Update README.md

README.md

@@ -42,7 +42,7 @@ print(text)
 ```
 
 ## Evaluation Performance
-Note: WER calculated with newmm tokenizer for Thai segmentation.
+WER calculated with newmm tokenizer for Thai segmentation.
 | Model                                   |       CV18 (WER)       |        Gowejee (WER)      |     LOTUS-TRD (WER)    |      Thai Dialect (WER)    |        Elderly (WER)       |     Gigaspeech2 (WER)      |       Fleurs (WER)         |     Distant Meeting (WER)  |       Podcast (WER)        |
 |:----------------------------------------|:----------------------:|:-------------------------:|:----------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|
 | whisper-large-v3                        |         18.75          |          46.59            |         48.14          |           57.82            |           12.27            |           33.26            |           24.08            |           72.57            |           41.24            |
@@ -50,6 +50,7 @@ Note: WER calculated with newmm tokenizer for Thai segmentation.
 | thonburian-whisper-th-large-v3-combined |         7.62           |          22.06            |         41.95          |           26.53            |           1.63             |           25.22            |           13.90            |           64.68            |           32.42            |
 | monsoon-whisper-medium-gigaspeech2      |         11.66          |          20.50            |         41.04          |           42.06            |           7.57             |           21.40            |           21.54            |           51.65            |           38.89            |
 | pathumma-whisper-th-large-v3            |         8.68           |           9.84            |         15.47          |           19.85            |           1.53             |           21.66            |           15.65            |           51.56            |           36.47            |
+**Note:** WER for other models not target fine-tuned on dialect datasets may be less representative of dialect performance.
 
 ## Limitations
 More information needed