[ASR Pipe] Update init to set model type and subsequently call parent init method (#28486)

* add image processor arg

* super

* rm args

src/transformers/pipelines/automatic_speech_recognition.py

@@ -17,11 +17,10 @@ from typing import TYPE_CHECKING, Dict, Optional, Union
 import numpy as np
 import requests
 
-from ..modelcard import ModelCard
 from ..tokenization_utils import PreTrainedTokenizer
 from ..utils import is_torch_available, is_torchaudio_available, logging
 from .audio_utils import ffmpeg_read
-from .base import ArgumentHandler, ChunkPipeline, infer_framework_load_model
+from .base import ChunkPipeline
 
 
 if TYPE_CHECKING:
@@ -35,7 +34,7 @@ logger = logging.get_logger(__name__)
 if is_torch_available():
     import torch
 
-    from ..models.auto.modeling_auto import MODEL_FOR_CTC_MAPPING_NAMES, MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
+    from ..models.auto.modeling_auto import MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
 
 
 def rescale_stride(stride, ratio):
@@ -155,11 +154,15 @@ class AutomaticSpeechRecognitionPipeline(ChunkPipeline):
         model ([`PreTrainedModel`] or [`TFPreTrainedModel`]):
             The model that will be used by the pipeline to make predictions. This needs to be a model inheriting from
             [`PreTrainedModel`] for PyTorch and [`TFPreTrainedModel`] for TensorFlow.
+        feature_extractor ([`SequenceFeatureExtractor`]):
+            The feature extractor that will be used by the pipeline to encode waveform for the model.
         tokenizer ([`PreTrainedTokenizer`]):
             The tokenizer that will be used by the pipeline to encode data for the model. This object inherits from
             [`PreTrainedTokenizer`].
-        feature_extractor ([`SequenceFeatureExtractor`]):
-            The feature extractor that will be used by the pipeline to encode waveform for the model.
+        decoder (`pyctcdecode.BeamSearchDecoderCTC`, *optional*):
+            [PyCTCDecode's
+            BeamSearchDecoderCTC](https://github.com/kensho-technologies/pyctcdecode/blob/2fd33dc37c4111417e08d89ccd23d28e9b308d19/pyctcdecode/decoder.py#L180)
+            can be passed for language model boosted decoding. See [`Wav2Vec2ProcessorWithLM`] for more information.
         chunk_length_s (`float`, *optional*, defaults to 0):
             The input length for in each chunk. If `chunk_length_s = 0` then chunking is disabled (default).
 
@@ -190,10 +193,9 @@ class AutomaticSpeechRecognitionPipeline(ChunkPipeline):
         device (Union[`int`, `torch.device`], *optional*):
             Device ordinal for CPU/GPU supports. Setting this to `None` will leverage CPU, a positive will run the
             model on the associated CUDA device id.
-        decoder (`pyctcdecode.BeamSearchDecoderCTC`, *optional*):
-            [PyCTCDecode's
-            BeamSearchDecoderCTC](https://github.com/kensho-technologies/pyctcdecode/blob/2fd33dc37c4111417e08d89ccd23d28e9b308d19/pyctcdecode/decoder.py#L180)
-            can be passed for language model boosted decoding. See [`Wav2Vec2ProcessorWithLM`] for more information.
+        torch_dtype (Union[`int`, `torch.dtype`], *optional*):
+            The data-type (dtype) of the computation. Setting this to `None` will use float32 precision. Set to
+            `torch.float16` or `torch.bfloat16` to use half-precision in the respective dtypes.
 
     """
 
@@ -203,77 +205,14 @@ class AutomaticSpeechRecognitionPipeline(ChunkPipeline):
         feature_extractor: Union["SequenceFeatureExtractor", str] = None,
         tokenizer: Optional[PreTrainedTokenizer] = None,
         decoder: Optional[Union["BeamSearchDecoderCTC", str]] = None,
-        modelcard: Optional[ModelCard] = None,
-        framework: Optional[str] = None,
-        task: str = "",
-        args_parser: ArgumentHandler = None,
         device: Union[int, "torch.device"] = None,
         torch_dtype: Optional[Union[str, "torch.dtype"]] = None,
-        binary_output: bool = False,
         **kwargs,
     ):
-        if framework is None:
-            framework, model = infer_framework_load_model(model, config=model.config)
-
-        self.task = task
-        self.model = model
-        self.tokenizer = tokenizer
-        self.feature_extractor = feature_extractor
-        self.modelcard = modelcard
-        self.framework = framework
-
-        # `accelerate` device map
-        hf_device_map = getattr(self.model, "hf_device_map", None)
-
-        if hf_device_map is not None and device is not None:
-            raise ValueError(
-                "The model has been loaded with `accelerate` and therefore cannot be moved to a specific device. Please "
-                "discard the `device` argument when creating your pipeline object."
-            )
-
-        if self.framework == "tf":
-            raise ValueError("The AutomaticSpeechRecognitionPipeline is only available in PyTorch.")
-
-        # We shouldn't call `model.to()` for models loaded with accelerate
-        if device is not None and not (isinstance(device, int) and device < 0):
-            self.model.to(device)
-
-        if device is None:
-            if hf_device_map is not None:
-                # Take the first device used by `accelerate`.
-                device = next(iter(hf_device_map.values()))
-            else:
-                device = -1
-
-        if is_torch_available() and self.framework == "pt":
-            if isinstance(device, torch.device):
-                self.device = device
-            elif isinstance(device, str):
-                self.device = torch.device(device)
-            elif device < 0:
-                self.device = torch.device("cpu")
-            else:
-                self.device = torch.device(f"cuda:{device}")
-        else:
-            self.device = device if device is not None else -1
-        self.torch_dtype = torch_dtype
-        self.binary_output = binary_output
-
-        # Update config and generation_config with task specific parameters
-        task_specific_params = self.model.config.task_specific_params
-        if task_specific_params is not None and task in task_specific_params:
-            self.model.config.update(task_specific_params.get(task))
-            if self.model.can_generate():
-                self.model.generation_config.update(**task_specific_params.get(task))
-
-        self.call_count = 0
-        self._batch_size = kwargs.pop("batch_size", None)
-        self._num_workers = kwargs.pop("num_workers", None)
-
         # set the model type so we can check we have the right pre- and post-processing parameters
-        if self.model.config.model_type == "whisper":
+        if model.config.model_type == "whisper":
             self.type = "seq2seq_whisper"
-        elif self.model.__class__.__name__ in MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES.values():
+        elif model.__class__.__name__ in MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES.values():
             self.type = "seq2seq"
         elif (
             feature_extractor._processor_class
@@ -285,11 +224,7 @@ class AutomaticSpeechRecognitionPipeline(ChunkPipeline):
         else:
             self.type = "ctc"
 
-        self._preprocess_params, self._forward_params, self._postprocess_params = self._sanitize_parameters(**kwargs)
-
-        mapping = MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES.copy()
-        mapping.update(MODEL_FOR_CTC_MAPPING_NAMES)
-        self.check_model_type(mapping)
+        super().__init__(model, tokenizer, feature_extractor, device=device, torch_dtype=torch_dtype, **kwargs)
 
     def __call__(
         self,