Update README.md

README.md

@@ -26,10 +26,10 @@ model-index:
     metrics:
        - name: Test WER
          type: wer
-         value: 0.08108860330598514
+         value: 0.08040489847285752
        - name: Test CER
          type: cer
-         value: 0.027241712812152218
+         value: 0.02745409135817121
   - task: 
       name: Speech Recognition
       type: automatic-speech-recognition
@@ -40,10 +40,10 @@ model-index:
     metrics:
        - name: Test WER
          type: wer
-         value: 0.06541946111307212
+         value: 0.05763423147821312
        - name: Test CER
          type: cer
-         value: 0.02205785796827398
+         value: 0.019116372518121653
   - task: 
       name: Speech Recognition
       type: automatic-speech-recognition
@@ -54,19 +54,33 @@ model-index:
     metrics:
        - name: Test WER
          type: wer
-         value: 0.1506717480848443
+         value: 0.13993147784578666
        - name: Test CER
          type: cer
-         value: 0.09562445266717665
+         value: 0.08941471299857044
+  - task: 
+      name: Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Robust Speech Event - Catalan Dev Data
+      type: speech-recognition-community-v2/dev_data
+      args: ca
+    metrics:
+       - name: Test WER
+         type: wer
+         value: 0.26797799000695716
+       - name: Test CER
+         type: cer
+         value: 0.15358004827031377
 ---
 
 <!-- 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. -->
 
-# wav2vec2-xls-r-300m-ca
+# wav2vec2-xls-r-300m-ca-lm
 
 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - CA dataset.
-It achieves the following results on the averaged across datasets test set:
+It achieves the following results on the averaged across datasets test set (without the LM):
 - Loss: 0.2758
 - Wer: 0.1792
 
@@ -125,7 +139,29 @@ The following hyperparameters were used during training:
 | 1.1407        | 5.33  | 10000 | 0.2768          | 0.1798 |
 | 1.1401        | 5.59  | 10500 | 0.2780          | 0.1816 |
 | 1.1333        | 5.86  | 11000 | 0.2748          | 0.1750 |
-
+| 1.1571        | 6.13  | 11500 | 0.2808          | 0.1708 |
+| 1.1505        | 6.39  | 12000 | 0.2726          | 0.1692 |
+| 1.1519        | 6.66  | 12500 | 0.2749          | 0.1654 |
+| 1.136         | 6.93  | 13000 | 0.2765          | 0.1643 |
+| 1.1326        | 7.19  | 13500 | 0.2706          | 0.1668 |
+| 1.1342        | 7.46  | 14000 | 0.2665          | 0.1638 |
+| 1.1286        | 7.72  | 14500 | 0.2669          | 0.1636 |
+| 1.1243        | 7.99  | 15000 | 0.2619          | 0.1623 |
+| 1.1173        | 8.26  | 15500 | 0.2652          | 0.1604 |
+| 1.1129        | 8.52  | 16000 | 0.2610          | 0.1598 |
+| 1.1091        | 8.79  | 16500 | 0.2608          | 0.1584 |
+| 1.1053        | 9.06  | 17000 | 0.2633          | 0.1664 |
+| 1.1004        | 9.32  | 17500 | 0.2594          | 0.1662 |
+| 1.0995        | 9.59  | 18000 | 0.2623          | 0.1569 |
+| 1.0964        | 9.86  | 18500 | 0.2624          | 0.1597 |
+| 1.09          | 10.12 | 19000 | 0.2577          | 0.1578 |
+| 1.089         | 10.39 | 19500 | 0.2574          | 0.1531 |
+| 1.0864        | 10.66 | 20000 | 0.2556          | 0.1546 |
+| 1.0806        | 10.92 | 20500 | 0.2548          | 0.1583 |
+| 1.0842        | 11.19 | 21000 | 0.2550          | 0.1542 |
+| 1.0805        | 11.45 | 21500 | 0.2561          | 0.1524 |
+| 1.0722        | 11.72 | 22000 | 0.2540          | 0.1566 |
+| 1.0763        | 11.99 | 22500 | 0.2549          | 0.1572 |
 
 ### Framework versions