fix low-precision audio classification pipeline (#35435)

* fix low-precision audio classification pipeline

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* add test

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* fix format

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* fix torch import

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* fix torch import

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

* fix format

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

---------

Signed-off-by: jiqing-feng <jiqing.feng@intel.com>

src/transformers/pipelines/audio_classification.py

@@ -212,6 +212,8 @@ class AudioClassificationPipeline(Pipeline):
         processed = self.feature_extractor(
             inputs, sampling_rate=self.feature_extractor.sampling_rate, return_tensors="pt"
         )
+        if self.torch_dtype is not None:
+            processed = processed.to(dtype=self.torch_dtype)
         return processed
 
     def _forward(self, model_inputs):

tests/pipelines/test_pipelines_audio_classification.py

@@ -17,7 +17,11 @@ import unittest
 import numpy as np
 from huggingface_hub import AudioClassificationOutputElement
 
-from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
+from transformers import (
+    MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
+    TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING,
+    is_torch_available,
+)
 from transformers.pipelines import AudioClassificationPipeline, pipeline
 from transformers.testing_utils import (
     compare_pipeline_output_to_hub_spec,
@@ -32,6 +36,10 @@ from transformers.testing_utils import (
 from .test_pipelines_common import ANY
 
 
+if is_torch_available():
+    import torch
+
+
 @is_pipeline_test
 class AudioClassificationPipelineTests(unittest.TestCase):
     model_mapping = MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
@@ -127,6 +135,33 @@ class AudioClassificationPipelineTests(unittest.TestCase):
         output = audio_classifier(audio_dict, top_k=4)
         self.assertIn(nested_simplify(output, decimals=4), [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2])
 
+    @require_torch
+    def test_small_model_pt_fp16(self):
+        model = "anton-l/wav2vec2-random-tiny-classifier"
+
+        audio_classifier = pipeline("audio-classification", model=model, torch_dtype=torch.float16)
+
+        audio = np.ones((8000,))
+        output = audio_classifier(audio, top_k=4)
+
+        EXPECTED_OUTPUT = [
+            {"score": 0.0839, "label": "no"},
+            {"score": 0.0837, "label": "go"},
+            {"score": 0.0836, "label": "yes"},
+            {"score": 0.0835, "label": "right"},
+        ]
+        EXPECTED_OUTPUT_PT_2 = [
+            {"score": 0.0845, "label": "stop"},
+            {"score": 0.0844, "label": "on"},
+            {"score": 0.0841, "label": "right"},
+            {"score": 0.0834, "label": "left"},
+        ]
+        self.assertIn(nested_simplify(output, decimals=4), [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2])
+
+        audio_dict = {"array": np.ones((8000,)), "sampling_rate": audio_classifier.feature_extractor.sampling_rate}
+        output = audio_classifier(audio_dict, top_k=4)
+        self.assertIn(nested_simplify(output, decimals=4), [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2])
+
     @require_torch
     @slow
     def test_large_model_pt(self):