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--- |
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license: apache-2.0 |
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base_model: facebook/wav2vec2-xls-r-300m |
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tags: |
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- generated_from_trainer |
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metrics: |
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- wer |
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model-index: |
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- name: asr-afmaay-wav2vec2 |
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results: [] |
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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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# asr-afmaay-wav2vec2 |
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. |
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It achieves the following results on the evaluation set: |
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<!-- - Loss: inf --> |
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- Wer: 0.5950 |
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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: 0.0003 |
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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: 500 |
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- num_epochs: 56 |
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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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| 5.6522 | 3.65 | 400 | inf | 0.9355 | |
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| 1.0451 | 7.31 | 800 | inf | 0.7379 | |
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| 0.7211 | 10.96 | 1200 | inf | 0.7056 | |
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| 0.5122 | 14.61 | 1600 | inf | 0.6823 | |
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| 0.3891 | 18.26 | 2000 | inf | 0.6832 | |
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| 0.2934 | 21.92 | 2400 | inf | 0.6586 | |
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| 0.2379 | 25.57 | 2800 | inf | 0.6384 | |
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| 0.1926 | 29.22 | 3200 | inf | 0.6492 | |
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| 0.1592 | 32.88 | 3600 | inf | 0.6353 | |
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| 0.1328 | 36.53 | 4000 | inf | 0.6411 | |
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| 0.1106 | 40.18 | 4400 | inf | 0.6169 | |
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| 0.0851 | 43.84 | 4800 | inf | 0.6102 | |
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| 0.0702 | 47.49 | 5200 | inf | 0.6098 | |
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| 0.0609 | 51.14 | 5600 | inf | 0.6022 | |
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| 0.0501 | 54.79 | 6000 | inf | 0.5950 | |
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### Framework versions |
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- Transformers 4.33.1 |
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- Pytorch 2.1.2 |
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- Datasets 2.19.2 |
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- Tokenizers 0.13.3 |
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