Update README.md

README.md

@@ -134,7 +134,7 @@ The model was trained on around 3400 hours of Spanish speech data.
 The performance of Automatic Speech Recognition models is measuring using Character Error Rate (CER) and Word Error Rate (WER).
 Table 1 summarizes the performance of the model with the Transducer and CTC decoders across different datasets.
 
- | Model     | MCV %WER/CER test |MLS %WER/CER test | Voxpopuli %WER/CER test |Fisher %WER/CER test |
+ | Model     | MCV %WER/CER |MLS %WER/CER | Voxpopuli %WER/CER |Fisher %WER/CER|
  |-----------|--------------|---------------|--------------|---------------|
  | RNNT head | 7.58/ 1.96 | 12.43 / 2.99 |9.59 / 3.67 | 30.76 / 11.49  |
  | CTC head  | 8.23 / 2.20 | 12.63 / 3.11 | 9.93 / 3.79 | 31.20 / 11.44  |
@@ -142,7 +142,7 @@ Table 1 summarizes the performance of the model with the Transducer and CTC deco
 
 Table 2 provides the performance of the model when punctuation marks are separated during evaluation, using both the Transducer and CTC decoders.
 
- | Model     | MCV %WER/CER test |MLS %WER/CER test | Voxpopuli %WER/CER test |Fisher %WER/CER test |
+ | Model     | MCV %WER/CER|MLS %WER/CER| Voxpopuli %WER/CER|Fisher %WER/CER|
  |-----------|--------------|---------------|--------------|---------------|
  | RNNT head | 6.79 / 2.16 | 11.63/ 3.96  |8.84/ 4.06| 27.88 / 13.40 |
  | CTC head  | 7.39 / 2.39 | 11.81 / 4.01  | 9.17 / 4.17| 27.81 / 13.14 |