📝 open fresh PR for pipeline doctests (#17073)

docs/source/en/pipeline_tutorial.mdx

@@ -39,7 +39,9 @@ While each task has an associated [`pipeline`], it is simpler to use the general
 2. Pass your input text to the [`pipeline`]:
 
 ```py
->>> generator("Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone")
+>>> generator(
+...     "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone"
+... )  # doctest: +SKIP
 [{'generated_text': 'Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone, Seven for the Iron-priests at the door to the east, and thirteen for the Lord Kings at the end of the mountain'}]
 ```
 
@@ -51,7 +53,7 @@ If you have more than one input, pass your input as a list:
 ...         "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone",
 ...         "Nine for Mortal Men, doomed to die, One for the Dark Lord on his dark throne",
 ...     ]
-... )
+... )  # doctest: +SKIP
 ```
 
 Any additional parameters for your task can also be included in the [`pipeline`]. The `text-generation` task has a [`~generation_utils.GenerationMixin.generate`] method with several parameters for controlling the output. For example, if you want to generate more than one output, set the `num_return_sequences` parameter:
@@ -60,7 +62,7 @@ Any additional parameters for your task can also be included in the [`pipeline`]
 >>> generator(
 ...     "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone",
 ...     num_return_sequences=2,
-... )
+... )  # doctest: +SKIP
 ```
 
 ### Choose a model and tokenizer
@@ -85,7 +87,9 @@ Create a [`pipeline`] for your task, and specify the model and tokenizer you've
 Pass your input text to the [`pipeline`] to generate some text:
 
 ```py
->>> generator("Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone")
+>>> generator(
+...     "Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone"
+... )  # doctest: +SKIP
 [{'generated_text': 'Three Rings for the Elven-kings under the sky, Seven for the Dwarf-lords in their halls of stone, Seven for the Dragon-lords (for them to rule in a world ruled by their rulers, and all who live within the realm'}]
 ```
 
@@ -93,7 +97,18 @@ Pass your input text to the [`pipeline`] to generate some text:
 
 The flexibility of the [`pipeline`] means it can also be extended to audio tasks.
 
-For example, let's classify the emotion from a short clip of John F. Kennedy's famous ["We choose to go to the Moon"](https://en.wikipedia.org/wiki/We_choose_to_go_to_the_Moon) speech. Find an [audio classification](https://huggingface.co/models?pipeline_tag=audio-classification) model on the Model Hub for emotion recognition and load it in the [`pipeline`]:
+For example, let's classify the emotion in this audio clip:
+
+```py
+>>> from datasets import load_dataset
+>>> import torch
+
+>>> torch.manual_seed(42)  # doctest: +IGNORE_RESULT
+>>> ds = load_dataset("hf-internal-testing/librispeech_asr_demo", "clean", split="validation")
+>>> audio_file = ds[0]["audio"]["path"]
+```
+
+Find an [audio classification](https://huggingface.co/models?pipeline_tag=audio-classification) model on the Model Hub for emotion recognition and load it in the [`pipeline`]:
 
 ```py
 >>> from transformers import pipeline
@@ -106,12 +121,10 @@ For example, let's classify the emotion from a short clip of John F. Kennedy's f
 Pass the audio file to the [`pipeline`]:
 
 ```py
->>> audio_classifier("jfk_moon_speech.wav")
-[{'label': 'calm', 'score': 0.13856211304664612},
- {'label': 'disgust', 'score': 0.13148026168346405},
- {'label': 'happy', 'score': 0.12635163962841034},
- {'label': 'angry', 'score': 0.12439591437578201},
- {'label': 'fearful', 'score': 0.12404385954141617}]
+>>> preds = audio_classifier(audio_file)
+>>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
+>>> preds
+[{'score': 0.1315, 'label': 'calm'}, {'score': 0.1307, 'label': 'neutral'}, {'score': 0.1274, 'label': 'sad'}, {'score': 0.1261, 'label': 'fearful'}, {'score': 0.1242, 'label': 'happy'}]
 ```
 
 ## Vision pipeline
@@ -126,14 +139,10 @@ Specify your vision task and pass your image to the classifier. The imaage can b
 >>> from transformers import pipeline
 
 >>> vision_classifier = pipeline(task="image-classification")
->>> vision_classifier(
+>>> preds = vision_classifier(
 ...     images="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg"
 ... )
-[{'label': 'lynx, catamount', 'score': 0.4403027892112732},
- {'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor',
-  'score': 0.03433405980467796},
- {'label': 'snow leopard, ounce, Panthera uncia',
-  'score': 0.032148055732250214},
- {'label': 'Egyptian cat', 'score': 0.02353910356760025},
- {'label': 'tiger cat', 'score': 0.023034192621707916}]
+>>> preds = [{"score": round(pred["score"], 4), "label": pred["label"]} for pred in preds]
+>>> preds
+[{'score': 0.4335, 'label': 'lynx, catamount'}, {'score': 0.0348, 'label': 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor'}, {'score': 0.0324, 'label': 'snow leopard, ounce, Panthera uncia'}, {'score': 0.0239, 'label': 'Egyptian cat'}, {'score': 0.0229, 'label': 'tiger cat'}]
 ```