Build a demo with Gradio

In this final section on audio classification, we’ll build a Gradio demo to showcase the music classification model that we just trained on the GTZAN dataset. The first thing to do is load up the fine-tuned checkpoint using the pipeline() class - this is very familiar now from the section on pre-trained models. You can change the model_id to the namespace of your fine-tuned model on the Hugging Face Hub:

from transformers import pipeline

model_id = "sanchit-gandhi/distilhubert-finetuned-gtzan"
pipe = pipeline("audio-classification", model=model_id)

Secondly, we’ll define a function that takes the filepath for an audio input and passes it through the pipeline. Here, the pipeline automatically takes care of loading the audio file, resampling it to the correct sampling rate, and running inference with the model. We take the models predictions of preds and format them as a dictionary object to be displayed on the output:

def classify_audio(filepath):
    preds = pipe(filepath)
    outputs = {}
    for p in preds:
        outputs[p["label"]] = p["score"]
    return outputs

Finally, we launch the Gradio demo using the function we’ve just defined:

import gradio as gr

demo = gr.Interface(
    fn=classify_audio, inputs=gr.Audio(type="filepath"), outputs=gr.outputs.Label()
)
demo.launch(debug=True)

This will launch a Gradio demo similar to the one running on the Hugging Face Space: