JustAFool
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wav2vec2-large-xls-r-300m-vi-colab

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 ---
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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_11_0
+metrics:
+- wer
+model-index:
+- name: wav2vec2-large-xls-r-300m-vi-colab
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: common_voice_11_0
+      type: common_voice_11_0
+      config: vi
+      split: test
+      args: vi
+    metrics:
+    - name: Wer
+      type: wer
+      value: 0.6686410003290556
 ---
+<!-- 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-vi-colab
+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_11_0 dataset.
+It achieves the following results on the evaluation set:
+- Loss: 1.6916
+- Wer: 0.6686
+## 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: 30
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch | Step | Validation Loss | Wer    |
+|:-------------:|:-----:|:----:|:---------------:|:------:|
+| 7.495         | 4.6   | 400  | 3.4581          | 1.0    |
+| 1.2055        | 9.2   | 800  | 1.6062          | 0.7986 |
+| 0.3038        | 13.79 | 1200 | 1.5743          | 0.7307 |
+| 0.1831        | 18.39 | 1600 | 1.6441          | 0.7119 |
+| 0.1146        | 22.99 | 2000 | 1.6888          | 0.6977 |
+| 0.0876        | 27.59 | 2400 | 1.6916          | 0.6686 |
+### Framework versions
+- Transformers 4.38.2
+- Pytorch 2.2.1+cu121
+- Datasets 2.18.0
+- Tokenizers 0.15.2

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