Update README.md

README.md

@@ -25,18 +25,18 @@ model-index:
     metrics:
        - name: Test WER
          type: wer
-         value: 13.585485
+         value: 13.386893
        - name: Test CER
          type: cer
-         value: 4.850368
+         value: 4.795275
 ---
 # Wav2vec 2.0 large-voxpopuli-sv-swedish
-Finetuned version of Facebooks [VoxPopuli-sv large](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) model using NST and Common Voice data. Evalutation without a language model gives the following: WER for NST + Common Voice test set (2% of total sentences) is **6.30%**, WER for Common Voice test set is **13.59%** directly and **9.5%** with a 4-gram language model.
+Finetuned version of Facebooks [VoxPopuli-sv large](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) model using NST and Common Voice data. Evalutation without a language model gives the following: WER for NST + Common Voice test set (2% of total sentences) is **6.58%**, WER for Common Voice test set is **13.39%** directly and **9.5%** with a 4-gram language model.
 
 When using this model, make sure that your speech input is sampled at 16kHz.
 
 ## Training
-This model has been fine-tuned for 80000 updates on NST + CommonVoice and then for an additional 20000 steps on only CommonVoice. The additional fine-tuning on CommonVoce hurts performance on the NST+CommonVoice test set somewhat and, unsurprisingly, improves it on the CommonVoice test set. It seems to perform generally better though [citation needed].
+This model has been fine-tuned for 80000 updates on NST + CommonVoice and then for an additional 40000 steps on only CommonVoice. The additional fine-tuning on CommonVoce hurts performance on the NST+CommonVoice test set somewhat and, unsurprisingly, improves it on the CommonVoice test set. It seems to perform generally better though [citation needed].
 
 ## Usage
 The model can be used directly (without a language model) as follows: