bump version

CITATION.cff

@@ -12,8 +12,8 @@ authors:
     affiliation: Chemnitz University of Technology
     orcid: 'https://orcid.org/0000-0002-4932-2874'
     website: 'https://stefantaubert.com/'
-version: 0.0.1
-date-released: 2023-02-23
+version: 0.0.2
+date-released: 2023-08-11
 license: MIT
 url: https://github.com/stefantaubert/mean-opinion-score
-doi: 10.5281/zenodo.7670649
+doi: 10.5281/zenodo.8238259

README.md

@@ -5,8 +5,8 @@
 [![MIT](https://img.shields.io/github/license/stefantaubert/mean-opinion-score.svg)](https://github.com/stefantaubert/mean-opinion-score/blob/master/LICENSE)
 [![PyPI](https://img.shields.io/pypi/wheel/mean-opinion-score.svg)](https://pypi.python.org/pypi/mean-opinion-score/#files)
 ![PyPI](https://img.shields.io/pypi/implementation/mean-opinion-score.svg)
-[![PyPI](https://img.shields.io/github/commits-since/stefantaubert/mean-opinion-score/latest/master.svg)](https://github.com/stefantaubert/mean-opinion-score/compare/v0.0.1...master)
-[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7670649.svg)](https://doi.org/10.5281/zenodo.7670649)
+[![PyPI](https://img.shields.io/github/commits-since/stefantaubert/mean-opinion-score/latest/master.svg)](https://github.com/stefantaubert/mean-opinion-score/compare/v0.0.2...master)
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.8238259.svg)](https://doi.org/10.5281/zenodo.8238259)
 
 Python library for calculating the mean opinion score (MOS) and 95% confidence interval (CI) of the standard deviation (SD) of text-to-speech (TTS) ratings according to ["Ribeiro, F., Florêncio, D., Zhang, C., & Seltzer, M. (2011). CrowdMOS: An approach for crowdsourcing mean opinion score studies"](https://doi.org/10.1109/ICASSP.2011.5946971). To determine CIs, the authors used a two-way random effects model with the variables: diversity of intrinsic sentence quality, diversity of rater preference, and subjective uncertainty.
 
@@ -116,12 +116,12 @@ Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
 If you want to cite this repo, you can use this BibTeX-entry generated by GitHub (see *About => Cite this repository*).
 
 ```txt
-Taubert, S. (2023). mean-opinion-score (Version 0.0.1) [Computer software]. https://doi.org/10.5281/zenodo.7670649
+Taubert, S. (2023). mean-opinion-score (Version 0.0.2) [Computer software]. https://doi.org/10.5281/zenodo.8238259
 ```
 
 ## Changelog
 
-- v0.0.2 (unreleased)
+- v0.0.2 (2023-08-11)
   - Added:
     - commonly used 95% confidence interval calculation
 - v0.0.1 (2023-02-23)

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "mean-opinion-score"
-version = "0.0.1"
+version = "0.0.2"
 description = "Library for calculating the mean opinion score and 95% confidence interval of the standard deviation of text-to-speech ratings according to Ribeiro et al. (2011)."
 readme = "README.md"
 requires-python = ">=3.6, <3.12"

src/mean_opinion_score/calculation.py

@@ -3,9 +3,10 @@
 # Ribeiro, F., Florêncio, D., Zhang, C., & Seltzer, M. (2011). CrowdMOS: An approach for crowdsourcing mean opinion score studies. 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2416–2419. https://doi.org/10.1109/ICASSP.2011.5946971
 # ------------------------
 
-from math import inf, sqrt
+from math import sqrt
 
 import numpy as np
+from scipy.stats import norm
 from scipy.stats import t as scipy_t
 
 
@@ -30,8 +31,9 @@ def get_ci95_default(Z: np.ndarray) -> float:
   """
   Computes the 95% confidence interval.
   """
-  # 1.959963984540054
-  z_score = matlab_tinv(0.5 * (1 + 0.95), inf)
+  # matlab_tinv() doesn't work with inf for py36, py37
+  # z_score = matlab_tinv(0.5 * (1 + 0.95), inf)
+  z_score = norm.ppf(0.5 * (1 + 0.95))
   s = np.nanstd(Z)
   n = get_non_nan_count(Z)
   ci95 = z_score * s / sqrt(n)