Update README.md

README.md

@@ -8,6 +8,26 @@ metrics:
 - wer
 pipeline_tag: automatic-speech-recognition
 license: mit
+
+model-index:
+- name: Lemswasabi/wav2vec2-large-xlsr-53-842h-luxembourgish-14h-with-lm
+  results:
+  - task:
+      type: automatic-speech-recognition             # Required. Example: automatic-speech-recognition
+      name: Speech Recognition             # Optional. Example: Speech Recognition
+    metrics:
+      - type: wer
+        value: 11.68
+        name: Dev WER
+      - type: wer 
+        value: 10.71
+        name: Test WER
+      - type: cer
+        value: 2.64
+        name: Dev CER
+      - type: cer 
+        value: 2.31
+        name: Test CER
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -15,7 +35,9 @@ should probably proofread and complete it, then remove this comment. -->
 
 ## Model description
 
-More information needed
+We fine-tuned a wav2vec 2.0 large XLSR-53 checkpoint with 842h of unlabelled Luxembourgish speech
+collected from [RTL.lu](https://www.rtl.lu/). Then the model was fine-tuned on 14h of labelled
+Luxembourgish Speech from the same domain.
 
 ## Intended uses & limitations
 
@@ -47,4 +69,18 @@ The following hyperparameters were used during training:
 - Transformers 4.20.0.dev0
 - Pytorch 1.11.0+cu113
 - Datasets 2.2.1
-- Tokenizers 0.12.1
+- Tokenizers 0.12.1
+
+## Citation
+
+This model is a result of our paper `IMPROVING LUXEMBOURGISH SPEECH RECOGNITION WITH CROSS-LINGUAL SPEECH REPRESENTATIONS` submitted to the [IEEE SLT 2022 workshop](https://slt2022.org/)
+
+```
+@misc{lb-wav2vec2,
+  author = {Nguyen, Le Minh and Nayak, Shekhar and Coler, Matt.},
+  keywords = {Luxembourgish, multilingual speech recognition, language modelling, wav2vec 2.0 XLSR-53, under-resourced language},
+  title = {IMPROVING LUXEMBOURGISH SPEECH RECOGNITION WITH CROSS-LINGUAL SPEECH REPRESENTATIONS},
+  year = {2022},
+  copyright = {2023 IEEE}
+}
+```