Fix whisper doc (#19608)

* 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

* Correct whisper processor

* make fix copies

* remove sample docstring as it does not fit whisper model

* Update src/transformers/models/whisper/modeling_whisper.py
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

* fix, doctests are passing

* Nit

* last nit
Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

src/transformers/models/whisper/modeling_whisper.py

@@ -32,13 +32,7 @@ from ...modeling_outputs import (
     Seq2SeqModelOutput,
 )
 from ...modeling_utils import PreTrainedModel
-from ...utils import (
-    add_code_sample_docstrings,
-    add_start_docstrings,
-    add_start_docstrings_to_model_forward,
-    logging,
-    replace_return_docstrings,
-)
+from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
 from .configuration_whisper import WhisperConfig
 
 
@@ -46,8 +40,6 @@ logger = logging.get_logger(__name__)
 
 _CONFIG_FOR_DOC = "WhisperConfig"
 _CHECKPOINT_FOR_DOC = "openai/whisper-tiny"
-_PROCESSOR_FOR_DOC = "WhisperProcessor"
-_EXPECTED_OUTPUT_SHAPE = [1, 2, 512]
 
 
 WHISPER_PRETRAINED_MODEL_ARCHIVE_LIST = [
@@ -1005,14 +997,7 @@ class WhisperModel(WhisperPreTrainedModel):
         self.encoder._freeze_parameters()
 
     @add_start_docstrings_to_model_forward(WHISPER_INPUTS_DOCSTRING)
-    @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",
-    )
+    @replace_return_docstrings(output_type=Seq2SeqModelOutput, config_class=_CONFIG_FOR_DOC)
     def forward(
         self,
         input_features=None,
@@ -1029,7 +1014,25 @@ class WhisperModel(WhisperPreTrainedModel):
         output_hidden_states=None,
         return_dict=None,
     ):
+        r"""
+        Returns:
+
+        Example:
+         ```python
+         >>> import torch
+         >>> from transformers import WhisperFeatureExtractor, WhisperModel
+         >>> 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 = (
             output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states