Merge pull request #87 from susnato/patch-5

Fix broken links in Chapter 4

Author: MKhalusova

chapters/en/chapter4/classification_models.mdx

@@ -17,7 +17,7 @@ models for audio classification.
 Decoder-only models introduce unnecessary complexity to the task, since they assume that the outputs can also be a _sequence_
 of predictions (rather than a single class label prediction), and so generate multiple outputs. Therefore, they have slower
 inference speed and tend not to be used. Encoder-decoder models are largely omitted for the same reason. These architecture
-choices are analogous to those in NLP, where encoder-only models such as [BERT]((https://huggingface.co/blog/bert-101))
+choices are analogous to those in NLP, where encoder-only models such as [BERT](https://huggingface.co/blog/bert-101)
 are favoured for sequence classification tasks, and decoder-only models such as GPT reserved for sequence generation tasks.
 
 Now that we've recapped the standard transformer architecture for audio classification, let's jump into the different

chapters/en/chapter4/demo.mdx

@@ -1,9 +1,9 @@
 # Build a demo with Gradio
 
-In this final section on audio classification, we'll build a [Gradio]((https://gradio.app)) demo to showcase the music
+In this final section on audio classification, we'll build a [Gradio](https://gradio.app) demo to showcase the music
 classification model that we just trained on the [GTZAN](https://huggingface.co/datasets/marsyas/gtzan) dataset. The first
 thing to do is load up the fine-tuned checkpoint using the `pipeline()` class - this is very familiar now from the section
-on [pre-trained models](../classification_models). You can change the `model_id` to the namespace of your fine-tuned model
+on [pre-trained models](classification_models). You can change the `model_id` to the namespace of your fine-tuned model
 on the Hugging Face Hub:
 
 ```python

chapters/en/chapter4/fine-tuning.mdx

@@ -88,7 +88,7 @@ gtzan["train"][0]
 }
 ```
 
-As we saw in [Unit 1](chapter1/audio_data), the audio files are represented as 1-dimensional NumPy arrays,
+As we saw in [Unit 1](../chapter1/audio_data), the audio files are represented as 1-dimensional NumPy arrays,
 where the value of the array represents the amplitude at that timestep. For these songs, the sampling rate is 22,050 Hz,
 meaning there are 22,050 amplitude values sampled per second. We'll have to keep this in mind when using a pretrained model
 with a different sampling rate, converting the sampling rates ourselves to ensure they match. We can also see the genre