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--- |
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tags: |
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- generated_from_trainer |
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datasets: |
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- evanarlian/common_voice_11_0_id_filtered |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-xls-r-113m-id |
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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: evanarlian/common_voice_11_0_id_filtered |
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type: evanarlian/common_voice_11_0_id_filtered |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.6403468314731113 |
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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-113m-id |
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This model is a fine-tuned version of [evanarlian/distil-wav2vec2-xls-r-113m-id](https://huggingface.co/evanarlian/distil-wav2vec2-xls-r-113m-id) on the evanarlian/common_voice_11_0_id_filtered dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5214 |
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- Wer: 0.6403 |
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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: 0.0002 |
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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: 2 |
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- total_train_batch_size: 16 |
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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_ratio: 0.3 |
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- num_epochs: 5.0 |
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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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| 2.8452 | 0.61 | 1000 | 2.8065 | 1.0 | |
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| 1.3277 | 1.22 | 2000 | 1.0774 | 0.9330 | |
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| 1.025 | 1.84 | 3000 | 0.8000 | 0.8474 | |
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| 0.8497 | 2.45 | 4000 | 0.6812 | 0.7669 | |
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| 0.7678 | 3.06 | 5000 | 0.6125 | 0.7186 | |
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| 0.6886 | 3.67 | 6000 | 0.5758 | 0.6812 | |
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| 0.6318 | 4.29 | 7000 | 0.5420 | 0.6570 | |
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| 0.6086 | 4.9 | 8000 | 0.5214 | 0.6403 | |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.12.1 |
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- Datasets 2.7.1 |
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- Tokenizers 0.13.1 |
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