Update README.md

README.md

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----
-license: apache-2.0
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+---
+license: apache-2.0
+language: fi
+tags:
+- automatic-speech-recognition
+- fi
+- finnish
+---
+# Colloquial Finnish Wav2vec2-Base
+
+The base model pre-trained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. 
+
+**Note**: This model does not have a tokenizer as it was pre-trained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for more in-detail explanation of how to fine-tune the model.
+
+## 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 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).
+
+## Intended uses & limitations
+
+You can use this model for Finnish ASR (speech-to-text) and SER (Spoken Emotion Recognition) tasks. 
+
+### How to use
+
+See [this notebook](https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_Tune_XLS_R_on_Common_Voice.ipynb) for more information on how to fine-tune the model.
+
+### Limitations and bias
+
+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.