  In this work, we present Stellar Spectra Factory (SSF), a tool to generate
empirical-based stellar spectra from arbitrary stellar atmospheric parameters.
The relative flux-calibrated empirical spectra can be predicted by SSF given
arbitrary effective temperature, surface gravity, and metallicity. SSF
constructs the interpolation approach based on the SLAM, using ATLAS-A library
as the training dataset. SSF is composed of 4 data-driven sub-models to predict
empirical stellar spectra. SSF-N can generate spectra from A to K type and some
M giant stars, covering 3700 < Teff < 8700 K, 0 < logg < 6 dex, and -1.5 <
[M/H] < 0.5 dex. SSF-gM is mainly used to predict M giant spectra with 3520 <
Teff < 4000K and -1.5 < [M/H] < 0.4 dex. SSF-dM is for generating M dwarf
spectra with 3295 < Teff < 4040K, -1.0 < [M/H] < 0.1 dex. And SSF-B can predict
B-type spectra with 9000 < Teff < 24000K and -5.2< MG < 1.5 mag. The accuracy
of the predicted spectra is validated by comparing the flux of predicted
spectra to those with same stellar parameters selected from the known spectral
libraries, MILES and MaStar. The averaged difference of flux over optical
wavelength between the predicted spectra and the corresponding ones in MILES
and MaStar is less than 5%. More verification is conducted between the
magnitudes calculated from the integration of the predicted spectra and the
observations in PS1 and APASS bands with the same stellar parameters. No
significant systematic difference is found between the predicted spectra and
the photomatric observations. The uncertainty is 0.08mag in r band for SSF-gM
when comparing with the stars with the same stellar parameters selected from
PS1. And the uncertainty becomes 0.31mag in i band for SSF-dM when comparing
with the stars with the same stellar parameters selected from APASS.
