Update README.md

README.md

@@ -1,8 +1,89 @@
 ---
+tags:
+- automatic-speech-recognition
+- generated_from_trainer
 license: mit
 language:
 - lb
 metrics:
 - wer
 pipeline_tag: automatic-speech-recognition
----
+
+model-index:
+- name: Lemswasabi/wav2vec2-large-xlsr-53-842h-luxembourgish-11h-with-lm
+  results:
+  - task:
+      type: automatic-speech-recognition             # Required. Example: automatic-speech-recognition
+      name: Speech Recognition             # Optional. Example: Speech Recognition
+    metrics:
+      - type: wer
+        value: 9.98
+        name: Dev WER
+      - type: wer 
+        value: 10.09
+        name: Test WER
+      - type: cer
+        value: 2.27
+        name: Dev CER
+      - type: cer 
+        value: 2.22
+        name: Test CER
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# 
+
+## Model description
+
+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 11h of labelled
+Luxembourgish speech from the same domain. Additionally, we rescore the output transcription 
+with a 5-gram language model trained on text corpora from RTL.lu and the Luxembourgish parliament.
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 7.5e-05
+- train_batch_size: 3
+- eval_batch_size: 3
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 12
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 2000
+- num_epochs: 50.0
+- mixed_precision_training: Native AMP
+
+### Framework versions
+
+- Transformers 4.20.0.dev0
+- Pytorch 1.11.0+cu113
+- Datasets 2.2.1
+- 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}
+}
+```