# Check your understanding of the course material

### 1. What is a vocoder?

<Question
	choices={[
		{
			text: "An additional neural network that turns the spectrogram output of a transformer into a waveform.",
			explain: "Correct! ",
			correct: true
		},
		{
			text: "A type of transformer layer that is responsible for creating audio embeddings.",
			explain: ""
		},
		{
			text: "An additional neural network that preprocesses speech audio to remove background noise",
			explain: "",
		}
	]}
/>

### 2. Wav2Vec2 is an example of

<Question
	choices={[
		{
			text: "Seq2Seq architecture",
			explain: ""
		},
		{
			text: "CNN architecture",
			explain: ""
		},
		{
			text: "CTC architecture",
			explain: "Correct!",
			correct: true
		}
	]}
/>

### 3. What does a blank token in CTC algorithm do?

<Question
	choices={[
		{
			text: "Blank token indicates breaks between the individual words in the sentence.",
			explain: ""
		},
		{
			text: "Blank token is a predicted token that serves as a hard boundary between groups of characters. It makes it possible to filter out the duplicate characters",
			explain: "Correct!",
			correct: true
		},
		{
			text: "Blank token is used for sounds that do not match any tokens in the vocabulary, similar to <UNK> token for 'unknown'.",
			explain: ""
		}
	]}
/>

### 4. Which of the following statements about CTC models is FALSE? 

<Question
	choices={[
		{
			text: "CTC models use only the encoder part of the transformer architecture.",
			explain: ""
		},
		{
			text: "Wav2Vec2 & HuBERT use the exact same architecture but are trained differently.",
			explain: ""
		},
		{
			text: "CTC models tend to perform best for speech recognition compared to other architectures.",
			explain: "Correct!",
			correct: true
		}
	]}
/>

### 5. Whisper is an example of  

<Question
	choices={[
		{
			text: "Seq2Seq architecture",
			explain: "Correct!",
			correct: true
		},
		{
			text: "CNN architecture",
			explain: ""
		},
		{
			text: "CTC architecture",
			explain: ""
		}
	]}
/>

### 6. What is the easiest way to perform audio classification?

<Question
	choices={[
		{
			text: "Use encoder-decoder transformers on the audio waveform.",
			explain: ""
		},
		{
			text: "Use spectrograms and treat the task as an image classification problem.",
			explain: "Correct!",
			correct: true
		},
		{
			text: "Turn a CTC model into a general-purpose audio classifier by changing the labels and training it with a regular cross-entropy loss function.",
			explain: ""
		}
	]}
/>

### 7. True or false? When treating spectrograms as images for classification, you will always benefit from image data augmentation techniques, such as shifting an image, cropping it, or resizing. 

<Question
	choices={[
		{
			text: "True",
			explain: ""
		},
		{
			text: "False",
			explain: "Correct!",
			correct: true
		}
	]}
/>


<EditOnGithub source="https://github.com/huggingface/audio-transformers-course/blob/main/chapters/en/chapter3/quiz.mdx" />