# Check your understanding of the course material

### 1. What units is the sampling rate measured in?

### 2. When streaming a large audio dataset, how soon can you start using it?

### 3. What is a spectrogram?

### 4. What is the easiest way to convert raw audio data into log-mel spectrogram expected by Whisper?

A.
```python
librosa.feature.melspectrogram(audio["array"])
```

B.
```python
feature_extractor = WhisperFeatureExtractor.from_pretrained("openai/whisper-small")
feature_extractor(audio["array"])
```

C.
```python
dataset.feature(audio["array"], model="whisper")
```

### 5. How do you load a dataset from 🤗 Hub?

A.
```python
from datasets import load_dataset

dataset = load_dataset(DATASET_NAME_ON_HUB)
```

B.
```python
import librosa

dataset = librosa.load(PATH_TO_DATASET)
```

C.
```python
from transformers import load_dataset

dataset = load_dataset(DATASET_NAME_ON_HUB)
```

### 6. Your custom dataset contains high-quality audio with 32 kHz sampling rate. You want to train a speech recognition model that expects the audio examples to have a 16 kHz sampling rate. What should you do?

### 7. How can you convert a spectrogram generated by a machine learning model into a waveform?