fix audio pipeline with torchcodec input

src/transformers/pipelines/audio_classification.py

@@ -174,14 +174,7 @@ class AudioClassificationPipeline(Pipeline):
         if isinstance(inputs, bytes):
             inputs = ffmpeg_read(inputs, self.feature_extractor.sampling_rate)
 
-        if is_torch_available():
-            import torch
-
-            if isinstance(inputs, torch.Tensor):
-                inputs = inputs.cpu().numpy()
-
         if is_torchcodec_available():
-            import torch
             import torchcodec
 
             if isinstance(inputs, torchcodec.decoders.AudioDecoder):
@@ -224,10 +217,14 @@ class AudioClassificationPipeline(Pipeline):
                     self.feature_extractor.sampling_rate,
                 ).numpy()
 
+        if is_torch_available():
+            import torch
+
+            if isinstance(inputs, torch.Tensor):
+                inputs = inputs.cpu().numpy()
+
         if not isinstance(inputs, np.ndarray):
             raise TypeError("We expect a numpy ndarray or torch tensor as input")
-        if len(inputs.shape) != 1:
-            raise ValueError("We expect a single channel audio input for AudioClassificationPipeline")
 
         processed = self.feature_extractor(
             inputs, sampling_rate=self.feature_extractor.sampling_rate, return_tensors="pt"

src/transformers/pipelines/automatic_speech_recognition.py

@@ -365,12 +365,6 @@ class AutomaticSpeechRecognitionPipeline(ChunkPipeline):
         stride = None
         extra = {}
 
-        if is_torch_available():
-            import torch
-
-            if isinstance(inputs, torch.Tensor):
-                inputs = inputs.cpu().numpy()
-
         if is_torchcodec_available():
             import torchcodec
 
@@ -425,10 +419,15 @@ class AutomaticSpeechRecognitionPipeline(ChunkPipeline):
                 # can add extra data in the inputs, so we need to keep track
                 # of the original length in the stride so we can cut properly.
                 stride = (inputs.shape[0], int(round(stride[0] * ratio)), int(round(stride[1] * ratio)))
+
+        if is_torch_available():
+            import torch
+
+            if isinstance(inputs, torch.Tensor):
+                inputs = inputs.cpu().numpy()
+
         if not isinstance(inputs, np.ndarray):
             raise TypeError(f"We expect a numpy ndarray or torch tensor as input, got `{type(inputs)}`")
-        if len(inputs.shape) != 1:
-            raise ValueError("We expect a single channel audio input for AutomaticSpeechRecognitionPipeline")
 
         if chunk_length_s:
             if stride_length_s is None:

tests/pipelines/test_pipelines_automatic_speech_recognition.py

@@ -1148,7 +1148,7 @@ class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase):
             num_beams=1,
         )
 
-        transcription_non_ass = pipe(sample.copy(), generate_kwargs={"assistant_model": assistant_model})["text"]
+        transcription_non_ass = pipe(sample, generate_kwargs={"assistant_model": assistant_model})["text"]
         transcription_ass = pipe(sample)["text"]
 
         self.assertEqual(transcription_ass, transcription_non_ass)