return_tensors="pt", padding=padding, truncation="fusion").to(
+                torch_device
+            )
+
+            audio_embed = model.get_audio_features(**inputs)
+            expected_mean = EXPECTED_MEANS_FUSED[padding]
+
+            self.assertTrue(
+                torch.allclose(audio_embed.cpu().mean(), torch.tensor([expected_mean]), atol=1e-3, rtol=1e-3)
+            )
+
+    def test_batched_unfused(self):
+        EXPECTED_MEANS_FUSED = {
+            "repeatpad": 0.0016,
+            "repeat": 0.0019,
+            "pad": 0.0019,
+        }
+
+        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"
+
+        model = ClapModel.from_pretrained(model_id).to(torch_device)
+        processor = ClapProcessor.from_pretrained(model_id)
+
+        for padding in self.paddings:
+            inputs = processor(audios=audio_samples, return_tensors="pt", padding=padding).to(torch_device)
+
+            audio_embed = model.get_audio_features(**inputs)
+            expected_mean = EXPECTED_MEANS_FUSED[padding]
+
+            self.assertTrue(
+                torch.allclose(audio_embed.cpu().mean(), torch.tensor([expected_mean]), atol=1e-3, rtol=1e-3)
+            )