Fix pipeline test

src/transformers/models/audio_spectrogram_transformer/feature_extraction_audio_spectrogram_transformer.py

@@ -32,13 +32,13 @@
 
 class ASTFeatureExtractor(SequenceFeatureExtractor):
  r"""
- Constructs a Audio Spectrogram Transformer feature extractor.
+ Constructs a Audio Spectrogram Transformer (AST) feature extractor.
 
  This feature extractor inherits from [`~feature_extraction_sequence_utils.SequenceFeatureExtractor`] which contains
  most of the main methods. Users should refer to this superclass for more information regarding those methods.
 
- This class extracts mel-filter bank features from raw speech using TorchAudio and applies utterance-level cepstral
- mean and variance normalization to the extracted features.
+ This class extracts mel-filter bank features from raw speech using TorchAudio, pads/truncates them to a fixed
+ length and normalizes them using a mean and standard deviation.
 
  Args:
  feature_size (`int`, *optional*, defaults to 1):
@@ -47,6 +47,8 @@ class ASTFeatureExtractor(SequenceFeatureExtractor):
  The sampling rate at which the audio files should be digitalized expressed in Hertz per second (Hz).
  num_mel_bins (`int`, *optional*, defaults to 128):
  Number of Mel-frequency bins.
+ max_length (`int`, *optional*, defaults to 1024):
+ Maximum length to which to pad/truncate the extracted features.
  do_normalize (`bool`, *optional*, defaults to `True`):
  Whether or not to normalize the log-Mel features using `mean` and `std`.
  mean (`float`, *optional*, defaults to -4.2677393):
@@ -65,6 +67,7 @@ def __init__(
  feature_size=1,
  sampling_rate=16000,
  num_mel_bins=128,
+ max_length=1024,
  padding_value=0.0,
  do_normalize=True,
  mean=-4.2677393,
@@ -74,6 +77,7 @@ def __init__(
  ):
  super().__init__(feature_size=feature_size, sampling_rate=sampling_rate, padding_value=padding_value, **kwargs)
  self.num_mel_bins = num_mel_bins
+ self.max_length = max_length
  self.do_normalize = do_normalize
  self.mean = mean
  self.std = std
@@ -121,7 +125,6 @@ def normalize(self, input_values: np.ndarray) -> np.ndarray:
  def __call__(
  self,
  raw_speech: Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]],
- max_length: int = 1024,
  sampling_rate: Optional[int] = None,
  return_tensors: Optional[Union[str, TensorType]] = None,
  **kwargs
@@ -133,8 +136,6 @@ def __call__(
  raw_speech (`np.ndarray`, `List[float]`, `List[np.ndarray]`, `List[List[float]]`):
  The sequence or batch of sequences to be padded. Each sequence can be a numpy array, a list of float
  values, a list of numpy arrays or a list of list of float values.
- max_length (`int`, *optional*, defaults to 1024):
- Maximum length of the returned list and optionally padding length (see above).
  sampling_rate (`int`, *optional*):
  The sampling rate at which the `raw_speech` input was sampled. It is strongly recommended to pass
  `sampling_rate` at the forward call to prevent silent errors.
@@ -175,8 +176,8 @@ def __call__(
  if not is_batched:
  raw_speech = [raw_speech]
 
- # extract fbank features (padded/truncated to max_length)
- features = [self._extract_fbank_features(waveform, max_length=max_length) for waveform in raw_speech]
+ # extract fbank features and pad/truncate to max_length
+ features = [self._extract_fbank_features(waveform, max_length=self.max_length) for waveform in raw_speech]
 
  # convert into BatchFeature
  padded_inputs = BatchFeature({"input_values": features})

tests/pipelines/test_pipelines_common.py

@@ -155,6 +155,10 @@ def get_tiny_feature_extractor_from_checkpoint(checkpoint, tiny_config, feature_
  if hasattr(tiny_config, "image_size") and feature_extractor:
  feature_extractor = feature_extractor.__class__(size=tiny_config.image_size, crop_size=tiny_config.image_size)
 
+ # Audio Spectogram Transformer specific.
+ if feature_extractor.__class__.__name__ == "ASTFeatureExtractor":
+ feature_extractor = feature_extractor.__class__(max_length=24, num_mel_bins=16)
+
  # Speech2TextModel specific.
  if hasattr(tiny_config, "input_feat_per_channel") and feature_extractor:
  feature_extractor = feature_extractor.__class__(