Model save

README.md

@@ -2,8 +2,6 @@
 license: apache-2.0
 base_model: facebook/wav2vec2-large-xlsr-53
 tags:
-- automatic-speech-recognition
-- DewiBrynJones/banc-trawsgrifiadau-bangor-clean
 - generated_from_trainer
 metrics:
 - wer
@@ -17,10 +15,10 @@ should probably proofread and complete it, then remove this comment. -->
 
 # wav2vec2-xlsr-53-ft-btb-cy
 
-This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the DEWIBRYNJONES/BANC-TRAWSGRIFIADAU-BANGOR-CLEAN - DEFAULT dataset.
+This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.5400
-- Wer: 0.4066
+- Loss: 0.8068
+- Wer: 0.4827
 
 ## Model description
 
@@ -46,23 +44,25 @@ The following hyperparameters were used during training:
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
 - lr_scheduler_warmup_steps: 500
-- training_steps: 2000
+- training_steps: 2500
 - mixed_precision_training: Native AMP
 
 ### Training results
 
 | Training Loss | Epoch  | Step | Validation Loss | Wer    |
 |:-------------:|:------:|:----:|:---------------:|:------:|
-| No log        | 0.2278 | 200  | 2.9681          | 1.0    |
-| No log        | 0.4556 | 400  | 1.3578          | 0.8300 |
-| 3.3875        | 0.6834 | 600  | 0.9213          | 0.6513 |
-| 3.3875        | 0.9112 | 800  | 0.7288          | 0.5341 |
-| 0.8947        | 1.1390 | 1000 | 0.6245          | 0.4634 |
-| 0.8947        | 1.3667 | 1200 | 0.5952          | 0.4458 |
-| 0.8947        | 1.5945 | 1400 | 0.5781          | 0.4385 |
-| 0.7523        | 1.8223 | 1600 | 0.5620          | 0.4283 |
-| 0.7523        | 2.0501 | 1800 | 0.5437          | 0.4140 |
-| 0.6707        | 2.2779 | 2000 | 0.5400          | 0.4066 |
+| No log        | 0.2278 | 200  | 2.9657          | 1.0    |
+| No log        | 0.4556 | 400  | 1.2565          | 0.8238 |
+| 3.3708        | 0.6834 | 600  | 0.8799          | 0.6337 |
+| 3.3708        | 0.9112 | 800  | 0.7572          | 0.5468 |
+| 0.9128        | 1.1390 | 1000 | 0.6134          | 0.4571 |
+| 0.9128        | 1.3667 | 1200 | 0.5879          | 0.4429 |
+| 0.9128        | 1.5945 | 1400 | 0.6283          | 0.4655 |
+| 0.7779        | 1.8223 | 1600 | 0.6166          | 0.4566 |
+| 0.7779        | 2.0501 | 1800 | 0.6131          | 0.4648 |
+| 0.7465        | 2.2779 | 2000 | 0.6312          | 0.4722 |
+| 0.7465        | 2.5057 | 2200 | 0.7630          | 0.4722 |
+| 0.7465        | 2.7335 | 2400 | 0.8068          | 0.4827 |
 
 
 ### Framework versions