faster forward following what is done for images (#21906)

* faster forward following what is done for images

* add missing licence

src/transformers/pipelines/zero_shot_audio_classification.py

+# coding=utf-8
+# Copyright 2023 The HuggingFace Inc. team.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+from collections import UserDict
 from typing import Union
 
 import numpy as np
@@ -5,22 +20,17 @@ import requests
 
 from ..utils import (
     add_end_docstrings,
-    is_torch_available,
     logging,
 )
 from .audio_classification import ffmpeg_read
-from .base import PIPELINE_INIT_ARGS, ChunkPipeline
-
-
-if is_torch_available():
-    import torch
+from .base import PIPELINE_INIT_ARGS, Pipeline
 
 
 logger = logging.get_logger(__name__)
 
 
 @add_end_docstrings(PIPELINE_INIT_ARGS)
-class ZeroShotAudioClassificationPipeline(ChunkPipeline):
+class ZeroShotAudioClassificationPipeline(Pipeline):
     """
     Zero shot audio classification pipeline using `ClapModel`. This pipeline predicts the class of an audio when you
     provide an audio and a set of `candidate_labels`.
@@ -101,38 +111,36 @@ class ZeroShotAudioClassificationPipeline(ChunkPipeline):
         if len(audio.shape) != 1:
             raise ValueError("We expect a single channel audio input for ZeroShotAudioClassificationPipeline")
 
-        n = len(candidate_labels)
-        for i, candidate_label in enumerate(candidate_labels):
-            audios = self.feature_extractor(
-                audio, sampling_rate=self.feature_extractor.sampling_rate, return_tensors="pt"
+        inputs = self.feature_extractor(
+            [audio], sampling_rate=self.feature_extractor.sampling_rate, return_tensors="pt"
         )
-            sequence = hypothesis_template.format(candidate_label)
-            inputs = self.tokenizer(sequence, return_tensors=self.framework)
-            inputs["input_features"] = audios.input_features
-            yield {"is_last": i == n - 1, "candidate_label": candidate_label, **inputs}
+        inputs["candidate_labels"] = candidate_labels
+        sequences = [hypothesis_template.format(x) for x in candidate_labels]
+        text_inputs = self.tokenizer(sequences, return_tensors=self.framework, padding=True)
+        inputs["text_inputs"] = [text_inputs]
 
     def _forward(self, model_inputs):
-        is_last = model_inputs.pop("is_last")
-        candidate_label = model_inputs.pop("candidate_label")
-        outputs = self.model(**model_inputs)
+        candidate_labels = model_inputs.pop("candidate_labels")
+        text_inputs = model_inputs.pop("text_inputs")
+        if isinstance(text_inputs[0], UserDict):
+            text_inputs = text_inputs[0]
+        else:
+            # Batching case.
+            text_inputs = text_inputs[0][0]
 
-        # Clap does crossproduct scoring by default, so we're only
-        # interested in the results where audio and text and in the same
-        # batch position.
-        diag = torch.diagonal
-        logits_per_audio = diag(outputs.logits_per_audio)
+        outputs = self.model(**text_inputs, **model_inputs)
 
         model_outputs = {
-            "is_last": is_last,
-            "candidate_label": candidate_label,
-            "logits_per_audio": logits_per_audio,
+            "candidate_label": candidate_labels,
+            "logits_per_audio": outputs.logits_per_audio,
         }
         return model_outputs
 
     def postprocess(self, model_outputs):
-        candidate_labels = [outputs["candidate_label"] for outputs in model_outputs]
+        candidate_labels = model_outputs.pop("candidate_labels")
+        logits = model_outputs["logits"][0]
+
         if self.framework == "pt":
-            logits = torch.cat([output["logits_per_audio"] for output in model_outputs])
             probs = logits.softmax(dim=0)
             scores = probs.tolist()
         else: