docs(readme): add usage and references

CITATION.cff

@@ -0,0 +1,16 @@
+# SPDX-FileCopyrightText: Enno Hermann
+#
+# SPDX-License-Identifier: MIT
+
+cff-version: 1.2.0
+message: "If you use this software, please cite it as below. Also consider citing the Glow-TTS paper."
+title: "monotonic-alignment-search"
+abstract: "Implementation of monotonic alignment search from Glow-TTS"
+date-released: 2024
+authors:
+  - family-names: Hermann
+    given-names: Enno
+version: 0.1.0
+license: "MIT"
+url: "https://github.com/eginhard/monotonic_alignment_search"
+repository-code: "https://github.com/eginhard/monotonic_alignment_search"

README.md

@@ -4,6 +4,35 @@ SPDX-FileCopyrightText: Enno Hermann
 SPDX-License-Identifier: MIT
 -->
 
-Implementation of monotonic alignment search from
-[Glow-TTS](https://github.com/jaywalnut310/glow-tts) for easy reuse in other
-projects.
+# Monotonic Alignment Search (MAS)
+
+Implementation of MAS from [Glow-TTS](https://github.com/jaywalnut310/glow-tts)
+for easy reuse in other projects.
+
+## Usage
+
+MAS can find the most probable alignment between a text sequence `t_x` and a
+speech sequence `t_y`.
+
+```python
+from monotonic_alignment_search import maximum_path
+
+# value (torch.Tensor): [batch_size, t_x, t_y]
+# mask  (torch.Tensor): [batch_size, t_x, t_y]
+path = maximum_path(value, mask)
+```
+
+## References
+
+This implementation is taken from the original [Glow-TTS
+repository](https://github.com/jaywalnut310/glow-tts). Consider citing the
+Glow-TTS paper when using this project:
+
+```bibtex
+@inproceedings{kim2020_glowtts,
+    title={Glow-{TTS}: A Generative Flow for Text-to-Speech via Monotonic Alignment Search},
+    author={Jaehyeon Kim and Sungwon Kim and Jungil Kong and Sungroh Yoon},
+    booktitle={Proceedings of Neur{IPS}},
+    year={2020},
+}
+```