Passing keyword arguments

In this exercise, you will learn how to pass keyword arguments to model functions when used inside a pipeline.

For this you will be using the MusicGen small model from Meta, which is capable of generating high-quality music samples conditioned on text descriptions or audio prompts.

The pipeline module has been loaded.

This exercise is part of the course

Multi-Modal Models with Hugging Face

Exercise instructions

Load a text-to-audio pipeline using the facebook/musicgen-small model in the PyTorch framework.
Make a dictionary to set the generation temperature to 0.8 and max_new_tokens to 2.
Generate an audio array corresponding to the "Classic rock riff" prompt.

Hands-on interactive exercise

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

# Load a text-to-audio pipeline
musicgen = pipeline(task="____", model="____", framework="pt")

# Make a dictionary to set the generation temperature to 0.8
generate_kwargs = ____

# Generate an audio array passing the arguments
outputs = ____
sf.write("output.wav", outputs["audio"][0][0], outputs["sampling_rate"])

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.

Exercise 1: Hugging Face model navigation Exercise 2: How many models!?Exercise 3: Finding the most popular text-to-image model Exercise 4: Loading and preprocessing data Exercise 5: Text tokenizing Exercise 6: Image preprocessing Exercise 7: Audio preprocessing Exercise 8: Pipeline tasks and evaluations Exercise 9: Pipeline caption generation Exercise 10: Passing keyword arguments

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Exercise 11: Model evaluation on a custom dataset

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