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import gradio as gr
import torch
from transformers import pipeline
from timeit import default_timer as timer
username = "barto17" ## Complete your username
model_id = f"{username}/distilhubert-finetuned-gtzan"
device = "cuda:0" if torch.cuda.is_available() else "cpu"
pipe = pipeline("audio-classification", model=model_id, device=device)
# def predict_trunc(filepath):
# preprocessed = pipe.preprocess(filepath)
# truncated = pipe.feature_extractor.pad(preprocessed,truncation=True, max_length = 16_000*30)
# model_outputs = pipe.forward(truncated)
# outputs = pipe.postprocess(model_outputs)
# return outputs
def classify_audio(filepath):
start_time = timer()
"""
Goes from
[{'score': 0.8339303731918335, 'label': 'country'},
{'score': 0.11914275586605072, 'label': 'rock'},]
to
{"country": 0.8339303731918335, "rock":0.11914275586605072}
"""
preds = pipe(filepath)
# preds = predict_trunc(filepath)
outputs = {}
for p in preds:
outputs[p["label"]] = p["score"]
pred_time = round(timer() - start_time, 5)
return outputs, pred_time
title = "🎵 Music Genre Classifier"
description = """
just demo
"""
filenames = ['sample_audio.mp3', 'sample_2.mp3', 'sample_3.mp3' ]
filenames = [[f"./{f}"] for f in filenames]
demo = gr.Interface(
fn=classify_audio,
inputs=gr.Audio(type="filepath"),
outputs=[gr.Label(label="Predictions"),
gr.Number(label="Prediction time (s)")],
title=title,
description=description,
examples=filenames,
)
demo.launch()
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