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wav2vec2-large-xls-r-300m-tr-cv16.1

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 ---
-library_name: transformers
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 ---
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 ---
+license: apache-2.0
+base_model: facebook/wav2vec2-xls-r-300m
+tags:
+- generated_from_trainer
+datasets:
+- common_voice_16_1
+metrics:
+- wer
+model-index:
+- name: wav2vec2-large-xls-r-300m-tr-cv16.1
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: common_voice_16_1
+      type: common_voice_16_1
+      config: tr
+      split: test
+      args: tr
+    metrics:
+    - name: Wer
+      type: wer
+      value: 0.41599252148275984
 ---
+<!-- 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-large-xls-r-300m-tr-cv16.1
+This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_16_1 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.3356
+- Wer: 0.4160
+## 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: 0.0003
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 500
+- num_epochs: 2
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch | Step | Validation Loss | Wer    |
+|:-------------:|:-----:|:----:|:---------------:|:------:|
+| 5.669         | 0.39  | 400  | 1.2228          | 0.8840 |
+| 0.6809        | 0.78  | 800  | 0.6371          | 0.6557 |
+| 0.4224        | 1.17  | 1200 | 0.4607          | 0.5226 |
+| 0.3151        | 1.56  | 1600 | 0.3671          | 0.4457 |
+| 0.2633        | 1.95  | 2000 | 0.3356          | 0.4160 |
+### Framework versions
+- Transformers 4.38.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

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