update model card README.md

README.md

@@ -6,21 +6,9 @@ tags:
 - generated_from_trainer
 metrics:
 - wer
-datasets:
-  - jimregan/sbtal_riksdag_asr
 model-index:
 - name: Whisper Small Sv - Riksdag 100h
-  results:
-    - task:
-        name: Automatic Speech Recognition
-        type: automatic-speech-recognition
-      dataset:
-        name: SBTal Riksdag ASR
-        type: jimregan/sbtal_riksdag_asr
-      metrics:
-        - name: Test WER
-          type: wer
-          value: 720.3756
+  results: []
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -28,12 +16,10 @@ should probably proofread and complete it, then remove this comment. -->
 
 # Whisper Small Sv - Riksdag 100h
 
-This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the SBTal Riksdag ASR dataset.
+This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.4019
-- Wer: 720.3756
-
-That's not an error, the results really are that bad, and this should not be used by anyone, ever, except to get a good laugh. I'll try to run fine-tuning again, but don't hold your breath.
+- Loss: 0.4977
+- Wer: 1118.4718
 
 ## Model description
 
@@ -59,17 +45,33 @@ 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: 4000
+- training_steps: 20000
 - mixed_precision_training: Native AMP
 
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Wer       |
-|:-------------:|:-----:|:----:|:---------------:|:---------:|
-| 0.1349        | 0.11  | 1000 | 0.4679          | 732.5701  |
-| 0.1071        | 0.22  | 2000 | 0.4305          | 1417.2884 |
-| 0.0959        | 0.33  | 3000 | 0.4077          | 787.1881  |
-| 0.0691        | 0.43  | 4000 | 0.4019          | 720.3756  |
+| Training Loss | Epoch | Step  | Validation Loss | Wer       |
+|:-------------:|:-----:|:-----:|:---------------:|:---------:|
+| 0.1384        | 0.11  | 1000  | 0.4747          | 380.8335  |
+| 0.1186        | 0.22  | 2000  | 0.4513          | 1032.3900 |
+| 0.1056        | 0.33  | 3000  | 0.4385          | 582.0427  |
+| 0.0824        | 0.43  | 4000  | 0.4465          | 574.8907  |
+| 0.0961        | 0.54  | 5000  | 0.4199          | 1004.9138 |
+| 0.0939        | 0.65  | 6000  | 0.4478          | 866.2979  |
+| 0.0758        | 0.76  | 7000  | 0.4384          | 907.9496  |
+| 0.0741        | 0.87  | 8000  | 0.4264          | 641.1371  |
+| 0.0692        | 0.98  | 9000  | 0.4206          | 1142.6550 |
+| 0.0257        | 1.08  | 10000 | 0.4707          | 1152.4312 |
+| 0.0273        | 1.19  | 11000 | 0.4789          | 1100.2058 |
+| 0.021         | 1.3   | 12000 | 0.4763          | 1236.1719 |
+| 0.0163        | 1.41  | 13000 | 0.5035          | 924.8006  |
+| 0.0183        | 1.52  | 14000 | 0.4911          | 1285.1814 |
+| 0.024         | 1.63  | 15000 | 0.4861          | 1140.8284 |
+| 0.0158        | 1.73  | 16000 | 0.4793          | 1181.7597 |
+| 0.0167        | 1.84  | 17000 | 0.4759          | 1207.3064 |
+| 0.0231        | 1.95  | 18000 | 0.4801          | 1139.6964 |
+| 0.0054        | 2.06  | 19000 | 0.4934          | 1114.4842 |
+| 0.006         | 2.17  | 20000 | 0.4977          | 1118.4718 |
 
 
 ### Framework versions