Modify pipeline_tutorial.mdx (#22726)

generator(model="openai/whisper-large") always returns error. As the error says the generator expects an input, just like the .flac file above. Even the generator object has no parameters called model. While there are parameters which can be passed to generator like 'batch_size' but to pass a model i believe the the parameter has to be passed while instantiating the pipeline and not as a parameter to the instance.

I believe the correct term should be:

generator = pipeline(model="openai/whisper-large", device=0)

docs/source/en/pipeline_tutorial.mdx

@@ -81,10 +81,10 @@ If you want to iterate over a whole dataset, or want to use it for inference in
 In general you can specify parameters anywhere you want:
 
 ```py
-generator(model="openai/whisper-large", my_parameter=1)
-out = generate(...)  # This will use `my_parameter=1`.
-out = generate(..., my_parameter=2)  # This will override and use `my_parameter=2`.
-out = generate(...)  # This will go back to using `my_parameter=1`.
+generator = pipeline(model="openai/whisper-large", my_parameter=1)
+out = generator(...)  # This will use `my_parameter=1`.
+out = generator(..., my_parameter=2)  # This will override and use `my_parameter=2`.
+out = generator(...)  # This will go back to using `my_parameter=1`.
 ```
 
 Let's check out 3 important ones:
@@ -95,14 +95,14 @@ If you use `device=n`, the pipeline automatically puts the model on the specifie
 This will work regardless of whether you are using PyTorch or Tensorflow.
 
 ```py
-generator(model="openai/whisper-large", device=0)
+generator = pipeline(model="openai/whisper-large", device=0)
 ```
 
 If the model is too large for a single GPU, you can set `device_map="auto"` to allow 🤗 [Accelerate](https://huggingface.co/docs/accelerate) to automatically determine how to load and store the model weights.
 
 ```py
 #!pip install accelerate
-generator(model="openai/whisper-large", device_map="auto")
+generator = pipeline(model="openai/whisper-large", device_map="auto")
 ```
 
 Note that if  `device_map="auto"` is passed, there is no need to add the argument `device=device` when instantiating your `pipeline` as you may encounter some unexpected behavior!
@@ -114,7 +114,7 @@ By default, pipelines will not batch inference for reasons explained in detail [
 But if it works in your use case, you can use:
 
 ```py
-generator(model="openai/whisper-large", device=0, batch_size=2)
+generator = pipeline(model="openai/whisper-large", device=0, batch_size=2)
 audio_filenames = [f"audio_{i}.flac" for i in range(10)]
 texts = generator(audio_filenames)
 ```
@@ -287,4 +287,4 @@ pipe = pipeline(model="facebook/opt-1.3b", device_map="auto", model_kwargs={"loa
 output = pipe("This is a cool example!", do_sample=True, top_p=0.95)
 ```
 
-Note that you can replace the checkpoint with any of the Hugging Face model that supports large model loading such as BLOOM!
+Note that you can replace the checkpoint with any of the Hugging Face model that supports large model loading such as BLOOM!