finetune on 10000-20000

README.md

@@ -23,7 +23,7 @@ model-index:
     metrics:
     - name: Wer
       type: wer
-      value: 21.4960058097313
+      value: 29.24663420223432
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -33,8 +33,8 @@ should probably proofread and complete it, then remove this comment. -->
 
 This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the SLR Javanenese 41_35 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2504
-- Wer: 21.4960
+- Loss: 0.4200
+- Wer: 29.2466
 
 ## Model description
 
@@ -65,18 +65,18 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch  | Step | Validation Loss | Wer     |
-|:-------------:|:------:|:----:|:---------------:|:-------:|
-| 0.066         | 0.3003 | 100  | 0.2901          | 25.6596 |
-| 0.0693        | 0.6006 | 200  | 0.2879          | 24.0620 |
-| 0.0775        | 0.9009 | 300  | 0.2810          | 23.2147 |
-| 0.0331        | 1.2012 | 400  | 0.2727          | 23.7231 |
-| 0.0306        | 1.5015 | 500  | 0.2661          | 21.9075 |
-| 0.0352        | 1.8018 | 600  | 0.2570          | 20.8908 |
-| 0.0122        | 2.1021 | 700  | 0.2608          | 21.8833 |
-| 0.0113        | 2.4024 | 800  | 0.2552          | 21.4234 |
-| 0.0112        | 2.7027 | 900  | 0.2509          | 21.5686 |
-| 0.0115        | 3.0030 | 1000 | 0.2504          | 21.4960 |
+| Training Loss | Epoch | Step | Validation Loss | Wer     |
+|:-------------:|:-----:|:----:|:---------------:|:-------:|
+| 0.4922        | 0.16  | 100  | 0.6047          | 37.4678 |
+| 0.435         | 0.32  | 200  | 0.5572          | 35.9424 |
+| 0.5688        | 0.48  | 300  | 0.5090          | 33.5649 |
+| 0.4779        | 0.64  | 400  | 0.4799          | 31.8390 |
+| 0.4247        | 0.8   | 500  | 0.4540          | 30.8364 |
+| 0.42          | 0.96  | 600  | 0.4368          | 30.2492 |
+| 0.2276        | 1.12  | 700  | 0.4330          | 29.6333 |
+| 0.2137        | 1.28  | 800  | 0.4264          | 29.5832 |
+| 0.236         | 1.44  | 900  | 0.4215          | 29.2395 |
+| 0.1971        | 1.6   | 1000 | 0.4200          | 29.2466 |
 
 
 ### Framework versions