asr-africa
/

w2v-bert-2.0-yogera-lg-10hrs-v1

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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: 0.1597164303586322
 ---
+<!-- 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: 0.2858
+- Wer: 0.1597
+- Cer: 0.0355
+## 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: 8
+- eval_batch_size: 4
+- 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    |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
+| 0.8824        | 1.0   | 198  | 0.2803          | 0.2968 | 0.0591 |
+| 0.2156        | 2.0   | 396  | 0.2128          | 0.2389 | 0.0493 |
+| 0.1589        | 3.0   | 594  | 0.2110          | 0.2207 | 0.0458 |
+| 0.1277        | 4.0   | 792  | 0.1942          | 0.1964 | 0.0422 |
+| 0.1055        | 5.0   | 990  | 0.1698          | 0.1873 | 0.0390 |
+| 0.087         | 6.0   | 1188 | 0.1771          | 0.1879 | 0.0428 |
+| 0.0738        | 7.0   | 1386 | 0.1850          | 0.1856 | 0.0406 |
+| 0.0589        | 8.0   | 1584 | 0.1799          | 0.1681 | 0.0381 |
+| 0.0573        | 9.0   | 1782 | 0.1882          | 0.1863 | 0.0400 |
+| 0.0481        | 10.0  | 1980 | 0.2275          | 0.1664 | 0.0359 |
+| 0.0425        | 11.0  | 2178 | 0.2135          | 0.1696 | 0.0379 |
+| 0.039         | 12.0  | 2376 | 0.2035          | 0.1600 | 0.0354 |
+| 0.0351        | 13.0  | 2574 | 0.2095          | 0.1683 | 0.0366 |
+| 0.0326        | 14.0  | 2772 | 0.2070          | 0.1589 | 0.0353 |
+| 0.0302        | 15.0  | 2970 | 0.2526          | 0.1708 | 0.0367 |
+| 0.0308        | 16.0  | 3168 | 0.2441          | 0.1642 | 0.0367 |
+| 0.0255        | 17.0  | 3366 | 0.2504          | 0.1678 | 0.0365 |
+| 0.0213        | 18.0  | 3564 | 0.2844          | 0.1721 | 0.0377 |
+| 0.0225        | 19.0  | 3762 | 0.2602          | 0.1721 | 0.0383 |
+| 0.02          | 20.0  | 3960 | 0.2746          | 0.1610 | 0.0351 |
+| 0.0181        | 21.0  | 4158 | 0.2767          | 0.1668 | 0.0364 |
+| 0.0149        | 22.0  | 4356 | 0.2442          | 0.1633 | 0.0355 |
+| 0.0136        | 23.0  | 4554 | 0.2765          | 0.1677 | 0.0362 |
+| 0.0156        | 24.0  | 4752 | 0.2858          | 0.1597 | 0.0355 |
+### Framework versions
+- Transformers 4.45.2
+- Pytorch 2.1.0+cu118
+- Datasets 3.0.1
+- Tokenizers 0.20.1

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