Ammar sleepingcat4

Introducing MusicalityBench: A Living Benchmark for AI-Generated Songs

Official Announcement

We are excited to introduce MusicalityBench, a stateless, continuously updated benchmark for evaluating artificial intelligence-generated songs in real time.

Background

The concept of Musicality as a human evaluation metric was first introduced by Radford (https://arxiv.org/abs/2005.00341), where songs were judged by expert musicians against criteria for enjoyable, human-made music. MusicalityBench builds directly on this foundation, expanding the original definition of Musicality to keep pace with new models, new songs, and an evaluation landscape that never stops moving.

We have to change the following files:

Navigator/Subtopic_Query_Expansion.py
Navigator/get_paper_embeddings.py
Navigator/google_scholar_api.py
Navigator/outline_creation.py
Navigator/run_full_subtopic_expansion_process.py
Navigator/search_api.py
Navigator/subtopic_aspect_generation.py

	import numpy as np
	from scipy.misc import derivative

	def path(x):
	return a * x*2 + b x + c

	a = float(input())
	b = float(input())
	c = float(input())
	d = float(input())

	from sklearn import svm

	# Create an SVM classifier
	classifier = svm.SVC()

	## X Being the Energy
	### Y Being the Pressure (Particle Beam Pressure)

	classifier.fit(X, Y)

	import pandas as pd
	import matplotlib.pyplot as plt

	# Read the CSV file
	df = pd.read_csv('your_file.csv')

	# Extract the columns
	X = df.iloc[:, 0] # Column 1
	Y = df.iloc[:, 1] # Column 2

	from torchscale.architecture.config import EncoderConfig
	from torchscale.architecture.encoder import Encoder

	config = EncoderConfig(
	vocab_size=64000,
	hidden_size=1000,
	num_layers=10
	)

	model = Encoder(config)

	total_params = sum(p.numel() for p in model.parameters())
	print(f"Total parameters in the model: {total_params}")