Multiple typo fixes in NLP, Audio docs (#35181)

Fixed multiple typos in Tutorials, NLP, and Audio sections

docs/source/en/tasks/asr.md

@@ -112,7 +112,7 @@ The next step is to load a Wav2Vec2 processor to process the audio signal:
 >>> processor = AutoProcessor.from_pretrained("facebook/wav2vec2-base")
 ```
 
-The MInDS-14 dataset has a sampling rate of 8000kHz (you can find this information in its [dataset card](https://huggingface.co/datasets/PolyAI/minds14)), which means you'll need to resample the dataset to 16000kHz to use the pretrained Wav2Vec2 model:
+The MInDS-14 dataset has a sampling rate of 8000Hz (you can find this information in its [dataset card](https://huggingface.co/datasets/PolyAI/minds14)), which means you'll need to resample the dataset to 16000Hz to use the pretrained Wav2Vec2 model:
 
 ```py
 >>> minds = minds.cast_column("audio", Audio(sampling_rate=16_000))

docs/source/en/tasks/multiple_choice.md

@@ -419,7 +419,7 @@ Get the class with the highest probability:
 ```py
 >>> predicted_class = logits.argmax().item()
 >>> predicted_class
-'0'
+0
 ```
 </pt>
 <tf>
@@ -448,7 +448,7 @@ Get the class with the highest probability:
 ```py
 >>> predicted_class = int(tf.math.argmax(logits, axis=-1)[0])
 >>> predicted_class
-'0'
+0
 ```
 </tf>
 </frameworkcontent>

docs/source/en/tasks/question_answering.md

@@ -325,7 +325,7 @@ or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/no
 
 Evaluation for question answering requires a significant amount of postprocessing. To avoid taking up too much of your time, this guide skips the evaluation step. The [`Trainer`] still calculates the evaluation loss during training so you're not completely in the dark about your model's performance.
 
-If have more time and you're interested in how to evaluate your model for question answering, take a look at the [Question answering](https://huggingface.co/course/chapter7/7?fw=pt#post-processing) chapter from the 🤗 Hugging Face Course!
+If you have more time and you're interested in how to evaluate your model for question answering, take a look at the [Question answering](https://huggingface.co/course/chapter7/7?fw=pt#post-processing) chapter from the 🤗 Hugging Face Course!
 
 ## Inference
 
@@ -397,7 +397,7 @@ Tokenize the text and return TensorFlow tensors:
 >>> from transformers import AutoTokenizer
 
 >>> tokenizer = AutoTokenizer.from_pretrained("my_awesome_qa_model")
->>> inputs = tokenizer(question, text, return_tensors="tf")
+>>> inputs = tokenizer(question, context, return_tensors="tf")
 ```
 
 Pass your inputs to the model and return the `logits`:

docs/source/en/tasks/summarization.md

@@ -283,7 +283,7 @@ Pass your `compute_metrics` function to [`~transformers.KerasMetricCallback`]:
 ```py
 >>> from transformers.keras_callbacks import KerasMetricCallback
 
->>> metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set)
+>>> metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_test_set)
 ```
 
 Specify where to push your model and tokenizer in the [`~transformers.PushToHubCallback`]:

docs/source/en/tasks/translation.md

@@ -290,7 +290,7 @@ Pass your `compute_metrics` function to [`~transformers.KerasMetricCallback`]:
 ```py
 >>> from transformers.keras_callbacks import KerasMetricCallback
 
->>> metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_validation_set)
+>>> metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_test_set)
 ```
 
 Specify where to push your model and tokenizer in the [`~transformers.PushToHubCallback`]: