infinitejoy
/

wav2vec2-large-xls-r-300m-indonesian

@@ -20,8 +20,8 @@ should probably proofread and complete it, then remove this comment. -->
 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - ID dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.2750
-- Wer: 0.3575
 ## Model description
@@ -46,24 +46,35 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
-- lr_scheduler_warmup_steps: 2000
-- num_epochs: 50.0
 - mixed_precision_training: Native AMP
 ### Training results
 | Training Loss | Epoch | Step  | Validation Loss | Wer    |
 |:-------------:|:-----:|:-----:|:---------------:|:------:|
-| 2.8112        | 4.72  | 1000  | 2.7786          | 1.0    |
-| 1.3803        | 9.43  | 2000  | 0.4971          | 0.5788 |
-| 1.1894        | 14.15 | 3000  | 0.3605          | 0.4442 |
-| 1.1019        | 18.87 | 4000  | 0.3252          | 0.4236 |
-| 1.0366        | 23.58 | 5000  | 0.3127          | 0.4023 |
-| 1.0217        | 28.3  | 6000  | 0.3027          | 0.3953 |
-| 0.9774        | 33.02 | 7000  | 0.2924          | 0.3965 |
-| 0.9485        | 37.74 | 8000  | 0.2949          | 0.3755 |
-| 0.9332        | 42.45 | 9000  | 0.2815          | 0.3665 |
-| 0.9093        | 47.17 | 10000 | 0.2780          | 0.3623 |
 ### Framework versions

 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - ID dataset.
 It achieves the following results on the evaluation set:
+- Loss: 0.2759
+- Wer: 0.3256
 ## Model description
 - seed: 42
 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
 - lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 4000
+- num_epochs: 100.0
 - mixed_precision_training: Native AMP
 ### Training results
 | Training Loss | Epoch | Step  | Validation Loss | Wer    |
 |:-------------:|:-----:|:-----:|:---------------:|:------:|
+| 3.0387        | 4.72  | 1000  | 3.0892          | 1.0    |
+| 1.7911        | 9.43  | 2000  | 0.8451          | 0.6702 |
+| 1.2826        | 14.15 | 3000  | 0.4211          | 0.4166 |
+| 1.1802        | 18.87 | 4000  | 0.3508          | 0.4690 |
+| 1.1065        | 23.58 | 5000  | 0.3319          | 0.4662 |
+| 1.0921        | 28.3  | 6000  | 0.3056          | 0.3880 |
+| 1.0366        | 33.02 | 7000  | 0.2997          | 0.3665 |
+| 0.9988        | 37.74 | 8000  | 0.2972          | 0.3653 |
+| 0.9864        | 42.45 | 9000  | 0.2697          | 0.3371 |
+| 0.9558        | 47.17 | 10000 | 0.2739          | 0.3141 |
+| 0.9094        | 51.89 | 11000 | 0.2657          | 0.3533 |
+| 0.9034        | 56.6  | 12000 | 0.2699          | 0.3397 |
+| 0.8907        | 61.32 | 13000 | 0.2765          | 0.3470 |
+| 0.8631        | 66.04 | 14000 | 0.2774          | 0.3346 |
+| 0.8389        | 70.75 | 15000 | 0.2743          | 0.3365 |
+| 0.8214        | 75.47 | 16000 | 0.2778          | 0.3201 |
+| 0.8195        | 80.19 | 17000 | 0.2725          | 0.3286 |
+| 0.7994        | 84.91 | 18000 | 0.2782          | 0.3315 |
+| 0.7816        | 89.62 | 19000 | 0.2775          | 0.3363 |
+| 0.7816        | 94.34 | 20000 | 0.2731          | 0.3278 |
+| 0.7635        | 99.06 | 21000 | 0.2767          | 0.3259 |
 ### Framework versions