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--- |
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license: apache-2.0 |
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base_model: facebook/wav2vec2-large-xlsr-53 |
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tags: |
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- generated_from_trainer |
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datasets: |
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- xtreme_s |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-XLS-R-Fleurs-demo-google-colab-Ezra_William_Prod6 |
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results: |
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- task: |
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name: Automatic Speech Recognition |
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type: automatic-speech-recognition |
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dataset: |
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name: xtreme_s |
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type: xtreme_s |
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config: fleurs.id_id |
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split: test |
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args: fleurs.id_id |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.4653494985780572 |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# wav2vec2-XLS-R-Fleurs-demo-google-colab-Ezra_William_Prod6 |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.9273 |
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- Wer: 0.4653 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.001 |
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- train_batch_size: 16 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 32 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 600 |
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- num_epochs: 120 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:------:|:----:|:---------------:|:------:| |
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| 4.8946 | 18.18 | 300 | 2.8697 | 1.0 | |
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| 1.0963 | 36.36 | 600 | 0.8022 | 0.5904 | |
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| 0.1715 | 54.55 | 900 | 0.9387 | 0.5567 | |
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| 0.0953 | 72.73 | 1200 | 0.9120 | 0.5082 | |
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| 0.0651 | 90.91 | 1500 | 0.9362 | 0.4854 | |
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| 0.044 | 109.09 | 1800 | 0.9273 | 0.4653 | |
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### Framework versions |
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- Transformers 4.37.2 |
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- Pytorch 2.2.0+cu121 |
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- Datasets 2.16.1 |
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- Tokenizers 0.15.1 |
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