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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_Prod8 |
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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: 0.42037120191588084 |
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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_Prod8 |
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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: 0.9283 |
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- Wer: 0.4204 |
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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: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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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: 180 |
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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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| 4.7974 | 15.58 | 300 | 2.8438 | 1.0 | |
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| 1.0661 | 31.17 | 600 | 0.7520 | 0.5781 | |
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| 0.1697 | 46.75 | 900 | 0.8342 | 0.5181 | |
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| 0.1081 | 62.34 | 1200 | 0.8508 | 0.5010 | |
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| 0.0833 | 77.92 | 1500 | 0.9216 | 0.5014 | |
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| 0.0616 | 93.51 | 1800 | 0.9659 | 0.4859 | |
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| 0.0463 | 109.09 | 2100 | 0.9606 | 0.4646 | |
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| 0.0356 | 124.68 | 2400 | 0.9255 | 0.4578 | |
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| 0.0291 | 140.26 | 2700 | 0.9889 | 0.4503 | |
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| 0.0218 | 155.84 | 3000 | 0.9336 | 0.4371 | |
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| 0.0182 | 171.43 | 3300 | 0.9283 | 0.4204 | |
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### Framework versions |
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- Transformers 4.39.1 |
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- Pytorch 2.2.1+cu121 |
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- Datasets 2.18.0 |
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- Tokenizers 0.15.2 |
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