Update README.md

README.md

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----
-license: cc-by-nc-sa-4.0
----
+---
+language: "cs"
+tags:
+- Czech
+- KKY
+- FAV
+license: "cc-by-nc-sa-4.0"
+---
+
+# wav2vec2-base-cs-50k
+This is a monolingual Czech Wav2Vec 2.0 base model pre-trained from 50 thousand hours of Czech speech.
+
+This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for speech recognition, a tokenizer should be created and the model should be fine-tuned on labeled text data. 
+
+## Speech recognition results
+After fine-tuning, the model scored the following results on public datasets:
+- Czech portion of CommonVoice v16.0: **WER = 11.36%**
+
+See our paper for details.
+
+## Paper 
+The preprint of our paper (accepted to INTERSPEECH 2024) is available at [tbd]
+
+### All models released within the paper
+- https://huggingface.co/fav-kky/wav2vec2-base-cs-50k (monolingual Czech)
+- https://huggingface.co/fav-kky/wav2vec2-base-de-50k (monolingual German)
+- https://huggingface.co/fav-kky/wav2vec2-base-cs-en-100k (bilingual Czech+English)
+- https://huggingface.co/fav-kky/wav2vec2-base-cs-de-100k (bilingual Czech+English)
+- https://huggingface.co/fav-kky/wav2vec2-base-en-de-100k (bilingual English+German)
+- https://huggingface.co/fav-kky/wav2vec2-base-cs-en-de-150k (trilingual Czech+English+German)
+
+## Citation
+If you find this model useful, please cite our paper:
+```
+tbd
+```
+
+## Usage
+Inputs must be 16kHz mono audio files.
+
+This model can be used e.g. to extract per-frame contextual embeddings from audio:
+```python
+from transformers import Wav2Vec2Model, Wav2Vec2FeatureExtractor
+import torchaudio
+
+feature_extractor = Wav2Vec2FeatureExtractor.from_pretrained("fav-kky/wav2vec2-base-cs-80k-ClTRUS")
+model = Wav2Vec2Model.from_pretrained("fav-kky/wav2vec2-base-cs-50k")
+
+speech_array, sampling_rate = torchaudio.load("/path/to/audio/file.wav")
+inputs = feature_extractor(
+    speech_array, 
+    sampling_rate=16_000, 
+    return_tensors="pt"
+)["input_values"][0]
+
+output = model(inputs)
+embeddings = output.last_hidden_state.detach().numpy()[0]
+```
+
+## Related works