EzraWilliam
/

wav2vec2-XLS-R-Fleurs-demo-google-colab-Ezra_William_Prod5

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 ---
-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ---
+license: apache-2.0
+base_model: facebook/wav2vec2-large-xlsr-53
+tags:
+- generated_from_trainer
+datasets:
+- xtreme_s
+metrics:
+- wer
+model-index:
+- name: wav2vec2-XLS-R-Fleurs-demo-google-colab-Ezra_William_Prod5
+  results:
+  - task:
+      name: Automatic Speech Recognition
+      type: automatic-speech-recognition
+    dataset:
+      name: xtreme_s
+      type: xtreme_s
+      config: fleurs.id_id
+      split: test
+      args: fleurs.id_id
+    metrics:
+    - name: Wer
+      type: wer
+      value: 0.5365963179164795
 ---
+<!-- 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-XLS-R-Fleurs-demo-google-colab-Ezra_William_Prod5
+This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the xtreme_s dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.9790
+- Wer: 0.5366
+## 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: 0.001
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 2
+- total_train_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 600
+- num_epochs: 60
+- mixed_precision_training: Native AMP
+### Training results
+| Training Loss | Epoch | Step | Validation Loss | Wer    |
+|:-------------:|:-----:|:----:|:---------------:|:------:|
+| 4.9955        | 9.23  | 300  | 2.8534          | 1.0    |
+| 1.7522        | 18.46 | 600  | 0.7939          | 0.7079 |
+| 0.3374        | 27.69 | 900  | 0.8635          | 0.6423 |
+| 0.1617        | 36.92 | 1200 | 0.9916          | 0.5929 |
+| 0.1102        | 46.15 | 1500 | 0.9796          | 0.5648 |
+| 0.0815        | 55.38 | 1800 | 0.9790          | 0.5366 |
+### Framework versions
+- Transformers 4.37.2
+- Pytorch 2.2.0+cu121
+- Datasets 2.16.1
+- Tokenizers 0.15.1

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