Update README.md

README.md

@@ -14,9 +14,9 @@ The base model pre-trained on 16kHz sampled speech audio with [facebook/wav2vec2
 
 ## Model description
 
-The Finnish Wav2Vec2 Base has the same architecture and uses the same training objective as the English and multilingual one described in [Paper](https://arxiv.org/abs/2006.11477). It is pretrained on 158k hours of unlabeled Finnish speech, including [KAVI radio and television archive materials](https://kavi.fi/en/radio-ja-televisioarkistointia-vuodesta-2008/), Lahjoita Puhetta (Donate Speech), Finnish Parliament, Finnish VoxPopuli.
+The Finnish Wav2Vec2 Base has the same architecture and uses the same training objective as the English and multilingual one described in [Paper](https://arxiv.org/abs/2006.11477). It is pre-trained on 2600 hours of unlabeled colloquial Finnish speech from [Lahjoita puhetta (Donate Speech)](https://link.springer.com/article/10.1007/s10579-022-09606-3).
 
-You can read more about the pre-trained model from [this paper](TODO).
+You can read more about the pre-trained model from [this paper](TODO). The training scripts are available on [GitHub](https://github.com/aalto-speech/colloquial-Finnish-wav2vec2)
 
 ## Intended uses & limitations
 
@@ -31,3 +31,27 @@ See [this notebook](https://colab.research.google.com/github/patrickvonplaten/no
 This model was pre-trained with audio samples whose maximum length was 60 seconds so this model most likely works the best for quite short audios of similar length. However, you can try this model with a lot longer audios too and see how it works. If you encounter out of memory errors with very long audio files you can use the audio chunking method introduced in [this blog post](https://huggingface.co/blog/asr-chunking).
 
 A vast majority of the data used for pre-training was from the [Lahjoita puhetta (Donate Speech) corpus](https://link.springer.com/article/10.1007/s10579-022-09606-3) so this model might have biases towards colloquial Finnish.
+
+## Citation
+
+If you use our models or scripts, please cite our article as:
+
+```bibtex
+@inproceedings{getman24a_interspeech,
+  author={Yaroslav Getman and Tamas Grosz and Mikko Kurimo},
+  title={{What happens in continued pre-training? Analysis of self-supervised speech
+models with continued pre-training for colloquial Finnish ASR}},
+  year=2024,
+  booktitle={Proc. INTERSPEECH 2024},
+  pages={XX--XX},
+  doi={XXXX},
+  issn={XXXX-XXXX}
+}
+```
+
+## Team Members
+
+- Yaroslav Getman, [Hugging Face profile](https://huggingface.co/GetmanY1), [LinkedIn profile](https://www.linkedin.com/in/yaroslav-getman/)
+- Tamas Grosz, [Hugging Face profile](https://huggingface.co/Grosy), [LinkedIn profile](https://www.linkedin.com/in/tam%C3%A1s-gr%C3%B3sz-950a049a/)
+
+Feel free to contact us for more details 🤗