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import gradio as gr
import torch
from pyharp import ModelCard, build_endpoint, LabelList, AudioLabel, OutputLabel
from skey.key_detection import detect_key
# Check for GPU availability
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
# Create a ModelCard
model_card = ModelCard(
name="S-KEY Detection",
description="Major/Minor Key detection from audio using Self-supervised Key Detection.",
author="Deezer Research",
tags=["key detection", "mir"],
)
# Define the process function
@spaces.GPU
def process_fn(input_audio_path):
try:
ext = input_audio_path.split(".")[-1].lower()
detected_keys = detect_key(
input_audio_path,
extension=ext,
device=DEVICE,
cli=False
)
print("Raw output:", detected_keys)
if not detected_keys:
raise ValueError("No key detected")
detected_key = detected_keys[0] # because function returns list[str]
labels = LabelList()
labels.labels.append(
AudioLabel(
t=0.0,
duration=5.0,
label=detected_key,
amplitude=0.8,
description="Detected by S-KEY",
color=OutputLabel.rgb_color_to_int(255, 100, 100, 0.8),
)
)
return input_audio_path, labels
except Exception as e:
print("Error:", e)
raise e
# Build Gradio Endpoint
with gr.Blocks() as demo:
input_audio = gr.Audio(label="Input Audio", type="filepath").harp_required(True)
# output_text = gr.Textbox(label="Detected Key")
output_audio = gr.Audio(label="Output Audio", type="filepath")
output_labels = gr.JSON(label="Analysis Labels")
build_endpoint(
model_card=model_card,
process_fn=process_fn,
input_components=[input_audio],
output_components=[output_audio, output_labels],
)
if __name__ == "__main__":
demo.queue().launch()
|