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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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datasets: |
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- nb_samtale |
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metrics: |
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- wer |
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model-index: |
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- name: wav2vec2-classic-300m-norwegian-colab |
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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: nb_samtale |
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type: nb_samtale |
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config: annotations |
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split: test |
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args: annotations |
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metrics: |
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- name: Wer |
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type: wer |
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value: 0.7528477035956058 |
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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-classic-300m-norwegian-colab |
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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 nb_samtale dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.2190 |
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- Wer: 0.7528 |
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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: 30 |
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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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| 4.8141 | 2.57 | 400 | 3.0571 | 1.0 | |
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| 3.0777 | 5.14 | 800 | 2.9987 | 1.0 | |
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| 2.7311 | 7.72 | 1200 | 2.5705 | 0.9829 | |
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| 2.1302 | 10.29 | 1600 | 1.8399 | 0.9225 | |
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| 1.6827 | 12.86 | 2000 | 1.6372 | 0.8559 | |
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| 1.312 | 15.43 | 2400 | 1.8908 | 0.9172 | |
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| 0.9979 | 18.01 | 2800 | 1.7908 | 0.7890 | |
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| 0.7456 | 20.58 | 3200 | 1.8110 | 0.7720 | |
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| 0.592 | 23.15 | 3600 | 2.0024 | 0.7686 | |
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| 0.4946 | 25.72 | 4000 | 2.1173 | 0.7702 | |
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| 0.4093 | 28.3 | 4400 | 2.2190 | 0.7528 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu118 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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