update attention mask handling (#19385)

* update feature extractor params

* update attention mask handling

src/transformers/models/whisper/feature_extraction_whisper.py

@@ -218,6 +218,7 @@ class WhisperFeatureExtractor(SequenceFeatureExtractor):
         return_attention_mask: Optional[bool] = None,
         padding: Optional[str] = "max_length",
         max_length: Optional[int] = None,
+        sampling_rate: Optional[int] = None,
         **kwargs
     ) -> BatchFeature:
         """
@@ -255,11 +256,25 @@ class WhisperFeatureExtractor(SequenceFeatureExtractor):
                 - `'np'`: Return Numpy `np.ndarray` objects.
             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.
+                `sampling_rate` at the forward call to prevent silent errors and allow automatic speech recognition
+                pipeline.
             padding_value (`float`, defaults to 0.0):
                 The value that is used to fill the padding values / vectors.
         """
 
+        if sampling_rate is not None:
+            if sampling_rate != self.sampling_rate:
+                raise ValueError(
+                    f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
+                    f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
+                    f" {self.sampling_rate} and not {sampling_rate}."
+                )
+        else:
+            logger.warning(
+                "It is strongly recommended to pass the `sampling_rate` argument to this function. "
+                "Failing to do so can result in silent errors that might be hard to debug."
+            )
+
         is_batched = bool(
             isinstance(raw_speech, (list, tuple))
             and (isinstance(raw_speech[0], np.ndarray) or isinstance(raw_speech[0], (tuple, list)))

src/transformers/pipelines/automatic_speech_recognition.py

@@ -11,6 +11,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import inspect
 from collections import defaultdict
 from typing import TYPE_CHECKING, Dict, Optional, Union
 
@@ -259,9 +260,9 @@ class AutomaticSpeechRecognitionPipeline(ChunkPipeline):
             # Currently chunking is not possible at this level for `seq2seq` so
             # it's ok.
             align_to = self.model.config.inputs_to_logits_ratio
-            chunk_len = int(round(chunk_length_s * self.feature_extractor.sampling_rate / align_to)) * align_to
-            stride_left = int(round(stride_length_s[0] * self.feature_extractor.sampling_rate / align_to)) * align_to
-            stride_right = int(round(stride_length_s[1] * self.feature_extractor.sampling_rate / align_to)) * align_to
+            chunk_len = int(round(chunk_length_s * self.feature_extractor.sampling_rate / align_to) * align_to)
+            stride_left = int(round(stride_length_s[0] * self.feature_extractor.sampling_rate / align_to) * align_to)
+            stride_right = int(round(stride_length_s[1] * self.feature_extractor.sampling_rate / align_to) * align_to)
 
             if self.type not in {"ctc", "ctc_with_lm"}:
                 raise ValueError(
@@ -304,12 +305,18 @@ class AutomaticSpeechRecognitionPipeline(ChunkPipeline):
                     f"`input_features` or `input_values` key, but only has {model_inputs.keys()}"
                 )
 
+            accepts_attention_mask = "attention_mask" in set(inspect.signature(encoder.forward).parameters.keys())
+            if accepts_attention_mask:
                 attention_mask = model_inputs.pop("attention_mask", None)
                 tokens = self.model.generate(
                     encoder_outputs=encoder(inputs, attention_mask=attention_mask),
                     attention_mask=attention_mask,
                 )
+            else:
+                tokens = self.model.generate(inputs)
+
             out = {"tokens": tokens}
+
         else:
             stride = model_inputs.pop("stride", None)
             input_values = model_inputs.pop("input_values")