Update README.md

README.md

@@ -9,13 +9,13 @@ dataset_info:
     dtype: string
   splits:
   - name: male
-    num_bytes: 48393851.0
+    num_bytes: 48393851
     num_examples: 100
   - name: female
-    num_bytes: 70695334.0
+    num_bytes: 70695334
     num_examples: 100
   download_size: 117652455
-  dataset_size: 119089185.0
+  dataset_size: 119089185
 configs:
 - config_name: default
   data_files:
@@ -23,4 +23,61 @@ configs:
     path: data/male-*
   - split: female
     path: data/female-*
+license: cc
+task_categories:
+- text-to-speech
+language:
+- yo
+pretty_name: Yoruba-TTS-Test
+size_categories:
+- n<1K
 ---
+
+# IroyinSpeech TTS dataset
+
+IroyinSpeech TTS provides an high-quality speech synthesis dataset for Yorùbá language including male and female genders. 
+
+### Dataset Description
+- **Curated by:** [YorubaVoice Team](https://www.yorubavoice.com/about/)
+- **Funded by [optional]: [Imminent](https://imminent.translated.com/making-machines-speak-yoruba)
+- **Shared by [optional]:** [More Information Needed]
+- **Language(s) (NLP):** Yorùbá
+- **License:** CC-BY-NC 4.0
+
+### Dataset Sources [optional]
+
+- **Repository:** [NigerVolta YorubaVoice GitHub](https://github.com/Niger-Volta-LTI/yoruba-voice)
+- **Paper [optional]:** [IroyinSpeech](https://aclanthology.org/2024.lrec-main.812/) published at LREC-COLING 2024
+- **Demo [optional]:** 
+
+## Uses
+
+For automatic speech synthesis or text-to-speech of both male and female gender
+
+
+## Citation
+```
+@inproceedings{ogunremi-etal-2024-iroyinspeech,
+    title = "{{\`I}}r{\`o}y{\`i}n{S}peech: A Multi-purpose {Y}or{\`u}b{\'a} Speech Corpus",
+    author = "Ogunremi, Tolulope  and
+      Tubosun, Kola  and
+      Aremu, Anuoluwapo  and
+      Orife, Iroro  and
+      Adelani, David Ifeoluwa",
+    editor = "Calzolari, Nicoletta  and
+      Kan, Min-Yen  and
+      Hoste, Veronique  and
+      Lenci, Alessandro  and
+      Sakti, Sakriani  and
+      Xue, Nianwen",
+    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
+    month = may,
+    year = "2024",
+    address = "Torino, Italia",
+    publisher = "ELRA and ICCL",
+    url = "https://aclanthology.org/2024.lrec-main.812/",
+    pages = "9296--9303",
+    abstract = "We introduce {\`I}r{\`o}y{\`i}nSpeech corpus{---}a new dataset influenced by a desire to increase the amount of high quality, freely available, contemporary Yor{\`u}b{\'a} speech data that can be used for both Text-to-Speech (TTS) and Automatic Speech Recognition (ASR) tasks. We curated about 23,000 text sentences from the news and creative writing domains with an open license i.e., CC-BY-4.0 and asked multiple speakers to record each sentence. To encourage more participatory approach to data creation, we provide 5 000 utterances from the curated sentences to the Mozilla Common Voice platform to crowd-source the recording and validation of Yor{\`u}b{\'a} speech data. In total, we created about 42 hours of speech data recorded by 80 volunteers in-house, and 6 hours validated recordings on Mozilla Common Voice platform. Our evaluation on TTS shows that we can create a good quality general domain single-speaker TTS model for Yor{\`u}b{\'a} with as little 5 hours of speech by leveraging an end-to-end VITS architecture. Similarly, for ASR, we obtained a WER of 21.5."
+}
+}
+```