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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() |