End of training

README.md

@@ -7,7 +7,7 @@ base_model: openai/whisper-small
 tags:
 - generated_from_trainer
 datasets:
-- octava/indonesian-voice-transcription-1.4.9a-cv-fl
+- octava/indonesian-voice-transcription-1.4.9a-cv-fl-slrjv-md
 metrics:
 - wer
 model-index:
@@ -17,13 +17,13 @@ model-index:
       name: Automatic Speech Recognition
       type: automatic-speech-recognition
     dataset:
-      name: Extracted Youtube with CommonVoice11 and Fleurs
-      type: octava/indonesian-voice-transcription-1.4.9a-cv-fl
+      name: Extracted Youtube with CommonVoice11, Fleurs, OpenSLR, and MagicData
+      type: octava/indonesian-voice-transcription-1.4.9a-cv-fl-slrjv-md
       args: 'config: id, split: train'
     metrics:
     - name: Wer
       type: wer
-      value: 19.8005698005698
+      value: 19.96201329534663
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -31,10 +31,10 @@ should probably proofread and complete it, then remove this comment. -->
 
 # Optimized Whisper Small Id for Inspirasi
 
-This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Extracted Youtube with CommonVoice11 and Fleurs dataset.
+This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Extracted Youtube with CommonVoice11, Fleurs, OpenSLR, and MagicData dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.3393
-- Wer: 19.8006
+- Loss: 0.3376
+- Wer: 19.9620
 
 ## Model description
 
@@ -67,16 +67,16 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch  | Step | Validation Loss | Wer     |
 |:-------------:|:------:|:----:|:---------------:|:-------:|
-| 0.3919        | 0.1865 | 500  | 0.3850          | 23.0864 |
-| 0.2481        | 0.3730 | 1000 | 0.3674          | 22.3077 |
-| 0.2087        | 0.5595 | 1500 | 0.3528          | 21.4530 |
-| 0.1567        | 0.7460 | 2000 | 0.3456          | 22.1368 |
-| 0.1462        | 0.9325 | 2500 | 0.3452          | 20.5603 |
-| 0.0676        | 1.1190 | 3000 | 0.3451          | 19.8481 |
-| 0.0667        | 1.3055 | 3500 | 0.3433          | 20.0190 |
-| 0.0585        | 1.4920 | 4000 | 0.3427          | 20.0570 |
-| 0.053         | 1.6785 | 4500 | 0.3407          | 20.0760 |
-| 0.0662        | 1.8650 | 5000 | 0.3393          | 19.8006 |
+| 0.4122        | 0.1686 | 500  | 0.3999          | 24.8908 |
+| 0.2737        | 0.3373 | 1000 | 0.3655          | 22.4691 |
+| 0.2311        | 0.5059 | 1500 | 0.3491          | 21.5195 |
+| 0.1947        | 0.6745 | 2000 | 0.3339          | 21.5100 |
+| 0.169         | 0.8432 | 2500 | 0.3408          | 20.6363 |
+| 0.0875        | 1.0118 | 3000 | 0.3429          | 21.2726 |
+| 0.0877        | 1.1804 | 3500 | 0.3430          | 20.4748 |
+| 0.0726        | 1.3491 | 4000 | 0.3396          | 20.2469 |
+| 0.0741        | 1.5177 | 4500 | 0.3378          | 20.2754 |
+| 0.0675        | 1.6863 | 5000 | 0.3376          | 19.9620 |
 
 
 ### Framework versions