Model save

README.md

@@ -4,7 +4,7 @@ base_model: openai/whisper-small
 tags:
 - generated_from_trainer
 datasets:
-- mozilla-foundation/common_voice_17_0
+- common_voice_17_0
 metrics:
 - wer
 model-index:
@@ -14,15 +14,15 @@ model-index:
       name: Automatic Speech Recognition
       type: automatic-speech-recognition
     dataset:
-      name: mozilla-foundation/common_voice_17_0 id
-      type: mozilla-foundation/common_voice_17_0
+      name: common_voice_17_0
+      type: common_voice_17_0
       config: id
       split: None
       args: id
     metrics:
     - name: Wer
       type: wer
-      value: 0.16172444196946495
+      value: 0.05902826117221217
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -30,10 +30,10 @@ should probably proofread and complete it, then remove this comment. -->
 
 # whisper-small-id
 
-This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the mozilla-foundation/common_voice_17_0 id dataset.
+This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the common_voice_17_0 dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.3506
-- Wer: 0.1617
+- Loss: 0.0878
+- Wer: 0.0590
 
 ## Model description
 
@@ -61,18 +61,33 @@ 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: 5000
+- training_steps: 20000
 - mixed_precision_training: Native AMP
 
 ### Training results
 
-| Training Loss | Epoch   | Step | Validation Loss | Wer    |
-|:-------------:|:-------:|:----:|:---------------:|:------:|
-| 0.0547        | 3.8462  | 1000 | 0.2570          | 0.1687 |
-| 0.0034        | 7.6923  | 2000 | 0.3144          | 0.1589 |
-| 0.0011        | 11.5385 | 3000 | 0.3358          | 0.1615 |
-| 0.0007        | 15.3846 | 4000 | 0.3460          | 0.1614 |
-| 0.0006        | 19.2308 | 5000 | 0.3506          | 0.1617 |
+| Training Loss | Epoch   | Step  | Validation Loss | Wer    |
+|:-------------:|:-------:|:-----:|:---------------:|:------:|
+| 0.1875        | 0.8457  | 1000  | 0.1400          | 0.1099 |
+| 0.0852        | 1.6913  | 2000  | 0.1043          | 0.0857 |
+| 0.0387        | 2.5370  | 3000  | 0.0914          | 0.0757 |
+| 0.0153        | 3.3827  | 4000  | 0.0860          | 0.0818 |
+| 0.008         | 4.2283  | 5000  | 0.0878          | 0.0698 |
+| 0.005         | 5.0740  | 6000  | 0.0878          | 0.0745 |
+| 0.0033        | 5.9197  | 7000  | 0.0834          | 0.0651 |
+| 0.0029        | 6.7653  | 8000  | 0.0815          | 0.0627 |
+| 0.0014        | 7.6110  | 9000  | 0.0853          | 0.0627 |
+| 0.0013        | 8.4567  | 10000 | 0.0861          | 0.0641 |
+| 0.0005        | 9.3023  | 11000 | 0.0857          | 0.0633 |
+| 0.0005        | 10.1480 | 12000 | 0.0856          | 0.0620 |
+| 0.0007        | 10.9937 | 13000 | 0.0866          | 0.0605 |
+| 0.0005        | 11.8393 | 14000 | 0.0871          | 0.0614 |
+| 0.0002        | 12.6850 | 15000 | 0.0850          | 0.0596 |
+| 0.0004        | 13.5307 | 16000 | 0.0849          | 0.0600 |
+| 0.0001        | 14.3763 | 17000 | 0.0868          | 0.0592 |
+| 0.0002        | 15.2220 | 18000 | 0.0873          | 0.0593 |
+| 0.0001        | 16.0677 | 19000 | 0.0875          | 0.0585 |
+| 0.0001        | 16.9133 | 20000 | 0.0878          | 0.0590 |
 
 
 ### Framework versions