Video sentiment analysis with CLIP CLAP

Now you'll perform the emotion analysis of the advertisement you previously prepared using CLIP/CLAP. In order to make a multi-modal classification of emotion, you will be combining the predictions of these models using the mean (known as late fusion).

The video (video) and the corresponding audio (audio_sample) you created previously is still available:

Frames from the Bounce TV commercial

A list of emotions has been loaded as emotions.

This exercise is part of the course

Multi-Modal Models with Hugging Face

Exercise instructions

Make an audio classifier pipeline for zero-shot-audio-classification using the laion/clap-htsat-unfused model.
Make an image classifier pipeline for zero-shot-image-classification using the openai/clip-vit-base-patch32 model (a smaller variant of what we used in the video).
Use the image classifier pipeline to generate predictions for each image in the video.
Use the audio classifier pipeline to generate predictions for the audio_sample.

Hands-on interactive exercise

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

# Make an audio classifier pipeline
audio_classifier = ____(model="____", task="____")

# Make an image classifier pipeline
image_classifier = ____(model="____", task="____")

# Create emotion scores for each video frame
predictions = image_classifier(video, candidate_labels=emotions)
scores = [
    {l['label']: l['score'] for l in prediction}
    for prediction in predictions
]

avg_image_scores = {emotion: sum([s[emotion] for s in scores])/len(scores) for emotion in emotions}

# Make audio scores
audio_scores = ____(____, candidate_labels=____)

audio_scores = {l['label']: l['score'] for l in audio_scores}
multimodal_scores = {emotion: (avg_image_scores[emotion] + audio_scores[emotion])/2 for emotion in emotions}
print(f"Multimodal scores: {multimodal_scores}")

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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