Fix whisper for `pipeline` (#19482)

* update feature extractor params

* update attention mask handling

* fix doc and pipeline test

* add warning when skipping test

* add whisper translation and transcription test

* fix build doc test

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:
         """
@@ -261,6 +262,19 @@ class WhisperFeatureExtractor(SequenceFeatureExtractor):
                 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/models/whisper/modeling_whisper.py

@@ -31,13 +31,22 @@ from ...modeling_outputs import (
     Seq2SeqModelOutput,
 )
 from ...modeling_utils import PreTrainedModel
-from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
+from ...utils import (
+    add_code_sample_docstrings,
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    logging,
+    replace_return_docstrings,
+)
 from .configuration_whisper import WhisperConfig
 
 
 logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "WhisperConfig"
+_CHECKPOINT_FOR_DOC = "openai/whisper-tiny"
+_PROCESSOR_FOR_DOC = "openai/whisper-tiny"
+_EXPECTED_OUTPUT_SHAPE = [1, 2, 512]
 
 
 WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST = [
@@ -982,7 +991,14 @@ class WhisperModel(WhisperPreTrainedModel):
         return self.decoder
 
     @add_start_docstrings_to_model_forward(WHISPER_INPUTS_DOCSTRING)
-    @replace_return_docstrings(output_type=Seq2SeqLMOutput, config_class=_CONFIG_FOR_DOC)
+    @add_code_sample_docstrings(
+        processor_class=_PROCESSOR_FOR_DOC,
+        checkpoint=_CHECKPOINT_FOR_DOC,
+        output_type=Seq2SeqModelOutput,
+        config_class=_CONFIG_FOR_DOC,
+        expected_output=_EXPECTED_OUTPUT_SHAPE,
+        modality="audio",
+    )
     def forward(
         self,
         input_features=None,
@@ -999,26 +1015,6 @@ class WhisperModel(WhisperPreTrainedModel):
         output_hidden_states=None,
         return_dict=None,
     ):
-        r"""
-        Returns:
-
-        Example:
-
-         ```python
-         >>> import torch
-         >>> from transformers import WhisperModel, WhisperFeatureExtractor
-         >>> from datasets import load_dataset
-
-         >>> model = WhisperModel.from_pretrained("openai/whisper-base")
-         >>> feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-base")
-         >>> ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
-         >>> inputs = feature_extractor(ds[0]["audio"]["array"], return_tensors="pt")
-         >>> input_features = inputs.input_features
-         >>> decoder_input_ids = torch.tensor([[1, 1]]) * model.config.decoder_start_token_id
-         >>> last_hidden_state = model(input_features, decoder_input_ids=decoder_input_ids).last_hidden_state
-         >>> list(last_hidden_state.shape)
-         [1, 2, 512]
-         ```"""
 
         output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
         output_hidden_states = (

tests/pipelines/test_pipelines_automatic_speech_recognition.py

@@ -26,6 +26,8 @@ from transformers import (
     AutoTokenizer,
     Speech2TextForConditionalGeneration,
     Wav2Vec2ForCTC,
+    WhisperForConditionalGeneration,
+    WhisperProcessor,
 )
 from transformers.pipelines import AutomaticSpeechRecognitionPipeline, pipeline
 from transformers.pipelines.audio_utils import chunk_bytes_iter
@@ -308,6 +310,52 @@ class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase, metaclass=Pipel
         output = asr(data)
         self.assertEqual(output, {"text": "Un uomo disse all'universo: \"Signore, io esisto."})
 
+    @slow
+    @require_torch
+    @require_torchaudio
+    def test_simple_whisper_asr(self):
+        speech_recognizer = pipeline(
+            task="automatic-speech-recognition",
+            model="openai/whisper-tiny.en",
+            framework="pt",
+        )
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
+        filename = ds[0]["file"]
+        output = speech_recognizer(filename)
+        self.assertEqual(output, {"text": " Mr. Quilter is the apostle of the middle classes, and we are glad to"})
+
+    @slow
+    @require_torch
+    @require_torchaudio
+    def test_simple_whisper_translation(self):
+        speech_recognizer = pipeline(
+            task="automatic-speech-recognition",
+            model="openai/whisper-large",
+            framework="pt",
+        )
+        ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation").sort("id")
+        filename = ds[40]["file"]
+        output = speech_recognizer(filename)
+        self.assertEqual(output, {"text": " A man said to the universe, Sir, I exist."})
+
+        model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-large")
+        tokenizer = AutoTokenizer.from_pretrained("openai/whisper-large")
+        feature_extractor = AutoFeatureExtractor.from_pretrained("openai/whisper-large")
+
+        speech_recognizer_2 = AutomaticSpeechRecognitionPipeline(
+            model=model, tokenizer=tokenizer, feature_extractor=feature_extractor
+        )
+        output_2 = speech_recognizer_2(filename)
+        self.assertEqual(output, output_2)
+
+        processor = WhisperProcessor(feature_extractor, tokenizer)
+        model.config.forced_decoder_ids = processor.get_decoder_prompt_ids(task="transcribe", language="it")
+        speech_translator = AutomaticSpeechRecognitionPipeline(
+            model=model, tokenizer=tokenizer, feature_extractor=feature_extractor
+        )
+        output_3 = speech_translator(filename)
+        self.assertEqual(output_3, {"text": " Un uomo ha detto allo universo, Sir, esiste."})
+
     @slow
     @require_torch
     @require_torchaudio

tests/pipelines/test_pipelines_common.py

@@ -178,8 +178,16 @@ class ANY:
 class PipelineTestCaseMeta(type):
     def __new__(mcs, name, bases, dct):
         def gen_test(ModelClass, checkpoint, tiny_config, tokenizer_class, feature_extractor_class):
-            @skipIf(tiny_config is None, "TinyConfig does not exist")
-            @skipIf(checkpoint is None, "checkpoint does not exist")
+            @skipIf(
+                tiny_config is None,
+                "TinyConfig does not exist, make sure that you defined a `_CONFIG_FOR_DOC` variable in the modeling"
+                " file",
+            )
+            @skipIf(
+                checkpoint is None,
+                "checkpoint does not exist, make sure that you defined a `_CHECKPOINT_FOR_DOC` variable in the"
+                " modeling file",
+            )
             def test(self):
                 if ModelClass.__name__.endswith("ForCausalLM"):
                     tiny_config.is_encoder_decoder = False