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 music samples based on text descriptions or audio prompts.

The pipeline module has been loaded, and the soundfile library is available as sf.

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 called generate_kwargs to set the generation temperature to 0.8 and max_new_tokens to 1.
Generate an audio array using the "Classic rock riff" prompt, adjusting the generation parameters with your generate_kwargs dictionary.

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 and max_new_tokens to 1
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

4.9+

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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: Preprocessing different modalities 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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