| --- |
| license: apache-2.0 |
| base_model: facebook/wav2vec2-xls-r-300m |
| tags: |
| - generated_from_trainer |
| datasets: |
| - common_voice |
| metrics: |
| - wer |
| model-index: |
| - name: wav2vec2-large-xls-r-300m-dutch-fast-colab |
| results: |
| - task: |
| name: Automatic Speech Recognition |
| type: automatic-speech-recognition |
| dataset: |
| name: common_voice |
| type: common_voice |
| config: nl |
| split: test |
| args: nl |
| metrics: |
| - name: Wer |
| type: wer |
| value: 0.5791529354326864 |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # wav2vec2-large-xls-r-300m-dutch-fast-colab |
|
|
| This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.7816 |
| - Wer: 0.5792 |
| |
| ## Model description |
| |
| More information needed |
| |
| ## Intended uses & limitations |
| |
| More information needed |
| |
| ## Training and evaluation data |
| |
| More information needed |
| |
| ## Training procedure |
| |
| ### Training hyperparameters |
| |
| The following hyperparameters were used during training: |
| - learning_rate: 0.0003 |
| - train_batch_size: 8 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 16 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: linear |
| - lr_scheduler_warmup_steps: 300 |
| - num_epochs: 3 |
| |
| ### Training results |
| |
| | Training Loss | Epoch | Step | Validation Loss | Wer | |
| |:-------------:|:-----:|:----:|:---------------:|:------:| |
| | 4.6588 | 0.82 | 200 | 2.9541 | 1.0 | |
| | 2.237 | 1.65 | 400 | 1.2580 | 0.8380 | |
| | 0.5734 | 2.47 | 600 | 0.7816 | 0.5792 | |
| |
| |
| ### Framework versions |
| |
| - Transformers 4.32.1 |
| - Pytorch 2.0.1+cu118 |
| - Datasets 2.14.4 |
| - Tokenizers 0.13.3 |
| |
