Adding `audio-classification` example in the doc. (#20235)

* Adding `audio-classification` example in the doc.

* Adding `>>>` to get the real test.

* Removing assert.

* Fixup.

src/transformers/pipelines/audio_classification.py

@@ -16,6 +16,8 @@ from typing import Union
 
 import numpy as np
 
+import requests
+
 from ..utils import add_end_docstrings, is_torch_available, logging
 from .base import PIPELINE_INIT_ARGS, Pipeline
 
@@ -69,6 +71,24 @@ class AudioClassificationPipeline(Pipeline):
     raw waveform or an audio file. In case of an audio file, ffmpeg should be installed to support multiple audio
     formats.
 
+    Example:
+
+    ```python
+    >>> from transformers import pipeline
+
+    >>> classifier = pipeline(model="superb/wav2vec2-base-superb-ks")
+    >>> result = classifier("https://huggingface.co/datasets/Narsil/asr_dummy/resolve/main/1.flac")
+
+    >>> # Simplify results, different torch versions might alter the scores slightly.
+    >>> from transformers.testing_utils import nested_simplify
+
+    >>> nested_simplify(result)
+    [{'score': 0.997, 'label': '_unknown_'}, {'score': 0.002, 'label': 'left'}, {'score': 0.0, 'label': 'yes'}, {'score': 0.0, 'label': 'down'}, {'score': 0.0, 'label': 'stop'}]
+    ```
+
+    [Using pipelines in a webserver or with a dataset](../pipeline_tutorial)
+
+
     This pipeline can currently be loaded from [`pipeline`] using the following task identifier:
     `"audio-classification"`.
 
@@ -126,8 +146,13 @@ class AudioClassificationPipeline(Pipeline):
 
     def preprocess(self, inputs):
         if isinstance(inputs, str):
-            with open(inputs, "rb") as f:
-                inputs = f.read()
+            if inputs.startswith("http://") or inputs.startswith("https://"):
+                # We need to actually check for a real protocol, otherwise it's impossible to use a local file
+                # like http_huggingface_co.png
+                inputs = requests.get(inputs).content
+            else:
+                with open(inputs, "rb") as f:
+                    inputs = f.read()
 
         if isinstance(inputs, bytes):
             inputs = ffmpeg_read(inputs, self.feature_extractor.sampling_rate)