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neural-waveshaping-synthesis / scripts /time_forward_pass.py

akhaliq3

spaces demo

607ecc1 almost 3 years ago

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	import time

	import click
	import gin
	import numpy as np
	from scipy.stats import describe
	import torch
	from tqdm import trange

	from neural_waveshaping_synthesis.models.neural_waveshaping import NeuralWaveshaping
	from neural_waveshaping_synthesis.models.modules.shaping import FastNEWT


	@click.command()
	@click.option("--gin-file", prompt="Model config gin file")
	@click.option("--num-iters", default=100)
	@click.option("--batch-size", default=1)
	@click.option("--device", default="cpu")
	@click.option("--length-in-seconds", default=4)
	@click.option("--sample-rate", default=16000)
	@click.option("--control-hop", default=128)
	@click.option("--use-fast-newt", is_flag=True)
	def main(
	gin_file, num_iters, batch_size, device, length_in_seconds, sample_rate, control_hop, use_fast_newt
	):
	gin.parse_config_file(gin_file)
	dummy_control = torch.rand(
	batch_size,
	2,
	sample_rate * length_in_seconds // control_hop,
	device=device,
	requires_grad=False,
	)
	dummy_f0 = torch.rand(
	batch_size,
	1,
	sample_rate * length_in_seconds // control_hop,
	device=device,
	requires_grad=False,
	)
	model = NeuralWaveshaping()
	if use_fast_newt:
	model.newt = FastNEWT(model.newt)
	model.eval()
	model = model.to(device)

	times = []
	with torch.no_grad():
	for i in trange(num_iters):
	start_time = time.time()
	model(dummy_f0, dummy_control)
	time_elapsed = time.time() - start_time
	times.append(time_elapsed)

	print(describe(times))
	rtfs = np.array(times) / length_in_seconds
	print("Mean RTF: %.4f" % np.mean(rtfs))
	print("90th percentile RTF: %.4f" % np.percentile(rtfs, 90))


	if __name__ == "__main__":
	main()