asr-africa
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w2v-bert-2.0-yogera-lg-21hrs-v2

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 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
 library_name: transformers
+language:
+- lg
+license: mit
+base_model: facebook/w2v-bert-2.0
+tags:
+- generated_from_trainer
+datasets:
+- yogera
+metrics:
+- wer
+model-index:
+- name: wav2vec2-bert
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: Yogera
+      type: yogera
+    metrics:
+    - name: Wer
+      type: wer
+      value: 0.14867316851893853
 ---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# wav2vec2-bert
+This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the Yogera dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.2216
+- Wer: 0.1487
+- Cer: 0.0334
+## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 100
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
+| 0.6681        | 1.0   | 235  | 0.2226          | 0.2616 | 0.0533 |
+| 0.1666        | 2.0   | 470  | 0.1639          | 0.2013 | 0.0410 |
+| 0.1249        | 3.0   | 705  | 0.1608          | 0.1912 | 0.0416 |
+| 0.101         | 4.0   | 940  | 0.1573          | 0.1835 | 0.0416 |
+| 0.0833        | 5.0   | 1175 | 0.1567          | 0.1697 | 0.0378 |
+| 0.0715        | 6.0   | 1410 | 0.1589          | 0.1564 | 0.0346 |
+| 0.0624        | 7.0   | 1645 | 0.1634          | 0.1728 | 0.0408 |
+| 0.0541        | 8.0   | 1880 | 0.1592          | 0.1559 | 0.0341 |
+| 0.0464        | 9.0   | 2115 | 0.1788          | 0.1546 | 0.0336 |
+| 0.0434        | 10.0  | 2350 | 0.1641          | 0.1575 | 0.0353 |
+| 0.0385        | 11.0  | 2585 | 0.1854          | 0.1498 | 0.0333 |
+| 0.0358        | 12.0  | 2820 | 0.1915          | 0.1504 | 0.0345 |
+| 0.0308        | 13.0  | 3055 | 0.1747          | 0.1514 | 0.0328 |
+| 0.0283        | 14.0  | 3290 | 0.1966          | 0.1449 | 0.0329 |
+| 0.0274        | 15.0  | 3525 | 0.1882          | 0.1535 | 0.0342 |
+| 0.0246        | 16.0  | 3760 | 0.2199          | 0.1588 | 0.0362 |
+| 0.0212        | 17.0  | 3995 | 0.2108          | 0.1572 | 0.0355 |
+| 0.0188        | 18.0  | 4230 | 0.2173          | 0.1453 | 0.0320 |
+| 0.017         | 19.0  | 4465 | 0.2358          | 0.1444 | 0.0324 |
+| 0.0177        | 20.0  | 4700 | 0.2280          | 0.1548 | 0.0339 |
+| 0.0174        | 21.0  | 4935 | 0.2142          | 0.1484 | 0.0322 |
+| 0.0138        | 22.0  | 5170 | 0.2315          | 0.1489 | 0.0338 |
+| 0.0122        | 23.0  | 5405 | 0.2116          | 0.1483 | 0.0341 |
+| 0.0125        | 24.0  | 5640 | 0.2216          | 0.1487 | 0.0334 |
+### Framework versions
+- Transformers 4.45.2
+- Pytorch 2.1.0+cu118
+- Datasets 3.0.1
+- Tokenizers 0.20.1

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