Audio preprocessing

In this exercise, you will learn how to adjust the sampling rate of audio data, as well as how to use an automatic preprocessor. You will be working with the VCTK Corpus, which includes around 44-hours of speech data uttered by 110 English speakers with various accents.

The dataset has already been loaded.

This exercise is part of the course

Multi-Modal Models with Hugging Face

Exercise instructions

Resample the audio to a frequency of 16,000 Hz in the dataset using the .cast_column() method.
Load the audio processor using the pretrained openai/whisper-small model.
Preprocess the audio data of the first datapoint using the processor.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Resample the audio to a frequency of 16,000 Hz
dataset = dataset.____("____", ____(sampling_rate=____))

# Load the audio processor
processor = ____

# Preprocess the audio data of the 0th dataset element
audio_pp = ____(dataset[0]["____"]["____"], sampling_rate=____, padding=____, return_tensors="pt")
make_spectrogram(audio_pp["input_features"][0])

Edit and Run Code

This exercise is part of the course

Multi-Modal Models with Hugging Face

IntermediateSkill Level

5.0+

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Navigate the Hugging Face model hub, transform raw text, audio, and visual data into AI-friendly formats. Learn how to find the latest most popular models for tasks such as text generation and harness the power of pre-built pipelines.

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